DAVID ECCLES SCHOOL OF BUSINESS
UNIVERSITY OF UTAH
MARKETING 4600 Section 1 / 6600 Section 2
Draft December 10, 2012
Marketing Analysis and Decision Making in an Information Age
Spring 2013
T-TH 9:10-10:30 AM
BuC 106
Bill Moore
Office: SFEBB 7117
Phone: 581-5023 (office)
(435) 649 – 8859 (home)
E-Mail mktbm@business.utah.edu
COURSE OBJECTIVES
In this course, you will learn how to analyze marketing data to help make decisions about market
segmentation and target market selection; new product and service development; product
positioning; and allocation of marketing mix expenditures to accomplish objectives. Specifically,
we will use Excel Add-Ins to learn how to use and interpret:
 Conjoint analysis,
 Cluster analysis,
 Multidimensional Scaling (MDS), and
 Marketing mix response models.
This course will also provide an opportunity to improve your statistical and analytical skills as
well as build your proficiency with Excel.
REQUIRED COURSE MATERIAL
There is a required case packet which may be purchased from the book store.
An Excel add-in that is associated with this course will be in available on the computers in both
BuC 103 and the CRCC lab. Additionally you can download a copy for use on your own
computer. It is a large file, so download using a fast connection.
There is a file, MEXL-Utah2 Student License Information.doc in the ME>XL Tutorials folder in
Canvas that has instructions on how to download and activate the software. This document also
has all of the appropriate passwords.
Many of the cases in this class are included with the software and will be located in a folder
called “My Marketing Engineering.” Under the default installation of ME>XL this folder will be
placed in your “My Documents” or your “Documents” folder depending upon your operating
system options. For example in my computer, the Forte Hotel case is located at:
D:/Documents/My Marketing Engineering/Cases and Exercises/Forte Hotel Design (Conjoint)
/Forte Hotel Design Case (Conjoint).pdf
1
Because all these cases are in the Cases and Exercises subfolder of My Marketing Engineering,
in the syllabus, I will replace the first four levels of the path: D:/Documents/My Marketing
Engineering/Cases and Exercises/ with ME/.
POSSIBLE SUPPLEMENTAL COURSE MATERIAL
I have found the following book very useful for leaning about Excel. I have read various chapters
just to see what is there. Then I can go back when I need something. For example, I read the
chapter on Text functions, and then anytime I have been interested in manipulating text, I can go
back and figure out how to do it. It is about $26 from Amazon.
Winston, Wayne L. (2011), Microsoft Office Excel 2010: Data Analysis and Business Modeling,
Microsoft Press.
This book covers related material and is philosophically quite similar to the material covered in
this course. It costs about $17 from Amazon.
Jeffery, Mark (2010), Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should
Know, John Wiley & Sons, Inc.
GRADING
The grades will be weighted as follows:
Written Cases
Class Participation
Daily Assignments
First Conjoint Analysis Project
Second Conjoint Analysis Project
MDS and Clustering Project
ADBUDG Spreadsheet Project
40%
20%
20%
5%
5%
5%
5%
DESB guidelines for MKTG 4600 are that the average grade should be approximately 3.2.
Rather than assigning an A to grades higher than 90, scores will be curved to achieve this class
average. Therefore, to get an A or A- you need to be in the top quarter of the class. Typically
most of the students get full credit on the four major projects, so grade differentiation will occur
primarily in the first three categories.
CLASS PARTICIPATION
Class participation is an important indication of daily preparation. Thoughtful participation
facilitates the learning process and makes classes lively and interesting. Therefore, your grade for
class participation is based on the quality of your participation during the case
discussions. Absences, for any reason, will result in a zero in class participation for any case
discussion missed, including discussions of written cases. A maximum of two absences can be
made up by handing in a written case within one week of the missed session or at the start of the last
class meeting, whichever comes first.
2
I will attempt to distribute air time evenly, but that is a difficult job. Class participation is both of
our responsibilities. If you feel that you are not given an adequate chance to participate, come see
me early in the term. If you are particularly upset because I have not called on you during a
particular discussion, stop the class and say I am not calling on you. I will not be offended. Do not
wait until after final grades have been handed out before voicing a complaint.
You may not always be well prepared for a specific case due to personal circumstances. Although
you will not know in advance if your name is on my call list for that day, it is a good idea to inform
me before class if you are unprepared and wish to postpone formal participation. You do not need
to inform me as to why you wish your name removed from my list, if it is on my list. Obviously, I
do not recommend these requests as a repeated strategy! On the other hand, if you are not prepared,
and you take your chances that your name will not be called, you run the risk of zero participation
points for one of your few formal participation opportunities. I will have a list of alternative
participants; I will make a last minute substitution if you are on my list and request to be removed.
WRITTEN CASES
In addition to understanding how to apply these tools to marketing decisions, you need to be able to
communicate what you have done and why to someone else. To save space, assume that the
audience understands the technique you are applying and is knowledgeable about basic statistical
concepts, so you do not have to explain terms like R2 and statistical significance. Still, it is your job
to explain clearly what you did, why you did it, and how your recommendations follow from your
analysis.
Two cases must be submitted in written form and must be prepared in groups of two or three
(exceptions must be cleared with me). These will be assigned approximately ten days before they
are due. They are due in class on the date the case is discussed and will not be accepted late.
These word-processed reports should be in the form of a business memo to a person in the case and
cannot exceed 1000 words (approximately 4 pages). Tables and exhibits, which are not subject to
this 1000-word limitation, should be placed in an appendix. Leave 1" margins for my
comments. Write your name only on the backside of the last sheet.
DAILY ASSIGNMENTS
There will be approximately 10 to 12 assignments during the term. A hard copy of each
assignment is due at the start of the period on the day assigned. Late assignments will be
penalized. These are listed in the schedule portion of the syllabus in Canvas.
There will be a link to the assignment forms from the schedule portion of Canvas. Some of these
will be case preparation assignments covering basic analysis like parameter estimates or optimal
levels of marketing effort that will be the starting point of our classroom discussions. Because
these assignments cover only the basics, they are not a substitute for class participation. Other
daily assignments will be based on concepts we will be discussing in non-case sessions.
Feel free to discuss these assignments and the major projects with other people in class and work
together. However, everyone must do their own computer work – that is the only way you will
3
learn it. So you can sit next to another person and compare answers, but everyone must do their
own work.
MAJOR PROJECTS
There four major projects to help insure that you learn certain concepts in greater depth. These
are to be turned in electronically. You may turn in projects before their due date and I will
comment on them and allow you to resubmit corrected versions. For faster response, please
email these projects to me rather than send them through Canvas. The goal is to make sure you
can complete these four projects. Hopefully virtually everyone will get all possible points in this
portion of the course. They are due at 9:00 AM on the day assigned. Projects will be accepted for
a week after the due date for 80% credit. Instructions for these projects will be posted in the
schedule portion of Canvas.
If you want to contact me via email, please use my email address: mktbm@business.utah.edu
and not through Canvas.
Class Schedule
Tuesday January 8: Introduction
Introduction
 Marketing decision making
 Using models to improve decision making
 Linear response models
 Regression with Excel
Read:
 Canvas/First Day/Marketing Resource Allocation by Gupta. Read pp. 1-11 and a couple of
the applications.
 Canvas/Readings/Marketing Engineering Notes: Response Models – Linear Regression,
pp.1-9 (except the section on Interactions).
Preparation Exercises:
1. Open Canvas/First Day/Allegro.xls file. First, read the instructions on the Intro Worksheet.
Then use the Allegro “dumb” spreadsheet, Sheet1, to decide how to improve next year’s
performance. Then repeat with the “smart” spreadsheet, Sheet2.
2. Use Canvas/First Day/MedAdv.xls to determine the responsiveness to weight loss
advertising. Follow the instructions in the Instruction Sheet
Thursday January 10: Conjoint Analysis Lecture
Read:
 Canvas/Conjoint Analysis/Conjoint Analysis.doc
Preparation Exercises:
1. Download the file: Canvas/Conjoint Analysis/First Major Conjoint Project/AutoQuest.doc
4
Fill out the questionnaire, attempt to analyze your own conjoint data, and answer the
questions in AutoQuest.doc.
2. The following spreadsheet duplicates much of the analysis in this reading
Canvas/Conjoint Analysis/ConjointExample.xls
Try to duplicate the analysis on the Data Only sheet.
3. The following file provides the independent variable coding for the first three profiles:
Canvas/Conjoint Analysis/First Major Conjoint Project/ConjointStart.xls
Tuesday January 15: Conjoint Analysis – Lab
BuC 103
Major Assignment Due: First Conjoint Analysis Project
Read:
 Analyzing Consumer Preferences 9-599-112
 Getting Started with the ME>XL software and the Conjoint Analysis Tutorials.
 Forte Hotel Case at: /My Marketing Engineering/Cases and Exercises/Forte Hotel Design
(Conjoint)/Forte Hotel Design Case (Conjoint).pdf
Download and Activate Software on your PC:
Preparation Exercises:
1. Explore the ME>XL conjoint analysis software: open the spreadsheet: ME/Forte Hotel
Design (Conjoint)\ Forte Hotel Data (Conjoint, 3 Analysis).xls and click on ME>XL,
Conjoint
a. What are the average conjoint weights among the 40 people who filled out the
questionnaire?
b. Describe the four existing hotels.
c. What is the estimated market share of the four hotel designs already selected? (Use the
max utility choice rule for all market share estimates.)
Thursday January 17: Forte Hotel Case
Daily Assignment: Forte Hotel Case Assignment Sheet
Case and Spreadsheet:
ME/Forte Hotel Design Case (Conjoint) .pdf
ME/Forte Hotel Data (Conjoint, 3 Analysis) .xls
Preparation Questions / Instructions:
These data are a few years old and the market has changed somewhat. Start by assuming the data
represent preferences for typical business travelers. After answering the following questions,
think about how preferences may have changed and how you might incorporate that into your
analysis.
5
In the menu ME>XL/Conjoint/Run Analysis, the last two options under Market Share
Simulations are “… With New Product Profiles (from set),” which I will call the “simulator” as
it simulates the share of any given new hotel and “… With Optimal Product Profiles,” which I
will call the optimizer as it gives us the “right answer,” i.e., the hotel with the highest market
share.
1. Assume you do not have the simulator or the optimizer with this software. In the Conjoint
Analysis Template Sheet, there is a matrix containing the Respondents’ Preference
Partworths. This gives you the information on the individual conjoint weights. You can
create a row of average partworths on this spreadsheet under the individual weights. Given
this information, what hotel design would you recommend? Add it to the list of new product
profiles and see what share it generates by selecting Run Analysis and selecting “… With
New Product Profiles (from set),” under Market Share Simulations option.
2. Right below the Respondents Partworth matrix is a matrix of existing product profiles – these
are the current hotels serving the market. Given the information on consumer preferences,
existing hotels, and your own judgment what hotel design would you recommend? Again,
add it to the list of new product profiles and see what share it generates. Try several different
designs.
3. Look at the four designs that Hotel Forte management has come up with, Professional 1,
Professional 2, Tourist, and Deluxe under New Product Profiles. Based on what you know of
customer preferences (the conjoint weights) and the existing hotels, what do you think of
them? What changes would you suggest? Try those changes with the simulator by adding
them to the list of New Product Profiles and see what market share each gets.
4. Overall, try at least 10 different new hotel designs in an attempt to find ones that would be
successful.
5. Examine the four “optimal” profiles by selecting “… With Optimal Product Profiles,” under
the Market Share Simulations option. Why do you think each of these hotels were
successful? What does that say about market share maximizing products in general?
Tuesday January 22: Segmentation & Targeting – Lecture
Read:
 Cluster Analysis for Segmentation UVA-M-0748
 Canvas/Readings/Marketing Engineering Notes - Cluster Analysis, pp 31-39.
Thursday January 24: Segmentation and Targeting – Lab
BuC 103
Read:
 ME>XL Segmentation and Classification Tutorial.
Preparation Exercises:
Explore the Cluster Analysis Software:
1. Download: Canvas /Cluster Analysis/PDAData(Segmentation).xls
and click on ME>XL, Segmentation/Targeting
2. Cluster the data (k-means clustering, standardize the data) on the segmentation page into two
segments
6
a. Describe them in terms of attitudinal data
b. Discriminate with the data on the discrimination data page:
a. Describe each of the segments demographically
In the lab, we will cluster the segmentation data into 2, 3, 4, 5, and 6 cluster solution and use the
discrimination data for discrimination of each of those numbers of clusters (Put these solutions in
the same spreadsheet for ease of comparison), identify the difference between succeeding
solutions (i.e., how does the four cluster solution differ from the three cluster solution?),
determine the appropriate number of clusters to use as the basis of your analysis, and think about
which segment you would direct your marketing efforts to.
Look at Questionnaire on page 5 of ME/ConneCtor PDA 2001 (Segmentation)\Conglomerate
Incs New PDA Case (Segmentation and Targeting).pdf.
The questions X1 to X8, X14 and X15, and Z1 to Z17 are the same as the data in
PDAData(Segmentation).xls file.
Tuesday January 29: Segmentation – Case
Daily Assignment: ConneCtor PDA 2001 Assignment Sheet
Case and Spreadsheet:
ME/ConneCtor PDA 2001 Case (Segmentation and Targeting).pdf
ME/ ConneCtor PDA 2001 Data (Segmentation).xls
Modifications to the questions in the book:
1. How many segments did you use as the basis of your analysis?
a. Why did you choose that number of segments rather than one less?
b. Why did you choose that number of segments rather than one more?
2. What segment(s) did you choose to target?
a. How would you describe that segment in terms of both needs and demographics?
b. What were the primary reasons for choosing those segments?
c. What were the primary reasons why one would not choose those segments?
3. Describe your marketing mix for attacking those segments.
Thursday January 31: Conjoint Analysis and Clustering - Lab
BuC 103
Read:
 ME/Durr Case (Segmentation-Targeting-Conjoint).pdf.
Preparation Exercises:
With the spreadsheet: Canvas/Conjoint Analysis/Durr Data Class (Conjoint).xls
1. Create several possible new product profiles and estimate their expected contribution.
2. Cluster the conjoint weights and create a new Conjoint Analysis Template for one of the
segments.
7
3. Choose two potential new products and use Canvas/Conjoint Analysis/Durr Calculations
New.xlsx to determine the financial implications of introducing these two new products.
Start Second Major Conjoint Project:
1. Start to design a simple conjoint analysis questionnaire by determining the attributes and
levels for a product or service.
2. Input this into the conjoint analysis software and save.
3. Design two or three competitive products or services.
Tuesday February 5: Conjoint Analysis and Clustering – Case
Daily Assignment: Dürr Assignment Sheet
Case and Spreadsheet:
ME/Durr Case (Segmentation-Targeting-Conjoint).pdf
Canvas/Conjoint Analysis/Durr Data Class (Conjoint).xls
ME/Durr Data (Segmentation).xls
In addition to the questions in the case:
1. Determine the best two-product, product line, the appropriate segment for each, and your
selling proposition for each segment.
2. What is the forecast marginal revenue of your recommendation versus other possible
recommendations?
Thursday February 7: Targeting Segments of One – Lab
BuC 103
Read:
ME/Bookbinders Book Club Case (Customer Choice).pdf
Preparation: Start the analysis of the Bookbinders Book Club case
1. Estimate a response model using a regression model based on the ME/Bookbinders Book
Club Data (Consumer Choice).xls file
2. Determine which customers in the ME/Bookbinders Book Club Data (Consumer Choice)
Holdout Sample.xls file you would focus you effort on if you could target 10% of them.
Tuesday February 12: Targeting Segments of One – Case
Daily Assignment: BBBC Case Assignment Sheet
Case and Spreadsheet:
ME/Bookbinders Book Club Case (Customer Choice).pdf
ME/Bookbinders Book Club Data (Customer Choice).xls
ME/Bookbinders Book Club Data (Customer Choice) Holdout Sample.pdf
Assignment:
8
1. Use a regression model for the following:
a. Estimate the relationship between the independent variables and response to the offer on
the 1600 people in Bookbinders Book Club Data (Consumer Choice.xls file.
b. Use those coefficients to predict the likelihood of response from the 2,300 people in the
Bookbinders Book Club Data (Consumer Choice) Holdout Sample.xls file.
c. Order those customers from best to worst prospect.
d. Use their actual responses to determine how many and what percent of the people would
have responded if you would have taken the best potential prospects (e.g., the 10% best,
20% best, etc.)
e. Choose the percentile of potentially best prospects that would maximize your profits.
2. Answer questions associated with the case.
Thursday February 14: Lecture: Summary Conjoint Analysis and Introduction to Product
Positioning
Major Assignment Due: Second Conjoint Analysis Project
Read:
Analyzing Consumer Perceptions 9-599-110
Canvas/Readings/Marketing Engineering Notes – Perceptual Mapping, pp. 41 – 49.
Tuesday February 19: Product Positioning –Lab I
BuC 103
Read:
Canvas/Readings/Marketing Engineering Notes – Perceptual Mapping, pp. 41 – 49.
ME>XL Positioning Tutorial.
Preparation:
Use the Canvas/MDS – Product Positioning/SUV2003.xls data.
1. Do a positioning analysis using the class' perceptions and preferences.
2. Fill out your preferences for these SUVs in the SUV Data (Yours) and locate your ideal
point and preference vector. Do they make sense.
3. Do the same thing using regression in Excel.
Thursday February 21: Product Positioning – Lab II
BuC 103
Read:
ME/Blackberry Pearl Case.pdf
Explore the Product Positioning Software:
ME/Blackberry Pearl Data (Positioning).xls spreadsheet.
Click on ME>XL, Positioning.
Tuesday February 26: Product Positioning – Case
9
Daily Assignment: Blackberry Pearl Case Assignment Sheet
Case and Spreadsheet:
ME/Blackberry Pearl Case.pdf
ME/Blackberry Pearl Data (Positioning).xls
Assignment:
For all questions use both the tabular and perceptual mapping data.
1. Describe the two or three underlying perceptual dimensions of the space. How do people in
each of the three segments perceive the BlackBerry Pearl?
2. How would you describe the three preference segments? Does BlackBerry Pearl appeal to
any segments other than the business segment?
3. Is the BlackBerry Pearl positioned to be successful? Which segments would you target and
how would you reposition it?
4. Describe your marketing program.
Thursday February 28: Product Positioning – Work on the Clustering and MDS Project and
Introduction to Marketing Resource Allocation
BuC 103
Read:
Canvas/First Day/Gupta, “Contemporary Perspectives: An Overview of Marketing Mix Resource
Allocation and Planning Models and Approaches”
Lecture Preparation:
Come with any questions about the Clustering and MDS Project
Tuesday March 5: Introduction to Response Models
Third Major Project Due: MDS and Clustering Project
Read:
 Canvas/Readings/Marketing Engineering Notes: Linear Regression, pp. 1 – 11.
 Canvas/Readings/Marketing Engineering Notes: Linearizable Response Models, pp. 17 – 23.
Preparation:
1. Use Canvas/Marketing Mix Response Models/Linear Models/MedAdv.xls to determine the
responsiveness to weight loss advertising. First read instructions on Sheet0, and then look at
Sheet0.5. The other sheets and charts have my solutions.
2. In Canvas/Marketing Mix Response Models/Multiplicative Models/
LinearizableResponseModels.xlsx, look at the intro sheet and study sheets 1, 2, and 2a as
well as the accompanying charts in that spreadsheet.
3. Duplicate the estimation of the log – log model from Sheet2.
Thursday March 7: Estimating non-linear response models - Multiplicative Models
10
Daily Assignment: Medical Advertising Assignment Sheet
Read:
 Design of Price and Advertising Elasticity Models UVA-M-0805
 Canvas/Readings/Marketing Engineering Notes: Linearizable Response Models, pp. 17 – 23.
Preparation:
1. Complete the exercise on advertising budgeting with a linear response function on all four
remaining categories – Set medical information budgets for two of the four remaining
categories. Last names A – L do Quit Smoking and Cancer and last names M – Z do
Physician Referral and Medical Information.
2. Estimate linear and multiplicative models for sheet3 in Canvas/Marketing Mix Response
Models/Multiplicative Models/LinearizableResponseModels.xlsx and find the profit
maximizing level of advertising.
3. Look at the Allegro Case spreadsheet, Canvas/Marketing Mix Response
Models/Multiplicative Models/AllegroCase.xls We will estimate a multiplicative model
using the market place data and find the optimal level of price and advertising.
Spring Break March 10 – 17, 2013
Tuesday March 19: Multiplicative Response Models Lab
BuC 103
Daily Assignments: Linearizable Response Models Spreadsheet Assignment Sheet and
Allegro Spreadsheet Assignment Sheet
Preparation:
1. Estimate linear and multiplicative models for sheet3 and find the profit maximizing level of
advertising.
2. Use Canvas/Marketing Mix Response Models/Multiplicative Models/AllegroCase.xls, to
estimate a multiplicative model using the market place data and a regression. Find the
optimal level of price and advertising.
3. I will estimate multiplicative response models for the Atlanta Kroger data set in
Canvas/Marketing Mix Response Models/Multiplicative Models/CheeseClass.xls
Thursday March 21: Multiplicative Response Models – Summary and Introduction to
Nonlinear Least Squares
Daily Assignment: Cheese Spreadsheet Assignment Sheet
Preparation:
1. Follow the instructions and finish estimating the multiplicative response models for the
Albany Price Chopper data in: Canvas/Marketing Mix Response Models/Multiplicative
Models/CheeseClass.xls
11
2. Study the example of estimating nonlinear least squares functions in Canvas/Marketing Mix
Response Models/Nonlinear - AdBudg Models/NonlinearLeastSquares.xls. Start with the
IntroReg sheet. Perform a nonlinear least squares estimation for the Quit Smoking data. You
should get the same answer as you do with linear regression.
Tuesday March 26: Multiplicative Response Models – Case
Daily Assignment: Measuring Price Promotion Effects Assignment Sheet
Case and Spreadsheet:
Measuring Price Promotion Effects
Measuring Price Promotion Effects Spreadsheet
Preparation:
1. Summarize the data
a. What is the frequency of promotions, what is the average promotional and nonpromotional price, average promotional and non promotional volume, average cost,
etc.
b. Estimate a multiplicative model of the sales for each brand that is a function of both
D
own and other brand price, display, and feature advertising: Sit  a0  a1Dit  Pit2 a3 jt  Pjt4
i, j = x, y.
2. Determine the average cost and price data, estimate retailer profit in the different conditions.
3. Assume the manufacture (i.e., concentrate producer – bottler combination) has a variable
cost of $.014 per oz. What would you advise?
Thursday March 28: Estimating non-linear response models
BuC 103
Daily Assignment: Nonlinear Least Squares Spreadsheet for Quit Smoking
Read:
 Canvas/Readings/Marketing Engineering Notes: Estimating Nonlinear Models with Solver,
and ADBUDG, pp. 26 – 30.
Preparation for lab:
1. Study the example of estimating nonlinear least squares functions in: Canvas/Marketing Mix
Response Models/Nonlinear - AdBudg Models/NonlinearLeastSquares.xls
2. Start with the IntroReg sheet. Perform a nonlinear least squares estimation for one of the
other medical response functions you estimated earlier. You should get the same answer.
2. Study the way to estimate an ADBUDG function and create a profit model from it. Start with
the IntroADBUDG and IntroProfit sheets.
3. If time permits, we will look at estimating ADBUDG models from judgmental data. This will
be used in the Conglomerate Promotional Analysis next week. See the spreadsheet (Look
12
only at the 5Obs sheets): Canvas/Marketing Mix Response Models/Nonlinear - AdBudg
Models/ADBUDGJudgmentClass.xls
Tuesday April 2: Practice using Solver to estimate nonlinear response functions
BuC 103
Daily Assignment: ADBUDG Model Assignment Sheet
Preparation for lab:
Use Canvas/Marketing Mix Response Models/Nonlinear - AdBudg Models/AdBudgModel.xlsx
to estimate linear and ADBUDG response functions.
1. After you have estimated the ADBUDG function, assume that the price per unit is $1.00 and
variable cost per unit is $.25. Graph the profit function over a range of values of advertising
per capita between $0 and $30, and determine the optimal level of marketing effort. Using
both:
a. A graphical method to approximate the optimal level
b. Solver to find the exact optimum
2. Read: Canvas/Marketing Mix Response Models/Conglom Promotional Analysis (Solver).pdf
3. Explore Canvas/Marketing Mix Response Models/Nonlinear - AdBudg Models/Conglom
Promotional Analysis Data (Solver).xls
In Excel, click on ME>XL, Resource Allocation, Resource Allocation Tutorial
a. Fill in formulas for gross and net profit for New York.
b. Determine the optimal promotional level for New York.
c. Use 0% as a starting value and find the optimal promotional level for New York.
d. Recalibrate the response function for New York to fit the assumption that the
promotion impact of a saturation level of promotion will be 2 times current level, not
2.7 times. Repeat steps b. and c.
Look at the discussion questions for April 4. Do you have any questions about them?
Thursday April 4: Response Models – Case
Daily Assignment: Conglomerate Promotional Exercise Assignment Sheet
Case and Spreadsheet:
Canvas/Marketing Mix Response Models/Nonlinear - AdBudg Models/Conglom Promotional
Analysis (Solver).pdf
Canvas/Marketing Mix Response Models/Nonlinear - AdBudg Models/Conglom Promotional
Analysis (Solver).xls
Preparation for case:
1. Do the analysis suggested in the case for the four scenarios on page 2.
a. In scenario 2, the TOTAL Planned promotional budget should be less than or equal to the
Baseline TOTAL budget, $2,013,437, but is reallocated across the four cities.
b. In scenario 3, set three of the four cells equal to the remaining one, but allow ALL four
cells to vary.
13
2. Estimate the ADBUDG parameters using the following data with Excel. Then build a profit
function, graph it, and use solver to find an optimal level of promotional spending.
Promotional Spending ($MM)
$0.00
$0.44
$0.87
$1.31
$5.00 ( very large amount)
Sales (MM Units)
6.3
6.7
8.0
9.3
11.8
Price to the trade $2.25, cost to deliver to the trade $1.69 (including manufacturing shipping,
and allocated overhead)
3. What happens to the optimal spending level if the market saturates at 13.8M units rather than
11.8M units? What happens to the optimal spending level if the cost of delivery drops to
$1.39 from $1.69 per unit (use the old saturation level)?
Tuesday April 9: Sales Force Allocation – Lab
BuC 103
Fourth Major Project Due: Spreadsheet Project
Read:
The Resource Allocation Tutorial.
Syntex Laboratories (A) Case
Preparation:
Explore the Sales Resource Allocation Software
ME/Syntex Products Data (Resource Allocation).xls
In Excel, click on ME>XL, Resource Allocation, Resource Allocation Tutorial
Thursday April 11: Response Models Lecture
Read:
 Designing Marketing Experiments UVA-M-0839
 Canvas/Readings/Marketing Engineering Notes: Interactions, pp. 5 – 8.
Tuesday April 16: Sales Force Allocation – Case
Daily Assignment: Syntex Laboratories Case Assignment Sheet
Case and Spreadsheet:
Syntex Laboratories (A) Case
ME/Syntex Products Data (Resource Allocation).xls
ME/Syntex Specialties Data (Resource Allocation).xls
14
Things to think about:
For your analysis, use the model output rather than Exhibits 7 – 10 in the case – they are quite
similar, but generate the numbers with the computer model.
As you are combining the model with your judgment to answer the following questions, think
about:
1. At what levels of budget and allocation do you have the most confidence in the model’s
predictions?
2. If you feel Syntex is misallocating effort to one product or specialty, what do you think about
its allocations to other products or specialties?
3. The model is based on a number of assumptions, with which ones are you most and least
comfortable?
Questions:
1. If the sales force is maintained at its current size (about 430 reps), how does the current
allocation compare to the optimal allocation as indicated by the model?
2. What sales force size and allocation would you recommend for each of the next two or three
years? Would you allocate sales calls by product, specialty, or some combination?
3. What are the primary benefits and limitations of using this approach?
Thursday April 18: A/B Marketing Experiments: Case
Daily Assignment: Ohio Art Assignment Sheet
Case and Spreadsheet:
Advertising Experiments at the Ohio Art Company UVA-M-0752
Canvas/Marketing Experiments/Ohio Art.xls
Questions:
1. Were the Etch A Sketch and Betty Spaghetty advertising campaigns effective in increasing
sales?
2. What were the primary differences between the two experiments?
3. Project the impact of the Betty Spaghetty campaign if it was expanded nationally at the same
time of the year. What would you project if it was expanded nationally in the holiday season?
Tuesday April 23: Multivariate Marketing Experiments: Case
Daily Assignment: Progressive Insurance Assignment Sheet
Case and Spreadsheet:
Progressive Insurance: Multivariable Testing UVA-M-0762
Canvas/Marketing Experiments/UVA-S-M-0762(Progressive).xls
Questions:
1. What did you think of the way Progressive designed and conducted this test?
2. What were the results?
15
3. Which factors should Progressive apply to future mailings?
Instructions:
Use only the totals, i.e., ignore the four customer groups
16