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When Dunkin’ announced the revamp of its rewards program in early October, there was fierce consumer backlash. One headline blared, “What idiot do you think I am?” But behind the scenes, Dunkin’s decisions come from years of menu growth, and exemplify how a carefully planned loyalty strategy pays off over the long term.

To get the full story, I went back to the start of Dunkin’ Rewards, originally called DD Perks. The program launched in 2014, when the Dunkin’ menu focused on hot and iced coffee, with very few specialty drinks in between. Back then, Dunkin customers earned 5 points for every dollar spent. After spending $40, a customer who redeemed points for a free Dunkin iced coffee ($2.69) enjoyed a 6.7% discount which was in keeping with the typical 6-7% discount rate for QSRs at the time.

But as Dunkin’s menu grew, their rewards program did not. Over time, Dunkin’ found customers were redeeming for more expensive drinks and enjoying a significant discount. As the modern Dunkin’ menu included much more expensive drinks ($6-7), customers were gaining redemption values of up to 15%, which exceeds Paytronix’s recommended range of 7-8% value for QSRs.

I suspect there were greater reasons for a revamp, driven by customer feedback. First, customers may have found the rewards limiting. DD Perks did not extend to Dunkin’s growing food menu, which would have left many food-buying customers dissatisfied. The program also lacked a recognition element – there was no way to recognize the most valuable customers. Lastly, Dunkin’ may have felt the pressure from its competitors to switch over to a the most popular loyalty model in the QSR space, bankable points.

So, what does the new Dunkin’ Rewards look like? The new bankable points program awards 10 points on every dollar spent to be redeemed on items of their choosing across the entire menu. To cover any point discrepancies, Dunkin’ awarded their customers 150 bonus points for participating in the new program.

The real innovation from Dunkin, however, comes in its boosted status. A great example of loyalty gamification, customers gain access to the boosted status once they visit 12 times in a calendar month. It’s the first time we’ve seen a restaurant concept use a visit challenge monthly, rather than by year. With an offer cadence you’d see in convenience stores, the boosted status is a new pathway for coffee QSRs to engage their most frequent customers.

The new Dunkin’ Rewards is producing impressive results. In its first full week, the program produced one of the best week-on-week loyalty sales increases ever for Dunkin. Additionally, 20% of active members have already earned the Boosted Status, indicating most of the brand’s high frequency members have stuck around.

But what about all the media backlash? You may ask. In short, this is typical. If a program conserves the rewards customers have earned and is designed to provide comparable, (if not better) value, your customers will love it.

Online ordering is changing the way many brands think about the guest relationship. Digital channels offer a level of convenience that is hard to beat, however removing some of the face-to-face communications creates an inherent risk that poor experiences will not be heard about or addressed. Paytronix FEEDback^SM offers a solution to this by giving your guests an easy way to provide their comments and reviews – good and bad – while making it easy for you, your franchisees, or unit managers, to respond to guests and recover guests who have poor experiences.

Configurable response options within FEEDback^SM save you time while also making your guests feel heard, deterring them from posting negative reviews publicly before you have had the chance to recover from the situation. If your pre-configured options are not a fit in response to a guest’s review, you can also customize the response, including an option to offer a discount or coupon to encourage a return visit.

The newest FEEDback^SM feature makes responding to guests easier than ever. The new AI-enhanced response feature analyzes reviews – even those where guests don’t leave comments- and drafts a response email based on guest sentiment. This AI to IA^SM feature learns continuously and can respond in ways tailored to your brand, including accents, and signature sign off lines, allowing you to maintain a consistent brand voice while improving brand reputation, retaining guests and saving time!

Before creating your email draft, your team will have the opportunity to determine the best course of action to take. The FEEDback^SM page includes the guest’s ordering history and previous behavior to allow your team to choose whether or not to give a discount or just provide a response.

The AI response feature is available to all Paytronix Order & Delivery customers. Reach out today to learn more or to have it activated on your existing account.

As customers’ tastes lean increasingly digital, brands can take action now to capitalize on ease of use.  That means not only making the ordering and reviewing process easier but enabling the process to intelligently adapt to the customer.

The solution: a smart order-and-delivery process, which can be facilitated through artificial intelligence (AI). Paytronix called this Artificial Intelligence to Individual Action, or AI to IA^SM.

Paytronix has integrated AI into all its products to make the customer experience easier and more intelligent. Using one of the largest datasets of consumer transactions in the industry, Paytronix has one of the strongest models with deeper insights that drive individual action through artificial intelligence, referred to as AI to IA^SM.  You can set out to create great guest experiences and run new campaigns, confident that decision engines are using insights and making recommendations that have worked for years.

Paytronix has published The Order & Delivery Report 2022, which details this trend and the data surrounding it. This blog and the ones that follow will highlight findings from that report, as well as sheds new light on recently acquired data.

Sentiment Analysis

For instance, what if your technology could bring a more in-depth understanding to your reviews? Consider the role that sentiment plays. The rating an establishment receives and the words that accompany that rating may actually tell two different stories.

Paytronix data shows that 15% of orders with a rating of 4.5 or better actually have a negative sentiment and could benefit from action, while 3-star reviews most frequently contain negative sentiment.

Sentiment analysis, which can be accomplished using AI, is one tool that can help. Sentiment analysis enables the store manager to identify negative reviews that otherwise would have been missed and act quickly to recover the situation. This is important because sentiment scores are nearly as accurate a predictor of whether a patron will return as the ratings themselves. And as the below examples show, high ratings can hide poor customer experience.

Intelligent Menu Design

Another exciting implication of AI is the potential to influence customer ordering choices through smart menu design and recommendations.

Some establishments have long or complex menus, causing customers to gloss over some options or potential add-on items. Understanding where to place options on the menu and when to suggest an additional item can make a big difference in the order total.

Multivariant menu testing can determine how to help customers make sense of large menus in a way that’s beneficial for restaurants and c-stores. Working with Paytronix, National Coney Island — well-known for their large, detailed menu with plenty of options for everyone — utilized multivariant menu testing to determine if AI to IASM could reduce customer frictions with the menu.

The results were eye-opening, delivering an additional 3.5% conversion rate and $300 monthly sales lift per store.

Intelligent Menu Recommendations

Another example of helpful AI to IA^SM is intelligent recommendations. Recommendations are not a new feature: “Would you like fries with that?” has been a staple for a long time. But adding intelligent recommendations to digital orders provides customers a superior ordering experience while increasing revenue.

Uno Pizzeria & Grill is running a trial of intelligent recommendations, and early results have been encouraging. Uno reported that guests were twice as likely to accept the AI’s recommendations over ones manually programmed by staff. This produced a 1-3% lift on average checks from intelligent recommendation orders in comparison to no-recommendation orders, increasing Uno’s orders by between $100 and $150 per store per month.

Conclusion

There is no denying the ongoing challenge to the hospitality industry. In the wake of a global pandemic that brought the loss of over 6 million people worldwide and wreaked economic havoc, restaurants and c-stores are now wrestling with supply chain and labor disruptions amid a customer experience revolution. The world has not stopped changing, and, to succeed, the hospitality industry must embrace our new circumstances.

What is clear is that the customer experience of the future will be different than it was in 2019, before the pandemic. C-stores and restaurants will need to use technology to cater to guests wherever they are and give those guests the flexibility to receive their food through different channels. Payment must be effortless, with preferred selections front and center. And the experience of the future will enable guests to be heard when things don’t go right. AI can do all of this while removing frictions from the experience. Indeed, the future of guest experience is bright.

The risk of poor reviews may cause some establishments to shy away from digital ordering. While this is a legitimate concern, there are proven strategies restaurants and c-stores can implement to bounce back from a poor review and solidify customer loyalty.

Paytronix has published The Order & Delivery Report 2022, which details this trend and the data surrounding it. This blog and the ones that follow will highlight findings from that report, as well as sheds new light on recently acquired data.

What’s in a review?

It’s important to consider the potential implications of a review. Not surprisingly, customers who give the restaurant or c-store a rating of 4.9 or higher are most likely to return. While 1-star ratings may not be the most pleasant to receive, they’re not necessarily indicative of a customer’s behavior, as customers who leave 1-star ratings are as likely to return as those who leave a 2.4-star rating. Customers leaving a 1-star review may be acting in the heat of the moment or reacting without consideration, or may even be leaving simple reviews to speed through the process. Reviews with more nuance are typically written with more scrutiny.

The best indicator of whether a patron will return is if they received exemplary food, service, and value. Customers who give 5-star reviews in those particular categories are the most likely to revisit, and the three categories correlate with likelihood to return almost exactly.

However, as the ratings drop, value becomes more important than both food and service. At 1- and 2- star ratings, customers are more forgiving of poor food and service experiences than they are if they feel their dollar was not well-spent. Customers require high marks across the board to give an establishment a top rating, but they will be satisfied knowing they got their money’s worth even if the food and service are marginal.

Saving a customer relationship

A poor review should be viewed as an invitation, not a dismissal. Our data indicates restaurant and c-store operators can provide positive guest experiences in the wake of a bad review. To salvage the guest relationship after a less-than-stellar review, offering a coupon may be the best move a restaurant or c-store can make. Not only does it improve the chance a guest will return, but the marketing investment for this type of incentive makes sense. Time is of the essence when a customer has a poor experience. The Paytronix FEEDback^SM tool relays customer reviews to the store manager immediately. The store manager can then change a quality control issue and send a guest recovery coupon and message right away. By implementing a guest recovery coupon strategy for online ordering, restaurants and c-stores can win back up to 13% of customers who left a 1-star review.

Moreover, the coupon results in an average increase in Customer Lifetime Value (CLV) of $9.20 – a 4x return – meaning that as long as a restaurant or c-store’s profit margins are greater than 25% (as we know from experience most are), then the coupon is more than worth its cost.

No one wants to see a 1-star review. But with a planned coupon strategy and the right technology to put control in the front-line manager’s hands, brands can flip the script and help turn negative reviews into profitable customers.