A retail bank wishes to grow its credit-card subscriber base by offering balance transfers to customers with competing credit-card issuers. The bank wants to identify the factors that influence the acceptability of such balance-transfer offers amongst credit-card holders. Identification of customer segments that have high propensity to respond positively to balance transfers would enable the bank to focus its acquisition campaigns on such segments, thereby controlling acquisition costs as well as growing its credit-card subscriber base. Predictive modelling and segmentation techniques are deployed to develop prospect response propensity models that allow the bank to achieve its dual objectives of growing the credit-card business as well as controlling customer acquisition costs.
A retail bank wishes to identify prospects that would be open to accept a balance-transfer offer on their credit cards. The bank is looking towards growing its credit-cards business, and wants to use balance-transfer offers to subscribers with competing credit-card issuers as a vehicle to drive growth in its subscriber base.
Balance-transfer offers are an effective lever for credit-card issuers to grow their subscriber base. Since such offers are extended to existing credit-card holders (albeit with competing issuers), it is relatively easier to assess the credit-worthiness of the prospects due to the availability of their credit histories. This reduces due diligence efforts without compromising on risk management, thereby controlling application processing times, efforts and costs.
The retail bank wants to predict the likelihood that a prospect, when approached with a balance-transfer offer, will accept such an offer. Specifically, the bank wishes to identify the factors that influence the prospects’ propensity to respond positively to such offers. This would enable the bank to limit its customer acquisition efforts to credit-card holders that are relatively more likely to get converted into the bank’s customers, thus controlling acquisition costs as well as growing the credit-card subscriber base.
Exploratory analysis is conducted on the retail bank’s historical experience with balance-transfer campaigns for credit-card subscribers. The propensity of prospects to respond positively to such offers depends on a host of factors, such as customer demographics (age, gender, marital status, income level, academic background, professional profile, family size, etc) as well as other features such as number of credit-cards held, credit limits, credit histories, credit scores, and other measures of indebtedness (such as existing home loans, vehicle loans, personal loans, etc).
Customer profiling techniques are deployed to identify the key factors that influence credit-card holders’ likelihood to accept balance-transfer offers. Predictive analytics and segmentation techniques are used to zero-in on customer segments that have a high propensity to accept balance transfers. Such prospect response propensity models enable the credit-card issuer to focus on customer segments that are most likely to get converted to the bank.
With the prospect response propensity model in place, the retail bank is able to focus on customers that have high propensity to accept the credit-card balance transfer offers. This enables the bank to effectively target prospects, thereby growing its credit-card subscriber base as well as controlling customer acquisition costs.