One major challenge restaurants and convenience stores still face in 2024 is providing personalized and memorable experiences for off-premises guests.
Fortunately, advanced personalization tools that collect data from first-party ordering platforms make it easier than ever to make off-premises experiences feel like in-person ones. These tools can drive order frequency by as much as 30 percent, win back dissatisfied guests, and help brands get more out of their marketing budgets.
Personalization begins and ends with data. It's about understanding your customer base through data insights, and leveraging those learnings to drive deeper engagement. Transforming your numbers into insights ensures your marketing efforts and other aspects of the guest experience resonate with guests on a personal level.
Segmentation is the key to enabling personalization. Segmentation means dividing guests by certain actionable factors. Typically, guest segments are based on a single factor, like visit frequency, average spend, or commonly ordered items. This approach has served the industry well, enabling marketers to run simple campaigns based on guests' visit frequency or sales history. But in today's guest engagement landscape, marketers have access to more holistic metrics like total visits, average check size, average days between visits, and likelihood to lapse. These metrics enable marketers to be more exacting with their promotions, increasing ROI.
AI makes it easier for marketers to execute personalized campaigns. With AI, operators can instantly segment guests based on a wide range of proven and predicted behaviors. The result is a series of actionable segments that can help marketers motivate desired actions through timely, personalized, and relevant messages. Personalizing guest relationships drives repeat business and gives guests more reasons to develop a natural affinity for your brand.
At Paytronix, we’ve been tracking three data- and insight-driven personalization techniques that are proving to be best practices for deepening guest relationships. These include loyalty integration, menu engineering, and automated surveys.
According to Restaurant Dive, data-driven personalization will be key to restaurant loyalty programs in 2024. But emotional connections to brands are dropping, according to a recent report from Salesforce, which found rates dropped from 62% in 2022 to 54% in 2023. Operators must do all they can today to keep their best guests happy.
Loyalty is a great way to maintain guest satisfaction and engagement. Integrating your loyalty program with online ordering can also increase order frequency by 18-30%. Leveraging guest behavior data and using AI and machine learning to create personalized marketing campaigns, operators can get the right messages to the right guests at the right times.
Smashburger, for example—a Denver-based burger chain with more than 227 stores across the US—personalized its loyalty program by “micro-segmenting” its customer base.
“Instead of just blasting guests with communications screaming about the Smash Club program, we … tailor communications to what guests enjoy,” said Loyalty Manager Lexi Ryan. “Offers are tied to core behavior so that guests get what they love.”
With its data-driven and AI-powered campaigns designed to boost order frequency, Smashburger developed a far more profitable campaign structure, while providing rewards that are statistically likely to resonate with guests.
Almost everyone is accustomed to getting recommendations when they’re shopping online or deciding what movies to watch. Your guests expect the same treatment when figuring out what to eat.
To personalize the online ordering experience, operators can leverage data about guests’ previous orders to display evident favorites, along with additional items that complement their regular choices. Known as menu clustering, this popular AI-powered activity turns upsell opportunities into real revenue.
Menu clustering identifies items that are commonly bought together, such as fuel and coffee at a convenience store, or rice bowls and lettuce wraps at a Chinese restaurant. Then the online ordering feature issues recommendations for a customer to consider before checking out.
Segmenting guests based on what menu items they typically bundle together allows marketers to act strategically. They can choose an item or category they’d like to grow—such as a new burger, or milkshakes—and pair it with a guests’ favorite item to sweeten the deal.
Another burger brand used menu clustering to market a new, limited-time burger to guests based on their behaviors around four key food segments—meals, fries, shakes, and vegetable-based items.
The marketing team launched four unique campaigns and provided guests with uniquely relevant offers. The operator achieved a 61x ROI with this approach, and avoided missteps that might have alienated guests, such as offering a discount on a beef burger to a vegetarian. Most important, menu clustering helped the brand achieve the following objectives around the new offering:
Menu-variant testing, also known as menu analysis or menu engineering, is another technique that can help you tailor your online ordering menu for individual guests. It’s a great way to optimize menu design for profitability and gauge the reception of new menu items, and the more you know about your guests, the more informed your menu variant testing will be.
One of the best things about menu variant testing is that you can simultaneously test an infinite number of menu variations to as many guest segments as you want, and the AI will tell you which of your tests are the most profitable.
Let’s say you operate a restaurant where pizza is a big draw. With menu variant testing, you can try one menu that puts pizza front-and-center, so guests don’t have to scroll to find it. You could also try putting pizza behind your appetizers to encourage guests to add other items to their order before they get to the pizza. And you could create another, simplified menu that only features items a guest normally buys. No matter what creative ideas you come up with, the system will tell you which are the most effective.
The goal of menu variant testing is to remove items that are unpopular and unprofitable. Popularity is determined by the number of sales, and profitability is determined by comparing an item’s contribution margin (selling price minus food cost) to the average contribution margin of all items. For items that are popular but unprofitable, operators may try to increase their contribution margins without impacting sales.
According to Nation’s Restaurant News, only 43% of operators obtain data from guest surveys that request feedback on order quality, temperature, timeliness, and other characteristics.
While many guests don’t respond to surveys, those who do provide valuable information you can use to improve the overall guest experience. Lots of guests appreciate when you ask them how their order was and if there are any ways you can improve—especially when there’s an incentive to get a special deal on a future visit for completing the survey.
Operators use surveys to gather insights, build richer guest profiles, and make improvements to keep guests happy, but surveys can be difficult to deploy and manage. Thanks to new AI-powered tools, however, surveys and follow-ups can be automated. This newfound flexibility is one of the reasons surveys are a big personalization trend for 2024.
If a guest tells you in a survey they had a great experience, you can send them an AI-generated thank-you with an offer attached. And if another guest was dissatisfied with their order, you can send an automatic apology with a special deal for giving you a second chance.
Operators who send sincere apologies benefit from significant service recovery, with apology recipients returning more frequently and leaving more positive feedback after future visits.
Personalized online ordering is becoming increasingly important for restaurants and c-stores in 2024, but most operators aren’t using data as wisely as they should be. Nation’s Restaurant News reported that 7 out of 10 operators question whether they're optimizing their guest data.
By leveraging advanced tools that collect information and use AI and machine learning to create personalized marketing campaigns, rewards, and menu options, operators can increase order frequency, anticipate future needs, and keep customers who may have had a negative experience.
Techniques such as loyalty program integration, menu engineering, and automated surveys are proven techniques for deepening guest relationships. By showing guests you understand their needs and desires, you’ll be able to drive more revenue from happier guests who maintain an emotional connection to your brand.