Editor's note: Bryan Urbick is founder and chairman of Consumer Knowledge Centre, a London research firm.
When undertaking market research projects for our clients we are regularly asked what is the shelf life of the data. It’s a good question. Companies invest huge amounts of money annually to ensure that they have the most up-to-date intelligence regarding their products and their marketplace and the longer the information is usable, the better.
The answer to the question depends a great deal on the type of research data being addressed. Excellent research, in our opinion, shouldn’t have a short life span. Indeed, we regularly undertake research reviews for our clients and, depending on the scope of the project, we would consider qualitative and quantitative data for the previous three to five years. There is little reason not to extend that time frame but we have yet to go beyond the five years.
Even though marketing teams will rightly try to squeeze the last drop of intelligence from the research, the reports are typically archived away, only to gather dust. The reasoning behind this is usually based on the premise that today’s insights will be irrelevant tomorrow. In reality, though, this could not be further from the truth. New product development will always benefit from past insights, particularly when the research design has accommodated a variety of market scenarios and potential product characteristics.
Don’t get me wrong. I am not suggesting to promulgate the negative thinking of “we’ve tried it before and it didn’t work.” Quite the contrary.
When the data are “alive” or recently obtained, the analyses are more often than not based on the specific objectives of the brand, product or category managers. And since their focus is generally on a single objective, a linear view is most likely taken and the insights reported accordingly.
Simply hibernating
As we have found with several projects, there is much that can be learned from reanalyzing past market research data. These insights are not dead and buried; often they are simply hibernating.
Good market researchers are, by their very nature, curious and inquisitive people. They can’t resist identifying patterns within patterns, seeking to see what is not obvious to mere mortals and separating black areas from white while looking at little else but a grey canvas of data. It can be a bit like peering into a kaleidoscope. All the pieces remain encased, but rotate the tube and they take on very different patterns of colors and shapes.
In market research, data mining is a process that generally involves the analysis of large quantities of data in order to extract previously unknown intelligence or interesting patterns of behavior. Consider qualitative mining. It can be a very cost-effective exercise not only as an add-on to a current research project but also when budgets are tight.
While research reviews consist primarily of overlaying existing research, it is always fascinating to see how scenarios unfold. It could be argued that each researcher will have a different take on the findings, yet we have found that when cross-referencing our findings, the fallout information contains key highlights that are consistent. This type of research works best when we build up layers of context, such as demographics over a given period of time, product categories, market sectors and so on. It may seem like we are sifting through a jumble of information – and to a certain degree that is what we are doing – but once you begin to tease out emerging trends and patterns, the solutions start falling into place.
A simple example of this took place when we worked on a major FMCG brand aimed at mothers for their children. We commenced by overlaying the data from research reports dating back three years that related to a broad cross section of product and category issues. In some of the category studies it was clearly established (not surprisingly) that mothers focused on healthy food solutions when purchasing specific types of food products for their children. Digging deeper, we came across several random insights that mothers felt they needed to “treat” their kids at meal times as well as in-between meals. At the time, this insight was given less weight because there were more important developments on which to concentrate.
The themes of treating their children and focusing on healthy food solutions seemed to gel but as we continued to dig down through the superimposed layers of product and category data, we uncovered controversial insights. Mothers felt obligated to give their children food they had a preference for, whether or not that specific food was healthy. When treating, this was particularly true. In a nutshell, the controversy was that the obligation to treat appeared to conflict with the mothers’ desire give their children healthy options. Combining these insights – healthy treats that kids would love – drove different innovation and new brand-building ideas for the client. Though seemingly obvious, it was never really considered previously, as the research for “healthy” and “treats” was done in separate projects.
Miss the nuances
This type of data review project works best when done manually. There is little doubt that a computer would sift through the data in a matter of hours. It would, however, miss the nuances and threads thrown up – those things for which the seasoned qualitative researcher is looking. Ultimately we don’t know what we are looking for until we uncover it. Currently no amount of computer programming can replace the connections made by the human brain. Unlike quantitative data, qualitative data sets have many more assumptions regarding drivers of attitudes and values and those values are defined by the individual who wrote the report. There are not standards as would be found in a statistics-based model.
When conducting reviews for our clients, our modus operandi is to group ideas and concepts into themes so that the data can be thoroughly explored and cross-referenced to ensure nothing is missed. Since we are reviewing a much larger data set than we would with a specific product or category research project, there is always a wide range of different connections that occur. We then overlay this with our understanding from other projects and experiences, creating a deeper understanding of the themes uncovered.
One of the key issues is to identify what disruptions must occur to change the rules of the categories. This can be clearly demonstrated as mentioned earlier, where, in one category, mothers focused on healthy food solutions for their children, whereas in another, they felt obligated to provide food their children had a preference for, irrespective of its healthfulness.
We saw this again in another data review project where kids would talk and act differently when out with their friends compared with how they behaved at home. When out and about, they would act their “aspirational age” (usually two or three years older than their chronological age). When at home, they acted one or two years younger than their chronological age. This is not that surprising but when it came to food in particular, innovation and the acceptance of new products was significantly slower than in other categories. Children might well say that they want to try new things but the reality is quite different. The specific research review demonstrated that, to be successful, marketers need to better address the paradox of kids’ developmental and aspirational drivers (not just with nutritional issues) but with the balance and control delivered by the familiar.
More holistic context
Reanalyzing past market research data is not simply a matter of recycling old information. The objective is to leverage existing learnings in a wider, more holistic context. Beyond merely going back over individual research projects one by one, you must literally superimpose all the insights and see where common threads start to appear. Taken on their own, these insights might have little to no impact, yet when seen in a cross-brand/cross-category context, these insights can start repeating and compounding, showing that something more fundamental may indeed be going on.
Then, when you couple hindsight with the creation of a multidimensional strategic framework, you can begin to separate the so-called “white space” areas of practical application from the theoretical “grey space” areas. This framework helps establish the starting points from which to discuss the best ways of tackling these white and grey areas – focusing on those that may provide the greatest potential. It may well be that what was fashionable a year ago is of little relevance today. But that’s not the important issue here. What is important are the drivers of change. Being able to accurately define them is one of the key advantages of the data mining process. From the resulting strategic overview, a series of potential directions can be drafted for future exploration and can, of course, be the subject of further research in their own right.
Explore new themes
These kinds of research reviews let brand and product category teams discover, without major expenditure, relevant insights that could be further exploited. Undertaken correctly this methodology should provide the internal confidence needed to explore new themes that ultimately lead to new brand extensions and positioning strategies.
So often there is a desire to look ahead and not shackle ourselves with past prejudices. This is a worthy objective. But there is a way to harness the power of hindsight and use it to look ahead. Research reviews, conducted properly with a qualitative mind-set, can do just that.