Catalogue Response Analysis A Case Study Based On Decision Tree Model

Angela Ju

Content 1. Research Objective

2. Data Summary

3. Model Building

4. Key Findings

Research Objective

First, the research tried to figure out the most

important factors that affect response rate

Second, through the research, we tried to

identify customers that are most likely to

respond the catalogue campaign

Data Summary

Data Size 48,356 Records 82 Variables

Impute Missing Value Mode for Nominal Variables Column mean for Interval Variables

Partition

60%: Training 40%: Validation 0%: Test

Decision Tree

Summary of Decision Tree

For the first three levels of decision tree, the splitting variables are Original Buy Date, Expenditure Last 24 Months and Food Expenditure

Highest three response-rate leaves are Node 3, Node 17 and Node 27

Decision tree has five levels and 10 leaves

Lowest three response-rate leaves are Node 6, Node 12 and Node 24

Key Findings

Top 6 Factors Influence Response Rate

Factor   Importance  

Expenditure  Last  24  Months   1.0000  

Original  Buy  Date   0.8682  

Food  Expenditure   0.5209  

Number  Quarters  W/Buy   0.4184  

Beauty  Expenditure   0.3229  

Women  Hosiery  Expenditure   0.3142  

01 The response rate of customers whose original buy date later than June 3rd, 2008 is 100%.

02 Customers who have the following characteristics have a response rate more than 50%: original buy date before June 3rd, 2008; last 24 months expenditure greater than 138; expenditure on food greater than 32.5 and expenditure on beauty greater than 15.5

03 Customers who become client before June 3rd, 2008 with a last 24 months expenditure less than 138 are least likely to respond the campaign. Their response rate is only 3.6%

Who Most likely To Respond the Campaign?

How Decision Tree Help to Enhance Business Decisions?

A Mini Case: Assume the business wanted to distribute catalogues to 4,500 customers, if they selected customers randomly, the response rate is only around 5%. However, if they chose all the 35 customers whose DTBUYORG >= 17715.5, and chose 4465 customers that DTBUYORG < 17715.5 and $ Last 24 Months >= 138.1, the response rate would be 15.06%.The lift here is 3. Therefore, with the help of decision tree, the response rate of the campaign would increase by two times.  

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T H A N K Y O U Angela Ju