AI/ML Teams

Yobi’s Knowledge Graph Improves Model Performance by 15%

AI/ML Teams 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 machine-readable, enriched customer behavior dataset (assembled by Twitter, Spotify, Meta, Amazon, and Uber alums) can be foundational for use cases like improving the overall customer experience, increasing conversions, and developing more granular micro segments of your customers.

What is an Embedding?

Embeddings (vectorized data) are the cornerstone of modern AI, allowing AI to process vast amounts of data quickly & efficiently, capturing non-linear relationships in data, and drawing correlations that might not be apparent to humans.

By converting consumer decisions into embeddings, AI can keep relationships and correlations between consumer behavior intact, providing AI with the signals necessary for modeling without exposing it to raw consumer data. All of this, in a format that is highly efficient, pre-processed, and native for AI/ML.

Traditional segmentation: Users in blue and orange are categorized into different user categories based on how they look, with the assumption that they will behave similarly.

Yobi’s innovative approach: Our AI/ML models identify how these users behave and creates new dimensions, clustering them into new user categories based on the most predictive 200+ signals derived from our knowledge graph.

Use Cases

1. Instant Personalization – provide a starting point for recommender systems (overcoming the cold start problem), by enriching your users’ profiles
2. Improve Ad Conversions – create more granular audience segments by understanding your users’ behavior, to deliver the right message, through the right channel, at the right time
3. Anticipate Churn – reactivate churned customers, and identify which segments share similar characteristics, by enhancing their user profiles
4. Prevent Fraud – identify which users might be more susceptible to fraud, and what normal behavior looks like, by creating a richer customer profile
5. Optimize Pricing & Demand Forecasting – leverage consumer behavioral signals from outside your ecosystem to provide insights on price sensitivity, competitive intelligence, and market trends

We recently partnered with Databricks to make these Embeddings available from within your ecosystem.