Adobe Analytics is the enterprise-grade web and app analytics platform used by many of the world's largest companies for digital measurement. Whether you're building custom reports in Analysis Workspace, pulling data through the Reporting API, or configuring new tracking in the Admin Console, understanding the full landscape of available dimensions and metrics is essential for effective analysis and data-driven decision making.
This guide provides a complete reference of every major dimension and metric category in Adobe Analytics as of 2026. We've organized them by scope and function, included technical names for implementation teams, and added practical context on when and how to use each one.
How Adobe Analytics Data Is Structured
Adobe Analytics processes data through server calls (also called hits or image requests). Each time a page loads or a tracked interaction occurs, a server call sends data to Adobe's collection servers. This data is organized into a report suite — the container that stores all data for a specific website, app, or business unit.
Data has three fundamental scopes: hit (individual page view or link click), visit (a session of consecutive hits ending after 30 minutes of inactivity or at midnight), and visitor (a unique person identified by cookie or cross-device ID). Every dimension and metric operates at one of these scopes, which determines how data is counted and attributed.
Adobe Analytics distinguishes between traffic variables (props) and conversion variables (eVars). Props are hit-scoped — they only apply to the server call where they are set. eVars persist across hits according to their expiration configuration, enabling conversion attribution to earlier interactions. Both can be customized with up to 250 instances each.
Visit and Visitor Dimensions
These dimensions describe the visit (session) and visitor (person) — answering questions about who is on your site, when they visit, how often they return, and what kind of visit it is. They are the foundation of audience analysis.
| Dimension | Scope | Description |
|---|---|---|
| Visit Number | Visit | Sequential visit count for the visitor (1st visit, 2nd visit, etc.) |
| New vs. Returning | Visitor | Whether the visitor is new (first visit) or returning |
| Days Since Last Visit | Visit | Number of days between the current and previous visit |
| Visit Depth | Visit | Number of page views in the visit (bucketed: 1, 2-3, 4-7, 8-14, etc.) |
| Time Spent per Visit | Visit | Total time from first to last hit in the visit (bucketed) |
| Day of Week | Hit | Day the hit occurred: Sunday through Saturday |
| Hour of Day | Hit | Hour the hit occurred (0-23 in report suite timezone) |
| AM/PM | Hit | Whether the hit occurred in the morning or afternoon |
| Quarter of Year | Hit | Calendar quarter (Q1, Q2, Q3, Q4) when the hit occurred |
| First Visit Date | Visitor | Date of the visitor's first ever visit to the site |
| Customer Loyalty | Visitor | Bucketed visit count: not a customer, new, return, loyal |
| Cross-Device Visitor ID | Visitor | Identifier linking the same person across devices (if CDA is configured) |
Page Dimensions
Page dimensions describe the content being viewed. These are the most fundamental traffic dimensions — they tell you what pages people see, how they navigate through your site, and how content is organized.
| Dimension | API Variable | Description |
|---|---|---|
| Page Name | pageName | Name of the page as set in the tracking code |
| Page URL | pageURL | Full URL of the page including protocol and path |
| Site Section | channel | Top-level content section (e.g., Products, Blog, Support) |
| Hierarchy | hier1-hier5 | Multi-level page classification (up to 5 hierarchy variables) |
| Server | server | Server or hostname serving the page |
| Entry Page | entryPage | First page viewed in the visit (landing page) |
| Exit Page | exitPage | Last page viewed before the visit ended |
| Previous Page | previousPage | The page viewed immediately before the current page |
| Next Page | nextPage | The page viewed immediately after the current page |
| Page Not Found | pageType=errorPage | Whether the page returned a 404 error |
| Content Type | contentType | Type of content on the page (article, product, category, etc.) |
Traffic Source Dimensions
Traffic source dimensions describe how visitors arrived at your site — the referring URL, search engine, campaign, or marketing channel. These are essential for understanding acquisition effectiveness and marketing ROI.
| Dimension | API Variable | Description |
|---|---|---|
| Referring Domain | referringDomain | Domain of the website that referred the visitor |
| Referrer | referrer | Full URL of the referring page |
| Referrer Type | referrerType | Category: typed/bookmarked, search engines, social networks, other websites |
| Search Engine | searchEngine | Search engine that referred the visitor (Google, Bing, Yahoo, etc.) |
| Search Keyword | searchKeyword | Search query used to find your site (limited due to search engine encryption) |
| Marketing Channel | marketingChannel | Classified channel: paid search, organic search, email, display, social, direct, etc. |
| Marketing Channel Detail | marketingChannelDetail | Specific detail within the channel (e.g., Google for organic search) |
| First Touch Channel | firstTouchChannel | Marketing channel from the visitor's first ever visit |
| Last Touch Channel | lastTouchChannel | Marketing channel from the most recent visit |
| Tracking Code | campaign | Campaign tracking code (from s.campaign or UTM parameters) |
Technology Dimensions
Technology dimensions describe the device, browser, and operating system used by the visitor. These help you understand your audience's technical profile and optimize user experience accordingly.
| Dimension | Description |
|---|---|
| Browser | Web browser name and version (Chrome 122, Safari 17, Firefox 123, etc.) |
| Browser Type | Browser family without version (Chrome, Safari, Firefox, Edge) |
| Operating System | OS name and version (Windows 11, macOS 15, iOS 18, Android 15) |
| Mobile Device | Specific mobile device model (iPhone 16, Samsung Galaxy S25, Pixel 9) |
| Mobile Device Type | Category: mobile phone, tablet, desktop, other |
| Screen Resolution | Display resolution of the visitor's screen (1920x1080, 2560x1440, etc.) |
| Color Depth | Color depth of the display (16-bit, 24-bit, 32-bit) |
| Connection Type | Network type: LAN, WiFi, 3G, 4G, 5G (when detectable) |
| JavaScript Version | JavaScript support level of the browser |
| Cookie Support | Whether the browser accepts first-party and third-party cookies |
| Language | Browser language setting (en-US, fr-FR, de-DE, etc.) |
Product Dimensions
Product dimensions describe items in your e-commerce catalog. Adobe Analytics uses the products variable (s.products) to capture product-level data including category, name, quantity, and revenue on each server call.
| Dimension | Syntax Position | Description |
|---|---|---|
| Product Category | 1st | Top-level category of the product (e.g., Apparel, Electronics) |
| Product Name | 2nd | Name or SKU identifier of the product |
| Product Quantity | 3rd | Number of units in the transaction |
| Product Revenue | 4th | Total revenue for the product line item |
| Product Events | 5th | Product-level events with values (e.g., event1=5 per product) |
| Product Merchandising eVars | 5th | Product-level eVar values (e.g., product color, size, brand) |
eVar Dimensions (Conversion Variables)
eVars are the custom conversion dimensions in Adobe Analytics. They persist beyond the hit where they are set, allowing you to attribute downstream conversion events to earlier interactions. This is the key differentiator from props. You can configure up to 250 eVars per report suite.
| Configuration | Options | Description |
|---|---|---|
| Expiration | Hit, Visit, Day, Week, Month, Quarter, Year, Never, Custom | How long the eVar value persists after being set |
| Allocation | Most Recent (Last), Original (First), Linear | Which value gets credit when multiple values are set before conversion |
| Type | Text String, Counter | Whether the eVar stores text values or increments numerically |
| Merchandising | Disabled, Conversion Variable Syntax, Product Syntax | Whether the eVar is used for product-level attribution |
| Reset | On purchase or manually | When the persisted value gets cleared |
Common eVar implementations: internal search term (eVar1), logged-in status (eVar2), user type (eVar3), product finding method (eVar4), campaign content (eVar5), and content author (eVar6). Each organization customizes eVars for their specific business needs.
Prop Dimensions (Traffic Variables)
Props are hit-scoped custom dimensions that only apply to the server call where they are set. They do not persist across hits, which makes them ideal for page-level analysis and pathing reports. You can configure up to 250 props per report suite.
| Feature | Description |
|---|---|
| Scope | Hit only — value applies to the single server call where it is set |
| Pathing | Props can be enabled for pathing reports (page flow, fallout) |
| List Props | Can be configured to accept multiple delimited values in a single hit |
| Correlations | Props can be correlated with other props for cross-dimension analysis |
| Classification | Values can be classified into groups using SAINT classifications |
Common prop implementations: page template (prop1), content category (prop2), author (prop3), publish date (prop4), and A/B test variant (prop5). Props are best for dimensions where you only need the value at the moment it is set, without needing to carry it forward to a later conversion.
Core Metrics
These are the fundamental quantitative measurements in Adobe Analytics — the numbers that tell you how your site is performing in terms of traffic, engagement, and user behavior. They form the basis of every report and analysis.
| Metric | Scope | Description | Formula / Notes |
|---|---|---|---|
| Page Views | Hit | Number of times a page was loaded (s.t() calls) | Every page load generates one page view, including reloads |
| Visits | Visit | Number of sessions (30-minute inactivity timeout) | Resets at midnight in report suite timezone |
| Unique Visitors | Visitor | Number of distinct people within the reporting period | Identified by first-party cookie. Cannot be summed across periods |
| Daily Unique Visitors | Visitor | Unique visitors counted per day | Same person on two different days = 2 daily unique visitors |
| Entries | Visit | Number of times a page was the first in a visit | Entry count for a page = how many visits started there |
| Exits | Visit | Number of times a page was the last in a visit | Exit count for a page = how many visits ended there |
| Bounces | Visit | Visits with only a single page view (single server call) | No second hit in the visit. Does not count link tracking calls |
| Bounce Rate | Visit | Percentage of visits with a single page view | (Bounces ÷ Entries) × 100 for a specific page |
| Time Spent per Visit | Visit | Average time between the first and last hit in a visit | Excludes bounces (no second hit to calculate duration) |
| Time Spent on Page | Hit | Time between the page view and the next server call | Last page in a visit has no time calculated (no next hit) |
| Average Page Depth | Visit | Average number of page views per visit | Total Page Views ÷ Total Visits |
| Reloads | Hit | Number of times a page was reloaded (same page loaded consecutively) | Counted when pageName equals the previous pageName |
| Single Page Visits | Visit | Visits where only one unique page was viewed | Different from bounces — single page visits can have multiple hits |
| Instances | Hit | Number of times a dimension value was set in a server call | Counts how many hits contained a value for the dimension |
| Occurrences | Hit | Number of hits where a dimension value was set or persisted | Includes persisted eVar values — higher than instances for eVars |
Participation Metrics
Participation metrics credit a success event to every dimension value that was seen during the visit where the event occurred — not just the most recent value. This gives a broader view of what contributed to conversions.
| Metric | Description | Formula / Notes |
|---|---|---|
| Revenue (Participation) | Credits revenue to every page/section viewed in the converting visit | Each page gets full credit — totals will exceed actual revenue |
| Orders (Participation) | Credits the order to every dimension value in the converting visit | Useful for understanding the full path to purchase |
| Custom Event (Participation) | Credits any success event to all dimension values in the visit | Available for any enabled success event |
Calculated Metrics
Calculated metrics are custom metrics you create in Adobe Analytics using formulas. They combine existing metrics with mathematical operators and functions to create derived measurements tailored to your business needs.
| Function Type | Examples | Description |
|---|---|---|
| Basic Math | Add, Subtract, Multiply, Divide | Combine metrics: Conversion Rate = Orders ÷ Visits |
| Statistical | Mean, Median, Percentile, Variance, Standard Deviation | Statistical analysis of metric distributions |
| Conditional | If, Greater Than, Less Than, Equals | Create metrics with conditional logic (if revenue > 0, count as converting) |
| Regression | Linear Regression, Exponential, Logarithmic, Power | Trend fitting and forecasting models |
| Cumulative | Column Sum, Cumulative Average, Cumulative | Running totals and averages across rows |
| Advanced | Row Max/Min, And/Or, Approximate Count Distinct | Complex row-level and logical calculations |
Conversion Metrics (Success Events)
Success events are the custom conversion metrics you define in Adobe Analytics. They track the business outcomes that matter to your organization — from purchases and revenue to form submissions and video completions. Up to 1000 events can be configured per report suite.
| Event Type | How It Works | Use Cases |
|---|---|---|
| Counter | Increments by 1 each time the event fires | Form submissions, registrations, downloads, video starts |
| Numeric | Passes a custom integer value with the event | Items in cart, pages viewed in session, articles read |
| Currency | Passes a monetary value with the event | Revenue, order total, subscription value, donation amount |
Built-in conversion events
| Metric | Event | Description |
|---|---|---|
| Cart Additions | scAdd | Items added to the shopping cart |
| Cart Removals | scRemove | Items removed from the shopping cart |
| Cart Views | scView | Times the shopping cart page was viewed |
| Checkouts | scCheckout | Checkout process initiated |
| Orders | purchase | Completed purchase transactions |
| Revenue | purchase (revenue) | Total monetary value of purchases |
| Units | purchase (units) | Total quantity of items purchased |
| Product Views | prodView | Views of product detail pages |
Segments as Virtual Dimensions
Segments in Adobe Analytics are dynamic filters that isolate subsets of your data. While not traditional dimensions, they function as virtual dimensions when applied to reports — allowing you to see any metric filtered by specific criteria without modifying the underlying data.
| Segment Container | Scope | Description |
|---|---|---|
| Hit Container | Hit | Filters individual server calls that match conditions |
| Visit Container | Visit | Includes all hits from visits where conditions are met |
| Visitor Container | Visitor | Includes all data from visitors who match conditions at any point |
Sequential segments use THEN logic to find visitors who performed actions in a specific order — for example, "viewed product page THEN added to cart THEN purchased." This enables funnel analysis and behavioral pattern identification that standard dimensions cannot capture.
Report Suite Dimensions
Report suite dimensions control how data is organized at the highest level. These are administrative settings that affect data collection, processing, and availability across all reports.
| Dimension | Description |
|---|---|
| Report Suite ID | Unique identifier for the data container |
| Report Suite Timezone | Timezone used for visit boundaries and date-based dimensions |
| Currency | Default currency for revenue and monetary metrics |
| Base URL | Primary domain tracked in the report suite |
| Multi-Suite Tagging | Whether data is also sent to global/rollup report suites |
| Virtual Report Suite | Filtered view of a parent report suite with its own segment and settings |
How to Use Adobe Analytics Metrics for Optimization
Knowing which metrics and dimensions to use for different analysis goals is essential. Here is a practical framework for selecting the right tools.
For traffic analysis
Use page views, visits, and unique visitors as your primary volume metrics. Break down by marketing channel and referring domain to understand acquisition. Use entry page to identify top landing pages and bounce rate to measure landing page effectiveness.
For conversion analysis
Track orders, revenue, and custom success events. Use eVars with appropriate expiration to attribute conversions to earlier interactions (internal search term, campaign content, product finding method). Build calculated metrics for conversion rate (orders/visits) and average order value (revenue/orders).
For content performance
Analyze page views by page name and site section. Use time spent on page as an engagement indicator. Track entries and exits to understand how content functions in the user journey. Enable pathing on props to visualize page flow and identify common navigation patterns.
For audience understanding
Use technology dimensions (browser, device, OS) to understand your technical audience. Apply geographic dimensions (country, region, city) for market analysis. Segment by new vs. returning visitors and visit number to understand loyalty patterns. Use cross-device analytics to de-duplicate visitors across devices.
Common Mistakes When Analyzing Adobe Analytics Data
Even experienced analysts make these mistakes when working with Adobe Analytics. Avoiding them will improve data accuracy and analysis quality.
1. Confusing instances with occurrences for eVars
Instances count hits where the eVar was explicitly set. Occurrences count hits where the eVar had a value (set or persisted). For eVars with long expiration, occurrences can be dramatically higher than instances. Use instances when you want to know how often a value was originally set, and occurrences when you want to know how many hits the value applied to.
2. Summing unique visitors across time periods
Like reach in Facebook Ads, unique visitors cannot be summed across time periods. A monthly unique visitor report showing 100K in January and 120K in February does not mean 220K unique visitors for Q1. Many of those visitors overlap. Always query unique visitors for the full period you need.
3. Using props when you need eVars
If you need to attribute a conversion event (purchase, lead, registration) to a dimension value that was set earlier in the session, you need an eVar — not a prop. Props do not persist, so they cannot be connected to events on subsequent hits. Use props only for hit-level analysis like page-by-page breakdowns.
4. Ignoring segment container scope
A hit-container segment returns only the specific hits that match. A visit-container segment returns all hits from visits that contain a matching hit. A visitor-container segment returns all data from visitors who ever matched. The same condition with different containers can produce drastically different results. Always verify your container scope matches your analytical intent.
5. Comparing metrics across different report suites
Different report suites can have different timezone settings, event configurations, eVar expirations, and processing rules. Comparing metrics between report suites without accounting for these differences leads to flawed conclusions. Use virtual report suites or a global rollup for consistent cross-property analysis.
6. Overlooking time spent calculation limitations
Time spent is calculated as the difference between consecutive hits. The last page in a visit has zero time calculated because there is no next hit. Bounced visits also have zero time. This means time spent metrics systematically undercount actual engagement, especially for single-page content like blog posts. Consider supplementing with heartbeat tracking for more accurate time measurement.
