Case Studies

Personalized marketing strategy to increase customer spend

Posted by | Fuld & Company

Generating data-driven decisions for the marketing team using RFM analysis and K-Means clustering

Challenge

The client wanted to increase its customer spend and revenue from its existing customer base by personalizing promotional offers via digital marketing channels.

Solution

Our team leveraged its data analytics capabilities to come-up with the optimum solution for the client’s marketing team:

  • The process began with understanding the client’s customer behavior, by studying the entire customer life cycle from acquisition to churn, using data engineering and science technologies
  • A single source of truth was then created as a base for understanding customer purchasing behavior, and a recency, frequency, and monetary (RFM) analysis performed
  • The final step was to segment the customer base and match these segments with the best promotional offers for each customer group, using K-Means clustering, to maximize their spend

Impact

This approach to client segmentation to optimize digital marketing channels resulted in the client achieving:

  • An increase in spend for the targeted customer base of up to 48%
  • An increase in average spend per customer of to 31%
  • An overall increase in revenue of up to 29%

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