Signal & Noise

Customer insights research explains why customers behave, switch or churn. See how it differs from market research and what data and analysis it takes.

Most open text analysis tools stop at sentiment or category buckets. Here's how inductive theme discovery finds the patterns predefined categories miss.

How to analyse in-depth interviews (IDIs): within-case and cross-case coding, sample size, quote selection, and a worked example from a churn study.

Focus group transcription is harder than 1:1 interviews: crosstalk, speaker attribution, accuracy rates. How to transcribe, format and check a group transcript.

Most commercial qualitative research is built top-down: skim for themes, find quotes to fit. Academic research does it bottom-up. AI now makes that affordable at deadline.

Selling AI-assisted research on speed alone leads to shorter projects and thinner margins. The better pitch is what teams do with the time AI frees up.

Nobody accepts a black-box result from a spreadsheet. Why do we accept one from AI-assisted qualitative analysis? A case for showing the steps, not just the summary.

AI tools can show you the quote behind a finding. They can't show you what they never looked at. Here's why that gap decides whether a client trusts your research.

Clients expect AI to make research cheaper and faster. Agencies that compete on that basis commoditise themselves. Here's the alternative: compete on depth instead.

Fragmented VoC data produces contradictory findings, slows decisions, and lets the loudest team win instead of the best evidence. Here's how to size the real cost.

A vendor-neutral evaluation framework for qualitative analysis software: 7 scoring domains, the questions to ask, a pilot protocol, and the procurement annex.

Eight small research projects a customer insights team can run in days, using data you already have or can collect quickly, and publish as thought leadership.

Responsible AI in qualitative market research: 7 rules covering where AI helps, why synthetic respondents fail, and what human-in-the-loop really requires.

A line-by-line cost model for a qualitative study: recruitment, incentives, moderation, transcription and analysis, with 2026 rates and a worked example.

Interviews, NPS, tickets and sales calls rarely add up. How to run one stable category structure across all 8 customer feedback channels you collect.

Product managers run customer interviews but rarely analyse them well. Here's a practical guide to turning qualitative research into clear product decisions.

Most AI data analysis guides focus on numbers. But 80-90% of organisational data is unstructured text. This guide covers how AI handles both, with a decision table and practical workflow.

AI document analysis tools range from single-PDF chatbots to systematic multi-document analysis. Learn which tier fits your use case and when chat-with-your-PDF breaks down.

A practical guide to getting real insight from open-text engagement survey responses: coding frameworks, subgroup analysis, and presenting findings to leadership.

A practical guide to analysing customer interviews at scale: coding strategy, synthesis across 30–100+ interviews, segment analysis, and QA for AI-assisted analysis.

A practical guide to NPS verbatim analysis at scale: coding frameworks, segment differences, closing the loop, and tools for analysing 500+ NPS open-text comments.

How PE and VC deal teams run and analyse qualitative primary research in CDD, from expert calls to customer references, on tight deal timelines.

A practical guide to B2B VoC programmes: the four data sources, running customer interviews, synthesising across sources, and turning insights into decisions.

A bank of 40+ win-loss interview questions organised by topic, plus a guide to structuring 30-minute calls, handling off-script moments, and coding responses consistently.