Data Mining Coursework B064536

Introduction

Variables

Ranking by

Likelihood to

Redeem

Total

Coupon

Redeemed

Total

Participants

in Category

(Total Coupon

Redeemed/Total

Participant)*100

Age 35-44 years old 587 120565 0.49%

45-54 years old 761 169188 0.45%

25-34 years old 351 83584 0.42%

Marital

Status Married 1010 203642 0.50%

Unknown 743 203455 0.37%

Single 238 71354 0.33%

Income 35-99K 1275 273663 0.47%

100K+ 320 79425 0.40%

Under 34K 396 125363 0.32%

Homeowner

Description Unknown 304 5232 5.81%

Renter 64 6977 0.92%

Homeowner 1529 298685 0.51%

Household

Composition 2 Adults With Kids 608 108905 0.56%

1 Adult With Kids 141 28276 0.50%

2 Adults No Kids 655 153491 0.43%

Ranking by

Campaign

Effectiveness

Coupon

Redeemed

Total

Coupon

sent

Percentage

B 247 25872 0.95%

A 1724 444895 0.39%

C 20 7684 0.26%

People who redeem coupons are not from your main customer base

Largest campaign, Type A is not effective

Data from the demographic, coupon and campaign datasets were merged

together. A total of 478451 valid samples were generated. 50% of the samples

were used for model training purpose. 50% of the samples were used for

testing purpose. The dataset was imbalance as there were only 1991 redeemed

coupons out of 478451 valid samples. Balancing node in SPSS Modeler was

used to correct imbalance in dataset. The proportion of redeemed coupons was

increased by a factor of 245 for model training and testing. The model was

build using CHAID and the set of input variables that provide the highest

accuracy percentage were included in the decision tree. Different

combinations of input variables were tested during the working process and

their accuracy readings were recorded and compared.

CHAID is a classification method that uses Chi-square statistics to perform

optimal splits for decision tree building. CHAID will firstly conduct a detailed

analysis of the input variables and dependent variable. Next, it will test for

significance using Chi-square test of independence. CHAID will select an

input field that has the smallest p-value (most significant). If a variable has

more than two categories, these are weighed up, and categories that show least

difference in outcome are grouped together. This merging process stops when

all remaining categories are significantly different from each other. CHAID

was identified as the most accurate classification model for this dataset by auto

classifier node. A manual comparison amongst CHAID, CART and C5.0 were

also conducted. Result ascertains the analysis of auto classifier node.

When deciding who to send coupon to, retailer should consider these factors

in following sequence:

1. Homeowner description

2. Income description

3. Household composition

4. Household size

5. Marital status

According to CHAID classification, retailer should send coupons to:

1. People who are homeowners, probable owners and probable renters

2. People’s income who are 35-99K

3. Household composition with 2 adults with and without kids

4. A household with 1 to 3 person

5. Married

The model developed has an accuracy of 63.14%. This means that the model

is able to correctly classify (predict) those who would redeem coupon and

those who would not redeem coupon with an accuracy of 63.14%.

5.80%17.47%

25.20%35.36%

7.96% 8.21%

19-24 25-34 35-44 45-54 55-64 65+

Age

Married

43%

Single

15%

Unknown

42%

Marital Status

Under 34K

26%

35-99K

57%

100K+

17%

Income

62.43%

29.32%5.70% 1.46% 1.09%

Homeowner Unknown Renter Probable Owner Probable Renter

Homeowner Description

32.08%22.76%

18.04%11.32%

5.91% 9.88%

2 Adults NoKids

2 Adults WithKids

Single Female Single Male 1 Adult WithKids

Unknown

Household Composition

The aim of this project is to generate some meaningful insights for the retailer

using exploratory data analysis (SPSS Modeler). It also aim to build a model

that can accurately predict which customers are more likely to redeem

coupons and those that would not.

Who are your customers?

Age 45-54 Years Old

Married

35-99K

Homeowner

2 adults No Kids

Who are redeeming coupons right now?

Data Manipulation

Chi-squared Automatic Interaction Detection

Results

Discussion and Recommendations

There is one contradiction between data analysed and CHAID prediction. Data

analysed revealed that unknown and renter are more likely to redeem coupon.

However, CHAID predict that homeowners, probable owners and probable

renters are more likely to redeem coupons.

The other factors such as marital status, income and household composition

are consistent between data analysed and CHAID. Both indicate that people

who are married with income between 35-99K and household with 2 adults

with and without kids are more likely to redeem coupons.

One recommendation is to evaluate and re-design type A campaign as it is not

effective. Secondly, campaign should be more targeted to lower cost. Target

audience should be its main customer base as CHAID’s prediction coincides

with the characteristics of its main customer base.