What Is Event Taxonomy: Complete Definition & Framework

An event taxonomy is a structured system for naming, organizing, and categorizing user actions and their properties across your app or website. Think of it as a filing system for all the ways people interact with your product.

Every time someone clicks a button, views a page, or completes a purchase, that’s an event. Event properties are the details that add context—like which button they clicked or what they bought. User properties describe the person taking the action, such as their subscription level or device type.

A taxonomy organizes these events into clear categories with consistent naming conventions. For example, all button clicks might follow the pattern “button_clicked”, with properties such as "button_name” and “page_location.” This consistency lets anyone on your team understand what happened without having to guess.

Event, property, and user identifiers

The three core components of event tracking work together to capture the whole picture:

• Events: Specific user actions like button_clicked, video_played, checkout_started
• Event properties: Details describing the event, like button_name, video_duration, cart_value
• User properties: Attributes of the person, like subscription_plan or device_type

Clear event naming and organization directly affect your ability to . When event names stay consistent across platforms and teams, the same query produces the same results every time.

Messy taxonomies create duplicate events that split metrics across multiple lines. For instance, “sign-up,” "sign_up,” and “user_sign-up” might all track the same action, making appear lower than they actually are.

Faster analysis and experiments

Consistent naming conventions enable quick insights without . When web and use the same event names, you can compare behavior across platforms in a single chart.

becomes simpler when assignment, exposure, and conversion events follow the same schema. Teams can segment users with shared properties and track trends across product releases using repeatable queries.

Lower data costs and rework

Disorganized event tracking creates duplicates, , and mismatched definitions. These issues trigger debugging cycles, data backfills, and re-instrumentation work.

Clear naming conventions prevent duplicate events and conflicting property types. Stable definitions reduce the risk of drawing incorrect conclusions from inconsistent data.

Event taxonomy includes three main elements: event names, event properties, and naming conventions. A common pattern uses verb_noun structure like “button_clicked” or “checkout_completed,” written in lowercase with underscores.

Events often sit in a hierarchy by product area, such as Account > Authentication > password_reset. This structure groups related actions so that analysis follows natural product flows.

Required fields to capture

Every event needs a name, timestamp, and user identifier. Many implementations also include session identifiers, platform details, and app versions for cross-platform analysis.

Optional enrichments that add context

Additional properties provide deeper insights into user behavior and context:

• Geographic data: country, region, city
• Marketing attribution: utm_source, utm_medium, utm_campaign
• Session details: session_id, session_duration, pages_viewed
• Device information: , os_version, browser
• Product context: product_id, category, price, discount_applied

1. Define business goals and North Star metrics

Start with the your team tracks, such as sign-ups, purchases, activation rates, and retention. Event definitions align with business outcomes like and long-term engagement.

Map critical from first touch to conversion. Identify the specific actions that indicate progress toward your goals, such as account_created → onboarding_completed → first_purchase.

2. Audit current events and gaps

Review existing event tracking for inconsistencies and missing actions. Look for variations like “sign-up” versus “sign_up,” or uncaptured steps in important flows like or failed payments.

Document which conversions and key behaviors aren’t currently tracked. Identify events that exist in one platform but not others, creating gaps in cross-platform analysis.

3. Draft naming conventions and schemas

Establish consistent syntax using lowercase letters, underscores, and verb_noun patterns. Property names follow the same rules with stable data types—prices as numbers, currencies as standardized codes, timestamps in ISO format.

Create a style guide that covers:

• Event naming: button_clicked, not Button_Clicked or buttonClicked
• Property types: price (number), currency (string), timestamp (ISO 8601)
• Hierarchy rules: category > subcategory > specific_action

4. Validate with Amplitude funnel and journeys charts

Test your taxonomy by building key in Amplitude Analytics. Create flows like product_page_viewed → add_to_cart → checkout_completed to confirm events capture the user journey accurately.

Use journey charts to identify the most common paths to conversion and drop-off points. If events don’t flow logically or miss critical steps, refine the taxonomy before full implementation.

5. Publish, document, and train teams

Create central documentation listing every event, property, data type, and owner. Include examples and implementation notes for developers and analysts.

Train product, marketing, and engineering teams on the taxonomy. Establish a governance process for requesting new events, approving changes, and managing versions over time.

Data governance maintains taxonomy quality through ownership, approval workflows, and change management. Clear processes prevent ad-hoc additions that break consistency.

Ownership and approval workflows

Assign specific roles for taxonomy management:

• Event requesters propose new events with clear use cases
• Data stewards review naming and check for duplicates
• Domain owners approve events within their product areas
• Technical implementers add tracking code and test functionality

A typical approval process includes proposal submission, naming review, domain approval, implementation, testing, and documentation updates.

Version control and change logs

Track all taxonomy changes with version labels like “proposed,” "active,” "deprecated,” and “removed.” Document what changed, when, why, and who approved it.

Breaking changes—like renaming events or changing property types—require migration plans for existing dashboards and queries. Deprecation windows give teams time to update their analysis before old events stop collecting data.

Duplicate events and silent breaks

Inconsistent naming creates false duplicates that split metrics. When “sign-up,” "sign_up,” and “user_sign-up” all track registrations, conversion rates appear fragmented across multiple event names.

Property changes can break analysis without warning. Renaming “plan” to “subscription_plan” or changing a price from string to number breaks existing queries and dashboards.

Point solutions like Mixpanel, Heap, and Google Analytics each maintain separate event definitions, increasing the risk of inconsistencies. Amplitude's integrated keeps event schemas, properties, and governance aligned in one system.

Over-tracking low-value data

Tracking every interaction inflates data volume and obscures important patterns. Events like mouse movements or frequent scroll updates add noise that crowds out meaningful actions.

Focus on outcome-driven events that connect to business goals: account_created, product_viewed, add_to_cart, checkout_completed. Use properties to add context rather than creating separate events for minor variations.

Onboarding funnel cleanup

Clean taxonomy enables precise onboarding analysis with events like app_opened → sign_up_started → sign_up_completed → onboarding_step_viewed → onboarding_completed.

Amplitude's funnel analysis reveals exactly where users drop off when each step has a unique, consistent name. Breakdowns by platform, app version, or experiment variant show which segments struggle at specific points.

Properties like step_name and step_index enable detailed analysis of individual onboarding screens. When session_id and user_id link events together, you can track re-entry patterns and completion across devices.

Marketing attribution consistency

requires the same acquisition and conversion events across all channels. Consistent properties like utm_source, utm_medium, and campaign_id enable accurate campaign performance measurement.

Amplitude's unified platform connects conversions to original campaigns when user identifiers remain consistent. Cross-channel dashboards reflect a single set of campaign values without reconciliation between tools.

Event taxonomy forms the foundation for reliable product decisions. Consistent events and properties flow directly into analysis, experimentation, and audience management.

Amplitude provides integrated analytics, experimentation, and audience tools in one platform. A single taxonomy powers funnels, journeys, cohorts, experiments, and audiences without translation between point solutions.