Decision Tree Model: Catalogue Response Analysis
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Transcript of Decision Tree Model: Catalogue Response Analysis
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