AI-Driven Hypothesis Testing & Validation in Quick-Service Restaurants
In a competitive retail market where consumer preferences shift rapidly, brands must efficiently gather and analyze customer feedback to drive decision-making. Our client, a leading quick-service restaurant chain, wanted to better understand customer-identified areas for improvement within a fast-growing and highly competitive product category. Customer feedback was essential for guiding product innovation and refining marketing strategies. To generate both in-depth and broadly representative findings, we needed a research methodology capable of capturing rich consumer insights at scale across thousands of respondents.
KS&R leveraged Amplify, our AI-driven research platform, to enhance our ability to test and validate business hypotheses through automated, large-scale analysis. The Amplify solution enabled us to uncover deeper insight and accelerate time to insight in three key areas:
1. Hypothesis testing with AI-generated follow-ups: An open-ended question with AI-generated follow-ups was integrated into an online survey of 6,000 consumers. The AI probed the client’s key hypotheses only when relevant topics emerged, keeping feedback focused and unbiased. Initial consumer responses to open-ended questions are often brief and lacking in detail. Follow-ups prompted richer, more detailed responses – especially in areas the client cared about most.
2. Accelerating structured insights: Feedback was consolidated and processed using a secure large language model (LLM) to quickly analyze and categorize open-ended responses. The LLM identified key themes and supported the development of a structured coding framework. Human oversight ensured alignment with research objectives. Once finalized, the LLM coded each response into relevant categories, reducing a task that would take days into hours. Research-on-research showed that AI-generated coding was more consistent and reduced human subjectivity compared to traditional methods.
3. Building a complete picture of customer feedback: Responses were filtered by code, and AI-assisted qualitative summaries were generated to provide a more complete understanding of consumer feedback within each category. Summaries were ranked by the number of mentions, offering a clear sense of priority and actionability.
Hypotheses were either validated with actionable details or deprioritized based on customer feedback. The Amplify solution delivered faster, deeper insights, providing a holistic view of consumer sentiment along with key segment-level findings. By augmenting data processing with AI, time to insights was accelerated by several days. This allowed the client to efficiently identify trends and gaps within their product category, driving smarter product innovations and more targeted marketing communications.
