Why are retailers struggling with Big Data?
Through a special arrangement, what follows is a summary of an article from Retail Paradox, RSR Research’s weekly analysis on emerging issues facing retailers, presented here for discussion.
When RSR recently asked retailers about the status of capabilities related to capturing and analyzing Big Data, we found that no capability was reported to have been “implemented” by up to 50 percent of retailers and less than a quarter claimed to be “satisfied” with what they had implemented.
The industry has a “half empty or half full” problem. The optimistic view is we’re in the very early days when it comes to truly leveraging non-transactional data. The pessimistic view is that retailers are just too slow to change.
Whether slow or fast, retail remains a fundamentally reactive business. While heavily depending on the quality of the data (both internal and external), Big Data promises to virtually eliminate the lag time to reaction by predicting events before they occur. Beyond internal transactional data, it can take in consumer path-to-purchase and social data, as well as exogenous data like weather, competitive, consumer psychographic profiles, calendared events (such as concerts or sporting events), and manufacturer and supply chain data.
So will Big Data cause retailers to change their model? In a way, the opportunity that Big Data represents might be distracting retailers from the real question: “What value are you trying to deliver?”
Retailers need to answer these questions:
- What level of intimacy is needed to be relevant? Some needs are highly predictable, others much less so. The granularity of customer-specific data needed to make a highly relevant offer is closely related to the specificity of the need (for example, offering basic commodities doesn’t require much customer specificity);
- What level of relevance is needed to be relevant? This is closely related to the level of customer “intimacy” required, but relates to the granularity of the offer itself. Extreme localization isn’t necessary for every brand;
- How much data do you really need? How much analytical sophistication do you need to apply what you know to be relevant to customers?
Getting an answer to “What value are you trying to deliver?” has to come first and you don’t need technology to answer it. But you may need technology to deliver it and do need technology to tell you whether you are getting the job done in a way that will satisfy customers and generate profits for you. That’s when your version of Big Data becomes important.
DISCUSSION QUESTIONS: Are retailers failing to leverage Big Data due to caution around technology investments or something more? How does industry thinking have to change to drive more relevancy with consumers?