Comprehensive event tracking aims to record the entire product surface: every click, page view, hover, and form field interaction under one taxonomy. Its logic is simple: missing data cannot be collected later, so it is safer to record everything, even if you never use it.
In practice, this produces a crowded event stream that is costly to maintain, hard to query, and noisy enough to hide useful signals. It also fails to answer the questions early-stage teams actually face. They need to know whether the product works, not whether every surface has been covered.
Early-stage products generally need ten to fifteen well-tracked events. The challenge is choosing them.
Use the Product Lifecycle, Not the UI
Most teams define their first taxonomy by moving through the interface screen by screen and choosing what to record. That organizes events around UI location instead of lifecycle stage. "settings_tab_clicked" and "profile_page_viewed" then sit beside "onboarding_completed", with no signal that the last event matters ten times as much as either of the first two.
A better lens is the product lifecycle: which stages take someone from a first visit to becoming an active, retained user? In most SaaS products, these are acquisition, signup, activation, first value moment, habit formation, and retention. Track one to three events for each stage to show whether it works.
Acquisition events identify the user's source. Signup confirms registration is complete, while activation records the first meaningful in-product action. The first value moment depends on the product: for Mitzuo, it is receiving a first query answer; for a collaboration tool, a first shared document; for a task manager, a first completed task. Habit formation means the core action happens a second or third time. Retention means the user returns.
Ten Events for the Core Questions
For most early-stage B2B SaaS products, these events answer 80 percent of the questions you will need:
user_signed_up: the user completed registration. Properties: signup source, plan selected (if applicable), referral attribution. This is the top of your activation funnel.
onboarding_started and onboarding_completed: the user began and finished your onboarding flow. If your onboarding is a single screen, these might collapse into one. If it is a multi-step flow, track both so you can see where users drop off.
core_action_performed: this is the event for whatever your product fundamentally does. For a query tool it is "query_run." For a scheduling tool it is "meeting_scheduled." Name it for what it is in your domain. This is the single most important event in your taxonomy. You will query it constantly. Properties: source context, any relevant configuration state.
integration_connected (if applicable): for products that require connecting an external service before delivering value, integration completion is often the highest-correlation activation event. Track it explicitly with the integration type as a property.
feature_X_used for your two or three highest-priority features: if you are actively developing specific features and want to know whether users reach them, track direct interaction with those features. Keep this list short. Two to three events maximum, for your current focus areas. Prune them as features move from experimental to core.
user_returned or a daily/weekly active signal: some teams track explicit return events; others derive this from the presence of core_action_performed events on separate calendar days. Either works. What matters is having a clean way to measure whether users come back after their first session.
subscription_upgraded or payment_completed (if applicable): if your product has a paid tier, this event is the revenue event. It should have a clean, consistent definition and be triggered by the actual conversion, not a UI click that might not complete.
What to Deliberately Leave Out
Navigation events such as page views, tab clicks, and menu interactions are high-volume but low-signal early on. They show where users went, which is rarely actionable before the core loop is validated. Defer them until a specific navigation question exists.
Error events belong in application monitoring, not product analytics. Use your error tracking system for exceptions and logs. Putting them in the product stream adds pollution and makes it unclear whether an event represents a user action or a system failure.
Early-stage teams can rarely act on micro-interactions such as hover, focus, or scroll depth. Scroll depth is an exception for a media product when it proxies content engagement. In most SaaS products, these events add noise rather than useful signal.
This does not mean those events should never be tracked. The distraction is collecting them before the core lifecycle events work. Cover the lifecycle first, then expand to answer specific unanswered questions rather than guessing what may prove useful.
Naming Conventions for Later
Get event names right early. Renaming them later can break queries and dashboards and make historical comparisons inconsistent. These conventions tend to hold up:
Use noun-verb order rather than verb-noun: user_signed_up rather than signup_user, query_run rather than run_query. Noun-verb groups related events naturally when you sort alphabetically.
Use past tense for completion events: onboarding_completed, not complete_onboarding. This makes it clear the event fires after the action is done, not when it is initiated.
For events that have both a start and an end, track both with consistent naming: export_started and export_completed, not export_initiated and export_done. Inconsistent naming within pairs creates confusion about which event to use in funnel analysis.
Use a consistent property for the user identifier across all events. If you use user_id, use it everywhere. Not userId in some events and user_id in others. The difference is invisible when you write the event handler but extremely visible when you try to join across event types.
Expanding the Taxonomy Over Time
Expand the taxonomy in response to questions. If the current events cannot answer a product question, add the event that can. Do this after the question arises, not in anticipation of hypothetical ones.
This keeps the taxonomy lean and purposeful: every event exists to answer a question. Retire it when that question no longer matters because a feature was deprecated, an experiment ended, or the concern was resolved. The result reflects the questions the product actually produces, not everything that might someday be tracked.
Once the taxonomy reaches around 30 to 40 events, conduct a deliberate audit. Which events are queried often, and which have not been used in three months? Consider pruning the latter. A smaller, actively used taxonomy is easier to maintain, faster to query, and clearer for new team members.
