Every business would like to have an accurate crystal ball. But since they don't exist, predictive analytics is the next best thing.
Predictive analytics is the systematic study of historical results, of sales or other aspects of a business, to determine patterns and predict future performance. It's almost always done with iterative software that takes input, often supplied continuously, and usually makes preprogrammed adjustments for seasonal, cyclical and other factors.
Predictive analytics may be systematic, but there is nothing automatic or easy about designing or using it. Identifying and prioritizing the factors that can affect performance–whatever type of performance that may be–and deciding how much weight to give them is one of the toughest jobs in business planning.
For this reason, predictive analytics has made more progress in some aspects of CPG retailing than in others. It all depends on the "organizational culture," says Anbu Mani, advisory partner in retail and consumer practice at PwC.
"More often than not, organizations have gone primarily from [being] institution driven–meaning, 'I've been here for many years, so trust me, it will work'–to more data-driven," Mani says. "Decisions are made with facts and data that support the decision-making process."
Broadly speaking, the most widespread, developed use of predictive analytics in CPG retailing, say industry observers, is in marketing and shopper outreach.
"That's where we have seen the marketing function as a whole leverage predictive analytics to get close to consumers, be it a loyal customer or a one-off customer," Mani says. "With either scenario, there is a big push to understand who the customer is and how can we address their needs to provide a personalized experience across all channels."
Personalization, or "marketing to the customer," is accomplished by applying predictive analytics to as narrow a group of shoppers as possible.
"Customer loyalty analysis is used to optimize customer segmentation to improve upsell and cross-sell opportunities," says Russ Hill, senior director of global retail industry marketing and analytics product marketing for SAP. "This is managed through stronger relationship definition in order to create customer segmentations, identify the most loyal and profitable customers, and develop targeted promotions."
The idea is to narrow the target a much as possible, says Jed Alpert, vice president of marketing for consulting firm 1010data.
"CPGs and retailers have used many modeling techniques to cluster consumers into 'like groups' as well as predict their shopping patterns," Alpert says. "Typically these are on an aggregated/sampled data set and not on a per consumer basis, reducing their accuracy."
Obviously, the most desirably "narrow" target would be shoppers as individuals. That's within the reach, but not yet the grasp, of retailers, thanks to ever-increasing Big Data, Alpert says: "The accumulation of large amounts of data (Big Data) on each shopper is opening the door to building much better models of shopper behavior."