Case study
How a partner used behavioral intelligence at scale.
A pilot processing 3.4M profiles, integrated in under 10 hours.
3.4M
profiles processed
−40%
churn (reported)
2.3x
conversion (reported)
3.5x
ML accuracy (reported)
<10h
to integrate (observed)
Metrics reported by the pilot. How they were measured ↓
The challenge
The data already existed. It needed interpreting.
The partner needed to reduce churn and improve conversion at scale, without relying on surveys or declared demographic data, which have limited coverage and go stale quickly.
The application already captured clicks, timing, and navigation, but used those events only for aggregate analytics, not for individual, real-time decisions.
The gap wasn't a lack of data. It was the lack of a layer that understood the person behind each session.
The deployment
The same pipeline every Fluence customer uses.
Integration
Fluence connected to the existing event stream. No new, invasive data collection.
Signals
Clicks, response timing, uncertainty signals, and navigation patterns.
Deployment
In-production pilot, integrated in under 10 hours.
Scale
3.4M behavioral profiles processed during the pilot.
Results
How the results were measured.
Churn, conversion, ML accuracy, and integration time are outcomes reported by this specific deployment after adding Fluence behavioral context.
This page does not describe a detailed statistical methodology (test design, cohort size, or time window). Pilot results may not generalize to another product, user base, or industry. Detailed methodology is available on request.
See how Fluence measures incremental impact
What we learned
The value came from interpreting existing data better, not from collecting more.
Uncertainty signals and comparison patterns, ignored by aggregate analytics, helped predict churn before it showed up in traditional metrics.
Limitations
One customer, one industry, one product context.
Behavioral context is probabilistic: every inference carries a confidence level, and these results don't guarantee an identical outcome elsewhere.
See how this would work in your operation.
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