Project Idea from MRKT 350 (courtesy Dwayne Ball)
Instructions for Project 1: (Testing to find the best target
customers for a cross-selling opportunity)
The Story:
You are the marketing manager for the Wonderful Wines of the World Company (see the
document “Our Imaginary Enterprise.”). Your goal in this project is to define the best possible
target market you can for the local wine-tasting promotion that you intend to cross-sell by
telephone to a well-defined target market of your customers.
The local wine-tasting is an idea of your president. He has decided that the company can make a
little money and possibly build a little good will by hosting a wine-tasting at the home of local
volunteer customers in each locality. The company plans to charge $50 to attend the winetasting, and the average to provide the tasting per customer will be $25. Thus, the gross profit
per customer attending is $25. However, it will cost $3.20 per customer invited to make a
telephone invitation (through a telemarketing firm) and handle the reservations. Thus, you need
at least 12.8% of those invited to break even on this. Note: you have customer permission to call
each of your customers on his or her home or mobile telephone.
However, a test promotion amongst 20,000 randomly-selected customers reveals that only 7.5%
accept the invitation and pay the $50, generating $25 profit. Thus, if you were to telephone your
entire customer database of 300,000 people (actually 280,000 now that you have already invited
20,000), you would lose almost $400,000 on the promotion.
Your task:
Is to find a way to make as much money as possible from this promotion. Clearly, you will have
to mine the test data set to determine what sub-group of your total customer database should be
telephoned. If you can choose a group with a higher than 12.8% probability of accepting the
invitation, you can make money. The more people in this group, and the higher the response
rate (acceptance rate), the more money you will make.
It is your job to decide who in your customer base should receive this promotion.
The end result of your efforts will be a set of rules for the promotion. For example, your logic
might imply that we should "send the promotion to everyone," or "send the promotion only to
those customers that have been with the company at least 790 days, are known to have no
children at home, and have an estimated household income under $60,000 per year." So, your
goal is (at least a week before the project due date) to send me a set of instructions (“logic”) that
identify how to decide which customers get the promotion. Note that I expect each person in
your project group to be able to explain fully exactly how you got the result you got.
How will you do this? In short, by testing.
Project Idea from MRKT 350 (courtesy Dwayne Ball)
You may download 2 test samples of customers in SPSS-PC files of 10,000, taken randomly
from your customer database of 500,000 from the “Marketing 350” folder on the Public drive:
Master Customer File 1 and Master Customer File 2.
These are the two random samples of 10,000 customers from your database that were telephoned
with an invitation to a local wine tasting. This is called a test promotion. Therefore, in
addition to the basic data on the customers, there is a field (variable) called winetast that is "1"
for a customer if he/she accepted the invitation, and a "0" if he/she didn't.
Probably you will want to start exploring the data using data set 1. You will explore the results
of the test promotion you have downloaded using SPSS and decide how to formulate your rules
for sending the promotion to the entire customer list. We will discuss in class ways to explore
results and figure out how to segment and target customers for greater profit. But, it will be
important to note that you will want to estimate how much profit you would make from the
remaining customers in the entire customer database, using the logic you developed on the test
sample.
In order to estimate your profits amongst the 280,000 remaining customers in the database, you
should apply the logic to the second data set, then enter the results into the spreadsheet that gives
profit projections (this is downloadable from the same place you will get the data).
Next, as part of your report, you will give me the rules (or “logic”) you wish to apply in order to
decide whether or not to telephone each individual customer in the main database. These rules
must be expressed as a series of logical statements using the variable names and values, or as a
scoring equation. We will cover this in class.
For example, if you wanted to invite only customers that were between the ages of 35 and 65 or
had an estimated household income of between $45,000 and $85,000, then your logic for whom
to invite would be:
invite = 0;
if (age > 35 and age <66) or (income >45000 and income < 85001) then invite = 1;
As part of the target market identification request in the report, you will specify what you expect
to be the approximate percentage of customers that will be invited by telephone, according to
your rules. When I execute your rules on the main customer data set, I will send you an e-mail
to let you know if your percentage was approximately correct. If not, we'll try to figure out what
is wrong and reformulate your rules.
Once you have turned in your logic “for good” when the project is due, I will apply your rules to
the entire remaining customer database of 280,000 customers, and report to you on how many
customers responded to your promotion, of the ones your logic sends it to. I will also report
your actual profits or losses.
I highly recommend that you e-mail your logic to me at least a week before the project is
due. Send me: a) the logic statement, as above, and b) the fraction of customers that would be
Project Idea from MRKT 350 (courtesy Dwayne Ball)
invited by that logic. Do not send me your response rate or profit estimate, I can’t do anything
with that until the project is written up and turned in. But, if I know your logic and the estimated
fraction invited, I can catch some errors before you commit yourself to using the logic to define
the target market and initiate the campaign. Once you turn in your report, the logic will be used,
the campaign will be initiated, and the profit will be calculated.
How will your success be evaluated?
If this were the real world, of course, nearly all of your evaluation as a marketing manager would
be based on profits and losses. However, this is not the real world, and your evaluation will be:
Written report, 50 points. How well does the process you used to decide on target segments for
the promotion appropriately use the concepts and methods you have learned in the class? How
much did you learn from the exercise about the customers that could be applied to future
business problems that are not the same as this one? (that’s key). Did you apply the logic to
both datasets and use the second one for your projections? And, how well can you explain your
logic and write the report in a concise, informative fashion? The format of the written report is
below.
Profit, 50 points. This is scaled as an approximate fraction of the profit that is possible if you use
the ideas in class moderately well. The top team, in terms of profit, will get all 50 points
(although they have to make at least some profit to do so). The second team will get fewer
points, and so on, and how much fewer depends on how close they are to the top team. A team
that makes half the profit of the top team will get 25 of the 50 points, for example. Teams
showing a loss will get no points in this category. Don’t worry – if you know what you are
doing and work at it, you’ll probably make a profit close that of the top team and get nearly all of
the 50 points.
By the way, the maximum profit you could make, in theory, if you were to invite exactly and
only that small fraction of your customer base consisting of every single customer who would
accept, and not invite anyone who wouldn't accept, is about $434,000. If you sent the promotion
to all 280,000 remaining customers in your database, you would lose almost $400,000. These
figures include the test promotion losses of $24,000, which will be added into your profit/loss
balance. Naturally, you will not make anywhere close to the theoretical maximum profit –
maybe not even half of it – but you might make a decent amount of profit, in the low hundreds of
thousands of dollars. That’s what would happen in real life.
Where do we get the data? The basic test data, in two SPSS-PC data sets of 10,000
randomly-selected customers (Master Customer File 1 and Master Customer File 2), can be
found on the public drive in the Marketing 350 folder.
One final suggestion: Your group is advised to not share data or methods or any other
information on this project with other groups. Keep in mind that you are in competition with the
other groups to be the best at making profits, and their superiority may lose you points.
Project Idea from MRKT 350 (courtesy Dwayne Ball)
Format of the Written Report: (use these headings in large boldface type in
your report)
1. Title page with names of group and title of project
2. Description of problem (1 or 2 paragraphs)
3. Description of data-mining method (1 or 2 paragraphs). Include things like the data
set(s) used, the statistical methods (like crosstabulations) employed, and the crossvalidation with the second data set.
4. Request for target market identification. Give the logical statement(s) to be used to
identify the target market to whom invitations will be made.
5. Expected results (show a spreadsheet with calculations, like the one demonstrated in
class, of expected target market size, number of orders, expected profits, and so on).
6. Knowledge gained (1 or 2 paragraphs). Statement of what the company would have
learned from the data-mining exercise, if it were real – for example, specific relationships
between variables, trends in the data, anything that struck your eye as possibly useful in
the future. This knowledge should have nothing to do with the invitation to the winetasting, but should reflect some knowledge of what variables seem to be related to other
variables, possible different segments with different behaviors, etc.