Power AI with Yobi:
The Largest Consented US Dataset

Petabytes of consumer signals grounded by trillions of dollars of spend, modeled at 200B parameters.

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Why Yobi?
Now available on the Databricks Marketplace →
Trusted by:

The World’s First Foundation Model Built on Consumer Decisions

Yobi combines ML and behavioral science to understand how shoppers think to predict their future commercial actions.

  • Uncover hidden patterns in shopper behavior
  • Build dynamic, differentiated data estates
  • Capture external data without violating privacy
  • De-risk data acquisition and AI modeling

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

Model consumer behavior

Predict commercial intent.

AI-driven consumer insights

Understand what drives outcomes.

Overcome cold start

Instantly personalize customer experiences.

Reduce customer churn

Respond in real time to shifting consumer trends.

Catch fraud before it happens

Identify human vs bot traffic with external data.

Safely Personalize Customer Experience to Increase Engagement & ROI

Resolve gaps and blind spots in your data estate by adding Yobi’s predictive behavioral features.

  • Rich representations of customer behavior
  • Fine-tuned to drive ML performance
  • Exclusive external data that is 100% opt-in
  • Cleansed and privacy-safe

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Break away from static audience targeting by deploying custom AI audiences dynamically tuned to your business KPIs.

  • Find your next best customer
  • Retain engagement
  • Acquire market share
  • Grow lifetime value

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340M+

U.S. Consumer Profiles

+500m

Emails

115M+

U.S. Households

70B

Daily Ad Events

Read our Whitepaper on using Embeddings as Pre-Trained Features for AI/ML →

FAQs

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 work with marketing platforms?
Yobi seamlessly plugs into your existing marketing stack to deliver dynamic audience models specifically tuned to your business.
How do I use Yobi in my own environment?
Yobi is now available in the Azure and Databricks marketplaces to empower customer to enrich their data estates or find new customers, without leaving their own ecosystems.
What makes Yobi unique?
Yobi licenses the Largest US-based Consented Consumer Dataset (85% exclusive), combining 300M consumer profiles and $2.5T in consumer spend, to identify commercial intent.
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?
Foundation Models (e.g. GPT-4, Claude 2, Gemini, Llama 2, Mixtral 8x7B) are the cornerstone of modern AI. They learn from large amounts of data and form relationships between the data so that they can be retrieved and explained. The data, and relationships between them, are preserved via Embeddings for quick and efficient processing.
How would Embeddings be used for AI?
By converting Consumer Behavior datasets into Embeddings, Yobi preserves relationships and correlations between consumer behavior, providing the necessary signals about commercial intent while preserving user privacy. These Pre-Trained Features can then be used to overcome cold start, hyper-personalization, prevent churn and fraud, and improve the performance of AI.

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