Almost everyone has experienced a quick food run that turned into a 45-minute ordeal, but such delays are bad news for both quick-service restaurants (QSRs) and patrons. Long drive-through lines can overwhelm employees and frustrate customers, occasionally irritating the latter enough that they leave without making purchases. It is no wonder that so many big-name QSRs are turning to the latest digital technologies to tackle this problem.
McDonald’s acquired artificial intelligence (AI) startup Dynamic Yield for $300 million in March of 2019 to integrate the latter’s solutions into its customer relationship management (CRM) platform. The new system will maximize profits by enticing drive-through customers to order additional or more expensive menu items, increasing output per second for each location.
Dynamic Yield’s system gathers data on McDonald’s customers, and records environmental factors, such as location, precipitation and temperature. It then uses AI to analyze the data, and determine which items consumers would be most likely to purchase via drive-through, allowing smart screens to display products like McFlurries on warm days or hot coffee on cold days, for example. Some McDonald’s locations already use such solutions, and the company plans to eventually add the technology to 14,000 U.S. restaurants via drive-throughs, self-service kiosks and its mobile app.
Implementing this high-level data analysis at such a massive scale may seem like a Herculean task, but McDonald’s is far from the only major QSR chain attempting to do so. Starbucks spent much of 2019 enhancing its own CRM system with an AI solution called Deep Brew to automate menial tasks. KFC and Taco Bell are testing similar concepts at their U.S.- based self-service kiosks and drive-throughs.
Using complex data systems to boost business is sometimes seen as a cold, calculating way to run customer service-focused industries, but QSRs are using AI-based CRM tools to benefit both themselves and their consumers. The following Deep Dive explores how such software solutions can personalize and enhance consumers’ fast food experiences.
Personalizing CRM With AI And Machine Learning
CRM refers to the collection of procedures — usually software solutions — that businesses use to interact with their customers. AI- and machine learning-supported systems are at the cutting edge of CRM, and the market for AI-related solutions is rapidly expanding.
Some estimates claim that AI-enabled CRM software is on course to increase global business revenue by as much as $1.1 trillion from 2017 to 2021. Using such technologies to enhance CRM capabilities allows QSRs to provide customers with AI-enabled screen access at select drive-throughs, offering personalization that would be impossible to match by relying on traditional Excel spreadsheet analysis. QSRs have historically used the latter method to review customers’ data.
Many QSRs are taking AI-powered solutions even further, providing mobile apps that allow customers to access such services wherever they are — or at least wherever they are before pulling into drive-throughs. Sonic introduced a mobile app in May that authenticates customers’ identities when they log in, retrieving their data and providing them with customized menus catered to their tastes. The app then allows them to change their menu options, and place orders using voice-recognition technology.
Such AI-powered solutions bring the personalization offered at brick-and-mortar locations to customers’ cars, homes or anywhere else they use merchants’ apps. Consumers once stepped up to registers at their usual fast food locations and ordered from familiar faces who knew their favorite items by heart. They can now enjoy similar experiences via digital technology, thanks to AI-enabled CRM software.
Expanding Service: The Cloud Factor
AI and machine learning are useful tools, but both run on data. This means QSRs must collect and store both customer and environmental information to harness the full potential of these technologies.
Such data has historically been stored on in-house servers, but many QSRs are forgoing these solutions for cloud-based servers — outsourced by third-party providers for several reasons. Cloud servers are cheaper to set up and maintain, and they streamline the obtaining and sharing of data because they can be accessed from any location with Wi-Fi.
Starbucks is a prime example of a QSR that uses cloud-based data storage capabilities to expand its reach. It announced in July that it was teaming up with technology startup Brightloom on a cloud-based solution to bring order-ahead and other services to customers around the globe. The reason was simple: The coffee chain operated in 80 markets, but its mobile app and order-ahead services were available in only half of them. The proposed cloud solution would bring these features to all its customers.
Freeing Employees From Monotony
Offering customers personalized services is just one benefit of AI-enabled CRM tools. These solutions can also give employees more time to foster personal relationships with brick-and-mortar customers.
Many QSRs see integrated AI- and machine learning-based CRM systems as tools that can automate routine functions, enabling human workers to concentrate on customer service. Starbucks CEO Kevin Johnson said the company’s goal for its Deep Brew AI is less about reducing costs and enhancing efficiency, but more to focus on “nurturing humanity” by giving monotonous tasks to machines, allowing employees to engage in more creative, rewarding pursuits. Such prioritization can give workers more time and energy to foster strong interpersonal relationships with customers.
Relying on machines and algorithms to perform basic tasks can reduce human error as well, and these faster automated processes can reduce the time customers spend in line. QSR Chipotle Mexican Grill has seen success with its own AI-based ordering system, which was introduced earlier this year. The system converses with drive-through customers, and transfers the information to human employees, who can more quickly begin working on orders. It also ensures that orders are not misheard, and shields employees from dealing with difficult patrons. The company hopes to implement the system at all 2,500 U.S. locations by 2020.
The industry is growing increasingly reliant on cutting-edge technologies like AI, machine learning and cloud-based data storage, but this does not mean QSRs are no longer delivering personalized customer service. These technologies are instead helping businesses to find new ways to deliver high-quality customer service, while reducing human strain. Thus, it falls on individual QSRs to determine which technologies should enhance their CRM systems, and how those solutions can be tailored to provide ideal customer experiences.