# Welcome Personalization that feels 1:1, delivered at scale. Welcome to Moonbrush. This documentation is your guide to integrating data, activating audience intelligence, and launching safe, high‑impact messaging—without exposing sensitive attributes or secrets. *** ### Who This Is For * **Product & Growth** — launch playbooks, test ideas, read impact. * **Marketing & CX** — craft copy, choose channels, respect fatigue & consent. * **Data & ML** — ship events, manage features, monitor drift, and lift. * **Engineering** — integrate SDKs/APIs, operate reliably. *** ### What Moonbrush Does (at a glance) ``` Sources → Ingest → Identity & Consent → Feature Store → Models → Decision API → Channels (web, app, (pixels, SDKs, (durable+ (psychographics, (ranked actions (email, push, commerce, ads) server) ephemeral) personas, uplift) & explanations) in‑app, site) ``` * **Psychographics & Personas**: infer motivations, decision styles, and persona probabilities from behavior. * **Intent Feed**: capture live actions and context; emit enriched, real‑time features. * **Consumer Graph**: governed person record; safe views expose only approved fields for activation. * **Audience Playbooks**: turn signals into exactly‑what‑to‑say/where/when—with caps and guardrails. > Sensitive or protected attributes are **quarantined** and never used for activation without explicit communication, data usage agreement, and applicable SLA with end-user. *** ### Onboarding Checklist * [ ] Establish data & privacy owner(s) and consent scopes. * [ ] Send a minimal set of events (page view, search, add‑to‑cart, conversion). * [ ] Data dumb and analysis for target audience understanding, micro-segmentation, and specific activation notes/quirks (relative to vertical, company, brand, etc) * [ ] Custom psychographic model development * [ ] Persona and playbook definition, guidelines, and activation * [ ] Create holdouts and baseline metrics. * [ ] Ship your first audience via Moonbrush platform, direct integration to preferred MarTech, or via API (coming soon) *** ### Safety & Governance * **Consent**: purpose‑limited processing (analytics/personalization); revocation respected. * **Sensitive fields**: quarantined; excluded from APIs, templates, and routing. * **Guardrails**: frequency caps, fatigue, sensitive‑category blocklists, quiet hours. * **Explainability**: decision reason codes per request. * **Research-Driven:** in-depth research papers available upon request, justifying all standard models, personas, and playbooks available within the Moonbrush platform. *** ### Document Map #### Foundations * Architecture Overview * Data Governance & Consent * Identity & Stitching #### Data Plane * **Intent Feed** — capture → enrich → segment * **Consumer Graph** — person record & safe views #### Modeling * **Psychographic Modeling** — features, estimators, feedback loops * **Standard Persona Models** — taxonomy, assignment, calibration #### Activation * **Audience Playbooks** — objectives, triggers, templates, measurement * Decision API — ranking & reason codes #### Build & Operate * SDKs & Events Reference * Observability & Testing * Change Management & Versioning * SLAs & Reliability *** ### Conventions Used in This Doc * **Code** in monospace blocks; replace `` and IDs with your own. * **YAML/JSON** examples are **illustrative** and may omit optional fields. For security purposes, YAML/JSON examples may point to specific models, personas, playbooks, or columns that do not exist within the Moonbrush platform. * Times are **EST** on the intent feed and in **UTC** in all other instances, unless otherwise stated. *** ### Support & Feedback * Use your workspace **support channel** or contact your account team in the console. * Found an issue in the docs? Open a request with the page title and a brief description. *** ### Changelog (Docs) * \*COMING SOON!\* *** *© Moonbrush. All examples avoid sensitive categories and are for demonstration only.* ### Jump right in

:people-group:

Consumer GraphCore operational and reference database for persona analysis, micro-segmentation, and activation playbooks/pages/7FvWQMF0kTK7HGhlQfmo

:chart-user:

Standard Psychographic ModelsPsychographic enrichments offered to all Moonbrush users (custom models always available)https://github.com/GitbookIO/gitbook-templates/blob/main/product-docs/broken-reference/README.md

:globe-pointer:

Intent FeedCapture audiences as they're looking for your product collected from internet, social, purchase activity and more!/pages/QPzbTvC6XsT5gERiU43E
# Consumer Graph
Specifications Release Date: September 19, 2025\ Total Rows: 289.9M\ Version: 3.1.8\ File Size: 249.0 GB (parquet)
### Purpose A governed, person‑level record used to power analytics, measurement, and **policy‑compliant** segmentation. It consolidates identity, contact metadata, firmographics, location context, modeled psychographic/behavioral scores, and neighborhood indices. **Activation against protected or sensitive attributes is blocked**; those fields exist only for compliance auditing, fairness analysis, and model QA. *** ### Row Granularity & Keys * **Grain**: one row per internal person (`MOONBRUSH_ID`). * **Primary key**: `MOONBRUSH_ID` (opaque, non‑PII). Recommended `NOT NULL` constraint in DDL. * **Surrogate IDs**: None. Use `MOONBRUSH_ID` for joins to decisioning, intent feed, and persona outputs. ### Update Cadence & SLAs * **Upserts**: event‑driven plus scheduled batch compaction. * **Timeliness targets**: profile attributes ≤ 24h; streaming scores ≤ 1h; `LAST_UPDATED` always refreshed on write. *** ### Schema Overview (Groups) The full DDL is provided in engineering assets. Below is a grouped view of major columns and intent. Example columns listed are intentionally not exhaustive. #### 1) Identity & Contact (PII → masked) * `MOONBRUSH_ID`, `FIRST_NAME`, `LAST_NAME` * Emails: `BUSINESS_EMAIL`, `ADDITIONAL_PERSONAL_EMAILS[]`, `PROGRAMMATIC_BUSINESS_EMAILS[]` * Phones: `MOBILE_PHONE`, `DIRECT_NUMBER`, `PERSONAL_PHONE` * Links: `LINKEDIN_URL` * Timestamps & statuses: `BUSINESS_EMAIL_VALIDATION_STATUS`, `BUSINESS_EMAIL_LAST_SEEN`, `PERSONAL_EMAILS_VALIDATION_STATUS`, `PERSONAL_EMAILS_LAST_SEEN` #### 2) Personal Location (PII → masked) * `PERSONAL_ADDRESS`, `PERSONAL_ADDRESS_2`, `PERSONAL_CITY`, `PERSONAL_STATE`, `PERSONAL_ZIP`, `PERSONAL_ZIP4` * Geo enrichments: `METRO_NAME`, `COUNTY_NAME`, `COUNTY_FIPS`, `CBSA_FIPS`, `ZIPCODE_LAT`, `ZIPCODE_LONG`, `TIMEZONE_FROM_ZIP`, `DENSITY` #### 3) Demographic Proxies (Governed) * `GENDER`, `AGE_RANGE`, `MARRIED`, `CHILDREN`, `HOMEOWNER`, `INCOME_RANGE`, `NET_WORTH` #### 4) **Sensitive/Protected Attributes (Quarantined)** * `MODELED_ETHNICITY`, `POLITICAL_IDEOLOGY`, `RELIGIOUS_ALIGNMENT_SCORE`, and related ideology/values scores.\ **Policy**: these fields are **never** used for targeting, bidding, or personalization decisions. They live in restricted views for fairness monitoring and compliance only. #### 5) Firmographics (B2B context) * Person role: `JOB_TITLE`, `JOB_TITLE_NORMALIZED`, `SENIORITY_LEVEL`, `SENIORITY_LEVEL_2`, `DEPARTMENT`, `DEPARTMENT_2` * Company: `COMPANY_NAME`, `PRIMARY_INDUSTRY`, `COMPANY_DOMAIN`, `COMPANY_LINKEDIN_URL`, `COMPANY_PHONE`, `COMPANY_REVENUE`, `COMPANY_EMPLOYEE_COUNT`, `COMPANY_SIC`, `COMPANY_NAICS`, `COMPANY_ADDRESS*`, `COMPANY_COUNTRY` * Change stamps: `COMPANY_LAST_UPDATED`, `JOB_TITLE_LAST_UPDATED` #### 6) Behavioral & Engagement Scores * Channel: `EMAIL_ENGAGEMENT_SCORE`, `SMS_ENGAGEMENT_SCORE`, `DIGITAL_AD_ENGAGEMENT_SCORE` * Shopping & subscription: `SUBSCRIPTION_PURCHASE_SCORE`, `LOYALTY_PROGRAM_RESPONSIVENESS_SCORE`, `SUBSCRIPTION_FATIGUE_SCORE`, `COUPON_CONVERSION_SCORE` * Device/context: `MOBILE_DEVICE_ENGAGEMENT_SCORE`, `DESKTOP_DEVICE_ENGAGEMENT_SCORE`, `DAYPART_RESPONSIVENESS_SCORE`, `WEEKDAY_VS_WEEKEND_ACTIVITY_SCORE`, `RECENCY_FREQUENCY_WINDOW_RFW_SCORE` #### 7) Psychographic/Decision‑Style Scores (modeled) * Examples: `RISK_TOLERANCE_SCORE`, `NOVELTY_SEEKING_BEHAVIOR_SCORE`, `PRICE/QUALITY BIASES` (e.g., `BARGAIN_VS_QUALITY_BIAS_SCORE`), `AUTHORITY_RESPONSE_SCORE`, `SKEPTICISM_SCORE`, `RELATIONSHIP_RELIANCE_SCORE`, `TRUST_SIGNAL_SENSITIVITY_SCORE`, `DEAL_HUNTING_SCORE`, `BRAND_LOYALTY_SCORE`, `BRAND_SWITCHING_SCORE`, `ETHICAL_CONSUMPTION_SENSITIVITY_SCORE`, `PRIVACY_SENSITIVITY_SCORE`, `PURCHASE_RATIONALITY_SCORE`, `INFORMATION_OVERLOAD_TOLERANCE_SCORE`, and others as defined in DDL. #### 8) Civic/Values/Ideation Indices (Governed) * Examples: `CIVIC_ENGAGEMENT_INTENSITY_SCORE`, `POLITICAL_MOBILIZATION_SCORE`, `DEI_ALIGNMENT_SCORE`, `PATRIOTIC_ALIGNMENT_SCORE`, `ENVIRONMENTAL_RISK_AVERSION_SCORE`.\ **Use**: monitoring/model QA only; **blocked** for activation. #### 9) Neighborhood & Geo‑Social Indices * Examples: `LOCAL_OUTLIER_SCORE`, `NEIGHBORHOOD_HOMOGENEITY_SCORE`, `SOCIAL_EXPOSURE_SCORE`, `CULTURAL_AFFINITY_INDEX`, `COMMUNITY_VELOCITY_SCORE`, `CONFORMITY_PRESSURE_INDEX`, `GEO_SOCIAL_INFLUENCE_DENSITY`, `BEHAVIORAL_DISPARITY_SCORE`, `CIVIC_COHESION_SCORE`, `SPRAWL_INTENSITY_INDEX`, `GEO_ECONOMIC_PRESSURE_SCORE`, `LOCAL_IDEOLOGY_CONSISTENCY_SCORE`. #### 10) Program Flags & Attributes * `P0001`…`P0407` (char(1)) and `A0001`…`A0102` (char(1)) represent **binary program/attribute flags**. Values are `"Y"/"N"` or `"1"/"0"` depending on upstream. The **codebook** defines each flag’s meaning and governance tier. #### 11) History & Graph Adjacent * Arrays: `WORK_HISTORY[]`, `EDUCATION_HISTORY[]`, `RELATED_DOMAINS[]`, `SOCIAL_CONNECTIONS[]` * Free‑text: `COMPANY_DESCRIPTION` #### 12) Operational Columns * `LAST_UPDATED` (row timestamp), `CC_ID`, `STATE_ID`, `STATE` *** ### Governance & Policy * **Consent & Purpose Limiting**: all writes require aligned purpose; downstream views enforce consent scopes. * **Sensitive Attributes**: segregated in *quarantined* schema/views; accessible only to approved roles for fairness, bias monitoring, and regulatory reporting. **Not surfaced to decisioning APIs**. * **PII Masking**: dynamic masking on emails, phones, and street addresses by role. * **Row Access**: regional/tenant scoping via row access policies. #### Example (Masking & Row Policies — illustrative) ```sql -- Mask emails/phones for non-privileged roles CREATE OR REPLACE MASKING POLICY mp_mask_email AS (val STRING) RETURNS STRING -> CASE WHEN CURRENT_ROLE() IN ('ANALYST_PII','ADMIN') THEN val ELSE REGEXP_REPLACE(val, '(^.).+(@.+$)', '\\1***\\2') END; ALTER TABLE UNIVERSAL_PERSON_ENRICHED MODIFY COLUMN BUSINESS_EMAIL SET MASKING POLICY mp_mask_email; -- Row Access by region consent (example predicate) CREATE OR REPLACE ROW ACCESS POLICY rap_region AS (PERSONAL_STATE STRING) RETURNS BOOLEAN -> CURRENT_ROLE() IN ('ADMIN','ANALYST_US') AND PERSONAL_STATE IN ('CA','NY','TX'); ALTER TABLE UNIVERSAL_PERSON_ENRICHED ADD ROW ACCESS POLICY rap_region ON (PERSONAL_STATE); ``` > The exact predicates/roles are maintained by security engineering and may differ by tenant, region, and operation. *** ### Recommended Physical Design (SnowSQL) * **Clustering**: `CLUSTER BY (MOONBRUSH_ID, LAST_UPDATED)` for point lookups and incremental updates. * **Search Optimization**: enable on commonly filtered fields (e.g., `COMPANY_DOMAIN`, `PERSONAL_ZIP`, `BUSINESS_EMAIL`) for selective queries. * **Micro‑batch Upserts**: MERGE on `MOONBRUSH_ID` with `LAST_UPDATED` winner rules. #### Example Upsert (illustrative) ```sql MERGE INTO UNIVERSAL_PERSON_ENRICHED t USING @stage/person_delta.parquet s ON t.MOONBRUSH_ID = s.MOONBRUSH_ID WHEN MATCHED AND s.LAST_UPDATED > t.LAST_UPDATED THEN UPDATE SET (FIRST_NAME, LAST_NAME, JOB_TITLE, LAST_UPDATED) = (s.FIRST_NAME, s.LAST_NAME, s.JOB_TITLE, s.LAST_UPDATED) WHEN NOT MATCHED THEN INSERT (MOONBRUSH_ID, FIRST_NAME, LAST_NAME, JOB_TITLE, LAST_UPDATED) VALUES (s.MOONBRUSH_ID, s.FIRST_NAME, s.LAST_NAME, s.JOB_TITLE, s.LAST_UPDATED); ``` *** ### Access Patterns * **Person lookup** ```sql SELECT MOONBRUSH_ID, EMAIL_ENGAGEMENT_SCORE, BRAND_LOYALTY_SCORE, LAST_UPDATED FROM UNIVERSAL_PERSON_ENRICHED WHERE MOONBRUSH_ID = 'SUB-7f91b1c6'; ``` * **Join to Intent Feed & Personas** (safe view) ```sql SELECT p.MOONBRUSH_ID, i.occurred_at, pr.persona_top, pr.p_top FROM SAFE_PERSON_VIEW p JOIN ENRICHED_INTENT_STREAM i USING (MOONBRUSH_ID) JOIN PERSONA_ASSIGNMENTS pr USING (MOONBRUSH_ID) WHERE i.occurred_at >= DATEADD(day, -7, CURRENT_TIMESTAMP()); ``` * **Neighborhood index query** ```sql SELECT PERSONAL_ZIP, AVG(CIVIC_COHESION_SCORE) cc, AVG(SPRAWL_INTENSITY_INDEX) sprawl FROM SAFE_PERSON_VIEW GROUP BY 1 ORDER BY cc DESC; ``` *** ### Views & Exposure To prevent accidental misuse, exposure is via tiered views: 1. **`SAFE_PERSON_VIEW`** — drops PII, sensitive attributes, and all ideology/religion proxies; retains engagement and non‑sensitive psychographic scores. 2. **`RESEARCH_PERSON_VIEW`** — access‑controlled; includes additional modeled features for R\&D (no activation). 3. **`QUARANTINED_PERSON_VIEW`** — restricted; includes sensitive attributes for fairness/QA only. Illustrative creation: ```sql CREATE OR REPLACE VIEW SAFE_PERSON_VIEW AS SELECT MOONBRUSH_ID, EMAIL_ENGAGEMENT_SCORE, SMS_ENGAGEMENT_SCORE, BRAND_LOYALTY_SCORE, RISK_TOLERANCE_SCORE, NOVELTY_SEEKING_BEHAVIOR_SCORE, DAYPART_RESPONSIVENESS_SCORE, WEEKDAY_VS_WEEKEND_ACTIVITY_SCORE, -- ... (approved columns only) LAST_UPDATED FROM UNIVERSAL_PERSON_ENRICHED; ``` *** ### Data Quality & Monitoring * **Completeness**: non‑null rate of `MOONBRUSH_ID`, freshness of `LAST_UPDATED`. * **PII Hygiene**: valid email/phone patterns, DPV codes for addresses. * **Drift**: distribution shifts on key scores; PSI/KS alerts. * **Governance**: audit trail of column access; query pol # Standard Psychographic Models
Specifications Release Date: September 03, 2025\ Number of standard psychographic models: 108\ Version: 3 (2.78%), 3.0.1 (8.33%), 3.0.2 (39.81%), 3.1.2 (2.78%), 3.1.3 (46.30%) (model dependent)\ Values: scores from 0 to 1, up to 8 decimal places
## Standard Psychographic Models | behavioral\_model | definition | score\_directionality | | ------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | email\_engagement\_score | Measures the likelihood that an individual will open, read, and interact with email-based outreach and campaigns. | High score (close to 1) = highly responsive to email campaigns, likely to open, click, and convert from email. Low score (close to 0) = unlikely to open or interact with email content, suggesting other channels may be more effective. | | sms\_engagement\_score | Measures the likelihood that an individual will open, read, and respond to SMS-based outreach or campaigns. | High score (close to 1) = highly responsive to SMS campaigns, likely to open promptly and take action. Low score (close to 0) = unlikely to engage via SMS, suggesting alternative channels may be more effective. | | digital\_ad\_engagement\_score | Measures how likely someone is to notice, engage with (click, view, watch), or be influenced by digital ads across formats including display, social media, video, and native placements. | High score (close to 1) = highly receptive to digital advertising, more likely to notice, interact, and convert from online ads. | | impulse\_buy\_score | Measures the likelihood that an individual will make quick, unplanned purchases with minimal deliberation. | Low score (close to 0) = less responsive to digital ads, suggesting reliance on alternative outreach channels such as email, SMS, direct mail, or in-person engagement. | | subscription\_purchase\_score | Predicts the likelihood that an individual will sign up for and maintain recurring subscriptions such as streaming services, software, subscription boxes, newsletters, apps, or memberships. | High score (close to 1) = highly likely to initiate and maintain subscription services, often showing loyalty to recurring brands. Low score (close to 0) = less likely to commit to ongoing subscriptions, preferring one-time or short-term purchases. | | overextension\_risk\_score | Measures the likelihood that an individual will spend beyond their means, potentially using credit cards, installment payment plans, or loans to finance purchases that may not be financially prudent or necessary. | High score (close to 1) = more likely to take on debt or financing for discretionary or luxury purchases despite limited means. Low score (close to 0) = more financially cautious, less likely to use credit for non-essential items. | | brand\_loyalty\_score | Measures the likelihood that an individual will remain committed to a single brand over time, maintaining repeat purchases once converted. | High score (close to 1) = highly likely to repurchase from the same brand and resist switching. Low score (close to 0) = more likely to explore and purchase from multiple competing brands. | | coupon\_conversion\_score | Measures the likelihood that an individual will use coupons, discounts, or promotional codes when making a purchase. | High score (close to 1) = highly likely to seek and redeem coupons or promotional offers before purchasing. Low score (close to 0) = less motivated by discounts, more likely to purchase without promotional incentives. | | urgency\_response\_score | Measures the likelihood that an individual will respond to time-sensitive offers such as “limited supply,” “countdown timers,” or “this deal won’t last” messaging. | High score (close to 1) = highly likely to take action quickly when presented with time-limited or scarce offers. Low score (close to 0) = less influenced by urgency cues, more likely to maintain deliberation regardless of scarcity. | | influencer\_response\_score | Measures the likelihood that an individual will be persuaded to change opinions, adopt beliefs, or purchase a product or service based on the endorsement or promotion of an influencer, celebrity, public figure (non-political), or news personality, specifically due to their fame or public recognition rather than subject matter expertise. | High score (close to 1) = highly likely to be influenced by well-known personalities regardless of expertise. Low score (close to 0) = less influenced by fame, more likely to base decisions on expertise, product quality, or other factors. | | luxury\_purchase\_tendency | Measures the likelihood that an individual will purchase premium or luxury goods, services, or experiences regardless of functional necessity or utility. | High score (close to 1) = highly likely to prioritize premium or luxury options over standard alternatives, even when utility is equivalent. Low score (close to 0) = more focused on practicality, value, or cost-efficiency over brand prestige. | | expert\_response\_score | Measures the likelihood that an individual’s opinions, beliefs, or purchase decisions will be influenced by a subject-matter expert’s recommendation or guidance, separate from influence by celebrities or authorities. | High score (close to 1) = highly likely to follow advice or make purchases based on recommendations from qualified experts. Low score (close to 0) = less influenced by expert input, relying more on personal preference, peer input, or other factors. | | authority\_response\_score | Measures the likelihood that an individual will be influenced by an authority figure or organization promoting or discouraging a product, service, idea, or belief, in contrast to skepticism-oriented traits. | High score (close to 1) = highly likely to be swayed by authority-backed positions or endorsements. Low score (close to 0) = less influenced by authority figures or organizations, potentially exhibiting skepticism toward official sources. | | relationship\_reliance\_score | Measures the extent to which personal relationships influence an individual’s daily life and decision-making. | High score (close to 1) = daily choices and beliefs are strongly influenced by immediate social network. Low score (close to 0) = socially independent or isolated, making decisions with minimal external social influence. | | emotional\_sensitivity\_score | Measures the extent to which an individual’s emotions influence their decisions and actions. | High score (close to 1) = decision-making is strongly guided by emotions and affective responses. Low score (close to 0) = decision-making is less influenced by emotional factors, favoring rational or detached evaluation. | | ethical\_guidelines\_score | Measures the likelihood that an individual’s decisions are guided primarily by their ethics, morals, principles, religion, or political/personal ideology. | High score (close to 1) = decisions are strongly influenced by personal ethics, morals, or ideological beliefs. Low score (close to 0) = decisions are less tied to values-based considerations, with greater influence from practical, situational, or external factors. | | skepticism\_score | Measures the likelihood that an individual will act in opposition to or reject the recommendations, beliefs, promotions, or viewpoints of authority figures, experts, and, in some cases, influencers. | High score (close to 1) = more likely to act against advice or positions promoted by authorities, experts, or influencers. Low score (close to 0) = more likely to accept and align with recommendations from these sources. | | research\_depth\_score | Estimates the likelihood that an individual will thoroughly research, compare options, and engage in multi-touch discovery before making a decision. | High score (close to 1) = highly likely to invest significant time and effort in researching before deciding. Low score (close to 0) = more likely to make quick decisions with minimal research. | | deal\_hunting\_score | Estimates how price-sensitive an individual is and their likelihood of responding to discounts, sales, or coupons. | High score (close to 1) = highly likely to seek out and act on deals, discounts, and coupons before purchasing. Low score (close to 0) = less motivated by price reductions, more likely to purchase without waiting for promotions. | | trust\_signal\_sensitivity\_score | Measures the likelihood that an individual is influenced by third-party trust indicators such as reviews, ratings, testimonials, guarantees, and certifications. | High score (close to 1) = highly responsive to trust signals and more likely to be persuaded by credible third-party validation. Low score (close to 0) = less influenced by external credibility markers, relying more on personal judgment or other factors. | | brand\_switching\_score | Estimates the likelihood that an individual will switch brands within a given category, often driven by value-seeking tendencies, lower brand attachment, promotional responsiveness, or openness to experimentation. | High score (close to 1) = highly likely to change brands when presented with compelling alternatives or incentives. Low score (close to 0) = more likely to remain with the same brand regardless of competitive offers. | | loyalty\_program\_responsiveness\_score | Measures the likelihood that an individual will engage with and respond positively to loyalty-based or subscription-based programs, including streaming services, software, SaaS products, and membership rewards systems. | High score (close to 1) = highly likely to join, use, and maintain participation in loyalty or subscription programs. Low score (close to 0) = less likely to perceive value in or engage with these programs, may resist sign-up or retention efforts. | | subscription\_fatigue\_score | Measures the likelihood that an individual is less willing to sign up for new subscription services due to factors such as market saturation, financial strain, or established behavioral patterns. | High score (close to 1) = more likely to resist new subscription commitments, requiring stronger incentives or alternative models. Low score (close to 0) = more open to adopting additional subscriptions when relevant. | | financial\_cautiousness\_score | Measures the likelihood that an individual approaches financial decisions conservatively, prioritizing security, research, and risk avoidance over impulse or speculative spending. | High score (close to 1) = highly cautious with finances, more likely to research and delay decisions before spending. Low score (close to 0) = more willing to take financial risks or spend impulsively. | | social\_validation\_sensitivity\_score | Measures the likelihood that an individual will respond to social proof, peer reviews, influencer promotion, or crowd-based validation such as ratings, popularity indicators, or trending status. | High score (close to 1) = highly influenced by social validation and more likely to follow popular trends or majority opinions. Low score (close to 0) = less influenced by social proof, relying more on personal judgment or independent research. | | exclusivity\_affinity\_score | Measures the likelihood that an individual is attracted to exclusivity, luxury, high-status positioning, premium packaging, or gated access. | High score (close to 1) = highly attracted to exclusive, status-oriented, or limited-access products and experiences. Low score (close to 0) = less motivated by exclusivity, more focused on utility, availability, or value. | | value\_seeker\_score | Measures the likelihood that an individual prioritizes overall value and utility per dollar spent, favoring affordable or efficient options without necessarily focusing on discounts. | High score (close to 1) = highly likely to choose products or services offering strong utility and cost efficiency. Low score (close to 0) = less driven by value optimization, potentially prioritizing brand, status, or other non-utility factors. | | trend\_sensitivity\_score | Measures the likelihood that an individual will quickly respond to trending products, styles, or cultural phenomena, acting on trend momentum rather than solely sustained interest. | High score (close to 1) = highly likely to act quickly on trends and new releases. Low score (close to 0) = less influenced by trend momentum, more focused on long-term appeal or personal preference. | | time\_to\_purchase\_sensitivity\_score | Measures the likelihood that an individual will act on a purchase decision quickly when influenced by urgency cues, economic flexibility, and prior exposure to related signals. | High score (close to 1) = highly likely to finalize purchases quickly once prompted. Low score (close to 0) = more likely to delay purchases regardless of urgency cues or prior exposure. | | mobile\_device\_engagement\_score | Measures the likelihood that an individual primarily interacts with content or campaigns via mobile devices. | High score (close to 1) = primarily engages through mobile devices, likely to respond better to mobile-first campaigns. Low score (close to 0) = more desktop- or offline-focused engagement, requiring alternative channel optimization. | | desktop\_device\_engagement\_score | Measures the likelihood that an individual primarily engages with content or campaigns via desktop or laptop rather than mobile devices. | High score (close to 1) = primarily engages via desktop or laptop, likely to respond better to desktop-optimized campaigns. Low score (close to 0) = more mobile-focused engagement, requiring mobile-first campaign strategies. | | risk\_tolerance\_score | Measures the likelihood that an individual will take financial or personal risks, informed by factors such as economic environment, relevant intent signals, and stability indicators. | High score (close to 1) = more likely to take significant risks in financial or personal decisions. Low score (close to 0) = more risk-averse, favoring stability and security over uncertain opportunities. | | convenience\_over\_quality\_score | Measures the likelihood that an individual prioritizes convenience over product or service quality. | High score (close to 1) = more likely to choose convenience-focused options such as delivery apps or quick-purchase platforms over higher-quality alternatives. Low score (close to 0) = more likely to prioritize quality, craftsmanship, or premium service over convenience. | | locality\_affinity\_score | Measures the likelihood that an individual prefers shopping locally, engaging with nearby businesses, or showing geo-specific brand loyalty. | High score (close to 1) = more likely to favor local businesses and regionally relevant brands. Low score (close to 0) = less influenced by location, more open to national or global alternatives. | | ethical\_consumption\_sensitivity\_score | Estimates the likelihood that an individual will prioritize ethical factors such as sustainability, labor practices, environmental impact, and social causes when making purchasing decisions. | High score (close to 1) = highly likely to weigh ethical considerations heavily in purchasing decisions. Low score (close to 0) = less likely to prioritize ethical or sustainability factors when choosing products or services. | | trial\_willingness\_score | Measures the likelihood that an individual is willing to try new products, services, or subscriptions, particularly those with freemium, sample, or trial-based onboarding. | High score (close to 1) = highly likely to engage with trials, samples, or freemium models. Low score (close to 0) = less inclined to test or adopt without proven value or existing trust. | | privacy\_sensitivity\_score | Measures the likelihood that an individual is cautious about sharing personal data or engaging with tracking-based experiences. | High score (close to 1) = highly cautious about data sharing and tracking-based engagement. Low score (close to 0) = less concerned about privacy, more willing to exchange personal data for convenience or personalization without explicit privacy invasion (applies to implied/hidden privacy vs explicit data exploitation). | | purchase\_rationality\_score | Measures the likelihood that an individual will prioritize logic, budgeting, long-term value, and research over emotional or social influences when making purchases. | High score (close to 1) = more likely to make purchases based on rational evaluation, budgeting, and thorough research. Low score (close to 0) = more likely to make purchases influenced by emotions, social pressures, or impulse. | | emotionally\_charged\_response\_score | Measures the likelihood that an individual will react strongly to emotionally provocative messages such as fear, hope, pride, outrage, or nostalgia. | High score (close to 1) = highly likely to respond to emotionally charged messaging with increased engagement or action. Low score (close to 0) = less affected by emotional appeals, more influenced by rational or neutral communication. | | information\_overload\_tolerance\_score | Measures the ability of an individual to process and engage with complex, detailed messaging such as long-form articles, reports, or comparison charts, versus a preference for simpler, visual, or emotionally-driven content. | High score (close to 1) = more likely to engage with in-depth, detailed information. Low score (close to 0) = more likely to disengage from complex content and prefer concise or visually simplified messages. | | authority\_defiance\_score | Measures the tendency to resist, reject, or act in opposition to directives, recommendations, or messaging from authority figures, institutions, or government bodies, representing the inverse of authority responsiveness. | High score (close to 1) = highly likely to oppose or dismiss authority guidance. Low score (close to 0) = more likely to comply with or accept authority-backed positions. | | algorithmic\_trust\_score | Measures the comfort level of an individual with algorithms making decisions about content, services, or recommendations (e.g., Spotify playlists, TikTok feeds, AI product suggestions). | High score (close to 1) = comfortable with and trusting of algorithmic recommendations. Low score (close to 0) = less trusting of algorithmic systems, preferring self-directed or human-curated options. | | brand\_idealism\_alignment\_score | Measures the degree to which an individual aligns with brands that emphasize values-first positioning, such as sustainability, social equity, DEI, or empowerment. | High score (close to 1) = highly aligned with values-forward brands. Low score (close to 0) = less motivated by brand values, more influenced by other factors such as price, convenience, or performance. | | civic\_engagement\_intensity\_score | Predicts the likelihood that an individual will participate in civic activities such as voting, signing petitions, attending town halls, or engaging with political content. | High score (close to 1) = highly likely to engage in civic and political activities. Low score (close to 0) = less likely to participate in formal civic engagement, even when aware of issues. | | novelty\_seeking\_behavior\_score | Estimates whether someone gravitates toward the new, unusual, or avant-garde versus preferring established brands, norms, or routines. | High score (close to 1) = more likely to seek out and adopt new, unfamiliar, or experimental options. Low score (close to 0) = more likely to choose stability, familiarity, and tradition over novelty. | | policy\_focused\_persuasion\_score | Measures whether a person is more likely to respond to data-driven, issue-centric arguments rather than appeals rooted in values, identity, or tribal alignment. | High score (close to 1) = more likely to be persuaded by facts, metrics, and issue-specific reasoning. Low score (close to 0) = more likely to be swayed by identity, emotional tone, or shared values rather than hard data. | | tribe\_conformity\_score | Measures whether someone tends to align closely with the social and cultural expectations of their demographic, ZIP code, or political identity group. | High score (close to 1) = highly likely to conform to in-group norms and expectations. Low score (close to 0) = more likely to act independently of group pressures or community patterns. | | deliberative\_decision\_making\_score | Measures how long and how thoroughly someone thinks before acting, especially for major purchases, ideological changes, or brand switching. | High score (close to 1) = likely to take extended time before making a decision, incorporating multiple inputs. Low score (close to 0) = likely to act quickly with limited evaluation. | | media\_layer\_saturation\_score | Measures how tolerant a person is to multi-touchpoint exposure (ads on TV, phone, laptop, mailers) without experiencing tune-out or fatigue. | High score (close to 1) = can sustain higher repetition before disengaging. Low score (close to 0) = likely to experience ad fatigue quickly. | | moral\_absolutism\_vs\_relativism\_score | Infers whether someone tends to interpret social or political issues in black-and-white, absolute terms versus with nuance, flexibility, and contextual variation. | High score (close to 1) = moral absolutism, fixed principles. Low score (close to 0) = moral relativism, context-dependent principles. | | real\_world\_versus\_digital\_world\_bias\_score | Indicates whether someone leans toward offline/physical world engagement or digital-first behavior across shopping, events, and interaction channels. | High score (close to 1) = stronger bias toward real-world, offline experiences. Low score (close to 0) = stronger bias toward digital-first interactions. | | intellectual\_engagement\_score | Measures appetite for complex, long-form, or idea-dense content that requires focus and deeper processing. | High score (close to 1) = high interest in detailed, thought-provoking material. Low score (close to 0) = preference for concise, easily digestible formats. | | bargain\_vs\_quality\_bias\_score | Captures whether someone’s decision-making prioritizes price sensitivity or premium quality. | High score (close to 1) = bargain-first decision bias. Low score (close to 0) = quality-first decision bias. | | patriotic\_alignment\_score | Measures whether someone responds positively to national pride, military association, tradition, and flag-first messaging. | High score (close to 1) = strong patriotic alignment. Low score (close to 0) = low alignment or potential disengagement from nationalist framing. | | environmental\_risk\_aversion\_score | Measures sensitivity to environmental risks such as climate change, pollution, food safety, and ecological degradation. | High score (close to 1) = highly environmentally risk-averse. Low score (close to 0) = low prioritization of environmental risk. | | anonymity\_desire\_score | Captures the extent to which someone values anonymity beyond general privacy, both in communication and purchasing behavior. | High score (close to 1) = strong desire for anonymity. Low score (close to 0) = low concern for anonymity beyond basic privacy. | | dei\_alignment\_score | Indicates whether someone aligns with or resists diversity, equity, and inclusion (DEI)-related language, themes, and cultural cues. | High score (close to 1) = strong alignment with DEI values and framing. Low score (close to 0) = low or negative alignment with DEI framing. | | openness\_to\_conspiracy\_non\_mainstream\_ideas | Measures receptivity to alternative or non-mainstream explanatory narratives, including conspiratorial frames. | High score (close to 1) = more likely to consider or adopt alternative/non-mainstream explanations. Low score (close to 0) = more likely to disregard or avoid fringe or counter-narrative claims, favoring mainstream consensus. | | psychological\_security\_seeking\_score | Estimates preference for safety, certainty, and predictability in information and choices. | High score (close to 1) = prioritizes stability, safety signals, and low-variance options. Low score (close to 0) = more comfortable with change, ambiguity, and short-term volatility. | | polarization\_tolerance\_score | Captures comfort with ideologically charged, high-conflict environments and content. | High score (close to 1) = more tolerant of polarizing content and likely to engage despite controversy. Low score (close to 0) = averse to divisive framing; prefers neutral, bridge-building messages. | | social\_responsiveness\_score | Measures propensity to notice, acknowledge, and respond to social cues and outreach. | High score (close to 1) = highly likely to respond, comment, or share. Low score (close to 0) = more passive consumption; limited interactive engagement. | | risk\_mitigation\_behavior\_score | Estimates tendency to reduce perceived risk using reviews, guarantees, trials, backups, or expert validation. | High score (close to 1) = frequently seeks protections and contingency options before acting. Low score (close to 0) = less likely to add safeguards; more willing to proceed without formal mitigation. | | religious\_value\_alignment\_score | Captures alignment with faith-oriented values, language, and moral framing. | High score (close to 1) = decisions more strongly guided by religious values and norms. Low score (close to 0) = religious framing has limited impact on choices. | | political\_mobilizability\_score | Measures readiness to engage in civic or political actions when prompted. | High score (close to 1) = more likely to take tangible political/advocacy actions. Low score (close to 0) = awareness may be present, but action-taking is unlikely. | | experiential\_learning\_score | Captures preference for hands-on proofs, demos, trials, and practice-based learning. | High score (close to 1) = prefers to learn by doing; responds to demos, samples, trials. Low score (close to 0) = prefers research, reviews, and documentation before trying. | | repetition\_receptivity\_score | Estimates tolerance for and benefit from repeated exposures and consistent cues. | High score (close to 1) = can handle higher frequency and repetition without disengaging. Low score (close to 0) = fatigues quickly; needs lower frequency and more creative variety. | | narrative\_persuasion\_score | Measures responsiveness to story-driven, character-centric messaging. | High score (close to 1) = more responsive to story-led persuasion. Low score (close to 0) = more responsive to factual, data-led arguments. | | financial\_optimization\_score | Captures emphasis on budgets, total cost of ownership, savings, and ROI. | High score (close to 1) = actively optimizes spend via comparisons, incentives, and efficiencies. Low score (close to 0) = less focused on optimization; decisions driven by convenience, emotion, or brand. | | identity\_anchoring\_score | Estimates the extent to which stable identity facets anchor preferences and behaviors. | High score (close to 1) = decisions more strongly anchored to self-identity and community affiliation. Low score (close to 0) = identity cues play a limited role; broader, universal appeals are more effective. | | daypart\_responsiveness\_score | Represents typical time-of-day engagement patterns across morning, afternoon, evening, and late night. | High score (close to 1) = engagement most likely to occur in late evening or night hours, often tied to 9-5/traditional working schedules, leisure-oriented activity, or nighttime browsing habits. Low score (close to 0) = engagement most likely in early morning or daytime hours, often tied to retirees, early risers, or structured daily schedules. | | weekday\_vs\_weekend\_activity\_score | Indicates whether engagement tends to concentrate on weekdays or weekends. | High score (close to 1) = activity spikes during weekends, suggesting personal-time engagement patterns, leisure browsing, or discretionary spending focus. Low score (close to 0) = activity spikes during weekdays, often tied to work-mode consumption, professional engagement, or daytime research activity. | | recency\_frequency\_window\_rfw\_score | Composite of behavioral recency and frequency within a defined observation window. | High score (close to 1) = frequent and recent behavioral signals, indicating high responsiveness, rapid feedback cycles, and strong stimulus-response momentum. Low score (close to 0) = infrequent or dated activity patterns, often tied to deliberate decision-making, slower engagement cycles, or lower digital stimulus exposure. | | individualism\_vs\_collectivism\_score | Profiles orientation along a personal autonomy vs group cohesion continuum. | High score (close to 1) = strong individualism, prioritizing autonomy, self-expression, and personal decision-making over group norms or collective expectations. Low score (close to 0) = strong collectivism, valuing social cohesion, group harmony, and community norms over personal preference. | | temporal\_orientation\_score | Describes preference for present-focused versus future-oriented planning horizons. | High score (close to 1) = strongly future-oriented, emphasizing long-term planning, investment in future outcomes, and visionary thinking. Low score (close to 0) = strongly past-oriented, emphasizing tradition, heritage, and historical precedent. Mid-range scores indicate present-orientation, with focus on immediate experiences and short-term goals. | | control\_locus\_internal\_vs\_external | Describes perceived source of control over outcomes along an internal–external locus continuum. | High score (close to 1) = internal locus of control, believing personal effort and choices determine life outcomes. Low score (close to 0) = external locus of control, attributing outcomes to fate, luck, systems, or other external forces. | | stability\_vs\_change\_preference\_score | Profiles preference for consistency versus novelty and change. | High score (close to 1) = strong change preference, openness to disruption, innovation, and novel experiences. Low score (close to 0) = strong stability preference, favoring routine, predictability, and minimizing disruption. | | rationalism\_vs\_emotional\_intuition\_score | Describes dominant decision-making mode along analytic reasoning versus intuitive feeling. | High score (close to 1) = strong rationalism, favoring logic, data, and analysis over emotion in decision-making. Low score (close to 0) = strong emotional intuition, relying on gut feeling, personal resonance, and affective cues. | | civic\_engagement\_and\_agency\_score | Measures participation in community/civic life and perceived personal efficacy. | High score (close to 1) = highly engaged in civic, social, or environmental causes, showing strong personal agency in driving change. Low score (close to 0) = low civic engagement, disinterest in activism, and minimal participation in cause-driven actions. | | institutional\_trust\_and\_skepticism\_score | Profiles orientation toward established institutions and authorities along a trust–skepticism continuum. | High score (close to 1) = strong trust in institutions, accepts authority-backed messaging. Low score (close to 0) = skeptical of institutions, prefers grassroots or independent sources. | | freedom\_vs\_security\_trade\_off\_tendency | Describes preference when personal liberties and public safety/order are in tension. | High score (close to 1) = prioritizes personal freedom and autonomy. Low score (close to 0) = prioritizes safety, order, and regulatory protections. | | materialism\_vs\_meaning\_orientation | Profiles motivational emphasis on material utility/status versus purpose, values, and meaning. | High score (close to 1) = material success-focused, status-motivated. Low score (close to 0) = meaning-oriented, purpose- or values-driven. | | conformity\_vs\_individual\_expression | Captures social style along norm-conforming versus self-expressive orientation. | High score (close to 1) = prioritizes individuality, nonconformist tendencies. Low score (close to 0) = prefers conformity and social alignment. | | authoritarian\_vs\_libertarian\_tendencies\_score | Profiles governance values along authority–order versus autonomy–liberty preferences. | High score (close to 1) = authoritarian-leaning, values centralized authority. Low score (close to 0) = libertarian-leaning, favors minimal governance. | | technological\_optimism\_vs\_tech\_skepticism | Captures attitude toward new technologies along an optimism–skepticism continuum. | High score (close to 1) = embraces new technology, early adopter. Low score (close to 0) = cautious or resistant to technology adoption. | | zero\_sum\_vs\_abundance\_mindset | Describes default outlook on resource dynamics along scarcity/zero-sum versus growth/abundance framing. | High score (close to 1) = abundance-oriented, collaborative. Low score (close to 0) = zero-sum, competitive. | | narrative\_framing\_victim\_vs\_hero\_identity | Profiles preferred identity framing in narratives along victimhood/protection versus agency/overcoming themes. | High score (close to 1) = hero identity, self-driven. Low score (close to 0) = victim identity, advocacy-oriented. | | local\_outlier\_score | Measures how much an individual diverges from local socioeconomic, cultural, and behavioral norms within their immediate geography. | High score (close to 1) = markedly different from local norms. Low score (close to 0) = similar to local population averages. | | neighborhood\_homogeneity\_score | Quantifies how closely an individual’s attributes align with those prevalent in their neighborhood, indicating social and cultural uniformity. | High score (close to 1) = high similarity to local neighbors. Low score (close to 0) = more diverse/dissimilar from neighbors. | | social\_exposure\_score | Measures the extent of routine contact with people who differ in behaviors, demographics, or beliefs across one’s social environments. | High score (close to 1) = regularly exposed to diverse perspectives and backgrounds. Low score (close to 0) = primarily interacts within a homogeneous social group. | | cultural\_affinity\_index | Reflects the concentration and influence of dominant local cultural markers (e.g., language, cuisine, media) shaping everyday preferences. | High score (close to 1) = strong alignment with dominant local cultural markers. Low score (close to 0) = weaker cultural alignment, possibly more cosmopolitan or detached from local traditions. | | community\_velocity\_score | Captures the rate of demographic, economic, and social change in a community that reshapes norms, preferences, and adoption patterns. | High score (close to 1) = rapid change and demographic turnover. Low score (close to 0) = stable, slow-changing community profile. | | conformity\_pressure\_index | Measures the strength of normative social pressure in a geography to align with prevailing group values and behaviors. | High score (close to 1) = high social pressure to conform. Low score (close to 0) = low social pressure, higher tolerance for difference. | | geo\_social\_influence\_density | Captures the concentration of locally influential individuals and network pathways that amplify message spread and behavioral adoption. | High score (close to 1) = high influencer concentration and network impact. Low score (close to 0) = low influencer density and influence reach. | | behavioral\_disparity\_score | Assesses heterogeneity of day-to-day behaviors within a shared geography, indicating need for segmented targeting and varied messaging. | High score (close to 1) = high intra-neighborhood behavioral variance. Low score (close to 0) = behaviorally homogeneous area. | | civic\_cohesion\_score | Measures collective participation in civic life and the presence of shared norms around public engagement and mutual responsibility. | High score (close to 1) = high neighborhood-level civic participation. Low score (close to 0) = low civic engagement at community level. | | sprawl\_intensity\_index | Describes the degree of low-density, car-dependent urban form that shapes mobility, daily routines, and local touchpoint frequency. | High score (close to 1) = low-density sprawl. Low score (close to 0) = dense, walkable environment. | | geo\_economic\_pressure\_score | Aggregates signals of financial strain to reflect household purchasing power and sensitivity to price, risk, and short-term constraints. | High score (close to 1) = high local economic stress. Low score (close to 0) = economically stable area. | | local\_ideology\_consistency\_score | Captures how aligned political, social, and cultural beliefs are within a geography, indicating the strength of shared worldviews. | High score (close to 1) = strong internal ideological alignment. Low score (close to 0) = mixed or ideologically diverse area. | | emotional\_reactivity\_score | Measures the baseline amplitude of affective responses to emotionally salient stimuli across positive and negative cues. | High score (close to 1) = high emotional reactivity. Low score (close to 0) = low reactivity, even-keeled. | | optimism\_bias\_score | Captures the tendency to overestimate positive outcomes and underestimate risks in judgments and planning. | High score (close to 1) = future-hopeful, aspirational. Low score (close to 0) = skeptical or cautious about the future. | | anxiety\_sensitivity\_score | Measures propensity to perceive threat and interpret anxiety cues as harmful, elevating vigilance and avoidance. | High score (close to 1) = highly sensitive to anxiety triggers. Low score (close to 0) = low sensitivity to fear-based messaging. | | empathy\_responsiveness\_score | Captures capacity to recognize, resonate with, and act on others’ experiences conveyed through personal narratives. | High score (close to 1) = highly empathetic, responds strongly to others’ stories. Low score (close to 0) = low empathetic response in decision-making. | | frustration\_tolerance\_score | Measures ability to sustain goal-directed behavior under friction, delays, ambiguity, or setbacks. | High score (close to 1) = high tolerance for friction and setbacks. Low score (close to 0) = quick to disengage when challenged. | | social\_validation\_dependence\_score | Captures reliance on peer consensus, endorsements, and visible social proof when making decisions. | High score (close to 1) = strong dependence on external validation. Low score (close to 0) = self-reliant in decision-making. | | emotional\_volatility\_index | Reflects the frequency and intensity of mood shifts that affect consistency of engagement over time. | High score (close to 1) = frequent/intense emotional changes. Low score (close to 0) = emotionally stable baseline. | | nostalgia\_affinity\_score | Measures positive affect and engagement elicited by cues from one’s past or culturally significant eras. | High score (close to 1) = strong emotional pull toward past references. Low score (close to 0) = minimal nostalgia effect. | | shame\_aversion\_score | Captures motivation to avoid contexts or messages that threaten self-worth through social disapproval or inadequacy cues. | High score (close to 1) = avoids shame-based appeals. Low score (close to 0) = more responsive to guilt or shame framing. | | hope\_orientation\_score | Measures the tendency to approach the future with agency, possibility, and goal-directed optimism. | High score (close to 1) = hopeful, future-positive. Low score (close to 0) = pessimistic or resigned about the future. | ### Purpose This section explains how Moonbrush constructs, updates, and serves psychographic audience models and live behavioral profiles. It covers data sources, modeling approaches, feedback loops from prior campaigns (>100M recipient interactions), and how real‑time intent signals shape on‑the‑fly personalization. *** ### Architecture at a Glance 1. **Data Ingestion** → streaming (events API, web/mobile SDKs) + batch (S3/Blob, warehouse). 2. **Identity & Consent** → deterministic + probabilistic stitching; per‑region consent enforcement. 3. **Normalization & Enrichment** → schema harmonization, PII vaulting, third‑party enrichment (optional). 4. **Feature Store** → durable traits (demographic, psychographic baselines) + ephemeral signals (intent, context). 5. **Modeling Layer** → psychographic estimators, sequence models, causal/uplift models, bandits. 6. **Serving & Orchestration** → real‑time inference (<100ms p95), segment builder, decisioning API. 7. **Feedback Loop** → outcome events, holdouts, counterfactual logging, continuous retraining. *** ### Data Inputs #### 1) Demographic & Firmographic (optional) * **People-level**: age band, region, language, job title/function, income, net worth, gender, device class, tenure, acquisition channel. When applicable, credit scores, religion, ethnicity, and political orientation. * **Account-level**: plan tier, seat count, industry, ARR band. * **Governance**: stored in a restricted PII vault; only hashed/derived features are exposed to models. #### 2) Behavioral History * **Event streams**: pageviews, searches, add‑to‑cart, trial activations, feature use, subscription events. * **Engagement**: opens, clicks, plays, scroll depth, dwell time, repeat visits, session recency and frequency. * **Commerce**: impressions → view → click → purchase funnels; AOV; SKU taxonomy; return/cancel events. #### 3) Campaign Outcomes (>100M interactions) * **Granular outcomes**: delivered, viewed, clicked, conversion, churn/unsubscribe, complaint. * **Controls/holdouts**: random and stratified holdouts attached to each experiment. * **Attribution signals**: last‑touch, time‑decay, and causal lift estimates (see Feedback Loop). #### 4) Intent & Context (real‑time) * **Live actions**: search queries, filter selections, cart edits, hover/dwell, video quartiles, feature toggles. * **Circumstances**: time of day, device/network, geotemporal context, inventory/pricing state. * **Interests**: content categories, keyword embeddings, product affinities inferred from co‑view/ co‑buy graphs. *** ### Psychographic Foundations We map observed signals to standard psychographic constructs commonly used in marketing and UX research: * **Values & Motivations** (e.g., price‑sensitivity vs. quality‑seeking; autonomy/competence/relatedness proxies). * **Attitudes** (brand sentiment, risk tolerance, novelty‑seeking). * **Interests & Lifestyles** (content themes, activity clusters, media diets). * **Decision Styles** (research‑heavy vs. impulse; social proof reliance; deal‑driven vs. feature‑driven). > Note: Moonbrush estimates *probabilistic propensities* for these constructs using observed behavior; it does not store or expose sensitive attributes or diagnoses. *** ### Feature Engineering #### Durable Features (updated daily–weekly) * **Affinity vectors**: item/content embeddings aggregated with time‑decay (λ\_durable). * **Consistency & cadence**: session periodicity, inter‑purchase intervals, brand loyalty indices. * **Value signals**: predicted LTV, elasticity proxies (response vs. price/promo), churn risk. #### Ephemeral Features (updated in seconds) * **Session intent**: current query/category, last N actions, dwell‑weighted recency vector. * **Context**: device, locale, bandwidth class, local time bucket, inventory/price snapshot. * **Volatility**: rapid pivots across categories, abandonment micro‑events, hesitation markers. **Time‑decay weighting** * For an event with age *t* (hours), weight w(t)=e−λtw(t) = e^{-\lambda t}w(t)=e−λt. Separate λ for durable vs. ephemeral streams. *** ### Modeling Layer #### Psychographic Estimators * **Self‑supervised embeddings** over sequences of content/product interactions (Skip‑gram/CBOW‑style + transformer encoders) to learn latent interest spaces. * **Calibrated classifiers/regressors** map latent vectors → psychographic propensities (values, attitudes, decision styles). Platt or isotonic calibration ensures probabilistic outputs. #### Sequence & Preference Models * **Next‑action/next‑best‑content**: transformer or GRU with attention over recent events. * **Collaborative filtering**: matrix factorization / neural CF for cold‑start smoothing via item metadata. #### Causal & Uplift Modeling * **Treatment effect** estimation via meta‑learners (T‑, X‑, DR‑learners) with stratified holdouts. * **Policy optimization**: contextual bandits (Thompson/Bootstrapped) allocate variants to maximize uplift subject to guardrails (fatigue, fairness constraints). *** ### Feedback Loops (from Past Campaigns) **Signals captured**: impression → attention (view/dwell) → engagement (click/feature use) → outcome (conversion/retention) → long‑term value. **Learning pipeline** 1. **Outcome logging** with exposure IDs and control groups for counterfactual estimation. 2. **Attribution**: time‑decay and causal lift are computed per variant and audience slice. 3. **Weight updates**: model features and bandit priors updated using Bayesian/posterior refreshes. 4. **Safeguards**: novelty caps, fatigue scores, and fairness regularizers prevent over‑targeting. **Implicit feedback score (example)** si=1.0⋅click+0.2⋅view+0.05⋅dwell\_min−2.0⋅complaints\_i = 1.0\cdot\text{click} + 0.2\cdot\text{view} + 0.05\cdot\text{dwell\\\_min} - 2.0\cdot\text{complaint}si​=1.0⋅click+0.2⋅view+0.05⋅dwell\_min−2.0⋅complaint Scores feed ranking models and calibration layers; coefficients are tuned per vertical. *** ### Live Behavioral Model Moonbrush combines **durable psychographic baselines** with **ephemeral intent** in real time. **Fusion step (per request)** 1. Retrieve subject’s durable vector ddd and propensities (daily snapshot). 2. Compute session intent vector eee from the last K events (K≤50, 30–120s window). 3. **Context gating**: apply masks for device/locale/inventory constraints. 4. **Blend**: v=αd+(1−α)ev = \alpha d + (1-\alpha) ev=αd+(1−α)e, where α\alphaα is learned per user‑state and task. 5. **Decision**: rank actions/offers/content with a utility function combining uplift, fatigue, and constraints. **Latency targets**: p50 < 40ms, p95 < 100ms at the decision API (varies by deployment). *** ### Personalization Outputs * **Recommendations**: content/product offers, feature tips, UI surfaces. * **Messaging**: subject lines, send times, channel selection (email/push/in‑app), frequency caps. * **Audiences**: dynamic segments based on propensities (e.g., “high novelty‑seeking in category X”). * **Insights**: top drivers of response; cohort comparisons; lift with confidence intervals. *** ### Quality, Safety & Governance #### Privacy & Consent * Region‑aware consent capture; purpose‑limited processing; DSAR/erasure supported. * PII is stored in a vault; models access only anonymized IDs and derived features. #### Bias & Fairness * Pre‑deployment audits: subgroup performance, calibration error, and uplift parity. * On‑going monitors: drift detectors, false‑positive cost bounds, and harm heuristics (e.g., exclude sensitive categories). #### Observability * **Dashboards**: feature drift, label leakage checks, latency/error budgets. * **Experimentation**: sequential tests with early‑stopping; variance control with CUPED/covariates. #### Data Retention * Ephemeral session data: TTL 7–30 days (configurable). * Durable aggregates: 6–24 months with periodic downsampling; raw logs in cold storage per policy. **Research-Driven:** In-depth research papers available upon request, justifying all standard models available within the Moonbrush platform. *** ### Versioning & Change Management * **Model registry** with semantic versioning (MAJOR.MINOR.PATCH). * **Rollouts**: canary by traffic slice and audience; auto‑rollback on KPI regressions. * **Reproducibility**: training manifests (data snapshot, feature definitions, hyperparameters). *** ### Interfaces #### Ingestion * **Streaming**: `POST /v1/events` (JSON), SDKs (JS, iOS, Android). * **Batch**: S3/Blob drop or warehouse connectors; daily hourly sync. #### Decisioning * **Personalize**: `POST /v1/decide` → ranked items with explanations and eligibility notes. * **Segments**: `POST /v1/segments/query` → materialized audience IDs. * **Insights**: `GET /v1/experiments/{id}/lift` → lift, CI, and drivers. *** ### Configuration & Controls * **Guardrails**: frequency/fatigue caps, exclusion lists, sensitive‑category blocks. * **Regionalization**: model routing by locale/market; feature toggles by tenant. * **Explainability**: per‑decision reason codes (top‑k features) and consent trace IDs. *** ### Limitations & Assumptions * Quality depends on event fidelity and consented coverage; cold‑start users fall back to content‑based and contextual policies. * Psychographic constructs are inferred; they are proxies—not ground‑truth beliefs or identities. *** ### Glossary * **Durable features**: slowly‑changing aggregates reflecting longer‑term tendencies. * **Ephemeral intent**: rapidly‑changing, session‑level signals reflecting current goals. * **Uplift**: difference in outcomes between treated vs. control. * **Fatigue**: decreased engagement due to over‑messaging or over‑exposure. ### Implementation & Use Cases | behavioral\_model | use\_case | implementation\_strategy | | ------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | email\_engagement\_score | Rebuild lifecycle to let high‚ propensity users receive richer newsletters while low‚ propensity users get concise, value‚ dense alerts‚Äîlifting opens and reducing unsubscribes. | Create segments by score quantiles; map to 3 creative depths; enforce send‚ time optimization and frequency caps in ESP; measure lift via CUPED‚ adjusted OR/CTR/LTV. | | sms\_engagement\_score | Move time‚ sensitive offers (flash sales, pickup notices) to SMS for users who reliably engage on text, while routing others to email/push. | Set score>0.7 to SMS journey with quiet‚ hours; integrate with Twilio; failover to email after 2 unreads; track incremental conversion vs. control. | | digital\_ad\_engagement\_score | Prioritize high‚ score audiences for rich media/video placements and suppress low‚ score segments to reduce wasted impressions. | Sync audiences to ad platforms; bid up by score; set frequency caps by score; run geo holdouts; optimize to qCPA and attention metrics. | | impulse\_buy\_score | Turn discovery moments into instant conversion with one‚ tap checkout and limited‚ window bundles for spontaneous buyers. | Trigger quick‚ buy CTAs when score>0.75; pre‚ fill payment; 20‚ minute cart locks; measure uplift in same‚ session AOV and checkout latency. | | subscription\_purchase\_score | Promote annual plans and founder‚ tier bundles to users predisposed to subscribe, reducing churn‚ risk cohorts‚Äô exposure to long commitments. | Surface annual vs. monthly by score; add extended trials for mid‚ scores; evaluate via 90‚ day retention and plan mix shift. | | overextension\_risk\_score | Protect customers and revenue by offering softer payment plans and spending safeguards to users at risk of financial overextension. | Gate high‚ ticket offers when score>0.7 risk; show BNPL with budgeting tips; add friction for credit checks; monitor default rate and complaint volume. | | brand\_loyalty\_score | Create VIP lanes‚Äîearly access, surprise‚ and‚ delight, and concierge support‚Äîto deepen advocacy among loyalists. | Score‚ based tiering; unlock perks at thresholds; invite to beta programs; track NPS uplift, referral rate, and repeat purchase cadence. | | coupon\_conversion\_score | Deploy smart offers only where they change outcomes‚Äîstopping blanket discounts while doubling ROI on promotion‚ sensitive shoppers. | Offer recommendation engine using elasticity by score; suppress discounts for low‚ sensitivity; test offer value ladders; read via promo ROI and margin. | | urgency\_response\_score | Use deadline, scarcity, and countdown mechanics only for audiences that respond positively to urgency cues. | Attach limited‚ time banners when score>0.6; throttle frequency to avoid fatigue; evaluate conversion speed delta and backlash metrics. | | influencer\_response\_score | Route creator‚ led narratives to users who trust social voices; use UGC over studio ads for these segments. | Map score to creator whitelists; dynamic ad stitching with UGC; measure view‚ through conversions and save‚ to‚ cart actions. | | luxury\_purchase\_tendency | Introduce limited editions, craftsmanship stories, and concierge trials to customers with elevated luxury propensity. | Personalize PDP modules by score; invite to private drops; track premium SKU mix and appointment bookings. | | expert\_response\_score | Lead with white‚ papers, expert reviews, and certifications for audiences who rely on authority and credentials. | Score‚ gated content hubs; progressive disclosure of specs; attribute via assisted conversions and content dwell time. | | authority\_response\_score | Feature endorsements, seals, and standards compliance where authority cues close the gap in trust. | Inject compliance badges and case citations by score; A/B test placement density; monitor trust lift and checkout completion. | | relationship\_reliance\_score | Leverage community managers and 1:1 advisors for segments that decide through relationships rather than ads. | Assign human advisors when score>0.65; schedule consults; CRM notes into Snowflake; track win rate and time‚ to‚ close. | | emotional\_sensitivity\_score | Use story‚ driven creative that evokes authentic emotion, increasing salience without overwhelming sensitive audiences. | Dynamic tone banding by score; cap intensity; measure recall lift and sentiment safety via reaction signals. | | ethical\_guidelines\_score | Ensure regulated copy and disclaimers are foregrounded for compliance‚ sensitive segments without hurting UX for others. | Score‚ aware compliance components; auto‚ expand disclosures; log acceptance; audit via compliance pass‚ rate and bounce change. | | skepticism\_score | Neutralize doubt with transparent pricing, side‚ by‚ side comparisons, and third‚ party validation. | Trigger comparison widgets and independent reviews when score>0.6; monitor bounce drop and demo request lift. | | research\_depth\_score | Build long‚ form guides and interactive calculators for deep researchers to accelerate confident decisions. | Serve calculators and spec filters by score; cookie persist reading progress; optimize for assisted conversions. | | deal\_hunting\_score | Orchestrate dynamic bundles and total‚ cost‚ of‚ ownership framing to convert value‚ maximizers without eroding margin. | Score‚ tiered bundle pricing; show TCO charts; apply personalized coupon ceilings; read via margin‚ aware conversion. | | trust\_signal\_sensitivity\_score | Surface reviews, guarantees, and security badges prominently for trust‚ driven adopters to compress time‚ to‚ buy. | Guarantee badges + review snippets above the fold when score>0.6; evaluate return rate and first‚ time buyer lift. | | brand\_switching\_score | Capture churn‚ prone shoppers with switcher incentives and painless migration flows from competitors. | Detect score>0.7; offer import tools, matched pricing; track competitive win‚ backs and churn reduction. | | loyalty\_program\_responsiveness\_score | Scale point‚ based incentives, streaks, and gamified tiers where they meaningfully change behavior. | Auto‚ enroll high‚ scores; bonus multipliers during key windows; analyze tier ascents and incremental trips. | | subscription\_fatigue\_score | Prevent churn by downsell paths, pause options, and usage‚ based billing for subscription‚ tired customers. | Score‚ gated cancel intercepts; easy pause; proactive right‚ sizing; track churn saved and CSAT. | | financial\_cautiousness\_score | Offer refundable trials, extended warranties, and price locks to reduce perceived downside for cautious buyers. | Expose risk‚ reversal modules by score; measure trial‚ to‚ paid and refund rates. | | social\_validation\_sensitivity\_score | Feature ‚Äúpeople like you‚Äù testimonials and live social proof for those who rely on peer signals. | Inject cohort‚ matched reviews and counters; throttle to avoid spam; track add‚ to‚ cart and dwell lift. | | exclusivity\_affinity\_score | Run invitation‚ only drops and member‚ only forums that trade on scarcity and belonging. | Issue NFT/QR passes tied to score; monitor waitlist conversion and secondary demand. | | value\_seeker\_score | Orchestrate dynamic bundles and total‚ cost‚ of‚ ownership framing to convert value‚ maximizers without eroding margin. | Score‚ tiered bundle pricing; show TCO charts; apply personalized coupon ceilings; read via margin‚ aware conversion. | | trend\_sensitivity\_score | Accelerate launch communications and influencer collabs to audiences who chase what‚Äôs next. | Priority waitlists; creator teasers; metric: first‚ 24h conversion and share velocity. | | time\_to\_purchase\_sensitivity\_score | Compress funnels for quick actors and add nurturing for slower deciders to maximize both cohorts. | Adaptive flow length by score; short checkout vs. info‚ drip; measure cycle‚ time and CVR. | | mobile\_device\_engagement\_score | Lean into vertical video, tap‚ to‚ buy, and wallet passes for mobile‚ first users. | Serve AMP/PWA; Apple/Google wallet offers; attribute incremental via geo‚ matched holdouts. | | desktop\_device\_engagement\_score | Prioritize comparison tables, multi‚ pane configurators, and deep docs for desktop‚ leaning researchers. | Expose advanced filters; persist comparison sets; optimize for assisted conversions. | | risk\_tolerance\_score | Promote bold, novel offerings and early‚ access betas to higher risk‚ tolerant customers. | Flag score>0.7 to beta invites; add safety rails; monitor adoption and complaint rate. | | convenience\_over\_quality\_score | Win with speed‚Äîsame‚ day delivery, one‚ click support, and simplified SKUs for convenience‚ first buyers. | Prioritize fast‚ ship badges and short PDPs; SLA‚ backed delivery windows; read via repeat fast‚ ship usage. | | locality\_affinity\_score | Anchor creative in neighborhood identity‚Äîlocal causes, teams, and landmarks‚Äîto boost resonance. | Dynamic geo‚ inserts from ZIP; local inventory callouts; measure store visits via geo‚ lift. | | ethical\_consumption\_sensitivity\_score | Lead with sustainability, fair‚ trade, and impact receipts for ethically motivated consumers. | Expose lifecycle impact cards; verified supplier badges; track premium mix and advocacy posts. | | trial\_willingness\_score | Use generous trials, sandbox demos, and reversible commitments to convert explorers. | Score‚ tiered trial length; in‚ trial nudges; KPI: trial activation‚Üípaid conversion. | | privacy\_sensitivity\_score | Offer cookieless experiences, contextual ads, and local data storage options to privacy‚ first users. | Deploy consent‚ aware UX; suppress cross‚ site tags; monitor opt‚ in rates and session depth. | | purchase\_rationality\_score | Lead with cost‚ benefit proofs, benchmarks, and calculators to win analytic decision‚ makers. | Embed ROI models and peer comparisons; track demo‚ to‚ close lift and discount compression. | | emotionally\_charged\_response\_score | Use story‚ driven creative that evokes authentic emotion, increasing salience without overwhelming sensitive audiences. | Dynamic tone banding by score; cap intensity; measure recall lift and sentiment safety via reaction signals. | | information\_overload\_tolerance\_score | Tune information density‚Äîmicro‚ explainer for low tolerance, full spec sheets for high tolerance. | Adaptive content length by score; progressive disclosure; measure scroll depth and comprehension quiz. | | authority\_defiance\_score | Use autonomy‚ affirming messaging and peer success stories instead of top‚ down directives. | Hide authority cues for high‚ defiance; emphasize choice/control; assess sentiment and conversion lift. | | algorithmic\_trust\_score | Lean on recommendations and auto‚ optimize journeys where users trust algorithms to curate for them. | Expose ‚ÄòFor You‚Äô rails and auto‚ bundles; allow override; measure acceptance and dwell. | | brand\_idealism\_alignment\_score | Tie campaigns to purpose pillars and community impact to galvanize value‚ aligned audiences. | Score‚ matched impact stories; donation matching; track brand lift and share of voice. | | civic\_engagement\_intensity\_score | Boost participation in nonpartisan community initiatives (drives, townhalls, volunteering) among civically active users. | Trigger localized civic invites; RSVPs in‚ app; measure attendance and volunteer hours (nonpartisan). | | novelty\_seeking\_behavior\_score | Seed experimental features, AR try‚ ons, and limited concepts to novelty seekers who spread word‚ of‚ mouth. | Score‚ gated labs access; social share hooks; read via UGC volume and viral K‚ factor. | | policy\_focused\_persuasion\_score | Deliver balanced, factual policy explainers and service information to users who prefer issue‚ centric content (nonpartisan). | Serve neutral policy briefs with citations; interactive FAQs; track knowledge gain via quizzes and completion. | | tribe\_conformity\_score | Use community norms and shared symbols to increase relevance while avoiding exclusionary tones. | Community‚ coded creative variants by score; safeguard checks; monitor sentiment and inclusivity metrics. | | deliberative\_decision\_making\_score | Offer compare‚ and‚ contrast decision aids and scenario planners for slow, careful choosers. | Scenario planners; save/return workspaces; KPIs: decision quality proxies, return visits. | | media\_layer\_saturation\_score | Right‚ size frequency across channels to hit recall without fatigue, cutting waste and complaints. | Holistic frequency cap by score; rotate formats; model diminishing returns; track saturation index and CPA. | | moral\_absolutism\_vs\_relativism\_score | Frame messages in clear, principle‚ driven terms for audiences that prefer firm moral anchors. | Tone library with absolutist frames; brand safety review; measure trust and clarity ratings. | | real\_world\_versus\_digital\_world\_bias\_score | Choose physical demos/pop‚ ups vs. virtual trials based on where users prefer to experience value. | Route high real‚ world bias to IRL events; digital bias to VR/interactive; measure attendance vs. session depth. | | intellectual\_engagement\_score | Lead with thought leadership, research cohorts, and deep dives for intellectually curious users. | Invite to salons/webinars; unlock expert AMAs; metrics: content completion, lead quality. | | bargain\_vs\_quality\_bias\_score | Orchestrate dynamic bundles and total‚ cost‚ of‚ ownership framing to convert value‚ maximizers without eroding margin. | Score‚ tiered bundle pricing; show TCO charts; apply personalized coupon ceilings; read via margin‚ aware conversion. | | patriotic\_alignment\_score | Local service and heritage storytelling for audiences aligned with civic pride (nonpartisan). | Use community service spotlights and heritage campaigns; track favorability and local engagement. | | environmental\_risk\_aversion\_score | Offer eco‚ safe products and mitigation plans where environmental risk weighs heavily in decisions. | Expose safety certifications; climate impact calculators; monitor eco‚ SKU mix and opt‚ in to green shipping. | | anonymity\_desire\_score | Provide guest checkout and privacy‚ preserving support for users who value anonymity. | Enable masked emails, guest flows, and private chat; measure conversion without account creation. | | dei\_alignment\_score | Design inclusive creative and partnerships that reflect DEI values for aligned audiences. | Representation audits; partner with inclusive creators; track sentiment lift and brand trust. | | openness\_to\_conspiracy\_non\_mainstream\_ideas | Combat rumor adoption with transparent sourcing and myth‚ busting hubs that reinforce verifiable facts. | Serve fact‚ checked explainers; highlight sources; monitor rumor belief drop (brand safety focus). | | psychological\_security\_seeking\_score | Reduce uncertainty with guarantees, clear steps, and predictable outcomes to unlock momentum. | Add checklists, progress bars, and guarantees; measure drop in abandonment and support tickets. | | polarization\_tolerance\_score | Safely discuss contested topics using balanced frames for users comfortable with high‚ conflict content. | Civility guardrails; dual‚ perspective modules; track completion and complaint rate. | | social\_responsiveness\_score | Accelerate community management by prioritizing DMs/comments from highly responsive users to spark network effects. | Queue weighting in CRM; first‚ response SLAs; measure reply chains and resolution speed. | | risk\_mitigation\_behavior\_score | Bundle risk‚ reducers‚Äîwarranties, trials, expert chat‚Äîinto offers for risk‚ averse segments. | Show mitigation stack by score; log which elements drive conversion; optimize mix. | | religious\_value\_alignment\_score | Respectfully align copy to faith‚ compatible values in appropriate contexts (e.g., charity, community service). | Value‚ aligned wording library; review board; track relevance lift while enforcing sensitivity policies. | | political\_mobilizability\_score | Increase participation in nonpartisan civic actions like registration reminders and community meetings. | Trigger location‚ aware, factual notices; no partisan calls; measure verified completions (nonpartisan). | | experiential\_learning\_score | Replace specs with hands‚ on demos, sandboxes, and try‚ before‚ you‚ buy flows for learners by doing. | Interactive demos unlocked by score; capture behavior telemetry; track trial‚Üíadoption. | | repetition\_receptivity\_score | Use spaced‚ repetition messaging to build memory without annoyance for repetition‚ friendly users. | Cadence planner with Ebbinghaus spacing; control fatigue via feedback loops; measure recall and conversion slope. | | narrative\_persuasion\_score | Tell character‚ driven stories to move story‚ sensitive audiences from awareness to action. | Long‚ form videos/podcasts sequenced by score; assess narrative completion and lift. | | financial\_optimization\_score | Orchestrate dynamic bundles and total‚ cost‚ of‚ ownership framing to convert value‚ maximizers without eroding margin. | Score‚ tiered bundle pricing; show TCO charts; apply personalized coupon ceilings; read via margin‚ aware conversion. | | identity\_anchoring\_score | Frame benefits through stable identity facets‚Äîrole, life stage, community‚Äîto increase fit. | Identity tokens in creative; segment by score; track resonance and repeat engagement. | | daypart\_responsiveness\_score | Shift sends and bids to the hour when each user is most active to capture intent peaks. | Per‚ user send‚ time models; bid modifiers by hour; KPI: response rate lift at optimal windows. | | weekday\_vs\_weekend\_activity\_score | Adjust promotions to weekday vs. weekend modes‚Äîwork vs. leisure‚Äîto match behavioral rhythms. | Calendar‚ aware targeting; rotate creative themes; measure conversion by day type. | | recency\_frequency\_window\_rfw\_score | Re‚ activate lapsed users and throttle over‚ messaged actives using R/F windows personalized by score. | RFW gates in journey builder; reinvigoration offers for dormant cohorts; track reactivation rate. | | individualism\_vs\_collectivism\_score | Position benefits as personal achievement vs. community impact depending on cultural orientation. | Dual headline variants; locale testing; evaluate via engagement split. | | temporal\_orientation\_score | Package near‚ term wins vs. long‚ term payoff in line with users‚Äô planning horizons. | Two‚ track messaging (quick wins vs. investments); monitor plan selection and refund risk. | | control\_locus\_internal\_vs\_external | Empower internal‚ locus users with DIY tools; offer guided services to external‚ locus users. | Self‚ serve vs. concierge toggles; measure satisfaction and completion rates. | | stability\_vs\_change\_preference\_score | Promote continuity upgrades vs. bold redesigns according to appetite for change. | Variant routing; rollback option for cautious users; track upgrade adoption and churn. | | rationalism\_vs\_emotional\_intuition\_score | Balance stats vs. storytelling to match preferred decision style and increase persuasion efficiency. | Creative mixer that weights facts vs. feels by score; multivariate test; measure persuasion lift. | | civic\_engagement\_and\_agency\_score | Mobilize volunteers and donors for community projects using agency‚ affirming messaging (nonpartisan). | Local project feeds; impact trackers; measure sign‚ ups and completion rates. | | institutional\_trust\_and\_skepticism\_score | Neutralize doubt with transparent pricing, side‚ by‚ side comparisons, and third‚ party validation. | Trigger comparison widgets and independent reviews when score>0.6; monitor bounce drop and demo request lift. | | freedom\_vs\_security\_trade\_off\_tendency | Frame benefits in terms of autonomy vs. safety depending on dominant concern. | Tone pivot library; pre‚ test for reactance; measure engagement lift and complaint reduction. | | materialism\_vs\_meaning\_orientation | Position offers as status/utility vs. purpose/impact to match core motivation. | Dual value props; attribution on which frame closes; optimize creative mix. | | conformity\_vs\_individual\_expression | Offer customizable styles to expression seekers and curated defaults to conformity‚ leaning users. | Toggle customization depth; inventory guardrails; track return rate and satisfaction. | | authoritarian\_vs\_libertarian\_tendencies\_score | Use clarity/order framing vs. autonomy/choice framing to match governance preferences (nonpartisan, values‚ oriented). | Message variants with policy‚ neutral language; measure comprehension and comfort signals. | | technological\_optimism\_vs\_tech\_skepticism | Neutralize doubt with transparent pricing, side‚ by‚ side comparisons, and third‚ party validation. | Trigger comparison widgets and independent reviews when score>0.6; monitor bounce drop and demo request lift. | | zero\_sum\_vs\_abundance\_mindset | Frame outcomes as expanding‚ the‚ pie vs. fair reallocation depending on mindset. | Outcome framing tests; cohesion metrics; conversion by frame. | | narrative\_framing\_victim\_vs\_hero\_identity | Position the user as protected vs. empowered protagonist depending on identity resonance. | Story arc switcher; safety rails against victim‚ blame; track lift and sentiment safety. | | local\_outlier\_score | Spot contrarian micro‚ clusters in a town and seed niche products they‚Äôll champion despite local norms. | Detect high outliers; micro‚ influencer seeding; read via cluster‚ level adoption and spillover. | | neighborhood\_homogeneity\_score | Standardize creative in uniform areas to gain scale and reduce variant waste. | One‚ to‚ few templates; geo testing; KPI: CPM efficiency and store lift. | | social\_exposure\_score | Introduce cross‚ cultural products in high‚ exposure areas where diverse networks accelerate diffusion. | Geo‚ graph overlays; place pop‚ ups at transit hubs; track cross‚ sell and referral spread. | | cultural\_affinity\_index | Localize language, cuisine, and symbolism so campaigns feel native to the culture of a neighborhood. | Dynamic creative localization; community review councils; measure authenticity scores and conversion. | | community\_velocity\_score | Time launches to fast‚ changing neighborhoods that adopt quickly and set regional trends. | Velocity heatmaps; early seeding; KPI: speed‚ to‚ 50% adoption vs. control areas. | | conformity\_pressure\_index | Use consensus cues in high‚ pressure towns; celebrate individuality where pressure is low. | Swap norms vs. uniqueness messages; sentiment audits; measure engagement and complaints. | | geo\_social\_influence\_density | Concentrate micro‚ events around local opinion hubs to spark cascading adoption. | Identify hubs; invite networks; track cascade depth and event‚ to‚ sale ratio. | | behavioral\_disparity\_score | Deploy hyper‚ segmented variants in heterogeneous zip codes to match micro‚ cluster needs. | Cluster mapping; DCO with guardrails; KPI: variant ROI vs. broad creative. | | civic\_cohesion\_score | Partner with local orgs in cohesive communities to co‚ host drives and brand activations. | Co‚ branding kits; sign‚ up flows; track co‚ sponsored participation and halo lift. | | sprawl\_intensity\_index | Shift to drive‚ time radio, CTV, and highway OOH where car dependency rules. | Geo‚ based channel plan; optimize dayparts; measure store visits and web uplift by DMA. | | geo\_economic\_pressure\_score | Emphasize affordability, payment flexibility, and guarantees in high‚ strain areas. | Local price testing; payment plans; metric: conversion at constant margin and default rates. | | local\_ideology\_consistency\_score | Use broadly resonant, nonpolarizing community themes where worldview consensus is strong. | Neutral frames; stakeholder pre‚ reads; track complaints and approval ratios. | | emotional\_reactivity\_score | Use emotionally vivid, human‚ centered stories to make messages unforgettable for reactive audiences. | Tone/intensity dial; monitor sentiment safety; track recall and share rate. | | optimism\_bias\_score | Lean into future‚ gain framing for audiences inclined toward positive expectancy. | Gain‚ framed copy variants; validate with uplift in aspirational SKU take‚ rate. | | anxiety\_sensitivity\_score | Reduce fear with clear action steps and reassuring cues that convert concern into control. | Threat‚Üísolution templates; safety checklists; measure compliance and conversion. | | empathy\_responsiveness\_score | Activate giving and community actions with authentic first‚ person stories. | Story capture pipeline; match donor follow‚ ups to impact; track donation rate and retention. | | frustration\_tolerance\_score | Offer advanced flows to patient users and one‚ tap options to low‚ tolerance users to keep both moving. | Adaptive UX depth; progress indicators; KPI: task completion and drop‚ off delta. | | social\_validation\_dependence\_score | Show live counters, trending tags, and neighbor purchases to nudge action. | Real‚ time social widgets; dedupe spam; monitor conversion and trust signals. | | emotional\_volatility\_index | Time outreach to positive windows and keep tone consistent to avoid mood‚ based whiplash. | Event‚ triggered throttling; sentiment tracking; KPI: variance reduction in response. | | nostalgia\_affinity\_score | Revive heritage SKUs and retro campaigns to unlock emotionally anchored loyalty. | A/B test era motifs; limited runs; track uplift among high‚ affinity cohorts. | | shame\_aversion\_score | Replace guilt cues with empowerment framing to motivate without alienation. | Positive CTA library; private outreach; track complaint rate vs. conversion. | | hope\_orientation\_score | Lead with vision and plausible pathways to a better outcome to mobilize hopeful audiences. | Vision decks + stepwise plans; measure engagement with roadmap content and sign‚ ups. | # Standard Personas
Specifications Release Date: September 06, 2025\ Number of standard personas: 463 \ Version: 2.0.1 \ Values: Boolean
### Persona Canon \[Categorizations & Definitions] | persona\_name | category | sub\_category | short\_description | | ---------------------------------------------------- | ------------------------------------------ | -------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | | Highly Engaged Email Users | Behavioral Engagement Patterns | Engagement Profile | Frequently open and engage with email marketing. | | SMS-Responsive Shoppers | Behavioral Engagement Patterns | Engagement Profile | Regularly interact with promotional text messages. | | Multi-Channel Engagers | Behavioral Engagement Patterns | Engagement Profile | Actively engage across email, SMS, and ads. | | High Intent Ad Clickers | Behavioral Engagement Patterns | Engagement Profile | Frequently click on paid ads with purchase signals. | | Low Friction Funnel Movers | Behavioral Engagement Patterns | Engagement Profile | Glide through sales funnels with minimal resistance. | | Silent But Loyal | Behavioral Engagement Patterns | Engagement Profile | Rarely engage but consistently purchase or return. | | Content-Driven Subscribers | Behavioral Engagement Patterns | Engagement Profile | Motivated by value-rich or educational content. | | Re-Engagement Candidates | Behavioral Engagement Patterns | Engagement Profile | Previously active but now require revival. | | Impulse Buyers | Purchase Drivers & Conversion Signals | Purchase Behavior | Make fast, emotion-driven purchases with little deliberation. | | Coupon-Conscious Shoppers | Purchase Drivers & Conversion Signals | Purchase Behavior | Motivated by discounts and promotional codes. | | Subscription-Ready Audience | Purchase Drivers & Conversion Signals | Purchase Behavior | Open to recurring purchase models or trials. | | Risk-Averse Converters | Purchase Drivers & Conversion Signals | Purchase Behavior | Hesitate until reassured or incentivized to act. | | Price-Sensitive Households | Purchase Drivers & Conversion Signals | Purchase Behavior | Purchase decisions heavily influenced by cost. | | Brand-Loyal Buyers | Purchase Drivers & Conversion Signals | Purchase Behavior | Consistently choose familiar brands over alternatives. | | Subscription Skeptics | Purchase Drivers & Conversion Signals | Purchase Behavior | Skeptical of subscriptions or long-term commitments. | | High Affinity + Low Action | Purchase Drivers & Conversion Signals | Purchase Behavior | Show interest but delay action or conversion. | | Mission-Driven Purchasers | Purchase Drivers & Conversion Signals | Purchase Behavior | Prioritize values and purpose when making purchases. | | Hesitant but Interested | Purchase Drivers & Conversion Signals | Purchase Behavior | Need extra reassurance or social proof to convert. | | Visual-First Learners | Messaging Receptivity & Content Affinity | Content Preference | Prefer image-rich or visually guided experiences. | | Scroll-Stopping Video Fans | Messaging Receptivity & Content Affinity | Content Preference | Drawn to compelling video content in feeds. | | Social Proof Seekers | Messaging Receptivity & Content Affinity | Content Preference | Rely on reviews, testimonials, and peer validation. | | Thoughtful Article Readers | Messaging Receptivity & Content Affinity | Content Preference | Engage with long-form written content to inform choices. | | Data-Driven Decision Makers | Messaging Receptivity & Content Affinity | Content Preference | Base decisions on facts, comparisons, and data. | | Multi-Modal Consumers | Messaging Receptivity & Content Affinity | Content Preference | Switch between formats depending on context. | | Passive Browsers | Messaging Receptivity & Content Affinity | Content Preference | Observe more than act, but stay aware of offerings. | | Podcast-Oriented Professionals | Messaging Receptivity & Content Affinity | Content Preference | Prefer audio-based learning and professional podcasts. | | Deep-Dive Explorers | Messaging Receptivity & Content Affinity | Content Preference | Dive deep into specific topics before making decisions. | | Mobile-Optimized Viewers | Messaging Receptivity & Content Affinity | Content Preference | Engage best with content optimized for mobile viewing. | | UGC-Driven Women | Decision-Making Style & Influence Pathways | Influence Sensitivity | Women influenced by relatable content from real users. | | Creator-Friendly Shoppers | Decision-Making Style & Influence Pathways | Influence Sensitivity | Engage positively with influencer and creator content. | | Brand Advocates | Decision-Making Style & Influence Pathways | Influence Sensitivity | Willing to promote and endorse favored brands. | | Cause-Driven Sharers | Decision-Making Style & Influence Pathways | Influence Sensitivity | Share causes and campaigns they believe in. | | Everyday Trend Followers | Decision-Making Style & Influence Pathways | Influence Sensitivity | Adopt trends from peers and mainstream sources. | | Passive Loyalists | Decision-Making Style & Influence Pathways | Influence Sensitivity | Remain loyal but don’t actively promote. | | Micro-Influencer Candidates | Decision-Making Style & Influence Pathways | Influence Sensitivity | Have strong micro-influence within specific circles. | | Visual Influence Responders | Decision-Making Style & Influence Pathways | Influence Sensitivity | React well to visually appealing product showcases. | | Family-Centric Promoters | Decision-Making Style & Influence Pathways | Influence Sensitivity | Promote products that support or benefit their families. | | Reluctant Referrers | Decision-Making Style & Influence Pathways | Influence Sensitivity | Need nudging to refer or advocate to others. | | High Net Worth Purchasers | Financial & Value Sensitivity | Value Tier / Financial Signals | Have disposable income and willingness to spend. | | Middle-Income Mainstream | Financial & Value Sensitivity | Value Tier / Financial Signals | Represent the average consumer with stable income. | | Financially Overextended | Financial & Value Sensitivity | Value Tier / Financial Signals | Struggle with debt or spending beyond means. | | Coupon-Reliant Buyers | Financial & Value Sensitivity | Value Tier / Financial Signals | Depend on deals and discounts to make purchases. | | Subscription-Savvy Savers | Financial & Value Sensitivity | Value Tier / Financial Signals | Comfortable with subscriptions that offer savings. | | Luxury-Inclined Optimizers | Financial & Value Sensitivity | Value Tier / Financial Signals | Interested in luxury when paired with value. | | Budget-Cautious Parents | Financial & Value Sensitivity | Value Tier / Financial Signals | Juggle needs of family and financial caution. | | No-Frills Spenders | Financial & Value Sensitivity | Value Tier / Financial Signals | Spend conservatively, prioritizing necessities. | | Upsell-Ready Professionals | Financial & Value Sensitivity | Value Tier / Financial Signals | Open to upgrades or higher-value offers. | | Deal-Only Converters | Financial & Value Sensitivity | Value Tier / Financial Signals | Only convert when deals are too good to pass. | | Ready-to-Buy Actives | Lifecycle & Loyalty State | Lifecycle & Intent | Actively browsing with immediate buying potential. | | Mid-Funnel Researchers | Lifecycle & Loyalty State | Lifecycle & Intent | Comparing options and gathering info mid-journey. | | Top-of-Funnel Browsers | Lifecycle & Loyalty State | Lifecycle & Intent | Just becoming aware or exploring possibilities. | | Cart Drop-Off Risks | Lifecycle & Loyalty State | Lifecycle & Intent | Show intent but abandon carts or checkout. | | Re-Engagement Needed | Lifecycle & Loyalty State | Lifecycle & Intent | Require win-back strategies to return to action. | | Education-First Leads | Lifecycle & Loyalty State | Lifecycle & Intent | Need education before committing to purchase. | | High-Intent Category Seekers | Lifecycle & Loyalty State | Lifecycle & Intent | Searching for something specific in your category. | | Urgency-Triggered Shoppers | Lifecycle & Loyalty State | Lifecycle & Intent | React to time-sensitive offers or urgency cues. | | Conversion-Ready Subscribers | Lifecycle & Loyalty State | Lifecycle & Intent | Just need a final push before purchasing. | | Passive Considerers | Lifecycle & Loyalty State | Lifecycle & Intent | Thinking passively, might convert with right trigger. | | Millennial Decision Makers | Demographics & Identity Composition | Targeting Demographics | Make household or professional decisions regularly. | | Affluent Urban Professionals | Demographics & Identity Composition | Targeting Demographics | Live in cities and have disposable income. | | Young Suburban Families | Demographics & Identity Composition | Targeting Demographics | Young families seeking value and convenience. | | Retired or Near-Retired | Demographics & Identity Composition | Targeting Demographics | Older individuals with stable lifestyle and needs. | | Single Urban Renters | Demographics & Identity Composition | Targeting Demographics | Single professionals in rental-heavy areas. | | College-Educated Suburbanites | Demographics & Identity Composition | Targeting Demographics | Well-educated and located in suburban regions. | | High Net Worth Homeowners | Demographics & Identity Composition | Targeting Demographics | Wealthy, property-owning consumers with assets. | | Working Moms | Demographics & Identity Composition | Targeting Demographics | Juggle careers and family, often multitasking. | | Rural Self-Starters | Demographics & Identity Composition | Targeting Demographics | Live outside major cities and value independence. | | Gen Z First-Time Buyers | Demographics & Identity Composition | Targeting Demographics | Younger shoppers making first major purchases. | | Convenience-Driven Shoppers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Prioritize ease and speed in shopping experience. | | Brand Loyalists | Psychographics & Motivational Archetypes | Psychographics & Mindset | Stick with trusted names and familiar brands. | | Deal Seekers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Actively seek out bargains and low prices. | | Aspiration-Driven Consumers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Driven by dreams of upward mobility or success. | | Community-Oriented Buyers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Value local causes and social connection. | | Exploration-Driven Shoppers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Enjoy trying new things and discovering brands. | | Pragmatic Purchasers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Make logical, measured purchasing decisions. | | Trend Seekers | Psychographics & Motivational Archetypes | Psychographics & Mindset | Follow fashion, tech, or lifestyle trends closely. | | Research-Driven Skeptics | Psychographics & Motivational Archetypes | Psychographics & Mindset | Analyze deeply and question claims before buying. | | Emotion-First Responders | Psychographics & Motivational Archetypes | Psychographics & Mindset | Buy based on gut feeling or emotional pull. | | Extreme Coupon Reliants | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Require strong discounts to convert. Often from low-income brackets and highly promo-sensitive. | | Deal-Triggered Shoppers | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Actively browse but only purchase when there's a compelling offer. | | Value-Driven Pragmatists | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Cost-conscious but still evaluate product quality. | | Incentive-Boosted Converters | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Sitting on the fence — a well-timed discount tips them into buying. | | Luxury Deal Seekers | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | High-income but still motivated by value. Great for aspirational discounting. | | Occasional Bargain Hunters | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Look for deals during key retail moments (e.g., holidays, back-to-school). | | Promotional Fatigue Risks | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Overexposed to discounts — may be tuning them out. | | Default Discount Expecters | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Conditioned to never pay full price — require ongoing incentives. | | Coupon-Savvy Professionals | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Middle-income earners who strategically use promos but remain brand-loyal. | | Upsell-Resistant Discounters | Purchase Drivers & Conversion Signals | Coupon & Promo Sensitivity | Hard to move up the value chain — always optimizing for cost. | | Flash Sale Converters | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Respond quickly to limited-time deals; highly reactive to urgency cues. | | Impulse-Driven Buyers | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Make rapid decisions based on emotional triggers or FOMO. | | Deadline-Triggered Shoppers | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Wait until the last minute, then act right before a promotion ends. | | Deal Chasers with FOMO | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Motivated by the fear of missing out rather than product need. | | Last-Minute Subscribers | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Delay action but convert under urgency — especially with recurring models. | | Fast Funnel Movers | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Move quickly from awareness to conversion with minimal resistance. | | Inertia Break Candidates | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Low urgency and require a strong nudge to move forward. | | Slow-Deciders with Conversion Potential | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Need extra time and social proof before acting. | | Panic Buyers | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Overreact to urgency-based messaging — may overconvert or churn. | | Young Urgent Actors | Purchase Drivers & Conversion Signals | Urgency Responsiveness | Younger users who convert fast with time-sensitive creative. | | True Impulse Buyers | Purchase Drivers & Conversion Signals | Impulse Tendencies | Highly reactive and make unplanned purchases with little friction. | | Emotion-Driven Shoppers | Purchase Drivers & Conversion Signals | Impulse Tendencies | Purchase decisions are guided by emotion, not logic. | | Checkout Clickers | Purchase Drivers & Conversion Signals | Impulse Tendencies | Tend to convert on first or second exposure, often without promo. | | Stress Spenders | Purchase Drivers & Conversion Signals | Impulse Tendencies | Impulse behavior linked to financial or emotional pressure. | | Flashy Followers | Purchase Drivers & Conversion Signals | Impulse Tendencies | Swayed by influencers and cultural hype — quick to click. | | Late-Stage Impulsers | Purchase Drivers & Conversion Signals | Impulse Tendencies | Appear cautious initially but convert quickly when triggered. | | Discount-Driven Impulsers | Purchase Drivers & Conversion Signals | Impulse Tendencies | Impulse actions triggered mostly by promo content. | | Younger Impulse Segment | Purchase Drivers & Conversion Signals | Impulse Tendencies | Gen Z / Millennial users who act quickly without much deliberation. | | Impulse-Ready But Brand-Attached | Purchase Drivers & Conversion Signals | Impulse Tendencies | Open to impulsive behavior but tend to stay within trusted brands. | | Impulse Fatigue Risks | Purchase Drivers & Conversion Signals | Impulse Tendencies | Previously high impulse behavior, now slowing or burning out. | | Trial-Oriented Explorers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Eager to try new products and services with little commitment. | | Subscription-Ready Converters | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | High likelihood to convert to paid after trial. | | Freemium Optimizers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Use trials without intent to convert — want long-term free value. | | Fatigue-Prone Subscribers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Saturated by subscriptions — cautious or resistant to new ones. | | Promo-Sensitive Signups | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Require discounts or incentives to start trials or subs. | | Utility-Focused Subscribers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Subscribe for function, not brand or novelty. | | Subscription Skeptics | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Generally cautious about recurring charges; need clear value. | | Impulse Trialers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Try products quickly when exposed — often via email or SMS. | | Loyalty-Linked Subscribers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Join programs or subscriptions tied to brands they already trust. | | One-and-Done Samplers | Purchase Drivers & Conversion Signals | Trial & Subscription Behavior | Try once, rarely renew — often trial hopping. | | Loyalty Anchors | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Deep brand attachment; unlikely to switch or need heavy incentives. | | Program-Driven Stickiness | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Strong affinity for loyalty programs and rewards ecosystems. | | Conditional Loyalists | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Loyal to a point — may switch if value or service drops. | | Incentive-Locked Repeaters | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Appear loyal but driven more by recurring promotions. | | Switch-Ready Shoppers | Purchase Drivers & Conversion Signals | Loyalty Dynamics | High brand switching propensity — very persuadable. | | Fatigue-Driven Flight Risks | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Subscribed in the past but likely to churn or abandon loyalty models. | | Loyal Advocates | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Loyal, vocal, and brand-aligned — likely to promote and defend. | | Disillusioned Former Loyalists | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Were loyal, but recent signals show waning attachment. | | Value-First Swappers | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Will switch brands if the deal or utility is better. | | Upscale Brand Monogamists | Purchase Drivers & Conversion Signals | Loyalty Dynamics | Wealthy consumers who stick with legacy or premium brands. | | Chronic Brand Switchers | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Frequently change brands; loyalty is minimal and price or novelty-driven. | | Value-Driven Brand Changers | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Switch when better deals or cost/value efficiency is evident. | | Promo-Responsive Swappers | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Respond well to brand-switching incentives or comparison messaging. | | Trend-Based Switchers | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Shift brands based on social momentum, influencers, or trends. | | Youthful Experimenters | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Younger audiences with low loyalty and high curiosity. | | A/B Testers | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Not disloyal, but like to try different brands before choosing a favorite. | | Brand Nomads | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Rarely stick to one brand, especially in low-stakes or commodity categories. | | Frustration-Based Switchers | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Recent drop in loyalty combined with rising switch intent. | | Loyal-Until-Better-Offer | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Loyal under normal conditions, but promotional pressure triggers switch. | | Brand-Hoppers with FOMO | Purchase Drivers & Conversion Signals | Brand Switching Behavior | Try new brands often due to social proof, influencer hype, or trend anxiety. | | Burned-Out Subscribers | Purchase Drivers & Conversion Signals | Subscription Fatigue | Highly fatigued — likely unsubscribed from multiple services already. | | Skeptical of Recurring Charges | Purchase Drivers & Conversion Signals | Subscription Fatigue | Sub fatigue plus low trust in long-term commitments. | | Deal-Dependent Subscribers | Purchase Drivers & Conversion Signals | Subscription Fatigue | Only consider subscriptions with strong, ongoing savings. | | Trial-Only Consumers | Purchase Drivers & Conversion Signals | Subscription Fatigue | Frequently try, rarely convert — sample-focused behavior. | | Subscription Resisters | Purchase Drivers & Conversion Signals | Subscription Fatigue | Explicit behavioral signals that suggest recurring models are a no-go. | | Subscription Treadmill Users | Purchase Drivers & Conversion Signals | Subscription Fatigue | Maintain many active subs but show rising signs of burnout. | | Fatigue + Value-First Audience | Purchase Drivers & Conversion Signals | Subscription Fatigue | Will cancel if the value isn’t immediately apparent. | | Low-Income Fatigue Segment | Purchase Drivers & Conversion Signals | Subscription Fatigue | Subscription exhaustion tied to financial strain. | | Brand-Weary Loyalists | Purchase Drivers & Conversion Signals | Subscription Fatigue | Formerly loyal subscribers beginning to check out. | | Urgency-Triggered Trialers | Purchase Drivers & Conversion Signals | Subscription Fatigue | Still respond to urgency or FOMO despite subscription fatigue. | | Affluent Free-Spenders | Financial & Value Sensitivity | Spending Power Signals | High income and net worth with low spending resistance. | | Upscale Pragmatists | Financial & Value Sensitivity | Spending Power Signals | Wealthy but thoughtful with money — prefer justified spend. | | Mass-Affluent Professionals | Financial & Value Sensitivity | Spending Power Signals | Strong income signals, mid-high net worth — open to upsell. | | Middle-Income Mainstream | Financial & Value Sensitivity | Spending Power Signals | Represent a large, stable core of buyers. | | Budget-Constrained Spenders | Financial & Value Sensitivity | Spending Power Signals | Likely to spend, but under tight financial pressure. | | Cautious High Earners | Financial & Value Sensitivity | Spending Power Signals | High income, but deliberate and conservative with purchases. | | Status-Driven Spenders | Financial & Value Sensitivity | Spending Power Signals | Drawn to exclusivity, even at premium prices. | | Subscription-Comfortable Buyers | Financial & Value Sensitivity | Spending Power Signals | Can afford recurring costs and show high adoption. | | High Net Worth Optimizers | Financial & Value Sensitivity | Spending Power Signals | Wealthy users still seeking practical ROI or value. | | Financially Stretched, Deal-Motivated | Financial & Value Sensitivity | Spending Power Signals | Sensitive to cost, but can convert if value is clear. | | Financially Overextended | Financial & Value Sensitivity | Overextension Risk Profile | High risk of spending beyond means — vulnerable to debt or churn. | | Low-Income Risk Audience | Financial & Value Sensitivity | Overextension Risk Profile | Living paycheck to paycheck; hard to convert without strong incentive. | | Impulse-Driven Risk Segment | Financial & Value Sensitivity | Overextension Risk Profile | Emotion-led spending habits combined with financial pressure. | | Caution-Zone Spenders | Financial & Value Sensitivity | Overextension Risk Profile | Not critically overextended, but need persuasive value to act. | | Subscription-Strained Shoppers | Financial & Value Sensitivity | Overextension Risk Profile | Under financial pressure from recurring services. | | Price-Sensitive, Risk-Averse | Financial & Value Sensitivity | Overextension Risk Profile | High caution and low financial flexibility — may disengage easily. | | Deal-Reliant Outspenders | Financial & Value Sensitivity | Overextension Risk Profile | Spend more than they can afford — rely on promotions to rationalize. | | Moderate Risk, Responsive to Structure | Financial & Value Sensitivity | Overextension Risk Profile | On the edge, but likely to respond to clear budgeting or loyalty messaging. | | Aspirational Risk Takers | Financial & Value Sensitivity | Overextension Risk Profile | Stretch financially to pursue status or lifestyle upgrades. | | Recovering Over-Spenders | Financial & Value Sensitivity | Overextension Risk Profile | Previously high risk, now slowing spending patterns. | | Frugal Family Shoppers | Financial & Value Sensitivity | Household Value Orientation | Prioritize savings and functionality for multi-person households. | | Budget-Cautious Parents | Financial & Value Sensitivity | Household Value Orientation | Need to balance household needs with limited financial flexibility. | | Cost-Efficient Homeowners | Financial & Value Sensitivity | Household Value Orientation | Prefer products/services that provide long-term value for the household. | | Promo-Optimized Households | Financial & Value Sensitivity | Household Value Orientation | Plan around promotions, bundles, or loyalty-based savings. | | Family-Centered Practical Spenders | Financial & Value Sensitivity | Household Value Orientation | Seek products that meet everyday needs without overspending. | | Cautious Domestic Decision-Makers | Financial & Value Sensitivity | Household Value Orientation | Play the long game — slow to commit without clear utility. | | Two-Income Value Prioritizers | Financial & Value Sensitivity | Household Value Orientation | More financial room but still plan carefully. | | Price-Sensitive but Brand-Aware | Financial & Value Sensitivity | Household Value Orientation | Care about value but not purely price-driven. | | Simplicity-First Utility Buyers | Financial & Value Sensitivity | Household Value Orientation | Purchase only what’s necessary — efficiency over excitement. | | Risk-Averse Home Value Seekers | Financial & Value Sensitivity | Household Value Orientation | Need strong reassurance and long-term savings to engage. | | Deep-Dive Researchers | Decision-Making Style & Influence Pathways | Research Behavior Profile | Extensively compare, verify, and read before acting. | | Validation-Driven Evaluators | Decision-Making Style & Influence Pathways | Research Behavior Profile | Seek third-party reviews and social proof to validate choices. | | Slow & Thorough Converters | Decision-Making Style & Influence Pathways | Research Behavior Profile | Move through the funnel cautiously and deliberately. | | Education-First Buyers | Decision-Making Style & Influence Pathways | Research Behavior Profile | Require a knowledge base before committing — ideal for content marketing. | | Comparative Shoppers | Decision-Making Style & Influence Pathways | Research Behavior Profile | Actively explore competing brands before choosing. | | Cautious but Persuadable | Decision-Making Style & Influence Pathways | Research Behavior Profile | Rely on facts and structure, but can be nudged with clarity. | | Mid-Funnel Analysts | Decision-Making Style & Influence Pathways | Research Behavior Profile | Still deciding — high info consumption, mid urgency. | | Brand-Aware Researchers | Decision-Making Style & Influence Pathways | Research Behavior Profile | Stick to familiar brands, but still conduct diligence. | | High-Intent, High-Info Seekers | Decision-Making Style & Influence Pathways | Research Behavior Profile | Have clear purpose but want to be 100% sure. | | Functional Knowledge Gatherers | Decision-Making Style & Influence Pathways | Research Behavior Profile | Research primarily for practical/utility-based purchases. | | Validation-First Converters | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Need to see reviews, testimonials, or certifications to move forward. | | Third-Party Trust Seekers | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Prefer verified or socially endorsed content before purchase. | | Risk-Aware Buyers | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Cautious by nature — require guarantees, transparency, and structure. | | Parental Safety Prioritizers | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Trust and safety are top priorities due to family status. | | Skeptical Researchers | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | High research engagement and low impulsivity — take convincing. | | Senior Trust Dependents | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Older buyers who require familiarity and credibility to convert. | | Brand Reassurance Seekers | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Loyal but still want signals of consistency and quality. | | Low Net Worth, High Trust Needs | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Budget-conscious buyers who need extra risk reduction. | | Content-Guided Believers | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Rely on value-driven content to develop trust. | | Moderate Trust Builders | Messaging Receptivity & Content Affinity | Trust Signal Sensitivity | Some trust needed, but less sensitive than highly risk-averse groups. | | Emotional Impulse Responders | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Act on feeling, inspiration, or status appeal — not logic. | | Rational Evaluators | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Require logic, clarity, and structure — low tolerance for fluff. | | Social Emotionally Motivated Buyers | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Make decisions based on peer behavior, identity, or social validation. | | Urgency-Led Emotional Converters | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Act in the moment — quick decisions under pressure. | | Security-Oriented Rationalists | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Prioritize safety, transparency, and credibility above all. | | Emotive Brand Loyalists | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Motivated by trust, affinity, and consistency. | | Efficiency-Driven Realists | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Rational, value-conscious, time-limited buyers. | | Trend-Fueled Impulsives | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Respond emotionally to newness, popularity, or aesthetics. | | Balanced Dual-Motivators | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Combine logic and emotion in a blended decision process. | | Emotionally Motivated Skeptics | Psychographics & Motivational Archetypes | Emotional vs Rational Motivators | Feel deeply but still need reassurance before converting. | | Now-Window Buyers | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Active, late-funnel, and ready to convert quickly. | | Mid-Funnel Movers | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Considered but warming up — likely to convert with the right trigger. | | Delayed Decision-Makers | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Early-stage prospects — need nurturing and more info. | | Impulse Prone Accelerators | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | May convert fast due to emotional pull or time-based CTA. | | Cautious Evaluators | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Will take their time — likely rational, risk-aware. | | Test-and-Decide Buyers | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Require a sample or low-friction trial to move forward. | | Brand-Conditioned Converters | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Respond faster if familiar with the brand. | | Age-Driven Speed Seekers | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Younger, mobile-first buyers with fast purchase rhythm. | | Desktop-Dominant Deliberators | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | Tend to browse via desktop, indicating longer cycles. | | Intent Surfers | Purchase Drivers & Conversion Signals | Time-to-Purchase Velocity | High frequency, but still uncommitted — watching but not acting yet. | | Mobile-First Shoppers | Behavioral Engagement Patterns | Device Engagement Pattern | Strong mobile affinity — likely browsing on the go, responsive to SMS/push, short attention span. | | Desktop-Primary Evaluators | Behavioral Engagement Patterns | Device Engagement Pattern | Longer sessions, research-heavy, likely on laptop or work device. | | Cross-Device Fluid Browsers | Behavioral Engagement Patterns | Device Engagement Pattern | Comfortable switching between devices — respond to omnichannel strategy. | | Mobile-Only Convenience Seekers | Behavioral Engagement Patterns | Device Engagement Pattern | Fast-decision buyers with strong mobile signals and preference for frictionless UX. | | Work-Hour Desktop Users | Behavioral Engagement Patterns | Device Engagement Pattern | Most active during 9–5, desktop-focused, likely in office environments. | | Evening Mobile Browsers | Behavioral Engagement Patterns | Device Engagement Pattern | Scroll-heavy behavior outside of work hours, often after 6PM. | | SMS-Responsive Mobile Users | Behavioral Engagement Patterns | Device Engagement Pattern | Likely to respond to text offers, fast tap-through buyers. | | Email-on-Desktop Consumers | Behavioral Engagement Patterns | Device Engagement Pattern | Email-responsive but only via desktop — slower cadence, longer reads. | | Mobile Video-First Scrollers | Behavioral Engagement Patterns | Device Engagement Pattern | High affinity for visual content on mobile. | | Email-First Converters | Behavioral Engagement Patterns | Channel Affinity | Most responsive to email campaigns; prefer thoughtful, info-rich touchpoints. | | SMS-Responsive Shoppers | Behavioral Engagement Patterns | Channel Affinity | Strong responders to SMS and push; expect short-form urgency-driven messaging. | | Ad-Driven Browsers | Behavioral Engagement Patterns | Channel Affinity | Likely to click or engage via paid media — responsive to display or social ads. | | Multi-Channel Engaged | Behavioral Engagement Patterns | Channel Affinity | Engages reliably across all major outreach methods — strong full-funnel candidate. | | Social-Influenced Buyers | Behavioral Engagement Patterns | Channel Affinity | Responds more to social proof, influencers, and peer-based channels. | | Mobile-Only Tappers | Behavioral Engagement Patterns | Channel Affinity | Highly mobile-driven behavior — engages primarily via phone-based channels. | | Desktop Email Loyalists | Behavioral Engagement Patterns | Channel Affinity | Email responders who prefer traditional desktop browsing — slower but deliberate. | | Ad-Skipping Inbox-Checkers | Behavioral Engagement Patterns | Channel Affinity | Avoid paid ads but frequently engage with email content. | | SMS-Focused Flash Sale Fans | Behavioral Engagement Patterns | Channel Affinity | React well to time-limited deals and urgency messaging on mobile. | | Channel Ambivalent | Behavioral Engagement Patterns | Channel Affinity | Low scores across the board — difficult to reach via direct channels. | | Privacy-Conscious & Cautious | Messaging Receptivity & Content Affinity | Privacy Sensitivity | Strong preference for minimal tracking, low-retargeting tolerance, and careful data handling. | | Data-Sharing Skeptics | Messaging Receptivity & Content Affinity | Privacy Sensitivity | Hesitant to opt in or create accounts; prefer brands with strong privacy stances. | | Email-Only Opt-In Users | Messaging Receptivity & Content Affinity | Privacy Sensitivity | Willing to receive email but reject other tracking or SMS outreach. | | Low-Trust Holdouts | Messaging Receptivity & Content Affinity | Privacy Sensitivity | Rarely engage via trackable channels; may use ad blockers or burner info. | | Privacy-Minded Professionals | Messaging Receptivity & Content Affinity | Privacy Sensitivity | High education/income, desktop preference, and measured online behavior. | | Progressive Privacy Advocates | Messaging Receptivity & Content Affinity | Privacy Sensitivity | Sensitive to ethics and privacy — expect strong messaging around transparency. | | Cautious but Convertible | Messaging Receptivity & Content Affinity | Privacy Sensitivity | May share data if value is clear — require strong upfront messaging. | | Silent Browsers | Messaging Receptivity & Content Affinity | Privacy Sensitivity | High intent with low trackable engagement; likely using privacy tools. | | Legal-Driven Privacy Seekers | Messaging Receptivity & Content Affinity | Privacy Sensitivity | More responsive in regulated categories (finance, healthcare, insurance). | | Low Sensitivity, High Yield | Messaging Receptivity & Content Affinity | Privacy Sensitivity | Minimal privacy concern — more flexible with targeting and retargeting. | | Cautious & Conservative Buyers | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Strong aversion to financial or product risk. Require guarantees, proof, and security. | | Risk-Aware Researchers | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Not impulsive — research thoroughly and mitigate risk via due diligence. | | Trial-Driven Evaluators | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Lower risk takers, but willing to test before full commitment. | | Calculated Risk-Takers | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Accept some risk for value or upside — usually mid-funnel, cost-benefit driven. | | Risk-Tolerant Explorers | Psychographics & Motivational Archetypes | Risk Tolerance Profile | More open to trying new brands, investing, or adopting unproven offers. | | Impulse-Led High Risk-Takers | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Likely to act quickly with low friction — minimal fear of negative outcomes. | | Status-Seeking Risk Players | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Motivated by exclusivity and prestige — risk accepted for premium payoff. | | Financially Secure Adventurers | Psychographics & Motivational Archetypes | Risk Tolerance Profile | High income/net worth and high risk tolerance — open to luxury, investing, experimentation. | | Disengaged Risk-Neutrals | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Neither particularly cautious nor bold — act based on convenience or inertia. | | Risk-Averse Parents | Psychographics & Motivational Archetypes | Risk Tolerance Profile | Highly cautious segments often tied to household protection, children, or insurance mindset. | | Trend-Driven Early Adopters | Psychographics & Motivational Archetypes | Trend Responsiveness | High likelihood to act on new styles, products, or culture shifts. Responsive to social/influencer velocity. | | Mobile-First Trend Watchers | Psychographics & Motivational Archetypes | Trend Responsiveness | Follow trends heavily on mobile and social apps — ideal for reels, drops, or TikTok-style formats. | | Influencer-Aligned Buyers | Psychographics & Motivational Archetypes | Trend Responsiveness | Most likely to follow and purchase based on influencer recommendations. | | Trend-Flexible but Cautious | Psychographics & Motivational Archetypes | Trend Responsiveness | Aware of trends, but apply discretion — seek value or trust before acting. | | Style-First Converters | Psychographics & Motivational Archetypes | Trend Responsiveness | Motivated by aesthetic and social capital — often seen in fashion, beauty, or lifestyle categories. | | Social Circle Validators | Psychographics & Motivational Archetypes | Trend Responsiveness | React to what peers adopt rather than creators or brands. | | Trend-Immune Pragmatists | Psychographics & Motivational Archetypes | Trend Responsiveness | Focus on utility or value — unlikely to respond to trend-based messaging. | | Younger Trend Seekers | Psychographics & Motivational Archetypes | Trend Responsiveness | Gen Z / Young Millennial cluster — high trend sensitivity + mobile preference. | | Mid-Funnel Style Samplers | Psychographics & Motivational Archetypes | Trend Responsiveness | Observe trends before committing — good fit for retargeting and trial offers. | | Flash Drop Responders | Psychographics & Motivational Archetypes | Trend Responsiveness | Highly responsive to limited-time launches and cultural spikes. | | Luxury-Oriented Buyers | Psychographics & Motivational Archetypes | Exclusivity Affinity | High-income, high-exclusivity preference. Respond best to premium product lines and elite messaging. | | Limited Edition Responders | Psychographics & Motivational Archetypes | Exclusivity Affinity | Motivated by scarcity, gated access, and invitation-only drops. | | Brand-Status Loyalists | Psychographics & Motivational Archetypes | Exclusivity Affinity | View brands as identity signals — prefer exclusive or aspirational brands. | | Experience-Driven Elites | Psychographics & Motivational Archetypes | Exclusivity Affinity | Prioritize exclusive experiences or memberships over deals or savings. | | Trend-Luxury Hybrids | Psychographics & Motivational Archetypes | Exclusivity Affinity | Blend fashion-forward tastes with exclusivity — ideal for drops and collabs. | | Influencer-Driven Prestige Shoppers | Psychographics & Motivational Archetypes | Exclusivity Affinity | Follow aspirational creators and mirror their choices. | | Selective Deal Seekers | Psychographics & Motivational Archetypes | Exclusivity Affinity | Still want value, but only if it feels elevated — avoid “cheap” framing. | | Upscale Mobile Buyers | Psychographics & Motivational Archetypes | Exclusivity Affinity | Browse and buy luxury goods via mobile-first journeys. | | Younger Aspirationals | Psychographics & Motivational Archetypes | Exclusivity Affinity | Mid-income but exclusivity-driven — motivated by perception/status. | | Premium Re-Engagement Targets | Psychographics & Motivational Archetypes | Exclusivity Affinity | Once-engaged with high-end content, now silent — prime for relaunch campaigns. | | Mission-Driven Buyers | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Prioritize ethical sourcing, sustainability, DEI, and fair trade — core to purchasing behavior. | | Eco-Optimized Shoppers | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Sustainability-first — will switch brands to reduce waste or carbon footprint. | | Ethical but Budget-Conscious | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Values ethics, but needs price alignment — responds to sustainable value bundles. | | Lifestyle-Aligned Greens | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Tend to buy ethical products as a reflection of lifestyle and identity. | | Young Conscious Consumers | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Gen Z or young millennial skew — responsive to ethical storytelling and values branding. | | Green-Curious Pragmatists | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Interested in sustainability but not yet highly motivated — good fit for education and nudge campaigns. | | Cause-Based Campaign Responders | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Donate, volunteer, or support campaigns when tied to tangible impact. | | Exclusivity-Conscious Eco Buyers | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Buy sustainable and ethical products, but only when premium or upscale. | | Green Re-Engagement Segment | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Previously active on sustainability content but recently dormant — ripe for retargeting. | | ZIP-Based Sustainability Cluster | Psychographics & Motivational Archetypes | Green & Ethical Orientation | Lives in high-density, eco-forward ZIPs — likely aligned with community-driven sustainability trends. | | Locally Loyal Shoppers | Demographics & Identity Composition | Regional & Local Identity | Prioritize businesses, brands, or events tied to their region or city. | | Community-Driven Consumers | Demographics & Identity Composition | Regional & Local Identity | Responsive to “support local” campaigns and civic engagement. | | ZIP Cluster Loyalists | Demographics & Identity Composition | Regional & Local Identity | Live in ZIPs with high local density and consistent local engagement behavior. | | Hyper-Local Family Anchors | Demographics & Identity Composition | Regional & Local Identity | Family-oriented, stationary households with high local affinity. | | Regional Event Responders | Demographics & Identity Composition | Regional & Local Identity | Engaged by festivals, town events, or localized campaigns. | | Mid-Income Localists | Demographics & Identity Composition | Regional & Local Identity | Not high-income, but show strong local loyalty — especially for practical goods and services. | | Suburban Anchors | Demographics & Identity Composition | Regional & Local Identity | Located in ZIPs with high homeownership and moderate density — often prefer community over national brands. | | Urban Regionalists | Demographics & Identity Composition | Regional & Local Identity | Show strong local behavior in dense metro ZIPs — ideal for city-focused targeting. | | Local Storefront Re-Engagement | Demographics & Identity Composition | Regional & Local Identity | Once engaged with local content but inactive recently — good fit for reactivation via neighborhood messaging. | | ZIP-Pride Shoppers | Demographics & Identity Composition | Regional & Local Identity | Over-index on region-based apparel, sports, or town loyalty. | | High-Density Urbanites | Demographics & Identity Composition | Urbanicity & ZIP Traits | Live in dense metro ZIPs with high rental rates and mobile-first habits — often younger, fast-moving, and convenience-led. | | Suburban Anchors | Demographics & Identity Composition | Urbanicity & ZIP Traits | Stable, home-owning ZIPs with moderate density — often family-oriented or brand-loyal. | | Rural Pragmatists | Demographics & Identity Composition | Urbanicity & ZIP Traits | Low-density, budget-conscious areas — likely to favor utility, value, and conservative messaging. | | High-Education Enclaves | Demographics & Identity Composition | Urbanicity & ZIP Traits | ZIPs with very high education rates — skew toward high trust signal sensitivity and thoughtful content. | | Low-Poverty ZIP Optimizers | Demographics & Identity Composition | Urbanicity & ZIP Traits | Reside in affluent ZIPs with high purchase power and low promo sensitivity — prefer quality and experience over discounts. | | Mid-Income Commuter Zones | Demographics & Identity Composition | Urbanicity & ZIP Traits | Located in moderate-density ZIPs with long commutes and moderate-to-high income — convenience is king. | | Green-Conscious ZIPs | Demographics & Identity Composition | Urbanicity & ZIP Traits | Located in regions with high eco-awareness — strong match for sustainability and ethical campaigns. | | Data-Conscious Suburbanites | Demographics & Identity Composition | Urbanicity & ZIP Traits | Affluent, privacy-sensitive ZIPs — cautious about tracking and retargeting. | | Younger Urban Movers | Demographics & Identity Composition | Urbanicity & ZIP Traits | Metro ZIPs with low homeownership, high density, and trend-responsiveness — ideal for viral and mobile-first campaigns. | | Budget-Tight ZIP Segments | Demographics & Identity Composition | Urbanicity & ZIP Traits | High rent burden and poverty — cost-sensitive, value-first audience. | | Young Independence Seekers | Demographics & Identity Composition | Demographic Composition | Gen Z & early Millennials without dependents — exploratory, price-aware, and trend-responsive. | | Midlife Dual-Income Households | Demographics & Identity Composition | Demographic Composition | Aged 35–54, married or partnered, moderate-to-high income — high convenience and routine orientation. | | Affluent Empty Nesters | Demographics & Identity Composition | Demographic Composition | Age 55+, no children in household, strong purchase power — loyalty- and trust-driven. | | Young Families | Demographics & Identity Composition | Demographic Composition | Under 45 with children — responsive to utility, convenience, and mid-funnel nurturing. | | Single Urban Professionals | Demographics & Identity Composition | Demographic Composition | Age 25–44, no dependents, lives in urban ZIPs — trend and device-forward. | | Middle-Income Educated Consumers | Demographics & Identity Composition | Demographic Composition | College-educated, mid-income — responsive to value and ethical messaging. | | Female-Led Households | Demographics & Identity Composition | Demographic Composition | Female-identifying with dependents — over-index on trust, value, and mission-driven content. | | High-Income Knowledge Workers | Demographics & Identity Composition | Demographic Composition | Upper-income, educated, desk-based — respond to desktop-first, B2B, and high-trust flows. | | ZIP-Driven Family Clusters | Demographics & Identity Composition | Demographic Composition | Lives in ZIPs with high child presence and family orientation — localized family campaigns perform well. | | Senior Stability Segment | Demographics & Identity Composition | Demographic Composition | Age 65+, high homeownership — conservative, trust-reliant, longer decision cycles. | | Young Independence Seekers | Demographics & Identity Composition | Marital & Family Signals | Gen Z & early Millennials without dependents — exploratory, price-aware, and trend-responsive. | | Midlife Dual-Income Households | Demographics & Identity Composition | Marital & Family Signals | Aged 35–54, married or partnered, moderate-to-high income — high convenience and routine orientation. | | Affluent Empty Nesters | Demographics & Identity Composition | Marital & Family Signals | Age 55+, no children in household, strong purchase power — loyalty- and trust-driven. | | Young Families | Demographics & Identity Composition | Marital & Family Signals | Under 45 with children — responsive to utility, convenience, and mid-funnel nurturing. | | Single Urban Professionals | Demographics & Identity Composition | Marital & Family Signals | Age 25–44, no dependents, lives in urban ZIPs — trend and device-forward. | | Middle-Income Educated Consumers | Demographics & Identity Composition | Marital & Family Signals | College-educated, mid-income — responsive to value and ethical messaging. | | Female-Led Households | Demographics & Identity Composition | Marital & Family Signals | Female-identifying with dependents — over-index on trust, value, and mission-driven content. | | High-Income Knowledge Workers | Demographics & Identity Composition | Marital & Family Signals | Upper-income, educated, desk-based — respond to desktop-first, B2B, and high-trust flows. | | ZIP-Driven Family Clusters | Demographics & Identity Composition | Marital & Family Signals | Lives in ZIPs with high child presence and family orientation — localized family campaigns perform well. | | Senior Stability Segment | Demographics & Identity Composition | Marital & Family Signals | Age 65+, high homeownership — conservative, trust-reliant, longer decision cycles. | | Executive Decision-Makers | Demographics & Identity Composition | Seniority & Decision Role | Senior-level roles with high authority and budget discretion. | | Mid-Level Managers | Demographics & Identity Composition | Seniority & Decision Role | Operationally focused, often key influencers on tools or vendor choices. | | Departmental Influencers | Demographics & Identity Composition | Seniority & Decision Role | Job titles suggest input into tools, systems, or vendors — not top-level, but persuasive. | | Independent Operators / Freelancers | Demographics & Identity Composition | Seniority & Decision Role | Self-employed or solo professionals — high-risk tolerance, fast decision cycles. | | Procurement-Aligned Roles | Demographics & Identity Composition | Seniority & Decision Role | Roles that actively select or negotiate purchases — key in B2B and ops tooling. | | ICs with High Influence | Demographics & Identity Composition | Seniority & Decision Role | Not managers, but control key systems — often in technical or creative domains. | | HR & People Ops Buyers | Demographics & Identity Composition | Seniority & Decision Role | Targetable for tools related to benefits, wellness, culture, or compliance. | | Finance & Budget Gatekeepers | Demographics & Identity Composition | Seniority & Decision Role | Involved in budget allocation or vendor evaluation — esp. SMB and mid-market. | | Ops-Heavy Roles | Demographics & Identity Composition | Seniority & Decision Role | Responsible for tooling and implementation across many verticals. | | IT/System Roles | Demographics & Identity Composition | Seniority & Decision Role | Own infrastructure decisions or integrations — key for SaaS and workflow tools. | | Mobile-First Navigators | Decision-Making Style & Influence Pathways | Tech Usage Traits | High mobile engagement, shorter sessions, often responsive to SMS, push, or tap-to-act UX. | | Desktop-Oriented Users | Decision-Making Style & Influence Pathways | Tech Usage Traits | Engaged primarily through laptop or workstation — prefer detailed content and structured navigation. | | Mixed-Device Switchers | Decision-Making Style & Influence Pathways | Tech Usage Traits | Hybrid users with balanced desktop and mobile behaviors — engage across channels and time windows. | | Tech Progressives | Decision-Making Style & Influence Pathways | Tech Usage Traits | High trial willingness, trend sensitivity, and mobile preference — early adopters by behavior. | | Digital Skeptics | Decision-Making Style & Influence Pathways | Tech Usage Traits | Low device engagement scores, high privacy concerns, and older demo — minimal digital trust. | | App & Subscription Friendly | Decision-Making Style & Influence Pathways | Tech Usage Traits | Comfortable with app-based tools and recurring models — often mid-income, busy, convenience-led. | | Tech-Averse Traditionalists | Decision-Making Style & Influence Pathways | Tech Usage Traits | Low trial score, low device engagement, and resides in ZIPs with below-median tech usage. | | Device Power Users | Decision-Making Style & Influence Pathways | Tech Usage Traits | High usage across all digital surfaces — extremely responsive to multi-touch campaigns. | | On-the-Go Browsers | Decision-Making Style & Influence Pathways | Tech Usage Traits | Younger, mobile-skewed, low attention span — quick converters or bounce-prone. | | Homebound Desktop Reliants | Decision-Making Style & Influence Pathways | Tech Usage Traits | Older users, desktop-dependent, longer sessions during work hours — B2B-relevant. | | Institutional Trust Seekers | Decision-Making Style & Influence Pathways | Trust Style | Rely on expert reviews, credentials, guarantees, and third-party validation. | | Peer-Based Validators | Decision-Making Style & Influence Pathways | Trust Style | Look to social proof, reviews, and crowd consensus — highly responsive to popularity signals. | | Brand Familiarity Loyalists | Decision-Making Style & Influence Pathways | Trust Style | Trust what they know — favor brands with repeated exposure or long presence. | | Emotionally-Driven Believers | Decision-Making Style & Influence Pathways | Trust Style | Motivated by story, tone, and emotional alignment — less dependent on hard proof. | | Skeptics & Second-Guessers | Decision-Making Style & Influence Pathways | Trust Style | Require layered validation, often slow to convert — need reassurance across touchpoints. | | Low-Trust Independents | Decision-Making Style & Influence Pathways | Trust Style | Make decisions solo — dismissive of marketing or peer influence. | | Data-Driven Evaluators | Decision-Making Style & Influence Pathways | Trust Style | Trust logic, comparison tools, and factual benefits — less swayed by emotion or story. | | Repetition-Built Trusters | Decision-Making Style & Influence Pathways | Trust Style | Become more trusting with each touch — frequency plays a key role. | | Risk-Aware Trust Builders | Decision-Making Style & Influence Pathways | Trust Style | Especially cautious due to financial or personal stakes — slow and signal-dependent. | | Influencer-Led Converters | Decision-Making Style & Influence Pathways | Trust Style | Motivated by endorsements and identity-based validation. | | Cautious Planners | Financial & Value Sensitivity | Financial Posture | Budget-oriented, risk-averse, and deliberate — respond to structured savings and guarantees. | | Overextended Spenders | Financial & Value Sensitivity | Financial Posture | May be in financial strain — likely to convert on short-term deals, but churn risk is high. | | Value-Conscious Pragmatists | Financial & Value Sensitivity | Financial Posture | Prefer utility, durability, or ROI — responsive to "smart spend" narratives. | | Financially Fluid | Financial & Value Sensitivity | Financial Posture | Higher-income, low-caution, open to premium or aspirational offers. | | Impulse-Prone Buyers | Financial & Value Sensitivity | Financial Posture | Less planning, higher spend likelihood on convenience or trend — may disregard long-term cost. | | Security-First Mindset | Financial & Value Sensitivity | Financial Posture | Prioritize safety, coverage, and future stability — ideal for insurance, health, or finance brands. | | Stretching Middle Tier | Financial & Value Sensitivity | Financial Posture | Likely mid-income, value-seeking but still aspirational — careful tradeoff evaluators. | | Big Spenders with Flexibility | Financial & Value Sensitivity | Financial Posture | High net worth, low caution, trend-sensitive — targets for premium or status-positioned offers. | | Risk-Inclined Opportunists | Financial & Value Sensitivity | Financial Posture | Younger or entrepreneurial users more willing to invest or experiment financially. | | Financially Guarded Elders | Financial & Value Sensitivity | Financial Posture | Older, higher caution, and often responsive to conservative financial products. | | Short-Burst Scrollers | Behavioral Engagement Patterns | Attention Span Tendency | Prefer punchy, high-velocity content — often mobile-first with fast engagement/drop-off. | | Quick Converters | Behavioral Engagement Patterns | Attention Span Tendency | Likely to take action on first or second touch — low need for repetition. | | Multi-Touch Nurturers | Behavioral Engagement Patterns | Attention Span Tendency | Require several exposures — convert over time, often influenced by storytelling or proof. | | Long-Form Learners | Behavioral Engagement Patterns | Attention Span Tendency | Prefer in-depth, explanatory, or comparison-style content — ideal for whitepapers, demos. | | Bounce-Prone Browsers | Behavioral Engagement Patterns | Attention Span Tendency | Tend to drop off quickly — need retargeting or frictionless UX. | | Slow Decision Makers | Behavioral Engagement Patterns | Attention Span Tendency | Often return to the same content over multiple sessions — conversion window is long. | | Selective Clickers | Behavioral Engagement Patterns | Attention Span Tendency | Low open/click rates but high dwell once engaged — careful evaluators. | | Repetitive Content Consumers | Behavioral Engagement Patterns | Attention Span Tendency | Consume similar themes frequently — ideal for serialized or episodic content. | | Swipe & Skip Types | Behavioral Engagement Patterns | Attention Span Tendency | Exposed to lots of content but little stickiness — optimize for immediacy or hooks. | | Anchored Session Viewers | Behavioral Engagement Patterns | Attention Span Tendency | Prefer desktop sessions with longer dwell — high impact possible with fewer impressions. | | Aspirational Aligners | Psychographics & Motivational Archetypes | Social Identity Signals | Driven by image, lifestyle, and status signals — want to reflect success or trendiness. | | Tribe-Driven Participants | Psychographics & Motivational Archetypes | Social Identity Signals | Engage deeply with niche or group-based content — behavior mirrors identity-based belonging. | | Low-Identity-Conscious Users | Psychographics & Motivational Archetypes | Social Identity Signals | Unlikely to anchor decisions in social identity — prefer value or function. | | Cultural Momentum Followers | Psychographics & Motivational Archetypes | Social Identity Signals | Follow what's popular or “in” — image and momentum matter. | | Subcultural Loyalists | Psychographics & Motivational Archetypes | Social Identity Signals | Deeply aligned with specific genres, fandoms, or niches — long-tail communities. | | Cause-Driven Signalers | Psychographics & Motivational Archetypes | Social Identity Signals | Identity linked to social or ethical causes — likely to share or engage with mission-aligned content. | | Influencer-Mirrored Personas | Psychographics & Motivational Archetypes | Social Identity Signals | Self-perception shaped by influencer behaviors and recommendations. | | Conformity-Prone Actors | Psychographics & Motivational Archetypes | Social Identity Signals | Likely to conform to peer behavior — highly responsive to trends and popularity cues. | | Individualist Buyers | Psychographics & Motivational Archetypes | Social Identity Signals | Identity-driven but not follower-types — unique self-image influences brand selection. | | Community-Bound Converts | Psychographics & Motivational Archetypes | Social Identity Signals | Conversion tied to belonging, not just product value — amplify through shared identity. | | Silent But Seen | Behavioral Engagement Patterns | Re-Engagement Indicators | Viewed content multiple times but no action — likely stalled or skeptical. | | Burned-Out Subscribers | Behavioral Engagement Patterns | Re-Engagement Indicators | History of engagement but recent falloff — likely fatigued. | | Loyalty Drift Risk | Behavioral Engagement Patterns | Re-Engagement Indicators | Previously responsive to loyalty or promo cues, now disengaged. | | Urgency Lapsed | Behavioral Engagement Patterns | Re-Engagement Indicators | High urgency tendencies but no recent conversions — may have missed window. | | One-Touch Falloffs | Behavioral Engagement Patterns | Re-Engagement Indicators | Engaged once, never again — could be friction or mismatch. | | Over-Exposed Skimmers | Behavioral Engagement Patterns | Re-Engagement Indicators | Hit with multiple exposures across channels, low engagement — retarget smart or pause. | | Mid-Funnel Dormants | Behavioral Engagement Patterns | Re-Engagement Indicators | Was once active in the journey, now cold — prime for a value reminder or social proof. | | Promo-Only Rebounders | Behavioral Engagement Patterns | Re-Engagement Indicators | Reactivate only for discounts or perks — predictably promo-driven. | | Device-Shift Dormants | Behavioral Engagement Patterns | Re-Engagement Indicators | Switched devices/platforms — may have lost context or dropped due to UX. | | Lapsed Yet Loyal | Behavioral Engagement Patterns | Re-Engagement Indicators | Once-engaged loyalists — they’ve drifted but still resonate with your brand values. | | Top-of-Funnel Explorer | Lifecycle & Loyalty State | Lifecycle Stage | Early-stage — consuming lightly, possibly researching. | | Mid-Funnel Evaluator | Lifecycle & Loyalty State | Lifecycle Stage | Comparisons underway — not ready, but not casual either. | | Intent-Driven Prospector | Lifecycle & Loyalty State | Lifecycle Stage | Actively considering — signal-rich behavior but hasn’t converted yet. | | Trial-Stage Engager | Lifecycle & Loyalty State | Lifecycle Stage | Engaged in test or freemium — likely to tip toward conversion. | | Newly Converted | Lifecycle & Loyalty State | Lifecycle Stage | Just crossed the line — needs onboarding and reinforcement. | | At-Risk First-Time Buyer | Lifecycle & Loyalty State | Lifecycle Stage | Converted but shows low signs of stickiness. | | Repeat Buyer | Lifecycle & Loyalty State | Lifecycle Stage | Shows repeat behavior or multi-touch post-purchase. | | Loyalist Advocate | Lifecycle & Loyalty State | Lifecycle Stage | Brand loyal, reward receptive, and high response rate — core audience. | | Dormant/At-Risk | Lifecycle & Loyalty State | Lifecycle Stage | Lapsed from any stage — currently needs reactivation. | | Subscription Churn Watch | Lifecycle & Loyalty State | Lifecycle Stage | Active or recent sub, but fatigue or drop signals appearing. | | Loyalty Program Champions | Lifecycle & Loyalty State | Loyalty Loop Positioning | Already engaged in loyalty mechanics — reward them, deepen the bond. | | Habitual Repeater | Lifecycle & Loyalty State | Loyalty Loop Positioning | Not in the loyalty program but shows repeated behaviors — auto-pilot buyer. | | Value-First Retainer | Lifecycle & Loyalty State | Loyalty Loop Positioning | Loyal but driven by value — not perks or emotional loyalty. | | Wavering Loyalist | Lifecycle & Loyalty State | Loyalty Loop Positioning | Previously loyal, now disengaging or showing fatigue. | | Unaware Loyalist | Lifecycle & Loyalty State | Loyalty Loop Positioning | Loyal through habit — hasn’t joined rewards or official programs. | | Transactional Loyalist | Lifecycle & Loyalty State | Loyalty Loop Positioning | Repeats due to routine or utility, not emotional connection. | | Early-Cycle Loyalist | Lifecycle & Loyalty State | Loyalty Loop Positioning | Recently joined, shows potential for retention but not yet locked in. | | Churn-Stage Loyalist | Lifecycle & Loyalty State | Loyalty Loop Positioning | Loyal before, now dropping or resisting engagement. | | Loyalty Program Curious | Lifecycle & Loyalty State | Loyalty Loop Positioning | Not yet engaged, but profile suggests likely to join with right nudge. | | Perk-Only Retainers | Lifecycle & Loyalty State | Loyalty Loop Positioning | Stick around only if perks are present — vulnerable to better offers. | | Individualism vs. Collectivism | Psychology/World View Models | Psychology/World View Models | | | Temporal Orientation (Past / Present / Future Focus) | Psychology/World View Models | Psychology/World View Models | | | Moral Absolutism vs. Moral Relativism | Psychology/World View Models | Psychology/World View Models | | | Control Locus (Internal vs. External) | Psychology/World View Models | Psychology/World View Models | | | Stability vs. Change Preference | Psychology/World View Models | Psychology/World View Models | | | Rationalism vs. Emotional Intuition | Psychology/World View Models | Psychology/World View Models | | | Civic Engagement & Agency | Psychology/World View Models | Psychology/World View Models | | | Institutional Trust & Skepticism | Psychology/World View Models | Psychology/World View Models | | | Freedom vs. Security Trade-off Tendency | Psychology/World View Models | Psychology/World View Models | | | Materialism vs. Meaning Orientation | Psychology/World View Models | Psychology/World View Models | | | Conformity vs. Individual Expression | Psychology/World View Models | Psychology/World View Models | | | Authoritarian vs. Libertarian Tendencies | Psychology/World View Models | Psychology/World View Models | | | Technological Optimism vs. Tech Skepticism | Psychology/World View Models | Psychology/World View Models | | | Zero-Sum vs. Abundance Mindset | Psychology/World View Models | Psychology/World View Models | | | Narrative Framing: Victim vs. Hero Identity | Psychology/World View Models | Psychology/World View Models | | | Local Outlier Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Neighborhood Homogeneity Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Social Exposure Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Cultural Affinity Index | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Community Velocity Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Conformity Pressure Index | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Geo-Social Influence Density | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Behavioral Disparity Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Civic Cohesion Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Sprawl Intensity Index | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Geo-Economic Pressure Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Local Ideology Consistency Score | Geo-Social Relativity Layer | Geo-Social Relativity Layer | | | Emotional Reactivity Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Optimism Bias Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Anxiety Sensitivity Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Empathy Responsiveness Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Frustration Tolerance Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Social Validation Dependence Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Emotional Volatility Index | Emotional Disposition Layer | Emotional Disposition Layer | | | Nostalgia Affinity Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Shame Aversion Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Hope Orientation Score | Emotional Disposition Layer | Emotional Disposition Layer | | | Commercial Intent Persona Score | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Political Activation Persona Score | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Malleability & Persuasion Resistance Index | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Identity Anchoring Score | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Narrative Resonance Profile | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Behavioral Contradiction Index | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Lifestyle Intent Cluster | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Temporal Commitment Score | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | | Cognitive Load Sensitivity Score | Psychographic Archetype Cluster | Psychographic Archetype Cluster | | ### Purpose This section describes how Moonbrush defines, constructs, validates, and serves **standard persona models** that translate rich psychographic and behavioral signals into actionable archetypes for product, marketing, and CX teams. It covers persona taxonomy, data pipelines, modeling, versioning, governance, and API interfaces. *** ### Definitions * **Persona**: A probabilistic archetype describing consistent patterns of motivations, attitudes, needs, and behaviors observed in a population. Personas are outputs of modeling—not manually curated stereotypes. * **Baseline Persona**: A durable, longer‑horizon archetype inferred from historical signals (weeks–months). * **Live Persona Overlay**: A session‑level modifier that shifts a subject’s likelihood of belonging to specific personas based on ephemeral intent and context. * **Persona Canon**: The registry of approved personas (IDs, definitions, guardrails, owners, version). *** ### Taxonomy Personas are organized into a two‑level taxonomy: 1. **Primary Persona Families** (e.g., *Value Optimizer*, *Quality Seeker*, *Explorer*, *Skeptic*, *Loyalist*). 2. **Sub‑Personas** tuned to verticals or product surfaces (e.g., *Deal‑First Explorer (Content)* vs. *Trial‑First Explorer (Product)*). Each persona has: **ID**, **Name**, **One‑line Summary**, **Motivations**, **Frictions**, **Decision Style**, **Channel Preferences**, **Content Hooks**, **Risk Flags**, **KPIs**, **Eligibility/Exclusion Rules**. *** ### Data Inputs Moonbrush derives persona probabilities from the same inputs described in Psychographic Modeling: 1. **Demographic/Firmographic (optional)**: coarse bands only; never used as the sole determinant. 2. **Behavioral History**: event sequences, engagement, commerce, feature use. 3. **Campaign Outcomes**: exposures, responses, lifts from >100M interactions. 4. **Intent & Context (real‑time)**: live actions, query context, inventory/price state. > Sensitive attributes (e.g., health, religion, political views) are excluded by policy and enforced at ingestion and feature derivation. *** ### Feature Engineering * **Latent interest vectors** from self‑supervised sequence models. * **Decision‑style indicators**: research depth, novelty‑seeking, social proof reliance, price sensitivity proxies. * **Engagement cadence**: recency/frequency, session periodicity, attention depth. * **Value & risk**: predicted LTV, churn propensity, elasticity signals. * **Context affinity**: device/locale time‑bucket response patterns. Time‑decayed aggregations differentiate **durable** vs. **ephemeral** features (see Psychographic Modeling for formulas). *** ### Persona Construction #### 1) Candidate Discovery * Dimensionality reduction of latent spaces (UMAP/autoencoders) → **candidate manifolds**. * Density‑aware clustering (HDBSCAN/k‑means++) across multiple resolutions. * Objective combines cluster separation and business utility. #### 2) Persona Synthesis * Auto‑summarize salient features (SHAP/IG top‑drivers). * Generate **persona cards** (see template) with human‑in‑the‑loop review. * Register candidates in the **Persona Canon** with semantic versioning. #### 3) Calibration & Labeling * Train calibrated classifiers that output P(personak∣x)P(\text{persona}\_k\mid x) for each k. * Enforce **exclusion/eligibility rules** (e.g., sensitive categories, safety guardrails). *** ### Persona Assignment For a subject xx: 1. Compute durable baseline probabilities pkdp\_k^d. 2. Compute ephemeral overlay pkep\_k^e from session intent. 3. Blend with learned task‑specific weight α\alpha: pk=αpkd+(1−α)pkep\_k = \alpha p\_k^d + (1-\alpha)p\_k^e. 4. Apply **decision policy**: top‑1 (argmax), **top‑N with thresholds**, or **mixture routing** (weight actions by pkp\_k). Latency targets align with the decision API: p50 < 40ms, p95 < 100ms. *** ### Persona Card Template **ID**: `PER---` **Name**: *e.g., Deal‑First Explorer* **Summary**: Concise description of motivations and decision style. **Motivations**: (price wins, novelty, convenience, status, quality…) **Frictions**: (risk aversion, complexity sensitivity, time pressure…) **Behaviors**: (research depth, channel touchpoints, preferred evidence…) **Channel & Timing**: (email/push/in‑app; best hour/day patterns) **Content Hooks**: (try‑it‑now, comparison charts, social proof, bundles…) **KPIs**: (click‑to‑trial, assisted conversion, retention uplift, AOV) **Safety/Guardrails**: exclusions, fatigue limits, sensitive categories blocked **Representative (synthetic) quote**: “Show me the differences and a quick way to try.” *** ### Persona JSON Schema (excerpt) ```json { "$schema": "https://json-schema.org/draft/2020-12/schema", "title": "Persona", "type": "object", "required": ["id", "name", "summary", "kpis", "version"], "properties": { "id": {"type": "string"}, "name": {"type": "string"}, "family": {"type": "string"}, "version": {"type": "string", "pattern": "^\\d+\\.\\d+\\.\\d+$"}, "summary": {"type": "string"}, "motivations": {"type": "array", "items": {"type": "string"}}, "frictions": {"type": "array", "items": {"type": "string"}}, "behaviors": {"type": "array", "items": {"type": "string"}}, "channels": {"type": "array", "items": {"type": "string"}}, "content_hooks": {"type": "array", "items": {"type": "string"}}, "kpis": {"type": "array", "items": {"type": "string"}}, "eligibility": {"type": "string"}, "exclusions": {"type": "string"} } } ``` *** ### Example Persona Archetypes (illustrative) 1. **PER-EXP-dealfirst-1.0** — *Deal‑First Explorer* * **Motivations**: novelty + savings; responds to bundles and limited‑time trials. * **Behaviors**: high browse breadth; quick pivots across categories; reacts to price anchors. * **Content Hooks**: “Try before buying,” comparison grids, bundle prompts. * **KPIs**: trial start rate, assisted conversion.
2. **PER-QLT-craft-1.0** — *Quality‑Focused Crafter* * **Motivations**: durability, craftsmanship, community validation. * **Behaviors**: long reads, review mining, returns low; prefers deep dives. * **Content Hooks**: expert reviews, specs, lifetime guarantees. * **KPIs**: AOV, return rate, 90‑day retention.
3. **PER-SKP-proof-1.0** — *Skeptic Seeking Proof* * **Motivations**: risk minimization; needs evidence and social proof. * **Behaviors**: dense research sessions, repeat comparisons, low initial CTR, high post‑proof conversion. * **Content Hooks**: case studies, third‑party validations, transparent pricing. * **KPIs**: conversion after proof exposure, refund rate.
4. **PER-LOY-habit-1.0** — *Habitual Loyalist* * **Motivations**: convenience, trust, predictability. * **Behaviors**: consistent cadence, high brand depth, low cross‑category exploration. * **Content Hooks**: reorder nudges, loyalty perks, and early access. * **KPIs**: order frequency, retention uplift. > Replace or extend with vertical‑specific personas during implementation. *** ### Evaluation & QA * **Cluster quality**: silhouette/NMI/ARI across bootstraps. * **Stability**: Jaccard overlap of assignments between epochs; drift (PSI/KS on key features). * **Business lift**: incremental impact on targeting vs. baselines (A/B or interleaving tests). * **Calibration**: Brier/log loss of persona probability models; reliability diagrams. * **Fairness**: parity checks on error/lift across non‑sensitive cohorts; counterfactual tests. *** ### Updates & Refresh Cadence * **Durable (baseline) retrain**: weekly/bi‑weekly. * **Overlay (live) refresh**: seconds–minutes via streaming features. * **Canon review**: monthly; add/remove personas based on drift and utility. * **Deprecation**: mark persona as `sunset` with migration/playbook guidance. *** ### Serving Interfaces #### Decisioning * `POST /v1/personas/assign` → `{ subject_id, persona_probs: {id→p}, top_persona }` * `POST /v1/decision` with `routing_strategy="persona_mixture"` to weight actions by persona probabilities. #### Registry * `GET /v1/personas` → list canon with versions, owners, guardrails. * `GET /v1/personas/{id}` → full card + eligibility/exclusion logic. #### Segments * `POST /v1/segments/query` with `where persona_id IN (...) AND p >= τ` to materialize audiences. *** ### Governance & Ethics * **No sensitive‑attribute targeting**; explicit blocks in eligibility rules. * **Consent & purpose limits**: persona use restricted to consented contexts. * **Explainability**: per‑decision reason codes: top‑k features that drove persona assignment. * **Fatigue & harm prevention**: exposure caps; adverse‑action checks. * **Research-Driven:** In-depth research papers available upon request, justifying all standard personas available within the Moonbrush platform. *** ### Change Management & Versioning * **Model registry** with `MAJOR.MINOR.PATCH`. * **Canary** rollouts; automatic rollback on KPI regression or fairness alarms. * **Changelogs**: document added/removed personas, schema changes, and policy updates. *** ### Limitations & Assumptions * Cold-start coverage is limited; fallbacks utilize content- and context-based Personas are **probabilistic** and **context‑dependent**; avoid rigid, one‑size‑fits‑all use. * Cold‑start coverage is limited; fallbacks use content‑ and context‑based routing. * Over‑segmentation can dilute lift; prefer fewer, high‑utility personas. *** ### Glossary * **Persona Canon**: the authoritative registry of persona definitions. * **Overlay**: session‑level adjustments applied on top of durable persona probabilities. * **Mixture routing**: blending multiple strategies weighted by persona probabilities. # Activation Playbook Make data actionable
Specifications Release Date: September 16, 2025\ Version: 1.1.0
### Purpose Audience Playbooks translate Moonbrush’s psychographic models, persona probabilities, and intent feed into **concrete actions and words**: who to target, when to speak, what to say, how to say it, and on which channel—under guardrails that protect users and comply with policy. This section defines the playbook object model, governance, and ready‑to‑ship examples. > **Scope & Safety**: Playbooks may **not** target or tailor creative using sensitive/protected attributes or their close proxies. Sensitive fields are quarantined and never exposed to decisioning. Use only approved signals (see *SAFE\_PERSON\_VIEW*). *** ### What is a Playbook? A **Playbook** is an executable strategy that combines: 1. **Objective** (e.g., trial start, first purchase, reorder, feature adoption) 2. **Eligibility & Triggers** (persona codes, intent patterns, thresholds) 3. **Channel Plan** (email, push, in‑app, SMS, on‑site, ads) 4. **Message Templates** (microcopy with variables and tone rules) 5. **Frequency & Fatigue Controls** 6. **Measurement** (primary KPI, guardrail KPIs, holdouts) 7. **Fallbacks** (low‑data and safety fallbacks) *** ### Playbook Object (YAML) ```yaml id: PB-TRIAL-NUDGE name: Trial Nudge version: 1.0.0 objective: "Start a free trial within 7 days" primary_kpi: trial_start_rate secondary_kpis: [unsubscribe_rate, complaint_rate, downstream_retention_30d] eligibility: persona_any: [PER-EXP-dealfirst-1.0, PER-QLT-craft-1.0, PER-SKP-proof-1.0, PER-LOY-habit-1.0] thresholds: convert_7d_min: 0.15 fatigue_max: 0.8 consent: [personalization] excluded_categories: [Sensitive] triggers: - type: event when: add_to_cart OR pricing_page_view window: 3d - type: inactivity when: no_conversion window: 5d channels: - email - push - in_app frequency_caps: unit: day per_subject: 1 global_max: 3 localization: [en-US] creative: voice_rules: reading_grade_max: 8 avoid: [fear, urgency_shaming] prefer: [clarity, benefit_first, social_proof_light] variables: - name: product_name - name: key_benefit - name: trial_length_days - name: comparison_hook - name: social_proof_snippet templates: email: subject: | {{persona switch}} PER-EXP-dealfirst-1.0: Try {{product_name}}—see what you get in {{trial_length_days}} days PER-QLT-craft-1.0: Experience {{product_name}} quality—free for {{trial_length_days}} days PER-SKP-proof-1.0: See how {{product_name}} compares—free {{trial_length_days}}‑day trial PER-LOY-habit-1.0: Your smooth start with {{product_name}}—on us for {{trial_length_days}} days preheader: "Start now. Cancel anytime." body: | {{benefit_block}} {{comparison_hook}} {{cta primary="Start your free trial"}} {{safety_note}} push: title: "Start your {{trial_length_days}}‑day trial" body: | {{persona switch}} PER-EXP-dealfirst-1.0: Quick setup, real value—try it now PER-QLT-craft-1.0: Explore the quality—free trial PER-SKP-proof-1.0: See the proof—start free PER-LOY-habit-1.0: Make it part of your flow—on us cta: "Start free" in_app: banner: "Try {{product_name}} free for {{trial_length_days}} days" subcopy: "No commitment. Cancel anytime." cta: "Start trial" experiments: holdout: 10% strategy: Thompson variants: [copy_v1, copy_v2] reason_codes: ["clear_benefit", "low_friction", "social_proof_light"] ``` > The templating grammar supports persona‑aware branches (`persona switch`), variables, CTA components, and safety notes (unsubscribe/consent reminders where applicable). *** ### Tone & Microcopy Matrix (by Persona) | Persona | Voice & Tone | Evidence Emphasis | CTA Style | Example Phrase | | ------------------------------------------------- | --------------------------------- | -------------------------------------- | ---------------------------- | ------------------------------------ | | **Deal‑First Explorer** (`PER-EXP-dealfirst-1.0`) | energetic, concise, value‑forward | quick comparison, savings | "Start free", "See it now" | “Try it now—see what you unlock.” | | **Quality‑Focused Crafter** (`PER-QLT-craft-1.0`) | confident, craft‑oriented | specs, guarantees, expert takes | “Explore”, “See quality” | “Experience the finish and feel.” | | **Skeptic Seeking Proof** (`PER-SKP-proof-1.0`) | calm, precise, transparent | benchmarks, case studies, side‑by‑side | “See the proof”, “Compare” | “Here’s how we perform, in numbers.” | | **Habitual Loyalist** (`PER-LOY-habit-1.0`) | warm, reliable, helpful | continuity, convenience | “Set it up”, “Keep it going” | “Fits right into your routine.” | **Do**: plain language, benefit first, respectful opt‑outs.\ **Don’t**: exploit fear, imply sensitive traits, or gate core info behind clicks. *** ### Channel‑specific Guidance #### Email * **Subject**: ≤ 50 chars; avoid spammy terms; one clear benefit. * **Preheader**: clarify value or safety (cancel anytime, privacy‑first). * **Body**: * First paragraph = benefit + outcome. * Middle = evidence (1–2 bullets). * End = single primary CTA. #### Push * Title ≤ 45 chars, body ≤ 120 chars. Direct verb + benefit. Respect device quiet hours. #### SMS * ≤ 160 chars. Always include brand, value, **STOP to opt out**. #### In‑App/On‑site * Minimalist banner + one CTA. Show reasons on hover/expand. *** ### Segmentation Recipes * **Trial Nudge**: persona in {EXP, QLT, SKP, LOY} ∧ `convert_7d ≥ 0.15` ∧ `fatigue ≤ 0.8` ∧ consent includes personalization. * **Compare Grid**: `persona_any:{SKP, QLT}` ∧ recent `pricing_page_view` within 24h. * **Loyalty Perk**: `persona_any:{LOY}` ∧ `order_frequency ≥ cohort_p75` ∧ not exposed in last 30 days. > Implement via `POST /v1/segments/query` or decision policy with persona mixture routing. *** ### Microcopy Blocks (Reusable) ```yaml benefit_block: PER-EXP-dealfirst-1.0: "Unlock {{key_benefit}} in minutes—no credit card required." PER-QLT-craft-1.0: "Experience premium {{product_name}} performance—built for longevity." PER-SKP-proof-1.0: "Independent benchmarks show {{comparison_hook}}—see details." PER-LOY-habit-1.0: "Set it up once—enjoy {{key_benefit}} every day." safety_note: "You're receiving this because you opted into personalization. Unsubscribe anytime." ``` *** ### Ready‑to‑Use Playbooks #### PB-TRIAL-NUDGE (Conversion) * **Trigger**: pricing page → add to cart abandoned within 72h. * **Primary KPI**: trial starts (7d). * **Sample SMS**: “{{product\_name}} free for {{trial\_length\_days}} days. Explore {{key\_benefit}}—no commitment. Reply STOP to opt out.” #### PB-COMPARE-GRID (Consideration → Conversion) * **Trigger**: competitor page visits or frequent spec lookups. * **Creative**: side‑by‑side grid; neutral tone; link to independent review. * **Example CTA**: “See the proof.” #### PB-LOYALTY-PERK (Retention) * **Trigger**: high loyalty score + nearing reorder window. * **Offer**: early access/bundle convenience; avoid deep discounts. * **Example Push**: “Early access unlocked—your favorites, one tap.” #### PB-REORDER (Replenishment) * **Trigger**: expected depletion window ±3d. * **Tone**: helpful reminder. * **Example In‑App**: “Running low? Reorder in 2 taps.” *** ### Guardrails & Ethics * Blocklists: **Sensitive** categories and attributes; ideology/religion/ethnicity not available to templates or routing. * Caps: per‑channel and cross‑channel fatigue; quiet hours; novelty limits. * Safety language: include consent/unsubscribe where required; no dark patterns. * **Research-Driven:** In-depth research papers available upon request, justifying all standard playbooks available within the Moonbrush platform. *** ### Measurement & Feedback Loops * **Holdouts** per playbook (min 5–10%). * **Attribution**: time‑decay plus uplift estimates. * **Diagnostics**: failure reasons (fatigue, policy block, low confidence). * **Auto‑tuning**: contextual bandits swap copy variants within caps. *** ### API Quickstart * **Decide**: `POST /v1/decide` with persona probs, session context → ranked playbooks + templates. * **Render**: client fills variables (`product_name`, `key_benefit`, etc.) and respects caps. * **Log**: exposure → outcome with `trace_id` for learning. **Request (abridged)** ```json { "subject_id": "SUB-7f91b1c6", "persona_probs": {"PER-EXP-dealfirst-1.0": 0.72, "PER-QLT-craft-1.0": 0.18}, "context": {"recent_events": ["pricing_page_view"], "fatigue": 0.2, "consent": ["personalization"]} } ``` **Response (abridged)** ```json { "recommendations": [ {"playbook_id": "PB-TRIAL-NUDGE", "score": 0.86, "variables": {"trial_length_days": 14, "product_name": "Moonbrush", "key_benefit": "hyper‑personal previews", "comparison_hook": "2× faster setup"}}, {"playbook_id": "PB-COMPARE-GRID", "score": 0.64} ], "reason_codes": ["clear_benefit","low_friction"] } ``` *** ### Compliance Checklist (per playbook) * [ ] Uses only approved signals from SAFE\_PERSON\_VIEW * [ ] Consent scope verified (analytics/personalization) * [ ] Frequency caps set and enforced * [ ] Sensitive categories excluded * [ ] Holdout configured ≥ 5% * [ ] Localization reviewed * [ ] Copy passes tone rules (readability, no fear appeals) *** ### Limitations & Assumptions * Cold‑start fallback uses contextual and content‑based templates. * Persona probabilities are estimates; prefer mixture routing when confidence is moderate. * Results depend on event fidelity and consented coverage. # Intent Feed Capture → ID Match → Enrichment → Segmentation
Specifications Release Date: September 17, 2025\ Total rows (daily): 2B+\ Version: 1.2.2\ File Size: \~60G parquet\ Update Freq: Daily
### Purpose The Intent Feed ingests raw intent signals (cookies, pixels, SDK events, social interactions, commerce activity, and partner feeds), normalizes and matches them to an internal subject ID, enriches with models (interests, risk/fraud, value), and emits persona codes and playbook codes for **instant segmentation** and activation. *** ### Architecture Overview 1. **Capture**: pixels, SDKs, server‑to‑server, partner uploads. 2. **Normalize**: schema harmonization, bot/fraud filtering, consent enforcement. 3. **Identity**: deterministic/probabilistic stitching → `subject_id` (internal ID). 4. **Enrich**: embeddings, category affinities, price sensitivity, churn/propensity, compliance checks. 5. **Classify**: compute persona probabilities → persona codes; map to playbook codes. 6. **Serve**: stream to feature store, segments, and decision API; batch sink to warehouse. 7. **Feedback**: outcomes loop into model refresh and playbook tuning. Latency targets: stream p50 < 50ms, p95 < 120ms from capture → enriched record available in the feature store (deployment‑dependent). *** ### Data Sources * **Web**: first‑party cookies, pageview/search/cart pixels, consent banner states, referrer UTM. * **Mobile**: app SDK events (screen views, taps, purchases); device class/network only (no raw PII in feed). * **Commerce**: checkout starts/completes, SKU lines, refunds/returns, subscription changes. * **Social & Ads**: engagement callbacks (view/click), campaign/line‑item IDs; partner event streams (server‑to‑server). * **Partner/Offline**: POS batches, loyalty systems, call‑center outcomes, cooperative membership. > Sensitive attributes and special‑category data are not ingested. All sources must carry consent/legitimate‑interest flags; region‑aware enforcement occurs at ingestion. *** ### Identity & Consent #### Identifiers Accepted * `cookie_id` (first‑party), `session_id` * `hashed_email` (SHA‑256, lower‑cased, trimmed) when consented * `app_instance_id` (random app GUID) * `account_id` / `customer_id` (first‑party) * `partner_subject_key` (for clean‑room mapped IDs) #### Stitching * **Deterministic** precedence: account\_id > hashed\_email > app\_instance\_id > cookie\_id. * **Probabilistic** backfill: co‑occurrence + device/geo/time windows, capped with confidence thresholds. * Output: `subject_id` (opaque, internal), linkage graph with decay (edge TTL). #### Consent & Purpose Limiting * Required fields: `consent.purpose=[analytics|personalization|ads]`, `consent.region`, `consent.scope`. * Policy engine hard‑blocks enrichment/activation when consent is missing or revoked. *** ### Event Normalization #### Canonical Intent Event * **Required**: `event_id` (ULID), `occurred_at` (ISO‑8601 UTC), `source`, `event_type`, `consent`, `capture_context`. * **Optional**: `item` (SKU or content), `value`, `currency`, `metadata` (kv), `page_context`, `campaign_context`. **Deduplication**: idempotency on `(event_id)`, and near‑duplicate suppression via LSH on `(subject_id, event_type, item, ±5s)`. **Bot/Fraud Filtering**: signature/heuristics (JA3, header entropy, rapid‑fire patterns), partner IP allowlists, anomaly scores. *** ### Enrichment Models * **Interest Embeddings**: session‑aware text/item2vec to derive topical vectors and category affinities. * **Propensities**: conversion, churn, upgrade, repeat purchase, price sensitivity. * **Quality Signals**: fraud likelihood, bot probability, complaint risk. * **Context Features**: time‑bucket responsiveness, device/network cohort performance. * **Value Signals**: short‑term value (STV) and predicted LTV bands. Outputs are calibrated and written to the Feature Store keyed by `subject_id`. *** ### Persona & Playbook Classification * **Persona Codes**: map P(personak∣x)P(\text{persona}\_k\mid x) to canonical codes (e.g., `PER-EXP-dealfirst-1.0`). * Assignment strategies: top‑1 with threshold, top‑N mixture, or abstain if uncertainty high. * **Playbook Codes**: deterministic/ML rules translating enriched features + persona to activation strategies, e.g., * `PB-TRIAL-NUDGE`, `PB-REORDER`, `PB-COMPARE-GRID`, `PB-LOYALTY-PERK`. * Guardrails: fatigue caps, exclusion lists (sensitive categories), fairness constraints. *** ### Stream Output Record (Post‑Match) ```json { "record_id": "01JC6Z8B9Q9K4AS3H7X5S3N1PZ", "subject_id": "SUB-7f91b1c6", "occurred_at": "2025-09-21T14:05:37Z", "source": "web_pixel", "event_type": "add_to_cart", "item": {"id": "SKU-1234", "category": "brushes/digital"}, "value": 19.99, "currency": "USD", "consent": {"region": "US", "purpose": ["analytics","personalization"], "scope": "first_party"}, "enrichments": { "affinity": {"categories": [{"id": "digital_art", "score": 0.86}]}, "propensity": {"convert_7d": 0.34, "repeat_30d": 0.21}, "value": {"ltv_band": "M"}, "quality": {"bot_prob": 0.01} }, "persona": { "codes": ["PER-EXP-dealfirst-1.0"], "probs": {"PER-EXP-dealfirst-1.0": 0.72, "PER-QLT-craft-1.0": 0.18} }, "playbooks": ["PB-TRIAL-NUDGE", "PB-COMPARE-GRID"], "routing": {"eligible": true, "reasons": ["fatigue_ok","policy_pass"]}, "trace_id": "TRC-5b2a" } ``` *** ### Schemas #### Intent Event (Capture Layer) ```json { "$schema": "https://json-schema.org/draft/2020-12/schema", "title": "IntentEvent", "type": "object", "required": ["event_id","occurred_at","source","event_type","consent"], "additionalProperties": true, "properties": { "event_id": {"type": "string"}, "occurred_at": {"type": "string", "format": "date-time"}, "source": {"type": "string", "enum": ["web_pixel","mobile_sdk","server","partner_batch"]}, "event_type": {"type": "string"}, "consent": { "type": "object", "required": ["region","purpose","scope"], "properties": { "region": {"type": "string"}, "purpose": {"type": "array", "items": {"type": "string"}}, "scope": {"type": "string", "enum": ["first_party","clean_room","partner"]} } }, "identifiers": { "type": "object", "properties": { "cookie_id": {"type": "string"}, "session_id": {"type": "string"}, "hashed_email": {"type": "string"}, "app_instance_id": {"type": "string"}, "account_id": {"type": "string"}, "partner_subject_key": {"type": "string"} } }, "page_context": {"type": "object"}, "campaign_context": {"type": "object"}, "item": {"type": "object"}, "value": {"type": "number"}, "currency": {"type": "string"}, "metadata": {"type": "object"} } } ``` #### Enriched Intent Record (Post‑Match) ```json { "$schema": "https://json-schema.org/draft/2020-12/schema", "title": "EnrichedIntentRecord", "type": "object", "required": ["record_id","subject_id","occurred_at","source","event_type","consent","enrichments"], "additionalProperties": true, "properties": { "record_id": {"type": "string"}, "subject_id": {"type": "string"}, "occurred_at": {"type": "string", "format": "date-time"}, "source": {"type": "string"}, "event_type": {"type": "string"}, "item": {"type": "object"}, "consent": {"type": "object"}, "enrichments": { "type": "object", "required": ["affinity","propensity","value","quality"], "properties": { "affinity": {"type": "object"}, "propensity": {"type": "object"}, "value": {"type": "object"}, "quality": {"type": "object"} } }, "persona": { "type": "object", "required": ["codes"], "properties": { "codes": {"type": "array", "items": {"type": "string"}}, "probs": {"type": "object"} } }, "playbooks": {"type": "array", "items": {"type": "string"}}, "routing": {"type": "object"}, "trace_id": {"type": "string"} } } ``` *** ### Playbooks * **a Definition**: executable strategies with guardrails (eligibility, exclusions, exposure caps) and message/offer templates. * **Examples**: * `PB-TRIAL-NUDGE` — trigger a low‑friction trial prompt with comparison grid. * `PB-REORDER` — reorder reminder with last‑purchase context. * `PB-LOYALTY-PERK` — perk/early access for loyal cohorts. Playbooks are selected via rules or a policy model that consumes persona probabilities + enrichments. *** ### Serving & Interfaces * **Streaming**: `POST /v1/events` (capture), `GET /v1/stream/subjects/{id}` (tail enriched records). * **Batch**: daily S3/Blob exports (Parquet) partitioned by `dt=YYYY‑MM‑DD/region`. * **Segments**: `POST /v1/segments/query` supports filters such as `persona.codes ANY IN [...]` and `playbooks ANY IN [...]`. * **Decisioning**: `POST /v1/decide` accepts current context and returns ranked actions + playbook recommendations. *** ### Quality, Safety & Governance * **Compliance**: consent gating, purpose limiting, DSAR/erasure; clean‑room join paths for partner data. * **Bias/Fairness**: exclude protected categories; monitor lift and error parity across non‑sensitive cohorts. * **Security**: PII vaulting; transport encryption; signed pixel/SDK payloads; replay protection. * **Observability**: dedupe rate, bot rate, stitch confidence, enrichment latency, model drift. *** ### Retention & TTLs * **Raw capture**: 30–90 days (configurable) with access controls. * **Enriched features**: 6–24 months (downsampled); linkage edges decay with inactivity. * **Consent logs**: retained per regulatory requirements. *** ### Error Handling & Edge Cases * **Unmatched events** → `subject_id = null` → queued for backfill (bounded retry) or aggregated at the cohort level. * **Low consent scope** → enrichment/playbooks omitted; analytics only. * **High bot/fraud score** → drop or quarantine with `routing.eligible=false`. *** ### Versioning & Change Management * Schema evolution via `MAJOR.MINOR.PATCH`; producers/consumers validated in CI. * Backward‑compatible changes favored; deprecations announced with migration guidance. *** ### Limitations & Assumptions * Browser privacy controls (ITP/ETP) limit cookie longevity; first‑party capture and server events are recommended. * Identity stitching quality depends on consented identifiers and traffic mix. * Playbook efficacy is context‑dependent; continuous testing is required. # Changelog New updates and improvements ## v2.1.0 - September 2025
Added **Playbook (Sep 10–22)** * New Playbook tab introduced as a campaign-facing counterpart to Personas. * Categories: Messaging & Positioning, Channels & Platforms, Offers & Incentives, Exposure & Cadence. * “Highlight Card” shows the top option for the parent audience, including field, match %, rationale, and lookalike rules (plain-English explanations). * Heatmaps display all enrichment inputs across each section with gradient scales (per section color: blue, purple, teal, orange). * Other creative formats are listed in a horizontally scrollable row; selecting an option populates the preview card. * Integrated audience context (parent audience, record count, option ranking).
**Export Report (Sep 12–18)** * Downloadable enriched report introduced. * Export modal supports audience CSV, enriched PDF report, and segment CSVs. * File resizing and internal vs external views are supported. * A cap on max records was introduced to manage performance.
**FAQ Page (Sep 14–17)** * New FAQ/Help center tab. * Structured Q\&A by categories (onboarding, enrichment, search, exports). * Expand/collapse cards with consistent styling.
**Usage Page (Sep 15–20)** * New Usage tab under admin. * Tracks record usage per user and organization (total counts, not unique IDs). * Default allocation: 2M records. * Usage bar shows % consumed vs remaining, with green used/white remaining. * Chart tracks usage trends at the user and org level, reset at the billing cycle end.
**Workflow Page (Sep 18–22)** * New Workflow tab providing guided steps through upload → enrich → build segments → export. * Includes visual progress indicators.
**Sample Data (Sep 20–21)** * Default demo audience seeded into “My Audiences” for first-time users. * Ensures new users can preview insights, persona flows, and playbook without uploading their own file.
Changed/Improved **Segment** **Builder** * Action buttons moved to sticky row at the bottom of the screen for consistency with modals.
**Segment Detail** * Now mirrors the Audience screen layout more closely. * Added option to search for new users by shared behavior from a segment ({persona\_name} – {parent\_audience\_name}).
**Export Flow** * Unified modal for CSV, segment, and report downloads. * Consistent styling across export confirmations and success toasts.
#### Fixed * Addressed minor chart rendering bugs in preview heatmaps (mismatched % thresholds). * Corrected edge cases where the enriched report download would misclassify internal vs external format.
Fixed alignment issues in the FAQ cards and the Usage chart legend.
Fixed * Addressed minor chart rendering bugs in preview heatmaps (mismatched % thresholds). * Corrected edge cases where the enriched report download would misclassify internal vs external format. * Fixed alignment issues in the FAQ cards and Usage chart legend.
*** ## v2.0.0 - September 2025
Added **Audience Overview** * Sub-tab navigation was introduced, replacing horizontal toggles. * Top metrics redesigned: Audience Type, Top Segment by match %, and Download Audience modal with export options (audience CSV, enriched report \[placeholder PDF], top segments CSVs). * Enrichment Scores Preview: live average scores shown directly in the overview. * Behavioral Analysis Section: all enrichment scores were standardized into grouped categories and displayed as uniform cards. * Intent Signals Preview: high-level chart of audience intent by topic (7-day and 30-day views) is now fully functional with live data.
**People Search / Intent Search** * Save & Search relocated to the top of results preview. * The success modal is added after completing a search. * State filters are available for searches. * The current audience context is preserved when returning from searches to the overview.
**Persona & Category Views** * Persona Analysis supports drill-through: selecting a persona opens Segment Detail View. * Category Detail View implemented with live data for deeper trait exploration.
**Segment Builder** * Child audiences display parent audiences in the header for clear lineage. * New Segment Count calculates unique individuals across multiple personas.
**Global Enhancements** * Modal & tooltip system added (first applied to enrichment/persona elements). * Buttons in Segment Builder were repositioned to the top of the screen for clarity.
Change/Improved **Layout & Styling** * Audience Overview header simplified: oversized top cards removed in favor of direct background header. * Demographic insights compacted to match Segment Detail styling. * Charts for gender, age, and income redesigned with consistent formatting, axis labels, and colors. * Audience Concentration pie chart now shows top 3–5 states with “Other” grouping.
**Intent Insights** * Corrected table visuals: decreases now show red down arrow, icons standardized.
**System Performance** * Eliminated duplicate backend queries when switching audiences/personas. * Early steps toward reducing load times, including staged query triggers and static table exploration.
Fixed * Search Queries: resolved mis-formatted queries that produced incorrect “OR” logic and dropped enrichment filters. * Upload Mapping: standardized upload mapping to match against Moonbrush universal ID. * Audience Context: intent searches no longer clear the active audience; context persists across navigation. * Persona Analysis: duplicate “device” and “channel” categories removed from default persona profiles. * Authentication: major issue fixed where users could bypass login; authentication now required for access. * Intent Feed: audiences generated via Intent Search now save reliably.
## v1.2.0 - August 2025
Added **Data Model & Pipelines (Aug 1–5)** * Introduced core tables/schemas for app data. * Established deterministic keys (MOONBRUSH\_ID, plus hashed joins like SHA256\_LC\_HEM) for consistent identity joins across sources. **Flexible Uploads → Automatic Matching & Enrichment (Aug 3–9)** * Upload Modal accepts flexible CSV/XLSX schemas with auto header mapping (fuzzy detection for common name variants). * Validation rules: First Name (required) + one of Phone / Email / Address (or ZIP). **100+ Native Enrichments (Aug 6–18)** * Expanded the foundation set to 100+ continuous \*\_SCORE columns spanning key behavioral, engagement, and psychographic traits. * Implemented ZIP-aware adjustments, multi-stage wrappers/normalization, and robust outlier handling across pipelines. * Each enrichment is implemented via production-grade SQL/logic blocks with consistent naming, null behavior, and bounds (0–1). **People Search (Native) (Aug 10–16)** * Consumer View & Business View search modes powered by Moonbrush Consumer Graph, consolidated to “People Search.” * Enrichment Filters: Strong Match (≥0.7) and Weak Match (≤0.3) toggles per enrichment trait. * Personal Filters: marital/children/homeowner flags; contact presence; income, net worth, age ranges; location; gender; phone presence. * Professional Filters: seniority, job title (fuzzy), department, state/ZIP, company fields (name, phone, industry, revenue, size, NAICS/SIC, LinkedIn URL). * Live Preview updates in place; output saves as a new audience. **“My Audiences” (Aug 12–18)** * Unified table drives list of uploaded/searched audiences with: records, creation date, list type, and top enrichment tags. * Row actions: Search, Download, View Insights. **Audience View – Overview (Aug 14–20)** * Header (audience name, size from matched IDs). * Top Persona Fit Tabs (Purchase, Messaging, Engagement). * Enrichment Panels: Purchase, Psychographic & Messaging, Engagement & Channel. * Demographic Variance: Gender, Age, Income, and Audience Concentration. * Preview Table with Personal/Business toggles. **Person Detail View (Aug 17–21)** * Overview: demographics, contact, professional, and location from a single row. * Behavioral Profile: enrichment chips + top outliers. **Segment Builder – Profile Selection (Aug 18–22)** * Introduced Profile Groups → Categories → Personas hierarchy * Dynamic tags show match rates; users can check persona buckets to save as a new audience. **Profile Detail & Category Detail (Aug 20–26)** * Profile Detail: overview table (top personas, % audience, use cases). * Category Detail: persona cards with name, tag, records matched, % audience, rule snapshot, description, and View Segment. **Segment Detail + Download (Aug 23–26)** * The segment page shows matched records, % of audience, rule snapshot, and demographic breakdowns. * Download the segment and return to Audience after saving. **Intent – Taxonomy & Search (Aug 22–28)** * Three-level taxonomy UI (Category → Subcategory → Topic). * Multi-topic selection persists; results save as a new audience. **Explore Intent – Overview Workspace (Aug 24–30)** * Saved Searches panel. * Pinned Category center panel with volume, daily change, and audience overlap. * Intent Insights Table with time filter (24h/7d/30d) and group toggle. **Global UX Systems (Aug 12–31)** * Tooltips are standardized as click-trigger popovers. * Breadcrumbs added across Audience/Intent hierarchies * Notifications (header bell): “Audience uploaded” / “Search results ready.” **Responsive charts and animations unified across components.**
Changed/Improved * Normalization & Scoring Consistency (Aug 6–18): wrappers for scaling, null safeguards, ZIP adjustments; harmonized naming. * Search Filter UX (Aug 15–20): reorganized groups, improved fuzzy matching, stricter industry code handling. * Audience Charts (Aug 18–23): standardized axis ticks, totals sum to 100%, added state breakdown. * Routing & Breadcrumbs (Aug 20–28): stable routes Audience → Profile → Persona; Intent → Category → Topic.
Fixed * Upload Mapping (Aug 9–12): resolved collisions and false positives. * Enrichment Outliers (Aug 12–16): corrected overflow in device-engagement composites. * Search Preview Drift (Aug 17–19): preview cards now always reflect active filters. * Intent Joins (Aug 26–27): standardized ID join precedence.
## v1.1.0 — July 2025
Added * Audience Charts & Graphs (Jul 25–27): Audience Concentration, Age Distribution, Income Distribution. * Micro Segment Preview (Jul 26): styling updated, “Find Similar Audiences” button routes to Intent Search. * UI Cohesion (Jul 25–27): unified fonts, colors, padding, alignment, and accent scheme.
Changed/Improved * Login Screen (Jul 24): placeholder replaced with finalized imagery. * Navigation (Jul 24–25): removed unused tabs. * Home Screen (Jul 25): ensured all 4 cards present, aligned filter bar, and “Upload Data.” * Audience Screen (Jul 25–27): corrected chart math, standardized padding, ensured charts populate with data. * General Styling (Jul 25–27): standardized card and table formatting. * B2B Search (Jul 26): “Add Prospects To” button tagged as “Coming Soon.”
Fixed * Chart Accuracy (Jul 25–27): corrected percentages and pie chart logic. * UI Gaps (Jul 25): closed alignment issues in filter bar and upload section.
## v1.0.0 — July 2025
Added * MVP structure (Jul 3–6): Home, Audience, Company/Contact Search, Intent. * Audience lifecycle (Jul 3–4): Pending Enrichment → Enriched states. * Manual enrichment (Jul 3–6): \~10 initial scores; applied manually. * Initial persona rules (Jul 3–5): threshold-based personas. * Upload flow (Jul 6–7): validation and templates. * My Audiences (Jul 6–7): unified list of uploaded/searched audiences. * Embedded intent portal (Jul 6–7). * Company/Contact Search (Jul 11–12): query builder, preview, results table, details view. * Enrichment modal (Jul 11): selection, preview, confirmation, status updates. * Find Similar Audiences CTA (Jul 3–6). * ICP Preview CTA (Jul 3–6): placeholder. * Home overview cards (Jul 16). * Get Started modal (Jul 16)
Changed / Improved * Single-audience model (Jul 7–8). * Unified audience selector (Jul 7–12). * Tabs removed (Jul 11–12). * Search tips (Jul 14–16). * Tooltips for My Audiences (Jul 16). * Recent audiences default (Jul 6–7). * Upload gating & errors (Jul 6–7).
Fixed * Selector/toggle mismatches (Jul 11–12). * Upload CTA state (Jul 6–7). * Enrichment balance (Jul 3–5). * Persona application drift (Jul 3–5).
Removed / Deferred * Multi-audience compare (Jul 7–8). * B2B/B2C tabs (Jul 11–12).