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Bank customer retention automation – churn reduction

Bank customer retention process automation with real-time personalized offers during customer interaction

30 %

shorter service time for a single customer

7 x

departments engaged in the process

40 +

integration services

5 +

integrated systems

Challenge

A customer calls to leave – the agent has minutes and a single opportunity

Customer retention in the call center channel requires real-time offer personalization. However, customer service agents lacked a tool capable of recommending the optimal retention offer in real time – based on both operational customer data and information gathered during the conversation. The result was standardized offers, low effectiveness, and high operational costs.

Key Requirement

A system that combines operational data with live conversation inputs and, in real time, recommends a personalized retention offer to the customer service agent.

Project

Operational data + conversation data = personalized retention offer

The project focused on analyzing available banking data and defining how additional insights could be captured during live customer conversations. Establishing a complete data set enabled diversification and personalization of retention offers.

Finture developed a balance between data requirements and call handling time – ensuring that consultants received a ready-to-use recommendation within seconds, without extending the duration of the interaction. The solution was continuously monitored and improved based on production data. Close collaboration with product departments and a carefully designed UX enabled customer service agents to efficiently present targeted promotional offers.

Solution

30% faster handling, lower churn, measurable impact

The implementation reduced handling time for each retention case by 30% and enabled the bank to decrease its churn rate. Customer service agents now see a personalized offer in a single interface – without switching systems or manually searching for products.

Key Decision

Continuous monitoring and iterative improvements based on production data allowed the system to steadily increase recommendation accuracy. The model learned from real retention outcomes, not only historical data.

Results

30 %

shorter service time for a single customer

7 x

departments engaged in the process

40 +

integration services

5 +

integrated systems

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Client

TOP 5 bank in Poland (Client retention)

Sector

Banking

Service

Business process automation

Technologies

Java, JavaScript, BPM

Competencies

Hyperautomation, Digital Transformation

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Krzysztof Chyliński – portret

Krzysztof Chyliński

Head of Advisory

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