CASE STUDY
Advanced CLTV Model Increases Profitability, Nets 20% Improvement on ROI
Customer Lifetime Value (CLTV) model helps banking leader design targeted marketing strategies for different segments.
It can be difficult for any business to match customers to the right products. Our client – a well-known Middle Eastern banking giant – needed to understand customer lifetime values across a range of financial products, including auto loans, personal loans, credit cards, and current account savings account (CASA) products. Their current model wasn’t meeting their needs, so we used our experience in advanced data analytics and multivariate modeling to deliver one that could, well, deliver.
Increased Clarity = Increased ROI
Developing a more accurate and powerful CLTV model allowed the client to:
Realize a 20% improvement in overall marketing ROI.
Increase customer profitability by gaining a larger market share of high-value customers.
Segment existing customers by lifetime value and evaluate their profitability.
Not only did this help the client design more profitable marketing strategies, but it also increased the revenue from their high-value segment.
Improving an Existing Model’s Methodology
Customer Lifetime Value modeling is a way to predict how much future revenue will be generated by that customer. It weighs the customer’s past, current, and projected behavior for both existing and future products. An accurate estimation of CLTV requires multiple variables (including tenure, salary, interest rate, payment behavior, limit utilization, spending trends, account balances, and more). Customers are grouped into behavior-based cohorts and this information is used to shape various decisions and actions.
After examining the existing model, we identified areas for improvement in the client’s transition matrix methodology. So, we created a cashflow and attrition matrix for each group. Then we calculated the CLTV by multiplying the last cashflow by the appropriate cashflow and attrition ratios and summed the values for the desired time period.
Peering into Future Revenue Potential
The result of this process was a model with significantly more accurate outputs – providing a better understanding of revenue potentialities and a clearer path forward for the marketing department and the company as a whole.
