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Oakland Business Review Article
(click here to read the report
in the published site)
Can science help sell cars?
BY TODD DAVIS,
oakland@mbizreview.com
Dec. 22, 2005
Retailing pioneer John Wanamaker - among the first to embrace
retail advertising in the mid-1800s - is credited with the phrase:
"Half the money I spend on advertising is wasted; the trouble is, I
don't know which half."
It's an axiom the advertising industry hasn't been able to shake,
since advertising is emotional and often unpredictable beyond never
anticipating results greater than a single percentage point.
But scientist Ranjit Roy and automotive marketing veteran Michelle
Morrissett want to change that perception. They call it MetaMarketing.
Using statistical analysis known as the "Taguchi method" to test
the performance of advertising before it runs, the two say
MetaMarketing has the potential to dramatically improve advertising
efficiency similar to the gains achieved by applying the practice to
engineering.
"You never know how effective your advertising is," said Morrissett,
president of Troy-based Fouresquare LLC, a marketing and business
development firm launched in 2003.
"This helps narrow the bull's-eye instead of scattering advertising
all around."
A direct-mail trial run this summer for Hank Graff Chevrolet in
Davison resulted in a 60 percent gain in responses over traditional
advertising, the Fouresquare principals said.
Morrissett said future applications could include Internet
marketing programs, advertising agencies and other retail advertisers.
New applications, new results
Roy, through his Bloomfield Hills-based company, Nutek Inc., has
been teaching the theories of Japanese scientist Genichi Taguchi to
engineers and managers of Fortune 500 companies since 1987.
Taguchi's "design of experiment" methodology provides the framework
for performing multiple test parameters simultaneously. Traditional
testing methods compare two sources of influence at a time - repeated
for validation - before comparison with another set of data.
It has become an accepted form of quality control and validation
for ISO/QS-9000 and Six Sigma practitioners around the globe, Roy
says.
"It's applicable in many areas - from cars to Campbell's soup," he
said. "Many who followed it were so preoccupied with manufacturing, it
wasn't applied to other avenues."
Having worked in marketing for Ford Motor Co. for 16 years,
Morrissett also realized the potential Taguchi's could have on the
industry.
"(Design of experiment) is a tried and true discipline in many
environments," she said. "Now we're applying it to the marketing and
advertising arena."
For the pilot program at Graff Chevrolet, Morrissett and Roy
analyzed a variety of marketing and advertising criteria that they
gathered through a series of focus groups with sales representatives
and customer surveys.
Roy crunched the data in a program based on Taguchi's method,
narrowing 128 potential advertising options to eight.
Morrissett created eight ads based on the results of the program,
mailing 1,000 direct-mail pieces in August based on each option.
The most-effective ad was a letter with two colors, a picture of
Hank Graff in the upper right-hand corner and not much text. The
returns demonstrate that an easy-to-understand offer and brand
recognition work, Morrissett said.
According to dealership general manager Chris Graff, the results,
delivered in October, were "astronomical."
"Statistically, they proved one of the eight ads was far more
effective than the others," he said.
Morrissett compared 5,481 target customers to customers -
identified through Graff's customer database - who purchased new cars
during the test period. She said 2,519 were eliminated from the count
due to duplication and technical difficulties obtaining data from the
dealership's computer system.
Of the target customers, 59 bought new cars from Graff Chevrolet
during the campaign. That's less than a 1 percent response rate
overall, but an improvement of 61.2 percent over Graff's own previous
best campaign.
Compared to the dealership's "average performing campaign," the ads
resulted in a 135 percent improvement, Graff said.
Still, Graff said he remains a skeptic, holding off on final
judgment.
"We're going to run that (same) sample again to validate the
results," he said.
Once a "best method ad" is identified, a client can reuse the
advertising and marketing data as long as they want, Morrissett said.
"The bull's-eye always moves," she said.
"It's reasonable to re-test after 12-18 months as market conditions
change."
The costs can vary, depending on the complexity of the preparation
and research, according to Morrissett, but are expected to be 5
percent-10 percent of the prospective campaign budget.
Todd Davis is a freelance writer.
MetaMarketing Studies Help
Auto Dealer Achieve Improved Sales
Date:
11/22/05 Press Release by Automation Alley, Michigan.
Nutek, Inc.,
a Bloomfield Hills of Michigan-based quality engineering consulting
firm and Fouresquare LLC, a Troy of Michigan-based
marketing & business development firm are pleased to share their first
test case of scientifically designed advertisement for an automobile
dealership. Under the name Metamarketing, the consulting companies
initiated a study to optimize the direct marketing mail strategy of
Hank Graff
Chevrolet of Davison,
Michigan,
one of the top 5 largest Chevrolet dealerships in the State of
Michigan, and achieved the following results.
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61.2%
improvement in vehicle sales over dealership identified best ever
campaign
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125%
improvement in vehicle sales over the average performing campaign
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This
represents improvement in actual vehicle salesnot
just response rates
The newly launched MetaMarketing strategy uses Taguchi
Experimental Design technique to run live marketing/advertising
experiments. This proprietary service is offered by Nutek in
collaboration with Fouresquare LLC of Troy, Michigan. The results
identify optimal elements (delivery type, ad content, colors, etc.)
that will provide the greatest ROI for every advertising dollar
spent.
Chris Graff, General Manager at our pilot dealership
has agreed to give personal testimonials to businesses considering
utilizing our Metamarketing service. We will provide his
name and contact information to you for references upon request. In
his own words, Its difficult to believe the results are real, but
thats what the numbers show.
Nutek, Inc.
is an engineering consulting company founded by its
president Dr. Ranjit K Roy in 1987. It specializes in training and
implementation of the Taguchi experimental design technique for
product and process design improvement by the manufacturing
industries. Experimental design technique has a long proven track
record in manufacturing processes. The technique is expected to be
equally effective in scientifically identifying what works the best.
We are looking for data that tells us how the customer response is
affected by a long list of uncontrollable factors says Nuteks
president Dr. Ranjit Roy.
Fouresquare
LLC specializes in marketing with a key focus on branding and
imaging. As part of their on-going evolution, a new marketing response
optimization technique called Metamarketing was
developed. This new process gives us the ability to open
the boundaries related to traditional marketing campaign design.
Metamarketing is a mathematically based process that statistically
analyzes and optimizes the performance of marketing tools being used.
They include but are not limited to; direct mail, newspaper, web site,
point of purchase materials. Michele Morrissett,
President of Fouresquare LLC has 20 years of experience in the
automotive industry. Her skill at effectively consolidating marketing
materials ranging from printed materials to auto shows, while
ultimately reducing costs was gained during her time at one of the Big
Three. Most advertisers today do not always have a quantitative
measure of the response. With use of DOE it is possible to
statistically determine what works and what does not. says Ms.
Morrissett.
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(Read the article below to learn about
how the
Design of Experiment (DOE) technique may apply to your
activities)
DOE
demystified
By Ranjit
K. Roy, Ph.D., P.E. (M.E.)
Nutek Inc.
Since the early 1980s,
Design of Experiments (DOE) has been used by industries world-wide to
improve product and process designs. DOE can save significant amounts of
time and money for its users because it streamlines the process of
selecting the best of many possible choices, and does so with validity. It
is one of the techniques prescribed by the ISO/QS-9000 and TS 16949
standards for quality improvement.
DOE is about choices
Everyday we make choices
and generally, the more the choices the better. Consider some of the
common choices we make every day. My wife keeps four popular brands of
soft drinks in the kitchen refrigerator. In this case, I have one item (a
soft drink) with four available options. In making selections like this we
face two issues: (1) the number of choices available, and (2) how the
desirable option is selected.
 The number of options
available to choose from can become overwhelming if we face more than
three items, or factors, each of which offers multiple substitution
possibilities. For large numbers of factors, each of which has a large
number of options (called “levels”), the combinations/permutations formula
lets you calculate the total possibilities. At this point, simply realize
that the number of total possibilities rapidly becomes very large. Seven
factors with two levels in each produces 128 possibilities. 15 factors
with two levels in each produce 32,768 possibilities. When the
possibilities are known, how do you decide which one is best for you? The
most factually revealing method is to test each and every possibility –
that is, if you can afford the time and cost. Often, you can’t. And, in
that situation, you will need a more formal selection methodology.
Selecting desirable options
Choices and outcome
possibilities in business and scientific investigations are endless.
Whenever performance (output/results) is subject to influence from more
than two factors, overlooking all the possibilities may cause some
opportunities to be lost. But can we always afford to look at all
possibilities?
The problem compounds
with the number of possibilities, which range from eight possibilities for
three factors (which can be inputs, ingredients, or variables) to over
32,000 possibilities for 15 such factors that offer only two choices each.
Finding the best among all possible options is like trying to find a
needle in a haystack. Testing all conditions is almost never an option.
The situation is similar to polling efforts for presidential elections. It
is never practical to talk to all voters. Pollsters make their predictions
after talking with a necessarily limited number of voters whom they assume
to constitute a reliable sample. Statistical science tells us that the
larger the sample the higher the accuracy of prediction.
 When
looking for the most desirable among a huge number of possibilities, help
comes from the DOE technique. It defines the smallest sample (a few
conditions among all the possibilities) that you will need to look at, and
what those combinations are. When you have designed an experiment, you
will have answers to crucial questions such as what is the minimum number
of tests you must conduct in order to assure a reliable result, and how
must these tests be conducted?
DOE enables you to
predict the behavior of the factors involved in an experiment as part of
determining the most desirable option(s). In the election polling example,
the sample size is much smaller than the number of all voters, so some
kind of statistical calculation is needed to predict (an estimation of
expected value) the final results. Similarly, in DOE, statistical
calculations are needed because only a fraction of all possibilities
generally can be looked at.
Where is DOE used?
DOE produces the most
benefit when it is used in the early stages of product design. It can be
used with simulation and “virtual” engineering, as well as later when
design has progressed to the point where prototypes are built and
manufacturing processes are being structured. DOE-based investigations can
be created by using the simulation model, or by using physical hardware.
Most manufacturing processes offer opportunities to perfect or at least
significantly improve performance with DOE. No matter which phase of
engineering (concept, design, or production) uses it, DOE is most
practical and effective for applying a finishing touch or for fine-tuning
something that is already working at least reasonably well.
The number of individual
combinations you need to test depends on the number of factors included in
the prerequisite study. As a result of that study, you will find that the
number of combinations (or, configurations) needed to investigate will
typically range between four configurations for three factors with two
choices in each and 16 configurations for fifteen factors with two choices
in each. Usually, you want to repeat tests with each combination to check
for variability from sample to sample. This, of course, increases the
number of test samples for the investigation, but is vastly more efficient
than studying each of hundreds or thousands of possible combinations.
DOE’s application
includes three major tasks: (1) experiment planning, (2) experiment
design, and (3) analysis of results. Among these, design and analysis are
routine tasks similar to all sizes of experiments. These are areas in
which technical advice is relatively easy to obtain and where automated
software is appropriate. The most important step, however, is planning.
This is what impacts the outcome the most. Your knowledge of the project
comes into play here. This is where you will decide, generally as a group
and by consensus, how you should go about judging the experiment’s
performance, what your yardstick of the results should be, which factors
you believe will most influence the results, and the choices that you will
allow in the factors.
Dr. Genichi Taguchi
formulated the process of laying out standardized sets of tests to suit
different experimental situations. Dr. Taguchi provided guidelines in the
form of tables of numbers. These rectangular tables of numbers help
experimenters prescribe the minimum number of tests to be carried out for
a given situation; that is, the number of factors and their choices to be
included in the study.
One such table of numbers
(known as L-4 array), shown in Fig. 1, can be used to investigate and
select the most desirable configuration comprising three factors with two
choices in each. In the popcorn example, four conditions prescribed by DOE
that will point to the most desirable parameter combination can be laid
out by replacing the character notations A, B, and C with the factors in
the table (Fig. 2). Choices for each parameter are shown below.
Figure 1. L-4 Array
|
Test Cond. |
A |
B |
C |
|
#1 |
1 |
1 |
1 |
|
#2 |
1 |
2 |
2 |
|
#3 |
2 |
1 |
2 |
|
#4 |
2 |
2 |
1 |
In the popcorn example,
to carry out the experiment, the machine is operated under the four test
conditions. A simple analysis of the results (for example, the amount of
popped or unpopped kernels) helps determine the best settings for the
machine. The predicted condition is then confirmed by testing a few more
samples just in case some mischievous variables sneaked into the tests.
Figure 2. Test Lay Out
|
Test Cond. |
Heat |
Oil |
Plate |
|
#1 |
low |
coconut |
stainless |
|
#2 |
low |
vegetable |
copper |
|
#3 |
high |
coconut |
copper |
|
#4 |
high |
vegetable |
stainless |
For the popcorn vendor,
the bottom line is that his machine is set up for optimal performance on
the basis of only four tests. As the number of possible combinations
increases, potential savings from using DOE grow dramatically. Shift from
the popcorn vendor to the automotive parts manufacturer whose profit
margin has been shaved by competition to a dollar per part. He contracts
to deliver a million parts. If his design engineers fail to foresee and
prevent problems on the production line, or worse, if failures occur once
his customer delivers vehicles containing faulty parts, the results can be
disastrous. Nutek Inc.
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application specialist, is well versed in the 