Marketing Analytics case
azanah
example_-_bookbinders.pptxDatabase for BookBinders Book Club Case
Predict response to a mailing for the book, Art History of Florence, based on the following variables accumulated in the database and the responses to a test mailing:
Gender
Amount purchased
Months since first purchase
Months since last purchase
Frequency of purchase
Past purchases of art books
Past purchases of children’s books
Past purchases of cook books
Past purchases of DIY books
Past purchases of youth books
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Drivers of the RFM Model
Recency
Frequency
Monetary
Value
Time/purchase occasions since the last purchase
Number of purchase occasions since first purchase
Amount spent since the first purchase
R
F
M
Total RFM Score: R Score + F score + M Score
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Example RFM Model Scoring Criteria
| R | Months from last purchase | 13-max | 10-12 | 7-9 | 3-6 | 0-2 |
| Score | 5pts | 10 | 15 | 20 | 25 | |
| F | Frequency | > 30 | 21-30 | 16-20 | 11-15 | 0-10 |
| Score | 5pts | 10 | 15 | 20 | 25 | |
| M | Amount purchased | > 400 | 301-400 | 201-300 | 101- 200 | 100 |
| Score | 50 | 45 | 30 | 15 | 10 |
Implement using Nested If statements in Excel
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Variables that represent recency, frequency, and monetary value are the following:
Recency: Months since last purchase (Last_purchase)
Frequency: Total number of purchase (Frequency)
Monetary value: Total money spent on BBBC (Amoun_purchased)
Computing Scores Based on Regression
Run regression model to predict probability of purchase:
Probability of Choice (0 or 1) = a0 +a1 x Gender+a2 x Income +…
Note that predicted choice probabilities from the regression model need not necessarily lie between 0 and 1, although most of the probabilities will fall in that range.
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The Customer Choice (Logit) Model in MEXL
The probabilities lie between 0 and 1, and sum to 1.
The model is consistent with the proposition that customers pick the choice alternative that offers them the highest utility on a purchase occasion, but the utility has a random component that varies from one purchase occasion to the next.
The primary objective of the model is to predict the probabilities that the individual will choose each of several choice alternatives. The model has the following properties:
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