Increased cost savings due to marketing optimization
Improved efficiency of the model by 25%
The client is a multi-state community-focused bank serving in New York, Maryland, and other states in the US and provides banking, investment, insurance, and mortgage services to its clientele. The client wanted to leverage data from multiple sources such as clickstream, account, and transaction history to build machine learning models. It also wanted to use Google Ads Customer Match API to reach out to users with high conversion propensity scores.
Quantiphi helped in mapping the probability scores against different customer identifiers such as user login ID and WebID so that model outputs can be used to reach out to customers on different channels. Quantiphi created profiles and segments of people seeking mortgage services from banks to allow enhanced retargeting and customer experience.