Unlocking the power of machine learning with a turnkey solution

By offering an integrated solution for companies both large and small to leverage the power of machine learning in their operations, Bite Kiosk works with a variety of restaurants across the industry, allowing restaurants to focus on what they do best: providing great service and making great food. Bite’s algorithm uses reinforcement learning to provide recommendations to customers, and Bite Kiosk works out of the box to integrate seamlessly into existing workflows and deliver results. 

Steve Truong, Head of Product at Bite, explains how machine learning algorithms predict recommendations by extracting information from the existing data they’re provided. “Machine learning algorithms take a large dataset and use the data to train the algorithm. The algorithm’s parameters determine the output given some input,” says Truong. The algorithm’s parameters could include the preferences of other customers at the restaurant, environmental factors such as weather and time of day, and the user’s own historical preferences, if they’ve previously opted into facial recognition. 

In particular, the Bite algorithm is a reinforcement learning algorithm that improves over time. Truong explains, “Over time, you reinforce the learning by feeding the algorithm data and telling the algorithm whether or not the answer it predicted was right or wrong. Then, it will internally adjust its own parameters to more closely approximate a better guess the next time it gets asked the same question. As you feed the algorithm more and more of this data, it will get better and better at seeing patterns and produce more accurate results.” 

Because of the machine learning algorithm, Bite’s recommendations are more powerful and flexible than upsell recommendations from cashiers. “Just saying ‘Would you like fries with that?’ is not enough. The machine learning we’re developing asks Would you like ‘X’ with that?” where ‘X’ is what the customer would say yes to every time,” says Truong. 

Ultimately, Bite Kiosk is a turnkey solution that allows companies to bring the power of machine learning to the guest experience without additionally complicating the restaurant’s operations. “Our solution comes in an integrated package. We’re not a general purpose machine learning consultancy,” says Truong. 

Unlike data analytics derived from a machine learning consultancy that have to be consciously integrated into the workflow, Bite Kiosk applies its findings immediately. “Using machine learning to improve restaurant operations doesn’t make sense until you can put it into practice, and kiosks are the ideal way to put it into practice. Kiosks are the perfect medium for reinforcement learning because they provide instant feedback. As we learn your taste profiles, we can reorder the entire menu board on the kiosk based on what you like.” Bite gives its clients the ability to provide customers with entirely bespoke guest experiences tailored to their individual needs and preferences. In doing so, Bite is leveraging technology to revolutionize the restaurant guest experience, enabling restaurant brands to provide their guests a level of personalized hospitality never before possible.

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.