Use case: review analysis
Product Review Thematic Analysis with AI
Automatically extract themes from G2, Capterra, app store and Amazon reviews. Apercu groups reviews into AI-labeled themes with counts and examples.
By Apercu team · Last updated
Product review thematic analysis shows what customers consistently praise, criticize and request across hundreds or thousands of reviews. Apercu groups review text into AI-labeled themes with counts and examples, so your team can move from anecdotes to numbers.
Why product reviews are hard to analyze at scale
One review is easy to read. Hundreds are a different problem, and teams usually either read a small sample or leave the data untouched.
You cannot read them all. A product with 400 reviews on one platform already has more text than an analyst can process properly in a day.
Samples give you anecdotes. Reading 20 reviews tells you what a few people said. Analyzing all of them tells you whether an issue affects 8 percent or 32 percent of reviewers.
The signal is in the patterns. One review saying "onboarding is confusing" is a data point. Fifty saying it is a roadmap priority.
How to analyze product reviews with AI
- Export or collect your reviews in a spreadsheet. One review per row, with the review text in one column. You can combine several platforms in one file.
- Split positive and negative reviews. Use star ratings, or the pros and cons fields that sites like G2 and Capterra provide, and analyze each group separately. This shows what customers love and what they criticize without the two blending together.
- Upload to Apercu and add context. Confirm the suggested text column and optionally describe the data, for example "1 and 2 star reviews of a project management app". Context helps the AI write clearer theme labels.
- Review the AI-labeled themes. Each theme shows its label, how many reviews it contains, its percentage and real examples. Rename themes or move reviews if something is in the wrong group.
- Export or share. Download JSON on any plan, or CSV, Excel and a PDF report with a summary and recommendations on paid plans.
How to extract themes from G2, Capterra and app store reviews
Review sites structure their reviews differently. Setting up the file to match gives you sharper themes.
| Source | How reviews are structured | How to set up the analysis |
|---|---|---|
| G2 | Separate answers to "What do you like best?" and "What do you dislike?" | Keep likes and dislikes in separate columns and analyze each one |
| Capterra | Separate Pros and Cons fields plus an overall comment | Analyze Pros and Cons separately |
| App Store and Google Play | One review text with a star rating | Split by rating: 1 to 2 stars vs 4 to 5 stars |
| Amazon and Trustpilot | One review text with a star rating | Split by rating, and by product if you sell several |
Apercu analyzes one text column at a time, and the export lists each review with its theme. To compare platforms, products or releases, run a separate analysis for each segment and compare the theme percentages.
What product review analysis reveals
- What customers consistently praise: the 3 to 5 strengths that come up again and again, which make strong marketing and sales messages
- What customers consistently criticize: recurring friction points, ranked by how many reviewers mention them
- What competitors' customers want: run the same analysis on competitor reviews to find gaps and positioning angles
- How reviews change over time: analyze each quarter's reviews and check whether problem themes shrink after a fix
Teams that use Apercu for review analysis
- Product managers: prioritize the roadmap with counts instead of anecdotes
- Marketers: reuse the words customers use when they praise the product
- Competitive intelligence: find competitors' recurring weaknesses
- Founders: catch frustrations early, before they turn into churn
- UX researchers: add app store review data to usability findings
- Consultants: deliver structured review analysis to clients without days of reading
Related review analysis pages
- How to extract themes from G2 reviews: a full step-by-step guide
- App review analysis for App Store and Google Play reviews
- Customer feedback analysis for NPS comments, support tickets and interview notes
- Open-ended survey response analysis for qualitative survey answers
- Automated thematic analysis for a broader alternative to manual coding
Frequently asked questions
What is thematic analysis of product reviews?
It is the process of finding the recurring topics across many reviews, for example learning that 40 percent of negative reviews mention battery life or that 35 percent of positive reviews praise ease of setup. Apercu automates it with AI, so you do not have to read and tag every review.
How do I automatically extract themes from G2 reviews?
Put your G2 reviews in a spreadsheet with one review per row, keeping the 'What do you like best?' and 'What do you dislike?' answers in separate columns. Upload the file to Apercu and run one analysis on the likes column and one on the dislikes column. Each run returns AI-labeled themes with counts and example reviews.
Can Apercu analyze reviews from multiple sources at once?
Yes. Combine reviews from Amazon, Trustpilot, G2, Capterra, the App Store, Google Play or any other platform into one CSV or Excel file with the review text in a single column. To compare platforms with each other, run a separate analysis per platform.
How many product reviews can I analyze at once?
Up to 2,500 reviews per analysis on the free plan, 50,000 on the Analyst plan ($14.99/mo) and 100,000 on the Pro plan ($39.99/mo).
Does Apercu identify sentiment in product reviews?
No. Apercu groups reviews by what they are about, not by whether they are positive or negative. For the clearest results, split reviews by star rating or into pros and cons, and analyze each group separately.
Can I use Apercu to analyze competitor reviews?
Yes, if you have lawful access to the review data and the platform's terms allow your use. Competitor reviews often reveal recurring complaints, unmet needs and positioning opportunities.
How is Apercu different from reading reviews manually?
Reading and tagging 500 reviews by hand typically takes 4 to 8 hours. Apercu groups and labels them in minutes and applies the same method to every review, so the counts are consistent and easy to repeat next quarter.