Problem

Customer behaviour keeps changing, and marketers need a way to build strategies appropriately to target different customer segments. For instance, loyalty, win-back potential, nascent etc.

Solution

Lister built a Recency Frequency Monetary (RFM) solution, based on customer purchase and engagement patterns, to process close- to- real- time feeds and overlay the data over the marketing database. This enables marketers to use the data for behavioral segmentation. This was also used as a churn predictor model by way of looking at RFM trends and tagging each customer under segments as “Loyalist,” “Potential,” “Nascent,” “Hibernator "and “Vanisher”

Key features

• Customer level engagement and purchase RFMs
• RFM product category level calculations
• Engagement RF calculations for every channel
• RFM trends used to calculate the customer lifetime value (CLTV) month-wise and predict churn
• RFM data feeds to MAPs like OMC, SFMC, Braze, Adobe Marketing Cloud to target the right audience for the relevant programs

Benefits

• Improved cross-sell, up-sell
• Improve CLTV
• Prevent/delay churn by creating relevant win-back programs