Product analytics event instrumentation best practices

Overview

This guide teaches you best practices for instrumenting events in product analytics.

Prerequisites

Before LaunchDarkly enables hosted product analytics for your organization, ensure:

• Your SDK is sending custom events. These events should represent the product interactions you wish to analyze in product analytics.
• Events are instrumented using the user context. This ensures events are accurately attributed and user-level metrics such as MAU, retention, and segmentation function properly in product analytics. Product analytics currently supports the user context. To learn more about how to track events using the user context, read Context kinds.

After both these prerequisites are met, we can enable hosted product analytics for your organization.

Best practices for event instrumentation

Here are some best practices for event instrumentation:

Event instrumentation should answer questions, not just measure actions

Before deciding where to track events, define the questions you want to answer.

For example:

• How is our core conversion workflow performing, and where are users dropping off?
• How often are users coming back and engaging with the product over time?
• Are there any unexpected or anomalous patterns in usage that signal friction or opportunity?

From these questions, work backward to identify the minimum set of events needed to answer them. This ensures the first iteration of instrumentation is efficiently constructed but also analytically complete, not just technically correct.

Here’s a high-level example process to follow:

1. List the top three to five product or business questions you want to answer.
2. Map each to a measurable user action or state change.
3. Instrument the events that directly feed those insights.

Use an effective event design and naming framework

A clear, company-wide convention for event naming is essential to scaling product analytics. Without it, data fragments quickly, because the same behavior gets tracked under different names. Over time, dashboards become less trustworthy because it becomes unclear whether the same behavior is being tracked under different names.

Maintain a shared, version-controlled taxonomy document that defines event names, firing logic, and required properties. This ensures every team speaks the same analytics language.

Think of the taxonomy as your product’s analytics dictionary, and take the time to develop a framework that all contributors understand and use. Establishing consistency here drives clean insights everywhere else.

Recommendations for effective naming conventions

Follow a human-readable, object-action framework (for example, “Project Created”, “User Invited”, “Report Exported”). This approach emphasizes what entity was acted upon and what consequences occurred as a result, making it intuitive and easy to scan in the product analytics UI.

Use title case for event names to improve readability. Avoid symbols or mixed styles like “button.click.createScenario”, which combine snake, camel, and dotted casing and are hard to interpret at scale.

While the object-action convention is our recommended framework for event naming, there are a few other patterns you might use or encounter. These are included here for reference and context.

Adopt one convention organization-wide and enforce it with reviews or linting

Use a human-readable case for clarity in the product analytics UI. Document every event in your taxonomy with its purpose, owner, and properties. Review these periodically to deprecate unused or redundant events.

The goal is consistency and readability; event names should make sense to anyone reviewing analytics, not just engineers.

Where to place track calls

Go beyond surface-level interactions. Focus on moments of intent (or “user purpose”) and moments of success (or “user outcome”).

Good instrumentation coverage should capture a full funnel:

Ensure that tracking spans multiple layers:

• Front-end [client-side] events: interaction-level behavior (clicks, navigation).
• Back-end [server-side] events: durable state transitions (object creation, API success or failure responses).

Enrich events with details

To make analytics meaningful, go beyond event names. Capture event-level and user-level properties that provide context for segmentation and insight. LaunchDarkly product analytics supports both, letting you analyze not just what happened, but who did it and under what conditions.

• Event-level properties describe the details of the interaction and are tracked when the event occurs. For example, source, item_count, result, duration. Use these properties to segment behavior by relevant product events.
• User-level properties describe who performed the event and their relationship to your product. They are associated with each user. For example: user_id, planTier, region, device, city, etc. Use these to segment behavior by persona, geography, and more.

Keep property names consistent and well-documented, and standardize a small core set across all events. This ensures analyses in product analytics remain comparable and scalable as your instrumentation grows.

Start focused and scale intentionally

“Start small” doesn’t mean “track less.” It means design your instrumentation intentionally. Begin with a complete analytical storyline, such as user onboarding or another important user journey. Ensure that for that flow you can calculate activation percentage, completion percentage, retention percentage, and drop-off reasons. After you validate those calculations, expand coverage by tracking different user journeys, not random features.

This approach creates immediate insight loops and lets you avoid event sprawl.