Skip to content

Using RFM analysis to re-design the push message campaign for mobile app.

Notifications You must be signed in to change notification settings

RainyChenShiYu/RFM-Analysis-for-Mobile-App-Push-Messaging

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 

Repository files navigation

RFM-Analysis-for-Mobile-App-Push-Messaging

Background:

  • Tuango is one of the major “deal-of-the-day” websites in China. The website’s business model is similar to that of Groupon, promoting discounted gift certificates that can be used at local or national retailers. The pronunciation of “Tuango” in Chinese sounds similar to “group buying,” which refers to the fact that customers are buying as a big group for each “deal.”
  • Tuango team wanted to re-evaluate the execution process of the campaign, which was targeting all consumers without considering the true marginal cost of each message.

Goal:
To identify the most profitable customer segments using RFM analysis on customer databases.

Procedure:

  • Utilized RFM analysis to evaluate the efficiency of a push message marketing campaign on Tuango in order to identify the most profitable customer segments for targeted marketing strategies.
  • Performed preliminary and quintile analysis in R to ascertain the relationship of order size and response rate among customers based on their recency, frequency, and monetary habits.
  • Pinpointed profitable groups using the remaining test sample and calculated optimal profitability.
    Please read the uploaded file to review the code.

Key Results:
Increased projected campaign profit by 116,883 RMB relative to previous years.

About

Using RFM analysis to re-design the push message campaign for mobile app.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published