Beyond the Buzz: How AI, Analytics are Bringing Digital Innovation to Restaurants & QSRs

At the top of nearly every “trending topics” list for restaurants, and many other industries, is the shift toward embedding artificial intelligence (AI) and machine learning into marketing and customer experience.

Picture this: A tech-centric restaurant sector powered by insights gained from machine learning algorithms. Sounds great, but what would it look like?

Data gold rush

Some of the software solutions you’re currently using – think employee scheduling or point of sale (PoS) systems – contain a goldmine of insights that can be used to help run your restaurant like a well-oiled machine.

Adding AI into the mix is not the sci-fi leap that pop culture has led us to imagine – in fact, it is much simpler than that. Essentially, a real-time data platform ingests data provided from a PoS terminal or other employee and customer interfaces and then different models and algorithms are employed to create a deeper understanding of your business performance.

In a nutshell, we’re talking about processing billions of data points, in a matter of milliseconds. Once this information is collected and analyzed, the software works to identify common patterns and trends. These metrics are combined into easily digestible scores and indicators, and then aggregated into a customized dashboard for interpretation and action by management and analysts.

Digital innovation is on the menu

Up to this point, much of the buzz around AI in restaurants tend to focus on three areas: robots, delivery bots, and chatbots. However, for the restaurant industry, there are several areas where the deployment of AI solutions has the potential for exciting innovations:

• More agility: In placing, processing, and fulfilling an order. For example, it can lower latency by using cloud services to record and manage simultaneous orders.
• Higher throughput: Focus on processing more orders simultaneously, thereby managing the top line and driving a higher sales throughput.
• Greater accuracy: Less order errors stemming from a more direct channel between orders placed and served. Higher grade deep learning algorithms can even track accents or dialects and suggest corrective features.
• Inter-operability: Seamless connections from the order kiosk to the kitchen and central tracking system gives higher visibility for ops staff to look at material, analytics, service and results. Sharing data seamlessly with multiple operating teams via data streams and maintaining an up-to-date system of records.
• Personalization at scale: Localized offers, daily special up-sells, cross-selling based on ambient noises (i.e. kids in the car can mean more kid friendly meals if the BOT suggests). Plugged in for future versions to have ‘most favorable offers’ OR ‘most ordered items’ for loyalty members when they are recognized by the system.
• Customer delight & employee satisfaction: Taking the focus out of multi-tasking, so staff can focus on actual food and service personalization. This area still requires some human oversight to ensure quality of interaction.
• Data mining & analysis: More data collected can give far greater insights into menu items, redesigning offers, specials, franchisee insights, supply chain and future strategy.

As these technologies mature, more innovations will filter into each aspect of the customer experience.

In closing

As retailers and brands take advantage of internet-enabled technologies and use AI to better track and respond to shopper behaviors and purchasing habits while factoring in broader contextual cues, services will shift from reactive to proactive.

For the quick service restaurant industry, with annual sales totaling $799 billion, AI solutions don’t just bring the potential for new financial success, they can also generate insights that offer concrete competitive advantages.

About the Author - Akash Mohan