Loyalty Analytics for a Global Grocery Store Chain

  • 3%

    Decline in customer attrition

  • 7%

    Annualized cost savings

  • >5

    Projects delivered


CLIENT CHALLENGES
  • A global grocery retail chain, with over 1,500 stores in 20 countries, wanted to identify its most loyal customers, to initiate a loyalty program
  • The client’s marketing team understood that it would be costly to acquire new customers in a highly saturated market; therefore, it wanted to maximize value through existing customers
  • The client expected grading of existing customers on the basis of loyalty and customized offers on loyalty cards to help it increase the wallet share, lifetime value growth, and retention rate
OUR APPROACH
  • Extracted three-year transactional and customer data from the SQL server and prepared data in a usable format with required fields (the RFM analysis output consisted of three fields for each customer: Recency, Frequency, and Monetary Value. Each of these components was assigned a score [1 to 5])
  • Applied an independent method to rate customers on R, F, and M dimensions and normalized variables
  • Applied two-step clustering algorithm on normalized RFM variables to arrive at the optimal number of clusters (i.e. 3),and classified customers based on loyalty
  • Connected demographic characteristics (e.g. age, gender, marital status, no. of children, city, education, income, etc.) to profile clusters
IMPACT DELIVERED
  • The client successfully implemented the loyalty program,customized as per different grades of loyalty identified
  • The loyalty program resulted in a 7% increase in revenue from existing customers in the first year
  • The customer attrition rate decreased by 3% after implementation of the loyalty program
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