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Machine Learning in the QSR

Sona Sharma on November 25, 2020

Understanding How Machine Learning is Transforming the Restaurant Experience

To keep up with the constantly-evolving food service landscape, quick service restaurants (QSRs) are increasingly utilizing technology and powerful tools like artificial intelligence (AI) and machine learning (ML) to automate and personalize the dining experience. 

This technological evolution has accelerated due to COVID-19, which encouraged customers that previously did not engage with restaurants digitally to adopt innovative new channels and download mobile apps. In fact, the pandemic is predicted to have accelerated the digitalization of customers by 3 to 4 years across all industries, resulting in QSR customers who expect greater convenience than ever before. Due to the pandemic, 72% of restaurants implemented enhanced delivery and online/mobile ordering—channels that will likely remain dominant in a post-COVID world. 

As part of increased adoption of innovative technology, QSRs are turning to sophisticated capabilities such as machine learning (ML) to engage loyal customers and deliver tailored experiences no matter how the customer chooses to interact with them. Through ML, QSRs are better able to distinguish, anticipate, and respond to customer data in order to provide better service. 

The current state of machine learning in QSR

Restaurants today are using AI and ML to not only improve their own workflows but also to deliver better customer service. Mobile ordering and delivery have been steadily growing in demand and exploded exponentially in 2020. Since 2014, digital ordering and delivery grew 3 times faster than dine-in traffic. An eMarketer “State of Food Delivery” survey revealed that over 52% of all respondents utilize a restaurant’s app or website to order their meals.

While ML was first implemented by larger restaurant chains—and one report revealed that two-thirds of restaurant owners are not quite ready to embrace it yet—a competitive and increasingly digitally-savvy world has emphasized the necessity of a platform that can utilize predictive insights to engage customers in the right moment with the right offer. Artificial intelligence (AI) and ML collect crucial data, providing key information about customer behavior that restaurants can use to improve the customer experience and increase revenue. One survey reported that 31% of AI-focused companies had increased revenues, 22% improved their market share, and 21% say it helped them expand globally.

The forward-looking trends of AI and ML in Quick Service 

Technology adoption in the QSR is accelerating. With the rapid rise of digital ordering and delivery, AI and ML will continue to prove integral for understanding customer’s needs and automatically intervening with the right offer or incentive. Here are just a few channels and technologies where ML can play a vital role in the QSR:

  • Smart Kiosk Technology: The self-service kiosk serves as an ideal channel for ML. Once a customer logs into their loyalty program—informing the QSR who they are—the smart kiosk can use historical purchases (e.g. saved “favorites) and rewards (e.g. free cookie) to upsell customers. Further, still, smart kiosks can reduce order errors and encourage customers to explore new areas of the menu, providing the restaurant with greater insight into customer preferences and tastes.
    Example: Taco Bell In-Store Kiosk
  • Mobile Apps: The number of consumers using mobile apps for ordering and receiving food has grown substantially in the past year. Many restaurants, from large chains to small mom-and-pop shops, offer in-app technology that assists customers in their purchasing decisions. By utilizing location-specific data, they can connect customers with the nearest branch of the restaurant. In addition, they can provide order suggestions, delivery tracking, coupons, and rewards programs based on customer preferences and behavior. 

    Example: Starbucks Mobile App

  • Voice Activation: Voice activation, propelled by at-home assistants like Amazon’s Alexa and Google’s Home, is a burgeoning channel for all industries—including QSR. This has required QSRs to design an experience that is easy for customers to follow without visual cues, and still provide the QSR opportunities to personalize and upgrade as a customer places their order.Example: Wingstop Voice-Activated Ordering
  • Touchless Ordering: One-third of consumers report that they are very or extremely likely to select a merchant based on the availability of digital and touchless offerings. Touchless options such as digital wallets and facial recognition require QSRs to adopt an omnichannel approach to the dining experience that serves up the same exceptional experience to customers no matter where they interact.

    Example:
    KFC Facial Recognition Technology

Today’s customers demand an omnichannel, personalized customer experience. Through AI and ML, QSRs can utilize predictive insights to provide better customer service, increase average ticket sizes, and foster long-lasting customer loyalty. Contact ZineOne today to discover how our Intelligent Customer Engagement (ICE) platform can help you use AI and ML to personalize every customer journey.