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Event Tracking Basics for Product Teams

Jan 21, 2026EventDash Team

Why event tracking still goes wrong

Event tracking seems simple until you ship a feature and realize nobody agrees on what a "conversion" means. The fix is not more dashboards. The fix is a shared, repeatable plan for how you name, capture, and validate events.

This guide covers the minimum viable approach that scales.

Start with outcomes, not clicks

Before you list events, define the outcomes you care about:

  • Activation: what is the moment a user first sees value?
  • Engagement: what behavior indicates recurring value?
  • Retention: what brings users back after a week or month?

Map events to those outcomes, not the other way around.

Use a clear naming convention

Pick one format and stick to it. A simple, readable option:

object_action

Examples:

  • project_created
  • report_shared
  • billing_updated

Avoid vague names like clicked without the object. Context makes events useful.

Capture only the properties that matter

Every event can include properties, but most do not need them. A good rule:

  • Required: identifiers you need for grouping (plan, role, device)
  • Nice to have: details that help explain behavior (feature flag, variant)
  • Avoid: anything personal or sensitive

If a property will not be used in analysis, do not send it.

Validate events before shipping

Create a small checklist for every new event:

  • Name matches the convention
  • Properties are documented
  • Sample payloads look correct
  • Event appears in staging dashboard

If you do this once, every sprint, you prevent data drift.

Keep tracking lightweight

EventDash supports HTML attribute tracking, which lets teams add analytics without writing new JavaScript. That keeps tracking close to the UI and easier to maintain. The result is less code and fewer regressions.

Final checklist

  • [ ] Outcomes are defined
  • [ ] Events are named consistently
  • [ ] Properties are minimal and useful
  • [ ] Events are validated before release

Consistency beats volume. A small, clean event model will outperform a noisy one every time.