Problem

Both manual and semi-automated (using traditional systems) forecasting of customer churn is inefficient and has low accuracy because it fails to reveal all the data relationships and patterns, identify anomalies, and take timely actions aimed at customer retention.

Solution

A customer churn prediction system built on the basis of special machine-learning algorithms that identifies in real-time customers that are prone to churn 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;
• Reducing 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