When we analyze __data especially, masses of data do not automatically mean masses of information useful to decision makers. More data also means more risk of inaccurate or incomplete data, as explained in What Is data Linkage And Why Does It Matter? Data, data, everywhere, Nor any drop to drink...Also see Stuff Happens for some of the dangers of data dredging.
Cost and speed often favor secondary research, but not always, and it usually makes more sense to think of them as complementary than competing methodologies. To clarify what I mean by this, consider why we conduct marketing research in the first place. Here are some questions MR tries to answer:
- Who uses our product category? How do they differ from those who do not? Who knows about our brand?
- Do we understand the ways people actually use the products in the category, e.g., how and for what occasions? How do purchase and usage differ by occasion?
- How do consumers perceive the category? Is it different from the way we do? What other categories do we compete against?
- Who buys our brand? Are there different segments of consumers with different needs?
- How do they shop our category? Is the purchase mostly for themselves or for others? Is it mainly impulse, autopilot or planned? Where do they shop? What media do they use?
- Why do they buy our brand more (or less) often than competitor brands? What do they like most and least about our brand and the competition?
- Do the brands in our category have distinct images which are associated with purchase behavior? Is our brand seen as low end, high end, as providing value for money? Does this match the positioning we’ve tried to create for it?
- Are there missing SKUs in our lineup? Conversely, are there SKUs or variants we should drop?
- Is our product difficult to understand or use? What do consumers want that is not currently offered by any brand in the category? What do they think of our ideas for new products?
- How well is our marketing working?
- Do some kinds of people respond more (or less) to our marketing? How is our marketing (and competitors' marketing) changing consumer expectations for the category and affecting their shopping and purchase behavior?
Some of these can be addressed in part with existing data or standardized research products, but many also require primary customized research. Our answers are usually not just "out there" waiting to be found or easily discovered by canned research products.
For example, detailed information regarding attitudes, shopping behavior, usage and demographics often must be purposely collected and linked together using multivariate statistical techniques to provide the answers decision makers need. By contrast, a piecemeal approach - looking at only a few variables at a time - can confuse or mislead decision makers. Multivariate analysis is a far superior way to analyze data and typically works best when incorporated into the design of the research. This means primary research.
What about AI and machine learning? There is science fiction and there is reality, as I discuss in Some Things AI Can Do and Some Things It Can't.
Please see Combining Smart Design with Smart Analytics, Why Segment? and Taking Quantitative Marketing Research to a Higher Level for more thoughts on marketing research and ways I think it can move forward.
I hope you’ve found this interesting and helpful!
Bio: Kevin Gray is President of Cannon Gray, a marketing science and analytics consultancy. He has more than 30 years’ experience in marketing research with Nielsen, Kantar, McCann and TIAA-CREF. Kevin also co-hosts the audio podcast series MR Realities.
Original. Reposted with permission.
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