A large restaurant chain was growing rapidly and along with the growth came a significant increase in the number of customer reviews received. The company preferred a centralized review management as this gave them great insights into issues and trends across locations but the small team was becoming overloaded with review volume. They were already employing a platform to automatically respond to no-comment reviews and used a series of templates as starting suggestions for personalized responses. But as the review volume doubled year-over-year, the small response team needed a more capable solution without sacrificing review response quality.

LocalClarity has been at the forefront of incorporating the growing capabilities of generative AI, with the AI-powered review response suggestions added to the platform in March of 2023. Was saw this challenge as an excellent opportunity to combine the best parts of our Intelligent Auto Response campaigns with customized response suggestions, while leaving humans at the ultimate gatekeepers.

An entirely new workflow was created so that each incoming review automatically received a fully-informed review response that incorporated details on the location, reviewer, rating, comments, and additional custom details on the brand’s unique details and tone of voice. These response suggestions are then dropped into an Approval Box for moderation. The corporate response team simply needed to evaluate the proposed response and either click send or append a final snippet.

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The new workflow allowed the team to answer 105% more reviews per session compared to their prior, template driven processes. After a few edits to the custom brand details (background and tone-of-voice), the initial response suggestions we accepted without edits the majority of the time, helping the team achieve previously impossible levels of efficiency without sacrificing quality.

Moving the AI-powered review response suggestions directly into a streamlined approval workflow enhanced the restaurant chain’s ability to manage its rapidly growing review volume while also significantly boosting response team efficiency.  Manual workload was reduced while maintaining strong customer engagement at scale.