Data Mining Case Study – One page summary

The best way to look for customers is to search for people who resemble them. The case study on pages 40–45 of your book is about creating a profile for your potential customers. Your job for this assignment is to complete the following.
Read the case on pages 40-45 in the Supplemental Textbook. (attached)
Prepare a one-page summary of its methodology.

Identify three census tracts or zip codes near you that you are familiar with.

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Find their respective scores for each of the index dimensions used in the case.
Calculate their scores.
Write a one-page summary of the results, and answer the following questions.
What are your recommendations for the publication, given the results?
Are the results what you would expect, given your knowledge of the characteristics of the selected census tracts or zip codes?

The paper must be completed with Times New Roman font in 12-point. More information is attached including an example.
case1.docx

biam510_week_1_case_details.pdf

biam510_w1_case_study_example_v2.docx

Unformatted Attachment Preview

Picking Appropriate Messages Even when selling the same basic product or service, different
messages are appropriate for different people. A classic example is the trade-off between price and
convenience. Some people are very price sensitive, and willing to shop in warehouses, make their
phone calls late at night, and always change planes to get a better deal. Others will pay a premium for
the most convenient service. A message based on price will not only fail to motivate the convenience
seekers, it runs the risk of steering them toward less profitable products when they would be happy to
pay more.
A Data Mining Example: Choosing the Right Place to Advertise
One way of targeting prospects is to look for people who resemble current customers. Through
surveys, one nationwide publication determined that its readers have the following
characteristics:
• ?59 percent of readers are college educated.
• ?46 percent have professional or executive occupations.
• ?21 percent have household income in excess of $75,000/year.
• ?7 percent have household income in excess of $100,000/year.
Understanding this profile helps the publication in two ways: First, by targeting prospects
who match the profile, it can increase the rate of response to its own promotional efforts.
Second, this well-educated, high-income readership can be used to sell advertising space in
the publication to companies wanting to reach such an audience.
Because the theme of this section is targeting prospects, let’s look at how the publication
used the profile to sharpen the focus of its prospecting efforts. The basic idea is simple.
When the publication wants to advertise on radio, it should look for stations whose listeners
match the profile. When it wants to place billboards, it should do so in neighborhoods that
match the profile. When it wants to do outbound telemarketing, it should call people who
match the profile. The data mining challenge was to come up with a good definition of what
it means to match the profile.
Understanding this profile helps the publication in two ways: First, by targeting prospects
who match the profile, it can increase the rate of response to its own promotional efforts.
Second, this well-educated, high-income readership can be used to sell advertising space in
the publication to companies wanting to reach such an audience.
Because the theme of this section is targeting prospects, let’s look at how the publication
used the profile to sharpen the focus of its prospecting efforts. The basic idea is simple.
When the publication wants to advertise on radio, it should look for stations whose listeners
match the profile. When it wants to place billboards, it should do so in neighborhoods that
match the profile. When it wants to do outbound telemarketing, it should call people who
match the profile. The data mining challenge was to come up with a good definition of what
it means to match the profile.
This table calculates a score based on the proportion of the audience that agrees with each characteristic.
For instance, because 58 percent of the readership is college educated, Amy gets a score of 0.58 for this
characteristic. Bob, who did not graduate from college, gets a score of 0.42 because the other 42 percent
of the readership presumably did not graduate from college. This is continued for each characteristic, and
the scores are added together. Amy ends with a score of 2.18 and Bob with the higher score of 2.68. His
higher score reflects the fact that he is more similar to the profile of current readers than is Amy.
The problem with this approach is that while Bob looks more like the profile than Amy does, Amy looks
more like the audience the publication really targets — namely, college-educated, higher-income
individuals. The success of this targeting is evident from a comparison of the readership profile with the
demographic characteristics of the U.S. population as a whole.
Compared to the overall population, the readership is better educated, more professional, and better paid.
In Table 2-2, the “Index” columns compare the readership’s characteristics to the entire population by
dividing the percent of the readership that has a particular attribute by the percent of the population that
has it. The readership is almost three times more likely to be college educated than the population as a
whole. Similarly, they are only about half as likely not to be college educated. By using the indexes as
scores for each characteristic, Amy gets a score of 8.42 (2.86 + 2.40 + 2.21 + 0.95) versus Bob with a
score of only 3.02 (0.53 + 0.67 + 0.87 + 0.95). The scores based on indexes correspond much better with
the publication’s target audience. The new scores make more sense because they now incorporate the
additional information about how the target audience differs from the U.S. population as a whole.
Data by Census Tract
The U.S. government is constitutionally mandated to carry out an enumeration of the population every 10
years. The primary purpose of the census is to allocate seats in the House of Representatives to each state.
In the process of satisfying this mandate, the census also provides a wealth of information about the
American population.
Even in non-census years, the U.S. Census Bureau (www.census.gov) surveys the American population
using questionnaires containing detailed questions about income, occupation, commuting habits, spending
patterns, and more. The responses to these questionnaires provide the basis for demographic profiles.
The Census Bureau does not release information about individuals. Instead, it aggregates the information
by small geographic areas. The most commonly used is the census tract, consisting of about 4,000
individuals on average. Although census tracts do vary in size, they are much more consistent in
population than other geographic units, such as counties and zip codes.
The census does have smaller geographic units, blocks and block groups; to protect the privacy of
residents, some data is not made available below the level of census tracts. From these units, it is possible
to aggregate information by county, state, metropolitan statistical area (MSA), legislative districts, and so
on. The following figure shows some census tracts in the center of Manhattan:
One philosophy of marketing is based on the old proverb “birds of a feather flock together.”
People with similar interests and tastes live in similar areas (whether voluntarily or because
of historical patterns of discrimination). According to this philosophy, marketing to people
where you already have customers and in similar areas is a good idea. Census information
can be valuable, both for understanding where concentrations of customers are located and
for determining the profile of similar areas
TIP
When comparing customer profiles, keeping in mind that the profile of the population
as a whole is important. For this reason, using indexes is often better than using raw
values.
BIAM510 Week 1 Case Study: Additional Details
This document provides additional details on how to complete the BIAM510 Week 1 Case Study.
Finding Scores for Index Dimensions on the Census Bureau website
The index dimensions used in the case are those listed in Table 2-1 and Table 2-2 in the textbook (Linoff
& Berry, 2011): the percentages of people who are college educated, have professional or executive
occupations, have household incomes over $75,000 per year, and have household incomes over
$100,000 per year. To calculate the scores for the census tracts or zip codes you have chosen, you will
need to find these percentages for each of your census tracts or zip codes.
Here is an example of one way to find the percentage of college educated individuals in a given zip code:
1. Go to the Census Bureau website at http://www.census.gov, scroll to the bottom of the home page,
and click the link to the American Fact Finder tool.
2. Select the Guided Search option.
3. Click Get Me Started.
4. On the Start page, select “I’m looking for information about people” and click Next.
5. On the Topics page, click the + beside Education to expand the category; click Educational Attainment
to add it to your selections; then click Next.
6. On the Geographies page, enter the zip code for which you want information ; click Go to add it to
your selections; then click Next.
7. On the Race/Ethnic Groups page, because we do not need information broken down by in this way,
click Skip This Step.
8. Review the list of tables and select the one that seems most likely to contain the data you need,
without too much extraneous data. The same information may be available from more than one table.
In this case, click on Educational Attainment.
9. The educational attainment data for the selected zip code will be displayed.
You may need to do some additional interpretation or calculations using these data to derive the single
“percent college educated” number that is needed for calculating the score for this dimension. For
example, should you use the data for only the population 25 years and over, or combine it with the data
for population 18 to 24 years? Should “college educated” include only those with a bachelor’s degree or
higher, or should you add those with an associate’s degree, or those with some college but no degree?
Think carefully, use your best judgment, and document any choices or assumptions that you make in
your paper.
Repeat the above process to find the population percentage for each of the four index dimensions in
each of your three chosen census tracts or zip codes.
Calculating Scores
The process of calculating a fitness score for a census tract or zip code is described under “Measuring
Fitness for Groups of Readers” on pp. 44-45 in the textbook. Note that in Figure 2-6 on p. 45, the
headings for Goal and Tract in the callout boxes are reversed; and that the goal values listed for each
index dimension in this figure are close to, but do not precisely match, the values given for the
publication’s readership elsewhere in the case. The process can be summarized as follows:
1. For each index dimension, calculate a fitness score by dividing the population percentage in the
census tract or zip code (from your research on the Census Bureau site) by the corresponding population
percentage for the magazine’s readership (given on p. 40, and in the first column of Table 2-1 on p. 41
and Table 2-2 on p. 42).
2. If the fitness score for an index dimension is greater than 1, set it to 1 (because 1 indicates a perfect
match).
3. Average the fitness scores for all dimensions to create an overall fitness score for the census tract or
zip code.
For example, the following table shows the fitness score calculation for zip code 90808:
Index Dimension
College Educated
Professional or Executive
Income > $75K
Percentage for Zip Code Goal Percentage for
90808 (from Census
Publication Readership
Bureau website)
(from Table 2-1 of text)
48%
58%
27%
46%
28%
21%
Income > $100K
17%
Overall Score
Fitness Score
Calculation
48%/58% = 0.83
27%/46% = 0.59
28%/21% = 1.33;
set to max of 1.00
7% 17%/ 7% = 2.42; set
to max of 1.00
(0.83 + 0.59 + 1.00
+ 1.00)/4 = 0.85
This calculation does not use the percentages for the US population as a whole. These are used when
calculating the fitness score for an individual, as described on p. 42 of the textbook, but not when
calculating the fitness score for a group, as described on pp. 44-45.
Fitness scores calculated in this way range from 0.00 to 1.00, with a higher score indicating a better
match between the population of the census tract or zip code and the publication’s current readership.
Completing the Case Study Paper
Write a one-page summary of the methodology described in the case study as presented in the
textbook.
Research the data for three census tracts or zip codes, and calculate the overall score for each, as
described in the case study and the preceding sections. Write a one-page summary of your results,
including your recommendations for the publication, and an assessment of whether the results align
with your expectations based on your knowledge of the census tracts or zip codes.
The paper must be completed with 12-point Times New Roman font.
References
Linoff, G. S., & Berry, M. J. A. (2011). Data mining techniques (3rd ed.). Indianapolis, IN: Wiley.
Choosing a Communication Channel – Case
Study
BIAM510-64240: Case Study
Keller Graduate School of Management
Professor: Joylene Ware
EXAMPLE
March 2016
1
Choosing a Communication Channel – Case
Study
Prospecting is an important part of any successful company. There are many prospecting
strategies that can be used to gain an advantage over competition. The use of census data is one,
which when properly used, can yield results that are beneficial in determining the appropriate
communication channel for different demographics. As I searched though the census.gov website, I
was “mining data” that was useful in determining the relationships between the different index
dimensions in an effort to come up with the best zip code/census tract for advertising the company
publication. An understanding of these dimensions not only helps in increasing the rate of response
to the promotional effort but can also be helpful to sell advertising space in the publication to the
company wanting to reach this audience.
When calculating the fitness scores for the three zip codes—75024, 75035, and 75093—I
used three index dimensions (listed in the tables below), and the scores determined which zip code
the publication would benefit the most from. The results determined that publishing in zip codes
75024 and 75093 would help the company reach its intended audience because of their high score
of 0.50. These zip codes have high percentages of college graduates, professional executives, and
also high numbers of people earning more than $75k or $100k.
Having lived in two of the zip codes, the results are what I would expect. I saw all sorts of
publications being delivered on the doorsteps of these people, making me believe they enjoyed
reading these publications, for whatever reason(s). So, targeting these zip codes for the publication
would benefit the company.
INDEX DIMENSION
College Educated
Professional/Executive
Income > $75K
Income > 100K
Overall Score
INDEX DIMENSION
College Educated
Professional/Executive
Income > $75K
Income > 100K
% FOR ZIP
CODE – 75024
28
18
12
4
% FOR ZIP
CODE – 75093
25
15
8
6
GOAL % FOR
PUBLICATION
58
46
21
7
GOAL % FOR
PUBLICATION
58
46
21
7
2
FITNESS SCORE
CALCULATION
28/58 = 0.48
18/46 = 0.39
12/21 = 0.57
4/7 = 0.57
(0.48+0.39+0.57+0.57)/4=0.50
FITNESS SCORE
CALCULATION
25/58 = 0.43
15/46 = 0.33
8/21 = 0.38
6/7 = 0.86
Choosing a Communication Channel – Case
Study
Overall Score
INDEX DIMENSION
College Educated
Professional/Executive
Income > $75K
Income > 100K
Overall Score
(0.43+0.33+0.38+0.86)/4=
0.50
% FOR ZIP CODE
– 75035
17
12
8
5
GOAL % FOR
PUBLICATION
58
46
21
7
FITNESS SCORE
CALCULATION
17/58 = 0.29
12/46 = 0.26
8/21 = 0.38
5/7 = 0.71
(0.29+0.26+0.38+0.71)/4 =
0.41
http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml
References
Linoff, Gordon S., Michael Berry. Data Mining Techniques: For Marketing, Sales, and Customer
Relationship Management, 3rd Edition. John Wiley & Sons P&T, 2011. VitalBook file.
http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml
3

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