Strata Sneak Peek: Why Nobody Does It Better Than Wal-Mart
One of the first big data big analytics chasm crossers.
This has been a long couple of weeks at PatternBuilders. We’ve been working on our latest analytics platform release (sleep deprived engineers translates into “fun” for the entire team) and putting the finishing touches on our Strata Conference session presentation, “Retail: Lessons Learned from the Original Data-Driven Business and Future Directions.”
Now I don’t want to steal any thunder from the actual session, but as is the case with most big data and analytics discussions, there is simply too much great material (no pun attended) and too little time to go over it all. So, I decided to blog about some of the material that got cut out.
By material, I mean the Wal-Mart story. No, not the story about how it was founded by Sam Walton in 1962, blah, blah, blah. I want to talk about how it got to be number 1 on the Fortune 500 list, a spot that it has frequently occupied over the years.
Many of us at PatternBuilders cut our teeth on the retail sector, working with/for channels or for companies that support the industry in the ERP, retail management, and demand forecasting spaces. In fact, our experiences trying to use standard analytics technology to solve problems in the retail and financial sectors is what made us believe in Big Data (and the problems that arise from it) before it became a buzzword. Keep in mind that retail was surfing big data before almost any other industry (outside of financial services) and in my humble opinion, nobody in retail does Big Data better than Wal-Mart.
Before I talk about Wal-Mart specifically, just a little bit of retail history for those of you who haven’t spent years working in the space:
- It’s über competitive. The big box stores are in a constant battle for more market share which translates into more customers and more sales.
- It has very thin margins. This means that every penny counts and retailers are über (there’s that word again) focused on widening those margins in order to squeeze more profit out of their stores.
- It lives and dies by the 4P’s—price, product, place, and promotion. Whoever “does” the 4P’s best wins and, it goes without saying, that all the retailers have lots and lots of data to work with. Think about it for a moment: inventory management systems, retail management systems, distribution and logistics tracking systems, POS systems, and customer loyalty tracking systems—all these systems cranking out lots and lots of data, every minute, hour, and day of the year.
What makes Wal-Mart so special in all of this? Before anyone else, they understood that all this aggregated data represented their competitive advantage: the ability to predict customer buying behavior. Way back in 1991, Wal-Mart invested $4 billion to create RetailLink, their über sales database, and use innovations like bar codes and EDI (pre-Internet) before anyone else did. As technology “innovated,” Wal-Mart embraced it, adding to the ways in which they collected and used data. In fact, according to a New York Times article in 2004 (click here to read the article—please note that you will have to login/register with the NYT to access it), Wal-Mart is considered to have “more data about the products it sells and its shoppers’ buying habits” than anyone else in the business:
With 3,600 stores in the United States and roughly 100 million customers walking through the doors each week, Wal-Mart has access to information about a broad slice of America – from individual Social Security and driver’s license numbers to geographic proclivities for Mallomars, or lipsticks, or jugs of antifreeze. The data are gathered item by item at the checkout aisle, then recorded, mapped and updated by store, by state, by region.
Famously secretive about their data and how they use it (for example, in 2001 they decided to no longer share their sales data with companies like IRI who would then sell it to other retailers), in 2004 Wal-Mart revealed that it had more than 460 terabytes of data stored on mainframes. At that time, the Internet was considered to be half its size. Now, much has changed since then in terms of big data but outside of science, the government, and social media, Wal-Mart is still considered to have one of the largest civilian databases in the world. And they use their data to drive pretty much every decision they make about their business:
- Stocking products based on expected demand at a specific point in time (based on a myriad of factors such as day, time, weather, special events, etc.).
- Supplier replenishment (inventory is expensive so you want to have “just enough” to meet demand but never run out and you need to know where it is located at any given time in the supply chain).
- Customer buying behavior (not just on what products are purchased, but what products make up a specific buying basket).
- The list goes on and on.
Wal-Mart is watching everything that is happening in their business in as near to real-time as they can get; they are able to proactively respond to events and get product where it needs to be before anyone else. Take Hurricane Charlie for example. Looking at the buying patterns just before the hurricane hit, they discovered that Strawberry Pop-Tart sales increased about 7 times and beer was their top-selling item. Before the next hurricane, they made sure that all the stores in its path were stocked with Strawberry Pop-Tarts and beer and both of those items sold quickly.
Will the Internet change who’s on top? Some are saying that it will—that Amazon will out-Wal-Mart, well, Wal-Mart! I think that the jury is still out on this. If you look at Wal-Mart’s past history, this is a company that is not afraid to change, to innovate, and to embrace new technology and new ways of doing business. My bet is that it will be on top for a while.
Now, if you aren’t going to Strata next week and would like to read more about our session, stay tuned as the next few weeks will include more posts about how retail will continue to drive Big Data innovations for itself and other industries, with a focus on our predictions for what’s coming in terms of technology and governance (hint: mobile is a game changer). We will also be blogging from the conference—letting you know what we are seeing and hearing—and tweeting too. If you’re there, please stop by our session and say hello. We “eat, sleep, and drink” (really, we do) big data and big analytics and look forward to hearing your BD/BA stories!