We take a bit of a detour this week from my usual weekly posts on research insights and methodology primers to go back in time to the very first ever market research and data analytics project conducted.
The story may be somewhat apocryphal but worth recounting as it has implications for today's data analytics practitioners. The story was first relayed to me by my professor at the Lundquist School of Business at the University of Oregon.
The year was 1869 and the innovation of the day was Campbells Soup introduced by the Campbell Soup Company of Camden, New Jersey. The company and its ad agency decided on a regional rollout strategy, initially targeting the larger population centers of the time till nationwide coverage was achieved.
The marketing media plan was full page ads in major newspapers, the only wide-reaching medium of its day (radio would not be invented for at least another decade).
When the company began its roll-out in Boston, the two major newspapers were vying for the lucrative Campbell's account were the very conservative Boston Herald and the liberal, working-class paper, the Boston Globe. Both entities, locked in fierce battle for the account, pleaded their case for why their readership was most likely to adopt the new innovation:
- The Boston Globe put forth the hypothesis that its mostly working-class readership would find appeal in the convenience of the new innovation for its convenience given long hours they spent at factory jobs of the day.
- The Herald on the other end contended that their conservative and mostly middle class and upper-class readership would appreciate and adopt canned soup for its novelty and innovation.
Both set out to make it’s case to the company and its agency. Since the soup was already available in stores throughout the Boston area, the Herald embarked on an experiment. Since Boston neighborhoods at the time were highly segmented by social class, the company set about examining garbage cans in different neighborhoods looking for evidence of product consumption in the form of empty soup cans in their garbage cans. When the tallies were compiled, it turned out that significantly more Campbell's soup consumption was happening in the non-working-class neighborhoods. The Boston Herald's hypothesis appeared to hold true that the novelty factor was attractive to the leisure class. In addition, a psychographic factor that came into play was that since a large portion of working-class families at the time were recent immigrants, the traditional family values they held were a barrier in deviating from soup cooked in the old-fashioned way. The new innovation as an anathema to their values and way of life.
The Boston Herald presented their "research findings" and won Campbell’s account! As I indicated, the story has implications for today's practitioners of the market research craft. Two major lessons emerge here:
- The importance of telemetry data's inclusion in data sets. The "telemetry data" the Boston Herald used to make and win it's case was empty soup cans. We can imagine the data set looked something like empty soup cans per 100 households/unit of time tallied by neighborhood (zip codes had not yet been introduced). Because a lot of primary research data is commissioned using external agencies with access to consumer panels and tools and methodology expertise to analyze the data, the data is often reviewed, assimilated and acted upon in isolation from other data points that are at an organization's disposal. How much more insightful would it be to triangulate and analyze survey data, say on intention to purchase a product extension, with engagement data with the current product? The expected and obvious correlation may be with amount of engagement, but what if it was with engagement with a particular feature or mode of using the product? Or a certain license type they purchased?
- The inclusion of psychographics or lifestyle factors. In the Campbell's Soup use case, psychometrics could only be obtained by interviewing members of the target segment of working-class families and eliciting their barriers to adoption of the new innovation despite the convenience it could provide them. Quantitative data such as the number of empty soup cans in their neighborhood's garbage cannot reveal the WHY behind the purchase or adoption behavior. Likewise, in our Trail-to-Conversion analytics offering, in-depth follow-on interviews with non-converters conducted by my analysts reveal eye-opening and actionable insights including items they would not be comfortable conveying to their vendor due to a need for preserving relationships, etc. To this end, those of you who have worked with me and my team know how huge a proponent and evangelist I am for including qualitative data in market research studies. These insights into the WHY behind purchase and usage patterns are a gold-mine and a major source of enrichment to quantitative data sets. If you have not yet obtained a copy of my eBook titled "The Qualitative Edge" here is a link for a complimentary download: https://www.siliconvalleyrg.com/qualedgedownload
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Alan Nazarelli is Founder & CEO of Silicon Valley Research Group. Based in San Jose, CA with offices in Seattle and New York, the company works with the world’s most innovative brands to provide timely and actionable market intelligence and strategic guidance to enable them to make well-informed decisions to positively impact revenues and profits and to achieve their growth targets. Connect with Al on Linked in
Topics: Market Research Best Practice
