Loyalty Trends 2024: Data and Analytics Drive Performance

Key Takeaway: Leveraging advanced data analytics, automation, and artificial intelligence is vital for understanding guests, delivering more effective loyalty experiences, and maximizing customer lifetime value. 

In today's hyper-competitive restaurant and convenience store landscape, where 2,286 new convenience stores and 53,800 new restaurants open annually in the US, harnessing the power of data and automation is critical for creating and supporting a successful loyalty program. A recognized leader in data-driven loyalty marketing—Starbucks—attributed 57% of all US sales in 2023 to Rewards members, according to eMarketer.

Unfortunately, most restaurants are not fully leveraging the customer data they collect to drive such performance. According to the Nation’s Restaurant News (NRN) 2024 Restaurant Technology Outlook, only 20% of operators say they “definitely” optimize their customer data, while another 25% “probably” optimize it: 

Operators need expertise in capturing, aggregating, and analyzing data to strengthen restaurant and convenience store loyalty programs by creating personalized loyalty experiences. 

It’s time for operators to move beyond a name, email address, and birthdate for each guest. Today’s best loyalty programs use real-time transactional and behavioral data to create guest experiences that increase visits, sales, and customer lifetime value (CLV). Examples include using a guest’s location to promote a new offer and in-app nudges to complete a purchase when an abandoned cart seems imminent. 

Expectations for seamless customer experiences in every industry have been changed forever by data-driven category leaders and category creators like Starbucks, Amazon, Uber, Spotify, Netflix, Tesla, Apple, and others. One-click purchasing, free OS upgrades, and personalized playlists are just some of the ways organizations have raised customer experience expectations, showing they understand what individual customers want and need to feel appreciated. 

To replicate this level of personalization in hospitality and retail, restaurants and convenience stores must recognize the value of guest data for building loyalty programs that stand out. 

Power Your Loyalty Program with First-Party Data 

First-party data is the information you collect directly from guests who come into your stores, order from you online, engage with your marketing materials, participate in your loyalty program, and use your mobile app. First-party data is directly under your control, and it’s becoming more important than ever as third-party cookies crumble.

Every guest interaction generates data you can use to develop and continually improve your loyalty initiatives. By tracking each guest’s order history, frequency, spending, marketing engagement, mobile app usage, and behavior around points and rewards, you can lay a solid foundation for personalizing the entire loyalty experience. 

For example, by analyzing order histories, a guest who regularly orders salads could automatically earn bonus points for healthy choices or receive offers for new veggie-forward menu items. A busy parent who only visits on weekends may appreciate surprise rewards throughout the week. 

As Forbes notes, “With accurate, detailed customer profiles, brands can create highly personalized marketing campaigns that cater to individual preferences and needs. Personalized marketing drives higher engagement rates, conversions, and customer satisfaction.” 

Incorporate Third-Party Data to Improve Guest Engagement 

While first-party data provides the foundation for delivering personalized guest experiences, integrating third-party data can provide an extra level of context around guest ages, genders, lifestyles, interests, and life stages. This kind of data enrichment gives you new ways to understand and target your best guests for optimal loyalty participation. 

Hospitality Technology offers an example based on a recent collaboration between Paytronix and PYMNTS. The study found that college-educated adults and consumers who earn more than $100,000 annually exhibit the highest engagement rates with local restaurant loyalty programs, at 50%: 

“With data enrichment, you could identify who has a college education  

within your customer loyalty program and earns more than $100,000  

per year. Then you could target those customers more aggressively  

based on their online or in-store engagement with your brand, their  

purchase history, and milestones, such as birthdays.” 

Using third-party data can also help you segment guests by age, making it easier to create targeted messaging using language that resonates with different generations.  

How AI and Machine Learning Are Changing the Loyalty Game 

With unified guest data as a foundation, restaurant and convenience store operators can make better use of advanced artificial intelligence (AI) and machine learning (ML) tools that drive loyalty program performance, engagement, and spend. NRN’s 2024 Restaurant Technology Outlook shows that operators are hungry for AI’s advanced analytics capabilities: 37% of foodservice operators are currently using AI, while 35% are not currently using AI but want to incorporate it into their business:

Currently, only 17% of operators use AI specifically for loyalty. Fast-casual restaurants lead the pack at 25%, followed by quick-serve restaurants at 18% and casual dining restaurants at 16%.  

Demand for AI and ML is growing because the technology helps operators act on individual guest preferences and behaviors. Below are four examples of how AI and ML are supercharging restaurant and convenience store loyalty programs with automation:

1. Intelligent Guest Segmentation 

One of the biggest challenges loyalty programs face is effectively segmenting customers to deliver tailored messaging and relevant offers. This is where machine learning shines. AI-powered cluster analysis can sift through vast pools of customer data — purchase history, frequency, lifetime value, to name a few — to automatically surface ultra-granular micro-segments like customers who order on Fridays at 5:30 pm or those who buy a specific drink on Tuesdays. 

With these intelligent segments defined, brands can execute hyper-personalized campaigns tailored to each group's unique attitudes and mindsets. For example, an urban professional cluster might receive happy hour incentives and promotions for new premium menu items. Suburban family segments could get kid-friendly offers and meal subscription incentives. 

The possibilities expand even further when incorporating real-time signals like location and operational data. AI models can detect when high-value guests are nearby during non-peak hours and automatically deploy mobile push notifications. For loyalty members stuck in long lines, surprise offers for future redemption could salvage the experience. 

By leveraging machine learning to identify precise customer clusters and moments that matter most, operators can exponentially increase the relevance of every loyalty communication and experience. 

2. Predictive Churn Analytics 

Another powerful application of AI and ML for loyalty programs is utilizing predictive analytics to identify the risk of customer churn before it happens. Sophisticated models can be trained to analyze shifts in individual purchasing patterns and engagement metrics like visit frequency. 

By detecting leading indicators of lapsing behavior, for example, operators can automatically trigger 1:1 win-back campaigns aimed at retaining their most valuable loyalty members. If a guest who previously visited twice a month suddenly goes four or five weeks without a transaction, AI can prescribe a targeted offer or incentive customized specifically for their purchase preferences.  

3. Conversational Responses 

With the ubiquity of online reviews and social media, brands are under pressure to provide timely, personalized responses to customer feedback — both positive and negative. However, manually crafting personalized replies can become unsustainable as volumes increase. This is where conversational AI comes into play. 

By leveraging large language models (LLMs) and natural language processing (NLP), AI can automatically generate personalized, real-time responses to customer comments at scale. Whether it's issuing a sincere apology for a negative experience along with a make-good offer or doubling down on praise with a surprise that delights, AI allows brands to nurture guest relationships in an automated yet still personal way. 

Tailored interactions driven by conversational AI have been shown to significantly boost guest satisfaction and retention rates. Loyal customers feel truly heard, while lapsed or at-risk guests receive the attention needed to rebuild the relationship. 

4. Frictionless Guest Experiences 

Upselling and cross-selling are crucial revenue drivers, but making relevant recommendations for individual guests can be challenging. Machine learning is changing that by automatically clustering menu items based on purchase affinities. AI models can analyze each guest's order history and taste profile to intelligently surface the most appealing upshot items during every transaction. 

For loyalty programs, this translates into opportunities to showcase exclusive members-only menus or bonus point multipliers for trying new items aligned with preferences. According to eMarketer, 53.9% of consumers say brand recommendations make them feel known and appreciated. 

While personalization is critical for digital channels, innovative technologies are making it possible to identify loyal guests upon their physical arrival through capabilities like license plate recognition and geofencing. Using these tools, operators could automatically detect when a loyalty member enters the premises or pulls into the drive-thru lane. Once known, the member could get a tailored greeting or see their current rewards balance on a digital ordering screen.

By seamlessly blending AI's predictive abilities into every channel, operators create a frictionless path to drive incremental spend and keep guests coming back. 

The Paytronix AI Advantage 

Paytronix is at the forefront of helping restaurant and convenience store brands operationalize AI to drive loyalty program performance: 

• Uno Pizzeria & Grill's guests are 2x more likely to accept AI-powered product recommendations, lifting average monthly online orders by $125. Automated feedback responses are driving a 29% increase in future orders.
  

• Primanti Bros. lifted frequency by 90% and spend by 50% using AI to identify and re-engage lapsed guests with tailored win-back offers. Despite offering discounts of $5 to $10, the brand saw a substantial return on its campaign investment. 

• Smashburger achieved a 20% increase in spend and a 16% lift in visits by leveraging AI for loyalty program micro-segmentation. Personalized campaigns and offers tied to individual preferences and behaviors delivered a 61x ROI. 

As these examples demonstrate, investing in unified customer data, advanced analytics, and AI capabilities is powering the future of effective loyalty strategies. Unlocking these powerful technologies allows operators to understand their guests as individuals, automatically optimize interactions, and ultimately drive sustained engagement, spend, and maximum CLV. 

Leveraging advanced data and AI is no longer a luxury; it's a necessity for brands to remain competitive and deliver the level of personalized loyalty experiences today's consumers continue to expect. Operators who fail to prioritize these capabilities risk losing guests to brands that offer more relevant and meaningful connections. 

To learn more about how Paytronix can help you develop a high-performing, data-driven loyalty program, download our loyalty development guides for restaurants or convenience stores, or contact us today.