300 Reviews at 4.2 Beats 15 at 5.0 #shorts

Say there are two plumbers in the same town.

Same work. Same prices. Same twelve years of experience.

One has a 4.2 Rating while the other has a perfect 5.0 but on an average Tuesday the guy with the 4.2 receives more calls from customers.

How is that possible?

Think back five years ago, when you needed a service, you’d skim the list of service providers google gave and pick the one with the most stars.

But now, most people would rather ask ChatGPT to recommend a service provider.

The question is, would ChatGPT look for them the same way as you?

Research shows it doesn’t.

Review sites are among the first places AI will go looking but most of the time it can’t actually read what your customers wrote because it sits behind logins it can’t get through.

So it works from what it can see. How many. What average. And when the last one landed.

Which is why your star rating isn’t the thing you think it is.

Somebody tested this across a hundred and twenty thousand AI recommendations, five industries, every major model.

In four out of five, how many reviews you had predicted whether you got named better than how good they were.

Three hundred reviews at four point two beats fifteen at a perfect five.

And the other factor is the date.

A business whose newest review is from 2023 raises a question nobody can answer. Is this place still going?

Which means this was never a reputation problem.

It’s a rhythm problem.

Instead of asking for reviews when you remember to you need a system with the rhythm built into it. This is what my partners at HighLevel offer.

Every customer gets asked, automatically, a few days after the job.

Same message, every time, whether you remembered or not.

Follow and I’ll keep showing you what’s actually working.