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Imagine a world where marketing researchers have never designed a Usage and Attitude (U&A) study, a concept or product test, or an advertising pretest on their own. That world may not be far off – many younger marketing researchers have only been exposed to standardized ready-made research products, Google Analytics and big data.
First, a few definitions. When we analyze data collected for purposes other than our own or summarize research done by others, we are conducting secondary research, also known as desk research. When we design and execute research for our own purposes, this is primary research. In MR, primary research has traditionally meant consumer surveys, hall tests, in-depth interviews or focus groups.
Standardized proprietary research products are now very popular in MR and fall in-between primary and secondary research. Most are designed for a single purpose, such as concept screening, and can be customized only to a limited degree. Many are very simple, but some have an analytics black box operating out of view.
I have been a member of two marketing science teams responsible for developing standardized proprietary methodologies. They have advantages over fully customized primary studies, including norms that can be used by clients as benchmarks, more consistent quality and less need for experienced, highly-trained researchers.
However, they also have many drawbacks, one being that one-size-fits-all may not fit anyone very well. Computer scientists are fond of saying there is no free lunch, meaning that no algorithm is best for every situation. This is true even if we slap the “AI” label on it. Moreover, experts in any field frequently disagree about which is the best approach in a given set of circumstances.
Product category and country also must be considered. Dog food is not diamonds and banks are not BMWs. What works in the US might fail in neighbouring Mexico, for various reasons. Survey questions only work well across categories and countries when they are highly generic. If you want to dig deep, you’ll probably have to customize your research design and analytics. This clearly applies to qualitative research as well.
There may be legal or regulatory barriers in some countries which restrict our ability to collect or access customer data required by a proprietary method. There are also differences in internet access and device penetration. Social media are not identical in every country, and automated translations can break down in the face of colloquial language and regional and generational dialects.
Shoppers, buyers, users and decision makers are not always the same people and, even within the same product category and country, there is substantial heterogeneity among these groups of people. This must be taken into account.
Client corporate culture and management style also should be considered, not to mention brand size and heritage. Furthermore, some client organizations are very marketing research-savvy while others are complete newbies.
Returning to secondary research – and big data especially – masses of data do not automatically mean masses of information useful to decision makers. Many large data files are heavily imputed and error-ridden. Furthermore, even with high-quality data, it’s quite easy to find something that isn’t really there, as explained in Stuff Happens.
Cost and speed often favour secondary research, but not always, and it usually makes more sense to think of them as complementary than competing methodologies. 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?
- How do consumers perceive the category? Is it different from the way we do? What other categories do we compete against?
- Do we understand the ways people actually use the products in the category, e.g., how and for what purposes and occasions?
- Who knows about our brand? Who buys our brand? Are there different segments of consumers with different needs?
- What do consumers want not currently offered by any brand in the category? What do they think of our ideas for new products?
- 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?
- 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? Does ours match the positioning we’ve tried to create for it?
Some of these can be addressed in part with secondary data or standardized research products, but many also require customized primary research. All our answers are not just “out there” waiting to be found or easily discovered with ready-made research products. For example, detailed information regarding attitudes, shopping behaviour, usage and demographics frequently must be expressly collected and linked together using suitable statistical methods to provide the answers decision-makers need. This means customized primary research.
Data collection is now very fast and inexpensive compared a decade or so ago. In addition, we have a huge array of statistical and machine learning tools that will run on ordinary computers, as this link shows. Marketing is more complex than it used to be, and changing in ways that are often unpredictable. The time is ripe for customized primary research.
One last thought – if we lose our competence at customized primary research, who will design the next generation of proprietary research products?
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