Between March and August 2020, one in five consumers switched brands, and seven in ten tried new digital shopping channels.
NB: This is an article from McKinsey & Co
The retail sector experienced ten years of growth in digital penetration in a matter of months. But the resulting surge in data has not provided marketers with substantially better understanding of their customers, because their companies’ outdated data modeling isn’t able to capture these shifts with the necessary granularity and speed.
Rather than using the data to try to better target customers and tailor messages, many marketers have reverted to mass communications and promotions. As one CMO told us, “I’ve largely retreated to mass marketing instead of data-driven marketing because customer behavior is changing so fast I can’t trust my historical data and models.”
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But some marketers are accepting the data for the bounty it is and, rather than stepping back from precision marketing, are doubling down. A consumer-goods company, for example, anticipated that sales of beauty products would spike as communities eased out of lockdown. Marketing teams tracked reopenings on a county basis, using epidemiological statistics, municipal reporting, and traffic data to determine where to focus their media spend. These tactics drove a double-digit increase in sales.
Similar insights helped a business service provider get a jump on another emerging trend. Business registration and employment data showed that small healthcare providers in major metropolitan areas were growing at a much faster rate than other small and midsize businesses. Armed with that insight, the company created healthcare-specific product bundles and has launched paid media ads to target those businesses and locales. These moves, combined with other, similarly data-driven campaigns, are poised to increase sales in a core product by more than 10 percent.
Companies that hone their precision marketing in these ways can drive significant customer acquisition during periods of convulsive change. Capturing this opportunity, however, will require brands to update their modeling—from pulling in new sorts of data to retraining algorithms—in order to both keep pace with changing needs and expectations as well as anticipate shifts in customer behavior.
New challenges to account for
Precision-marketing models are trained to recognize and draw inferences from behavioral patterns. An algorithm might learn, for instance, that customers who make more than two visits to a store’s website within a two-week period are 30 percent more likely to make a purchase. Such indicators can trigger tailored offers to convert browsers into buyers, allowing marketers to direct their acquisition efforts and spend toward the most profitable segments.
But buyer behavior has changed significantly since the pandemic began, rendering the relationship rules baked into many existing data models invalid. Externalities that once seemed incidental, such as customer mobility, now have outsize importance. Is visitation down because customers can’t get to the store or because they no longer wish to shop there? Many marketing teams simply don’t know. A Fortune 100 CMO said, “The indicators for the new opportunities we face are not contained in our own data.”
