CASE STUDY

Using Customer Data to Develop Models
for Long-Term Profitability

ABSTRACT

Marketing executives at a top US credit card issuer were interested in cross-selling more business products to their >20mm consumer card holders.  Based on their research, their penetration rate was at less than 25% of its potential.  By employing customer-level analytics across marketing channels, products, and segments, and combining these with a real-time investment optimization engine, we were able to help them drive a 40% lift in customer NPV, $1B in incremental card spending, and a 5% lift in new customers acquired. 

 

Clearly Define Your Business Objectives

Marketing executives at a major US credit card issuer were interested in cross-selling more business products to their >20mm consumer card holders, and deriving higher profits from these customers.  Based on their research, the penetration rate of business products among likely small business owners was at less than a quarter of its potential.  They had 6 main business products, 4 marketing channels, and were interested in developing a segmented strategy.

 

Aquire & Synthesize Relevant Data

We began by leveraging financial data to derive a set of relationships for calculating customer-level profitability.  Next we built a predictive model to identify high-likelihood small business owners among the consumer base.  Finally, we combined firmographic and demographic data, card usage behavior, and response data for historical campaigns, to create a comprehensive modeling dataset.  We used this dataset to develop customer- , product- , and channel-specific profitability models, which we were able to populate for the entire customer base. 

 

Develop an Action Plan

We fed the results of these models into a real-time investment optimization engine, which can quickly compare a large number of investment allocation scenarios and calculate the optimal distribution and sequencing of offers by segment and channel. 

Partner with You to See it Through

 

Partner with You to See it Through

We worked with their Risk and IT teams to implement the models and optimization engine within their production scoring systems, allowing them to be leveraged for future campaigns and updated as new products and data became available.  Together, we were able to help drive a 40% lift in customer NPV, $1B in incremental card spending, and a 5% lift in new customers acquired.

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