Sales & Traffic Dashboard

What is the Sales & Traffic Dashboard?

The Sales & Traffic Dashboard is a powerful analytical tool designed to help you understand every aspect of your Amazon marketplace performance. Think of it as your command center for monitoring how your products are performing, how customers are discovering your listings, and how effectively your sales efforts are converting visitors into buyers.

This dashboard consolidates complex sales and traffic data into an easy-to-understand format, allowing you to see patterns, identify opportunities, and make informed decisions about your business strategy. Whether you’re tracking daily sales performance, analyzing customer behavior trends, or comparing business-to-business (B2B) versus consumer sales, this tool provides the insights you need at a glance.

Key Terminology (Glossary)

Before diving into the features, it’s essential to understand the language used throughout this dashboard. These terms appear frequently and understanding them will help you make the most of the tool.

Sales Metrics:

Units Ordered: The total number of individual product units that customers have purchased during your selected time period. Each unit represents one physical item sold.
Units Ordered B2B: Similar to Units Ordered, but specifically tracking purchases made by business buyers (Amazon Business customers) rather than individual consumers.
Total Units Ordered: The combined count of both regular consumer units and B2B units ordered. This gives you the complete picture of all units sold.
Total Order Items: This represents the number of distinct line items in customer orders. If a customer orders three different products, that counts as three order items, even if they’re in the same order.
Total Order Items B2B: The number of distinct line items specifically from business buyers.
Sales Amount: The total monetary value of products ordered by regular consumers. This is calculated by multiplying the price of each order item by the number of units sold.
Sales Amount B2B: The total monetary value of products ordered specifically by business buyers.
Total Sales Amount: The combined monetary value of all sales, including both consumer and B2B purchases.
Average Sales Per Order Item: This tells you how much revenue, on average, each order item generates. It’s useful for understanding the typical value of individual purchases.
Average Sales Per Order Item B2B: The average revenue per order item specifically from business buyers.
Average Units Per Order Item: This metric shows how many units, on average, are included in each order item. For example, if customers typically buy two units at a time, this would show 2.0.
Average Units Per Order Item B2B: The average number of units per order item specifically from business buyers.
Average Selling Price: This represents the average price at which each unit was sold. It’s calculated by dividing total sales by total units sold.
Average Selling Price B2B: The average price per unit specifically for business buyer purchases.
Units Refunded: The total number of units that customers returned and received refunds for during your selected period.
Refund Rate: A percentage showing what portion of your total units sold were refunded. This helps you understand product quality and customer satisfaction levels.
Claims Granted: The number of A-to-z Guarantee Claims that Amazon approved. These are customer protection claims that go beyond standard returns.
Claims Amount: The total monetary value of all A-to-z Guarantee Claims that were granted.
Shipped Product Sales: The monetary value of products that were not only ordered but also confirmed as shipped. This differs from Sales Amount because it only counts sales that have progressed to the shipping stage.
Units Shipped: The total number of units that were physically shipped to customers.
Orders Shipped: The total number of distinct orders that were shipped out.

Traffic Metrics:

Page Views: The total number of times customers viewed your product listings on Amazon. Each time someone loads your product page, it counts as one page view.
Page Views B2B: The number of times your product listings were viewed specifically by Amazon Business customers.
Browser Page Views: Page views that occurred when customers were using Amazon’s website through a web browser (like Chrome, Safari, or Firefox) on their computer or mobile device.
Browser Page Views B2B: Browser page views specifically from business buyers.
Mobile App Page Views: Page views that occurred when customers were using Amazon’s mobile application on their smartphones or tablets.
Mobile App Page Views B2B: Mobile app page views specifically from business buyers.
Sessions: A session represents a visit to your Amazon product pages by a customer. All activity within a 24-hour period by the same customer is considered part of one session. This is different from page views because one session can include multiple page views.
Sessions B2B: Sessions specifically from Amazon Business customers.
Browser Sessions: Sessions that occurred when customers were using Amazon’s website through a web browser.
Browser Sessions B2B: Browser sessions specifically from business buyers.
Mobile App Sessions: Sessions that occurred when customers were using Amazon’s mobile application.
Mobile App Sessions B2B: Mobile app sessions specifically from business buyers.
Buy Box Percentage: This critical metric shows how often your offer appears in the Amazon Buy Box—the prominent “Add to Cart” box on product pages. A higher percentage means more customers see your offer as the primary option, which typically leads to more sales.
Buy Box Percentage B2B: How often your offer appears in the Buy Box specifically for business buyers.
Conversion Rate: Also called “Unit Session Percentage,” this shows what percentage of customer sessions resulted in a purchase. It’s calculated by dividing units ordered by sessions and multiplying by 100.
Order Item Session Percentage: This metric shows what percentage of sessions resulted in at least one order item being purchased.
Order Item Session Percentage B2B: The percentage of business buyer sessions that resulted in at least one order item purchase.
Unit Session Percentage B2B: The conversion rate specifically for business buyers, showing what percentage of their sessions resulted in purchases.
Average Offer Count: The average number of competing offers available for your products during the selected time period. This helps you understand competitive pressure.
Average Parent Items: The average number of parent products you had listed for sale during the time period. Parent items are the main product listings that may have variations (like different sizes or colors).
Feedback Received: The total number of customer reviews and ratings you received during the period.
Negative Feedback Received: The number of negative reviews or low ratings you received.
Negative Feedback Rate: The percentage of all feedback that was negative. This is calculated by dividing negative feedback by total feedback.

Interface Terms:

Granularity: This refers to the time interval used to group your data. You can view data by day (daily), by week (weekly), or by month (monthly). Daily granularity shows the most detail, while monthly provides a broader overview.
Date Range: The specific period of time you want to analyze. You can select custom dates or use preset ranges like “This Year” or “Last Month.”
ASIN: Amazon Standard Identification Number—a unique identifier for each product listing on Amazon. When you expand a date row, you’ll see performance data broken down by individual ASINs.
Expandable Rows: Some rows in the table can be expanded to show more detailed information. Clicking the arrow icon next to a date will reveal product-level (ASIN) breakdowns for that day.
Column Management: The ability to show, hide, and reorder the columns (metrics) displayed in your table. This lets you customize the view to focus on the metrics most important to you.
Chart Visualization: A graphical representation of your selected metrics over time, making it easier to spot trends and patterns.
Trendline: When viewing a single metric in the chart, a dashed line appears showing the overall trend direction. This helps you see if a metric is generally increasing, decreasing, or staying stable over time.

The “Why”: Benefits of Using This Tool

Understanding why this dashboard matters will help you use it more effectively. Here are the key benefits:

1. Comprehensive Performance Visibility

Instead of checking multiple reports or spreadsheets, everything you need to know about your sales and customer behavior is in one place. You can see how many units you sold, how much revenue you generated, how many people visited your listings, and how effectively those visits converted into sales—all in a single view.

2. Data-Driven Decision Making

By seeing trends over time, you can identify what’s working and what isn’t. For example, if your conversion rate is declining while page views are increasing, you might need to improve your product listings or pricing. If B2B sales are growing faster than consumer sales, you might want to focus more marketing efforts on business buyers.

3. Early Problem Detection

The dashboard helps you catch issues before they become major problems. A sudden spike in refund rate might indicate a quality issue. A drop in Buy Box percentage could signal pricing or fulfillment problems. By monitoring these metrics regularly, you can address issues quickly.

4. Performance Optimization

Understanding which metrics are underperforming helps you know where to focus improvement efforts. If your conversion rate is low, you might need better product images or descriptions. If your Buy Box percentage is low, you might need to improve your seller metrics or adjust pricing.

5. Time Efficiency

Rather than manually calculating averages, percentages, and totals from raw data, the dashboard does all the math for you instantly. You can analyze months of data in seconds instead of spending hours in spreadsheets.

6. Trend Analysis

The chart visualization makes it easy to spot patterns. You might notice that sales spike on weekends, that mobile app sessions are growing faster than browser sessions, or that certain months consistently perform better. These insights help you plan inventory, marketing campaigns, and business strategies.

7. Competitive Intelligence

Metrics like Average Offer Count help you understand competitive pressure. If you see this number increasing, you know more sellers are competing for the same customers, which might require adjusting your strategy.

8. Customer Behavior Insights

By seeing the breakdown between browser and mobile app usage, you can understand how customers prefer to shop. This information can guide decisions about where to focus optimization efforts—for example, ensuring your product listings look great on mobile devices if most traffic comes from mobile apps.

Interface Overview

Page Layout and Structure

When you first open the Sales & Traffic Dashboard, you’ll see a well-organized interface designed to give you quick access to all the information you need. Let’s break down each section:

Header Section

At the top of the page, you’ll find the page title “Sales & Traffic” along with several important controls:

Date Range Selector: This allows you to choose the time period you want to analyze. You can select from preset options like “This Year,” “Last Month,” “Last 7 Days,” or choose custom dates using the calendar picker.
Granularity Selector: This dropdown lets you choose how your data is grouped—by day (Daily), by week (Weekly), or by month (Monthly). Daily shows the most detail with one row per day, while Monthly provides a higher-level overview with one row per month.
Marketplace Selector: If you sell in multiple Amazon marketplaces, you can select which one to view. The dashboard shows data for one marketplace at a time.

Main Content Area

Below the header, you’ll find the main data visualization area, which consists of two primary components:

The Data Table: A comprehensive table showing all your sales and traffic metrics organized by date. Each row represents one time period (day, week, or month depending on your granularity setting), and each column represents a different metric.
The Chart Visualization: Below the table, you’ll see an interactive chart that graphically displays selected metrics over time. This makes it easier to spot trends and patterns.

Understanding the Data Table

The data table is the heart of the dashboard. Let’s explore its components in detail:

Table Structure

Fixed First Column (Expander): The leftmost column contains an arrow icon (► or ▼) that allows you to expand rows to see product-level details. There’s also a pin icon that controls whether the first column stays visible when you scroll horizontally.
Date Column: The second column shows the date for each row. Dates are formatted in a readable format like “Jan 15, 2024” or “Week of Jan 1, 2024” depending on your granularity setting.
Metric Columns: All subsequent columns display different metrics. The number of columns visible depends on your column management settings (which we’ll cover in detail later).
Table Footer: At the bottom of the table, you’ll see a “Total” row that sums up all the values in each column for the entire selected date range. This gives you quick access to overall totals without having to calculate them manually.

Row Expansion Feature

Many rows in the table can be expanded to reveal more detailed information. When you click the arrow icon (►) next to a date, the row expands to show a breakdown by individual products (ASINs). Each expanded row shows:

Product image (if available)
Product name or display name
SKU (Stock Keeping Unit) identifier
The same metrics as the main table, but calculated specifically for that product

This product-level view is incredibly valuable for understanding which products are driving your performance. You can even click on a product to open a detailed view showing that product’s performance over time.

Column Headers

Each column header serves multiple functions:

Title: The name of the metric (e.g., “Units Ordered,” “Sales Amount,” “Conversion Rate”)
Info Icon: Many column headers include a small information icon (ℹ️) that, when hovered over, displays a tooltip explaining what that metric means and how it’s calculated.
Sort Arrows: Clicking on a column header sorts the table by that metric. You’ll see an up arrow (↑) for ascending order, a down arrow (↓) for descending order, or a double arrow (⇅) when no sorting is applied.
Chart Toggle Button: Most metric columns include a small chart icon that allows you to add that metric to the chart visualization below the table. When a metric is added to the chart, the button appears highlighted.

Understanding the Chart Visualization

The chart area provides a graphical view of your selected metrics, making trends and patterns much easier to identify than looking at raw numbers in a table.

Chart Components

X-Axis (Horizontal): Represents time, with dates displayed along the bottom. The format adjusts based on your granularity—daily charts show individual dates, while monthly charts show month names.
Y-Axis (Vertical): Represents the values of your metrics. The chart automatically creates separate axes for different types of metrics:
- Left axis for counts (units, sessions, page views)
- Right axis for currency values (sales amounts, prices)
- Right axis for percentages (conversion rates, Buy Box percentage)
Data Lines: Each metric you’ve added to the chart appears as a colored line. The lines are smooth curves that connect data points, making trends easy to follow.
Data Points: Small circles mark each actual data point on the lines. When you hover over these points, you’ll see detailed information in a tooltip.
Trendline: When viewing a single metric, a dashed line appears showing the overall trend. This trendline uses statistical analysis to show whether the metric is generally increasing, decreasing, or staying stable.
Legend: Below the chart, you’ll see colored badges representing each metric currently displayed. You can click the X icon on any badge to remove that metric from the chart.

Chart Interactions

Hover: Moving your mouse over any point on a line displays a tooltip showing the exact date and values for all visible metrics at that point in time.
Metric Selection: Click the chart icon in any column header to add that metric to the chart. Click it again (or click the X on the legend badge) to remove it.
Zoom and Pan: The chart automatically adjusts to fit your selected date range. As you change date ranges, the chart updates accordingly.

Column Management System

One of the most powerful features of this dashboard is the ability to customize which columns you see and in what order. This section explains how to use these customization features.

Accessing Column Management

Look for a settings icon (⚙️) or “Manage Columns” button, typically located near the top-right of the table. Clicking this opens a dialog box or dropdown menu where you can control column visibility and ordering.

Showing and Hiding Columns

In the column management interface, you’ll see a list of all available metrics with checkboxes next to each one. Here’s how to use this:

To Hide a Column: Uncheck the box next to any metric you don’t want to see. The column will immediately disappear from the table.
To Show a Column: Check the box next to any hidden metric to make it visible again.
Select All / Deselect All: Most column management interfaces include buttons to quickly show or hide all columns at once, which is useful when you want to start fresh with your customization.

Why Hide Columns?

You might want to hide columns for several reasons:

Focus: If you’re analyzing conversion rates, you might hide shipping-related columns to reduce clutter and focus on traffic and sales metrics.
Screen Space: On smaller screens, hiding less-important columns makes the table easier to read and navigate.
Workflow: Different business questions require different metrics. You might create one view for daily operations (showing units, sales, and conversion rates) and another for weekly reviews (showing averages and totals).

Reordering Columns

The column management interface also allows you to change the order in which columns appear. This is typically done through drag-and-drop functionality:

Drag to Reorder: Click and hold on a column name in the management interface, then drag it to your desired position. Other columns will automatically shift to make room.
Logical Grouping: Many users prefer to group related metrics together. For example, you might put all sales-related columns together, followed by all traffic-related columns, followed by all percentage/rate columns.
Most-Used First: Another common strategy is to place the metrics you check most frequently on the left side of the table, so they’re always visible without scrolling.

Saving Your Preferences

Your column visibility and ordering preferences are automatically saved. The next time you open the dashboard, your custom view will be restored. This means you can set up your ideal view once and it will persist across sessions.

Resetting to Default

If you ever want to return to the default column configuration, look for a “Reset” or “Restore Defaults” button in the column management interface. This will restore all columns to their original visibility and order.

Understanding Input Fields and Controls

Date Range Selection

The date range selector is one of the most frequently used controls. Here’s how to use it effectively:

Preset Options: Quick-select buttons for common ranges like “Today,” “Yesterday,” “Last 7 Days,” “Last 30 Days,” “This Month,” “Last Month,” “This Year,” “Last Year,” and “All Time.”
Custom Date Range: Click the calendar icon to open a date picker where you can select any start and end date. This is useful for:
- Comparing specific periods (e.g., this year’s Q1 vs. last year’s Q1)
- Analyzing a specific campaign period
- Reviewing performance for a custom reporting period
Date Limitations: Note that data is typically available up to two days ago. This is because Amazon needs time to process and finalize sales and traffic data. You cannot select today or yesterday’s date in most cases.

Granularity Selection

The granularity dropdown offers three options:

Daily: Shows one row per day. This provides the most detailed view and is best for:
- Identifying daily patterns (e.g., weekend spikes)
- Troubleshooting specific date issues
- Detailed performance analysis
Weekly: Groups data by week, showing one row per week. This is useful for:
- Weekly performance reviews
- Identifying weekly trends
- Reducing data volume for longer time periods
Monthly: Groups data by month, showing one row per month. Best for:
- Long-term trend analysis
- Monthly reporting
- High-level performance overviews

Important Note on Granularity: Changing granularity doesn’t change your date range—it only changes how the data within that range is grouped. For example, if you select “This Year” with daily granularity, you’ll see 365 rows (one per day). The same date range with monthly granularity shows 12 rows (one per month), with each row containing the sum or average of that month’s daily data.

Understanding Buttons and Actions

Pin/Unpin First Column

The pin icon (📌) in the first column header toggles whether the expander and date columns stay visible when you scroll horizontally. When pinned (default), these columns remain fixed on the left side, making it easy to see which date you’re looking at even when viewing columns far to the right. When unpinned, these columns scroll with the rest of the table.

Expand/Collapse Rows

The arrow icons (► or ▼) in the first column allow you to expand date rows to see product-level details. Clicking ► expands the row, and clicking ▼ collapses it. You can expand multiple rows simultaneously to compare product performance across different dates.

Sort Columns

Clicking any column header sorts the table by that metric. The first click sorts ascending (lowest to highest), the second click sorts descending (highest to lowest), and the third click removes the sort. The current sort direction is indicated by arrows in the column header.

Add to Chart

The chart icon (📈) in column headers adds that metric to the chart visualization. When a metric is added, the icon appears highlighted. Clicking it again removes the metric from the chart. You can add multiple metrics to compare them visually.

Export/Download

Many dashboards include an export function (typically a download icon) that allows you to export your data to Excel or CSV format. This is useful for:

Creating custom reports
Performing additional analysis in spreadsheet software
Sharing data with team members
Archiving historical data

Refresh Data

A refresh button (typically a circular arrow icon) reloads the data from Amazon’s servers. This is useful if you’re expecting new data to be available or if you suspect the displayed data might be outdated.

Comprehensive User Guide

Getting Started: Your First Analysis

Step 1: Accessing the Dashboard

Navigate to the Sales & Traffic Dashboard from your main navigation menu. The dashboard loads automatically with default settings, typically showing data from the beginning of the current year to two days ago, with daily granularity.

Step 2: Understanding the Default View

When the dashboard first loads, you’ll see:

A table with multiple columns showing various metrics
Rows representing each day (or week/month depending on granularity)
A chart area below (which may be empty until you add metrics)
Column management controls

Take a moment to familiarize yourself with the layout. Notice that some columns show whole numbers (like Units Ordered), some show currency (like Sales Amount), and some show percentages (like Conversion Rate).

Step 3: Selecting Your Date Range

Click on the date range selector at the top of the page. For your first analysis, try selecting “Last 30 Days” to get a manageable amount of data to review. This gives you enough data to see patterns without being overwhelmed.

Step 4: Choosing Your Granularity

Select “Daily” from the granularity dropdown if it’s not already selected. Daily granularity gives you the most detailed view and is recommended for your first analysis.

Step 5: Reviewing Key Metrics

Scroll through the table and notice the following key columns:

Date: When the data was recorded
Units Ordered: How many products you sold
Sales Amount: How much revenue you generated
Page Views: How many times customers viewed your listings
Sessions: How many customer visits occurred
Conversion Rate: What percentage of visits resulted in sales

These five metrics give you a quick overview of your performance. High page views with low conversion rates might indicate a problem with your listings or pricing. High conversion rates with low page views might indicate you need more traffic.

Step 6: Adding Metrics to the Chart

Click the chart icon (📈) next to “Sales Amount” in the column header. You’ll see a line appear in the chart below showing your sales over time. Now click the chart icon next to “Page Views” to add that metric as well. The chart will now show both metrics, making it easy to see if sales and traffic move together.

Step 7: Expanding a Row for Product Details

Find a date row that interests you (perhaps a day with particularly high or low sales). Click the arrow icon (►) next to that date. The row will expand to show individual products (ASINs) that contributed to that day’s performance. This helps you understand which products are driving your results.

Step 8: Interpreting Your First Analysis

Look for patterns:

Are there days of the week that consistently perform better?
Do sales and page views move together, or are they independent?
Which products appear most frequently in your expanded rows?
Is your conversion rate stable, or does it fluctuate significantly?

These initial observations form the foundation for deeper analysis.

Scenario 1: Analyzing Weekly Performance Trends

Objective: Understand how your business performed week-over-week and identify any concerning trends.

Step-by-Step Process:

Set Your Date Range: Select “Last 90 Days” from the date range selector. This gives you approximately 12-13 weeks of data, enough to see weekly patterns.
Change Granularity: Select “Weekly” from the granularity dropdown. The table will now show one row per week instead of one row per day.
Customize Your Columns: Open the column management interface and ensure the following columns are visible:
- Date
- Units Ordered
- Sales Amount
- Page Views
- Sessions
- Conversion Rate
- Buy Box Percentage
Hide other columns temporarily to focus on these core metrics.
Sort by Date: Click the Date column header to ensure weeks are sorted chronologically (oldest to newest or newest to oldest—your preference).
Review the Table: Look at each week’s performance. Pay attention to:
- Whether metrics are increasing, decreasing, or staying stable
- Any weeks with unusual spikes or drops
- The relationship between traffic (Page Views, Sessions) and sales (Units Ordered, Sales Amount)
Calculate Week-over-Week Changes: While the table shows absolute values, you can mentally calculate percentage changes. For example, if Week 1 had 100 units ordered and Week 2 had 110 units ordered, that’s a 10% increase.
Add Key Metrics to Chart: Add “Sales Amount” and “Conversion Rate” to the chart. This visual representation makes trends immediately obvious. You’ll quickly see if sales are trending up (good) or down (concerning).
Identify Anomalies: Look for weeks that don’t follow the general trend. A week with unusually low sales might indicate:
- A stockout situation
- A competitor’s promotion
- A technical issue with your listings
- A seasonal pattern you weren’t aware of
Expand Interesting Weeks: For any week that stands out (positively or negatively), expand the row to see which products drove that performance. This helps you understand whether the trend is product-specific or across your entire catalog.
Document Your Findings: Make note of:
- Weeks with the best performance (and why)
- Weeks with concerning drops (and potential causes)
- Overall trend direction (improving, declining, or stable)
- Any patterns you notice (e.g., sales always drop in the first week of the month)

Interpreting Results:

Consistent Growth: If you see steady week-over-week increases in sales and traffic, your business is healthy and growing.
Declining Trends: If sales or conversion rates are consistently decreasing, investigate potential causes:
- Are competitors undercutting your prices?
- Have you received negative reviews affecting your Buy Box percentage?
- Is your inventory running low?
Volatile Performance: Large swings from week to week might indicate:
- Seasonal demand patterns
- Promotional activity (yours or competitors’)
- Inventory management issues
- External factors (holidays, events)

Scenario 2: Comparing B2B vs. Consumer Sales

Objective: Understand the difference between business buyer and consumer sales to optimize your strategy for each segment.

Step-by-Step Process:

Set Up Your View: Select a date range that gives you enough data—“Last 60 Days” is a good starting point. Use daily granularity to see day-by-day patterns.
Show B2B Comparison Columns: Ensure the following columns are visible:
- Units Ordered
- Units Ordered B2B
- Total Units Ordered
- Sales Amount
- Sales Amount B2B
- Total Sales Amount
- Average Selling Price
- Average Selling Price B2B
- Conversion Rate (which shows consumer conversion)
- Unit Session Percentage B2B (which shows B2B conversion)
Review the Totals Row: Scroll to the bottom of the table and look at the “Total” row. This shows aggregate numbers for your entire date range. Calculate:
- What percentage of total units came from B2B: (Total Units Ordered B2B / Total Units Ordered) × 100
- What percentage of total sales came from B2B: (Total Sales Amount B2B / Total Sales Amount) × 100
Compare Average Prices: Look at the difference between Average Selling Price and Average Selling Price B2B. Business buyers often purchase in larger quantities and may receive different pricing, so their average price per unit might differ from consumer purchases.
Analyze Conversion Rates: Compare Conversion Rate (consumer) with Unit Session Percentage B2B. This tells you which segment converts better. If B2B converts at 5% while consumers convert at 2%, business buyers are more likely to purchase after viewing your listings.
Add to Chart: Add “Sales Amount” and “Sales Amount B2B” to the chart to visually compare the two segments over time. You’ll quickly see if one is growing faster than the other.
Look for Patterns: Review the daily data to see if there are patterns:
- Do B2B sales spike on certain days of the week? (Business buyers might order more on weekdays)
- Are B2B sales more consistent or more volatile than consumer sales?
- Do both segments follow similar trends, or do they move independently?
Expand High-Performing Days: For days with particularly high B2B sales, expand the row to see which products business buyers prefer. This helps you understand your B2B product mix.
Review Traffic Sources: Check Browser Page Views vs. Mobile App Page Views for both segments. Business buyers might prefer one platform over another, which can inform where you focus optimization efforts.

Interpreting Results:

B2B-Heavy Business: If B2B represents a large portion of your sales, consider:
- Optimizing listings for business buyers (highlighting bulk pricing, business features)
- Focusing on products that appeal to businesses
- Understanding B2B buying cycles (they might order monthly or quarterly)
Consumer-Heavy Business: If most sales come from consumers, ensure:
- Your listings appeal to individual shoppers
- Your pricing is competitive for single-unit purchases
- Your product images and descriptions are consumer-friendly
Balanced Mix: If you have a good balance, you’re diversified and less dependent on either segment. This is generally a positive position.
Price Differences: Significant differences in average selling prices between segments might indicate:
- Volume discounts for B2B buyers
- Different product preferences (B2B buyers might prefer higher-end products)
- Pricing strategy opportunities

Scenario 3: Investigating a Drop in Conversion Rate

Objective: When your conversion rate suddenly decreases, identify the cause and determine corrective actions.

Step-by-Step Process:

Identify the Problem: Start by adding “Conversion Rate” to the chart. You’ll immediately see if there’s a drop and when it occurred. Note the date when the decline started.
Set Focused Date Range: Adjust your date range to include the period before the drop, during the drop, and after (if enough time has passed). For example, if the drop happened two weeks ago, view “Last 30 Days” to see the full picture.
Compare Related Metrics: Add the following metrics to the chart to see their relationship:
- Conversion Rate
- Page Views
- Sessions
- Buy Box Percentage
- Average Selling Price
Analyze the Timeline: Look at what happened around the time of the drop:
- Did Page Views increase while Conversion Rate decreased? (This might indicate lower-quality traffic)
- Did Buy Box Percentage drop? (This could mean you lost the Buy Box, reducing conversion opportunities)
- Did Average Selling Price increase? (Higher prices might reduce conversion rates)
- Did Sessions decrease? (Fewer sessions with the same sales would show as higher conversion, so this isn’t the issue)
Check for External Factors: Review the date of the drop:
- Was it a holiday or special event?
- Did a competitor launch a promotion?
- Was there a change in Amazon’s algorithm or policies?
- Did you make any changes to your listings, pricing, or inventory?
Expand Affected Days: Expand rows for days with low conversion rates and compare them to days with normal conversion rates. Look for differences in:
- Which products were viewed
- Which products sold
- Product mix (are low-converting days showing different products?)
Review Product-Level Data: For the specific days with low conversion, expand the rows and note:
- Which products received the most page views but didn’t convert
- Whether certain products consistently underperform
- If new products were introduced around the time of the drop
Check Traffic Sources: Review Browser vs. Mobile App metrics. A shift in traffic source (e.g., more mobile traffic) might affect conversion rates if your listings aren’t optimized for mobile.
Analyze Buy Box Performance: If Buy Box Percentage dropped around the same time, this is likely the cause. Losing the Buy Box means customers see competitor offers first, reducing your conversion opportunities.
Compare B2B vs. Consumer: Check if the drop affects both segments equally or if one is more impacted. This helps narrow down the cause.

Interpreting Results and Taking Action:

Buy Box Issue: If Buy Box Percentage dropped:
- Check your seller performance metrics
- Review your pricing competitiveness
- Ensure your inventory levels are adequate
- Verify your fulfillment method (FBA vs. FBM) is optimal
Traffic Quality Issue: If Page Views increased but Conversion Rate decreased:
- Review your advertising campaigns (are you attracting the right customers?)
- Check your product titles and keywords (are you ranking for relevant searches?)
- Ensure your product images and descriptions accurately represent your products
Pricing Issue: If Average Selling Price increased:
- Compare your prices to competitors
- Consider whether the price increase was justified
- Test temporary price reductions to see if conversion recovers
Product Mix Issue: If certain products are driving the low conversion:
- Review those products’ listings for quality issues
- Check for negative reviews that might have appeared
- Consider pausing or optimizing underperforming products
Seasonal or External Factor: If the drop aligns with an external event:
- Determine if this is a temporary issue that will resolve itself
- Adjust expectations for the period
- Plan for similar events in the future

Scenario 4: Optimizing for Mobile vs. Desktop Traffic

Objective: Understand how customers access your listings and optimize accordingly.

Step-by-Step Process:

Set Up Mobile vs. Desktop View: Select a recent date range (e.g., “Last 30 Days”) with daily granularity.
Show Traffic Source Columns: Ensure these columns are visible:
- Browser Page Views
- Mobile App Page Views
- Page Views (total)
- Browser Sessions
- Mobile App Sessions
- Sessions (total)
Calculate Percentages: While the dashboard shows absolute numbers, calculate percentages:
- Mobile App Page Views % = (Mobile App Page Views / Total Page Views) × 100
- Browser Page Views % = (Browser Page Views / Total Page Views) × 100
Do the same for Sessions. This tells you which platform drives more traffic.
Review the Totals Row: Check the aggregate numbers to see overall platform preference. If 70% of your page views come from mobile apps, mobile optimization is critical.
Add to Chart: Add “Browser Page Views” and “Mobile App Page Views” to the chart to see trends over time. Is mobile growing faster than desktop? This helps you prioritize optimization efforts.
Compare Conversion by Platform: Unfortunately, the dashboard doesn’t directly show conversion rates by platform, but you can infer:
- If mobile traffic is growing but overall conversion is stable or declining, mobile might be converting lower
- If browser traffic converts better, you might need to improve mobile listings
Analyze Day-by-Day Patterns: Look for patterns in when each platform is used:
- Do mobile app views spike on weekends? (Customers browsing on phones during leisure time)
- Do browser views spike during weekdays? (Customers shopping from work computers)
- Understanding these patterns helps you time promotions and inventory updates
Check B2B Platform Preferences: Review Browser Page Views B2B vs. Mobile App Page Views B2B. Business buyers might strongly prefer one platform, which informs how you communicate with them.
Review Product-Level Data: Expand a few rows and check if certain products get more mobile vs. browser traffic. Some product categories naturally appeal more to mobile shoppers.

Interpreting Results and Taking Action:

Mobile-Dominant Traffic: If most traffic comes from mobile apps:
- Ensure product images are clear and load quickly on mobile devices
- Optimize product titles and descriptions for mobile viewing (shorter, scannable)
- Test how your listings appear in the Amazon mobile app
- Consider mobile-specific advertising strategies
Browser-Dominant Traffic: If most traffic comes from browsers:
- Ensure your listings look great on desktop screens
- Use high-resolution images that benefit from larger screens
- Provide detailed descriptions that desktop users are more likely to read
- Consider browser-based advertising
Balanced Traffic: If traffic is evenly split:
- Optimize for both platforms equally
- Test your listings on both mobile and desktop
- Ensure consistent experience across platforms
Growing Mobile Trend: If mobile is growing faster:
- Prioritize mobile optimization
- Prepare for mobile to become your primary traffic source
- Invest in mobile advertising

Scenario 5: Monitoring Refund Rates and Customer Satisfaction

Objective: Track refund rates and feedback to maintain product quality and customer satisfaction.

Step-by-Step Process:

Set Up Quality Metrics View: Select a date range that gives you enough data—“Last 60 Days” is good for spotting trends.
Show Quality-Related Columns: Ensure these columns are visible:
- Units Ordered
- Units Refunded
- Refund Rate
- Feedback Received
- Negative Feedback Received
- Negative Feedback Rate
- Claims Granted
- Claims Amount
Review Refund Rate: Look at the Refund Rate column. A healthy refund rate varies by product category, but generally:
- Under 1% is excellent
- 1-3% is good
- 3-5% is acceptable but worth monitoring
- Over 5% indicates a problem
Add Refund Rate to Chart: Add “Refund Rate” to the chart to see trends. A sudden spike indicates a problem that needs immediate attention.
Compare Refund Rate to Sales: Add “Units Ordered” to the chart alongside “Refund Rate.” If refunds spike when sales spike, it might indicate:
- A quality issue with a specific batch of products
- A problem with a specific product that’s selling well
- Shipping issues during high-volume periods
Analyze Negative Feedback: Review the Negative Feedback Rate. This is often correlated with refund rates—products with quality issues generate both refunds and negative reviews.
Check Claims: Review Claims Granted and Claims Amount. A-to-z claims are serious—they indicate customers went beyond normal returns to file formal complaints. Multiple claims suggest significant problems.
Expand Problem Days: For days with high refund rates or negative feedback, expand the rows to see which products are causing issues. This helps you identify problem products quickly.
Calculate Totals: Review the totals row to see overall performance:
- Total Refund Rate = (Total Units Refunded / Total Units Ordered) × 100
- Total Negative Feedback Rate = (Total Negative Feedback / Total Feedback Received) × 100
Trend Analysis: Use the chart to identify:
- Whether refund rates are improving or worsening over time
- If there are seasonal patterns (e.g., refunds spike after holidays when customers return unwanted gifts)
- If recent changes (new products, new suppliers) correlate with refund rate changes

Interpreting Results and Taking Action:

High Refund Rate: If refund rate is above your target:
- Review product quality with suppliers
- Check for shipping damage (are products arriving broken?)
- Review product descriptions for accuracy (are customers receiving what they expect?)
- Consider temporarily pausing problematic products until issues are resolved
Spike in Refunds: If refunds suddenly increased:
- Identify the date of the spike
- Check what changed around that time (new product launch, supplier change, etc.)
- Review customer feedback for common complaints
- Take immediate corrective action
High Negative Feedback Rate: If customers are leaving negative reviews:
- Read the actual feedback to understand complaints
- Address common issues in product listings or quality
- Respond professionally to negative reviews
- Consider reaching out to customers who left negative feedback to resolve issues
Claims Issues: If you’re seeing A-to-z claims:
- These are serious and require immediate attention
- Review fulfillment processes
- Ensure accurate product descriptions
- Improve customer service response times
- Consider working with Amazon Seller Support to understand claim reasons
Improving Trends: If refund and negative feedback rates are decreasing:
- Continue current quality control measures
- Document what’s working
- Share best practices across your product catalog

Scenario 6: Exporting Data for External Analysis

Objective: Export your sales and traffic data to Excel or CSV for custom analysis, reporting, or sharing with team members.

Step-by-Step Process:

Prepare Your View: Before exporting, set up the dashboard exactly as you want the exported data:
- Select your desired date range
- Choose your granularity (daily, weekly, or monthly)
- Show/hide columns to include only the metrics you need
- Apply any sorting you want preserved
Locate Export Function: Look for a download or export button, typically represented by a download icon (⬇️) or “Export” text, usually located near the top of the page or in a menu.
Choose Export Format: Most dashboards offer:
- Excel (.xlsx): Best for detailed analysis with formatting, formulas, and multiple sheets
- CSV (.csv): Best for simple data transfer, database imports, or when Excel isn’t available
Initiate Export: Click the export button. The system will generate a file containing:
- All visible rows (dates) in your selected range
- All visible columns (metrics)
- The totals row (if applicable)
- Product-level data for expanded rows (if any were expanded)
Wait for Processing: Large date ranges or many columns may take a moment to process. The system will prepare the file and prompt you to download it.
Download the File: Once ready, your browser will download the file. Save it to a location where you can easily find it.
Open and Review: Open the exported file in Excel (or a spreadsheet application). Verify that:
- All expected dates are included
- All expected metrics are present
- Data values match what you saw in the dashboard
- Formatting is readable (numbers formatted correctly, dates recognizable)
Customize in Excel: Now you can:
- Create custom formulas and calculations
- Build pivot tables for different views
- Create charts and graphs
- Add commentary and analysis
- Share with team members who don’t have dashboard access

Best Practices for Exports:

Regular Exports: Consider exporting data weekly or monthly to maintain historical records outside the dashboard.
Consistent Format: Use the same column selection each time to ensure consistency across exports.
Date Naming: Name your exported files with dates (e.g., “Sales_Traffic_2024_01_15.xlsx”) to easily identify them later.
Backup Important Periods: Export data for important periods (product launches, promotions, seasonal peaks) for future reference.
Data Validation: Always spot-check exported data against the dashboard to ensure accuracy.

Advanced Usage: Customizing Your Workflow

Creating Custom Views for Different Purposes

You can use column management to create different “views” for different purposes, even though the system saves one set of preferences. Here’s how:

Daily Operations View: Show only the metrics you check daily:
- Units Ordered
- Sales Amount
- Conversion Rate
- Buy Box Percentage
Hide everything else to reduce clutter and focus on key performance indicators.
Weekly Review View: Show metrics useful for weekly analysis:
- All sales metrics (to see revenue trends)
- All traffic metrics (to see visitor trends)
- Conversion rates (to see efficiency)
- Averages (to see typical performance)
Hide detailed metrics like individual page view types.
Quality Monitoring View: When focusing on product quality:
- Units Ordered
- Units Refunded
- Refund Rate
- Feedback Received
- Negative Feedback Rate
- Claims Granted
Hide sales and traffic metrics to focus solely on quality indicators.
B2B Analysis View: When analyzing business buyers:
- All B2B-specific columns
- Comparison columns (regular vs. B2B)
- B2B conversion metrics
Hide consumer-only metrics.

Using Charts for Quick Insights

The chart visualization is powerful for spotting trends quickly. Here are strategies:

Single Metric Deep Dive: Add only one metric to see its trend clearly, including the automatic trendline that shows overall direction.
Related Metrics Comparison: Add related metrics together:
- Sales Amount + Page Views (to see if traffic drives sales)
- Conversion Rate + Buy Box Percentage (to see if Buy Box affects conversion)
- Units Ordered + Units Refunded (to see refund trends relative to sales)
Segment Comparison: Compare segments:
- Sales Amount + Sales Amount B2B (to compare segments)
- Browser Page Views + Mobile App Page Views (to compare platforms)
Problem Identification: When something seems wrong, add multiple metrics to the chart to see what changed together, helping identify root causes.

Efficient Navigation Tips

Use Keyboard Shortcuts: If available, learn keyboard shortcuts for common actions (sorting, expanding rows, etc.).
Pin Important Columns: Use the pin feature to keep date and expander columns visible, making it easier to navigate wide tables.
Sort Before Expanding: Sort by the metric you’re interested in before expanding rows. This puts the most relevant dates at the top.
Use Totals Row: Don’t manually calculate totals—use the totals row at the bottom for quick aggregate numbers.
Chart for Trends, Table for Details: Use the chart to identify trends and the table to see exact values and investigate specific dates.

The Logic “Under the Hood”

Understanding how the dashboard calculates metrics helps you interpret the numbers correctly and make better decisions. This section explains the mathematical formulas used, presented in a way that’s accessible without requiring advanced math knowledge.

Sales Calculation Formulas

Total Sales Amount Calculation

The dashboard calculates your total sales amount by combining consumer and B2B sales. The formula is:

$\text{Total Sales Amount} = \text{Sales Amount} + \text{Sales Amount B2B}$

This is straightforward addition: if you sold $1,000 to consumers and $500 to business buyers, your total sales amount is $1,500.

Average Sales Per Order Item

This metric tells you the average revenue generated by each order item. The calculation is:

$\text{Average Sales Per Order Item} = \frac{\text{Sales Amount}}{\text{Total Order Items}}$

For example, if your sales amount is $10,000 and you had 100 order items, your average sales per order item is $100. This means, on average, each order item generated $100 in revenue.

Average Sales Per Order Item B2B

The same calculation, but specifically for business buyers:

$\text{Average Sales Per Order Item B2B} = \frac{\text{Sales Amount B2B}}{\text{Total Order Items B2B}}$

Average Units Per Order Item

This shows how many units, on average, are included in each order item:

$\text{Average Units Per Order Item} = \frac{\text{Total Units Ordered}}{\text{Total Order Items}}$

If you sold 500 units across 100 order items, your average units per order item is 5.0. This means customers typically order 5 units per order item.

Average Selling Price

This is the average price at which each unit was sold:

$\text{Average Selling Price} = \frac{\text{Sales Amount}}{\text{Units Ordered}}$

If you generated $10,000 in sales from 500 units, your average selling price is $20 per unit.

Average Selling Price B2B

The same calculation for business buyers:

$\text{Average Selling Price B2B} = \frac{\text{Sales Amount B2B}}{\text{Units Ordered B2B}}$

Total Units Ordered

This combines consumer and B2B units:

$\text{Total Units Ordered} = \text{Units Ordered} + \text{Units Ordered B2B}$

Refund and Quality Calculation Formulas

Refund Rate

The refund rate shows what percentage of your sales were refunded:

$\text{Refund Rate} = \frac{\text{Units Refunded}}{\text{Units Ordered}} \times 100$

The result is expressed as a percentage. For example, if you sold 1,000 units and 20 were refunded, your refund rate is 2%. This means 2% of your sales resulted in refunds.

Negative Feedback Rate

This shows what percentage of your customer feedback was negative:

$\text{Negative Feedback Rate} = \frac{\text{Negative Feedback Received}}{\text{Feedback Received}} \times 100$

If you received 100 feedback entries and 5 were negative, your negative feedback rate is 5%.

Traffic and Conversion Calculation Formulas

Total Page Views

The dashboard combines browser and mobile app page views:

$\text{Page Views} = \text{Browser Page Views} + \text{Mobile App Page Views}$

This gives you the total number of times customers viewed your product listings, regardless of whether they used a browser or the mobile app.

Total Sessions

Similarly, total sessions combine browser and mobile app sessions:

$\text{Sessions} = \text{Browser Sessions} + \text{Mobile App Sessions}$

Conversion Rate (Unit Session Percentage)

This is one of the most important metrics. It shows what percentage of customer sessions resulted in a purchase:

$\text{Conversion Rate} = \frac{\text{Units Ordered}}{\text{Sessions}} \times 100$

For example, if you had 1,000 sessions and sold 50 units, your conversion rate is 5%. This means 5% of customer visits resulted in a purchase.

Unit Session Percentage B2B

The same calculation for business buyers:

$\text{Unit Session Percentage B2B} = \frac{\text{Units Ordered B2B}}{\text{Sessions B2B}} \times 100$

Order Item Session Percentage

This shows what percentage of sessions resulted in at least one order item being purchased:

$\text{Order Item Session Percentage} = \frac{\text{Total Order Items}}{\text{Sessions}} \times 100$

This differs from conversion rate because one session could result in multiple order items. If you had 1,000 sessions and 60 order items were purchased, your order item session percentage is 6%.

Percentage Breakdown Formulas

While the dashboard shows absolute numbers for browser vs. mobile app metrics, you can calculate percentages:

Browser Page Views Percentage

$\text{Browser Page Views \%} = \frac{\text{Browser Page Views}}{\text{Page Views}} \times 100$

Mobile App Page Views Percentage

$\text{Mobile App Page Views \%} = \frac{\text{Mobile App Page Views}}{\text{Page Views}} \times 100$

Note that these two percentages will always add up to 100%, since all page views are either from browsers or mobile apps.

Browser Sessions Percentage

$\text{Browser Sessions \%} = \frac{\text{Browser Sessions}}{\text{Sessions}} \times 100$

Mobile App Sessions Percentage

$\text{Mobile App Sessions \%} = \frac{\text{Mobile App Sessions}}{\text{Sessions}} \times 100$

Trendline Calculation

When you view a single metric in the chart, a trendline appears showing the overall direction. This trendline uses a statistical method called linear regression to find the “best fit” line through your data points.

The trendline calculation finds a line that minimizes the distance between the line and all your data points. The formula for the trendline is:

$y = mx + b$

Where:

$y$ is the predicted value
$x$ is the time point (day 1, day 2, etc.)
$m$ is the slope (how much the metric changes per time period)
$b$ is the y-intercept (the starting value)

The slope ( $m$ ) tells you the trend direction:

Positive slope: The metric is generally increasing over time
Negative slope: The metric is generally decreasing over time
Near-zero slope: The metric is relatively stable

The dashboard calculates this automatically, so you don’t need to do the math yourself. Just know that:

An upward-sloping trendline (left to right) means improvement
A downward-sloping trendline means decline
A flat trendline means stability

Aggregation Across Time Periods

When you change granularity (daily to weekly or monthly), the dashboard aggregates data differently:

For Count Metrics (Units Ordered, Page Views, Sessions, etc.):

Daily to Weekly: Sum all days in the week
Daily to Monthly: Sum all days in the month

For Currency Metrics (Sales Amount, Average Selling Price, etc.):

Daily to Weekly: Sum sales amounts, but recalculate averages
Daily to Monthly: Sum sales amounts, but recalculate averages

For Percentage Metrics (Conversion Rate, Refund Rate, etc.):

Daily to Weekly: Recalculate using weekly totals (e.g., weekly conversion = weekly units / weekly sessions)
Daily to Monthly: Recalculate using monthly totals

For Average Metrics (Average Selling Price, Average Units Per Order Item, etc.):

The dashboard recalculates averages using the aggregated totals, not the average of daily averages

For example, if Day 1 had an average selling price of $10 and Day 2 had $20, the weekly average isn’t $15 (the average of $10 and $20). Instead, it’s calculated as: (Total Sales for Week) / (Total Units for Week).

Understanding Zero Values and Missing Data

Sometimes you’ll see zeros in the table. This can mean:

No Activity: Genuinely no sales, traffic, or other activity for that metric on that date
Data Not Available: Amazon hasn’t provided data for that metric yet (common for very recent dates)
Not Applicable: Some metrics don’t apply to certain products or situations

The dashboard handles these cases by displaying “0” or leaving cells empty. When calculating totals or averages, zeros are included in the math, which is important to understand:

If you had 0 units ordered on a day, that day contributes 0 to your total units ordered
If you’re calculating an average and some days have 0, those days lower your average

Rounding and Precision

The dashboard displays numbers with appropriate precision:

Whole Numbers: Counts (units, sessions, page views) are shown as whole numbers with commas for thousands (e.g., 1,234)
Currency: Sales amounts are shown with currency symbols and typically no decimal places for large amounts, or two decimal places for smaller amounts (e.g., $1,234 or $12.34)
Percentages: Shown with two decimal places (e.g., 5.23%)
Averages: Shown with appropriate decimal places depending on the metric (e.g., Average Units Per Order Item might show 2.5)

When the dashboard calculates totals, it uses full precision internally and only rounds for display. This means the totals row might show slightly different numbers than if you manually added up the displayed values due to rounding in individual cells.