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Chapter 8: Time Series Analysis and Forecasting: Case Problem 2 Forecasting Lost Sales Book Title: Business Analytics Printed By: Jigar Jitendrak Patel ([email protected]) © 2021 Cengage Learning, Cengage Learning

Chapter Review

Case Problem 2 Forecasting Lost Sales

The Carlson Department Store suffered heavy damage when a hurricane struck on August 31. The store was closed for four months (September through December), and Carlson is now involved in a dispute with its insurance company about the amount of lost sales during the time the store was closed. Two key issues must be resolved: (1) the amount of sales Carlson would have made if the hurricane had not struck and (2) whether Carlson is entitled to any compensation for excess sales due to increased business activity after the storm. More than $8 billion in federal disaster relief and insurance money came into the county, resulting in increased sales at department stores and numerous other businesses.

The following two tables give (1) Carlson’s sales data for the 48 months preceding the storm and (2) the total sales for the 48 months preceding the storm for all department stores in the county, as well as the total sales in the county for the four months the Carlson Department Store was closed. Carlson’s managers asked you to analyze these data and develop estimates of the lost sales at the Carlson Department Store for the months of September through December. They also asked you to determine whether a case can be made for excess storm-related sales during the same period. If such a case can be made, Carlson is entitled to compensation for excess sales it would have earned in addition to ordinary sales.

Managerial Report

Prepare a report for the managers of the Carlson Department Store that summarizes your findings, forecasts, and recommendations. Include the following:

1. An estimate of sales for Carlson Department Store had there been no hurricane.

2. An estimate of countywide department store sales had there been no hurricane.

3. An estimate of lost sales for the Carlson Department Store for September through December.

In addition, use the countywide actual department stores sales for September through December and the estimate in part (2) to make a case for or against excess stormrelated

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sales.

Sales for Carlson Department Store ($ Millions)

Month Year 1 Year 2 Year 3 Year 4 Year 5

January 1.45 2.31 2.31 2.56

February 1.80 1.89 1.99 2.28

March 2.03 2.02 2.42 2.69

April 1.99 2.23 2.45 2.48

May 2.32 2.39 2.57 2.73

June 2.20 2.14 2.42 2.37

July 2.13 2.27 2.40 2.31

August 2.43 2.21 2.50 2.23

September 1.71 1.90 1.89 2.09

October 1.90 2.13 2.29 2.54

November 2.74 2.56 2.83 2.97

December 4.20 4.16 4.04 4.35

Table 17.27

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Department Store Sales for the County ($ Millions)

Month Year 1 Year 2 Year 3 Year 4 Year 5

January 46.80 46.80 43.80 48.00

February 48.00 48.60 45.60 51.60

March 60.00 59.40 57.60 57.60

April 57.60 58.20 53.40 58.20

May 61.80 60.60 56.40 60.00

June 58.20 55.20 52.80 57.00

July 56.40 51.00 54.00 57.60

August 63.00 58.80 60.60 61.80

September 55.80 57.60 49.80 47.40 69.00

October 56.40 53.40 54.60 54.60 75.00

November 71.40 71.40 65.40 67.80 85.20

December 117.60 114.00 102.00 100.20 121.80

Chapter 8: Time Series Analysis and Forecasting: Case Problem 2 Forecasting Lost Sales 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.

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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

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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: ,

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, , , ,

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. ,

, ,

, ,

,

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,

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”

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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.