Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
nexscope-ai avatar

Amazon Brand Analytics

  • 661 installs
  • 480 repo stars
  • Updated July 23, 2026
  • nexscope-ai/amazon-skills

amazon-brand-analytics is an agent skill that interprets Amazon Brand Analytics exports—Search Frequency Rank, Market Basket, Item Comparison, and demographics—for developers and sellers who need data-driven catalog and

About

amazon-brand-analytics is a Nexscope agent skill from the 53-skill Amazon-Skills collection that teaches coding agents how to read Brand Registry analytics exports and produce strategic reports. The skill walks through three analysis modes—SFR keyword gap tables, Market Basket cross-sell matrices, and Item Comparison competitive positioning—and outputs structured recommendations for listings, bundles, and ad spend. It expects you to export dashboard data (90-day SFR, 6–12 month basket, competitor comparison sets) rather than calling Amazon APIs directly. Developers reach for amazon-brand-analytics when interpreting click-share gaps, co-purchase rates above 25%, or seasonal keyword shifts across any of the 12 supported Amazon marketplaces. Pair it with amazon-keyword-research or amazon-competitor-monitoring for expanded keyword mining and competitor tracking after gaps are identified.

  • Search term reports
  • Market basket
  • Repeat purchase
  • Demographics
  • Share of voice

Amazon Brand Analytics by the numbers

  • 661 all-time installs (skills.sh)
  • +91 installs in the week ending Jul 26, 2026 (Skillselion tracking)
  • Ranked #404 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 3, 2026 (Skillselion catalog sync)
npx skills add https://github.com/nexscope-ai/amazon-skills --skill amazon-brand-analytics

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs661
repo stars480
Last updatedJuly 23, 2026
Repositorynexscope-ai/amazon-skills

How do you interpret Amazon Brand Analytics exports?

Interpret Amazon Brand Analytics—search terms, market basket, repeat purchase, demographics—to refine catalog, content, and ad strategy for registered brands.

Who is it for?

Developers or ecommerce engineers supporting Brand Registry sellers who already export SFR, Market Basket, or Item Comparison data and need structured interpretation.

Skip if: Skip amazon-brand-analytics when you lack Brand Registry access, need live API pulls instead of exported reports, or only want generic Amazon SEO without analytics data.

When should I use this skill?

User uploads or references Brand Analytics SFR, Market Basket, Item Comparison, or demographic exports and asks for keyword, bundle, or competitive strategy recommendations.

What you get

Brand Analytics strategic report with SFR gap tables, Market Basket cross-sell matrix, Item Comparison positioning, and prioritized 30-day action list.

  • Brand Analytics strategic report
  • Keyword gap table
  • Cross-sell bundle recommendations

By the numbers

  • Part of nexscope-ai/Amazon-Skills collection with 53 skills
  • Covers 3 Brand Analytics analysis modes: SFR, Market Basket, Item Comparison
  • Supports 12 Amazon marketplaces per repository README

Files

SKILL.mdMarkdownGitHub ↗

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 -g

Capabilities

  • 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 gaps
Review 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 data
Find seasonal trends and opportunity keywords from my Brand Analytics data for kitchen appliances

Three Analysis Modes

ModeInput RequiredOutputBest For
SFR AnalysisSearch Frequency Rank data exportKeyword opportunities, click/conversion gapsAdvertising optimization
Market BasketMarket Basket Analysis exportCross-sell opportunities, bundle recommendationsProduct strategy
Item ComparisonItem Comparison report dataCompetitive positioning insightsProduct development

Workflow

Step 1: Data Preparation

For SFR Analysis: 1. Export Search Frequency Rank report from Brand Analytics (last 90 days recommended) 2. Focus on top 100-200 keywords by search frequency rank 3. Note current click share and conversion share for each keyword

For Market Basket Analysis: 1. Export Market Basket Analysis report (6-12 months for pattern recognition) 2. Include both "Customers who bought X also bought Y" data 3. Filter for statistically significant purchase combinations (10+ co-purchases)

For Item Comparison: 1. Export Item Comparison report for your main ASINs 2. Include comparison data with top 5-10 competitors 3. 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 TAM

Integration 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 -g
Step 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 universe

With amazon-competitor-monitoring

npx skills add nexscope-ai/Amazon-Skills --skill amazon-competitor-monitoring -g
Step 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 changes

Requirements

⚠️ 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](https://www.nexscope.ai/?co-from=skill) — Your AI Assistant for smarter E-commerce decisions.

---

Built by [Nexscope](https://www.nexscope.ai/?co-from=skill) — research, validate, and act on e-commerce opportunities with AI.

Related skills

How it compares

Pick amazon-brand-analytics when you have Brand Registry dashboard exports to interpret; use amazon-keyword-research for autocomplete-driven keyword discovery without analytics access.

FAQ

Does amazon-brand-analytics require Brand Registry?

amazon-brand-analytics requires Amazon Brand Registry access because Brand Analytics dashboards are limited to registered brands. The skill interprets exports you download from Seller Central rather than fetching live data automatically.

What data formats does amazon-brand-analytics accept?

amazon-brand-analytics works with Search Frequency Rank, Market Basket Analysis, and Item Comparison exports from Brand Analytics. Recommended windows are 90 days for SFR and 6–12 months for basket pattern detection before synthesis.

Which Amazon marketplaces does amazon-brand-analytics cover?

amazon-brand-analytics supports Brand Analytics data from all Amazon regions in the Nexscope Amazon-Skills README, including US, UK, DE, FR, IT, ES, JP, CA, AU, IN, MX, and BR marketplaces.

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.