Signal & Noise blog

Practical ChatGPT prompts for qualitative data analysis — plus a clear-eyed account of where general-purpose AI falls short for rigorous qualitative work.

Most guidance on qualitative sample sizes is vague. Here is what the actual research on data saturation shows — and how to decide for your specific study.

NVivo costs over EUR 1000 per year and is rarely worth it for individual researchers. Here are the best free and affordable alternatives for PhD students doing qualitative analysis.

How to make qualitative research findings land with sceptical executives: structuring findings, using frequency language, and building the credibility trail that numbers-first audiences need.

A practical guide to user research synthesis: how to move from raw interview transcripts to structured findings and a stakeholder-ready narrative.

A practical guide to qualitative coding: what inductive, deductive, and abductive coding are, when to use each, and how AI tools are changing the process.

A practical guide to analysing 360-degree feedback: how to extract development priorities from qualitative comments and avoid the pitfalls of generic reports.

A practical guide to exit interview analysis: how to collect, code, and synthesise departure data to identify real attrition drivers and act on them.

How to extract competitive intelligence from customer interviews, NPS verbatims, and win-loss calls — and analyse it systematically with thematic analysis.

How AI is changing the synthesis of expert calls and customer reference interviews in commercial due diligence — and what it means for deal teams in practice.

A practical pre-publication checklist for qualitative research that used AI tools — covering documentation, traceability, transparency, and peer reviewer expectations.

Most research repositories become graveyards. Here is how to design one that stays current, surfaces relevant insights, and earns a place in real workflows.

A step-by-step guide to writing up thematic analysis findings — structuring your results section, reporting themes, and meeting peer reviewer standards.

How startups and scale-ups can run continuous customer research from discovery to churn using embedded AI interviews, without a dedicated research team.

Learn how to analyse employee survey results properly, from aggregate scores to open-text themes, and turn raw data into HR insights leadership will act on.

A methodologically grounded guide to AI-assisted thematic analysis: where AI accelerates the process, where it can't replace researcher judgement, and how to do it rigorously.

Learn how to import customer feedback CSVs into Skimle, set metadata fields, and let AI surface hidden themes by product, time period, and more.

NPS verbatim analysis reveals what the score never can. Learn how to turn open-text NPS comments into themes you can actually act on.

Win-loss analysis only works when you treat interviews as structured data. Learn the methodology for systematic theme discovery across your whole deal set.

Annual engagement surveys produce scores, not understanding. AI interviewers now make it possible to gather rich qualitative insights from hundreds of employees at the cost and speed of a survey.

App Store review analysis at scale reveals version-specific complaints, regional trends, and sentiment shifts that reading individual reviews never could.

How to use Skimle's manual editing tools to refine AI-generated codes, and how to export your full coding scheme to NVivo, MAXQDA, or ATLAS.ti via REFI-QDA.

Focus group transcript analysis requires a different approach to 1:1 interviews. Learn how to handle attribution, group dynamics, and dominant voices.

In this article we explain how to discover patterns in the data using Skimle's metadata features. Analyse differences in responses by time period, gender, type of organisation or any other variable