> For the complete documentation index, see [llms.txt](https://ailyze.gitbook.io/ailyze-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ailyze.gitbook.io/ailyze-docs/common-research-workflows/analyze-product-reviews.md). # Analyze Product Reviews This page shows you how to turn product reviews — from app stores, marketplaces, and review sites — into a ranked view of what users love, what they want, and what is broken. {% hint style="info" %} **What you need:** the Free Plan or higher, plus your reviews as a spreadsheet (XLSX or CSV), or a web link you collect first. **What you will have at the end:** [themes](/ailyze-docs/reference/glossary.md#theme) split into complaints, requests, and praise, each with [frequencies](/ailyze-docs/reference/glossary.md#frequency) and quotes. {% endhint %} ## Step 1: Get the reviews into Evidano You have two starting points: * **You already have a review export (CSV or XLSX):** go straight to **"Analyze xlsx/csv files"**. * **The reviews are live on the web:** use **"Get Social Media Web Data"** to collect them first. Evidano can pull reviews from many popular stores and review sites — including Amazon, the Apple App Store, Google Play, Trustpilot, Tripadvisor, and Yelp, among dozens of others. Download the ZIP, then analyze the spreadsheet. See [Collect social media and web data](/ailyze-docs/collect-research-data/collect-social-media-and-web-data.md).
Choosing a review source to collect
## Step 2: Analyze for product decisions 1. Start **"Analyze xlsx/csv files"** and upload the reviews. 2. At **"Choose the type of Analysis"**, pick **"Analyze text responses in selected columns"** and select the **review-text** column. (If your export also has rating, version, or date columns, pick **"Analyze text, numerical, categorical, etc. survey data across all columns"** so you can segment by them.) 3. In **"Configure Column Types"**, tag useful columns under **"Segments"**: star-rating band, app version, country, or product variant. 4. Let Evidano generate [themes](/ailyze-docs/reference/glossary.md#theme) and add an instruction like *"Separate bugs, feature requests, praise, and pricing complaints."* 5. Click **"Create Project and Start Analysis"**. {% hint style="info" %} **Tip:** Segmenting by **rating band** (1–2 stars vs 4–5 stars) instantly contrasts what unhappy reviewers complain about with what fans love. {% endhint %} ## Step 3: Read your results * **Themes** split into complaints, requests, and praise, with [frequencies](/ailyze-docs/reference/glossary.md#frequency) to help you prioritize the roadmap. * **Quotes** for each theme — the direct customer evidence for specs and PRDs (product requirements documents). * **Segment comparisons** — for example, a bug that only shows up in a specific app **version**, or a complaint concentrated in one **country**. * **A coded data table** so you can pivot reviews by theme yourself.
Product-review themes split into complaints, requests, and praise
See [How to read your results](/ailyze-docs/analyze-data/how-to-read-your-results.md) for a tour of every panel. ## Step 4: Make it actionable * Click **"Chat to generate report or chart"** and ask: *"List the top 10 feature requests by frequency, each with a quote and the versions where they appear."* * **"Download as CSV"** to feed your roadmap tool, or **"Download as DOCX"** for a summary. * Track changes over time by re-collecting reviews periodically and comparing. ## A realistic example > **Scenario:** A mobile app with **8,000 store reviews**. You collect them with **"Get Social Media Web Data"**, analyze the review text, and segment by **rating** and **version**. You discover a crash theme spiking in v4.2 (1–2 stars) and a strong "dark mode" request across all versions — both quantified and quote-backed for the next sprint planning. ## Next steps * [Collect social media and web data](/ailyze-docs/collect-research-data/collect-social-media-and-web-data.md) * [Analyze open-ended survey responses](/ailyze-docs/common-research-workflows/analyze-open-ended-survey-responses.md) * [Compare groups and segments](/ailyze-docs/analyze-data/compare-groups-segments.md) * [How to read your results](/ailyze-docs/analyze-data/how-to-read-your-results.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://ailyze.gitbook.io/ailyze-docs/common-research-workflows/analyze-product-reviews.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.