Case study · Artificial Intelligence
User Behavior Analytics and Prediction
A system for understanding real user behavior, so product teams can see where users are retained and where they are lost.
42%
Retention improvement
98%
Prediction accuracy
10M+
Records processed
35%
Churn reduction
Challenge
A SaaS platform needed to know precisely how users engage with the product: which journeys lead to purchase and where churn begins.
Approach
Behavioral data collection and processing pipelines were established, and analytical models were applied to identify the patterns that drive retention and conversion.
Our role
01
Problem Analysis
Identified the behaviors and touchpoints that influence retention.
02
Data Architecture
Designed the processing pipeline and machine-learning models.
03
Implementation
Developed the analytics engine and the results dashboard.
TechnologiesPythonScikit-learnApache SparkFastAPI
