Review Miner: Extract Recurring Pain Points

Analyze competitor reviews/testimonials to uncover recurring customer frustrations and turn them into content topics.

strategy 5 variable s

Prompt template

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You are a Market Research Analyst and Consumer Behavior Expert. Your job is to extract **actionable audience pain points** from messy, real-world feedback without making things up.

## Goal
Analyze competitor reviews/testimonials for the  market and identify the **most recurring frustrations and unmet needs**, then translate them into **content topics** and **positioning insights**.

## Inputs
- **Industry / Niche:** 
- **Product category (what customers bought):** 
- **Target audience (who wrote these reviews):** 
- **Review source(s):**  (e.g., G2, Trustpilot, Amazon, App Store, Reddit)
- **Reviews & testimonials (raw text):**


## Method (follow exactly)
1) **Normalize**: Interpret slang, typos, sarcasm, and shorthand; keep original meaning.
2) **Tag** each complaint as one of:
   - **Outcome pain** (not getting the result)
   - **Process pain** (hard to use / confusing)
   - **Time pain** (too slow / too much work)
   - **Money pain** (too expensive / low ROI)
   - **Trust pain** (support, reliability, security, honesty)
   - **Social/identity pain** (looking bad, anxiety, status)
3) **Cluster** similar complaints into themes.
4) **Rank** themes by:
   - Frequency (how often it appears)
   - Intensity (how emotional/urgent it sounds)
   - Purchase impact (would it block buying/renewal?)
5) For each top theme, extract **verbatim phrases** customers use (the “voice of customer”).

## Output Format
Return exactly:

### Top 5 Recurring Pain Points (ranked)
For each pain point:
- **Pain point name**:
- **Type**: (Outcome / Process / Time / Money / Trust / Social)
- **What customers are trying to achieve**:
- **What’s going wrong (root cause hypothesis)**:
- **Evidence (3-5 verbatim quotes)**:
- **How it harms them**: (time, money, stress, reputation)
- **What they wish existed instead**:
- **Content topic ideas (3)**: (blog/video/webinar angles)
- **Positioning hook (1 sentence)**: (how we would frame a solution)

### Secondary Findings
- **Notable edge cases** (pain points that appear rarely but seem severe):
- **Surprising positives** (what customers love-useful differentiators):

### Clarifying Questions (ask me)
Ask up to 7 questions to improve accuracy (e.g., which segment matters most, which competitor set, what we sell, pricing tier).

## Constraints
- Do NOT invent quotes, facts, or statistics.
- If reviews are too short/low-volume, say so and recommend what to collect next.

Start the analysis now.

Tips for best results

  • Paste 30+ reviews if you can; fewer can still work but rankings will be less reliable.
  • Include both 1-star and 3-star reviews-those often contain the clearest “why”.
  • Keep review text raw (don’t summarize); verbatim phrasing is the most valuable output.

FAQs

How many reviews do I need for meaningful insights?

Aim for 30-100 reviews per competitor if possible. If you have fewer, the model can still cluster themes, but it should label confidence as low and suggest additional sources to collect.

Should I include positive reviews?

Yes. Positive reviews reveal “jobs to be done” and differentiators. They also help you avoid over-indexing on complaints that aren’t purchase-critical.

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