How to Design User Analytics Events

User analytics is not “record every click.”

Good analytics turns behavior into answerable questions: where do users come from? Why do they sign up? Where do they drop off? Which features are actually used? What do users do before paying? Did the new release improve conversion?

If tracking is not designed, you do not get data. You get a pile of event names that nobody can interpret.

This article references official materials:

• Google Analytics 4 Recommended Events
• Google Analytics 4 Automatically Collected Events
• Segment Tracking Plan
• Mixpanel Property Reference

Start With Questions

Do not begin with “which events should we track?”

Start with business questions:

What is the conversion rate from visit to signup?
How many users complete the first key action after signup?
Which acquisition channels bring users who pay?
Which step has the highest drop-off?
Did the new feature get used after launch?
What do free users usually do before upgrading?

Analytics exists to answer questions, not to make the product look data-driven.

If an event does not help you make decisions, improve the product, or validate a hypothesis, it should not be prioritized.

Separate Events, Event Properties, and User Properties

Three concepts are often confused.

Event: what the user did
Event property: context for this specific action
User property: a longer-lived trait of the user or account

For example:

Event: checkout_started
Event properties: plan=pro, source=pricing_page, currency=USD
User properties: role=owner, company_size=1-10, signup_channel=seo

The event describes the action. Properties describe the action context. User properties describe the person or account.

Do not put everything into the event name. Avoid:

pro_user_from_pricing_page_started_usd_checkout

Use:

event: checkout_started
properties:
  plan: pro
  source: pricing_page
  currency: USD

This makes filtering, grouping, and comparison possible.

Keep Naming Stable

Once naming becomes messy, analytics data becomes hard to use.

Use a consistent format, such as snake_case:

page_viewed
signup_started
signup_completed
onboarding_completed
project_created
file_uploaded
checkout_started
payment_succeeded
subscription_cancelled

Avoid mixing:

Signup Success
signup_success
signUpSuccess
register_done
user_registered

Stable naming matters more than elegant naming.

Google Analytics 4 recommended events include names such as sign_up, login, and purchase. When a platform has a recommended event, reuse it when it fits. Define custom events only when needed.

Track Key Paths First

You do not need to track the whole product on day one.

Cover the most important paths first:

Homepage viewed
Pricing page viewed
Signup started
Signup completed
Onboarding completed
First project created
Core feature triggered
Checkout started
Payment succeeded
Subscription cancelled

For a content product, track:

Article impression
Article read
Table of contents click
Search
Bookmark
Share
Subscribe

For a tool product, track:

Template selected
File imported
Generation started
Generation succeeded
Export
Collaboration invite

Do not start by tracking every button. Many clicks do not answer core product questions.

Every Event Needs Properties

Events without properties are weak.

For project_created, include:

project_type
template_id
source
plan
team_id
is_first_project

For checkout_started, include:

plan
billing_cycle
currency
price
coupon_used
source

For article_read, include:

article_id
category
read_depth
referrer
locale

Do not add endless properties. Add the ones needed for common analysis: channel, version, plan, entry point, and user type.

Design Funnels, Not Isolated Events

A single event has limited value. Funnels answer better questions.

Signup funnel:

landing_viewed
signup_started
email_verified
profile_completed
onboarding_completed
activation_event_completed

Payment funnel:

pricing_viewed
checkout_started
payment_method_entered
payment_succeeded
subscription_activated

Core feature funnel:

feature_opened
input_added
generation_started
generation_succeeded
result_exported

When designing analytics, draw the funnel first instead of listing isolated events.

Define an Activation Event

Every product needs an activation event.

Activation is not signup. It is the moment when the user experiences product value.

Examples:

Project management tool: creates first project and invites a member
AI writing tool: generates first usable draft
Image tool: uploads an image and completes first export
Email tool: successfully sends first email
Data tool: connects data source and creates first chart

Signup is account creation. Activation means the user has started to understand value.

If you cannot define activation, onboarding will be hard to improve.

Maintain a Tracking Plan

Analytics needs a tracking plan.

Segment's Tracking Plan documentation describes it as a data spec for the events and properties you intend to collect.

Early on, a Markdown file or spreadsheet is enough:

Event name
Trigger timing
Page or surface
Event properties
Property types
Required or optional
Example values
Owner
Release version
Notes

Without a tracking plan, you will forget what events mean and which ones are still being sent.

Do Not Collect Sensitive Data

Analytics tools may be accessed by product, growth, support, operations, and external vendors.

Do not collect:

Passwords
Tokens
Verification codes
Identity documents
Card numbers
Full address
Private chat content
Raw uploaded file content
Sensitive personal information without consent

Use masking, grouping, or enums when analysis is needed.

For example, record plan=pro, not payment details. Record company_size=1-10, not employee names.

Events Need Versioning

Products change, and tracking changes with them.

When an event's meaning changes, do not change it silently.

Use these rules:

Add a new property instead of changing an old meaning
Create a v2 event when necessary
Record deprecation date in the tracking plan
Keep old and new events during a transition period
Check that key events still fire before release

The worst case is one event name meaning different things at different times. Historical data gets mixed together and analysis becomes misleading.

A Minimum Viable Analytics Plan

For an early indie product:

1. List 3 core business questions
2. Design one funnel for each question
3. Keep only key events in each funnel
4. Use snake_case for event names
5. Add necessary properties to every event
6. Define one activation event
7. Maintain a tracking plan
8. Never collect sensitive data
9. Check key events before each release

This is slower than automatic click tracking, but the data quality is much higher.

Final Thoughts

User analytics translates product questions into observable behavior data.

Do not collect data for its own sake. Start with questions, then events. Start with funnels, then buttons. Start with naming rules, then code.

A good analytics system should help you understand where users first experience value, where they give up, and which users are most likely to stay.

Next, we continue with growth entry points: How to Generate SEO Pages.