Customer Review Analysis Template (Free for Excel & Google Sheets)

A good review analysis template turns a pile of reviews into decisions. Without one, review reading is anecdotal: you remember the angriest comment, not the most common one. With one, every review becomes a row with a category, a severity, and an owner - and suddenly you can answer questions like "what is our number one complaint this quarter?" with a formula instead of a feeling. Below is the exact column structure and scoring system we use, free to copy into any spreadsheet.

The Review Analysis Template: 12 Columns

Create a sheet with these columns, in this order. The first five come straight from your export; the remaining seven are the analysis you add.

• • Date - when the review was posted, so you can trend everything by month.
• • Rating - the star rating as a plain number, ready for averages.
• • Platform - G2, Trustpilot, Amazon, and so on; complaints often differ by platform.
• • Reviewer segment - company size, plan, or user type when the platform shows it; a churn signal from enterprise reviewers means something different than one from free users.
• • Review text - the full verbatim text, never a summary, so quotes stay quotable.
• • Category - the one primary theme, chosen from the taxonomy below.
• • Sub-theme - a freer, more specific label ("checkout crash", "CSV import") that keeps nuance without exploding the category list.
• • Sentiment - Positive, Neutral, or Negative based on the text, because 4-star reviews often contain serious complaints.
• • Severity (1-3) - 1 is a minor annoyance, 2 blocks a workflow, 3 is a deal-breaker or churn reason; this is what separates loud from important.
• • Feature request (Y/N) - a simple flag so product can filter straight to the asks.
• • Competitor mentioned - the name of any rival referenced, which quietly builds a competitive intelligence dataset.
• • Action owner - the person or team who should act; a row without an owner is a complaint you have agreed to ignore.

Resist the urge to add more columns. Every extra field slows categorization down, and slow templates stop getting filled in. If a piece of information does not change a decision - who acts, what gets built, what gets escalated - it does not need a column.

A Starter Category Taxonomy

The Category column only works if the options are few and mutually exclusive. Start with these six, assign exactly one per review, and push anything more specific into Sub-theme:

Two rules keep the taxonomy honest. First, add an "Other" option - and treat it as a smoke alarm: if more than 10 percent of reviews land there, a real category is missing and it is time to add one. Second, review the taxonomy quarterly, not per review. Renaming categories mid-analysis breaks every COUNTIF and pivot downstream, so batch your changes.

How to Fill the Template Fast

Do not type reviews in by hand. Export them to CSV first with a Chrome extension - see our guides for G2, Trustpilot, Capterra, and Google Play, or browse all supported platforms. The export already gives you Date, Rating, Platform, and Review text as clean columns, so the data-entry half of the template fills itself. Then work newest-first through the analysis columns - most people can categorize 100-150 reviews per hour once the taxonomy is familiar.

A routine that speeds categorization up considerably: sort by rating and handle the 1-2 star reviews first, since they carry most of the severity-3 rows and deserve the freshest attention. Then batch similar reviews - once you have tagged three "checkout crash" complaints, the next one takes two seconds. Leave Sentiment for a second pass; flipping between "what category is this?" and "how do they feel?" on every row is what makes the work feel slow.

Add these formulas on a summary tab so the sheet reports on itself:

With the month helper in place, build a pivot table with months as rows, categories as columns, and a count of reviews as values. That single pivot answers "what is growing?" - the question raw review feeds never answer.

Turning the Sheet Into Charts

Three charts cover most stakeholder questions:

• • Pareto of complaint categories. A bar chart of negative reviews per category, sorted descending. In most products, two categories account for the bulk of complaints - this chart ends the "everything is on fire" debate.
• • Rating trend line. Average rating per month. A slow slide that is invisible day to day becomes obvious over six months.
• • Theme-by-month heatmap. Apply conditional formatting to the monthly pivot so growing themes literally darken over time.

For step-by-step chart and pivot instructions, see the full guide to analyzing exported reviews in Excel and Google Sheets.

When the Review Analysis Template Stops Scaling

Around 500+ reviews, manual categorization becomes the bottleneck: it takes hours per update, and category judgments drift between sessions and between teammates. At that point, keep the template as your reporting layer but let AI do the tagging. The Reviews Extractor AI analysis layer clusters full exports into themes and surfaces pain points, feature requests, and competitor mentions automatically, with exportable insights you can drop straight into a spreadsheet. It is part of the All Extensions + AI plan at $25/month on the pricing page - the same approach behind our analyses of 350+ HubSpot reviews and 1,020 Duolingo reviews.

Either way, the review analysis template above is the right place to start: it forces the discipline - one category, one severity, one owner per review - that makes any later automation trustworthy. For what to do with the findings, our guide to turning reviews into product feedback picks up where this template leaves off.

Frequently Asked Questions