This is the most basic report you'll want in your bag of tricks that tells you everything from a collection and overall STORE rating standpoint. This templates puts together a few recipes that tell a story of review acquisition. If you're looking for product review performace - check out the other templates around product reviews!
This report is made of two Insight Blocks -
The visual breakdown of 1–5 star ratings lets us immediately understand how customers are feeling without digging through individual reviews. It serves as a sentiment dashboard—a high-level metric we can monitor over time to catch red flags early or celebrate wins.
For your leadership team, this structure makes it easy to digest at a glance and builds a shared language for evaluating performance. It fuels faster stand-ups, more focused meetings, and sharper strategic priorities.
One of the most powerful components is the time-based ratings distribution table. It includes:
• Star counts across time ranges
• Total and negative review volume
• Percent negative
• Average rating
• Ratings standard deviation
This structured data transforms one-off customer opinions into trendlines. The inclusion of metrics like standard deviation is key—it tells us not just how we’re rated, but how consistent those ratings are. That helps us differentiate between one-time issues and systemic problems.
The “percent negative” field helps normalize review trends even if volume fluctuates—critical for seasonal businesses or periods of rapid growth.
Tags like Customer Service, Shipping, or Pricing provide a qualitative layer that’s easy to quantify. This kind of taxonomy helps us map reviews to operational categories and gives teams direct insight into what customers care about most.
For product, ops, and support teams, this structured tag data is gold. Instead of reading every review, you can track recurring themes and measure if changes you implement are having an effect.
Understanding how reviews are acquired—whether via Instant, Direct, or Accelerated channels—gives us clarity on how well our review collection processes are working. This attribution data allows us to double down on channels that produce high-quality, high-conversion reviews, which are essential for both SEO and trust-building.
