MCDONALD'S Bites On Big Data With $300 Million Acquisition

Credit from Wired


Mention McDonald’s to someone today, and they're more likely to think about Big Mac than Big Data. But that could soon change: The fast-food giant has embraced machine learning, in a fittingly super-sized way.


When you add an item to an online shopping cart, it’s the tech that nudges you about what other customers bought as well. Dynamic Yield reportedly had been recently valued in the hundreds of millions of dollars; people familiar with the details of the McDonald’s offer put it at over $300 million. That would make it the company's largest purchase since it acquired Boston Market in 1999.

The burger giant can certainly afford it; in 2018 alone it tallied nearly $6 billion of net income, and ended the year with a free cash flow of $4.2 billion. But that still doesn’t address the bigger question of why. For that, you have to head to the drive-thru.


Drive Time


Over the last several years, you may have noticed that the displays as you approach the McDonald’s drive-through—and inside the restaurant, for that matter—have gone digital. That’s just one of several significant, data-focused investments that both McDonald’s and its franchisees have made since CEO Steve Easterbrook took the helm in 2015. The company also launched an app and partnered with Uber Eats in that time, in addition to making a number of infrastructure improvements.

Here’s what that looks like in practice: When you drive up to place your order at a McDonald’s today, a digital display greets you with a handful of banner items or promotions. As you inch up toward the ordering area, you eventually get to the full menu. Both of these, as currently implemented, are largely static, aside from the obvious changes like rotating in new offers, or switching over from breakfast to lunch.


But in a pilot program at a McDonald’s restaurant in Miami, those displays have taken on new dexterity. Algorithms crunch data as diverse as the weather, time of day, local traffic, nearby events, and of course historical sales data, both at that specific franchise and around the world. In the new McDonald’s machine-learning paradigm, significant display real estate goes toward showing customers what other items have been popular at that location, and prompting them with potential upsells. Thanks for your Happy Meal order; maybe you’d like a Sprite to go with it.


McDonald’s was reticent to share any specific insights gleaned so far, or numbers around the personalization engine’s effect on sales. But it’s not hard to imagine some of the possible scenarios. If someone orders two Happy Meals at 5 o’clock, for instance, that’s probably a parent ordering for their kids; highlight a coffee or snack for them, and they might decide to treat themselves to a pick-me-up. And as with any machine-learning system, the real benefits will likely come from the unexpected.


McDonald’s defines those customer benefits broadly. Multiple executives noted that if the drive-thru is moving slowly, the menu can dynamically switch to show items that are simpler to prepare, to help speed things up. Likewise, the display could highlight more complex sandwiches during a slower period. And as with any online checkout experience, it’s unlikely that the drive-thru window will tell you that you’ve actually ordered too much. While customer satisfaction may be the goal, the avenues McDonald’s takes to get there will increase revenues along the way.


Personal Touch


McDonald’s didn’t spend over $300 million on a machine-learning company just to juice its drive-thru. Henry says he expects to see the technology in 1,000 locations within the next three months, eventually rolling out to the company’s 14,000 US restaurants and beyond. You can also expect McDonald’s to integrate its new machine-learning smarts not just broadly but deeply, albeit at a measured pace.


An important part of that focus is figuring out how to leverage the “personalization” part of a personalization engine. Fine-tuned insights at the store level are one thing, but Easterbrook envisions something even more granular.


As for what form that might ultimately take, Easterbrook raises a handful of possibilities. McDonald’s already uses geofencing around its stores to know when a mobile app customer is approaching and prepare their order accordingly. Easterbrook suggests you could extend that, in a strictly opt-in capacity, to the smartphone itself, using a sort of beacon technology. Or, he says, license plate recognition could let the system identify a specific customer as they approach, and adjust the digital menu accordingly based on their purchase history.

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