Analisador de Inteligência de Pesquisas: Transforme Respostas Brutas em Insights e Planos de Ação
Why this prompt matters
Survey data has a short shelf life. Leadership makes decisions based on gut feel when analysis is slow or shallow. A thorough synthesis that flags the top pain points, quotes real users, and ranks recommendations by impact turns survey data from a checkbox into a strategic asset.
What we use it for
You ran a 200-person customer satisfaction survey after your product launch and have a spreadsheet of responses — NPS scores, feature ratings, and 80 open-ended comments. You need a structured analysis ready for a board meeting in two hours.
Prompt
Act as a Senior Research Analyst specializing in survey data synthesis and stakeholder communication. CONTEXT: I have conducted a survey on the topic: [SURVEY TOPIC / PRODUCT / PROGRAM NAME] Survey size: [NUMBER] respondents Survey period: [DATE RANGE] Primary audience for this report: [EXECUTIVES / PRODUCT TEAM / HR / OTHER] SURVEY DATA: [PASTE YOUR SURVEY QUESTIONS AND RESPONSES HERE — include question text with each set of responses. For quantitative questions include totals or percentages. For open-ended questions include the actual responses or a representative sample.] TASK: Produce a complete Survey Intelligence Report with the following sections: 1. EXECUTIVE SUMMARY (3–4 sentences: who responded, top finding, top risk, top opportunity) 2. QUANTITATIVE HIGHLIGHTS - Key metrics with percentages - Notable score changes vs benchmark (if applicable) - Distribution anomalies worth flagging 3. SENTIMENT ANALYSIS - Overall sentiment: positive / neutral / negative with percentage breakdown - Sentiment by segment (if data allows): [SEGMENT VARIABLE e.g., department, plan tier, region] - Strongest positive signal - Strongest negative signal 4. KEY THEMES FROM OPEN-ENDED RESPONSES - List the top 5 themes with frequency count and representative verbatim quotes for each - Flag any themes that appear in both positive and negative contexts 5. SURPRISING OR COUNTER-INTUITIVE FINDINGS - Results that contradict assumptions or prior surveys 6. PRIORITIZED ACTION PLAN - 5 specific recommendations ranked by: (Impact × Urgency) / Implementation Difficulty - For each: what to do, who owns it, and a suggested success metric CONSTRAINTS: - Do not invent data or fabricate quotes — only use what is provided - Flag explicitly when a finding is based on a small sample (under 10 responses) - Avoid vague recommendations like "improve communication" — every action must be specific and measurable - Keep the executive summary to 4 sentences maximum OUTPUT FORMAT: Use the section headers above. Use bullet points within sections. Bold key findings. Include a section break between each major section.
Result
Executive Summary
Survey: Q2 2026 Customer Satisfaction Survey — CloudBridge Pro (N=187, April–May 2026, primary audience: Product and GTM leadership)
Overall satisfaction sits at 71% positive — 8 points below the industry benchmark of 79% for B2B SaaS platforms at this price tier. The strongest positive signal is onboarding quality (88% satisfaction), driven by the new in-app guided setup flow launched in March. The most urgent risk is API reliability: 34% of power users flagged downtime or latency issues as their top complaint, with three verbatim responses specifically mentioning considering alternatives. The highest-impact opportunity is expanding the native Salesforce integration, mentioned unprompted by 41 respondents.
Quantitative Highlights
- NPS Score: 32 (down from 41 in Q4 2025) — driven by detractor growth in the Enterprise tier
- Feature satisfaction: Reporting dashboard 62% | Onboarding 88% | API 54% | Mobile app 48% | Support response time 79%
- Renewal intent: 81% "definitely" or "probably" renewing — but only 61% in the 50–200 seat segment
- Distribution anomaly: Satisfaction scores are bimodally distributed — 43% rate overall experience 9–10/10, while 28% rate it 1–4/10. The middle is thin, suggesting a polarized user base.
Sentiment Analysis
- Overall: 71% positive / 16% neutral / 13% negative
- By tier: Starter 84% positive | Growth 73% positive | Enterprise 58% positive
- Strongest positive signal: Onboarding and customer success responsiveness — "best onboarding I've experienced in 10 years of SaaS" (VP Operations, 80-seat account)
- Strongest negative signal: API stability — "We've had three incidents this quarter that caused missed SLAs with our own clients" (CTO, 200-seat Enterprise account)
Key Themes from Open-Ended Responses
Theme 1 — API Reliability (n=63, 34%): Latency spikes, occasional 502 errors during peak hours. Quote: "The product is great when it works. The 'when it works' part is the problem."
Theme 2 — Salesforce Integration Gaps (n=41, 22%): Bi-directional sync requested; current read-only integration not meeting workflow needs. Quote: "We're manually copying data between systems — defeats the purpose."
Theme 3 — Reporting Flexibility (n=37, 20%): Custom date ranges and exportable raw data consistently requested. Quote: "I'd pay more for a real data export."
Theme 4 — Mobile App Functionality (n=29, 16%): Limited to read-only on mobile; approvals and quick actions requested.
Theme 5 — Pricing Transparency (n=18, 10%): Confusion around seat-count billing and overage charges. (Note: small sample — treat as signal, not statistically significant finding.)
Surprising Findings
- Support response time scores increased despite a 40% rise in ticket volume — suggests the March support team expansion is working ahead of schedule
- The cohort most likely to churn (61% renewal intent) is the 50–200 seat mid-market segment — not the smaller Starter tier as assumed
Prioritized Action Plan
- Resolve API reliability (Owner: Engineering, Q3): Implement rate limiting, dedicated Enterprise infrastructure tier, and real-time status page. Success metric: API-related support tickets down 60% by September.
- Launch bi-directional Salesforce integration (Owner: Integrations team, Q3): Scope write-back capability for the top 5 field types. Success metric: 50% of Salesforce-connected accounts using bi-directional sync within 90 days of launch.
- Ship custom reporting + CSV export (Owner: Product, Q3): Flexible date ranges and raw export. Success metric: Reporting satisfaction from 62% to 75%+.
- Mid-market retention program (Owner: CS, immediate): Proactive outreach to all 50–200 seat accounts with renewal in the next 90 days. Success metric: Renewal intent in segment from 61% to 75%+.
- Mobile approvals MVP (Owner: Mobile team, Q4): Add approve/reject actions for top 3 workflow types. Success metric: Mobile DAU up 25%.
Coletar dados de pesquisas é a parte fácil. Interpretar centenas de respostas abertas, identificar padrões significativos em dados quantitativos e transformar tudo isso em recomendações que uma equipe de liderança realmente colocará em prática — é aí que a maioria dos esforços de pesquisa trava.
Este prompt atua como um analista de pesquisa sênior. Você cola seus dados de pesquisa (ou um resumo das respostas) e ele retorna um relatório de análise estruturado com destaques estatísticos, detalhamento de sentimento, temas-chave das respostas qualitativas e uma lista de ações priorizadas. Funciona para pesquisas de satisfação do cliente, sondagens de engajamento de funcionários, formulários de feedback de produto, avaliações de conferências e pesquisa de mercado.
O Prompt
Quando Usar
Use este prompt após coletar respostas de pesquisa quando precisar sintetizar os resultados rapidamente para uma apresentação, atualização para o conselho ou sessão de estratégia interna. É especialmente valioso quando você tem uma mistura de respostas quantitativas (escala Likert, múltipla escolha) e qualitativas (abertas) e precisa analisá-las em conjunto.
O Que Torna Este Prompt Eficaz
A maioria das pessoas pede à IA para “resumir minha pesquisa”. Isso produz um resumo genérico. Este prompt força o modelo a assumir um papel de analista com entregas explícitas: padrões estatísticos, polaridade de sentimento, agrupamento de temas, destaques literais e um plano de ação ranqueado. Cada seção é obrigatória, não opcional. A saída é estruturada o suficiente para ser inserida diretamente em uma apresentação de slides ou memorando executivo.
Adaptando-o aos Seus Dados
Para grandes conjuntos de dados (500+ respostas), cole uma amostra representativa ou um resumo agregado — o modelo lida melhor com raciocínio analítico quando trabalha com dados pré-agregados em vez de linhas brutas. Para pesquisas menores (menos de 100 respostas), cole tudo. Sempre inclua o texto da pergunta junto com as respostas para que o modelo entenda o que estava sendo perguntado.