The initial head-scratching phase over the Amazon-Whole Foods deal is giving way to some aha moments as commentators absorb the idea of a mostly online retailer buying something as tangible and localized as an organic grocery chain. They’re pointing out that Amazon will be acquiring a first-class logistics operation, a global distribution hub for its own grocery delivery business, and the opportunity to sell to a broader range of high-income customers.
But much of the commentary so far has missed a crucial part of the story: It’s all about the data.
The Wall Street Journal was one of the few publications to emphasize this vital aspect of the proposed buyout. The Journal rightly points out that the deal would enable the new company to combine its online and in-store knowledge to better predict what goods to carry in-store and to offer cross-sell promotions to customers who shop at both venues.
All true. But there’s a deeper principle involved here. In general, a web company with high-quality data on its customers (Amazon, in this case) will have a much higher enterprise valuation than an equivalently sized “real economy” company with a less robust data set. For example, Walmart has four times the revenue but only half the valuation of Amazon.
The market correctly perceives a very high lifetime value for each Amazon customer because the company is so adept at using its data to lock in the buyer and target its marketing with tremendous precision. That data leverage is the key to the lifetime-value-per-customer metric, which is what also drives enterprise value. If the acquisition of Whole Foods goes through, Amazon will be able to add “real world” information to its massive collection of data about its virtual buyers. And it should be able to target their preferences better and increase profit as a proportion of spend per customer.
As I see it, here are the key lessons we should draw from the deal:
This is just the beginning. The Amazon-Whole Foods deal signals the dawn of an era in which data-centric web companies will buy more and more real-world assets and seek to turn those into even more data about consumer preferences and behavior.
Deep data confers competitive advantages that may be unassailable. The B2C companies with the cleanest and most comprehensive data sets on customer behavior will be the ones that are best positioned to leverage emerging AI strategies and techniques, as Amazon does. What’s more, those proprietary data sets are massive barriers to entry to upstart competitors. You can’t replicate ten years of Amazon buying history without being Amazon for 10 years.
It’s the depth and accuracy of the data that counts. The effectiveness of data-based barriers to entry doesn’t necessarily depend on how large the data sets are. True, in Amazon’s case, it’s certainly “big” data – but it’s the detail, consistency, and accuracy of the data that makes it meaningful. Because Amazon’s data flows in from its own platform, the company can leverage multi-year data, in a highly standardized format, with key buyer attributes understood at 100 percent accuracy.
Questions B2B leaders should be asking
For B2B organizations, this proposed merger of two retail giants should be food for some serious thought. How are you building and managing your proprietary data sets, particularly those that define your core commercial relationships – customers, partners, suppliers? How easily can you access essential data? Is it accurately curated so that it’s actionable? These are critical questions to consider as data continues to dominate as one of the most valuable components of business. Companies with high-quality data sets will be better set up for success, and for those without, now is the time to make it a priority.
Praful Saklani is cofounder of Pramata. He previously founded and served as CEO of Yatra Corp, was cofounder and managing partner of consulting firm Invotech Systems, and was an executive at Waterhealth International.