Skimle for product managers
Turn customer signals into product decisions
Product managers sit at the intersection of customer feedback and product direction. The challenge is never a shortage of data — App Store reviews, NPS open-ends, support tickets, customer discovery calls, win-loss interviews. The challenge is making sense of it systematically, without spending too much time or drowning in a sea of individual apps.
Skimle is a comprehensive, versatile and easy-to-use research tool that can access all types of text data (feedback, interviews, meeting transcripts etc.) and structure it into themes, and shows you exactly where each finding comes from. You can also collect data with Skimle Ask, our easy AI-assisted interview tool that is easy to embed to products. With Skimle, you can make decisions based on what customers actually said — not on what the loudest voice in the last product review happened to mention.
One tool for all your qualitative customer intelligence
Whether you are working through 2,000 App Store reviews, 500 NPS open-ends, or 30 in-depth customer discovery interviews, the analysis challenge is the same: too much text, too little time, too much risk of missing the pattern that matters. Skimle handles the volume systematically so you can focus on the interpretation.
- Analyse App Store reviews, NPS verbatims, and support tickets at scale
- Run AI-assisted customer discovery interviews without scheduling bottlenecks
- Synthesise findings from user interviews, win-loss calls, and stakeholder sessions
- Segment analysis by user type, platform, rating, geography, or any metadata variable
- Full traceability — every theme links to the exact customer quote
- GDPR-compliant EU storage, no data used to train AI models
TURN THOUSANDS OF CUSTOMER SIGNALS INTO CLEAR PRIORITIES
Gather and analyse customer comments, App Store reviews and NPS verbatims
Thousands of App Store reviews, NPS open-ends, and support tickets contain real signal — but reading through them manually is not realistic, and word-cloud tools lose the nuance that drives product decisions. Skimle reads every item systematically and gives you a structured theme hierarchy, each theme linked to the actual customer quotes, filterable by rating, platform, or time period.
How it works
- 1. Import the data
Upload CSV exports from your App Store, NPS tool, or support platform. Skimle accepts text, CSV, PDF, and Word — mix sources freely in one project.
- 2. Define what you are looking for
Focus the analysis around your current product questions — feature gaps, churn signals, onboarding friction. Or run an open-ended analysis and let themes emerge.
- 3. Review the theme structure
Skimle builds a hierarchy of themes across all responses, each with a count of supporting mentions and the actual customer quotes. Nothing is summarised away.
- 4. Segment by metadata
Filter themes by rating, platform, user type, or time period to understand which problems affect which users most acutely.
- 5. Export for your roadmap
Generate a structured report in Word or PowerPoint — customer evidence ready to drop into your next product review or board update.
CUSTOMER INSIGHT AT SCALE WITHOUT THE HIRING PLAN
Run continuous customer discovery without scaling your team
Continuous discovery makes sense in principle, but scheduling, conducting, and synthesising interviews at pace demands a research team most product organisations do not have. Skimle Ask lets you run AI-powered interviews at scale — customers respond in their own time, the AI probes for depth, and Skimle structures the findings as responses arrive.
How it works
- 1. Design your interview
Write your core discovery questions and set the context. Skimle Ask guides each respondent through a natural conversation, probing for depth on the answers that warrant it.
- 2. Embed or share the link
Add the interview link to your app, onboarding flow, churn survey, or support follow-up email. Respondents complete it when it suits them.
- 3. AI conducts the conversations
Each customer gets a personalised conversation. The AI handles follow-up questions, keeps the conversation on track, and captures nuance that closed surveys miss.
- 4. Responses are analysed automatically
As responses arrive, Skimle structures them into themes. You have a running picture of what customers are saying — without waiting for an analysis sprint.
- 5. Iterate on your questions
Update the interview guide as your product questions evolve. Export findings on a weekly or monthly cadence to feed into planning cycles.
ONE KNOWLEDGE BASE ACROSS ALL YOUR QUAL RESEARCH
Pull together findings from in-depth user, win-loss and stakeholder interviews
Most product teams accumulate qualitative research in silos — user interviews from last quarter, win-loss calls, stakeholder sessions — each in a separate folder, each invisible to the next project. Skimle lets you combine all of it into one knowledge base, identifies patterns across the full corpus, and keeps institutional knowledge in the organisation rather than in a researcher's head.
How it works
- 1. Import existing research
Upload transcripts from past studies alongside new ones. Skimle handles mixed formats — notes, transcripts, summaries — in a single project.
- 2. Organise by research programme
Tag documents by study type, date, or user segment using metadata. This lets you filter and compare findings across different research programmes.
- 3. Identify cross-cutting patterns
Skimle builds themes across the full corpus — surfacing what is consistent across user research, win-loss data, and stakeholder input, and what contradicts.
- 4. Explore what has changed
Compare how themes distribute across time periods or user cohorts. Track whether a problem is getting better or worse as you ship fixes.
- 5. Share findings with the team
Export a synthesis report that gives the whole product team access to the evidence base — not just the people who ran the research.
FAQ
Frequently asked questions
- How do I import App Store or Google Play reviews?
- Export your reviews as a CSV from App Store Connect, Google Play Console, or a third-party tool like AppFollow or Sensor Tower, then upload directly to Skimle. Skimle reads the review text and any metadata columns — rating, date, country — and makes them available for filtering and segmentation.
- Can Skimle analyse reviews in multiple languages?
- Yes. Skimle supports 100+ languages and can analyse reviews from global markets in a single project without translation. Themes are unified across languages, so you can see whether a usability complaint in German reviews also appears in English and Japanese ones.
- How is Skimle Ask different from a survey tool?
- Survey tools ask fixed questions and collect structured responses. Skimle Ask conducts a conversation — asking follow-up questions based on what the respondent says, the way a skilled interviewer would. You get the depth of a qualitative interview at the scale of a survey.
- Can I use Skimle for ongoing research rather than one-off projects?
- Yes. You can update a project with new responses as they arrive and re-run the analysis to update the theme structure. This makes Skimle well suited to continuous discovery programmes where you are collecting customer feedback on an ongoing basis.
- How does Skimle handle sensitive customer data?
- All data is stored on EU-based servers and never used to train AI models. Skimle is GDPR-compliant and provides Data Processing Agreements for organisations that require them. No customer data is shared with third parties.
- How quickly can I get results from a large dataset?
- A set of 500 NPS comments or App Store reviews is typically processed in under an hour. A set of 30 to 50 interview transcripts takes 1 to 2 hours. You upload, start the analysis, and come back to a structured theme view — no manual work required.
About Skimle
Built for professionals, by professionals
Skimle is based in Finland and built by people who understand both the research rigour that qualitative analysis demands and the pace that product development requires. We are trusted by researchers at over 30 universities, Finnish government ministries, consulting firms, and growing product teams. All data is stored within the EU with full GDPR compliance.
All data is stored within the EU and processed according to our strict GDPR policy and terms of service.
Want to learn more? Explore our Signal & Noise blog, our FAQ, and use cases by sector. For enterprise enquiries or to discuss a team plan, get in touch.