Why Primary Research?
Primary studies have always been a strength of marketing research. Many younger marketing researchers, however, have only been exposed to standardized ready-made research products or big data. This is a concern. What is the point of the word research in marketing research?
When we analyze data collected for purposes other than our own or summarize research done by others, we are conducting secondary research. Secondary research is also sometimes known as desk research.
When we design and execute research for our own purposes, this is primary research. The research may incorporate secondary research and data, and in MR has most often meant consumer surveys, hall tests or focus groups. These have long been the backbone of traditional marketing research.
Standardized proprietary research products, some fully or partly automated, are now common in MR and some marketing researchers have never had the opportunity to design a Usage and Attitude (U&A) study, a concept or product test, or an advertising pretest on their own, to cite a few examples. This is worrisome. Moreover, if the team designing a new proprietary method is not skilled at primary marketing research, it raises questions regarding how valid and useful it actually is, however sophisticated.
Standardized methods do have advantages, though, and here are a few which are often cited:
- Norms that can be used by clients as benchmarks
- Higher quality
- Consistent quality
- Easier to sell
- Less need for experienced, highly-trained researchers
- Operationally more efficient
- Higher margins
However, there are also downsides, which I summarize in Why Customize? A very important drawback is that one-size-fits-all may not fit anyone very well.
Returning to secondary research and big 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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