11 Nov Chapter 9 Case Problem: Grey Code Corporation.?? If using Excel or Minitab for your calculations, charts, and graphs, please copy and paste your work into the Word document.? Do not att
- In a single Word document, Chapter 9 Case Problem: “Grey Code Corporation.” If using Excel or Minitab for your calculations, charts, and graphs, please copy and paste your work into the Word document. Do not attach Excel or Minitab as separate documents.
- response should be a minimum of 2-3 pages . The font is Times New Roman, font size should be 12, and the paragraphs are single-spaced. There should be a minimum of one reference supporting your observations. Citations are to follow APA 7.0. double space.
– no plagiarism, need plagiarism report
11/11/22, 6:53 AM Print Preview
https://ng.cengage.com/static/nb/ui/evo/index.html?deploymentId=5994142305022023695824496950&eISBN=9780357131824&id=1635156284&sna… 1/5
Chapter 9: Predictive Data Mining Case Problem: Grey Code Corporation Book Title: Business Analytics Printed By: Jigar Jitendrak Patel ([email protected]) © 2021 Cengage Learning, Cengage Learning
Chapter Review
Case Problem: Grey Code Corporation
Grey Code Corporation (GCC) is a media and marketing company involved in magazine and book publishing and in television broadcasting. GCC’s portfolio of home and family magazines has been a long-running strength, but it has expanded to become a provider of a spectrum of services (market research, communications planning, web site advertising, etc.) that can enhance its clients’ brands.
GCC’s relational database contains over a terabyte of data encompassing 75 million customers. GCC uses the data in its database to develop campaigns for new customer acquisition, customer reactivation, and identification of cross-selling opportunities for products. For example, GCC will generate separate versions of a monthly issue of a magazine that will differ only by the advertisements they contain. It will mail a subscribing customer the version with the print ads identified by its database as being of most interest to that customer.
One particular problem facing GCC is how to boost the customer response rate to renewal offers that it mails to its magazine subscribers. The industry response rate is about 2%, but GCC has historically performed better than that. However, GCC must update its model to correspond to recent changes. GCC’s director of database marketing, Chris Grey, wants to make sure that GCC maintains its place as one of the top achievers in targeted marketing. The file Grey contains 38 variables (columns) and over 40,000 rows (distinct customers). The table appended to the end of this case provides a list of the variables and their descriptions.
Play the role of Chris Grey and construct a classification model to identify customers who are likely to respond to a mailing. Write a report that documents the following steps:
1. Explore the data. Because of the large number of variables, it may be helpful to filter out unnecessary and redundant variables.
2. Appropriately partition the data set into training, validation, and test sets. Experiment with various classification methods and propose a final model
11/11/22, 6:53 AM Print Preview
https://ng.cengage.com/static/nb/ui/evo/index.html?deploymentId=5994142305022023695824496950&eISBN=9780357131824&id=1635156284&sna… 2/5
for identifying customers who will respond to the targeted marketing.
3. Your report should include appropriate charts (ROC curves, lift charts, etc.) and include a recommendation on how to apply the results of your proposed model. For example, if GCC sends the targeted marketing to the top 10% of the test set that the model believes is most likely to renew, what is the expected response rate? How does that compare to the industry’s average response rate?
Variable Description
CustomerID Customer identification number
Renewal 1 if customer renewed magazine in response to mailing, 0 otherwise
Age Customer age (ranges from 18 to 99)
HomeOwner Likelihood of customer owning their own home
ResidenceLength Number of years customer has lived at current residence. Values: , , , , ,
, , , , , ,
, , , or more
DwellingType Identifies the type of residence. . ,
Gender , ,
Marital , , (divorced, widowed, etc.),
HouseholdSize Identifies the number of individuals in the household. Arguments are: ,
11/11/22, 6:53 AM Print Preview
https://ng.cengage.com/static/nb/ui/evo/index.html?deploymentId=5994142305022023695824496950&eISBN=9780357131824&id=1635156284&sna… 3/5
, , , ,
ChildPresent Indicates if children are present in the home. ;
;
Child0-5 Likelihood of child 0–5 years old present in home
Child6-12 Likelihood of child 6–12 years old present in home
Child13-18 Likelihood of child 13–18 years old present in home
Income Estimated income. Ranges from $5,000 to $500,000+
Occupation Broad aggregation of occupations into high level categories. Arguments are: ,
, ,
(blue collar type jobs), (caregivers, unemployed, homemakers),
HomeValue The estimated home value in ranges. Arguments are , ,
, , , , , ,
,
MagazineStatus Identifies the status for a customer based on their magazine business activity. ,
, ,
, ,
,
11/11/22, 6:53 AM Print Preview
https://ng.cengage.com/static/nb/ui/evo/index.html?deploymentId=5994142305022023695824496950&eISBN=9780357131824&id=1635156284&sna… 4/5
,
PaidDirectMailOrders Number of paid direct mail orders across all magazine subscriptions
YearsSinceLastOrder Years since last order across all business lines
TotalAmountPaid Total dollar amount paid for all magazine subscriptions over time
DollarsPerIssue Paid Amount/Number of Issues Served. Average value per issue (takes the subscription term into account)
TotalPaidOrders Total # of paid orders across all magazine subscriptions
MonthsSinceLastPayment Recency – # months since most recent payment
LastPaymentType Indicates how the customer paid on the most recent order. If it was credit order it will contain the billing effort number (how many bills were sent to collect payment).
, , ,
, , , ,
, , ,
, , ,
on ith billing
UnpaidMagazines Number of magazine titles currently in “unpaid” status for a given magazine customer
PaidCashMagazines Number of magazine titles currently in “paid cash” status for a given magazine customer
PaidReinstateMagazines Number of magazine titles currently in “paid reinstate” status for a given magazine customer
PaidCreditMagazines Number of magazine titles currently in “paid credit”
11/11/22, 6:53 AM Print Preview
https://ng.cengage.com/static/nb/ui/evo/index.html?deploymentId=5994142305022023695824496950&eISBN=9780357131824&id=1635156284&sna… 5/5
status for a given magazine customer
ActiveSubscriptions Number of different magazines the customer is in “Active” status
ExpiredSubscriptions Number of different magazines the customer is in “Expire” status
RequestedCancellations Number of different magazines the customer is in “Cancelled via Customer Request” status
NoPayCancellations Number of different magazines the customer is in “Cancelled for non-payment” status
PaidComplaints Number of different magazines the customer is in “Paid Complaint” status
GiftDonor Yes/No indicator as to whether the customer has given a magazine subscription as a gift
NumberGiftDonations Number of subscription gift orders for this customer
MonthsSince1stOrder Recency (in months) of 1st order for this magazine
MonthsSinceLastOrder Recency (in months) of most recent order for this magazine
MonthsSinceExpire Recency (in months) since the customer’s subscription has expired for this magazine. Negative values represent months until an active subscription expires
Chapter 9: Predictive Data Mining Case Problem: Grey Code Corporation Book Title: Business Analytics Printed By: Jigar Jitendrak Patel ([email protected]) © 2021 Cengage Learning, Cengage Learning
© 2022 Cengage Learning Inc. All rights reserved. No part of this work may by reproduced or used in any form or by any means – graphic, electronic, or mechanical, or in any other manner – without the written permission of the copyright holder.