| name | review-mining |
| description | Deep review extraction for an Amazon ASIN. Pulls multi-page reviews (1★/3★/5★ + recent verified) via SellerApp n8n MCP, then decomposes them into purchase drivers, objections, surprises, recurring questions, comparison mentions, and photo references — each tagged by frequency. Use when running `review-mining {ASIN}`, when an existing brief needs deeper review evidence, or when surfacing objections for a CVR-leak fix. |
review-mining — Claim-Level Review Decomposition
Reviews are the single most important CRO signal source. This skill pulls a wide-and-deep review sample, decomposes it using the 8-layer framework from ~/.claude/skills/cro/review-analysis.md, and outputs structured insights ready to feed /cro-content-plan, objection-killer, or any image/copy planning skill.
Invocation
review-mining {ASIN}
review-mining {ASIN} {marketplace}
review-mining {ASIN} --deep # 5 pages per star bucket instead of 3
Output
Writes to /tmp/cro-research/{ASIN}-reviews-{date}.md and opens.
Tools Used
n8n MCP wrapper (workflow 9RmjDT107uXtrImf). Tool prefix mcp__f32016b6-7c77-45e2-b4a2-70195c5f2d2d__*.
| Pull | Tool | Params |
|---|
| Verified positive | Get_Product_Reviews | rating=5, reviewer_type=verified_purchase, pages 1-3 |
| Negative | Get_Product_Reviews | rating=1, pages 1-3 |
| Critical-but-buying | Get_Product_Reviews | rating=3, pages 1-2 |
| Nuanced | Get_Product_Reviews | rating=4, page 1 |
| Recency baseline | Get_Product_Reviews | sort=recent, page 1 |
| With media | Get_Product_Reviews | media_type=multimedia, page 1 (photo refs) |
Phase 1 — Pull
Parallel batch where possible. Default = 3 pages per star bucket = ~30 reviews per bucket. --deep flag = 5 pages = ~50 per bucket.
Note: each review page costs 10 SellerApp tokens. Default mode = ~120 tokens per ASIN.
Phase 2 — Decompose (8-layer framework from cro/review-analysis.md)
For every review, extract:
- Aspect — what part of the product (taste, durability, sizing, packaging, instructions, etc.)
- Sentiment — positive / negative / neutral (with negation handling)
- Driver vs Barrier — does this push them to buy, or stop them?
- Modifier — qualifier ("loud at first" / "small for the price")
- Conditional — when does it apply? ("if you have small hands" / "for the price")
- Repurchase signal — explicit ("would buy again") / implicit (gifting, replacing)
- Competitor mention — did they compare to anyone?
- Photo evidence — did they post a photo? What does it show (demographic / use context)?
Phase 3 — Categorize Output
Aggregate decomposed claims into 6 buckets per 02-visual-content/listing-images.md rules:
A. Purchase Drivers (from 5★ + 4★)
Top 5-10 themes ranked by frequency. Each entry:
- Theme
- Frequency:
N/{total} reviews = X%
- Tag: 🔴
>30% mandatory visual / 🟡 10-30% supporting / ⚪ <10% nice-to-have
- Quote (one short example, fewer than 15 words, in quotes)
B. Objections (from 1-3★)
Top 5-10 themes. Same structure. Tag: 🔴 >20% of negatives preempt with image / 🟡 <20% address in A+ FAQ.
C. Positive Surprises (from 5★)
Things customers love that nobody pitched. Untapped selling points → candidates for slot 7 or A+ hero.
D. Recurring Questions / Confusions
Cross-cuts all star levels. "Wait, does it…?" / "I wasn't sure if…" → these are visual-FAQ candidates.
E. Competitor Mentions
Compared to whom, on what dimension, who won. Builds the differentiation column for competitor-sweep.
F. Photo Review Evidence
- Demographic distribution (age/gender/race/setting visible in customer photos)
- Use context (where they use it, when, with what)
- Defects shown (if any)
- This is the ground truth for "Right Demographic" rule — beats stock photo intuition.
Phase 4 — Write Output File
# Review Mining — {Title}
**ASIN:** {ASIN} | **Date:** {date} | **Sample size:** {N reviews across {breakdown}}
## Data Sources
| Bucket | Reviews pulled | Token cost |
|---|---|---|
| 5★ verified | {N} | 30 |
| 4★ | {N} | 10 |
| 3★ | {N} | 20 |
| 1★ | {N} | 30 |
| Recent | {N} | 10 |
| Media | {N} | 10 |
| **Total** | **{N}** | **~120** |
## A. Purchase Drivers
{table with theme, frequency, tag, quote}
## B. Objections
{table}
## C. Positive Surprises
{list}
## D. Recurring Questions
{list, each tagged: → image / → A+ FAQ / → bullet}
## E. Competitor Mentions
{table: competitor, dimension, who won, frequency}
## F. Photo Review Evidence
{demographic summary + use-context summary + 3-5 representative photo URLs}
## Slot Mapping (handoff to content planning)
| Driver / Objection | Slot recommendation | Image type |
|---|---|---|
| {Top driver} | Slot 2 | Benefit photography |
| {Top objection} | Slot 3 | Objection-handling visual |
| ... | ... | ... |
## Sentiment Trajectory
{Pull review_date from each — flag if recent reviews trend more negative; that's a regression signal}
Reference Files
~/.claude/skills/cro/review-analysis.md — full 8-layer framework + validation rules- Vault:
CRO-Knowledge-Base/01-research/review-mining.md
- Vault:
CRO-Knowledge-Base/MASTER-CRO-REFERENCE.md §2 (Review Mining is signal #1 of 4)
Quality Bar
Common Mistakes (from review-analysis.md validation rules)
- ❌ Treating "loud at first but you get used to it" as pure negative — it's conditional
- ❌ Counting "doesn't have X" feature requests as objections to existing features
- ❌ Ignoring 5★ reviews because "they're all positive" — they have the purchase drivers
- ❌ Missing comparison mentions ("better than {Brand}") — these are gold for differentiation
- ❌ Not weighting verified purchases higher than non-verified
Auto-Triggers
Runs (without prefix) when:
- User asks "what do reviews say about {ASIN}" / "mine reviews for X"
- A pipeline (
/cro-content-plan, objection-killer, cvr-leak-fix) needs the data
- User uploads a review export and asks for analysis (skill switches to file-input mode)
