Skip to content

Marketing Analytics project : Promotion email targeting with uplift and causal forest model

Notifications You must be signed in to change notification settings

tongxinguo/Customer-Targeting-for-Email-Promotion

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Customer Targeting for Email Promotion

Overview

In this experiment we were examining the impact of an email that was intended to drive purchases. We want you to evaluate whether the promotion is effective and who to target with the email campaign.

Method

Effectiveness Evaluation

  • Average Casual Effect ace

    • Customers generally spend $1.34 more with email promotion campaign.

    • Baseline variable selection

      • Add other variables as baseline variables
      • Compare standard errors
  • Slicing and Dicing Analysis compare

    • Our email campaign works the most effectively on customers who have purchased sav blanc compare
    • Sav blanc buyers are more affected by the email, leading to addition $2.05 in spending

Targeting/Prediction Methods

  • Uplift model

  • Casual Forest model

  • Customer Scoring (Score = 𝛕i|X *profit margin - unit cost) target

    • Targeted group characteristics

      • Visit website less than 10 times
      • Half of them are recent buyers(last purchase day < 60)
      • Half of them have purchased wine with value less than $50
    • Targeting would increase profit by reducing cost

Final Suggestion

Our email campaign is effective and we should target at customer with score>0

About

Marketing Analytics project : Promotion email targeting with uplift and causal forest model

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published