Customer segmentation based on analytics for customer risk, attrition, response likelihood and other aspects are de rigueur in financial services. But there is still an opportunity to improve results by sharpening the picture. Many lenders are still not using the most advanced predictive models—for example, customer behavior models based on card transaction data.
Refining segmentation strategies with information reflecting customer attitudes and sensitivities towards key business issues can also enhance incremental profitability. As an example, banks can use testing within specified segments to determine customer sensitivity to deposit rates. Deposit pricing sensitivity is an emerging concern for banks; knowing the exact price point at which a customer will likely defect is a powerful tool in improving customer retention efforts. Adaptive control testing allows a bank to uncover hidden customer sensitivities and drive profit-enhancing treatment strategies into more granular customer sub-segments.
Here’s an example—it stems from work we did with a major North American bank, which believed its long-term growth would come from its existing customers. The bank believed that it could improve its response rates to cross-selling efforts if it could include measures of customer sensitivity to specific product features into its segmentation scheme. Working with FICO, the bank used action-effect modeling techniques to rapidly understand the value of customer sensitivity to key offer types, including balance transfer offers, credit line offers, rewards upgrades and rate discounts.
The resulting cross-sell program showed the potential to increase profitability up to $7.00 per eligible customer over a 12-month period due to the improvement in targeting offers. With over 1 million customers eligible for the program, profit was expected to increase incrementally by 6%, directly attributable to increasing response rates and reducing costs. In addition, there are the harder-to-measure benefits on customer loyalty—not fatiguing the customer with inappropriate offers, but targeting them with more appropriate ones.