It’s often been said here at PAR that the restaurants of tomorrow are going to look nothing like the ones of today. More often, daily tasks are being completed with the support and insight of data, from what items are on the menu to how people purchase and pay for their food.
When McDonald’s bought Dynamic Yield in 2019 for $300 million, it felt like the industry had made the shift from one that was afraid of data and analytics to one fully embracing the future. Not to be outdone by the Golden Arches, other concepts, including YUM! Brands, Shake Shack, Domino’s, Wendy’s, Chipotle, and Chick-fil-A are doing their fair share of technology exploration as well. These brands have invested hundreds of millions of dollars into companies and internal products that could monumentally shift the restaurant dining experience as we know it.
But not every brand is McDonald’s, and very few concepts have the sort of capital the big guys do. For a vast majority of restaurants, they don’t have a dedicated IT team or data scientists on staff to crunch the numbers and analyze the data collected at their locations each day. For smaller brands, gaining access to reliable restaurant insights has streamlined and optimized the restaurant experience for customers and operators alike.
Enter machine learning, which uses POS, customer, sales, and other data to improve the customer experience. While some machine learning algorithms track inventory or align marketing efforts to specific customer segments, others boost the bottom line through upsells, cross-sells, and limited-time offerings.
Data Access and Implementation in Real-Time
At its core, machine learning is a burgeoning tech segment that uses algorithms to collect and learn from data to complete a given job more efficiently. The case for machine learning has been made in restaurants for several years now as an easy way to guide guests through the ordering process, control costs, and create memorable experiences while increasing upsell opportunities. For most concepts, it isn’t feasible to hire a consultant or company to dissect data and spit out relevant, real-time information consistently.
That’s where people like Sash Dias, Co-Founder and Chief Customer Officer of Incentivio, an all-in-one digital restaurant management software company, come into the picture. He says his company got its start by addressing the one critical need small and mid-size restaurant concepts share – understanding vast amounts of information, then applying it to real-world situations.
“Restaurants were assembling tech stacks, but they didn’t know what to do with the data, how to use the data,” Dias said. “The smaller brands didn’t have the expertise, didn’t have the funds, and didn’t have the five years that Starbucks and Panera Bread spent to get it right. They needed something out-of-the-box and ready to go; intelligence gleaned from the data is woven into the digital guest experience. We knew that to start, you had to have all these features in a single platform, all connected together in one place.”
Incentivio’s closed-loop system allows the company to compile POS data, marketing information, delivery, loyalty, CRM, and even geographical location to get to know a brand’s customers. Then it uses that information to tailor menu suggestions to those preferences. As a result, restaurants can offer the right customers appropriate upsells at the right time and create exceptional guest experiences.
“In the end, it’s about yield optimization. How do we deliver more margin dollars to the restaurant?” Dias said. “In the long term, how do we bring in margin data and use that to reprice, upsell, cross-sell, segment, and market to create margin and yield optimization.”
Machine learning techniques have already proven their worth to restaurant owners and operators, but it also helps guests, including those who may have dietary restrictions, discover new menu items they might not have considered and learn more about their favorite meals. By giving guests this additional information and insight as part of an upsell or cross-selling opportunity, they have more power to pick and choose what they want.
Smaller Concepts Win with Machine Learning
For years, this type of data analysis was only available to the largest chains because it’s costly and the data is usually stale before it can be acted upon. Machine learning, which includes everything from optional upsells on menu boards to automated cooking robots, inventory tracking cameras, and security systems bring an immense amount of control back to smaller concepts. And operators who see the value often see it quickly.
“For one of our customers, it’s delivered an additional $2,500 in revenue per month across their five locations, just from turning on the machine learning upsells feature,” Dias said. “That’s more than they currently pay for our entire platform, which includes so much more.”
Similar to how social media ads are geared to meet our specific needs and interests at the right time, restaurants use deep learning and other AI to compile and dissect customer information. This later helps their guests make better-informed decisions based on interests, previous purchases, and other criteria. In other cases, the restaurant’s menu boards and kiosks can make suggestions to customers based on the type of menu items already ordered, the weather outside, or upcoming holidays or events. Best yet, the system can be taught what NOT to offer too. For example, a customer who has already ordered a burger and fries will not be upsold another burger but could see a pop-up for a chocolate shake, cookie or combo.
If your eyes have glossed over, you’re not alone. Machine learning is an incredibly complex topic to discuss with most restaurant operators and owners. Getting it to work correctly requires a room full of bright minds working behind the scenes. For Incentivio, the need to continue improving and growing their offering pushes them to invest heavily in their talent, with plans to grow by 50-100% this year alone, ranging from engineers and roboticists to data scientists.
Unsupervised Learning = Real-World Results
For the concepts that use Incentivio, they don’t need to know how the product works. Instead, they want to see the business results it delivers. In their restaurants, the proof of concept is much easier to explain as their revenue increases almost from the moment the machine learning feature is implemented.
“What we’re delivering are outcomes,” Dias explained. “Real revenue and real margin dollars.”
Those outcomes get the concepts interested in asking more questions, explained Rajat Bhakhri, Dias’ colleague and CEO of Incentivio.
“As soon as the first results come in, operators start looking for more ways to optimize their data collection for better use,” Bhakhri said. “Those outcomes are the ones that reverse the customer back into us. They don’t spend time analyzing the data; they haven’t looked at it. But when they see the positive outcome, they start becoming curious. How did I get here? That’s when we start to explain the journey to them. That does two things; they have something tangible in front of them, and they start listening to you more.”
The Future of Future Tech
As the restaurant industry shifts toward more technology, the futures of machine learning and AI are incredibly bright. From drive-thru menu boards, in-store kiosks, and mobile apps offering relevant upsells, to better loyalty programs with personalized rewards and easier access to information, customers and operators alike will experience a more engaging ordering experience.
“If I like a certain item, I know I like it, and I’ll buy it,” Dias said. “But this has the potential to help people discover new menu items that they wouldn’t have tried, or perfect pairings.”
Over time, the technology will continue to be refined, and the customer experience will improve. As machine learning and other artificial intelligence technology take on more tasks traditionally performed by managers and their staff, those employees will have more free time to focus on creating the ultimate customer experience.