A few years ago, “Mr. Media” Thomas Koch cited Amir
that he does not see anyone in the market research industry who derives
insights from the vast amount of available data to create real value. The truth
ist that although Big Data is all the
rage on client side, the industry has not done much more than putting up Big
Data as a buzz word on the agenda while continuing to promote their traditional
As more than 80% of the
German population is online and of which more than 95% use Google, there is no
data source out there that is closer to covering the knowledge about consumer
behavior. Of course, our opinion is backed up by Google. Evidence for a
changing market is shown by the chart below presenting that Google searches for
market research have continuously dropped over 68% within the last 10 years in
Germany and international figures show similar results:
What should market
Instead of optimizing their
tools within traditional market research areas they should widen their
perspective to see the big picture. Digital data of all kind holds many
insights that – if integrated with traditional market research data and
knowledge (!) – brings companies closer to their customers than ever before. To
do so, we at td follow 5 key principles when conducting data science:
1. Customer first
Open the black box by arranging your data along the customer journey: New,
digital data will help you connecting customer behavior with underlying
attitudes. If done properly you will be able to understand the cause-and-effect
chains that drive your business.
2. Do less! More
Too many huge market research studies focus on too many too detailed questions.
While we do believe that it is important to understand drivers behind people’s
behavior, we do not believe that these drivers should be understood in every
market research study. To be able to isolate single impacts high data frequency
is needed. New data sources can measure consumer behavior in real-time. If
combined with market research results, always-on behavior becomes visible.
3. Better be
roughly right than exactly wrong!
We are big fans of Nate Silver’s work and fully endorse his perspective
on over-fitting models explaining incomplete observations. While they show high
R²-values, they totally miss out underlying relationships and can only lead to
wrong predictions by producing false positive results (see Silver 2013, The
Signal and the Noise: The Art and Science of Prediction).
4. Enjoy the
Joined in an ensemble of models, predictive models compensate for one another’s
limitations. The ensemble (of models) as a whole is more likely to predict
correctly than its component models are.
5. Stand on the
shoulders of giants!
Re-usage of qualitatively proven theories (think of Rogers innovation diffusion theory as base to develop differing
predictive algorithms based on the market adoption status of a product),
combined with implicit industry knowledge and scientific state-of-the-art
methodology will open your eyes on your data. While nobody can disagree with
scientifically proven theories, integrating them into the story you let your
data tell will help you make your insights become more viable.
market research needs now is change
In order to be
future-oriented, market research professionals have to actively break down
closed data silos. Those they have built themselves around their knowledge to
make it easier for other professions to see the value of traditional market
research. And those of other data professions by being more open to integrate
data from other, new sources (e.g. web analytics, search, social, CRM) into
their daily work to increase the value they deliver.
LET’S BREAK DOWN
DATA SILOS TOGETHER!
Smart Data expert with an engineering background, who has been part of TD Reply since day one. He is the visionary behind our data-driven marketing and innovation tools such as Pulse and Trend Sonar.