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Most companies have a multitude of marketing activities and
attendant marketing expenses. In the days when there were only
several marketing activities such as TV, radio, print and direct
mail, marketers could easily gauge response rates through testing.
However, with multi-million dollar marketing budgets and extensive
marketing activities that can number 50 and up, marketing response
for each activity becomes nearly impossible to test. Testing is
especially difficult when more than one marketing activity is
directed to a potential customer. Then, how can you determine
which activity worked best?
Econometrics is the answer to that question. Econometrics, which
is the application of mathematics and statistics to the study of
economic and financial data, is sweeping through industries.
Finally, we can figure out which 50 percent of advertising is
working! After completing the econometrics, a marketing and
financial model is developed that can then be optimized to yield
total budget recommendations and specific allocations among the
various marketing activities for optimal results. The following
case study describes how one Fortune 100 company was able to
determine response rates for various marketing activities and then
optimize their budget, accordingly.
Business Goal Discussion: The first step in the determining the best marketing
mix was to discuss the company’s business goals, which could
include profits, customer life-time value, market share,
customers, etc. Companies have different business goals and
sometimes multiple goals. In this case, our client’s goal was
increasing customer lifetime value. Next we interviewed executives
to get their opinions on the factors that drive customers to be
retained longer while spending more money, thus increasing
customer lifetime value.
Data Capture: Once we determined the drivers, we discussed the variables
that are measured to track their performance. Although a company
may track 50 variables, only 15 may influence business goals. The
variables can be internal, which include marketing spend, or
external, which can include the unemployment rate, for example.
Then we gathered the data and developed a database of all the
variables across time. Three years of weekly data should yield a
robust model.
Econometric Analysis:
Our team of PhDs performed regression analysis on the
database to determine which variables had impact on converting
prospects to customers and customers to retained customers. The
impact is defined as a coefficient of elasticity or impact value.
Certain variables have significant impact whereas others may have
little or no impact.
Budget Optimization:
The coefficients from the econometric analysis were loaded
into MarQuant Analytics’ proprietary software to determine the
optimal budget based on long-term customer value, acquisition and
retention value. Since that amount was more than the company
wanted to spend, the budget amount was set and the optimizations
rerun.
Recommendations:
The recommendations increased marketing spend in certain areas and
decreased others. For the same amount of budget as the previous
year, the company was able to increase its customer lifetime value
by 15 percent.
As marketing activities expand and the results of which are
increasingly hard to gauge, executives can use econometrics and
optimization to determine the best marketing mix to accomplish
their goals.
If you would like a demo of a case study, please contact Barbara
Lewis at (310) 471-8979 or
barbaralewis@marquantanalytics.com. Or you can
register for our 50-minute webinar on how econometrics and
optimization can help you accomplish your top-line and/or
bottom-line goals.
Barbara Lewis
President of
MarQuant Analytics
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