AGENT SKILLBY NEXSCOPE-AI
Amazon brand analytics
AT A GLANCE
Amazon brand analytics is an agent skill for brand. Unlock Brand Analytics insights for strategic growth. It is published by nexscope-ai in the nexscope-ai/amazon-skills GitHub repository (336 stars) under the MIT license. It is also listed under analytics. Install it with: npx skills add nexscope-ai/amazon-skills --skill amazon-brand-analytics
- SOURCE REPOSITORY
- nexscope-ai/amazon-skills (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- SKILL.MD SIZE
- 8.3 KB228 LINES
QUICK ANSWER
- WHAT IT IS01
- Unlock Brand Analytics insights for strategic growth. Requires Brand Registry, works with your data. The value is a focused slice of brand and messaging judgment, useful when several similar skills cover the same ground.
- INPUTS02
- Context for brand and messaging: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result for brand and messaging: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add nexscope-ai/amazon-skills --skill amazon-brand-analyticsREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 14
Amazon Brand Analytics 📊#
Unlock Brand Analytics insights for strategic growth. Requires Brand Registry — works with your data.
Installation#
npx skills add nexscope-ai/Amazon-Skills --skill amazon-brand-analytics -gCapabilities#
- Search Frequency Rank (SFR) analysis: Decode keyword opportunities, click share gaps, and conversion optimization
- Market Basket intelligence: Identify cross-sell opportunities, bundle strategies, and category expansion
- Item Comparison insights: Understand competitive positioning and customer consideration factors
- Demographic analysis: Extract customer segment insights and geographic opportunities
- Seasonal trend detection: Identify timing patterns and market shifts from search data
- Strategic recommendations: Convert raw data into actionable growth strategies
- Multi-marketplace support: Works with Brand Analytics from all Amazon regions
Usage Examples#
Users can ask naturally. Examples:
Analyze my Search Frequency Rank data for "wireless earbuds" — show keyword opportunities and click share gapsReview my Market Basket data for the last 6 months. What cross-sell and bundling opportunities do you see?Interpret my Item Comparison report for yoga mats — how do customers evaluate my product vs competitors?Generate Brand Analytics strategy report for Q4 combining SFR, Market Basket, and demographic dataFind seasonal trends and opportunity keywords from my Brand Analytics data for kitchen appliancesThree Analysis Modes#
| Mode | Input Required | Output | Best For |
|---|---|---|---|
| SFR Analysis | Search Frequency Rank data export | Keyword opportunities, click/conversion gaps | Advertising optimization |
| Market Basket | Market Basket Analysis export | Cross-sell opportunities, bundle recommendations | Product strategy |
| Item Comparison | Item Comparison report data | Competitive positioning insights | Product development |
Workflow#
Step 1: Data Preparation#
For SFR Analysis:
- Export Search Frequency Rank report from Brand Analytics (last 90 days recommended)
- Focus on top 100-200 keywords by search frequency rank
- Note current click share and conversion share for each keyword
For Market Basket Analysis:
- Export Market Basket Analysis report (6-12 months for pattern recognition)
- Include both "Customers who bought X also bought Y" data
- Filter for statistically significant purchase combinations (10+ co-purchases)
For Item Comparison:
- Export Item Comparison report for your main ASINs
- Include comparison data with top 5-10 competitors
- Note customer consideration patterns and demographic breakdowns
Step 2: Pattern Recognition#
Use the provided data to identify:
SFR Insights:
- Keywords with high search frequency but low click share (opportunity gaps)
- Conversion share significantly below click share (optimization needs)
- Seasonal search pattern changes
- Emerging keyword trends
Market Basket Patterns:
- Products with >25% co-purchase rate (strong bundle candidates)
- Category cross-over patterns (expansion opportunities)
- Price point correlations in purchase combinations
- Geographic or demographic purchase pattern differences
Item Comparison Analysis:
- Customer consideration factors ranked by importance
- Your brand's competitive strengths and weaknesses
- Price sensitivity patterns in your category
- Feature preferences by customer segment
Step 3: Strategic Synthesis#
Convert insights into actionable recommendations following the output format below.
Output Format#
Present analysis in this structure:
## Brand Analytics Strategic Report: [Brand/Category]
**Analysis Period:** [timeframe] | **Data Sources:** [SFR/Market Basket/Item Comparison]
**Marketplace:** Amazon [region] | **Report Date:** [current date]
### 1. Search Frequency Rank Opportunities
**Top Keyword Gaps:**
| Keyword | Search Rank | Your Click Share | Category Avg | Opportunity Score |
|---------|-------------|------------------|--------------|-------------------|
| "wireless earbuds waterproof" | #23 | 2.1% | 8.4% | High |
| "bluetooth headphones gym" | #45 | 0.8% | 5.2% | Medium |
| "noise cancelling earbuds" | #67 | 4.2% | 6.1% | Low |
**Seasonal Trends:**
- [Keyword] searches peak in [months] (+X% vs baseline)
- [Category] shows declining trend (-X% YoY)
- Emerging opportunity: [new keyword trend]
**Recommended Actions:**
1. Increase advertising spend on high-opportunity keywords
2. Optimize listings for gap keywords with low click share
3. Prepare seasonal campaigns for [upcoming peaks]
### 2. Market Basket Insights
**Cross-Sell Opportunities:**
| Product Combination | Co-Purchase Rate | Revenue Opportunity | Recommendation |
|--------------------|------------------|--------------------|--------------|
| Your Product + [Item A] | 34% | +$2.3M annually | Create bundle |
| Your Product + [Item B] | 28% | +$1.8M annually | Cross-promote |
| [Item C] + [Item D] | 25% | +$1.2M annually | New product opportunity |
**Category Expansion Insights:**
- 23% of customers also purchase [adjacent category]
- Geographic concentration: [region] shows 40% higher cross-category rate
- Demographic pattern: [age group] drives 60% of cross-category purchases
### 3. Competitive Positioning
**Item Comparison Analysis:**
**Customer Consideration Factors (Ranked):**
1. Price (43% primary factor)
2. Reviews/Rating (31% weight)
3. Brand Recognition (18% influence)
4. Feature Set (12% consideration)
**Your Competitive Position:**
✅ **Strengths:** Higher ratings (4.6 vs 4.2), strong brand recall in 35-54 demo
⚠️ **Weaknesses:** Price perception, limited feature differentiation
**Market Opportunities:**
- Premium segment under-served (15% price tolerance above current range)
- Feature gap: customers want [specific feature] (mentioned in 67% of comparisons)
- Geographic expansion: strong brand preference in [regions]
### 4. Strategic Recommendations
**Immediate Actions (Next 30 Days):**
1. Launch [product bundle] based on Market Basket data
2. Increase ad spend on [top 3 opportunity keywords]
3. A/B test premium pricing in [geographic segments]
**Q4 Strategy:**
1. Prepare seasonal campaigns for [trending keywords]
2. Develop [feature enhancement] to address competitive gap
3. Expand into [adjacent category] with [specific product]
**2027 Growth Plan:**
1. Full [category] expansion based on cross-sell data
2. Premium line development for feature-conscious segment
3. Geographic expansion focus on [high-opportunity regions]
**Projected Impact:**
- Bundle optimization: +$X.XM revenue
- Keyword optimization: +X% conversion rate
- Category expansion: +$X.XM TAMIntegration with Other Skills#
This skill works perfectly with other Brand Registry and competitive analysis skills.
With amazon-keyword-research#
npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -gStep 1: "Analyze my SFR data for keyword opportunities"
→ amazon-brand-analytics identifies click share gaps
Step 2: "Research long-tail variations of those opportunity keywords"
→ amazon-keyword-research expands the keyword universeWith amazon-competitor-monitoring#
npx skills add nexscope-ai/Amazon-Skills --skill amazon-competitor-monitoring -gStep 1: "Review my Item Comparison data for competitive positioning"
→ amazon-brand-analytics reveals competitor strengths/weaknesses
Step 2: "Set up monitoring for those key competitors"
→ amazon-competitor-monitoring tracks their strategy changesRequirements#
⚠️ Brand Registry Required: This skill requires access to Amazon Brand Analytics data, which is only available to Brand Registry participants. You must export data from your Brand Analytics dashboard to use this skill effectively.
Limitations#
This skill provides frameworks for interpreting Brand Analytics data but requires you to export and provide the raw data from Amazon's Brand Analytics dashboard. For automated Brand Analytics processing and real-time strategic recommendations, check out Nexscope (opens in a new tab) — Your AI Assistant for smarter E-commerce decisions.
Built by Nexscope (opens in a new tab) — research, validate, and act on e-commerce opportunities with AI.
FAQ
07 QUESTIONS
01What is the Amazon brand analytics agent skill?
Unlock Brand Analytics insights for strategic growth. Requires Brand Registry, works with your data. It is listed under brand and analytics in the 100xLab agent skills directory.
02How do I install Amazon brand analytics in Cursor, Claude Code, or Codex?
Run npx skills add nexscope-ai/amazon-skills --skill amazon-brand-analytics in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Amazon brand analytics, not every skill in the repository.
03What does the Amazon brand analytics SKILL.md cover?
The SKILL.md includes sections on Installation, Capabilities, Usage Examples, Three Analysis Modes, Workflow and Step 1: Data Preparation. Read the full text on this page or in plain markdown at /resources/agent-skills/nexscope-ai-amazon-skills--amazon-brand-analytics--1w94o94/raw.
04Which agents can use Amazon brand analytics?
Amazon brand analytics installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is Amazon brand analytics under?
The nexscope-ai/amazon-skills repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is Amazon brand analytics free to use?
Reading, copying and installing Amazon brand analytics costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of Amazon brand analytics?
Amazon brand analytics comes from https://github.com/nexscope-ai/amazon-skills, which has 336 GitHub stars. The SKILL.md is at https://github.com/nexscope-ai/amazon-skills/blob/HEAD/amazon-brand-analytics/SKILL.md. The author is nexscope-ai.
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ATTRIBUTION
Amazon brand analytics was written by its original authors and is published in nexscope-ai/amazon-skills (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.