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Data Mining Coursework B064536
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%
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%
Unknown 304 5232 5.81%
Renter 64 6977 0.92%
Homeowner 1529 298685 0.51%
2 Adults With Kids 608 108905 0.56%
1 Adult With Kids 141 28276 0.50%
2 Adults No Kids 655 153491 0.43%
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
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%.
19-24 25-34 35-44 45-54 55-64 65+
5.70% 1.46% 1.09%
Homeowner Unknown Renter Probable Owner Probable Renter
2 Adults No
2 Adults With
Single Female Single Male 1 Adult With
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
2 adults No Kids
Who are redeeming coupons right now?
Chi-squared Automatic Interaction Detection
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.