Sometime ago Whole Foods, an American supermarket chain formulated a marketing strategy targeting the millennial customers. They planned to launch a line of grocery stores specifically for that demographic. The announcement and strategy met with controversy and the retailer’s stock price dropped. The organic food retailer was criticized for thinking that the millennials could be singled out for wanting conveniently priced, organic food from modern, streamlined and innovatively designed outlets. The problem? Not only millennials but people of all ages cater to such products and such a convenient shopping environment. The basic fault in the marketing model was the assumption made by analytics that the announcement would make the millennial customers happy, while they had excluded a large portion of the market with the same needs. Making predictions without explanations, based on generalization is a practice with pitfalls. Knowing why people would be interested in a product or service and what events may change the scenario needs to be taken into consideration while planning a marketing strategy. This is where explanatory analytics is your newest addition to the predictive analytics package.
Explaining the ‘why’?
Market fluctuations have been keeping marketers always on their toes, that’s half the thrill of the job. However it is not as much thrilling when the calculations don’t add up and coveted business results are not achieved. Data and analytics have for a long time evolved to accommodate marketers needs better. One of those evolutions is explanatory data analytics (EDA).
While predictive analytics illustrates what will happen, if the present scenario continues, and the likely outcomes if nothing changes, explanatory analytics adds to the prediction the explanation of what will happen if things do change. In reality, market scenario changes, always. It’s hardly ever that straightforward. Explanatory analytics employs data visualization for presentation of data in variable scenarios and helps in understanding them better.
Make models to identify impacts
What Whole Foods did was to identify a market segment based on the demographic and predict their demands based on a hypothesis. What they lacked was the variables which were most influential to the outcome they expected. With the help of explanatory analytics, marketers can extract these variables, and based on those create varied models of marketing strategies to apply them in theory and test their impacts. Like in this case, planning a marketing strategy based on just the demographics couldn’t give the marketers the much-needed insight into the situation, the variable outcomes scenarios that might have evolved, thus leading to the failure of the campaign.
Various scenarios for changeable results
Anything could change in a certain area at a point of time, the budget, weather, trends, or the public mood in general. It’s not possible to forecast those changes while using predictive analytics, which gives a hypothetical scenario of a market segment where nothing changes. The results can’t be absolute due to lack of variables and assumption testing. With explanatory analytics the scenarios are more spread out in front of you to incorporate any number of scenarios with visualizations that helps you interpret the data in more than one way.
Facebook employs explanatory data analytics to better understand user behavior and social media trends.
Avoid mass generalization
Product developers and service providers are replicating individual needs with their offerings. Be it insurance or security cameras for home, each and every thing is customized to suit the consumers individually. Mass generalization of a market segment depending on geography, age or earnings won’t necessarily lead to a uniform audience. People have different lives and everyday occurrences affecting them differently; same demography doesn’t ensure consistent patterns over time. Marketing with a more realistic view of the market with two or three variations will help in implementing plans with long term impacts.
Target the right audience at the correct time
Why does a person buy the same brand of jeans over and over? Is it the price? Is it the availability? Why does he suddenly stop? Is it the new emerging brand with powerful marketing in the area? Details such as these help you plan a flexible, intuitive marketing plan that would interest consumers at individual levels. Planning a different strategy to market the brand of jeans with maybe a discount or personalized offer will be beneficial for the campaign. Explanatory modeling would suggest these variables at planning stage and help you foresee the evolving impact of the campaign under such circumstances.
Explanatory data analytics allows you to understand the ‘why’. If you understand why, you can change it or intercept changes, foresee changes, track the changing interests of a demographic. Rather than chasing a linear path of assuming only one outcome, explanatory analytics opens the door for different outcomes depending on the changed scenario. It opens new opportunities along the way and allows you to be prepared and use the variables which are directly or indirectly under your control to get the desired results from a marketing campaign.
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