O Analisador de Resultados de Pesquisa: Transforme Respostas Brutas em Temas, Sentimento e Ações
Porque é que este prompt importa
Manually reading and coding 340 open-ended responses takes a trained analyst 8-12 hours, and most teams skip that step entirely, reporting only the multiple-choice scores while ignoring the free-text answers that usually contain the most actionable insight. Without structured analysis, a genuinely fixable driver of churn — the specific negative theme mentioned by 40% of at-risk customers — stays buried in a spreadsheet nobody reads past row 20.
Para que o usamos
You just closed a customer satisfaction survey with 340 open-text responses to "What would make you more likely to renew?" and a board meeting in two days where you need to present findings, not raw data.
Prompt
Role: Act as a UX researcher and data analyst experienced in synthesizing open-ended survey data for product and business teams. Context: - Survey question(s) asked: [PASTE THE EXACT QUESTION(S) HERE] - Who will read this report: [E.G. PRODUCT TEAM, EXECUTIVE BOARD, CUSTOMER SUCCESS] - Decision this survey should inform: [E.G. WHETHER TO CHANGE PRICING, WHAT TO BUILD NEXT] - Raw responses: [PASTE ALL OPEN-TEXT SURVEY RESPONSES BELOW, ONE PER LINE] Task: Analyze all responses and produce a structured report with a thematic breakdown, sentiment per theme, supporting quotes, and prioritized action items. Constraints: - Group responses into no more than 6 themes; merge similar comments rather than creating a theme for every unique phrasing - Report frequency as an approximate percentage (e.g. "mentioned by ~30% of respondents"), not an exact count, since exact counts overstate precision on small samples - Sentiment per theme must be exactly one of: Positive, Negative, Mixed, Neutral — not a descriptive phrase - Flag any theme where sentiment is Negative AND frequency is high (top half of themes by frequency) as "Priority" - Include 2-3 verbatim quotes per theme (lightly edited only for typos), not paraphrases - Do not invent or infer a theme that isn't actually present in the responses — if you're unsure whether something is a real pattern or a single outlier, say so explicitly rather than including it as a theme - Tone for the report: [YOUR PREFERRED TONE: e.g. clinical / conversational] Output Format: 1. Executive summary (3-4 sentences) 2. Theme table: Theme | Frequency | Sentiment | Priority flag 3. Quotes section, grouped by theme 4. Prioritized action items, ranked, each tied to a specific theme
Resultado
Executive Summary: Across the sampled responses to "What would make you more likely to renew?", three themes emerged clearly: pricing/value concerns (mentioned by roughly half of respondents), a desire for better onboarding support (about a third), and requests for a mobile app (about a quarter). Pricing sentiment is strongly negative and should be treated as the top priority.
| Theme | Frequency | Sentiment | Priority |
|---|---|---|---|
| Pricing / Value | ~50% | Negative | 🔴 Priority |
| Onboarding Support | ~33% | Mixed | Medium |
| Customer Support Speed | ~17% | Negative | Medium |
| Mobile App Request | ~25% | Neutral | Low |
Pricing / Value — Quotes:
"I like the product but I genuinely can't justify $200/month for what we actually use."
"Feels like we're paying for features three tiers up that we'll never touch."
Onboarding Support — Quotes:
"First two weeks were confusing — a guided setup call would have saved us a lot of back-and-forth."
"Once we figured it out it was great, just wish someone had walked us through it."
Prioritized Action Items:
- Pricing / Value (Negative, ~50%): Pilot a tiered or usage-based pricing option for the segment citing feature bloat — the single highest-leverage fix given both frequency and sentiment severity.
- Customer Support Speed (Negative, ~17%): Audit response-time SLAs; a modest improvement here directly addresses a named renewal blocker.
- Onboarding Support (Mixed, ~33%): Pilot a guided onboarding call for new accounts next quarter — lower urgency than pricing but affects a larger share of respondents.
- Mobile App Request (Neutral, ~25%): Log as a roadmap feature request — neutral sentiment suggests a nice-to-have, not a renewal blocker.
A falha mais comum na análise de pesquisas gerada por IA é a proliferação de temas: peça a um modelo para classificar 300 respostas de texto aberto sem limite, e ele frequentemente produzirá 15 ou 20 microtemas que na verdade são apenas paráfrases uns dos outros. Este prompt resolve isso com um limite rígido de seis temas.
O sentimento é restrito a quatro rótulos fixos — Positivo, Negativo, Misto, Neutro — em vez de texto descritivo aberto, porque uma coluna de planilha que diz "geralmente frustrado mas grato pelas correções recentes" não pode ser ordenada ou filtrada. A regra da marca de Prioridade (sentimento Negativo mais alta frequência) faz o trabalho real de priorização.
A exigência de 2-3 citações literais por tema existe porque descobertas resumidas de pesquisas costumam ser questionadas em reuniões — alguém sempre pergunta "posso ver um exemplo real?" — e ter a linguagem real do cliente, não parafraseada, resolve essa questão imediatamente.
A restrição contra inventar temas não realmente presentes nas respostas importa mais do que parece: modelos de linguagem solicitados a encontrar padrões em dados ambíguos às vezes apresentam um tema plausível que é na verdade uma extrapolação, não algo que os respondentes realmente disseram.