Amazon Serp Analysis

Analyze Amazon market tracks/niches for go/no-go investment decisions. Given a seed keyword, scrapes SERP, expands related keywords to map the full track landscape, scores competition intensity, estimates demand, and delivers a structured recommendation with entry strategy. Trigger on: 赛道分析, 赛道研究, 值不值得做, 市场分析, 竞争分析, niche analysis, track analysis, 亚马逊赛道, 切入点, 要不要做这个品, 这个类目怎么样.

Handa@linbeihanda

Install

openclaw skills install @linbeihanda/amazon-serp-analysis

amazon-serp-analysis

Comprehensive Amazon niche analysis pipeline. Input: a seed keyword. Output: full track landscape map, competition scoring, demand assessment, and go/no-go recommendation with specific entry angles.

Requirements

omni-scraper skill installed and configured (required — provides SERP + PDP data)
amazon-listing-judge skill installed (optional — adds listing quality depth)
SellerSprite MCP tools (optional — adds keyword volume and search trends)
ClickHouse MCP (optional — adds historical brand analytics data)

Workflow

Step 1 — Seed SERP Scrape

Scrape Amazon search results for the seed keyword:

bash

curl -s -X POST "$CLAW_API_BASE/api/scrape" \
  -H "Content-Type: application/json" \
  -d '{
    "claw_key": "'"$CLAW_KEY"'",
    "url": "https://www.amazon.com/s?k=KEYWORD_URL_ENCODED",
    "mode": "scraper"
  }'

From the response parsed.results, extract for each position:

position, asin, title, price, rating, review_count, bought_past_month, sponsored, ac_badge

Stop if: parsed is null and body is also null — report error and stop.

Step 2 — Keyword Expansion (build track landscape)

Goal: identify 5–10 related search terms that form the full track.

If SellerSprite MCP is available, use it to expand keywords:

text

mcp__sellersprite-mcp__keyword_miner  { keyword: "<seed>", marketplace: "US" }
mcp__sellersprite-mcp__traffic_extend { keyword: "<seed>", marketplace: "US" }

Take the top 5–8 related keywords by search volume.

Fallback (no SellerSprite): Infer related keywords from SERP titles by:

Extracting noun phrases and modifiers from the top 10 product titles
Identifying variant terms (material, size, use-case, audience)
Generating 5–8 plausible search terms that a buyer might use
Label these as "inferred" in the output

If ClickHouse MCP is available, check brand analytics trend data:

sql

SELECT search_term, SUM(clicks) as total_clicks
FROM amazon.brand_analytics
WHERE search_term ILIKE '%<seed>%'
  AND date >= today() - INTERVAL 90 DAY
GROUP BY search_term
ORDER BY total_clicks DESC
LIMIT 10

This validates which related terms have real search volume.

Step 3 — Scrape 1–2 Key Related Keywords

Pick the 2 highest-volume related keywords from Step 2 and scrape their SERPs (same curl as Step 1). This reveals:

Whether the same products dominate multiple related SERPs (brand lock)
Price and review patterns across the broader track

Token budget: If context is getting long, skip this step and note it was skipped.

Step 4 — Deep-dive Top 5 Organic Products

For the top 5 non-sponsored products from the seed keyword SERP:

If amazon-listing-judge is available:

bash

uv run <amazon-listing-judge-skill-dir>/scripts/grade.py <ASIN>

Record the total score and key weaknesses.

Fallback: Use the SERP data already collected. Estimate listing quality as:

High (>80): has AC/BS badge + ≥4.5 stars + ≥1K reviews + bought_past_month
Medium (55-80): has some but not all signals
Low (<55): no badges, low reviews, no sales velocity signal

Step 5 — Competition Scoring

Calculate the following metrics from collected data:

5a. Review Barrier

text

avg_reviews = average review count of top 10 organic results

< 200 reviews avg → Low barrier (🟢 +2)
200–1,000 → Medium barrier (🟡 +1)
1,000–3,000 → High barrier (🔴 0)
3,000 → Very high barrier (🔴 -1)

5b. Brand Concentration

text

count unique brands in top 10 organic results

8–10 unique brands → Fragmented (🟢 +2)
5–7 unique brands → Moderate (🟡 +1)
3–4 unique brands → Concentrated (🔴 0)
1–2 brands dominating 5+ spots → Locked (🔴 -1)

5c. Demand Signals

Count how many of the top 10 organic results have "bought in past month" badge:

6–10 products → Hot track (🟢 +2)
3–5 products → Active (🟡 +1)
0–2 products → Low demand or seasonal (🔴 0)

5d. Listing Quality Gap

Average listing quality score of top 5 organic products:

Average score < 60 → Big quality gap (🟢 +2, easy to outcompete)
60–75 → Some gap (🟡 +1)
75 → Well-optimized (🔴 0)

5e. Price Range Health

text

min_price = lowest price among top 10
max_price = highest price among top 10
range_ratio = max_price / min_price

ratio > 3 → Wide range, room to position (🟢 +1)
ratio 1.5–3 → Moderate (🟡 0)
ratio < 1.5 → Commodity, price war risk (🔴 -1)

Competition Score Total (max 9)

text

competition_score = sum of all sub-scores above

7–9 → 🟢 Low competition — favorable
4–6 → 🟡 Moderate — conditional entry
0–3 → 🔴 High competition — difficult

Step 6 — Track Demand Score

Estimate overall demand level:

If SellerSprite available: use traffic_keyword_stat for monthly search volume.

From SERP signals:

Count products with "bought in past month" → quantify total estimated monthly units
Estimate: if avg = "1K+" per product × 10 products = ~10K monthly units in track
Note: this is a rough lower bound estimate

Seasonal flags:

If keyword contains holiday/event terms, flag as seasonal
If ClickHouse shows <3 months of search history, flag as trend/fad

Step 7 — Synthesis & Recommendation

Entry Assessment Matrix

Factor	Your Score	Implication
Competition score	X/9
Demand level	High/Med/Low
Price positioning room	Yes/No
Listing quality gap	Yes/No

Verdict

🟢 GO — competition_score ≥ 7 AND demand ≥ Medium 🟡 CONDITIONAL — competition_score 4–6 OR strong demand with one differentiator 🔴 NO-GO — competition_score ≤ 3 OR demand Low with high barrier

Entry Angle Candidates (pick the best fit)

Price disruption: If top products cluster at $20+, enter at $12–15 with comparable quality
Listing quality gap: If top listings score < 65, win on listing optimization alone
Underserved variant: If SERP titles share one attribute, find the missing variant buyers search for
Bundle/differentiation: If all products are standalone, add accessories or improved feature
Niche long-tail: If main keyword is crowded, target the highest-volume related term with lower competition

Output Format

Present the full analysis in this structure:

text

## 🔍 赛道分析：{seed_keyword}

### 赛道全貌
- **关键词组**: [seed] + [related1], [related2], ... (数据来源: SellerSprite/推断)
- **价格带**: $X–$Y，主力区间 $A–$B
- **市场容量估算**: 约 X 万月销量（基于月销标信号）

### 📊 SERP 竞品概览（前10有机结果）
| 排名 | ASIN | 标题(简) | 价格 | 评分 | 评论数 | 月销 | 徽章 |
|------|------|---------|------|------|--------|------|------|
...

### ⚔️ 竞争强度评分：X/9 — 🟢/🟡/🔴
| 维度 | 得分 | 说明 |
|------|------|------|
| 评论壁垒 | | 平均 X 条 |
| 品牌集中度 | | X 个独立品牌 |
| 需求热度 | | X/10 产品有月销标 |
| Listing 质量差距 | | 头部均分 X/100 |
| 价格带健康度 | | 区间比 X |

### 🔥 赛道热度
- 需求水平: High/Med/Low
- 季节性: 是/否
- 趋势信号: ...

### 🎯 结论

**裁定**: 🟢 GO / 🟡 有条件进入 / 🔴 NO-GO

**最佳切入点**: ...

**建议策略**:
1. ...
2. ...

**风险提示**: ...

Data Source Labels

Always label where each data point comes from:

[omni-scraper] — from SERP/PDP scrape
[SellerSprite] — from SellerSprite MCP
[ClickHouse] — from brand analytics DB
[inferred] — LLM reasoning from available data

If a tool was unavailable, explicitly state: "SellerSprite not available — keyword volume data omitted."

Amazon Serp Analysis

Install

amazon-serp-analysis

Requirements

Workflow

Step 1 — Seed SERP Scrape

Step 2 — Keyword Expansion (build track landscape)

Step 3 — Scrape 1–2 Key Related Keywords

Step 4 — Deep-dive Top 5 Organic Products

Step 5 — Competition Scoring

5a. Review Barrier

5b. Brand Concentration

5c. Demand Signals

5d. Listing Quality Gap

5e. Price Range Health

Competition Score Total (max 9)

Step 6 — Track Demand Score

Step 7 — Synthesis & Recommendation

Entry Assessment Matrix

Verdict

Entry Angle Candidates (pick the best fit)

Output Format

Data Source Labels

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