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%

Tags: , ,

Related Resources

Read More

Global medical equipment company discovers a new competitor…and a new market

A leading medical equipment manufacturer was concerned about a new entrant that had moved into one of its high-value, diagnostic […]

Read More

Identifying Drug Repurposing Collaboration Partners

A top pharma company actively working in the drug repurposing sector wanted to expand its portfolio by adding new indications […]

Read More

Identifying the most effective prophylactic/therapeutic oral anti-inflammatory agents

A global FMCG leader sought to expand their current oral care product range with a line of products that had […]

Subscribe to our mailing list for our latest updates:

Wednesday, February 15

10:30 ET | 15:30 GMT

Join us to learn about:

  • Cultural nuance issues
  • Methods to mitigate bias
  • Optimizing survey design
  • Interpreting global results