BACKGROUND AND CHALLENGE
- Alongside its insurance partners, a retailer offers relevant insurance products and services to its credit card database
- Names are selected randomly at various time intervals throughout the year which result in inefficient higher marketing costs
- The challenge was to create a framework and approach that improved efficiency of its insurance acquisition marketing costs
WHAT WE DID
- Built the following models:
- Upsell/Cross Sell Models by Insurance
- Contact Models Retention
- Product/Service
- Built a marketing contact database in order to utilize prior information based on marketing interaction.
- Allowed us to select right names with the right insurance offer and at the right time.
WHAT WAS THE RESULT?
- Below table looks at number of months of sales revenue that were required to pay back the initial acquisition costs
- Prior to any data science activity, approx. 50 months of sales revenue was the B/E point which decreased to approx. 8 months of sales revenue with our data science solutions.
