One of the biggest challenges banks face is identifying and successfully marketing to the most profitable customers. To stay competitive with tech-savvy fintechs, banks need to identify customers whose needs align with their offerings and avoid wasting effort and marketing budget on customers who aren’t likely to benefit from – or purchase – their products and services.
In this on-demand webinar, DataRobot’s Director of Banking H.P. Bunaes walks through the process of building a client profitability model with DataRobot’s automated machine learning platform and using it to improve profitability, client prospecting, and marketing ROI.
In this sesion H.P.:
- Shows how to develop and test a client profitability predictor model with DataRobot using real data
- Benchmarks profit vs. risk-averse strategies, compares the results, and proposes a high-profit strategy using the DataRobot model
- Describes insights gained from client profitability predictors to inform prospecting and target marketing
- Discusses benefits, use cases, and downside risks for client profitability models for credit card, wealth management, and auto lending lines of business
