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Customer researchers, Marketing managers, Product marketers, Founders, Growth teams
Prepare the Required Inputs listed in the Workflow Prompt. Use as much detail as necessary.
1. Copy the Workflow Prompt.
2. Paste it into your AI tool.
3. Replace the "Required Inputs"
4. Run the prompt.
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You are a customer survey analyst. Your task is to analyse survey responses and turn them into clear findings, practical insights, and recommended actions for marketing or customer strategy.
### Required Input
- Survey Goal: [Explain why the survey was run. Example: “understand why trial users do not upgrade”]
- Survey Questions: [Paste all questions, including response options where relevant]
- Survey Responses: [Paste raw responses or summarised response data]
- Audience Segment: [Who answered the survey. Example: “recent trial users”, “lost customers”, “newsletter subscribers”]
- Business Context: [Relevant context. Example: “conversion rate has dropped after a pricing change”]
- Decisions the Survey Should Inform: [Example: “landing page messaging”, “retention strategy”, “product positioning”]
- Known Limitations: [Example: “small sample size”, “mostly open-ended responses”, “incentivised survey”]
### Input Validation
Check whether the survey goal, questions, responses, and audience segment are clear enough to analyse. If sample size, question wording, response context, or intended decision is missing, ask targeted clarification questions. Pause until clarification is provided. Do not present weak data as conclusive.
### Instructions
Analyse the survey in relation to its stated goal. Separate what respondents clearly said from what can only be cautiously inferred. Look for repeated themes, minority but high-severity issues, contradictions between answers, differences across segments, emotional language, buying barriers, satisfaction drivers, and unmet needs.
Assess the quality of the survey data. Identify leading questions, ambiguous wording, missing response options, sample limitations, or gaps that may affect interpretation. Do not ignore flawed data; explain how it should limit confidence in the findings.
For open-ended responses, group answers into practical categories and include representative language. For quantitative responses, summarise patterns in plain English and explain what they may mean for the business decision. Where possible, connect survey findings to specific marketing, conversion, product, or customer experience actions.
Prioritise recommendations by likely impact, confidence, and effort. Include next-step research questions where the survey raises important issues but does not fully explain them.
### Output
Provide a complete Survey Analysis Report with these sections:
1. Executive Summary
- Most important findings
- Key decision implications
- Confidence level of the analysis
2. Survey Quality Review
- Strengths of the survey
- Limitations or bias risks
- Questions that may need improvement
- Data gaps to address
3. Theme Analysis
For each major theme include:
- Theme name
- What respondents said
- Representative phrases or response patterns
- Frequency or strength of signal
- Business implication
- Confidence level
4. Segment and Pattern Insights
- Differences between audience groups, if available
- Contradictions or unexpected findings
- High-severity minority signals
- Sentiment trends
5. Actionable Recommendations
- Marketing recommendations
- Messaging or positioning changes
- Product or service feedback
- Customer experience improvements
- Conversion opportunities
6. Priority Roadmap
- Quick wins
- Higher-impact strategic actions
- Follow-up research questions
- Metrics to monitor after changes
Add an impact-confidence-effort score to every recommendation.
Most Important Findings: Free trial users are not suffering from a lack of product value—68% achieved their primary goal during the trial. Instead, the upgrade drop-off is overwhelmingly a sticker-shock reaction to the new $79/month minimum tier, coupled with a tight 14-day window that causes users to run out of time just as they finish setting up.
Key Decision Implications: The $79 entry point is an insurmountable barrier for solopreneurs and small teams who only use 10% of the platform’s power. To recover the 22% conversion drop, the business must either introduce a usage-based “Starter” tier or significantly extend the trial window for accounts showing high setup activity.
Confidence Level of the Analysis: High. The sample size () is statistically robust for this cohort size, and the correlation between the quantitative barrier data and the qualitative complaints about pricing is undeniable.
Strengths of the Survey: Excellent timing. Surveying users within 30 days of trial expiration caught them while the friction points were still fresh in their minds. High response rate on the primary barrier question.
Limitations or Bias Risks: The $50 gift card incentive likely generated some “filler” answers in the open-text fields (e.g., “Good app,” “Nice”).
Questions That May Need Improvement: Question 2 (“What was the biggest barrier…”) forced a single choice. Many users who selected “Ran out of time” noted in the open feedback that they also felt it was too expensive once they saw the final price. A multi-select option or a secondary ranking question would have captured this compounding friction.
Data Gaps to Address: The survey did not ask for the respondent’s team size or company revenue. This makes it difficult to definitively isolate whether the pricing complaints are coming strictly from low-value hobbyists or viable mid-market teams.
What Respondents Said: The platform provides great value, but the jump to $79/month is impossible to justify for a single user or micro-business.
Representative Phrases: “I love the tool, but $79 a month just to automate 3 recurring spreadsheets is crazy.” / “You killed the cheaper plan. I can’t jump from free straight to eighty bucks.”
Frequency / Strength of Signal: Extremely High. 54% of respondents selected “Too expensive” as their primary barrier.
Business Implication: The new pricing model accidentally priced out a highly active, high-volume segment of advocates who don’t need enterprise-grade security or team seats.
Confidence Level: High.
What Respondents Said: 14 days is not enough calendar time to integrate a workflow tool into real business operations, especially when relying on busy technical colleagues for API keys.
Representative Phrases: “It took me 10 days just to get the IT guy to approve the webhook connection. By the time I built my first flow, my trial was over.”
Frequency / Strength of Signal: Medium-High. 29% of respondents selected “Ran out of time.”
Business Implication: The trial duration is optimized for simple software, but this platform requires data integrations. The clock runs out before the user experiences “Time-to-Value.”
Confidence Level: High.
What Respondents Said: The visual builder and template library are exceptional. Users genuinely enjoy using the product interface once they are inside.
Representative Phrases: “The drag-and-drop builder is the best I’ve used. No code required and it actually works.” / “The templates saved me hours of head scratching.”
Frequency / Strength of Signal: High. Appears in 72% of the open-ended text answers regarding what they “loved.”
Business Implication: This confirms there is no structural product quality issue or performance problem; this is strictly a positioning, lifecycle, and packaging failure.
Confidence Level: Medium-High.
The Activation Paradox: A fascinating…
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