Problem

Both manual and semi-automated (using traditional systems) forecasting of customer churn could be more efficient. It has low accuracy because it fails to reveal all the data relationships and patterns, identify anomalies, and take timely action to retain customers.

Solution

A customer churn prediction system built based on special machine-learning algorithms that identify customers prone to churn in real-time and runs the most appropriate retention scenario with a communication plan curated for each customer segment.

Result:

• Reducing the customer churn by up to 20% compared to conventional tools;
• Lowering the cost of marketing communications by 7–15%.

Implementation stages

PROJECT

Discussion of goals, objectives, and technical implementation approaches (3–5 days)

SCENARIO

Feasibility study based on the discovered customer issues (1–3 days)

PILOT PROJECT

Pilot implementation of the solution (1–3 months)

Scaling

Scaling