It’s become a ubiquitous sight for retailers: the customer hunched over a smartphone in a store browsing competitors’ prices and scouring consumer reviews before making a buying decision.
So, how can retailers break through to this consumer who’s so distracted by a deluge of information?
Companies need to start with understanding their target customers, according to a recent blog post from Harvard Business Review.
And that means using predictive analytics.
“It’s not only about like-kind segmentation based on customer needs and wants; it’s also about developing a deep understanding of customers’ connected behaviors, preferences, and usage patterns,” according to Jake Sorofman, the author of the HBR post.
“Brands measure these patterns and, increasingly, look to advanced ethnographic techniques to observe what isn’t often reliably reported by customers,” he says.
Companies should also:
Adopt a mobile-first strategy. Google says that 90% of consumers use multiple screens in sequence during one day, with 65% of purchases beginning on a smartphone.
“By pegging your efforts to mobile you ensure that experiences are optimized to what is fast becoming the primary use case – and you ensure that the physical constraints of the mobile medium gets the first-order attention it requires,” notes Sorofman.
Target the experience. “Big data means that we know more about consumers than they care to contemplate,” Sorofman says. “With mobile, targeting data encompasses elements of location and proximity.”
With the pervasive challenge that the aforementioned “smartphone hunch” brings to retailers, predictive analytics can be used to link data from multiple sources – including mobile devices, point-of-sale systems, shopper loyalty cards, social media, online shopping behavior and other sources – to gain insight into how shoppers likely will make certain purchasing decisions, notes Direct Marketing News.
“For example, by comparing the behavior of an individual shopper with the behaviors of statistically similar shoppers, a marketer can ‘predict’ what the future behaviors of the individual shopper might be,” the article notes.
“The accuracy of these predictions is in direct correlation to the amount of data available (both on the individual and the larger group), the quality of that data, and the expertise to know what information to mine for with the available resources,” notes Direct Marketing News. “With the right data, technology, and knowledge experts, predictive analytics can be – and in many instances already is – the single largest contributor to the growing gap between industry leaders and everyone else.”
While most retailers and CPG companies are already using some form of analytics, they are almost all focused on analyzing past behavior – targeting shoppers with marketing materials for items similar to what they’ve purchased in the past.
Predictive analytics, however, allows companies to customize marketing messages for seemingly unrelated items based on behavioral patterns that indicate what customers are likely to buy in the future.
In addition to helping retailers and CPG firms more effectively market to consumers, predictive analytics can also help companies better manage back-end operations. By helping retailers more accurately forecast demand, for example, retailers can better manage inventory and avoid running out of stock or having excess inventory.
“The bottom line is that predictive analytics enable retailers to truly ‘know their customer’ – down to individual wants, needs, and preferences. Gone are the days of being able to stay competitive using the backward-looking, intuition-based decision-making that has been the mainstay for decades,” according to Direct Marketing News.
“Future sales depend on knowing what your shoppers want – without even asking them. To succeed in today’s marketplace, retailers and CPGs need to fully embrace – and trust – the new data-driven analytics that are the undeniable future of retail,” the article notes.
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