Data Lift Per Deci Volume Number of Number of Cumulativ Cumulative Decile Cases Responses e Cases Responses 1 2 3 4 5 6 7 8 9 10 50000 4522 9.04% Decile Response Rate (a) Lift Per Decile Overall Response Rate (b) Cumulative Lift Lift Index (a/b) Cum Overall Cumulative Respons Response Lift Index e Rate Rate (e) (d/e) (d) Gains Cumulative Gain Responses Index Profitability Analysis Volume Decile 1 2 3 4 5 6 7 8 9 10 Contrib ution Margin Fixed Costs Operating Profit lysis Cum Operating Profit Data Predicted Number of Number of Cumulativ Cumulative Probability Cases Responses e Cases Responses Decile 1 2 3 4 5 6 7 8 9 10 50000 4522 9.04% Lift Per Decile Decile Overall Response Respons Rate e Rate (a) (b) Lift Index (a/b) Cumulative Lift Cum Overall Cumulative Respons Respons Lift Index e Rate e Rate (d/e) (d) (e) Gains Cumulative Responses Profitability Analysis Gain Index Predicted Contributio Probabilit n Margin y Decile 1 2 3 4 5 6 7 8 9 10 Fixed Costs ility Analysis Cum Operating Operating Profit Profit Course Number: Semester: MKTG 3509: Customer Data Analytics Summer 1, 2019 Homework 5: Using Logistic Regression Models / Evaluation of Multiple Models Objectives The objectives of this final exercise are to: 1) practice building and evaluating different models to identify which customers in our database should receive a future marketing communication from our company. You will create a univariate segmentation, and a predictive model using logistic regression analysis, 2) evaluate the lift and gains of each approach and 3) evaluate the profitability of each of the approaches. Assignment As a direct marketer of specialty books, the BookBinders Book Club has achieved steady growth in their customer base. Yet while sales have grown steadily, profits began falling when the database got larger and when the company diversified its book selection and increased the number of offers sent to customers. The falling profits have led Dave Lawton, BookBinders’ marketing director, to experiment with different database marketing approaches in order improve BookBinder’s mailing yields and profits. Dave began a series of live market tests, each involving a random sample of customers from the database. An offer for the current book selection is sent to the sample and then the sample customers' responses, either purchase or no purchase, are recorded and used to calibrate a response model for the current offering. The response model's results are then used to "score" the remaining customers in the database and select customers from the full customer database for the ‘rollout’ mailing campaign. Dave’s team continues to debate which of two methods will best identify potential targets for its new book, ‘The Art History of Florence’. The team is discussing two possible approaches to identify and target customers for ‘The Art History of Florence’: 1. Volume Segmentation (univariate segmentation using Totdol as the segmentation variable), or 2. Predictive Modeling using Logistic Regression Analysis Dave has a dataset containing the responses of a random sample of 50,000 customers to “The Art History of Florence.” He is eager to assess the potential value of each of three approaches as methods for predicting customer response and has asked you to complete the following analyses. Note: In order to complete this analysis you will need the two attached files: 1) BBB.sav, SPSS data file which contains all of the data needed to create volume deciles and to conduct the logistic regression analysis, and 2) Homework 5 Lift Gains Profit Analysis.xlsx, which contains 2 pre-formatted worksheets to assist you in your assessment of lift, gains and profitability. You will need to copy and paste data from SPSS output into Excel to complete your assessment. Due Date: Thursday, June 20 at 7:00 p.m. MKTG 3509, Summer 1, 2019 1 Dr. Christopher Monos During class we will develop two segmentation models: 1) We will segment customers based on volume. 2) We will identify the best logistic regression model and then use the predicted probabilities from the model to segment customers. In order to complete the assignment, you will need to: 1) Provide answers to the questions on the following pages of this document, and 2) Conduct a lift/gains analysis in Excel. You need to upload both this document and the Excel file which has been provided. Attention: This assignment requires you to perform another lift/gains/profit analysis. If needed, review the three videos in Fox Video Vault: 1) Evaluating Models: Lift Analysis, 2) Evaluating Models: Gains Analysis, and 3) Evaluating Models: Profit Analysis. MKTG 3509, Summer 1, 2019 2 Dr. Christopher Monos Question 1: Evaluate the following two Model Comparisons Comparison 1: 𝑝 𝑴𝒐𝒅𝒆𝒍 𝑪: ln ( ) = 𝛽0 + 𝜀 1−𝑝 𝑝 𝑴𝒐𝒅𝒆𝒍 𝑨: ln ( ) = 𝛽0 + 𝛽1 (𝑇𝑜𝑡𝑑𝑜𝑙) + 𝛽2 (𝐿𝑎𝑠𝑡) + 𝜀 1−𝑝 -2LL(C) PC -2LL(A) PA Improvement Chi Square Probability of Improvement Chi Square (sig) Interpretation of Findings (Include Improvement Chi Square and Probability of Improvement Chi Square ) Prediction Equation of Log Odds for Model A Using the Prediction Equation of Log Odds for Model A, what is the predicted probability of buying ‘The Art History of Florence’ for a customer with: Totdol = $419 Last = 3 Months since last purchase MKTG 3509, Summer 1, 2019 3 Dr. Christopher Monos Comparison 2: 𝑝 𝑴𝒐𝒅𝒆𝒍 𝑪: ln ( ) = 𝛽0 + 𝛽1 (𝑇𝑜𝑡𝑑𝑜𝑙) + 𝛽2 (𝐿𝑎𝑠𝑡) + 𝜀 1−𝑝 𝑝 ) = 𝛽0 + 𝛽1 (𝑇𝑜𝑡𝑑𝑜𝑙) + 𝛽2 (𝐿𝑎𝑠𝑡) + 𝛽2 (𝐴𝑟𝑡) + 𝛽2 (𝐶ℎ𝑖𝑙𝑑) + 𝛽2 (𝑌𝑜𝑢𝑡ℎ) + 𝛽2 (𝐿𝐶𝑜𝑜𝑘) + 𝛽2 (𝐷𝑜_𝐼𝑡) + 𝛽2 (𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒) + 𝛽2 (𝐺𝑒𝑜𝑔) + 𝜀 𝑴𝒐𝒅𝒆𝒍 𝑨: ln ( 1−𝑝 -2LL(C) PC -2LL(A) PA Improvement Chi Square Probability of Improvement Chi Square Interpretation of Findings (Include Improvement Chi Square and Probability of Improvement Chi Square ) Prediction Equation of Log Odds for Model A MKTG 3509, Summer 1, 2019 4 Dr. Christopher Monos Question 2: Which set of variables gives the best prediction of the dependent variable? What is your evidence? Question 3: Using the equation with all 9 independent variables, how are the odds of buying the ‘Art History of Florence’ affected by: a. # purchases of Art books (Art) – How do the odds of buying change for an additional Art book purchase? b. # purchases of Do it Yourself books (Do_it) - How do the odds of buying change for an additional Do_It Yourself book purchase? Question Answer 3a 3b MKTG 3509, Summer 1, 2019 5 Dr. Christopher Monos Question 4: Use the following cost information to assess the profitability of both modeling methods. Cost to Mail Offer to Customer Selling Price of ‘The Art History of Florence’ Contribution Margin $.50 $18.00 $6.00 Using the data from the Lift/Gains/Profitability Analysis that you completed in Excel, complete the following table. Method to Identify Customers Maximum Cumulative Operating Profit Mail All Customers in Database % of Customers Receiving Mailing Acquisition Costs per Customer ROI = Operating Profit/ Total Fixed Costs of Campaign 100% Volume Segmentation Logistic Regression Model Question 5: Which of the 3 approaches would you recommend be used in the future? Why? MKTG 3509, Summer 1, 2019 6 Dr. Christopher Monos 
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