It would seem to go against marketing 101, but is it possible that, in certain instances, strong sales and positive consumer feedback for a new product can be… bad?
Absolutely, if the consumers ponying up for that new product also have a demonstrated history of favoring items that fail. Those shoppers are, as marketing expert Eric Anderson and his colleagues described in a seminal 2015 research paper, “harbingers of failure.”
As it turns out, the people who purchased Diet Crystal Pepsi were more likely to have purchased Frito Lay Lemonade, researchers found. (If neither rings a bell, well, exactly.) And they kept purchasing — while they could, that is — only furthering the mirage of a supportive market.
“A one-time purchase of Diet Crystal Pepsi is partially informative about a consumer’s preferences,” the researchers wrote. “However, a consumer who repeatedly purchased Diet Crystal Pepsi is even more likely to have unusual preferences, and is more likely than other customers to choose other new products that will fail in the future.”
The research contradicted conventional marketing models, like the Bass diffusion model, that correlate strong early sales with a greater chance of long-term success. It showed, in short, that a quick start off the mark does not a marathon runner make.
The research also converged with the early days of retail’s embrace of big data. The authors analyzed two large samples of data from a national drugstore: one data set of individual customer transaction data, spanning more than 10 million transactions made using customer-loyalty cards over two years; and a sample of aggregate store-level transaction data, spanning 111 store locations in 14 states over more than six years.
The academic analysis may have been more post hoc than what we often see in contemporary business architectures — ingesting data to warehouses and lakes, then pipelined for analysis or reporting from Business Intelligence or data science teams — but it was nonetheless a triumph of data mining, or using large volumes of data to unearth significant patterns and anomalies.
Since the publication of Harbingers of Failure, the authors have been honored for the paper’s “significant, long-term contribution” to marketing. It even spawned a 2019 sequel (there are entire zip codes that are harbingers of product failure, and residents are more likely to donate to unsuccessful congressional candidates, too). And, of course, the proliferation of big data has only intensified, which would seemingly prompt companies to implement the paper’s findings and steer clear of such harbingers as much as possible. But that hasn’t exactly happened.
“I keep running into people who know about this … but firms are really struggling to figure out what to do about it,” Anderson told Built In. “Most of them have a shockingly narrow view of the world.”
Most companies only buy data that it perceives as directly relevant, Anderson said. A cosmetics company, say, will probably purchase beauty-related data from a consumer packaged goods market research firm like Nielsen or IRI, but that probably doesn’t tell them who’s drinking the 2021 version of Frito Lay Lemonade.
Cross-category data can be hard to come by because data sets are often rigidly segregated. Anderson recalled how, while at Ocean Spray, his team would purchase data that only covered red drinks. It provided eyes on the cranberry juice world, “but you didn’t see anything related to other beverages,” said Anderson. “It wasn’t the beverage database, it was the red drink database.”
Anderson believes that many-tentacled retailers and e-commerce sites like Amazon and Walmart are best poised to implement his research, in part because they possess reams of sales data across so many categories.
The harbinger effect, however, is tangentially related to several examples of how data mining and marketing do intersect in the real world.
