By Guest Blogger, Damian Fernandez-Lamela.
In my experience there are several top marketing analytics
mistakes that are common across different industries. Unfortunately, I have
seen these problems repeated over and over, in many companies, during my
career. If you want to be successful in analytics, you have to be particularly
careful to avoid these mistakes. Here is a short list:
·
Not adequately influencing the decision maker
It is a completely wasted
opportunity for the company if you are unable to influence the decision maker
using your data-driven analytics insights. In some cases the issue is the lack
of storytelling skills. Analytics professionals need to improve their
communications skills and be able to explain the stories that the data is
telling them in a way that business decision makers will easily understand.
Sometimes, the problem is the lack of sufficient effort in convincing all
necessary stakeholders. This is particularly challenging in large organizations
where you need to convince a sizable number of stakeholders before any
decisions are made. I know how frustrating it is to spend time on an analysis,
only to see it rapidly discarded; to ensure that doesn’t happen to you, improve
your internal “selling” skills. You and your organization will benefit
tremendously.
·
Not focusing on the most relevant business
problems
Many times, the most relevant
problems from a business-impact perspective are also the most complex and
difficult to solve from an analytics point of view. That means analytics
professionals might steer clear and head straight for the easy problems. “Easy
problems” are, for example, situations with readily available data or questions
about marketing activities that are simple to track. But who cares if you have
fantastic results from an advertising campaign test, if that campaign can only
potentially affect a tiny fraction of the company revenue? If your analytics
professionals fall into the trap of solving the easy problems rather than tackling
the most relevant problems, you might have to get them back on track. When you
are trying to decide where to focus your analytics efforts, look for the
biggest potential revenue impact, margin impact or cost reduction opportunities.
Then start your analytics there.
·
Lack of a holistic view
Many times in marketing analytics,
the lack of a holistic view becomes a problem. This is also called a problem of
“missing variables” in econometric modeling. For example, a few years ago, when
I was working at a high tech company, a friend of mine who was running paid
search advertising campaigns came to me, excited that his campaigns had hugely
increased their return on investment (ROI) over the past week. When I asked what
he’d done differently, he said that he had not changed anything. But it just so
happened that we had spent $10 million on a TV campaign that same week, which resulted
in the improved ROI in his paid search campaigns. This problem is particularly
common in digital marketing analytics, which tend to overlook influences from
offline media that are harder to track.
Conversely, in another company, I
saw that improving the customer experience in tech support calls generated a
much higher chance of repeat customers, cross sells, and upsells, and thus
higher long- term customer lifetime value. This
was outside the control of the Chief Marketing Officer of the company, but it
proved to be so important for our revenue that she brought it to the attention
of the Chief Operating Officer to improve the quality of the calls. Be sure you
are looking at all aspects of the business, not just your marketing department.
·
Using the wrong data
This problem encompasses a broad
range of mistakes like using the wrong data, using data that has noise, or using
data that is irrelevant. Your analysis is only as good as the data you’re using
to generate it. If you are using the wrong data, then all your analytics conclusions
based on that data are going to be inaccurate. There is a lot to be said about
the importance of the process of cleaning data before starting any analysis.
This typically involves looking at the data to identify noise patterns, remove
outliers, or complete any missing parts. For example, if you are using data
from two different tracking systems, an old one you used last year and a new
one you are using this year, you’ll need to normalize the information to be
able to join both sources of data for analyses and comparisons.
·
Lack of statistical significance
Unfortunately I have heard too
many times the expression that some information was “directionally correct” to
justify using test results that are not statistically significant. Let me be
clear: if a result is not statistically significant, that means you really
don’t know anything, so you cannot make a decision based on that information.
This also applies to results of statistical models that have components that
are not statistically significant.
·
Limitations of methodologies
If you don’t understand the
limitations of the analytics methodologies you are using, you will be tempted
to take results at face value and act upon them, instead of accounting for
those limitations. One way to approach these limitations is to cross validate results
using several techniques. For example, you can use a controlled in-market test
to confirm the results of an econometric model. A common problem to avoid is to
use the same tool to try to solve all situations. For example, some companies
or professionals specialize in certain techniques like applied neural networks,
agent- based modeling, or multi-linear
regression. In this case, every problem that they encounter they try to solve
with that same tool. Each analytics methodology has pros and cons, and being
aware of those will help you avoid plenty of mistakes.
In sum, in order to avoid making
marketing analytics mistakes, you need to adequately influence the decision
maker, focus on the most relevant business problems, apply a holistic view, use
the right data, make sure that your results are statistically significant and,
crucially, be aware of the limitations of the methodologies you use.
Damian Fernandez-Lamela
Senior Director, Marketing Analytics, RealPage, Inc
LinkedIn: https://www.linkedin.com/in/damianfernandezlamela
Twitter: @fernandezlamela
https://twitter.com/fernandezlamela