Signal & Noise
Skimle's resource center dedicated to high quality analysis using modern methods
AI for data analysis: a practical guide for text and unstructured data (2026)
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: how it works and how to use it well in 2026
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.
Qualitative primary research in private equity due diligence: a practical guide
How PE and VC deal teams run and analyse qualitative primary research in CDD, from expert calls to customer references, on tight deal timelines.
How to synthesise expert network calls: a step-by-step guide
Expert call synthesis done well turns 20 scattered call notes into structured, defensible findings. This step-by-step guide covers coding, divergence analysis, and deliverable writing.
Customer sentiment analysis: how to read the mind of customers using unstructured data
Customer sentiment analysis turns unstructured feedback (interviews, reviews, support tickets, open-text surveys) into actionable insight. Here's how to move beyond positive/negative scores to real un...
Product feedback analysis: moving from likes and dislikes to deeper product discovery
Most product feedback analysis produces feature request buckets. This guide shows how to go deeper: uncovering the mental models, workarounds and unmet needs that actually drive product decisions.
Qualitative research design: maximising insights within a limited budget
Every qualitative study faces the same constraint: too little time and money for how much you want to learn. Here's how to allocate your research budget across 5 phases, and how AI is reshaping the eq...
Voice-of-market research: building a complete market picture beyond the numbers
Voice-of-market research combines multiple data sources (reviews, Glassdoor, expert interviews, customer feedback) into a single coherent picture of market perception. Here's how to do it.
Consultant's guide to summarising interviews and finding themes
How consultants can move from raw interview notes to a credible, evidence-based synthesis. Covers note-taking for later coding, issue tree thinking, thematic consolidation, and presenting qualitative ...
Evidence-based strategy: how to make sense of unstructured data
Strategy decisions are only as good as the evidence behind them. This guide shows consultants and strategy teams how to collect, structure and synthesise unstructured data into sharp strategic conclus...
Consultant's guide to analysing large sets of qualitative data
How consultants can systematically analyse large volumes of qualitative data beyond interviews: industry reports, earnings calls, regulatory filings, expert papers, and customer documents. Covers work...
Exit interview questions: what to ask and how to analyse the results
Exit interviews are most valuable when they go beyond the obvious. This guide covers the best questions to ask, how to structure the conversation, and how to synthesise findings across many interviews...
Manual interview coding is too slow: how to speed up theme analysis without losing rigour
Bottom-up manual coding takes weeks. Top-down theme assignment is prone to bias. AI-assisted analysis offers a third path: the speed of automation with the rigour of systematic coding. Here's how it w...
From expert calls to client deliverable: the consulting research synthesis workflow
The end-to-end workflow for synthesising expert calls, management interviews, and documents into a structured consulting deliverable, with triangulation and evidence footnotes.
Informal interview questions: building rapport and getting beyond the obvious
Informal interviews produce richer data than formal ones when done well. This guide covers how to build rapport quickly, frame questions conversationally, and get to the insights that structured inter...
Interview tips: what to wear, how to introduce yourself, and how to follow up
Practical guidance on interview preparation: appropriate dress by setting, introducing yourself confidently, and writing a thank-you follow-up that keeps you in the frame. Plus: tips for interviewers ...
Qualitative market research: overview, methods and examples
Qualitative market research explores the 'why' behind customer behaviour. This guide covers the main methods, when to use them, real-world examples, and how AI is changing what qualitative research ca...
What is market research? Definition, types and examples
Market research is the systematic collection and analysis of information about markets, customers and competitors. This guide covers the main types, real-world examples, and how AI is opening new poss...
Building personas from real research data with AI: a practical guide
How to generate user or customer personas from real interview and survey data with AI, the persona types that exist, and how to avoid baking in bias from skewed input.
The Critical Incident Technique: a practical guide for UX and market research
The Critical Incident Technique (CIT) asks for specific remembered events, not general opinions. What it is, how to run it, and how to analyse incidents at scale.
Patient experience qualitative analysis with AI: a guide for healthcare providers and health plans
How to analyse patient and member free-text feedback at scale with AI, while handling health data anonymisation and care-touchpoint mapping properly.
Incident postmortem analysis with AI: finding themes across a year of retrospectives
How engineering leaders use AI to find recurring root-cause themes across dozens of incident postmortems, with traceability back to source.
Earnings call transcript analysis with AI: a guide for IR teams and analysts
How IR teams and analysts use AI for earnings call transcript analysis: management language, analyst Q&A themes, quarter-over-quarter shifts.
Qualitative MEL data analysis: a guide for NGO and international development teams
How Monitoring, Evaluation, and Learning (MEL) teams can analyse beneficiary interviews, FGDs, and MSC stories at scale, with donor traceability and native multi-language support.