Three QSR personalization strategies to reduce customer churn

Quick-serve restaurant (QSR) customers are a fickle bunch. The average QSR loses 30 to 40 percent of its customers every year. Most consumers visit their favorite restaurant less than twice a month. These numbers present a challenge and an opportunity for marketers. Brands can quickly increase revenue by building effective win-back campaigns with 1:1 personalization across every digital channel. 

Marketing can help turn bad experiences into good ones and keep customers returning for more. To start, 40 percent of guests leave a QSR after a negative dining experience, even if they’ve been loyal to the brand for years. Almost 90 percent of diners are bothered by inconsistent experiences from store to store, and 57 percent are less likely to return as a result.

But by analyzing customer data, building churn prediction models, and infusing 1:1 personalization to every win-back campaign, marketers can quickly influence customers to return and enjoy their favorite foods no matter why they churned.

Here are three ways marketers can personalize their customer retention strategies.

Use loyalty data to predict when customers may churn

Marketers can understand a customer’s likelihood to churn based on that person’s order history changes in their order pattern. By analyzing changes in visit frequency, reduced order value, and shifts in typical ordering days and times, brands can better predict customer churn and create personalized win-back campaigns.

Marketers can also build churn models based on changes in customer profile which could signal waning interest. Time-of-day and day-of-week changes could signify that a customer is likely to churn. Brands can also measure average check reduction, switching from full meals to single items, and changes in order location to build predict retention concerns and create personalized win-back strategies.

Create data-driven winback campaigns

Winback campaigns cannot be generic. Sending a generic message after a customer hasn’t ordered in 30 days may not move the needle. Instead, marketers can infuse 1:1 personalization into progressive win-back campaigns based on churn risk and length of inactivity.

Say a young professional with a busy schedule usually grabs dinner on their way home from work. If they haven’t ordered in 30 days, marketers can send an app notification between 5:00 and 7:00 pm to coincide with the customer’s traditional ordering schedule. A simple “are you missing your favorite meals?” – paired images of the customer’s usual orders – could be effective, if the channel and time of send are personalized for the individual customer.

Marketers can build in incentives after 60 days of inactivity, offering discounts on bundles of the customer’s favorite items with a loyalty tracker that gamifies the experience. Every aspect of the email or notification can be personalized – marketers can use location data to display the closest location and even the weather if it encourages delivery. 

Use 1:1 personalization to overcome bad experiences

Personalization can play a pivotal role in winning back consumers who had negative experiences. Whenever a customer leaves a poor review or returns an order using their mobile app, marketers need to act quickly to win them back. The first order of business is to apologize for the experience and include a survey for more detail on the customer’s experience. Acknowledging the mistake and giving customers a place to leave detailed comments could go a long way in rectifying the experience.

Marketers can then create automatic drip campaigns enhanced with loyalty rewards, images of their favorite food, and the closest location to encourage a new, more positive dining experience. By optimizing each email or app notification to the diner’s preferences, like their 

favorite daypart and the day and time they are most likely to engage with the brand, brands can help soothe hurt feelings after a less-than-optimal experience.

1:1 personalization with AI decisioning 

As QSR marketers create 1:1 personalization strategies, they will inevitably wonder how AI can help achieve their goals. Customers expect their favorite restaurant to understand their needs across every channel. New tools like AI decisioning are bringing marketing into a new era, allowing brands to meet customers wherever they are with unique, individualized experiences no matter how they order. 

AI and machine learning (ML) have rapidly evolved as essential tools in a marketer’s arsenal. One common method of using AI models to personalize are so-called next best action (NBA) models, which combine predictive models and manual testing to predict a customer’s “next best action,” and encode the result as business rules. 

These “predictive models” have their limits. They’re slow, static, and can only find “winners for segments” – they cannot understand customer behavior at an individual level. AI decisioning agents rely on a different type of ML, reinforcement learning, which is best-fit for making 1:1 decisions. An AI decisioning agent chooses the best action to take for each individual, and then autonomously experiments and continuously learns from future customer actions. 

To learn more about AI decisioning, read our whitepaper on personalization trends for QSR marketers.