Review Data and Statistics

At what rating threshold do customers stop clicking? Research shows most platforms filter at 4.0, and businesses below face a steep visibility cliff.

We analyzed thousands of review replies to find the overused words review replies that dilute your message. Learn what works instead.

Your first 10 reviews new business carries disproportionate mathematical weight. One outlier can sink your average for months. Here’s the cold-start math.

We analyzed 14,000 reviews to map weekday vs weekend reviews patterns. Discover when customers write, what sentiment shows up, and how to time your responses.

Photo reviews earn 35% more clicks and signal authenticity. We measured reviews with photos impact on trust, CTR, and local ranking for restaurants and dental practices.

Review recency weight affects both Google ranking and conversion. We break down how fresh reviews work, what the data shows, and why velocity matters.

A healthy star rating distribution follows a J-curve: heavy 5-star skew, minimal 2-3 stars. Learn the benchmark bands and warning signals.

Analysis of review length sentiment correlation shows longer reviews aren’t always negative. What the data reveals about word count and star ratings.

The yelp vs google rating difference isn’t coincidence. We explain why the same business earns 3.5 stars on Yelp and 4.2 on Google—and what it means for you.

Google subratings break down your overall score into Food, Service, Value, and Atmosphere. Learn how they work, what customers see, and how to use them.