Amazon Brand Analytics and Business Reports: Complete Guide

The difference between a data-driven Amazon seller and one flying blind is access to customer behaviour data. Brand Analytics reveals what customers search for, which products they buy together, and their demographics. Business Reports show your sales and traffic performance. Together, these datasets unlock competitive advantages that most sellers ignore. On the UK Amazon platform alone (amazon.co.uk), over 1,600 new sellers launch monthly; only those using data to refine strategy consistently outcompend price-cutters and guessers.

Accessing Brand Analytics: Brand Registry Requirements

Brand Analytics is not a free tool available to all sellers. It requires Amazon Brand Registry enrollment, which requires trademark registration. This is a significant barrier to entry for many sellers, which is why fewer than 30% of Amazon sellers have access to it.

Brand Registry Enrollment

To enrol in Amazon Brand Registry, you must own a registered trademark for your brand name or logo. The trademark can be registered in any country, but Amazon requires: trademark registration number, country of registration, and the registered holder name matching your Amazon seller account name.

Trademark registration timelines vary: UK, US, and EU trademarks typically take 4-12 months to register. Costs range from £200-£1,000 depending on country and legal complexity. The investment pays for itself within months through improved listing optimisation and advertising efficiency enabled by Brand Analytics access.

Applying for Brand Registry

Once you have trademark registration, log into Seller Central and navigate to Brands > Brand Registry > Register New Brand. Amazon verifies your trademark ownership (usually within 1-2 weeks). Once approved, Brand Analytics appears in your Seller Central dashboard under ‘Brand Analytics’.

If your brand is not yet registered, focus on the equivalent non-brand-analytics data: Business Reports (available to all sellers) provide ASIN-level traffic and conversion data. Use competitor research tools (Jungle Scout, Helium 10, MerchantWords) to gather search demand and keyword data until you’re enrolled.

Search Query Performance Dashboard

Search Query Performance is the highest-ROI Brand Analytics dashboard for most sellers. It shows every search term customers used to find your products in the Amazon search results, plus metrics that directly inform keyword strategy.

Metrics Explained

The dashboard displays:

Search Terms: The exact query customers searched. Example: “waterproof phone case”, “running shoes women’s blue”.
Search Frequency: How many searches for that term occurred within the reporting period (usually 13 weeks).
Number of Results: How many products appeared in search results for that term. High-volume terms may have 10,000+ results; niche terms may have 50-500.
Total Clickthrough Rate (CTR): Percentage of shoppers who searched that term and clicked on at least one product (any brand).
Your Conversion Rate: Percentage of customers who clicked your product after searching that term and purchased something.
Your % of Category Searches: What percentage of all searches for that term resulted in clicks to your products (market share).

Identifying High-Value Keywords

Sort by conversion rate (highest first) to identify high-intent keywords. These keywords convert above your average and represent customers actively looking to buy, not just browse. A keyword with 5% conversion rate is far more valuable than one with 0.5% conversion, even if the lower-converting keyword has higher volume.

Next, identify gaps: keywords with high conversion but low penetration (you capture <5% of category searches). These represent growth opportunities. If customers searching "red running shoes women's" convert at 6% but you only appear in 2% of those searches, increasing your visibility through PPC or organic ranking optimisation is high-ROI.

Example analysis:

High Volume, High Conversion, Low Penetration: Bid aggressively on PPC. This is money waiting to be captured.
High Volume, High Conversion, High Penetration: Maintain current position. Monitor for rank erosion.
High Volume, Low Conversion, Any Penetration: Irrelevant to your product. Don’t bid. It may be a generic category term capturing unqualified shoppers.
Low Volume, High Conversion, Low Penetration: These are niche keywords where you can build ranking cost-effectively. Invest in organic optimisation.

Seasonal Patterns in Search Behaviour

Compare Search Query Performance across reporting periods (Amazon provides 13-week rolling windows). Seasonal keywords (e.g. “Christmas gifts”, “garden furniture spring”) spike during specific months. Identify these patterns to plan advertising campaigns and inventory purchases months in advance.

Export data for seasonal analysis: If you see a keyword’s search frequency increases 300% in October-November, that’s your baseline for Q4 advertising planning.

Top Search Terms Report

The Top Search Terms report is a simpler view than Search Query Performance, showing the highest-volume search terms across your category or brand. It’s useful for understanding overall market demand but less actionable than Search Query Performance for individual keyword decisions.

Using Top Search Terms for Market Trend Analysis

Review monthly trends: Which terms are climbing in frequency? Which are declining? Ascending terms represent market tailwinds (growing customer demand); declining terms may signal category decline, competition emergence, or seasonal rotation.

Use Top Search Terms to validate new product development. If “eco-friendly” appears in top search terms and is growing month-over-month, there’s market demand for sustainable products in your category. Conversely, if a product category term is declining, reconsider investing heavily in that category.

Competitive Benchmarking

The report shows your top search terms, but to understand competitive positioning, you must compare against known competitors. If your top search term is “running shoes”, but competitors’ top searches are “marathon training shoes” and “trail running shoes”, they may be better positioned for niche segments.

This analysis informs product development and marketing positioning: Are you positioned as a generic player or a specialist? Data often reveals misalignment between your intended positioning and actual customer perception.

Market Basket Analysis

Market Basket Analysis reveals which products customers purchase together with your brand. This is gold for cross-sell strategy, bundling, and identifying product gaps.

Understanding the Data

The report shows:

Co-Purchased Products: Products customers buy in the same order as your brand’s products, including your own SKUs and competitor products.
Frequency: How often the combination appears in customer orders.
Category: The product category.

For example, if you sell women’s running shoes, Market Basket Analysis might show that 30% of customers who buy your shoes also buy running socks (your competitor’s product), 25% buy sports watches (competitor), and 15% buy insoles (your product or competitor).

Identifying Product Gaps and Opportunities

High-frequency co-purchases with competitor products represent gaps in your product line. If 25% of customers buy competitor sports watches with your shoes, consider developing your own watch or negotiating white-label distribution. This directly addresses customer needs revealed by shopping behaviour.

Use this data to inform SKU expansion decisions:

Which complementary product categories see the highest co-purchase rates?
Are these categories profitable enough to justify private label development or third-party distribution?
Can you bundle your products with high-frequency co-purchases to increase AOV (average order value)?

Bundling and Cross-Sell Strategy

Use Market Basket Analysis to create strategic product bundles. If 20% of customers who buy your running shoes also buy socks and insoles, bundle these three products at a discount to increase order value and reduce inventory fragmentation.

Monitor bundle performance: Do bundled products outconvert single-SKU listings? Do they increase customer lifetime value? A successful bundle increases AOV by 15-30% without eroding margins.

Repeat Purchase Behaviour

Repeat Purchase Behaviour shows what percentage of customers who buy your product once buy from you again. This metric benchmarks brand loyalty and indicates product quality and customer satisfaction.

Understanding Repeat Purchase Rates

The report breaks down repeat purchase rates by timeframe:

30-Day Repeat Rate: Customers who repurchase within 30 days of first purchase.
90-Day Repeat Rate: Customers who repurchase within 90 days.
365-Day Repeat Rate: Customers who repurchase within one year.

Industry benchmarks vary dramatically by category. Consumables (groceries, beauty, supplements) typically see 30-50% 90-day repeat rates. One-time purchases (furniture, appliances) may have 5-15% 90-day repeat rates simply because replacement cycles are longer.

Benchmarking Against Competitors

Brand Analytics allows comparison of your repeat purchase rate against category averages and top-selling brands (anonymised). If your 90-day repeat rate is 12% and the category average is 25%, investigate why:

Is product quality below competitors? (Review ratings, return rates.)
Is your price too high, driving customers to cheaper alternatives?
Is your product positioning non-consumable (customers don’t need to repurchase), whilst competitors position as consumable or subscription?
Are competitors running better loyalty programs (email, loyalty points) encouraging repeat purchase?

Improving Repeat Purchase Rates

Actionable improvements:

Quality and Consistency: Low repeat rates often signal quality issues. Audit product specifications and manufacturing consistency.
Email Marketing: Capture emails at purchase and run email campaigns encouraging repeat purchase (reorder reminders, loyalty discounts, new product launches).
Subscription or Auto-Replenishment: For consumables, offer subscription discounts (e.g. 5% off for monthly auto-ship) to increase repeat purchase predictability.
Product Bundles: Bundle complementary products to encourage larger, more frequent orders.
Loyalty Rewards: Implement points or rewards programs (if using a platform like Fiddle or Amazon-integrated solutions).

Even small improvements to repeat purchase rates compound significantly. A 5% improvement in 90-day repeat rate typically increases lifetime customer value 15-25%.

Demographics and Customer Insights

Demographics show your customer base composition: gender, age group, household income (in some markets), and shopping behaviour. This data validates marketing targeting and reveals underserved segments.

Interpreting Demographic Data

Brand Analytics shows:

Gender Distribution: Percentage of customers who are male, female, or unspecified. For example, 65% female, 30% male, 5% unspecified.
Age Groups: Distribution across age brackets (18-24, 25-34, 35-44, 45-54, 55-64, 65+). Use to validate that your product appeals to intended age groups.
Household Income (US only): Distribution across income brackets. Available in some US categories only.

Validating Marketing Strategy

Compare demographic data against your intended target market. If you’re marketing premium running shoes to athletes aged 25-44, but your customer demographics skew 45+, your positioning may be misaligned. Either adjust marketing to address the older demographic, or refine product positioning to attract younger audiences.

Use demographic insights to refine advertising targeting. Amazon advertising allows targeting by age group and gender. If your product performs 3x better with female customers aged 25-34, allocate 60-70% of advertising budget to that segment.

Identifying Underserved Segments

Sometimes demographics reveal unexploited opportunities. If your product appeals equally to men and women, but your brand positioning and marketing emphasise one gender, you’re leaving growth on the table. Test marketing messaging to underrepresented segments: a 10% increase in one underserved segment could drive 15-25% revenue growth.

Payment Reports and Financial Data

Beyond sales performance, understanding your cash flow and settlement schedule is essential for business health. Amazon’s Payment Reports provide detailed financial data.

Payments Summary

The main Payments Summary dashboard shows:

Total Account Balance: Total funds owed to you or total funds you owe Amazon (if you’ve had chargebacks or refunds exceeding available balance)
Available Funds: Cash you can withdraw immediately
Pending Funds: Sales revenue that will be available in the next settlement (typically 7-14 days)
Recent Payouts: List of recent transfers to your bank account, showing deposit dates and amounts

Statement View

Shows payment activity aggregated by settlement period and line type. You can view earnings by:

Sales and shipping revenue (by settlement date)
Refunds and returns (showing when refunds reduce your available balance)
Adjustments (chargebacks, disputed transactions, policy violations)
FBA fees and service fees (Amazon’s cuts)

Use Statement View to understand your payment rhythm and plan cash flow. If you process £5,000 in sales but £2,000 in refunds in a settlement period, your net funds available is £3,000.

Transaction View

Drill down to individual transactions within a payment period. You can see:

Order ID and transaction date
Amount and transaction type (sale, refund, fee)
Settlement date (when funds hit your account)

Transaction View is valuable for reconciling specific orders or investigating discrepancies between your records and Amazon’s.

Date Range Reports

Generate custom financial reports for any date range, based on dispatch date (when the order shipped) rather than payment received date. This is useful for accounting purposes: you can match revenue to the period when orders were fulfilled, independent of settlement delays.

Seller Coach: AI-Powered Recommendations

Amazon’s Seller Coach (available in Seller Central under Growth Opportunities) provides AI-driven recommendations tailored to your account. Unlike generic guides, Seller Coach analyses your specific account data and suggests actions likely to grow your sales.

Five Opportunity Types

Inventory Opportunities: Based on your sales velocity over the last 7 days, Seller Coach recommends which items you should restock. If a product sold 50 units last week, Seller Coach suggests restocking to meet expected demand. This prevents stockouts during peak demand.
Product Opportunities: Highlights SKUs with limited availability (low stock) and strong customer demand. If a product has 100+ page views but only 3 units in stock, Seller Coach recommends restocking to capitalise on demand.
Global Selling Opportunities: Identifies products that sell well domestically but are not listed internationally. If a UK bestseller has no US listing, Seller Coach recommends creating a US version to access a larger market.
Fulfilment Opportunities: Shows products with strong customer interest but not yet listed on FBA or SFP (Seller Fulfilled Prime). Migrating to FBA often increases Buy Box win rate and sales velocity, and Seller Coach quantifies the opportunity.
Low Price Opportunities: Identifies products where your price is 10-15% above competitor average without clear differentiation. Seller Coach suggests modest price reductions that could improve competitiveness.

Customizable Notifications

You can configure Seller Coach notifications to alert you by email when specific opportunities emerge. For example, “Notify me when an SKU reaches low inventory levels” or “Send weekly summaries of top opportunities”. This keeps you proactive rather than reactive to market changes.

Business Reports and ASIN-Level Analysis

Business Reports are available to all sellers (no Brand Registry required) and show your account sales, traffic, and product-level performance.

Sales and Traffic Reports (2026 Update)

Amazon’s Sales and Traffic reports provide comprehensive data for up to 2 years of historical performance. You can access and filter this data flexibly to analyse trends, seasonality, and performance by different dimensions.

Report Data Groups (Three Main Categories)

1. Account Sales Data

Sales: Total revenue from product sales
Orders: Number of orders placed
Average Order Value (AOV): Total sales / orders. Track this metric monthly; declining AOV suggests customers are buying fewer items per order (lost cross-sell opportunity) or smaller-value items.

2. Listing Performance Data

Sessions: Number of customer browsing sessions (visits) to your listings
Session Order Rate (Conversion Rate): Orders / Sessions. High-performing listings convert 5%+. Low-performing <2%. Use this to identify which listings need optimisation.
Buy Box Percentage: If multiple sellers exist for an ASIN, what % of sales come from you? 70%+ indicates you’re the primary seller.

3. Account Performance Data

Feedback Rating: Your overall seller feedback score (1-5 stars). Track monthly to monitor reputation health.
A-Z Claims: Number of customer claims filed against you (indicates quality issues or delivery problems). High A-Z claim rates trigger account warnings.
Refund Rate: Percentage of orders refunded. Track by product category; high refund rates indicate quality or fit issues.

Sales and Traffic by Date

Shows daily or weekly units sold, revenue, and sessions (customer browsing sessions). Track:

Session Conversion Rate: Orders / Sessions = conversion rate for your overall account. Track weekly trends; declining conversion suggests listing quality degradation or increased competitive pressure.
Units Per Session: Total units sold / Sessions = average items per customer. High values indicate strong cross-sell or bundle success.
Session Velocity: Do sessions increase or decrease week-over-week? Declining sessions signal rank erosion or reduced visibility. Investigate and adjust advertising or listings accordingly.

Filtering and Analysis

Sales and Traffic reports allow filtering by:

Date Range: Analyse specific periods (launch weeks, seasonal spikes, competitive events)
Fulfilment Method: Separate FBA vs FBM performance to understand which model performs better for your products
B2B vs Consumer: If you sell to both businesses and consumers, segment data to understand which channel is growing

Detail Page Sales and Traffic (ASIN-Level)

Shows performance for individual ASINs (Amazon Standard Identification Number: essentially your product listing). Key metrics:

Buy Box Percentage: If your ASIN has multiple sellers, what % of purchases come from you? 0-30% means you’re not winning the buy box; competitors are. 70%+ means you’re the primary seller.
Sessions: Customer views of that ASIN.
Conversion Rate: Units sold / Sessions. High conversion (5%+) indicates strong product, pricing, and listing quality.
Feedback Rating: Average star rating from customer reviews.

Identifying High-Performers and Underperformers

Sort ASIN-level reports by conversion rate. ASINs converting above 8% are strong performers and should receive advertising investment and visibility focus. ASINs converting below 2% need investigation:

Is the listing optimised (title, bullets, images, description)?
Is pricing competitive?
Are there negative reviews driving customers away?
Is the product image or listing poorly formatted?

Take action: optimise listings, adjust pricing, or consider discontinuing underperforming ASINs that can’t be salvaged.

Search Catalogue Performance (Brand Analytics + Business Reports)

The Search Catalogue Performance dashboard (Brand Analytics) combines search data with your sales performance. It shows how many searches for specific keywords resulted in your products appearing in search results, and what percentage of those impressions converted to clicks and sales.

This is crucial for understanding your organic ranking strength. If “running shoes” generates 100,000 searches monthly in your category, but you capture only 200 clicks (0.2% CTR), your ranking is weak. Improvement opportunities: optimise listing for that keyword, increase review count, improve image quality, or increase advertising to boost momentum.

Using Brand Analytics to Optimise Listings

Brand Analytics data directly informs listing optimisation decisions. Titles, bullet points, backend keywords, and descriptions should all align with customer search behaviour revealed in Brand Analytics.

Keyword Alignment in Titles and Bullets

Compare your current listing (title, bullet points) against your top search terms in Brand Analytics. If customers frequently search “blue running shoes women’s” and you have a blue women’s running shoe, but your title is “Premium Athletic Footwear for Female Runners”, you’re missing the exact keywords customers search.

Rewrite title to include high-volume, high-conversion keywords: “Blue Running Shoes for Women, Professional Athletic Footwear, Lightweight and Waterproof”. This matches customer search behaviour.

Prioritise keywords in title by: (1) search volume (higher first), (2) conversion rate (high-converting terms take precedence), (3) competition level (lower-competition terms offer quick ranking wins).

Backend Keyword Strategy

Backend keywords (hidden from customers but searchable by Amazon’s algorithm) should capture high-intent, lower-volume terms that don’t fit naturally in the title. Search Query Performance reveals misspellings, synonyms, and related terms that customers search but don’t appear in your listing.

Example: If customers search “trainers” (British English) and “sneakers” (American English) for running shoes, but your listing emphasises “athletic footwear”, add both terms to backend keywords.

See our guide to backend keywords for detailed strategy.

Content Refresh Based on Emerging Keywords

Monitor Brand Analytics monthly for emerging search terms. If a new keyword trending upward matches your product, add it to listing content before competitors do. Emerging keywords often have lower competition and faster ranking potential.

Informing Advertising Strategy with Search Data

Brand Analytics is your most direct path to high-ROI advertising keywords. PPC keyword selection should be data-driven, not guessed.

Keyword Bid Strategy from Search Query Performance

Prioritise PPC bids on keywords from Brand Analytics sorted by high conversion and moderate-to-high volume:

High Conversion, High Volume, Low Penetration: Bid aggressively. This is proven high-intent customer demand with market opportunity.
High Conversion, Moderate Volume, Any Penetration: Bid moderately. Stable, predictable ROI.
High Conversion, Low Volume, Low Penetration: Niche keywords with potential for organic ranking investment before PPC.
Low Conversion: Don’t bid regardless of volume. Customer intent doesn’t align with your product.

Avoid common PPC mistakes: bidding on high-volume but low-conversion keywords (brand-name terms, generic category terms), or ignoring low-volume, high-conversion keywords (long-tail, specific intent terms).

Competitor Visibility and ASIN Targeting

Brand Analytics shows “Your % of Category Searches” for each keyword. If a competitor appears in 15% of searches for a keyword whilst you appear in 3%, consider running competitor ASIN-targeting ads: target that competitor’s ASIN with your ads to display when customers view their listing.

This is effective for capturing in-market searchers considering alternatives. Your ad appears on competitor product pages, converting window shoppers into buyers.

Testing and Iterating on Keyword Strategy

Use Brand Analytics to benchmark previous keyword performance and test new keywords. Compare conversion rates quarter-over-quarter: Are your high-bid keywords still converting at expected rates? Are new keywords emerging that should receive increased budget?

For more detailed PPC strategy, see our Amazon PPC guide.

Limitations and Data Gaps

Brand Analytics is powerful but imperfect. Understanding its limitations prevents over-reliance on incomplete data.

Data Lag

Brand Analytics data is typically delayed by 1-2 days. You’re not seeing real-time customer search behaviour; you’re seeing 24-48 hour old data. This is acceptable for strategy and long-term decisions, but not for real-time bid adjustments or daily optimisations.

Aggregate Data Without Individual Customer Tracking

Brand Analytics shows aggregate trends but no individual customer journey data. You can see that customers searching “running shoes women’s” have 6% conversion rate, but you can’t see that customer #12345 searched the term and abandoned their cart. Individual customer behaviour requires tools like heat mapping or UTM-tracked data from external sources.

Competitor Data is Anonymised

When comparing your performance to competitors, competitor names are withheld. You see “Top 5 Sellers in Your Category” but not names. This limits competitive benchmarking. Use third-party tools (Jungle Scout, Helium 10) to fill this gap.