Counter Intelligence: Tapping Into Big Data to Drive Sales

Counter Intelligence: Tapping Into Big Data to Drive Sales

In our Counter Intelligence series, we’ve been exploring the innovative new tools retailers and marketers are using to better understand and predict consumer behavior. Acknowledging the role emotional intelligence plays in the buying process has been critical in the rise of practices like neuromarketing, which relies on such tools as fMRI, electroencephalogram (EEG) and eye tracking to uncover customers’ subconscious reactions to products and brands.

In this final part of our series, we’ll focus on another powerful research tool that’s gaining ground among retailers to drive sales: big data. Through the use of predictive analytics, retailers are now able to cross-reference a variety of information from massive data sets—like purchase history, demographics, social media sentiment, web browsing patterns and geographic location—to anticipate demand, provide availability at the right store location, and offer dynamic pricing and relevant and timely promotions to consumers. Perhaps the most well-publicized example of this practice comes from Target, which successfully analyzed data gleaned through its Guest ID program in order to identify pregnant shoppers and send them relevant coupons, boosting sales in its Mom and Baby category.

Clearly, companies that are tapping into big data are starting to see a competitive advantage. Interbrand’s 2014 Best Retail Brands report found that a common theme among the biggest gainers in brand value for 2014—Macy’s, Amazon and Whole Foods—is that they’re leveraging big data to better understand their customers. And usage is growing. Cumulative big data spending from 2011 through 2016 is expected to reach more than $232 billion, according to a Gartner survey of 720 Gartner Research Circle members worldwide conducted in 2013.

So how can the private brand industry utilize big data and predictive analytics? On the manufacturing side, big data can be used to predict consumer demand and increase operational efficiency, as well as manage product defects. According to the IRI Private Label 2013 Special Report, private brand manufacturers and retail partners should also utilize predictive analysis to determine pricing for entire categories, optimizing shelf space, assortment and promotions in accordance. Companies like Marketyze, for example, can match all competitive national and private brand products to compare pricing.

Although there’s certainly been a lot of buzz surrounding big data these days, it’s important to keep in mind its limitations. In a New York Times op-ed piece, Gary Marcus and Ernest Davis attest that while big data is very good at detecting correlations, it can’t identify which correlations are meaningful, and that no scientist, for example, would be able to solve a problem by crunching data alone. And Martin Lindstrom, author of Buyology: Truth and Lies About Why We Buy, advocates the use of big data in conjunction with neuromarketing to generate a more accurate “micro-macro perspective” of consumer behavior.

What is apparent here is that we’re on a new path to interpreting what consumers truly want. I, for one, can’t wait to see what’s in store next.

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