Improving promotion targeting for a credit card company
The credit card company needed to drive top-line revenue through increased credit card utilization. The company’s existing promotion methodology used the RFM (Recency, Frequency, Monetary) model to identify customer purchasing behavior, but it had limited data features. This credit card company wanted to do more. Management wanted to implement targeted next best offers for customers, which would maximize the response rates and drive incremental spending.
Neal Analytics helped the company identify customers with the highest latent spending potential by using advanced analytics and machine learning techniques. We helped develop a solution that combined historical customer purchasing patterns with the demographics of potential clients to generate micro-segments tailored to individual customers.
Neal Analytics also helped optimize next best offer recommendations by identifying the transaction category and the time when customers tend to spend heavily.
Neal Analytics built a solution that helped the company better allocate promotion budgets with highly targeted campaigns. The solution enhanced promotion recommendations over the existing and traditional RFM strategy. The customer was also able to identify their highest value customers to target for promotional campaigns, maximizing response rates.