Most QSRs Don’t Know Their Customers That Well. Do You?

Today’s digital technology makes knowing and satisfying your customers easier than ever before — theoretically. Most quick serve restaurants (QSRs) have been slow to adopt digital solutions that can help them learn more about their customers.

While customer satisfaction scores provide a helpful window, they don’t present the full picture. Most unhappy restaurant guests don’t complain directly to restaurants themselves.

As a result, QSRs often don’t know what most of their customers think about their brands — either nationally or at a franchise level.

The good news is that customer sentiment is an entirely avoidable blind spot for QSRs. Read on to learn how to obtain a clear view of your diners, one that helps you deliver exceptional customer experiences everywhere, all the time.

Use AI to Find, Hear and Engage With Your Customers 24/7

While you might not know what most of your customers think about your brand, that doesn’t mean they aren’t sharing their opinions. They are, and often in forums where lots of people are listening.

There’s plenty you can do to track, join and steer those conversations to help your business and diners alike. To do that, however, you’ll need to cover the digital waterfront, spanning Twitter, Facebook, Instagram, Yelp, Google Reviews, TikTok and more. This entails daily, round-the-clock social listening, content moderation and community engagement — both within your own digital channels and across many external ones.

While this may sound like a tall order, it’s highly doable. Using artificial intelligence (AI), you can scan for different keywords that could signal an issue your brand should act on, ranging from damage control (tainted ingredient) to amplifying a positive story (local QSR community service).

You can further understand customer sentiment by using AI-powered speech and text analytics to discover patterns in conversations that reveal where your QSRs excel and could improve. When you combine these technologies with semantic deep-learning engines, you can tag customer sentiment across key actionable areas. This doesn’t just help you triage immediate problems, but also lets you detect and get in front of evolving trends (e.g., drive-thru guests preferring to order digitally) so that you can maximize positive developments (faster drive-thru lanes designated for digital orders) and head off negative ones (digital orders ready too late/early).

Constant cross-channel monitoring also proves critical to managing negative reviews. These can pop up pretty much anywhere and everywhere, although most of them appear outside a QSR’s own digital forums. QSRs ignore such reviews at their peril, potentially increasing customer churn up to 15 percent if they don’t respond.

Time is of the essence; most online complainers expect a response within hours. Since roughly half of customers post reviews about their restaurant experiences — well above the percentage for most other categories — manually tracking their feedback can be a lengthy and challenging process^[1]. Using AI to find and flag negative feedback lets content moderation teams focus on quickly appeasing and then positively reengaging disgruntled reviewers. In clear-cut situations, AI itself can step in to appease and reengage customers, via app and website chat, SMS messages, email and other automated communications.

Centralize Omnichannel Insights to Drive Higher Customer Value

The most efficient and economical way to cover all your digital bases is to bring all QSR guest interactions onto one platform. Doing so gives you a single view into which touchpoints consumers favor for certain activities (e.g., checking reviews, ordering, etc.), as well as tracks when and where they use those channels. As a result, you waste neither the customer’s time nor your own, creating operational savings along the way.

You can also draw on data gathered from every prior interaction with a customer and then couple that with machine learning (ML) to make tailored upsell recommendations — either directly to the guest or to the QSR employee transacting with the customer.

For instance, consider a loyalty program customer who orders a chicken sandwich once a week, along with either a small shake or a large fries, but never both. The total charge is roughly the same for each order. Guessing that it’s price, not appetite, that’s preventing the customer from adding both the shake and fries, AI recommends a value meal including all three items. An integrated omnichannel platform ensures that this specific upsell recommendation would be made to the loyalty customer whether they were ordering via the QSR’s mobile app, website, kiosk or cashier.

A single, integrated platform also helps a brand manage customers based on where they are in their customer life cycle, ensuring both consistency and sufficient novelty to keep individuals engaged with the brand long-term.

Maximize Call Center Insights

Customer phone calls deliver free insights directly to you. But most QSRs squander this intelligence. Instead, they should analyze data from every call to understand its root cause and then aggregate it with data from every other call to discern patterns. This intelligence can then support call center representatives and their digital surrogates in real time to guide contextual responses, predict outcomes and generate AI/ML-based best next actions.

We recently helped a leading QSR holding company whose CSAT scores plateaued at around 25 percent. To address this problem, we transformed the company’s call centers through a combination of conversational AI, business data intelligence and customer analytics. Within just three months:

• CSAT jumped 90 percent

• First contact resolution improved 94 percent

• Average handling time fell 60 percent

As you seek to better satisfy your own customers, both now and across their customer lifetime, keep these points in mind:

Widen your feedback net: Supplement CSAT and NPS metrics with opinions expressed online so you learn how to satisfy customers beyond the ones you survey.

Connect the data dots: Be sure you can make sense of your customer intel. Data that isn’t paired with the right analytics capabilities tends to create an unhelpful feedback loop.

Find the right digital partner: Work with a provider who tailors solutions to your needs and offers quick scalability, fast results and long-term sustainability.

Want to learn more? Let’s talk. We’d love to hear from you.