The Big Picture:
A leading retail bank was facing low customer engagement and satisfaction with its customers. The existing analytical models on product propensities generated lower accuracy, and missed critical data elements, such as offline and online interactions and transactions, and prevented an objective arbitration of offers among multiple competing product offers. This resulted in sub-optimal customer experience and lower response rates.
To address the company’s challenges, a new next best product and service recommender was built using deep learning. It was designed to predict the top three recommendations from among a wide suite of products, and for services.
A single customer view was prepared with 4,000+ attributes such as customer product holdings, transactions, in-bank transactions, and online interactions.
The models were tested on a select population within the lead scoring platform and deployed centrally.
As a result of the engagement, customer product-offtake rates jumped by 60%, resulting in significantly higher marketing ROI.