Restaurant technology trends to watch out for in 2020

According to multiple outlets, 2019 was the year of the chicken sandwich. Social media feuds and new product debuts kept fried chicken top-of-mind within the QSR industry and beyond. But the chicken sandwich wasn’t the only important development of 2019. In our view, the theme of the year was lasting developments in technology: restaurants bet on big data and modified their operations to accommodate the continued rise of digital ordering. However, it’s clear that the movement has just begun. Here, we take a look at the trends that defined 2019 and are expected to go strong well into 2020. 

Betting big on technology

In the National Restaurant Association’s 2019 State of the Restaurant report, 70% of quick service restaurant operators planned to devote more resources to investing in technology, with 57% planning on investing more in back-of-house operations like POS systems and 41% planning on investing more in customer-facing technology like self-order kiosks. 

In collaboration with Baidu, China’s largest search engine, KFC debuted a facial recognition system designed to predict personalized menu options based on a customer’s age, gender, and mood. In collaboration with Yext, Taco Bell is enhancing its digital presence, ensuring that the brand pops up when customers use search terms like “fast food”, “Mexican food”, and “drive-thru.” 

Large multinational companies who are particularly well-placed to make investments in technology are at the forefront of this movement. McDonald’s acquired Dynamic Yield, a startup that provides retailers with decision logic technology, for $300 million in March. Just a month later, McDonald’s also announced it had acquired a 9.9% minority stake in Plexure, a mobile-app vendor. Then, in September, McDonald’s acquired Apprente, a company that builds conversational agents focused on fast-food ordering. Many predict that this will be another big year for M&A in the restaurant space. The year already kicked off with big news about the Yum! Brands acquisition of Habit Burger. But in 2020, will more restaurants follow McDonald’s lead in acquiring tech companies?

Using artificial intelligence in novel ways

McDonald’s acquisitions of Dynamic Yield and Apprente in 2019 reflected the industry trend of investing in artificial intelligence. With both of these acquisitions, McDonald’s is aiming to overhaul the entire drive-thru experience, using Dynamic Yield’s technology to show customers personalized drive-thru menus and Apprente’s technology to automate voice ordering. 

Other companies are making more modular changes. To improve phone ordering, Chipotle is testing a conversational voice bot, using voice recognition technology to interpret customers as well as machine learning to improve the algorithm after every conversation. In the field of marketing and rewards, TGI Fridays is using artificial intelligence to personalize mobile device notifications, and Punchh recently closed a $41 million round of funding in order to augment its AI algorithms, which generate targeted multi-channel marketing campaigns in order to foster brand loyalty. Other fun applications include Domino’s DOM Pizza Checker, which uses computer vision to check the quality of pizzas before they are delivered to customers in Australia and New Zealand, and Chick-fil-A’s customized artificial intelligence system that predicts foodborne illnesses based on social media mentions. 

Redesigning stores to be digital-first

To grapple with the ever-increasing influence of digital ordering, companies are testing out new store formats that prioritize off-premise orders. 

Some of these changes have been incremental, building infrastructure for customers to pick up their digital orders in a designated space. Firehouse Subs, a Florida-based sandwich chain, debuted a new restaurant format with an emphasis on pickup shelves to accommodate the off-premise orders that now make up 62% of sales. In a variation of the Firehouse pickup shelves, Pizza Hut debuted a new location with carry-out pizza lockers. 

However, some restaurants are also piloting takeout-only models. Marking the first time the international burger chain has debuted a new store format since launching drive thru restaurants in the 1980s, McDonald’s unveiled a new, take-out only store in London with kiosks and a reduced menu for convenience. Similarly, KFC opened an experimental drive-thru only location in Newcastle, New South Wales in November. Even delivery services like DoorDash are getting in on this trend: DoorDash is piloting a series of “ghost kitchens,” offering dedicated restaurant space to some of its partners (The Halal Guys, Nation’s Giant Hamburgers, Rooster & Rice, and Humphry Slocombe have already signed on) to prepare orders exclusively for DoorDash deliveries. We’ll see interesting results in 2020 as these innovative new restaurant models are launched and evaluated. 

The rise of the self-order kiosk

Major players like Burger King and McDonald’s have been experimenting with kiosk ordering since the mid-2000s, but it was not until recently that the restaurant industry began to see the mass adoption of self-order kiosks. This shift was set into motion by major restaurant brands like Subway, Panera, and Wendy’s, who began testing kiosks in between 2015-2017. Today, self-order kiosks are in two-thirds of Wendy’s locations. 

However, kiosks are no longer only reserved for major restaurant brands who can afford to make large technological investments. Companies like Bite, offering out-of-the-box solutions to smaller restaurant brands, are democratizing this valuable technology and making it available to brands of all sizes. In 2020, we’ll expect to see even more restaurants jump on the bandwagon with kiosk ordering, especially as it becomes an operational model that customers have grown to expect.

How leading restaurant brands are driving productivity with machine learning

Machine learning (ML), a subset of artificial intelligence, refers to systems that can learn from experience to solve specific tasks. ML algorithms use statistics and linear algebra to find and apply patterns in massive amounts of data that may be invisible to the naked eye. In recent years, ML algorithms have transformed consumer products ranging from music and video streaming services to search engines to retail e-commerce. By giving consumers personalized recommendations derived from analyzing the data they provide, these companies can optimize their products for each user, in essence providing a bespoke product to every single one of them. 

While the restaurant industry has been slower to adopt machine learning technology than other industries like those mentioned above, innovators at the biggest companies in the restaurant space are increasingly seeing that their firms cannot afford to ignore these technologies. Top executives at Domino’s even refer to the company as “an e-commerce company that sells pizza,” emphasizing the company’s tech-driven mindset. Below are some notable examples of restaurants driving productivity with machine learning:

Customer Experience

McDonald’s reached an $300 million acquisition agreement with Dynamic Yield, a machine learning startup that works with brands to personalize customer experiences. In particular, McDonald’s acknowledged that it planned to use Dynamic Yield’s decision technology to present customers with intelligent, dynamic Drive Thru menu displays based on factors like trending menu items, weather, and time of day. 

Food Safety

Chick-fil-A developed custom technology to track social media mentions in order to identify outbreaks of foodborne illnesses. Using custom software, Chick-fil-A is able to predict the likelihood of an emergent illness by identifying trends in phrases and keywords used in social media posts related to the brand.

Marketing

Dunkin’ Donuts is working with big data firm Splunk to drive loyalty through targeted promotions. Splunk provides insights into Dunkin’s customers’ habits and preferences, allowing Dunkin’ to target specific customers with offers and promotions relevant to them. 

Quality Assurance

Domino’s recently launched the DOM Pizza Checker in Australia and New Zealand, which uses computer vision to check the quality and consistency of pizza pies before they are delivered to customers. 

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It’s easy to see, however, that these decisions were made by large multinational companies who were able to put up large upfront investments in order to leverage machine learning solutions. Having identified that not every restaurant can afford to acquire technology startups, contract with consulting firms, and/or build technical solutions in house, some emerging restaurant tech companies are making this technology available to the industry at large. 

Read on to the next article to learn about how Bite is using machine learning to revolutionize the in-store ordering experience for restaurant brands both large and small.

Considering self-order kiosks? Here’s where to start

While kiosk ordering may have seemed like something of the distant future just a few years ago, today kiosks’ arrival in restaurants across the country seems all but inevitable. McDonald’s, Wendy’s, and Burger King have each all announced large investments into kiosk ordering in their stores, and other brands are taking notice. If you are a decision-maker at a quick-service restaurant brand, there is no doubt you have already begun to wonder about whether now is the right time for your brand to test kiosk ordering. This article covers a few insights we’ve gained from talking to executives across the industry as well as in testing out and expanding kiosk programs with our customers. There are three main questions you should consider. How will kiosk implementation affect store-level operations? Who in your organization will be affected by kiosk implementation? Finally, how will you test your kiosk program?

How will kiosk implementation affect store-level operations?

Implementing kiosk ordering is a big change—your employees and customers alike will need to adapt to this change, and so it is imperative that the transition be as seamless as possible. On the backend, opt for kiosk software that will integrate directly with your POS—this way you will only need to update menu information in one place, and orders will be sent to your kitchens just as they are when a cashier enters the order. From the perspective of your customers, be sure that the kiosk software you choose has a natural, responsive User Interface. A poor User Experience could cause your customers to feel frustrated and they may resent the presence of kiosks in your stores. It’s important that the experience of using kiosks in your stores not just be sufficient, but delightful. This will help win over customers who might be initially hesitant to this new change.

Who in your organization will be affected by kiosk implementation?

Any change in a large organization, even a small change, is likely to meet some friction. Because implementing kiosk will affect multiple departments in your organization—IT, Operations, Marketing, Franchising—you should gain buy-in from members of these departments sooner rather than later. Your choice of a kiosk solution is important here: it will be difficult to gain the support of those in Marketing, for example, if the kiosk software you hope to pilot poorly represents your brand or lacks data reporting functionality. It might be hard to win over your Director of Operations, on the other hand, if the kiosk implementation would involve a serious shift in your restaurant’s store-level operating model. As you move forward with a kiosk software solution, seek the input of other stakeholders involved so that you can enter the pilot with everyone in alignment.

How will you test your kiosk program?

Once you’ve decided kiosk is something you want to concretely explore, the next step is to run a test. It would be foolish, of course, to roll out a new technology in hundreds of locations across the country before testing it out in a few pilot stores. But while the insight to run a test is hardly revolutionary, designing the test so that you can actually learn what you set out to learn is more difficult than it might seem. As you begin to design your kiosk test, consider the following:

What do you want to achieve with kiosk long term?

Identify ideally just one and at most a few long-term goals for kiosk. Why did you decide to explore kiosk? What do you hope to get out of it?

What can you test in the short-term that will shed light on kiosk benefits in the long-term?

What is the outcome that will make you decide to expand the kiosk software into more stores and what outcome would lead you to pull the plug?

How will you measure this outcome throughout the testing period?

Be sure that the metrics are simple and you limit confounding variables that could cloud your results. In our tests, common metrics used are change in average order volume, change in throughput, and change in labor as a percent of sales—but the specific metrics you choose should correspond to your specific goals in implementing kiosk.

As you consider how to develop your kiosk test, keep in mind the importance of working with a kiosk partner that values customer success and will be willing to work with you to make kiosk a success in your stores.