Integrations to major MAPs like OMC, SFMC, Adobe marketing cloud and Braze to drive loyalty, engagement, win-back programs
Integrations to major MAPs like OMC, SFMC, Adobe marketing cloud and Braze to drive loyalty, engagement, win-back programs
RFM analytics

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
Outcomes Delivered