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Top Tips Overview

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When to Use the Grids versus the Chat

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Prompting Dos and Don’ts

✅ Rephrase your question: Just like with Google — if you don’t find what you’re looking for the first time, try asking differently. ✅ Ask specific questions: CoLoop uses your prompt and project description to find answers. If it’s unclear what to look up, the answer won’t be helpful. ✅ Ask simpler questions: CoLoop considers one thing at a time. If you ask three questions at once, the response will be less detailed than if you asked them one by one. ❌ Don’t ask quantitative questions: The AI chat isn’t reliable for “how many” or “how much” questions — use the analysis grid instead. ✅ Always check the full evidence panel: The quote you’re looking for might not be at the top — scroll to review everything. ✅ Use filters when possible: Segment filters reduce the amount of data CoLoop processes and improve accuracy. ✅ Reset the chat: When changing topics, reset the chat to avoid carryover from earlier questions.

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Prompting Practical Tips

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Detailed Example Prompts

For more involved analyses, here are detailed, ready-to-adapt prompts grouped by goal. Swap the placeholders — like [topic] or Segment A/B — for your own study.
Segment comparison
  • Compare Segment A and B’s top concerns. List each segment’s top 3–5 with quotes, then highlight where they diverge most.
  • Compare first reactions to Concept A vs B — emotional response, language used, and objections raised.
Contradictions and conflict
  • Find where participants contradict themselves. Quote both statements, note the context shift, and what it reveals.
  • Find topics where participants disagree. Group opposing camps with quotes and the underlying assumption driving each.
Surprises and outliers
  • Surface the 5 most surprising or counterintuitive findings. Explain why each is surprising with supporting evidence.
  • Identify outlier participants. Summarize their view and assess whether it’s fringe or an early signal.
Language and emotion
  • Identify recurring metaphors and phrases used to describe [topic]. Group by theme, note which recur vs one-offs.
  • Map emotional intensity across topics. Which spark strong emotion vs neutral mentions? Quote the strongest moments.
  • Run a rigorous qualitative analysis: top themes with frequency, key tensions, segment differences, and gaps. Quote and cite for each.
  • Run a JTBD analysis. For each job: functional, emotional, social; trigger; current alternatives; success criteria. Rank by frequency and intensity.
  • Run a thematic analysis. Produce themes → sub-themes with counts, quotes, and business meaning. Flag thin themes.
  • Build personas from this data. Each with behaviors, goals, frustrations, drivers, language, anchor quotes, and source participant IDs.
  • Run a decision-driver analysis for [key decision]. Separate stated reasons from revealed reasons where they diverge.
  • Run a journey analysis for [process]. Map stages, emotional highs/lows, friction points, and where journeys diverge by segment.
  • Identify the strongest new market opportunity. Specify segment, unmet need, why now, evidence strength, and key risks.
  • Suggest 3–5 product or feature opportunities. For each: unmet need, affected segments, evidence strength, impact vs effort.
  • Suggest 2–3 positioning directions based on participants’ language and emotional drivers. Include target segment and supporting quotes.
  • Recommend which segments to prioritize vs deprioritize, based on signal strength, intensity of need, and willingness to act.
  • What follow-up research is needed? Identify questions raised but unanswered, plus sample or methodological gaps.
  • Pressure-test [hypothesis]: evidence for, evidence against, what’s missing, and a confidence verdict (strong/mixed/weak/contradicted).
  • Find the strongest counter-arguments to [finding]. Quote participants whose views complicate it and explain a skeptic’s read.
  • Does the data support [new idea]? Identify evidence for/against, segments most/least likely to respond, and untestable assumptions.
  • Stress-test for sample bias. What users, contexts, or viewpoints are under-represented, and how might that skew conclusions?
  • What evidence would disconfirm my hypothesis that [X]? Specify what future research would need to show.
  • Write a compelling narrative for [key finding]: hook, finding, why it matters, 4–6 supporting quotes, nuance, implications.
  • Write a one-page executive summary: top 3 findings, implications, next steps. Assume the reader has 2 minutes.
  • Reframe [key finding] for [audience: product/marketing/leadership]. Adjust emphasis, language, and the “so what.”
  • Pull 10–15 of the most powerful verbatim quotes. One line of context each, tagged by theme.
  • Show the full evidence base for [claim]: every supporting quote, every contradicting quote, with participant IDs, then a verdict.
  • How many participants raised [topic], in what context, with what intensity? Separate unprompted mentions from prompted ones.
  • Audit [previous summary or finding] against the raw data. Flag overstated, under-evidenced, or unsupported claims.