Properties Report

The Properties report lets you analyze the custom properties attached to conversions, microconversions and purchased items (items), broken down by traffic source (UTM source / medium / campaign).

What This Report Shows

When you track conversions with custom properties (like product_category, plan_type, or payment_method), this report helps you understand:

• How events distribute across the values of a property for each source / medium / campaign
• Which traffic sources over- or under-index on specific property values
• Which products or categories each traffic source sells (with the Conv. Items data source)

The report counts events (or, for Conv. Items, units). It does not show revenue — for revenue by source use the Sources or Conversions reports.

Accessing the Report

1. Select a site from the site selector
2. Click Properties in the sidebar

Report Interface

Controls

Control	Description
View Mode	Toggle between Totals (absolute numbers) and Rates (percentages). See View Modes
Data Source	Toggle between Conversions, Microconversions, and Conv. Items. See Data Sources
Property Key	Select which property to analyze (e.g., currency, plan_type). Each option shows its event count. The first key is selected automatically
Export	Download the current breakdown as CSV or PDF

Data Sources

Data Source	What is counted	Notes
Conversions	One per conversion carrying the selected key	Includes every conversion type (e.g. purchase, lead) that carries the key. The items key is hidden here — analyze it with Conv. Items
Microconversions	One per microconversion carrying the selected key	Includes every microconversion type (e.g. view_item, add_to_cart) that carries the key
Conv. Items	Units of each product in the items array of your conversions	Each field inside an item (category, product_name, brand…) becomes a selectable key. An item with quantity: 2 counts as 2; an item without quantity counts as 1. quantity itself is not offered as a key

The report has no conversion-type or microconversion-type selector. If the same key is sent on several event types, their counts are added together — use distinct keys, or send the key only on the event you want to analyze.

Events where the selected key is missing or empty are left out of the breakdown.

Property Analysis Table

The main table shows:

Column	Description
Source / Medium / Campaign	UTM attribution for this row. An empty source is shown as (direct), an empty medium or campaign as (none). In Rates mode, an entrance count is also shown
Total / Share %	In Totals mode, the sum of all events for this UTM combination. In Rates mode the header changes to Share % (see View Modes)
[Property Values]	One dynamic column for each distinct value of the selected property

Rows are sorted by total, largest first. Click any column header to sort by the value it displays. The table is paginated (10, 50, 100 or 500 rows per page).

A summary above the table shows the selected property key, its number of distinct values, and the total events.

Heatmap Visualization

Property-value cells are color-coded. The legend above the table labels them > avg, ≈ avg and < avg:

Color	Meaning
Green	This value takes more than an even share of the row's total
Yellow	Within ±15% of an even share
Red	This value takes less than an even share of the row's total

The comparison is made within each row: a row's total is divided evenly across all the property's values, and each cell is compared with that even share. Green therefore means "this source leans towards this value", not "this source outperforms other sources". A value that is large for every source (for example your best-selling category) will be green on most rows — compare rows against each other before drawing conclusions.

View Modes

Totals Mode

• Shows absolute counts for each property value
• Useful for understanding volume distribution

Rates Mode

• Shows percentages instead of counts. For each row, the dashboard looks up the entrances recorded for that source / medium / campaign and shows that count under the source name
• When an entrance count is found, each cell shows events ÷ entrances, and Share % shows the row total ÷ entrances
• When no entrance count is found (the row shows 0 entrances), each cell shows its share of the row's total and Share % shows 100%
• Check the entrance count under the source before reading a Rates cell as a conversion rate

Available Properties Table

Below the main analysis, you'll find a summary of the property keys available for the selected data source and date range:

Column	Description
Property Key	The property name (click to analyze it in the table above)
Conversions	Count from conversion events (in Conv. Items mode, the item unit count appears here)
Microconversions	Count from microconversion events
Total	Combined count

Because the table follows the Data Source toggle, one of the two count columns is normally 0.

Filters

The Properties report has fewer filters than the traffic reports:

Filter	Applies to this report?
Date range	Yes — every table on the page
Comparison period	No — the report shows no comparison values
Segment (country, device type, browser, OS)	No for event counts. The filter bar stays visible, but property counts always include all countries, devices, browsers and operating systems. Active Segment filters only change the entrance counts used in Rates mode
Local filter builder / search	Not available on this report
UTM, channel or conversion-type filters	Not available. Rows are already split by source / medium / campaign; sort a column or export to CSV to narrow down

The only report-specific controls are the View Mode, Data Source and Property Key selectors described above. See Filters for how global filters work in other reports.

Example Use Cases

For online stores, see E-commerce use cases below.

SaaS: Plan Type Analysis

Understand which campaigns drive premium vs basic subscriptions:

sealmetrics.conv('subscription', 49.00, {
  plan_type: 'premium',
  billing_cycle: 'annual'
});

Analyze plan_type to optimize campaigns for higher-value plans.

Lead Gen: Lead Source Analysis

Track which forms and sources generate leads:

sealmetrics.conv('lead', 0, {
  form_name: 'contact',
  lead_source: 'pricing_page'
});

E-commerce use cases

These cases assume your purchase conversion carries transaction-level properties and an items array, as described in the E-commerce Setup Guide:

sealmetrics.conv('purchase', 267.97, {
  currency: 'EUR',
  payment_method: 'credit_card',
  coupon: 'SAVE10',
  items: [
    { product_name: 'Blue Running Shoes', product_id: 'SKU-123', price: 89.99, quantity: 2, category: 'footwear', brand: 'Acme' },
    { product_name: 'Sports Socks', product_id: 'SKU-456', price: 89.99, quantity: 1, category: 'accessories', brand: 'Acme' }
  ]
});

Attribution in every case is the source / medium / campaign of the session in which the event happened (last click per session).

Which campaigns sell which product categories?

1. Set Data Source to Conv. Items and Property Key to category.
2. Keep View Mode on Totals.
3. Read each campaign's row across the category columns.

How to read it: the counts are units sold per category for that campaign. Green cells show the categories a campaign leans towards. If a campaign built for one category mostly sells another, its targeting or landing page is attracting a different buyer than intended — adjust the creative or landing page, or move budget to the campaign that already sells that category. Remember the heatmap compares within the row: a category that dominates your whole catalogue will be green almost everywhere.

Needs: items with a category field on the purchase conversion.

Which traffic source sells a specific product?

1. Set Data Source to Conv. Items and Property Key to product_name (or product_id, or brand).
2. Click the column header of the product you care about to sort rows by units of that product.
3. Export to CSV if you need to share or pivot the result.

How to read it: the top rows are the sources, mediums and campaigns that sold the most units of that product. If a product you promote heavily barely appears in the rows of the campaign that promotes it, that campaign is not selling it. Product-level keys produce one column per product, so a large catalogue gives a wide table — use category or brand for an overview and product_name for a short list of key products.

Needs: items with the chosen field on the purchase conversion.

Which campaigns depend on discount codes?

1. Set Data Source to Conversions and Property Key to coupon.
2. Keep View Mode on Totals.
3. Compare each row's Total with that source's total conversions in the Sources report for the same date range.

How to read it: this table only counts orders that carried a coupon — orders without one are left out. A campaign whose coupon orders are close to its total orders is selling on discount; one with few coupon orders is bringing full-price buyers. The coupon columns also show which codes leak into channels they were not issued for (for example an affiliate code appearing under paid search). Review codes that show up in unexpected rows.

Needs: a coupon property on the purchase conversion, sent only when a code is used. Because the Conversions data source includes every conversion type, send coupon only on purchase.

Which categories get added to the cart but not bought?

1. Set Data Source to Microconversions and Property Key to category. Note the category mix for a source.
2. Switch Data Source to Conv. Items (key category) and compare the same source's mix of purchased units.

How to read it: a category that takes a large share of a source's add-to-carts but a small share of its purchased units is losing buyers between cart and order — check price, shipping costs or stock for that category, and look at the cart-to-checkout step in the Funnel report. Compare shares rather than raw numbers: one add-to-cart event can hold several units, and purchased units are counted by quantity.

Needs: an add_to_cart microconversion with a category property. The Microconversions data source adds together every microconversion type that carries category, so if you also send category on view_item or begin_checkout, those are counted too. Either send category only on add_to_cart, or use a distinct key such as cart_category. See Microconversions and the funnel events in the E-commerce Setup Guide.

Best Practices

1. Use Consistent Property Names

Standardize property names across your implementation:

// Good - consistent naming
sealmetrics.conv('purchase', value, { product_category: 'shoes' });

// Avoid - inconsistent
sealmetrics.conv('purchase', value, { category: 'shoes' });
sealmetrics.conv('purchase', value, { productCategory: 'shoes' });

2. Keep Property Values Clean

Use consistent, lowercase values:

// Good
{ plan_type: 'premium' }

// Avoid
{ plan_type: 'Premium' }
{ plan_type: 'PREMIUM' }

Values are stored as text. Numbers and booleans are converted to strings, so premium, Premium and PREMIUM appear as three separate columns.

3. Limit Property Cardinality

Every distinct value becomes a column in the analysis table. There is no hard limit on the number of values, but keys with many unique values (order IDs, prices, timestamps) produce tables too wide to read. Avoid using unique IDs as property values.

4. Track Meaningful Properties

Focus on properties that inform business decisions:

Good properties:

• product_category - Informs inventory and marketing decisions
• plan_type - Helps optimize pricing campaigns
• payment_method - Identifies payment preferences by source

Less useful properties:

• order_id - Too many unique values
• timestamp - Already tracked automatically
• user_email - Privacy concern, too granular

Requirements

The Properties report requires:

1. Conversions or microconversions with properties - Events must include custom properties
2. Purchases with an items array - Only for the Conv. Items data source

UTM parameters are not required: events without a recorded source are grouped under (direct).

If no properties are found, the report will display: "No properties found. Properties are set when tracking conversions and microconversions."

Related Documentation

• Event Properties Guide - How to implement properties
• E-commerce Setup Guide - Purchase, items and funnel events
• Conversions - Tracking conversions
• Microconversions - Tracking microconversions
• Sources Report - Traffic source analysis
• Funnel Report - Step-by-step drop-off