In a move that surprised many last week, Amazon announced its intention to purchase Whole Foods for $13.7B in cash. Amazon had been dabbling with traditional brick-and-mortar activities for a few years already - from owning a few physical stores to running experiments like “Amazon Fresh” and “Amazon Go” (more on this later).
When the news broke last week, critics saw it as a sign that the company had finally caved and made a large investment into physical stores in order to grow. What many didn’t see, however, is that this acquisition is, in fact, in complete alignment with Amazon’s view of the world of retail.
According to a recent survey of chief data executives, 47% of them believe that their firms are at risk of major disruption in the next decade. That disruption will most likely come from Data. And Amazon is using Data to reverse-engineer retail in a way that might change it forever.
The firm’s move marks the beginning of the end of retail as we've known it. Or maybe the beginning of the retail industry, as it should be. How does this relate to the disruptions coming to your industry?
Data: No longer a four-letter word
Retail is a ruthless business. It has incredible uncertainty and risk built-in. Groceries can go bad in unpredictable ways. The sale of accessories can be affected by unexpected fashion trends and unforeseen weather patterns.
To succeed, retailers have to compete on many fronts: they have to invest in the right location. They need to carry the right inventory at the right time. They have to operate with exceptional excellence and provide supreme convenience to extract razor-thin margins.
Historically, this industry took a while to adopt technology and Data to disrupt itself. It wasn’t until the 1970’s, when point-of-sale systems were introduced to replace the very limited electronic cash registers, that retailers could start tracking transactions, and tie them to orders and buyers in order to start managing inventory with more certainty.
In contrast, Amazon has approached the problem of retail in a more scientific way since day 1. In the Amazon world, every transaction is recorded, every buyer is known, every inventory movement is known, and perhaps more importantly, every possible next order can be suggested based on what visitors buy and browse. This playbook, based on the belief that all data must be collected, analyzed and used, is drastically different from retail’s traditional way of thinking.
Amazon’s move is a pretty clever one if you think about the luxury it will give the online company to reinvent and reengineer the process of buying, moving and selling goods: Whole Foods has higher margins than Amazon’s retail business (5% vs. 3%), and with 460 locations and a history of highly localized habits, Amazon will benefit from a trove of Data that it can mine to write the future.
What this means for you:
If your industry hasn’t been disrupted the way retail has, ask yourself how much time it will take. It’s no longer a matter of ‘if,’ it’s a matter of ‘when.’ Over 58% of executives report that they have invested over $100M in Data in order to get ahead of the curve. Do you have a Big Data playbook or strategy? If you don’t, I suggest you read or listen to some of the work published by Tom Davenport, author and professor at Babson College. You’ll need a data strategy before your industry gets its “Amazon moment.”
What other “Amazon moments” might look like...
Amazon will not be the only company to innovate in this space. If you’ve been following some of the latest trends, you’ll find that this acquisition is possibly only an indication of the drastic changes that will occur to impact all of us.
Here are a few examples of technology introductions that I believe will happen over the next three years:
- Facial recognition. Last week, The Economist unveiled stories of French retailers that had been using software for eight months to mine shoppers’ movements and facial expressions in real-time. When surprise, dissatisfaction, confusion or hesitation were detected, clerks were dispatched to help. Sales rose by 10%. Amazon’s recommendation engine is rumored to generate a 30% sales lift. 10% is not bad for a real-time and physical response system.
- Cashiers. According to research, the average American makes 1.5 trips to the grocery store a week and spends an average 53 hours a year roaming the aisles. If it feels like it’s more for you, it’s probably because it is (or because the process is so painful, it just feels longer). We’ve all gotten used to automatic cash registers that allow us to self-check items and skip the sometimes slow and painful human-operated lines. Four years ago, Amazon set on a path to eliminate wait all together and later introduced “Amazon Go,” an application shoppers can run on their phones as they roam the aisles. When they finish shopping, they simply walk out. A store with “Amazon Go” doesn’t have cashiers. The early tests of this technology were rumored inconclusive. However, it doesn’t change the fact that Amazon is trying hard to increase efficiency and convenience. I connected with an Amazon spokesperson on this very topic who assured me that Amazon had "no plans to use the technology it developed for Amazon Go to automate the jobs of cashiers at Whole Foods. No job reductions are planned as a result of the deal".
- Currency. This past week, I met with the amazing team of innovators at Canadian Tire, one of Canada’s most emblematic retailers. They educated me on their use of Big Data and the types of innovation they are driving forward. One of the interesting innovations they discussed was an invention that came in early in the life of the company: its own currency. Canadian Tire issues bills that shoppers use to shop at their store. This invention, which occurred pre-loyalty programs, created an entire economic system for a certain segment of Canadian Tire’s clients. What if Amazon created its own currency tomorrow? Would you use it?
What this means for you:
While everyone talks about the promise of Big Data, artificial intelligence and machine learning, those are just buzzwords unless you take a moment to identify the types of innovation that have already occurred in your industry. Innovators have already been using Big Data, AI and ML. And if you’re hearing about them now, it might be too late. To get started, don’t rush to buy technology to solve your problems. First, ask yourself: “What’s the equivalent out-of-the-box thinking occurring in my industry that resembles the three examples provided above?”
