Predictive analytics gives retail marketers the power to see into the future and know with almost complete certainty exactly what products shoppers will purchase, where they will make those purchases, and how their preferences will change in the short- and long-term.
Of course, all of this can be done simply by clicking a button—or at least that’s what many of us are being led to believe. While this is a profound exaggeration, marketers can leverage the knowledge of analytical experts to use data from traditional point-of-sake systems, shopper loyalty cards, social media, online shopping behavior, mobile devices, and a myriad of other sources to gain insight into how shoppers will likely make certain purchasing decisions, how they will respond to specific events (such as weather, holidays, sporting events, etc.), and when and where those decisions will occur.
If used effectively, progressive retailers and CPGs can leverage this ability to better serve their customers, improve ROI from marketing initiatives and outperform their competitors. So what’s stopping each of us from launching a predictive analytics team and getting this data right now?
First things first. Several questions must be addressed before companies move forward with a successful plan, including: How does predictive analytics work? What’s different about it versus the analytics that most industry professionals are doing today? What is necessary to make use of this competency? How can this ability optimize and improve overall bottom-line results?
In its simplest terms, predictive analytics uses a combination of datasets and mathematical formulas to predict the likelihood of some future event or action. In the case of retail, it can help predict how a shopper will likely act or react in the future. 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 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. 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 virtually every retailer and CPG is already using data analytics in some form, the vast majority have the opportunity to raise the bar on how they make the best use of the available data. For example, it’s common practice to look at past purchase behavior and target shoppers with marketing materials for products similar to those items purchased in the past (or commonly related items, like milk and cereal). However, with predictive analytics, it’s possible to send customized communications for seemingly unrelated items based on behavioral patterns that indicate what future purchases will likely be, even if on the surface those items appear to be completely unrelated.
A real game-changer in this realm is predictive modeling, which applies logic to the data by integrating historical patterns and external data to predict the future, improve decision-making, optimize business performance, and improve ROI. This is where the art of predictive analytics comes in. To craft models that can accurately forecast future behaviors and events, data scientists must first determine which combinations of attributes and variables are the most predictive of certain behaviors. Once these are identified, they can then be overlapped and applied to larger sets of data to anticipate everything from when a shopper is most likely to buy a particular product, to what brand he or she is most likely to favor, to whether a special offer will influence his or her decision.
One challenge for progressive retailers is determining how to develop the resources necessary to take advantage of this proverbial goldmine. Just as retailers don’t raise their own cows in order to sell milk in their stores or develop POS software systems to manage the sale of every item, high-level analytics is a competency they can’t expect to develop entirely own their own. Instead, they should look to develop a partnership with experts who specialize in consumer data and mathematical modeling.
As many retailers know, the cost to collect and warehouse consumer data can be significant. Now imagine obtaining the resources (human, technological, and intellectual) to effectively optimize the use of that data and incurring the costs associated with staying up-to-date on improvements in technology and information.
To provide for this need, retailers have two possible solutions. The first, and most cost-effective, is to make use of the investment that many of their partners have already made. The second option is to contract with a company that sells software and modeling services. This option allows retailers to bring the resources and competencies in-house and to manage the details of their analytics centrally. The decision of which approach to take is most often dictated by a combination of factors, including available investment capital, internal human resources, marketing and IT department synergies, and overall corporate strategy.
Once the right resources are in place, predictive analytics can help retailers and CPGs more effectively market to shoppers and manage backend operations. For example, it can help retailers manage inventory by more accurately forecasting demand, thereby avoiding out-of-stocks and reducing shrink by minimizing product spoilage. It can also help with promotional bundling and time-of-day optimization by determining what products should be sold together and at what time of the day and day of the week products sell best. This type of information can be leveraged to help customize in-store product sampling events for the exact days and times shoppers are looking for those items—going beyond the industry norm of simply scheduling events at the same time everyday, regardless of the item or target consumer.
Predictive analytics can also help optimize trade dollars by providing information based on customers’ behaviors, not just on total sales. For example, if a CPG wanted to do a wine sampling event at a retailer, it wouldn’t necessarily benefit from having the event at the top five locations with the highest overall sales. It would be better served by analyzing available data to determine which locations have the customers most likely to purchase wine and possibly even which varietals will appeal to shoppers at each location.
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.
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.