Intelligent Segmentation

Most companies track customer metrics—such as usage frequency, tenure, and support interactions—but few truly understand what these numbers reveal about their business health. Traditional correlation analysis might indicate that "higher usage correlates with lower churn", but it does not reveal where the critical thresholds lie, which segments are losing revenue, or why even the most engaged customers still leave.

This is where Smart Segmentation (binning) analysis with Weight of Evidence (WOE), Information Value (IV), KS statistics, and Gini coefficients transforms raw data into strategic intelligence. By segmenting continuous variables into discrete bins, we can uncover non-linear relationships, identify crucial tipping points, and measure the true predictive power of each feature.

Instead of simply knowing that "usage matters," we discover that customers with 8–30 uses still churn at 43%, meaning high activity does not guarantee retention. Instead of believing that "tenure builds loyalty", we find that customers at 30+ months churn at 56%—worse than new customers—indicating a value decay challenge. Instead of assuming "support shows engagement," we learn that 62% of customers make 5–10 support calls and 61% of them churn, exposing a fundamental product usability problem.

In the following three analyses, we examine:

Usage Frequency (IV: 0.120) — Why your power users are still leaving

Tenure (IV: 0.229) — Why time works against you, not for you

Support Calls (IV: 0.516) — Your strongest predictor and biggest crisis

Each analysis goes beyond the numbers to reveal root causes, competitive vulnerabilities, and actionable strategies. This is not just data analysis—it is a diagnostic of fundamental business model problems hiding in plain sight.