This post was written by Yaniv Avraham, Principal Data Product Manager at Start.io.

In digital advertising, knowing who you’re reaching matters just as much as knowing where you’re reaching them. While most ad-tech platforms optimize around supply-side signals – placement, geography, format, time of day – a massive layer of intelligence often goes untapped: the user themselves. 

That’s changing at Start.io. 

From Audiences to Intelligence 

Over the past year, Start.io built one of the industry’s most robust audience engines – dynamic Performance-based segments powered by high-value media KPIs like click-through rates, viewability, video completion, and conversion signals. These audiences already power external monetization across leading marketplaces including The Trade Desk, LiveRamp, and AdSquare. 

But we asked a bigger question: what if we could channel that same deep user understanding directly into our own algorithmic decision-making? 

The answer is the User-Level Feature Store – a new core layer in Start.io‘s media-Algo stack that transforms our first-party data from an external product into an internal competitive advantage. 

What It Does 

The Feature Store is a centralized repository of per-user behavioral and performance signals – engagement patterns, monetization history, activity profiles, and more – all computed offline at high frequency and served at bid time with minimal latency. 

Unlike traditional audience segments, which group users into discrete cohorts, the Feature Store produces rich, continuous feature vectors that our models consume directly. It’s not a segmentation output – it’s a model input layer. 

The architecture follows a clean hybrid design: intelligence is computed offline in our data science stack, then materialized into a high-throughput online store for real-time serving. At bid time, every decision is enriched with what we already know about the user – without adding latency to the serving path. 

Why It Matters 

This initiative touches every core algorithm in Start.io‘s media stack: 

  • Demand-Supply Matching becomes user-aware – learning which users each bidder truly values, not just which placements perform well on average. 
  • Bid Floor Optimization gets smarter – setting floors based on a user’s actual monetization potential rather than aggregate estimates. 
  • Supply-Side Throttling becomes surgical – preserving high-value users while filtering low-value traffic, protecting both revenue and partner relationships. 

The result is a 360-degree optimization model that spans user, supply, and demand – a significant leap from today’s supply-only approach. 

The Bigger Picture 

The Feature Store aligns with Start.io‘s broader infrastructure vision of building a modern, holistic, production-grade ML stack – one where features are shared, versioned, and decoupled from individual model code. It creates a foundation for future capabilities like user embeddings and generalized user representations that any model in the stack can leverage. 

For our partners and advertisers, this means better performance, more precise targeting, and higher return on ad spend – all powered by Start.io‘s unique first-party data asset spanning billions of mobile users worldwide. 

What’s Next 

The project is now in active development, with cross-functional teams across data science, algorithms, and platform engineering collaborating on architecture, feature prioritization, and early model experiments. Stay tuned for results from our first A/B tests down the year – we expect them to speak for themselves. 

The User-Level Feature Store: bringing what we know about the user into every bid decision.