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.

Personalization and the restaurant experience

With rapid developments in the sophistication of technology offerings in recent years, consumers have higher expectations than ever regarding personalization. In the U.S. adults are strapped for time, working more hours now than at any point in history, and the limited time these consumers do have will increasingly be given to brands that deliver an experience that is convenient and tailored to customers’ specific needs. Silicon Valley has been the first to answer this call for personalization, with digital native companies like Amazon, Netflix, and Spotify rapidly conquering their respective markets by offering consumers an experience that is individualized and puts ease-of-use above all else.

But while the personalization push has been led by companies operating primarily in digital spaces, consumers look for these same individualized experiences in physical spaces as well. McDonald’s recent acquisition of Dynamic Yield indicates that restaurants, too, will need to innovate toward hyper-personal experiences for their consumers if they wish to compete. In particular, small and medium size restaurant brands (<250 units), that may not have the same resources as a company like McDonald’s for large-scale acquisitions, will need to be thoughtful in partnering with cutting-edge tech companies that can help propel them along in their personalization journey.

Given the clear importance of personalization in the restaurant space, there are three key points on which a restaurant brand should focus in providing an individualized experience to guests that will keep them coming back to your restaurants: an ordering process that is designed for guests’ needs, reduced friction in customer tracking with facial recognition, and data-leveraged product development and marketing.

An ordering process that is designed for guests’ needs

While ten years ago customers might have found it strange for software to predict their cravings better than they did, today customers expect this level of intelligence from their tech. Spotify knows what song befits a sunday afternoon stroll on a rainy street. Amazon knows that a cart with olive oil and onions needs garlic. Consumers turn to software to plan their vacations and even help them meet their future spouses, so it is not surprising that they similarly expect restaurants to tailor the ordering process to their needs. Physical technology such as digital menu boards and self-ordering kiosks provide spaces for tech-driven personalization in the restaurant space. However, choosing the right software to power this hardware is key in ensuring an individualized experience for guests. Look for ordering software that makes recommendations based on your guests’ past experiences and tailors each step of the experience to their needs.

Reduced friction in customer tracking with facial recognition

There is, of course, no better feeling than returning to your favorite restaurant and being greeted by the same face that has greeted you for years. But with labor turnover rates in the QSR and fast casual space notoriously high and still increasing, this feels like an idyllic picture of the past for many restaurants. However, advanced technologies can re-introduce the ease associated with ordering from a server or cashier who remembers you and your favorite order. More than this, though, technologies like facial recognition also allow restaurants to collect data even from customers who have not signed up for loyalty programs, enriching customer data in new ways. Further, customers tracked outside of loyalty programs can be encouraged in creative ways to be brought into the fold of loyalty programs, for example by allowing customers to bank points before registering for the loyalty program and receiving their points upon signup.

Data-leveraged product development and marketing

But how can all this new data be used? Maybe you’ll predict the next super food, or see that consumers are cutting back on sugar before all the headlines in the media. Or perhaps you’ll see that, despite what media reports claim, your sales of glazed crullers are in fact higher than they’ve ever been, and this surge is sales is driven by primarily by men in their late sixties, and men who buy crullers tend to be some of your most loyal customers, so cutting crullers from the menu would be a costly error. When you have the data, you can cut out the guessing. Finding ways to allow customers to seamlessly share their information as part of their dining experience opens a whole world of data-driven product development and marketing. Brands that are innovative in this space will continue to expand their lead on competitors that fail to evolve. The benefits of a sophisticated data-backed operation—guess-free product development, precise audience metrics, etc.—are simply too powerful to ignore.