Context Lake In Practice: Preventing Customer Churn with Predictive AI

The Story of Streaming Su

Su is a product manager at a streaming service. Every month, thousands of subscribers cancel. The pattern is consistent: a subscriber's engagement drops, they skip a billing cycle, and then they're gone.

The company has predictive models. They can identify at-risk subscribers with decent accuracy. But by the time the models flag someone, it's often too late. The churn signal appears after the decision to leave has already been made.

The $280M question: what if the intervention could happen before the subscriber even realizes they're disengaging?

The Freshness Problem

Su's churn models run on batch-computed features. Engagement metrics are updated daily. Billing data is refreshed weekly. By the time the model scores a subscriber as at-risk, the opportunity for intervention has passed.

The models aren't wrong—they're late. The signal is there in the data, but the data is stale by the time it reaches the model.

Five Challenges in Building Predictive ML

Fragmentation: features scattered across multiple systems.

Staleness: batch pipelines introduce hours or days of delay.

Drift: features computed for training don't match serving-time features.

Sprawl: maintaining separate offline and online feature stores.

Concurrency: multiple models accessing shared features inconsistently.

How Tacnode Context Lake Addresses Churn

Tacnode Context Lake unifies feature computation, storage, and serving. Engagement signals are ingested in real-time. Features are computed incrementally as events stream in. Models score subscribers on live data.

The result: churn risk is identified when engagement first drops, not hours or days later. Interventions—personalized offers, content recommendations, support outreach—can happen in the critical window.

The Result

With fresh features, Su's models become proactive instead of reactive. Interventions succeed more often because they happen earlier. Customer lifetime value increases. The $280M loss becomes a recoverable opportunity.

The technology isn't magic. It's infrastructure designed for the requirements of real-time ML: fresh, consistent, unified.