To anticipate potential credit card profit and predict a prospect’s payments pattern.
A major regional financial services company conducted an enterprise-wide CRM analysis, which showed that its current credit card penetration within the bank customer base was less than 10% – significantly below the industry average. We were asked to lead the charge to develop a strategic solution to cross-sell credit cards to the existing retail bank customer base.
We had built a Machine Learning and AI driven Data Analytics Engine to predict potential credit card profit. The outcome facilitated prioritization for campaign selection. However, it is highly pivotal to select the right target, but it was only half the solution.
In addition to the profit model solution, we also developed a transactional behaviour determination based on a multinomial segmentation model, that can predict a prospect’s payments pattern. The combination of both these solutions empowered our customer’s market penetration and helped them to surpass the industry average.
Our predictive models enabled the client to target the most valuable prospects and extend the most relevant offers for their existing customer database. With the right combination of their internal financial and behavioural data and credit bureau, the predictive model solution scored a significant lift in identifying potential prospects who are more likely to be higher profitability accounts.