Fuel your models with real consumer signals
Give your models the creator, brand, media, and passion signals from real consumer behavior that demographic and click data can’t provide.
Generic data trains generic AI
Give your AI a competitive advantage with exclusive audience intelligence tied to real people and their behavior across major social platforms.
Request a demo
Powered by social behavior
InstagramFollows @teeboxtyler
YouTubeSubscribes to Back Nine Breakdown
TikTokHashtags #golftok
SubstackSubscribes to Fairway Notes
219M U.S. adults, 7 networks
Weekend Golfers
Audience segment
18%
of your audience
Media
Fore Play Podcast8.7xGolf Channel
Brands
Titleist8.1xCallaway6.3x
Interests
BourbonFantasy footballGrilling
Active on
InstagramYouTube
SubstackAnalyze with AI
Which creators reach Weekend Golfers?
AI insight
Lead with @teeboxtyler and Back Nine Breakdown. Both over-index with this segment and with Titleist buyers.
Connected via StatSocial MCP
Differentiated data for the AI you build & use
One person, multiple platforms
YouTube
Maya R.
Subscribes: trail running vlogs
@mayaruns.chi
Engages: @trailmixtaylor
TikTok
@mayamovesfast
Follows: run-club creators
Substack
Maya's Miles
Reads: training newsletters
Maya Reyes
Product designer, Chicago
Resolved Profile
One real person
- Demographics
- Favorite Creators
- Brand Affinities
- Media Preferences
- Political Leanings
- Personality Traits
- Interests
- Personas
AI-ready signals
Powering your AI systems
AI agents
Automated workflows
AI assistants
Via StatSocial MCP
AI focus groups
Digital Twins respond
Data environment
Add to your data stack
Add a new layer to your AI stack
Combine StatSocial with your first and third-party datasets to power the AI environments you already use, from Databricks to your own models, agents, and AI-powered focus groups.
Query StatSocial right from your AI assistant
Connect the StatSocial MCP server to Claude, ChatGPT, and other AI assistants to ask questions in plain language and get insights back without leaving the conversation.
Deliver to any AI environment
Access data via cloud share, clean room, data warehouse, or secure file transfer, in AI-ready formats for any model or analytics system.
AI applications built on real consumer intelligence
Propensity & scoring models
Predict purchase intent, churn risk, and lifetime value using signals that reveal what customers actually care about, not just what they clicked.
Lookalike modeling
Build lookalikes based on passions, creator and media affinities, and brand engagement instead of demographic proxies.
GenAI & LLM enhancement
Ground prompts and context in real audience intelligence so GenAI applications can personalize at scale.
Marketing mix modeling & attribution
Feed your MMM and attribution systems with differentiated audience signals for more accurate measurement and better ROI reads.
What data teams are saying
"StatSocial has become our go-to tool in providing robust and trustful insights and data to our clients. It's beneficial as it allows us to quickly gather data without always having to conduct primary research for the content that's available in StatSocial's platform."
"The customer service and support throughout integration setup with StatSocial has been second to none. StatSocial has excellent documentation and even better data. The data we get has boosted our client offerings by providing significant audience insights than they had before."
"We're often asked demographic and interest questions where little data exists, and where traditional tools like MRI and GWI can't drill down. StatSocial fills that gap."
Industry recognized. Client recommended.
Frequently asked questions
Everything teams usually ask before they get started.
StatSocial provides identity-resolved consumer profiles enriched with thousands of unique behavioral attributes, covering the influencers people follow, media they consume, brands they favor, their interests and passions, B2B firmographics, political leanings and more. Attributes are derived from real cross-platform social behavior rather than clicks or demographic proxies. For model builders, that means features describing what a person actually cares about, which is a signal most commercially available datasets do not contain.
Adding behavioral features gives models predictive signals that transactional and demographic data cannot supply. StatSocial attributes describing influencer affinities, brand preferences, and passions help models separate customers who look identical on age, income, and geography but differ sharply in intent. Teams apply this to purchase propensity, churn risk, and lifetime value scoring, where the lift usually comes from features that explain motivation rather than history.
Yes. StatSocial attributes can be injected into prompts, retrieval layers, or context windows so GenAI applications reason about a specific audience instead of a generic one. That grounds personalization, content generation, and agent behavior in observed consumer behavior. The alternative is a model inferring audience preferences from its training data, which produces plausible but unverifiable characterizations.
Match your customer file against StatSocial’s PeopleGraph of 219M U.S. adults, and StatSocial appends thousands of behavioral attributes to the records that resolve. The enriched file returns to your environment ready for feature selection and training. The same process works on survey, panel, and third-party datasets, which lets teams widen feature sets without new data collection.
Yes. StatSocial data arrives structured for direct integration into AI platforms, GenAI tools, and machine learning pipelines, with attributes delivered in a consistent machine-readable schema keyed to resolved individual identifiers. No manual reshaping, cleanup, or entity matching is required before training or inference. Records are continuously updated and joined on stable identity keys, which means enriched files can be refreshed without rebuilding the feature set each time.
StatSocial delivers by API, cloud data share, clean room, or secure file transfer, connecting to environments including Snowflake, Databricks, and LiveRamp as well as internal ML pipelines and analytics systems. Delivery method is usually chosen to match existing governance and data residency requirements rather than the use case, and clean room delivery lets teams enrich without moving customer records.
Yes. StatSocial licenses its behavioral data for model training, enrichment, and inference, sourced from publicly available social channels with no reliance on third-party cookies or device IDs, and compliant with CCPA and GDPR. Licensing terms and permitted uses are defined per agreement. This is the question most AI teams need answered before evaluation, since usage rights determine whether a dataset can enter a training pipeline at all.