Survey analysis, KPI summaries, competitor research, feedback clustering.
16 articles
80% of analytics work is wrangling messy input into something readable — AI excels at exactly this layer. This hub focuses on the "raw → conclusion" pipeline: survey clustering, KPI summaries, competitor comparison tables, feedback topic-modeling, chart reading, table interpretation, market-research summarization.
New to this hub? Read these three first to get a complete workflow:
Turn support tickets, sales calls, and product analytics into one actionable answer with AI. Tool picks, a copy-ready prompt, file-size limits, and the checks that stop bad numbers — as of June 2026.
Use AI to run open and axial coding on interview transcripts at scale, with Cohen's kappa reliability checks that catch hallucinated codes before they reach your findings.
Turn five PDFs and a folder of slides into a decision-ready one-pager — market size, trends, risks, recommendation — with sourced numbers, in 30-45 minutes.
Turn a finished A/B test into a one-page summary with winner, lift, CI, segment caveats, novelty risk, an SRM check, and a clean ship/hold/kill call.
Use ChatGPT, Claude, or Gemini to turn each chart into a 2-sentence takeaway that names the pattern and the implication. Copy-ready prompt, model notes, and accuracy checks for June 2026.
A June 2026 workflow for using AI deep-research tools to assemble side-by-side competitor matrices with per-cell citations, real white-space gaps, and zero hallucinated pricing.
Post a 4-line weekly Slack takeaway your team actually reads — not 'here's the dashboard link.' Lead with what moved, name the cause, surface one surprise, ask the right question.
Use AI as a critical analyst on A/B results: significance, sample-ratio mismatch, effect size, power, validity threats, and the right next action — with a copy-ready prompt.
An analyst's workflow for using AI to surface trends, anomalies, and testable hypotheses in monthly financial data — the inputs, the prompt, the validation checks, and where AI quietly miscalculates.
Turn a finished A/B test into a one-page summary with winner, lift, CI, segment caveats, novelty risk, an SRM check, and a clean ship/hold/kill call.
Use ChatGPT, Claude, or Gemini to turn each chart into a 2-sentence takeaway that names the pattern and the implication. Copy-ready prompt, model notes, and accuracy checks for June 2026.
A June 2026 workflow for using AI deep-research tools to assemble side-by-side competitor matrices with per-cell citations, real white-space gaps, and zero hallucinated pricing.
Post a 4-line weekly Slack takeaway your team actually reads — not 'here's the dashboard link.' Lead with what moved, name the cause, surface one surprise, ask the right question.
Use AI as a critical analyst on A/B results: significance, sample-ratio mismatch, effect size, power, validity threats, and the right next action — with a copy-ready prompt.
An analyst's workflow for using AI to surface trends, anomalies, and testable hypotheses in monthly financial data — the inputs, the prompt, the validation checks, and where AI quietly miscalculates.
Find the step with the biggest gap vs benchmark — not the biggest absolute drop — and surface the single highest-ROI test to run next, plus the tests not worth running.
Move from 'activation is up 4 points' to 'here is the most likely cause at medium confidence, here is what is still unproven, and here is the exact data that would resolve it' — without overclaiming causation.
Turn the week's metrics into a sharp KPI summary with AI — BLUF TL;DR, 3 wins, 3 risks, one ask — in 15 minutes instead of 90, without losing the narrative.
Turn five PDFs and a folder of slides into a decision-ready one-pager — market size, trends, risks, recommendation — with sourced numbers, in 30-45 minutes.
Use AI to run open and axial coding on interview transcripts at scale, with Cohen's kappa reliability checks that catch hallucinated codes before they reach your findings.
Turn a 12 × 12 retention cohort grid into a 3-sentence leadership readout that names the real problem: week-1 direction, long-tail shape, and the one outlier cohort worth digging into.
A repeatable June 2026 workflow for clustering open-ended survey responses with Claude or ChatGPT: verifiable themes, traceable quotes, and drift control.
Turn 200 survey responses into a one-page narrative organized by the 2-3 decisions the survey was meant to inform — with verbatim quotes, prior-contradiction flags, and an honest 'too thin to conclude' section. Tested prompts for ChatGPT and Claude, June 2026.
Compress a packed table into a one-paragraph plain-English summary plus three 'what to do with this' bullets — with a copy-ready prompt and tool picks verified for June 2026.
Turn 300-2,000 app reviews, NPS comments, or support tickets into 5-10 themes a PM can ship against this sprint. Prompt, QA checklist, and tool picks for June 2026.