Background Information: US book publishing industry is over $28 billion annually. The Sierra Book Club was established in 1986 for the purpose of selling specialty books through direct marketing. SBC is strictly a distributor and does not publish any of the books it sells. In anticipation of using database marketing, SBC made a strategic decision right from the start to build and maintain a detailed database about its members containing all the relevant information about them. Readers fill out an insert and return it to SBC, which then enters the data into its database. The company currently has a database of over a million readers and sends out a mailing about once a month.
SBC is exploring whether to use predictive modeling approaches to improve the efficacy of its direct mail program. For a recent mailing, the company selected a random sample of customers from its database and mailed a brochure for the book The Art History of Florence. SBC then developed a database to calibrate a response model to identify the factors that influenced these purchases. For purposes of analysis, we will use a subset of the database available to SBC. The data for this exercise appears in the file “Targeting.xlsx”. The sheet called “Data” contains data on 1600 customers, 400 of whom purchased the book, and 1200 that did not. There are 2300 observations for holdout prediction. Customer IDs starting with “T” are part of the training sample, while those starting with “H” are part of the holdout sample.
Here is a description of the variables used for the analysis:
Choice: Whether the customer purchased The Art History of Florence. 1 corresponds to a purchase and 0 corresponds to a non-purchase.
Gender: 0 = Female and 1 = Male
Amount purchased: Total money spent on SBC books
Frequency: Purchase frequency
Last_purchase (recency of purchase): Months since last purchase
First_purchase: Months since first purchase P_Child: Number of children’s books purchased P_Youth: Number of youth books purchased P_Cook: Number of cookbooks purchased
P_DIY: Number of do-it-yourself books purchased.
P_Art: Number of art books purchased
Objective: Sierra Book Club is considering a similar nationwide mail campaign for 1,000,000 customers. The allocated cost of the promotional mailing is $0.65/addressee (including postage). The book costs Sierra $15 to purchase and mail to a buyer. The company allocates overhead to each book at 45% of cost (i.e., 45% of $15). The selling price of the book is $31.95. Assume that the firm has decided to mail to only 10% of the 1,000,000 customers. (Note: All of this information is contained in the spreadsheet “Financial Calculations”).
Question 1: Machine Learning (8 points)
How much more profit would you expect the company to generate if they used a targeting model as compared to sending the mail offer to 10% of the entire list chosen at random?
Approach:
1) What % of customers bought (choice=1) in the “Train” and “Holdout” data?
a. Train
b. Holdout
2) Note: When running machine learning models (e.g., logistic regression; bootstrap forest, boosted trees, neural network models), the underlying Validation column has to be a “numeric” variable instead of a “character” variable. To convert the current Validation variable to a numeric one, do the following:
i. Click the Validation column (any cell).
ii. Click “Cols > Recode” and set the values according to the screenshot below. Click “Recode”.
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