Drive Customer Stickiness and Enhance the Bottom Line: The Power of LLMs and Generative AI in QSRs

In the fiercely competitive realm of Quick Service Restaurants (QSRs), the key to success lies in delivering exceptional customer experiences that create lasting impressions. Today's customers exhibit evolving preferences, demanding highly personalized interactions and showing reduced brand loyalty compared to previous generations. To gain a competitive edge and drive business growth, QSRs must seize the opportunities presented by cutting-edge technologies such as large language models (LLMs) and generative AI. 

By leveraging these transformative technologies, QSRs can revolutionize the customer experience, enhance operational efficiency and ultimately improve the bottom line. This article explores the substantial impact of LLMs and generative AI on QSRs, highlighting their ability to foster customer stickiness and drive financial success. 

Elevate customer experiences for stickiness 

In the modern landscape, QSR customers crave personalized experiences that cater to their unique preferences. By embracing LLMs and generative AI, QSRs can deliver seamless, tailored interactions that leave a lasting impact. These technologies utilize vast customer data to generate human-like language, responses and recommendations, elevating the customer journey. 

• Pizza Hut implemented LLM-powered chatbots (Pizza Hut Assistant) in 2016 that engage customers in conversational ordering experiences. These chatbots understand natural language and can provide personalized menu suggestions and dietary information and answer specific customer inquiries. This level of personalization creates a memorable experience, strengthening the customer's connection with the brand and fostering long-term loyalty.

Driving operational efficiency and cost reduction 

LLMs and generative AI offer QSRs more than just enhanced customer experiences; they also streamline operations and reduce costs. By leveraging these technologies, QSRs can automate manual processes, optimize order accuracy and enhance efficiency. McDonald's is a notable example of leveraging LLMs to improve operational efficiency. 

• Through self-order kiosks powered by LLMs, McDonald's customers can effortlessly place orders, customize their meals, and receive personalized recommendations based on their preferences and past orders. McDonald's accelerated this capability by acquiring AI company Apprente in 2019, which the chain has applied to its automated order-taking initiatives. This integration improves the customer experience, minimizes order errors, reduces labor costs and boosts operational productivity. With these automated processes in place, QSRs can serve more customers efficiently, leading to increased revenue generation and cost savings.

Grow revenue through personalization 

The power of LLMs and generative AI extends beyond operational efficiency, directly impacting revenue growth. These technologies enable QSRs to gather invaluable customer insights, creating opportunities for targeted marketing campaigns, personalized loyalty programs and innovative menu offerings. 

• Domino's Pizza exemplifies the potential for revenue growth through personalization. By leveraging LLMs and AI, Domino's implemented an advanced pizza delivery tracker in 2017 that provides real-time updates, estimated delivery times and personalized messages. This level of personalization deepens customer connections and drives repeat visits, positive reviews and word-of-mouth recommendations, ultimately increasing sales and bolstering the bottom line.
• Starbucks, a frontrunner in the QSR industry, has harnessed the potential of LLMs and AI to deliver personalized recommendations through its mobile app. Leveraging customer preferences, purchase history and location data, Starbucks suggests new beverages and offers customized promotions to individual customers. By analyzing past behavior and tailoring recommendations accordingly, Starbucks enhances the customer experience, increases order value and customer loyalty, and drives revenue growth.

Five Steps to Accelerate LLM adoption in Quick Service Restaurants 

Implementing LLMs and generative AI into a QSR business may seem daunting, but the potential rewards make it a worthwhile endeavor. Here are some steps QSRs can take to get started: 

1. Identify customer touchpoints: Determine the critical areas of customer interaction within your QSR, such as ordering, customization and delivery. These touchpoints will serve as the foundation for implementing LLMs and generative AI.
2. Data collection and analysis: Invest in systems and processes to collect and analyze customer data. This data will drive personalized experiences and inform targeted marketing efforts. Companies like Starbucks have successfully implemented customer data analytics to create customized offers and recommendations, increasing customer engagement and loyalty.
3. Implement chatbot technology: Integrate LLM-powered chatbots into your ordering systems to provide personalized recommendations, answer customer queries and facilitate smooth ordering experiences. This approach, as demonstrated by Pizza Hut's chatbot implementation, enhances customer engagement and strengthens brand loyalty.
4. Optimize operational efficiency: Explore the integration of LLMs into self-order kiosks or mobile apps to streamline ordering processes. McDonald's self-order kiosks, powered by LLMs, not only improve order accuracy but also offer personalized suggestions, resulting in increased customer satisfaction and operational efficiency.
5. Personalize marketing and loyalty programs: Leverage LLMs and generative AI to create targeted marketing campaigns and personalized loyalty programs. Domino's Pizza's use of LLMs in their delivery tracker is a prime example of personalized messaging that drives revenue growth by creating a unique and engaging customer experience.

QSRs can harness the power of LLMs and generative AI by following these steps and continuously adapting to customer needs. 

The risks to retail in failing to embrace AI

Retail chains that hesitate to adopt LLMs and generative AI face significant risks in an increasingly competitive market. Failing to meet the demand for personalized experiences can lead to customer attrition, decreased loyalty and revenue loss.  Arguably, this happens fastest in the highly-competitive QSR industry where brands vie for consumers' daily spend.

When retailers hesitate to leverage LLMs and AI for personalized marketing, customers receive generic promotions and offers that don't align with their preferences. This is a missed opportunity that results in disengagement and risks losing customers to competitors who provide more tailored experiences. Further, without a holistic data and AI strategy, retailers struggle to gather and analyze customer data effectively, missing out on opportunities for targeted marketing and optimized promotions. For QSRs specifically, it's time to leverage customer data to innovate product and service offers and customize their menus to individual customer preferences. If not, they risk flat or shrinking revenue and diminished brand relevance. 

Summary 

Adopting LLMs and Generative AI is not merely a technological trend but a strategic imperative for QSRs. By leveraging these powerful technologies, QSRs can deliver highly personalized experiences. From Pizza Hut's conversational chatbots to McDonald's self-order kiosks and Domino's personalized delivery tracker, industry leaders have showcased the transformative impact of LLMs on the customer experience. QSR executives must recognize these technologies' potential to shape their businesses' future, seizing the opportunities they present to cultivate long-lasting customer relationships, enhance operational efficiency and drive revenue growth in the competitive QSR landscape.