A Research Perspective: Using AI, ChatGPT, and more to Gain Franchise Insights

As you might expect of someone personally involved in franchise research, I’ve watched carefully how artificial intelligence tools like ChatGPT are transforming the way franchises can gain insights into their franchise operations. FRANdata has been offering such insights for years, and we have embraced these technologies to help our clients understand their businesses better, stay competitive, and drive growth. However, while AI offers numerous advantages, it also presents some challenges that we need to navigate carefully.

Advantages of AI tools

One of the most exciting benefits of AI tools is their ability to handle vast amounts of data. Traditional methods often struggle with the sheer volume and variety of data that franchises generate. AI can process this data seamlessly, integrating information from various sources to provide a comprehensive view of operations. For example, a hotel brand may use AI to analyze guest preferences and behaviors across its entire system, allowing the company to personalize guest experiences and enhance operational efficiency. That sort of global analysis would have been a major initiative a decade ago if it was possible at all.

What’s more, as promised by the hype, AI-driven data analysis is typically faster and more cost-effective than traditional methods. Automation reduces the need for extensive manual data processing, saving time and labor costs. AI tools can quickly identify patterns and trends, delivering insights that would take much longer to uncover using conventional approaches. 

AI can provide a deep understanding of customer sales patterns and store operations. By analyzing sales data and customer feedback, franchises can identify popular products, peak sales times, and customer preferences. 

Franchise operations can benefit from this analysis too. Support and compliance can monitor franchisee operations and ensure adherence to brand standards. AI’s ability to perform predictive analysis can allow these teams to foresee potential problems and address them proactively. 

AI tools can also help a franchise analyze competitive data, providing valuable insights into how other franchises in the industry are performing. This includes benchmarking performance against competitors, identifying market trends, and understanding competitive positioning. 

AI’s advanced capabilities, such as web scraping and social media analysis, can uncover new sources of information for this sort of comparison. By analyzing online reviews, social media posts, and competitor websites, AI tools provide a comprehensive view of the competitive landscape. This approach helps franchises stay informed about industry trends and customer sentiment.

Again, while this sort of analysis has been used before, now it is faster, cheaper, more comprehensive, and available as needed.

Challenges

Because of franchising’s unique business model, AI cannot completely understand it. There are some serious limitations to the analysis that these tools can do for a franchise. 

First and foremost is the difficulty of getting access to the data these tools need. Even if all FDDs were available to everyone, which they are not, we know that while the FDD provides some information, it’s often not detailed enough for in-depth analysis. Every franchise system has its unique qualities, and the data in an FDD often needs contextualization in order to truly understand what it is telling us. We invest a lot of time understanding the limitations and misinterpretations of franchise data. Further, the risk of AI hallucinations is real.

The major headlines that we all read about industry trends usually comes from gigantic research groups trying to analyze data (often incorrectly) from a handful of publicly traded brands, but the vast majority of franchises are privately held and do not publish extensive data that publicly traded companies are required to produce, limiting the availability of public information. Furthermore, public data only covers the franchisor and company units, ignoring all private company franchisees, something AI would struggle to understand. Finally, smaller brands have less of an online presence than larger brands, complicating web scraping. Without all this data, it is difficult for AI to produce an accurate and meaningful competitive and industry analysis. 

FRANdata has been actively using AI and learning its limitations. We realized long ago that primary research must be done to understand the complete picture of a franchise system and its peers. This includes conducting surveys, interviews, and field studies to gather firsthand information that complements AI-driven insights. 

AI tools offer significant advantages for franchises looking to gain deeper insights into their operations and competitive landscape. They are helping us to analyze large data sets, deliver real-time analysis, and offer cost-effective solutions for comprehensive data analysis. However, challenges of limited data (on franchisees in particular) and misleading and often bad information drive a necessary need for primary research to fully leverage AI’s potential. At FRANdata, we believe that by combining AI-driven insights with traditional research methods, franchise systems can enhance their operational efficiency, improve customer satisfaction, and maintain a competitive edge in the market.

As COO of FRANdata, Paul Wilbur is instrumental in building the research and consulting framework at FRANdata. He plays an integral role in the strategic development of FRANdata’s suite of franchise solutions. Nearly a 20-year veteran at the company, he is the franchise business model expert and plays a key role in fostering strategic advisory relationships with some of FRANdata’s biggest clients.