Amazon Brand Analytics

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 1bc9142374b1 · 19 Oct 2026Report an issue or request a re-scan

• Publishernexscope-ai GitHub organizationOwner of the repository this skill is read from
• Source foldernexscope-ai/Amazon-Skills/amazon-brand-analyticsThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict1bc9142374b1A new audit runs when the source changes
• Repository statusNot archivedLast push 54 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic…

---
name: amazon-brand-analytics
description: "Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies."
metadata: {"nexscope":{"emoji":"📊","category":"amazon"}}
---

# Amazon Brand Analytics 📊

Unlock Brand Analytics insights for strategic growth. Requires Brand Registry — works with your data.

## Installation

```bash
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

| 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:**
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 Exp

Excerpt, first 6,002 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

npx skills add nexscope-ai/Amazon-Skills --skill amazon-brand-analytics -g
npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g
npx skills add nexscope-ai/Amazon-Skills --skill amazon-competitor-monitoring -g

git clone https://github.com/nexscope-ai/Amazon-Skills
git -C Amazon-Skills checkout 1bc9142374b1

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source 1bc9142374b1

Questions about Amazon Brand Analytics

• github.com/nexscope-ai/Amazon-Skills/tree/main/amazon-brand-anal — the audited source at commit 1bc9142374b1 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology