Why It's Time to Unleash the Intelligent QSR

by Peter Schultz
Why it’s time to unleash the Intelligent QSR

The Covid-19 pandemic hyper-accelerated significant changes to the quick service restaurant (QSR) industry that for some had already been underway and others had in their plans. For example, many QSRs had been adopting and planning contactless ways to order and pay, via a mobile app or online. The pandemic made those experiences table stakes for surviving. Now, the QSR industry has an opportunity to not only survive but to transform itself to become stronger. Your customers are more loyal, but also more demanding. Most QSRs have done a good job adding and creating technology patches to address the changing needs of customers. Now is the time to acknowledge that “good” is not good enough and that it is easier than you think to fix and future proof your business. The first step: embracing an intelligent and adaptive digital fabric.

The Challenge: Frictionless is like a duck above the waterline

Let me tell you a story that underscores the challenges and opportunities for QSRs today. I recently used the mobile app of a growing QSR brand to order a burger for lunch. The app did exactly what I wanted it to do and what I expected it to do. It was intuitive, and it allowed me to make as many customizations as needed to make my burger perfectly. It allowed me to easily place my order and select an exact time for pick-up. But then a complication arose: as I drove to the restaurant to pick up my hot and juicy burger, I encountered an unexpected delay (If you have been to Atlanta, you know how bad and unpredictable the traffic can be). I had no way of using the app to modify my pick-up time while driving, nor did I think about anything other than getting to the restaurant as quickly as possible. I was only 10-12 minutes late to pick up my food, but by that time my food had become cold and dry. Everything about this experience up to biting into my burger was excellent. But no matter what the reason, cold food is just not satisfying. The experience I was left with the memory of a cold burger and soggy fries. And yet the app design was great, even more advanced than most QSRs three times the company’s size.

So what went wrong, you ask? What could that QSR have done better? Consider a scenario like this to understand what better looks like and why “good” was not good enough:

  • I place my order via the mobile app using AI-enhanced voice-to-text that allows me to customize my burger as if I were speaking to a human. While I am doing so, the connected system is smart enough to predict and recommend a complimentary menu item (perhaps a new or special food or drink) based on my purchase order history (and available at the chosen store location). This adds the element of surprise and delight to my ordering experience and builds a larger basket for the burger brand.
  • Then, using location tracking data to monitor my arrival time, the intelligence in the app and connected systems delivers my order to the kitchen at the exact time needed to deliver my food hot when I arrive (AKA just-in-time food prep).
  • Not only is my food piping hot when I arrive and delivered to me curbside, but my lunch is made even better because I now have that new dessert that was recommended, too.

What I’m suggesting is possible. But for most QSRs, disconnected and siloed data and systems are getting in the way. Many QSRs have accumulated vast storehouses of customer data. Those storehouses don’t talk to each other as quickly and effectively as they could. A room full of data scientists cannot work fast enough to analyze reams of customer data and detect patterns of behavior quickly enough to anticipate and respond to consumer behavior – like my trip to the local QSR being delayed by Atlanta traffic or my changing meal preferences from one day to the next (e.g., every Tuesday I eat sweets, and on Friday’s I prefer salty snacks).

But with an intelligent and adaptive digital fabric, data scientists don’t need to do the heavy lifting. Instead, AI-based computer technology applies machine learning to ingest all collected data and spit out a myriad of outcomes. If done correctly, a QSR can get a quick and comprehensive overview of the entire business in real-time. With delivered actionable insights, a connected visibility platform, and real-time customer data (non-PII), decision-makers can act faster and with better insight. The intelligent service can predict and personalize in the moment. Examples include:

  • A multi-brand owner can now see insights and analytics across its brands to run the company, while each individual brand can view insights most relevant to them. This level of intelligence would allow corporate HQ marketing to see potential issues before they happen, like two brands running competing marketing campaigns at the same time and potentially cannibalizing revenues.
  • A QSR can enable drive-thru digital menu screens with intelligence by adding predictive and personalized recommendations to provide a better customer experience and larger baskets.
  • A QSR can create loyalty through better customer engagement by enhancing current services with intelligence to deliver personalized and predictive offers, incentives, recommendations, and content based on variables such as:
  • The customer’s location and historical travel routes, purchase history and patterns, day-part, and other data the customer is willing to share.
  • External factors such as weather, seasonality, events, and holidays.
  • The QSR’s internal initiatives, such as marketing goals and inventory levels.

After all, loyalty is the outcome of good customer experiences.

An intelligent and adaptive digital fabric wraps data, systems, and vendor tech, creating a unified and intelligent ecosystem that breaks down the barriers of siloed data. QSRs quickly understand patterns in data (intelligence) to not only react but also to anticipate customer needs, business needs, and trends. The digital fabric allows the QSR to focus on their business instead of in their business while seamlessly connecting the different components of the QSR’s business:

  • Front-of-the-house, customer-facing operations.
  • Back-of-the-house support, inventory, labor, logistics, and systems.
  • Omnichannel elements: mobile, web, social, drive-thru, ordering, and delivery.
  • Customer experience and engagement, creating loyalty and driving revenues.

To get started:

  • First, assess what your pain points are. Are you trying to improve loyalty? Speed up drive-thru? Receive better insights? Capture a larger share of wallet with your customers?
  • Develop an agile plan to develop an intelligent and adaptive digital fabric. At Pactera EDGE, we suggest a crawl-walk-run approach that breaks down development into smaller stages to start quickly, achieve quick wins while creating a strong foundation, and then scale fast.

Covid-19 has accelerated the opportunity for QSRs to meet and exceed the needs of an increasingly more demanding customer base. Embracing an intelligent and adaptive digital fabric is no longer a nice-to-have, but rather the required foundation for transformation. The good news is that QSRs can build off their existing data infrastructure rather than replace it. Pactera Edge has centers of excellence across AI/machine learning, digital experiences and engineering, content and commerce, collection and curation of large AI Datasets, and supply chain. To learn more, contact Pactera EDGE.