Signal & Noise blog

Skimle's Agentic Chat lets researchers and AI agents work side by side on structured project data, with full source traceability and human control throughout.

How to analyse focus group data — from transcribing sessions to coding group dynamics, comparing across groups, and writing up. Includes a worked example.

How to analyse interview transcripts systematically — preparing data, coding approaches, building themes, and writing up. Covers manual and AI-assisted methods.

A complete walkthrough of using Skimle's Agentic Chat and MCP connections. Work together with smart agents to structure, analyse and visualise qualitative data. A practical approach to step to the fut...

A step-by-step guide to analysing customer interviews — from coding themes to synthesising insights — with practical examples for market researchers.

A step-by-step guide to analysing employee survey open-ended responses: from preparing and anonymising data to coding themes, segmenting findings, and presenting results.

Qualitative research for consultants operates under constraints that academic methods were never designed for. This guide covers tools, workflow, and best practices for consulting timelines.

How to pracrically anonymise interview transcripts in a business setting: replace identifiers, pseudonymise roles, and meet compliance requirements with Skimle Anonymise.

Step-by-step guide to anonymising and pseudonymising qualitative interview data for IRB, GDPR, and HIPAA compliance with Skimle Anonymise. Covers identifiers, audit trails, and methods documentation.

Upload audio or video to Skimle, select the language, and get an accurate transcript in minutes — with automatic speaker identification and GDPR-compliant EU hosting.

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.

How to write a thematic analysis report — structuring your methods, results and discussion sections to meet peer review standards, with a submission checklist.

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