Case Study

Reduce Customer Churn

Reduce Customer Churn

Analytical Framework

A leading payment bank in India serving a very large retail customer base across an extensive physical and digital network.

Type

Industry

Banking

Categories

AI GovernanceCollections & Recovery

Industry

Data AcceleratorModel Risk Management

The Challenge

Retaining existing customers is one of the most effective ways to grow revenue, yet customer retention teams often operate with limited resources.

Acquiring a new customer can cost up to five times more than retaining an existing one. While nurturing current customers delivers better outcomes, the challenge lies in identifying: – Which customers are most likely to churn – Why customers leave – How to prioritize retention efforts for maximum impact Without accurate predictions, retention strategies are often reactive and inefficient.

The Solution

iTuring.ai helped the payment bank proactively address churn using its AutoML-driven churn prediction capabilities.

The platform: – Analyzed historical churn behavior – Identified emerging patterns and trends in near real time – Predicted customers at risk of leaving within hours iTuring built a Customer Churn Prediction Model that: – Identified high-risk customers – Explained key drivers of attrition – Enabled segmentation based on customer value and risk The bank used these insights to design an event-based contact and retention strategy, focusing efforts on the most valuable and vulnerable customers.

IMPACT

Attrition Rate

8% ↓

Revenue Increase

USD 1.8 million ↑ annually

Savings on Value at Risk

17% ↑

WHY ITURING.AI

iTuring goes beyond identifying customers likely to churn.

The platform:
Explains why each customer is at risk
Enables prioritization based on risk and customer value
Supports actionable, targeted retention strategies

FAQs

01

Why was churn prediction important for the payment bank?

+

The bank needed to focus limited retention resources on customers most likely to leave and most valuable to retain

02

How did iTuring identify customers at risk of churn?

+

iTuring analyzed historical behavior and emerging patterns to accurately predict customers likely to churn.

03

What made this model better than previous churn models?

+

The model achieved higher accuracy (92% AUC) and provided clear explanations for customer attrition.

04

How were churn insights used by the business?

+

The bank implemented an event-based contact strategy tailored to customer risk and value segments.

05

What measurable outcomes did this solution deliver?

+

The bank reduced churn by 8%, increased annual revenue by USD 1.8 million, and protected 17% of balances at risk.

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