# What Is Event Taxonomy: Complete Definition & Framework

A complete guide to event taxonomy—how to prevent duplicate events, broken dashboards, and inconsistent metrics.

Source: https://amplitude.com/en-us/explore/data/event-taxonomy

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###### Learn how to design an event taxonomy that scales across web, mobile, and product areas

# What Is Event Taxonomy: Complete Definition & Framework

Learn how to build a future-proof taxonomy that supports funnels, journeys, cohorts, and experiments.

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Table of Contents

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If you track user behavior in a product, you collect many signals. Event taxonomy is the plan that makes those signals clear and useful.

It gives names to user actions, groups them in a logical way, and defines the details that describe each action. With a shared plan, teams read the data consistently.

Without a plan, events get messy, reports break, and teams debate definitions. A strong taxonomy turns raw clicks and views into trustworthy insight.

Browse this guide

- [What is an event taxonomy?](#definition)
  - [Event, property, and user identifiers](#event-property-and-user-identifiers)

- [Why a clear taxonomy drives better product decisions](#drive-better-product-decisions)

  - [Faster analysis and experiments](#faster-analysis-and-experiments)
  - [Lower data costs and rework](#lower-data-costs-and-rework)

- [Core building blocks of an event taxonomy](#building-blocks)

  - [Required fields to capture](#required-fields-to-capture)
  - [Optional enrichments that add context](#optional-enrichments-that-add-context)

- [Five steps to design a future-proof taxonomy](#steps-to-design-a-future-proof-taxonomy)

- [Governance rules to keep your data trustworthy](#governance-rules)

  - [Ownership and approval workflows](#ownership-and-approval-workflows)
  - [Version control and change logs](#version-control-and-change-logs)

- [Common pitfalls and how to avoid them](#pitfalls)

  - [Duplicate events and silent breaks](#duplicate-events-and-silent-breaks)
  - [Over-tracking low-value data](#over-tracking-low-value-data)

- [Real-world use cases with Amplitude Analytics](#use-cases)

  - [Onboarding funnel cleanup](#onboarding-funnel-cleanup)
  - [Marketing attribution consistency](#marketing-attribution-consistency)

- [Turn insight into action with a strong taxonomy](#insights-to-action)

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## What is an event taxonomy?

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

## Why a clear taxonomy drives better product decisions

Clear event naming and organization directly affect your ability to [analyze user behavior](https://amplitude.com/explore/analytics/digital-experience-analytics). 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 [conversion rates](https://amplitude.com/explore/metrics/conversion-rate-guide) appear lower than they actually are.

### **Faster analysis and experiments**

Consistent naming conventions enable quick insights without [data cleanup](https://amplitude.com/explore/data/data-cleaning-guide). When web and [mobile apps](https://amplitude.com/explore/analytics/mobile-analytics) use the same event names, you can compare behavior across platforms in a single chart.

[A/B testing](https://amplitude.com/explore/experiment/statistical-significance-guide) 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, [schema conflicts](https://amplitude.com/explore/data/what-is-data-mapping), 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.

## Core building blocks of an event taxonomy

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.

| Basic Event   | Enhanced Event        |
| ------------- | --------------------- |
| user\_clicked | button\_clicked       |
| page\_view    | product\_page\_viewed |
| purchase      | checkout\_completed   |

 

### **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:** [device\_type](https://amplitude.com/templates/user-device-usage-analysis-chart), os\_version, browser
- **Product context:** product\_id, category, price, discount\_applied

## Five steps to design a future-proof taxonomy

### **1. Define business goals and North Star metrics**

Start with the [key performance indicators](https://amplitude.com/explore/metrics/what-are-metrics-guide) your team tracks, such as sign-ups, purchases, activation rates, and retention. Event definitions align with business outcomes like [conversion optimization](https://amplitude.com/explore/experiment/conversion-rate-optimization) and long-term engagement.

Map critical [user journeys](https://amplitude.com/explore/digital-marketing/customer-journey-explained) 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 [abandoned carts](https://amplitude.com/templates/cart-abandonment-dashboard) 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 [funnels](https://amplitude.com/templates/funnel-analysis) 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.

## Governance rules to keep your data trustworthy

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.

## Common pitfalls and how to avoid them

### **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 [Digital Analytics Platform](https://amplitude.com/explore/data/data-analytics-platform) 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.

## Real-world use cases with Amplitude Analytics

### **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**

[Attribution](https://amplitude.com/explore/digital-marketing/marketing-attribution-guide) 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.

## Turn insight into action with a strong taxonomy

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.

[Try Amplitude for free today](https://app.amplitude.com/signup) to see how unified event tracking, governance, and analysis work together.

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