Reducing attrition among credit card clients

Tatra Bank used more targeted sales campaigns to reach customers and save accounts

With 40 percent market share, credit cards are a key product at Tatra banka. Founded in 1990 as Slovakia's first private lending institution, Tatra banka experienced slow growth and double-digit attrition when the global economic crisis hit. Hard times forced consumers in the saturated market to put away or cancel their credit cards – many of them falling behind in their payments. So the bank set a goal of reducing attrition by 30 percent. And it turned to SAS for the predictive analytics to achieve it.

"We couldn't afford losing clients of such a profitable and image-making product that defined us so much," says Marián Babic, Head of the Campaign Management Department.

We couldn't afford losing clients of such a profitable and image-making product that defined us so much.

Marian Babic
Head of the Campaign Management Department

Using technology to target customers

In planning their business strategy, Tatra banka's executives knew they could not approach tens of thousands of clients at the same time. And they couldn't rely on intuition to decide which clients to approach – or how to approach them.

"If you want to select the right clients from such a large pool, you need mathematical and statistical technology," says Babic.

The bank's first step was to establish several categories of credit card customers. In one group were customers who actively used their credit cards but still cancelled them. In another were customers who rarely used cards, and in a third group were customers who never used them. This segmentation enabled bank executives to target the right clients for promotions, thereby increasing campaign effectiveness and minimizing customer attrition.

"What is interesting about the private segment is that there is always a limiting factor," says Babic. "We could not approach all the clients; basically, we had to pick the right ones."

Improving data quality to support processes

The bank decided to create its predictive models in-house.

"We did not want to be dependent on external suppliers; we preferred to have our own team of experienced people who could promptly respond to our needs in the future," says Babic.

Since credit cards weren't the bank's only products, it also planned to use business analysis for bank accounts and mortgages. The bank later purchased a SAS solution that enabled it to cross-sell products to existing clients.

Before conducting any major analysis, however, the bank needed high-quality data to support its processes. Marek Bičár, Segment Manager, recognized early that analyses and business decisions couldn't be based on incomplete or incorrect data – and the data needed to be consolidated. Working with SAS Consulting, the bank mastered the processes of data collection, modeling and business interpretation. And now it can create a new model within one or two weeks.

With the help of SAS, the bank can create detailed segments and more effectively select customer groups for targeted sales or retention campaigns. It has also nearly reached its goal of reducing customer attrition by 30 percent.

Tatra banka has also seen measurable cross-selling results. "Today, figuratively speaking, our shots are more accurate. To achieve the same result we need fewer shots," says Babic.

The bank discovered that clients who are designated by the predictive model as having the highest potential to buy a certain product often accept the offer at a rate two or three times higher than randomly selected clients. In addition, analytics help the bank optimize communication. Babic says it's not important to swamp clients with advertising – it's more effective to keep them informed of new products and offers.

Planning for the future

Inspired by its analytics success, Tatra banka plans to continue using SAS to help it determine new pricing and service strategies based on specific customer qualities. "For example, what jobs the clients have, why those particular jobs, what sets them apart, what different products they would like to have, what type of communication they prefer and maybe what their growth potential is," says Babic.


Reduce attrition among credit card clients during a tough economy.


SAS® Predictive Analytics and Data Mining


  • Ability to predict which offers customers will accept helps reduce attrition by 30%.
  • Targeted customers accept offers at a rate two or three times higher than randomly selected customers.
  • Solidifies bank’s reputation as a business leader in Slovakia.


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