Explanatory Data Analytics Are Driving Marketing

Why predictive analytics are no longer enough


In Edward Bernays’ Crystallizing Public Opinion, he wrote: ‘The three main elements of public relations are practically as old as society: informing people, persuading people, or integrating people with people. Of course, the means and methods of accomplishing these ends have changed as society has changed.’ Human nature remains largely the same as it ever was, which has meant that much about appealing to people has remained the same since Bernays wrote that in 1923. However, technology has meant the means available to marketers for getting the word out have advanced tremendously.

A company’s ability to use data analytics can make or break their success, and marketing is one area where it has arguably had the most impact. According to Squiz research, 82% of marketers are now using an analytics tool, and the Wall Street Journal reported that spending on marketing analytics is expected to nearly double over the next two years, from 7% to 12%, as marketers overtake CTOs as a business’s biggest IT spender. Another survey of 308 CMOs and business unit directors by Forbes Insights, ‘The Predictive Journey: 2015 Survey on Predictive Marketing Strategies’, found that 86% of executives with experience in predictive analytics believe the technology has delivered a positive return on investment for their business.

Introducing data analytics is not, however, simply a case of buying a tool, sitting back and watching it churn out insights. Consumers’ interests and shopping habits are constantly evolving, and how the data is used should change accordingly. As data ages, it becomes irrelevant in terms of consumer value. It is for this reason that predictive analytics in marketing are, while highly useful, limited in how successful they can be. The next year should see a greater onus being placed on explanatory analytics in their place.

The limitations of predictive analytics was evidenced earlier in the year, when Whole Foods attracted controversy with its plan to launch a line of grocery stores geared towards millennials. The announcement was labeled variously as ’offensive’ and ‘stupid’, among other even less flattering terms, and the retailer’s stock price dropped. In Harvard Business Review, Robyn Bolton wrote that members of Generation X and Baby Boomers also want access to ‘lower-priced, organic, and natural foods.’ He pinned the error on Whole Food’s marketing models. The mere fact that someone is in a demographic doesn’t necessarily indicate certain preferences or behaviors - marketers need a greater understanding of why a demographic is interested, and know the best ways to capitalize on them.

Predictive analytics is an incomplete approach because it only gives you a likely outcome if nothing changes. It is useful only for understanding what the future will be like, it does not tell you why outcomes are likely, the correlations driving those outcomes, or how to intervene to change those outcomes. In order to alter an outcome, you have to be looking to explain why it will happen - a luxury afforded by explanatory modeling.

A good example of the difference between predictive and explanatory modeling is in healthcare. Predictive modeling is useful in that it would be able to give you an accurate estimate of, say, which hospitals require certain services. It doesn’t offer an explanation of why those areas require the most money, and would subsequently do little to address root causes and actually decrease illness rates. Explanatory modeling, on the other hand, will identify things that are having an impact. For instance, it would identify that smoking is prevalent among patients, that smoking causes a higher risk of cancer, and that raising the price of cigarettes will lead to less smoking and therefore less cancer in the area.

The same logic applies to marketing data, in which the understanding of consumer trends and how that will impact campaigns is paramount - something difficult to spot when relying simply on past data. Explanatory modeling mainly focuses on variables which are in control of the user, either directly or indirectly. Marketers should be trusted to use their intuition and experience to ask the analytics questions, and data should then be used to identify the correlations that matter. In light of this, there needs to be a shift in marketers’ attitudes towards data, away from looking simply at gathering as much as possible, which may work well for predictive analytics but is not so important in exploratory analytics. Rather, the focus should be on the quality of data received. Marketers need a clear strategy in place to ensure that they are all on the same page with what they want from the data, and communicate this through the team, only then will they be able to garner meaningful insights and drive growth for their organization.

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