Chick-fil-A has a landing page that consolidates their FAQs to help customers troubleshoot various issues. However, the problem with these pages is that the FAQ categories and content are filled with Chick-fil-A specific jargon, making it difficult for users to navigate and find relevant solutions. By the time customers reach the point of calling, they’re already frustrated, which makes it harder to retain them.
Historical site data revealed that 64% of users dropped off on specific FAQ pages and web forms, with 35% either leaving the site or choosing to call an agent. Through user research, I discovered that most users prefer to find answers on their own, but they’re often confused by the internal CFA language that doesn’t align with how they would typically describe their issues.
The CARES support form is slow, and the traditional search function is limited to Chick-fil-A-specific keywords, which makes it harder for users to find answers. Implementing NLM (Natural Language Processing) can bridge this gap by helping users solve their problems independently and more effectively.
Research and Insights
I conducted user research across across 7 general fast food customers who has submitted an issue with their order in the past month. Coming out of that, majority users said they didn’t want to call in and said the’d rather solve issues on their own.
I brainstormed several ways to leverage NLM as part of the holistic experience. Key feature updates included:
