How To Build a Tracking Strategy
A well-defined tracking strategy ensures your product data stays consistent and reliable, enabling teams to make data-informed decisions without wasting time on data cleanup.
This guide details how to create a tracking strategy that enables your team to capture the right events and properties, keep them consistent, and set them up for meaningful analysis.
What is a Tracking Strategy?
A tracking strategy is a plan for deciding which events and properties to capture, how to name them, and how to manage them over time.
In Mixpanel, this means using the data model (events, users, and properties) intentionally, so your data is structured for the KPIs that matter most to your business.
A tracking strategy is not just about what you log; it is about creating a shared framework so product, engineering, and analytics teams can trust the data and use it to answer business questions.
Building a Tracking Strategy
A good strategy is built step by step, not all at once. The practices below will help you move from a vague idea of “we should track more” to a clear, scalable framework your whole team can rely on.
Define the Events that Matter
Events are the backbone of Mixpanel. Start by mapping out the key user actions tied to your KPIs (e.g. sign-up started, checkout completed, feature used).
- ✅ Do choose events that represent meaningful behaviors connected to business goals.
- ❌ Do not track every click or interaction simply because it is possible, since doing so can clutter your analysis.
Learn more about Events.
Use Event Properties to Add Context
Events tell you what happened, properties tell you how it happened. Properties give you the flexibility to keep your event list small while still allowing rich analysis.
Utilize properties to avoid creating separate events for each variation.
Properties like plan_type, payment_method, or device can help keep your event list targeted and make analysis easier. Instead of hunting across multiple events, you can filter one event by property values.
Use consistent data types for predictable analysis.
Consistency in data types (e.g. string, numeric, or Boolean) ensures your metrics are accurate and comparable over time. For example, if a property is sometimes a string and sometimes a number, queries can break and charts can become unreliable.
Attach session-level context as super properties.
Promoting context like app version, device type, or campaign source to a super property auto-attaches it to every event, which saves engineering effort and guarantees important context is always available in your analysis.
Pro tip: Teams typically see the best results when they limit their schema to a manageable set of core events, enriched with properties for detail.
Capture User Profiles and Properties
Events capture behavior, while user profiles capture who the user is today (e.g. their email, location, and subscription level). When you pair event properties with user properties, you can compare past behavior with the current state.
For example, a user property for current_country = Singapore can be compared with an event geolocation property showing where they logged in previously. A user who travels often may track events in different locations despite their current location being somewhere else.
Learn more about User Profiles.
Tips for Choosing Properties
Properties, whether attached to events or stored on user profiles, add the context that makes your analysis meaningful. The right ones make it easy to answer business questions; the wrong ones add noise and confusion.
Use these guidelines to decide which properties to include.
Prioritize analysis needs.
Capture properties that directly support the questions your team needs to answer. For example, if “Which payment methods drive the most revenue?” is a key question for your team, you should prioritize including properties like payment_processor and payment_method.
Favor stability.
Pick properties that are unlikely to change meaning or format frequently. This keeps your reporting consistent over time.
Balance detail with simplicity.
More properties mean more flexibility, but too many can overwhelm dashboards and confuse stakeholders.
Reuse across events.
Standardize common properties like plan_type, device, or region so you can compare data across events instead of reinventing them each time.
Following these guidelines keeps your schema focused, while still giving you the flexibility to answer the questions that matter most.
Standardize Naming Conventions
Clear, consistent names make your data easier to use and trust. A naming convention prevents confusion, reduces duplicate events, and keeps your queries simple.
- ✅ Do pick one convention and use it everywhere.
- ❌ Do not mix formats like
SignUp,signup_started, andSignup Started.
As a best practice, use snake_case for all event names, properties, and property values (for example: checkout_completed, payment_method = credit_card). Snake case works well with APIs and avoids ambiguity.
Pro tip: Write down your naming rules in your tracking plan so engineers and analysts always know what to follow.
Document and Govern Your Tracking Plan
Your strategy only works if it is shared and enforced. Keep a living tracking plan and lean on Mixpanel’s governance features.
- Store your tracking plan in a shared location, such as a Google Sheet or Notion page.
- Keep your tracking plan updated as your product evolves.
- Share your tracking plan widely across product, engineering, and analytics teams.
Tools to Help with Data Governance
- Tracking Plan Template: Organize event names, properties, and owners in one place.
- Lexicon: Add descriptions, assign owners, and hide deprecated events right in Mixpanel.
Learn more about Data Governance.
Key Takeaways
- Only track events that tie directly to KPIs.
- Use properties to keep your schema flexible.
- Capture user profiles to combine “who” with “what.”
- Standardize names using snake_case.
- Document your plan and use governance features to keep data clean.
Check out our Onboarding Playbook on Building Your Tracking Strategy.
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