The Challenge

Guest feedback was spread across multiple review channels, making it difficult to maintain a complete view of what guests were saying.

Teams had to visit different platforms, read reviews individually and manually identify recurring issues.

For a hotel or multi-property operation, the problem becomes even larger as the volume of reviews grows.

Important patterns can easily disappear inside hundreds of individual comments, while management has no simple way to track whether recurring problems are improving or getting worse.

Our Approach

We looked at guest reviews as an operational data source rather than just individual comments.

The application was designed to automatically retrieve new reviews from multiple channels every day and store them in one structured environment.

AI is then used to analyse the review content and turn unstructured guest comments into information that can be tracked and compared over time.

This creates a consistent feedback model regardless of which channel the original review came from.

The Solution

We built a custom application that automatically checks connected review channels every day and retrieves new guest feedback.

The reviews are consolidated into one system and processed using AI to identify and organise the information contained within them.

Instead of manually reading through different platforms, teams can review feedback in one place, track recurring themes and understand how guest sentiment develops across properties and over time.

The underlying data also creates a reporting layer that can be used to identify operational issues and support management decisions.