How a Small View of Big Data Poses an Immediate Threat to Marketers

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For marketers, the most precious client data comes in two kinds: hip, buzz-wordy “big data,” and the lesser-known but comparably important “thick data.” Learning the difference and how to apply an understanding of thick data to their big data staffing skills will be increasingly important to every marketer’s career as the discipline becomes more and more analytics driven. Marketing analytics and big data recruiters will increasingly be on the look out for the rare talent that comprehends both aspects and aggressively hiring those individuals.

Thick data is typically produced by ethnographers and anthropologists, among others for observing the fundamental motivations and experiences of the individual. Big data is generated from the numerous touch points firms have with customers. To-date, thick data and big data have typically been promoted and utilized by different professions. Organizations grounded within the social sciences have been more involved with thick data. Big data continues to be favored by firms and individuals with a background in analytics, often working in corporate IT functions but more recently working in a marketing analytics staffing capacity. There’s been hardly any rapport and coordination between the two.

This data exclusivity is dangerous for marketers, especially in the long run. Incorporating both thick and big data together can remedy many of the issues that all kinds of marketing staffing talent often encounters with each type of data by itself:

  • Big data has the advantage of being largely unassailable because it is generated by the entire customer population rather than a smaller sample size. But on its own can only quantify human behavior, while being less insightful on its motivations. That is to say, it provides the “quantitative” results, but cannot explain the “why.”
  • Thick data’s power comes from its ability to establish hypotheses about why people behave as they do. It’s less capable of answering questions of “how much;” but it can offer some of the “why” that big data lacks. Some big data analytics experts refer to this as “qualitative.”

By combining the two kinds of information, analytically-minded marketing talent can assemble a comprehensive picture and complete answers to the problems facing their CMOs.

They’ll also become less dependent on what has so far been a cornerstone of all customer insights programs: namely, focus groups and endless studies that supposedly describe customers’ motivations and perceptions but in fact incorporate almost no strategic value.

A Case Study On the Pitfalls of a Narrow Data Mindset

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Take, for example, the case of a major European grocery chain that attempted to regain a hold on eroding market share and declining income. The CMO could see all this in his company’s big sales data, just as he could see that shoppers’ big weekend trips to the market—one of the key parts of his business—seemed to be disappearing. But he had no idea what was causing the change.

The marketing executive followed the classic CMO playbook to understand the trend and unleashed a large survey. More than 6,000 consumers in each market were asked more than 80 questions about everything from buying decisions and cost sensitivity to the importance of brands to occasions and emotions driving purchases.

The study didn’t produce any actionable insights from the organization’s big data staffing. When asked, people reported that price was the most important factor, but 80% also said, “I always choose high quality over low quality, even though it will cost me more.” And 75% of the so-called foodies said they regularly shopped at discount stores. It was a common belief among the management team that they were losing customers to the discount stores, but if that was really the case, why would people say they would pay for quality? The big data-centric approach alone couldn’t explain it.

An Extremely Informative Panel Discusses What Today’s CMOs are Demanding from their Marketing Analytics Experts:

video from Online Behavior

Left more confused than ever, the CMO commissioned  a report to come up with thicker data to get better insights on customers’ choices and lifestyles. Over two months’ length, a team of social scientists went shopping with buyers, observing them as they planned their shopping and prepared family meals.

As the executives looked at the findings from the study, a major shift in consumers’ lives was apparent. Not only had their food habits changed, but people’s whole social lives were different. Stable family routines were dissolving, and predicting what next week would look like was increasingly difficult. One of the most telling pieces of data was the disappearance of the family meal on weekdays. Families simply were not eating together at the same time every day. Many families also now had three or four different diets to consider. The dinner table had started moonlighting as a work station, pushing the sit-down dinner into different rooms.

The big data-centric approach alone couldn’t explain it.

This fundamental shift had a severe impact on shopping behavior. People were shopping more than nine times a week on average; one respondent shopped three times a day. People were not loyal to specific supermarkets but chose the ones that fit their need for fast, convenient shopping. Exhausted from working all day, the last thing they wanted was to carefully consider different prices at different supermarkets.

The study also revealed that the traditional assumptions around price versus quality were superficial. People didn’t categorize supermarkets by discount or premium. Rather, they seemed to be guided by the mood and experience of the stores. Some stores projected a mood of efficiency. Others felt fresh and local, and others seemed practical and thrifty, offering good everyday value.

The conclusion was apparent: the activities the company’s stores supplied were out of sync together with the consumers’ fact. In the place of emphasizing lowering prices, the supermarkets potential strategy was created over a different strategy: building special shopping experiences that fit into lives. To meet consumer needs, the CMO realized, a supermarket had to deliver shopping experiences that were both convenient and distinctive—in other words, a mood.

A More Powerful One-Two Data Punch

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As this case shows, it’s critical for talented marketing executives and their marketing analytics staffing to familiarize themselves with weaknesses and the advantages of the two data types. The big data was enough to sound the alarm, prompting the research into why shoppers were changing. The thick data afforded the necessary observations to understand what bigger trends were behind the numbers, and offered a badly needed new perspective on exactly what market the retailer was selling in. This illuminated the path for the CMO to develop a method of getting the retailer’s profits back on track.

Armed using a robust strategic framework, the marketing leaders can now review the big data sets to measure the qualitative studies. What’s the scale of the buying trends? What stores are affected most? This forward and backward between the things they believed were happening (big data) and exactly why (thick data) was critical to making a sound decision.

Marketers should revisit their consumer data strategies and collaborate closely with other customer-facing parts of the organization. Organizations as a whole should be shifting their talent acquisition strategy to work with big data recruiters who can bring in “thick data” expertise.

Melding big and thick and data that is thick together isn’t easy. It requires you to shift your focus from outdated ways of marketing and hiring people. But once you’ve witnessed real data power, you’ll reduce the vast amounts wasted on target groups and studies.

Article source: Harvard Business Review

Related Reading for Big Data Staffing Talent

Thick Data Closes the Gaps in Big Data Analytics

With thick data, you can’t always depend on numerics and algorithms to summarize a customer’s 360-degree experience. Here’s why you might want to adopt a thick data analytics approach. More

Big Data Needs Thick Data

Big Data can have enormous appeal. Who wants to be thought of as a small thinker when there is an opportunity to go BIG? The positivistic bias in favor of Big Data (a term often used to describe the quantitative data that is produced through analysis of enormous datasets) as an objective way to understand our world presents challenges for ethnographers. What are ethnographers to do when our research is seen as insignificant or invaluable? Can we simply ignore Big Data as too muddled in hype to be useful? More

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