NRF: Why Are Retailers Still Complaining About Big Data?
We hear the complaints about all the data that is now being captured from TLogs in the store and online leading to "a plethora of data but a dearth of wisdom." After walking the floor at NRF, I am confused why anyone would say this. I was amazed at the number of BI (business intelligence) vendors ready to help retailers cope with the volume of data they are collecting. Representatives from Teradata, Aldata, 1010data, and Micro Strategies were among the ones I saw or spoke with about this complaint. Further, ARTS (The Association for Retail Technology Standards) has created an RFP for BI solutions that describes the features necessary in a solution. Yet the complaint persists. I have come to the conclusion that there are plenty of viable solutions; the challenge for retailers is knowing what to want from the data.
BI tools open a whole new opportunity for gauging business performance. More importantly, new message-oriented interfaces make it possible to provide real or near-real time performance information for store operations. This combination of detail and timeliness makes it possible to provide feedback while there is still time to correct a bad situation. For example, knowing that sales on a promoted item have stopped is a sure way to warn people in the store that the shelf is empty. On the other hand, seeing that an item has higher sales than expected may lead to a special offer on another size of the same product. Targeted markdowns by store allow control of individual store inventories based on actual sales so that overstocks and returns are minimized. TPIs (tactical performance indicators) can be developed from the data that are focused on job performance and provide employees feedback on their efforts. TPIs also highlight the advantage of equipment upgrades or an improved business process.
The use of BI to evaluate consumer behavior, however, is still more art than science. But even here the retailer is striving for only three possibilities: the consumer buys a higher gross item; the consumer shops a department they have not previously shopped; or the consumer buys more of an item they are already using. Switching the consumer to a different item based on BI clearly depends on the category; few people will switch to private label cigarettes, for instance. But the BI tools should be able to tell in which categories the consumer buys multiple brands and enable the retailer to suggest better items.
The propagation of BI solutions raises another question: who really should be influencing consumer item selection; the brand product manager or the retailer? Retailers would naturally want to shift their customers to the higher gross private label items — probably not what the brand manager wants.
Discussion Questions: With tools apparently available, why are retailers still complaining about the difficulty in extracting wisdom from Big Data? Do retailers know what they want from the data? How are you seeing BI tools changing the influence of brands at retail?