When do you listen to online reviews?

We’ve been working with Cafecito, a local coffee shop, for a little over three months. Revenue is up 50%, profit is up over 100%, and the owners have more time to spend with their family. 

When we first partnered with them, the owners were stuck in a cycle of constantly reacting to every new review. Every week, they’d read dozens, and each week, they’d make changes: tweaking the menu, adjusting hours, retraining staff. It was exhausting, and often, the results didn’t follow. It’s absolutely important to listen to your customers, but it can be hard to distinguish between genuine feedback and someone having a bad day.

Instead of treating every review as equally important, we turned them into data points that were easy to compare. Using natural language processing (NLP), we assigned sentiment scores, a number indicating how positive or negative the content was, and subjects to each review and linked these directly to sales. We found that a few complaints about wait times weren’t worth significant changes, but a sustained dip in sentiment around drink quality was an early sign of a coming revenue decline.

By focusing on the signals that mattered, Cafecito stopped reacting to noise and started acting on the real feedback. Reviews improved, sales rose, and the team gained clarity on where to put their energy.

Not every business is ready to jump into NLP. You can recreate the same idea manually. Collect your recent reviews in a spreadsheet and tag each one by tone (positive, neutral, or negative), and by topic, like “service,” “pricing,” or “menu.” Then watch for trends. Even without coding, you’ll start to see the difference between passing opinions and feedback that signals a deeper issue.

The biggest lesson: when you treat reviews as anecdotes, they control your life. When you treat them as data, they guide you.