Improving promotion targeting for a credit card company

Improving promotion targeting for a credit card company

Challenges 

The credit card companies or banks drive top-line revenue through increased card utilization. They mostly focus on offering credit cards to their customers which allows them to earn revenue using interests. RFM (Recency, Frequency, Monetary) is a common promotion methodology used by companies to identify customer purchasing behavior of customers but has limited data features. This customer wanted to do more. The management of the credit card company wanted to implement targeted next best offers for its customers, which will maximize the response rates and drive incremental spending.  

 

Solutions 

Neal Analytics helped the company to identify customers with the highest latent spending potential using their advanced analytics 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 helped optimize next best offer recommendations by identifying the transaction category and the time when customers tend to spend heavily. 

 

advanced analytics

 

Results 

Neal Analytics built a solution that helped the company improve the allocation of promotion budgets, rather than spending a lot of money on highly targeted campaigns. The customer has focused more on promotion recommendations over the traditional and existing RFM strategy. The customer was also able to identify their highest value customers to target for promotional campaigns, maximizing response rates.