Convierte 500 reseñas de clientes en una hoja de ruta priorizada con este prompt de IA
Why this prompt matters
Without a systematic way to analyze feedback, product teams default to building what the loudest customer asked for last, not what would actually move retention or NPS. Missprioritized sprints cost engineering weeks on features that address no real pain. A single poorly-read feedback batch can push a team to solve a symptom instead of the cause, compounding both technical debt and user-experience debt for the next two quarters.
What we use it for
Your team just closed a quarterly NPS survey and has 400 open-text responses sitting in a spreadsheet alongside 200 unread App Store reviews from the past month. Sprint planning is on Friday and the team is debating what to build next, but no one has had time to read through 600 feedback items. You need clear signal from that noise before the meeting starts.
Prompt
Role: Act as a senior product analyst with expertise in customer research and data synthesis. Your job is not to summarize feedback — it is to extract signal, identify patterns, and translate customer language into actionable product decisions that a team can execute this quarter. Context: I have collected [NUMBER] pieces of customer feedback from [SOURCES: App Store reviews / Support tickets / NPS surveys / User interviews / Social media / Other]. The product is [BRIEF PRODUCT DESCRIPTION]. Our current development focus is [AREA: onboarding / core feature / performance / reliability / pricing / other]. I want to understand what customers are actually saying versus what they are asking for, identify the most painful issues versus nice-to-have requests, and prioritize what to fix or build next. Task: 1. Cluster the feedback into themes. Identify 5 to 8 meaningful themes — not generic categories like "UI" or "bugs", but specific patterns like "users cannot find the export function" or "onboarding flow loses users after step 3". 2. For each theme, estimate frequency (how many feedback items relate to this theme), sentiment intensity (how frustrated or delighted do users sound), and inferred business impact (if unresolved, what does the company lose?). 3. Identify the top 3 Quick Wins — issues that appear frequently, cause real friction, and are likely low effort to fix. 4. Identify the top 3 Strategic Priorities — themes that represent deeper product gaps with high customer impact. 5. Flag any feedback that suggests a customer segment or use case the product is not currently serving well. 6. Write one paragraph that could serve as an internal brief: what are customers really saying, in plain language? Constraints: - Do not treat feature requests at face value. Look for the underlying job the customer is trying to do. - Separate correlation (many people mentioned X) from urgency (these customers sound ready to churn over X). - Do not invent themes that are not actually supported by the feedback. - Keep technical jargon out of the internal brief — write it so a non-technical founder or executive can read it. - If the feedback volume is too small to draw reliable conclusions, say so clearly rather than speculating. Output Format: 1. Theme clusters (name, description, estimated frequency, sentiment intensity: low/medium/high, inferred business impact) 2. Quick Wins (3 items with reasoning) 3. Strategic Priorities (3 items with reasoning) 4. Underserved segments or use cases (if any) 5. Internal brief paragraph (2-4 sentences, plain language) 6. Confidence note (how confident are you in these findings given the data quality and volume?) Feedback to analyze: [PASTE YOUR CUSTOMER FEEDBACK HERE — reviews, tickets, survey responses, or interview notes]
Result
THEME CLUSTERS
1. Export and data portability Description: Users are trying to get their data out of the product into spreadsheets, other tools, or for reporting to clients. The current export button exists but produces CSV files with inconsistent column naming, no date filters, and no per-project scope. Estimated frequency: 27% of feedback items Sentiment intensity: High — several users have directly stated they are evaluating alternatives because of this Inferred business impact: Significant churn risk in the B2B segment; enterprise buyers consistently flag data ownership as a procurement requirement
2. Onboarding drop-off after step 3 Description: First-time users consistently report confusion at the workspace setup step. The term "workspace" does not match the mental model users arrive with, and the required fields feel like bureaucracy before they have seen any value. Estimated frequency: 19% of feedback items Sentiment intensity: Medium — frustration rather than rage, but correlates with low day-7 retention in support ticket volume Inferred business impact: Estimated 30-40% of trial signups do not complete setup. Improving this step is the highest-leverage acquisition funnel fix available.
3. Notification overload Description: Users are receiving too many emails and in-app pings for low-priority events. Several long-term customers report that they have disabled all notifications, which means they are also missing important alerts. Estimated frequency: 14% of feedback items Sentiment intensity: Medium — annoyance that is eroding trust with power users Inferred business impact: Notification fatigue is a known precursor to disengagement; if power users are tuning out, they are also missing important product updates and re-engagement touchpoints
4. Mobile app stability on Android Description: Android users specifically report crashes on the reports screen when viewing projects with more than 50 items. This is a distinct pattern from general performance complaints. Estimated frequency: 11% of feedback items Sentiment intensity: High — these users are blocked, not inconvenienced Inferred business impact: Android accounts for an estimated 38% of mobile sessions; crashes on a core screen are a direct retention risk
5. Pricing and tier confusion Description: Users do not understand what the Pro plan includes versus Free. Several churned users mentioned they paid for Pro expecting a feature that is actually on Enterprise only. Estimated frequency: 9% of feedback items Sentiment intensity: High — these users feel misled Inferred business impact: Pricing page clarity could recover failed upgrades and reduce churn in the $29/month tier
QUICK WINS 1. Fix the export CSV column naming and add a date range filter. Low engineering effort, directly addresses the most urgent churn signal. 2. Rename "workspace" to "project" in the onboarding flow and remove the two required fields that users skip or fill with placeholder text. 3. Add a notification frequency control (daily digest vs. real-time) as a single toggle. Existing notification infrastructure supports this; it is primarily a UI addition.
STRATEGIC PRIORITIES 1. Export and data portability — build a full export center with scoped exports, date filters, and PDF report output for client-ready deliverables. 2. Onboarding redesign — the step-3 confusion is a symptom of a deeper mismatch between product language and user mental models. A short Jobs-To-Be-Done research sprint before the next redesign would pay for itself. 3. Mobile reliability — Android crash patterns suggest the reports screen has an unhandled edge case at scale. This needs a dedicated bug sprint before the next mobile release.
UNDERSERVED SEGMENTS Freelancers managing multiple clients appear in roughly 15% of feedback but the product currently treats all projects as belonging to a single workspace owner. This segment is hitting limits the product was not designed for, and several are asking for client-portal or sharing features that would require a different product model.
INTERNAL BRIEF Customers who are churning or at risk are not unhappy with the product's core concept — they are blocked by execution gaps: they cannot get their data out easily, they cannot set up the product without hitting confusing terminology, and on Android they cannot reliably use a core screen. The clearest near-term wins are operational fixes, not new features. The most important strategic gap is that a meaningful segment of freelancers is using the product in a way it was not designed for, and either the product adapts or that segment churns to a competitor built for them.
CONFIDENCE NOTE Moderate to high. The sample of 600 feedback items is sufficient to identify the top themes with reasonable confidence, but frequency estimates should be treated as directional, not precise. The Android crash pattern and export frustration are robust findings. The pricing confusion theme is based on a smaller subset and should be validated with a targeted survey before committing significant design work.
La mayoría de los equipos de producto dedican más tiempo a debatir prioridades que a leer los comentarios que resolverían el debate. Los datos existen — reseñas de App Store, respuestas de texto abierto de NPS, tickets de soporte, notas de entrevistas de usuarios — pero procesar cientos de comentarios en bruto para tomar una decisión defendible sobre la hoja de ruta requiere un tiempo que la mayoría de los equipos no tienen antes de la planificación del sprint.
Este prompt le da a la IA un trabajo estructurado con esos comentarios. En lugar de pedir un resumen, pide extraer señales: agrupar los comentarios en temas significativos, puntuar cada uno por frecuencia y urgencia, separar la correlación del riesgo de abandono, y producir tres cosas que la mayoría de los análisis de comentarios pasan por alto — Quick Wins, Strategic Priorities, y una bandera para segmentos de usuarios desatendidos.
Qué hace diferente a este prompt
Los prompts genéricos de 'analiza mis comentarios' devuelven resúmenes genéricos. Este obliga al modelo a separar lo que los clientes dicen de lo que quieren decir. Un usuario que dice 'añade un modo oscuro' puede estar frustrado por la fatiga visual en sesiones largas — la necesidad subyacente es comodidad, no estética. La restricción de 'buscar la necesidad subyacente' empuja constantemente a los modelos más allá de las listas de funciones superficiales.
El requisito de nota de confianza es igualmente importante. Si pegas 12 reseñas y pides un análisis de temas, la mayoría de los modelos inventarán patrones. Este prompt indica al modelo que declare cuando la muestra es demasiado pequeña para extraer conclusiones fiables — algo que resulta sorprendentemente útil cuando tus datos son escasos.
Cómo usarlo
Rellena cuatro campos: el número de elementos de comentarios, de dónde provienen, una descripción del producto en una frase y tu área de enfoque de desarrollo actual. Luego pega los comentarios en bruto al final. El modelo se encarga del resto — agrupar, puntuar y escribir el párrafo de informe interno que puedes pegar directamente en un mensaje de Slack o en un documento de planificación.
Funciona mejor con Claude 4 Sonnet, GPT-5 o Gemini 2.5 Pro. Para volúmenes grandes de comentarios (más de 500 elementos), pega en lotes de 150-200 y pide al modelo que fusione los grupos de temas en una pasada final.
Cómo se ve el resultado
El prompt produce seis secciones estructuradas: grupos de temas con puntuaciones de frecuencia e impacto, Quick Wins con razonamiento, Strategic Priorities con razonamiento, segmentos desatendidos (cuando estén presentes), un informe interno en lenguaje sencillo y una nota de confianza sobre la calidad de los datos. El informe interno está diseñado para que un fundador o ejecutivo no técnico lo pueda leer sin edición.
El resultado de ejemplo en esta publicación se basa en un producto SaaS ficticio con 600 elementos de comentarios en cinco temas — desde fricción en la exportación hasta fallos en Android y confusión sobre precios. Es representativo de la profundidad y especificidad que el prompt produce de manera fiable.