Powerful Data to Drive Brand Loyalty, Business Growth, and Increased Profits

Now available on the Databricks Marketplace →
Trusted by:

The World’s First Knowledge Graph for Consumer Decisions, built by Twitter, Spotify, Meta, Amazon, and Uber alums

Forged at the Intersection of Consumer Behavior + AI

We use innovative machine learning and behavioral science to understand how humans think, learn, and make decisions.

  • Uncover mathematical patterns in human behavior
  • Identify correlations humans can’t see due to bias
  • Capture signals without violating user privacy
  • Further Ethical Modeling

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we are a data company! big clean private data behavior science third party data provider Data bricks

Behavior modeling

Predict consumer behavior.

AI-driven consumer signals

Understand what drives outcomes.


People in the U.S.







Largest Consented Behavioral Dataset in the United States

Petabytes of consumer signals combined with trillions of dollars of spend, modeled at 200B parameters.

  • Over 80% exclusive
  • Clean, Tested, Structured
  • Fully Consented Data
  • Ethically sourced

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Safely Personalize Every Experience to Increase Engagement & ROI

Utilize the 200+ most predictive consumer signals to enhance engagement, monetization, and relevance with your customers.

  • Privacy-Preserving Access
  • Preserve non-linear data relationships
  • Machine-Readable Format
  • No exposure to raw consumer data

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Personalization at scale

Keep customers engaged.


Increase conversions & ROI.

Overcome cold start problem

Convert more visitors into customers

Yobi’s Knowledge Graph is Ethically Democratizing the AI Revolution


Frequently Asked Questions

Why did we build Yobi?
AI performance is only as strong as the data that fuels it, and data has been hard to come by. Advanced AI/ML teams are investing heavily in AI efforts, but data scarcity is a problem that Gartner estimates will amplify 6,000% next year. Third-party data has become incredibly expensive and can’t be used for AI/ML efforts in raw format.
What are some use cases for Predictive Audiences?
Predictive Audiences can be applied in various use cases, such as leveraging Yobi’s predictive AI for customer acquisition, implementing “win back” strategies to predict and engage with lapsed customers likely to re-engage or buy, and optimizing cross-sell/upsell efforts to increase customer lifetime value by targeting existing multi-product customers and identifying potential next best customers.
How does Yobi use machine learning?
Yobi uses machine learning to create powerful representations of consumer behavior known as embeddings, extracting the signal from our rich behavioral dataset in order to make high-quality predictions about future actions.”
How do I use Yobi in my own environment?
Yobi is now available in the Azure & Databricks marketplaces and empowers customers to improve customer targeting and segmentation without creating additional data pipelines.
What makes Yobi unique?
Yobi licenses the Largest US-based Consented Behavioral Dataset (85% exclusive), combining 300M consumers with petabytes of signals across trillions of dollars in spend.
Why is it so difficult to use Consumer Data to build AI?
The three main reasons it’s so difficult to use consumer data to build AI models are; Accessing Data is Harder than Ever, Managing Data Remains Challenging, and Regulatory Uncertainty Creates Risk
What is an Embedding?
Embeddings (vectorized data) are the cornerstone of modern AI, allowing AI to process vast amounts of data quickly and efficiently, capturing non-linear relationships in data, and drawing correlations that might not be apparent to humans.
How can I use Data Enrichment?
Yobi customers can enrich their customer base with Yobi’s 200+ most predictive behavioral signals, from petabytes of consumer signals combined with trillions of dollars of spend, to identify new attributes to model against. This new enriched customer dataset can be foundational for use cases like improving the overall customer experience, increasing marketing conversions, and better understanding customer behavior.

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