Savvy marketers are rethinking their tech and data strategies to double down on precision marketing following COVID-19.
Between March and August 2020, one in five consumers switched brands, and seven in ten tried new digital shopping channels. 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in data has not provided marketers with substantially better understanding of their customers, because their companies’ outdated data modeling isn’t able to capture these shifts with the necessary granularity and speed.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.”
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.”Would you like to learn more about our Marketing & Sales Practice?Visit our Digital Marketing page
In addition, while patterns exist, they are harder to discern—and even when discerned, they can feel ephemeral, such as communities opening up only to lock down again. To tease out salient behavioral indicators in time to act on them, marketers need continually refreshed data from a variety of sources and at a far more detailed level—looking as deeply as the city-block level in some cases. However, many companies tend to rely on internally derived customer data, using modeling tools that were not built to handle large volumes of data.
Two other issues compound the challenges facing marketers. McKinsey data show that marketing budgets have been slashed for most companies, with six of ten marketers reporting major cuts. “My budget has evaporated,” said one senior marketer. “We have barely enough to execute our ‘must do’ marketing, let alone experiment with new tactics.”
The other issue is the rapid, large-scale shift to remote working. Data-driven marketing works best in agile settings, where teams can test and iterate in sprints. But with nearly two-thirds of employees working from home, marketing leaders have found it difficult to create an effective cadence. “In the past we used to go all in on marketing opportunities by having a command-center-like war room,” said one Fortune 100 CMO, “but with everyone working remotely, we haven’t been able to react as fast as we have in the past.”
How to make modeling more precise when everything else is in flux
While other organizations may have retreated to mass marketing, those that upgrade their modeling can be far more effective in generating revenue. Here’s what they need to do.
Tap new (and better) data
Precision marketing is only as good as the data behind it. New models with old data are still likely to provide inaccurate results. To hone their insights, leaders in the new normal will take a wide-angle approach to data collection by gathering not only behavioral trends and location-based insights but also third-party analytics on their business, customers, and competitors to complement their in-house customer data. Companies starting this journey are finding the most value in incorporating epidemiological data from government sources and customer-mobility and sales data from third-party providers into their models. Companies that extend their data gathering in these ways can identify upticks in demand and where new customers are coming from, as well as assess which customers in their existing base have increased spending and where lapsed customers have gone.
Before it updated its modeling approach, for example, a retail chain could only tell how many customers it was gaining or losing. The company then decided to pull in cell-phone data to scan changes in their competitors’ net traffic. That analysis showed that many of the customers they were gaining during the pandemic were coming from more expensive, specialty players, while those they were losing were heading to cheaper, larger-format players. On the basis of this information, the retailer transformed its onboard and churn-prevention campaigns. They sent emails advertising higher-end offerings to customers transitioning from specialty stores while touting bargain-oriented products to value-oriented customers at risk of churn.
In another example, a business-services provider tapped into new third-party data sources that identify key moments in the small-business life cycle. In one such effort, the provider aggregated data sources that indicated, with a lag of only one day, when new companies were being launched during the turbulence of COVID-19. Their salespeople reached out immediately with products and messages tailored to the needs of newly formed companies, such as systems tools. These collective efforts increased sales productivity by more than 25 percent.
Robust data can also allow companies to generate better competitor insights. By comparing third-party assortment, sales, and promotional data to their own figures, for instance, marketers can evaluate the strength of different value propositions and see which elements resonate with different groups of customers. They can then provide these groups with tailored messaging, content, and offers.
Article originally appeared on McKinsey.com.
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