JustAnalytics/Documentation

E-Commerce Reports

Revenue dashboards, product performance analytics, and purchase funnel visualization.

Overview#

The E-Commerce Reports section of your dashboard provides a complete picture of your store's performance. These reports are powered by the e-commerce events you send via JA.trackEcommerce() -- see E-Commerce Tracking for setup.

All e-commerce reports support date range selection, comparison periods, and filtering by traffic source, device type, geography, and custom segments.

Revenue Dashboard#

The Revenue Dashboard is your top-level view of store performance. Access it via Dashboard > E-Commerce > Revenue.

Key Metrics#

| Metric | Definition | |--------|-----------| | Total Revenue | Sum of all purchase event values minus refund event values | | Orders | Count of unique transaction_id values from purchase events | | Average Order Value (AOV) | Total Revenue / Orders | | Items Sold | Sum of all item quantities across purchase events | | Refund Rate | Refund count / Order count | | Revenue per Session | Total Revenue / Total sessions with e-commerce activity |

Revenue Over Time Chart#

A line chart showing daily revenue over the selected date range. Toggle between:

  • Revenue -- total revenue per day
  • Orders -- order count per day
  • AOV -- average order value per day

Enable Compare to previous period to overlay the prior period's data for trend analysis.

Revenue by Hour#

A heatmap showing revenue by day of week and hour of day. Use this to identify peak purchasing times and schedule promotions accordingly.

Example insight:
- Tuesday 2-4pm: highest revenue (office workers buying during breaks)
- Saturday 10am-12pm: second peak (weekend shopping)
- Sunday midnight-6am: lowest activity

Product Performance Table#

Navigate to Dashboard > E-Commerce > Products for detailed product-level analytics.

Default Columns#

| Column | Description | |--------|-------------| | Product Name | Item name from e-commerce events | | SKU | Item ID | | Category | Primary item category | | Revenue | Total revenue from this product | | Units Sold | Total quantity across all orders | | Views | Number of view_item events | | Add-to-Cart Rate | add_to_cart count / view_item count | | Purchase Rate | Purchase count / view_item count | | Avg. Price | Average selling price (accounts for discounts) | | Refund Rate | Refunded units / Sold units |

Sorting and Filtering#

  • Sort by any column to find top performers or problem products
  • Filter by category, brand, or price range
  • Search for specific products by name or SKU

Product Detail View#

Click any product row to see its detail page:

  • Revenue trend -- daily revenue chart for this product
  • Traffic sources -- which channels drive views and purchases of this product
  • Variant breakdown -- performance by variant (color, size)
  • Frequently bought together -- other products purchased in the same order
  • Funnel -- view_item > add_to_cart > purchase conversion for this product

Category Breakdown#

Navigate to Dashboard > E-Commerce > Categories for category-level analysis.

Category Tree#

If you use hierarchical categories (item_category, item_category2, item_category3), the report shows a tree structure:

Electronics                    $45,230  (52.3%)
  ├── Audio                    $18,400  (21.3%)
  │   ├── Headphones           $12,100  (14.0%)
  │   └── Speakers             $6,300   (7.3%)
  ├── Cables & Accessories     $15,830  (18.3%)
  └── Computer Accessories     $11,000  (12.7%)
Home & Garden                  $28,100  (32.5%)
  ├── Kitchen                  $16,200  (18.7%)
  └── Outdoor                  $11,900  (13.8%)
Apparel                        $13,120  (15.2%)

Category Metrics#

Each category row shows:

  • Total revenue and percentage of overall revenue
  • Number of products in the category
  • Units sold
  • Average product price
  • Top-selling product in the category
  • Revenue trend (sparkline)

Revenue by Traffic Source#

Understand which marketing channels drive the most revenue. Navigate to Dashboard > E-Commerce > Revenue by Source.

Source/Medium Table#

| Source / Medium | Revenue | Orders | AOV | Conv. Rate | Revenue % | |----------------|---------|--------|-----|------------|-----------| | google / organic | $28,450 | 312 | $91.19 | 3.2% | 32.9% | | google / cpc | $22,100 | 198 | $111.62 | 4.1% | 25.6% | | direct / none | $15,300 | 187 | $81.82 | 2.8% | 17.7% | | facebook / social | $8,900 | 145 | $61.38 | 1.9% | 10.3% | | newsletter / email | $7,200 | 89 | $80.90 | 5.5% | 8.3% | | bing / organic | $4,500 | 56 | $80.36 | 2.1% | 5.2% |

Key Insights#

  • Highest AOV -- which sources bring high-value customers
  • Highest conversion rate -- which sources are most efficient
  • Revenue share -- where the bulk of revenue comes from
  • Cost efficiency -- combine with ad spend data for ROAS calculations

Campaign-Level Drill Down#

Click any source to see campaign-level breakdown. UTM parameters (utm_source, utm_medium, utm_campaign, utm_content, utm_term) are parsed automatically from referral URLs.

Purchase Funnel Visualization#

Navigate to Dashboard > E-Commerce > Funnel for a visual funnel analysis.

Standard E-Commerce Funnel#

The default funnel tracks these steps:

View Item List → Select Item → View Item → Add to Cart → Begin Checkout → Add Shipping → Add Payment → Purchase

Funnel Visualization#

The funnel is displayed as a horizontal bar chart showing:

  1. Session count at each step
  2. Step-to-step conversion rate between adjacent steps
  3. Overall conversion rate from first step to purchase
  4. Drop-off count at each step

Funnel Metrics#

| Step | Sessions | Step Conv. | Overall Conv. | Drop-off | |------|----------|------------|---------------|----------| | View Item List | 25,000 | - | - | - | | Select Item | 15,500 | 62.0% | 62.0% | 9,500 | | View Item | 14,200 | 91.6% | 56.8% | 1,300 | | Add to Cart | 5,400 | 38.0% | 21.6% | 8,800 | | Begin Checkout | 2,300 | 42.6% | 9.2% | 3,100 | | Add Shipping | 2,050 | 89.1% | 8.2% | 250 | | Add Payment | 1,980 | 96.6% | 7.9% | 70 | | Purchase | 1,750 | 88.4% | 7.0% | 230 |

Segmented Funnels#

Apply segments to compare funnel performance across different user groups:

  • New vs. returning visitors -- do returning visitors convert better?
  • Mobile vs. desktop -- where is mobile checkout friction?
  • Traffic source -- which sources have the best funnel completion?
  • Country -- are there regional differences in checkout completion?

Drop-off Analysis#

Click any drop-off number to:

  1. View session replays of users who dropped off at that step
  2. See frustration signals (rage clicks, dead clicks, errors) at that step
  3. Compare the drop-off rate across device types and browsers
  4. Identify the most common exit pages after drop-off

Conversion Rate Analysis#

E-Commerce Conversion Rate#

The overall e-commerce conversion rate is calculated as:

E-Commerce Conversion Rate = Sessions with Purchase / Total Sessions

This is displayed as a trend line on the Revenue Dashboard.

Micro-Conversion Rates#

Track intermediate conversion rates to identify optimization opportunities:

| Micro-Conversion | Formula | Benchmark | |------------------|---------|-----------| | Browse-to-Detail | view_item / sessions | 40-60% | | Detail-to-Cart | add_to_cart / view_item | 8-15% | | Cart-to-Checkout | begin_checkout / add_to_cart | 30-50% | | Checkout-to-Purchase | purchase / begin_checkout | 50-80% |

Conversion by Segment#

Break down conversion rates by:

  • Device type -- mobile typically has 50-70% lower conversion than desktop
  • New vs. returning -- returning visitors convert 2-3x higher
  • Traffic source -- branded search has the highest conversion
  • Landing page -- which entry points lead to the most purchases
  • Day of week -- weekday vs. weekend conversion patterns

E-Commerce + Attribution Integration#

Revenue data integrates with JustAnalytics attribution models to assign credit to marketing touchpoints.

Available Attribution Models#

| Model | Description | |-------|-------------| | Last Click | 100% credit to the last touchpoint before purchase | | First Click | 100% credit to the first touchpoint | | Linear | Equal credit to all touchpoints | | Time Decay | More credit to touchpoints closer to purchase | | Position Based | 40% first, 40% last, 20% split among middle | | Data-Driven | ML-based model using your actual conversion data |

Attribution Report#

Navigate to Dashboard > E-Commerce > Attribution to see:

  • Revenue attributed to each channel under each model
  • Side-by-side comparison of attribution models
  • Path analysis showing common conversion paths
  • Assisted conversions (touchpoints that contributed but were not last)

Exporting Reports#

CSV Export#

Click Export > CSV on any report to download the data. Available exports:

  • Revenue summary (daily, weekly, monthly)
  • Product performance table
  • Category breakdown
  • Source/medium revenue
  • Funnel step data
  • Transaction-level detail

Scheduled Reports#

Set up automated email reports:

  1. Navigate to Settings > Scheduled Reports
  2. Click Create Report
  3. Select report type: Revenue Summary, Product Performance, etc.
  4. Set frequency: Daily, Weekly, or Monthly
  5. Add recipient email addresses
  6. Choose format: CSV or PDF

API Access#

Access e-commerce data programmatically:

curl "https://justanalytics.app/api/dashboard/ecommerce/revenue?siteId=SITE_ID&period=30d" \
  -H "Authorization: Bearer YOUR_SESSION_TOKEN"

Response:

{
  "totalRevenue": 86450.00,
  "orders": 987,
  "averageOrderValue": 87.59,
  "itemsSold": 2341,
  "refundRate": 0.032,
  "revenueByDay": [
    { "date": "2026-03-01", "revenue": 2840.00, "orders": 32 },
    { "date": "2026-03-02", "revenue": 3120.00, "orders": 37 }
  ]
}

Best Practices#

  1. Track all funnel steps -- the more steps you track, the more precisely you can identify drop-off points
  2. Use consistent item IDs -- always use SKU as item_id to avoid duplicate products in reports
  3. Include categories -- hierarchical categories enable powerful category analysis
  4. Track refunds -- refund tracking gives you net revenue (not just gross)
  5. Segment everything -- always look at e-commerce data by device, source, and user type
  6. Review weekly -- schedule a weekly review of the revenue dashboard and funnel report
  7. Compare periods -- use period comparison to spot trends and measure the impact of changes

Next Steps#