You've integrated your MMP SDK. Deep links are working. Attribution is flowing. But six weeks later, your marketing team is still making budget decisions based on install volume instead of revenue because nobody can trust the event data.

This isn't a technical problem. It's a planning problem.

Most teams approach event tracking like this: developers implement events as features get built, marketers request new parameters when campaigns launch, and product teams add their own tracking later.

Within three months, you have 47 events with inconsistent naming ("purchase" vs "Purchase_Complete" vs "transaction_success"), missing parameters that prevent cohort analysis, and no clear mapping between what users do and what marketing pays for.

The result? Marketing optimises for installs while finance asks why revenue per user keeps dropping. Product can't explain retention patterns because events don't capture the right context. And when you finally audit the mess, fixing it requires engineering sprints you don't have bandwidth for.

Here's what actually works: treating event taxonomy as infrastructure, not an afterthought. Before a single line of SDK code gets written, you need stakeholder alignment on what matters, how to measure it, and how events will flow into every system that depends on them.

This guide walks through the complete workflow, from audit to validation, with specific naming conventions, common failure points, and QA steps that prevent the costly rewrites most teams face.

Why 60% of Attribution Fails Because of Poor Event Structure

Bad event structure doesn't show up immediately. It reveals itself slowly, in symptoms that seem unrelated:

Week 4: Marketing can't segment high-value users from low-value users because events don't include product category or subscription tier.

Week 8: Finance questions your ROAS calculations because "revenue" events fire inconsistently (sometimes before payment confirmation, sometimes after).

Week 12: Product can't analyse onboarding drop-off because signup steps weren't tracked as distinct events.

Week 16: You discover 30% of conversion events have null values for critical parameters because developers didn't enforce required fields.

Each of these issues traces back to the same root cause: event taxonomy decisions were made in isolation by different teams, or worse, weren't made at all and evolved organically as needs arose.

The cost compounds. Marketing wastes budget on campaigns that look profitable at the install level but lose money at the revenue level. Product teams can't identify friction points in key flows. Analytics dashboards show conflicting numbers because events mean different things in different contexts.

When we audit attribution setups for growth teams, poor event structure is the single biggest predictor of measurement failure. Not SDK bugs. Not attribution window settings. Not postback configuration. Event taxonomy.

The good news: this is fixable before implementation, not after.

Phase 1: Audit Current Events and Define Business Outcomes

Start by mapping what you have, even if it's a mess. Export your current event list from your analytics platform (Mixpanel, Amplitude, Firebase, or wherever events currently flow). If you're starting fresh, skip to defining outcomes.

Current State Audit Checklist:

1. List every event currently being tracked (aim for complete inventory)

2. Document which systems consume each event (MMP, analytics, CRM, data warehouse)

3. Identify events used in active marketing decisions (ROAS calculations, audience targeting, campaign optimisation)

4. Flag events with inconsistent naming or duplicate purposes

5. Note missing parameters that limit analysis (user properties, revenue values, product details)

Most teams discover they're tracking 40-60 events, but only 8-12 actually inform decisions. The rest create noise.

Define Business Outcomes (Not Just Features):

Before rebuilding taxonomy, align stakeholders on what you're measuring and why. Run a 90-minute working session with marketing, product, engineering, and finance. Answer these questions:

• What user actions directly generate revenue? (purchases, subscriptions, ad views, bookings)

• What actions predict revenue? (signup completion, profile setup, first key action, repeat usage)

• What marketing touchpoints should we attribute conversions to? (paid ads, organic search, referrals, email)

• What cohort cuts do we need for budget decisions? (by channel, campaign, creative, geo, device)

• What events does finance need to reconcile revenue? (transaction IDs, timestamps, amounts, currency)

Output from this session should be a prioritised list of 15-25 core events that map to actual business questions, not features. For example:

Don't Track: "Button_Clicked_Homepage_SignUp" Do Track: "Signup_Started" with parameters for source, medium, campaign

Don't Track: "Product_Page_Viewed" Do Track: "Product_Viewed" with category, price, availability status

The difference: the first approach tracks interface interactions. The second tracks business-relevant behaviour with context that enables analysis.

Phase 2: Build Event Hierarchy (Standard vs Custom, Naming Conventions)

Event taxonomy needs structure. Without it, you end up with 200 events that nobody can navigate.

Three-Tier Event Hierarchy:

Tier 1: Standard Events (MMP Defaults)

Most MMPs including Linkrunner provide standard events optimised for ad platform integration. Use these for core conversion actions:

• install (automatic, don't implement manually)

• signup_complete

• purchase

• subscribe

• add_to_cart

• start_trial

Why use standard events? They map directly to ad platform optimisation goals (Meta's purchase event, Google's in-app conversion actions, TikTok's complete registration). This enables automatic postback configuration and better algorithm learning.

Tier 2: Business-Critical Custom Events

These capture actions specific to your business model but universal across your app:

For gaming apps: level_complete, daily_reward_claimed, currency_purchased For fintech: kyc_started, kyc_complete, first_transaction, recurring_payment For eCommerce: search_completed, filter_applied, checkout_started For EdTech: lesson_started, lesson_complete, assessment_passed

Tier 3: Product-Specific Events

Detailed product interactions that inform feature development and retention analysis:

• Onboarding flow steps

• Feature discovery moments

• Settings changes

• Share/referral actions

Naming Convention Rules (Non-Negotiable):

1. Use snake_case: signup_complete not SignupComplete or signup-complete

2. Start with action verb: purchase_complete not complete_purchase

3. Be specific but concise: subscription_renewed not user_subscription_renewal_event

4. Avoid platform indicators: signup_complete not ios_signup_complete (use platform as parameter instead)

5. Use past tense for completed actions: purchase_complete not purchase_completing

6. Standardise separators: choose underscore and stick with it everywhere

Bad Event Names vs Good Event Names:

Document every event in a central tracking plan (Google Sheet or dedicated tool). Include: event name, description, when it fires, required parameters, which teams use it, and which systems consume it.

Phase 3: Map Events to Marketing Funnel and Revenue Goals

Events don't exist in isolation. They flow into marketing optimisation, revenue reporting, and product analytics. Map each event to its purpose across systems.

Marketing Funnel Mapping:

Define which events represent each funnel stage:

Awareness: (typically tracked via ad platforms, not in-app events) Acquisition: install (automatic), app_open (first session) Activation: signup_complete, profile_complete, first_key_actionRevenue: purchase_complete, subscription_started, ad_view_completeRetention: session_start, daily_active, feature_usedReferral: referral_sent, referral_install

Each stage needs at least one event that marks progression. This enables funnel analysis and identifies where users drop off.

Revenue Event Requirements:

Revenue events need specific parameters to support accurate ROAS calculations and financial reconciliation:

Common mistake: Firing purchase events before payment confirmation. This inflates revenue in dashboards while actual payments fail. Always trigger revenue events after successful payment processing, not at checkout initiation.

SKAN Conversion Value Mapping:

For iOS campaigns, map events to SKAN conversion values (0-63 range). Prioritise by business value:

• 0-10: Install only, no meaningful action

• 11-20: Signup complete

• 21-30: First purchase (low value)

• 31-40: First purchase (medium value)

• 41-50: First purchase (high value)

• 51-63: Repeat purchases or high LTV indicators

Detailed SKAN mapping strategies are covered in our Strategic SKAN 4.0 Decoding guide.

Cross-System Event Flow:

Document which systems consume each event:

This prevents over-instrumentation. Not every event needs to flow everywhere. Product analytics events don't need MMP postbacks. Settings changes don't need CRM updates.

Understanding how events connect to marketing intelligence workflows ensures your taxonomy supports actual decision processes, not just theoretical completeness.

Phase 4: SDK Implementation with Parameter Standards

Implementation is where most event taxonomies break down. Developers need clear specifications, not ambiguous descriptions.

Event Implementation Specification Template:

For every event, provide developers with this format:

Parameter Standards (Enforce These):

1. Data Types Must Be Consistent:

   • Revenue: always float/decimal, never string

   • Boolean flags: true/false, not "yes"/"no" or 1/0

   • Timestamps: ISO 8601 format or Unix timestamp, pick one

   • IDs: strings, even if numeric (prevents truncation)

2. Handle Null Values Explicitly:

   • Required parameters: fail gracefully if missing, log error

   • Optional parameters: omit entirely if null (don't send "null" string)

3. Use Enums for Fixed Values:

// Bad: free-text entry
product_category: "Subscriptions"  // inconsistent capitalisation

// Good: defined enum
product_category: "subscription"  // from list: [subscription, physical_goods, digital_content]

1. Avoid Dynamic Event Names:

Platform-Specific Considerations:

Some parameters need different handling across iOS, Android, and web:

• Device IDs: Use platform-appropriate identifiers (IDFV on iOS, Android ID on Android)

• Attribution parameters: MMP SDKs auto-populate campaign source; don't override

• Revenue currency: Respect user's selected currency, don't force conversion

Modern MMPs including Linkrunner provide SDK helpers that validate parameter types at runtime, catching implementation errors before events reach production dashboards.

Phase 5: QA Validation Checklist (Test Scenarios, Expected vs Actual)

Most teams skip comprehensive QA, assuming if events appear in dashboards, they're working. Then they discover issues weeks later when making budget decisions based on bad data.

Pre-Launch Validation Protocol:

Test Environment Setup:

1. Configure separate test apps in your MMP (don't pollute production data)

2. Create test campaigns with unique identifiers

3. Use test devices registered in MMP dashboard

4. Set up real-time event debugger (most MMPs provide this)

Core Event Test Scenarios:

For each critical event, validate:

Scenario 1: Event Fires with Correct Parameters

• Trigger: Complete signup flow

• Expected: signup_complete event appears in MMP debugger within 30 seconds

• Validate: All required parameters present with correct data types

• Check: Event also appears in analytics platform (if integrated)

Scenario 2: Revenue Values Are Accurate

• Trigger: Purchase ₹299 product using test payment

• Expected: purchase_complete with revenue=299.00, currency=INR

• Validate: Revenue matches exactly (no rounding errors)

• Check: Transaction ID is unique and matches payment system

Scenario 3: Attribution Parameters Flow Correctly

• Trigger: Click test campaign link, install, complete signup

• Expected: signup_complete event attributed to correct campaign

• Validate: Campaign source, medium, campaign name all match

• Check: Deep link parameters preserved through install

Scenario 4: Events Don't Fire When They Shouldn't

• Trigger: Start signup but abandon halfway

• Expected: signup_complete does NOT fire

• Validate: Only signup_started appears, not completion event

• Check: Partial flows don't inflate conversion metrics

Scenario 5: Edge Cases and Error Handling

• Trigger: Complete purchase with network interruption

• Expected: Event queued and sent when connection restored

• Validate: No duplicate events when retrying

• Check: Failed payments don't fire revenue events

Validation Dashboard Cuts:

Create these specific views in your MMP to verify event quality:

1. Events by Type (Last 24 Hours): Should match expected volume patterns

2. Null Parameter Report: Flag events missing required fields

3. Revenue Event Audit: Sum of revenue events should reconcile with payment system

4. Attribution Match Rate: Percentage of events successfully attributed to source

5. Event Timing Analysis: Time between install and key events (spot delays)

Common Validation Failures:

Platforms like Linkrunner include built-in event validation that flags parameter mismatches and schema violations in real-time, reducing QA cycles from days to hours by catching issues before they reach production.

Phase 6: Documentation and Team Training for Consistency

Event taxonomy isn't a one-time implementation. It's ongoing infrastructure that needs maintenance as your product evolves.

Living Documentation Requirements:

Maintain a central tracking plan that every team can access. Minimum components:

Event Catalog:

• Event name

• Plain-English description

• When it fires (trigger condition)

• Required and optional parameters with data types

• Which systems consume this event

• Example payload

• Last updated date and change history

Implementation Guidelines:

• SDK initialisation checklist

• Parameter naming conventions

• Testing protocol

• Approval process for new events

Decision Record:

• Why we chose specific event names

• Trade-offs we considered

• What we explicitly decided NOT to track and why

Use tools appropriate to your team size. Google Sheets works for <50 events. Dedicated tracking plan tools (Avo, Iteratively) make sense for larger implementations.

Team Training Protocol:

New team members (especially developers and marketers) need onboarding on event taxonomy:

For Developers:

• Walk through tracking plan structure

• Explain why parameter consistency matters

• Show how to use MMP event debugger

• Review common implementation mistakes

• Practice: implement one test event end-to-end

For Marketers:

• Explain how events map to campaign goals

• Show which events drive ad platform optimisation

• Demonstrate dashboard cuts enabled by proper parameters

• Review how to request new events (approval process)

For Product Managers:

• Connect events to user journey mapping

• Show how events enable cohort analysis

• Explain event design trade-offs (granularity vs maintainability)

Change Management Process:

As your product evolves, you'll need to add events, modify parameters, or deprecate outdated tracking. Establish a formal process:

1. Request: Marketer/PM submits event request with business justification

2. Review: Growth lead + engineering lead assess feasibility and consistency with taxonomy

3. Specification: Create detailed event spec using template from Phase 4

4. Implementation: Developer implements with QA validation

5. Documentation: Update tracking plan and notify stakeholders

6. Monitoring: Confirm event data flows correctly for 7 days

Audit Cadence:

Schedule quarterly event audits:

• Review events that haven't fired in 90 days (candidates for deprecation)

• Check parameter usage patterns (are optional parameters being used?)

• Validate revenue reconciliation (MMP totals vs payment system)

• Interview teams about missing analytics capabilities

• Update documentation for any drift from specifications

Maintaining daily, weekly, and monthly KPI tracking routines becomes significantly easier when event taxonomy is treated as maintained infrastructure rather than fire-and-forget implementation.

Putting This into Practice

Event taxonomy done right transforms attribution from "we have data" to "we trust our data enough to move budget confidently". The difference shows up in operational speed: teams with solid event structure make budget reallocation decisions in days, not weeks, because they don't need to audit data quality every time they analyse performance.

Start with Phase 1 this week. Even if you're not ready to rebuild your entire tracking plan, understanding what you currently have and what business outcomes you need to measure clarifies the gap. Most teams discover they're tracking far more than they use, and missing the 5-10 events that would actually inform decisions.

If you're implementing a new MMP or migrating from another platform, this is your best opportunity to fix event structure. Don't just replicate your existing messy taxonomy in a new system. Use migration as the forcing function to align stakeholders, standardise naming, and build proper validation workflows.

For teams looking to operationalise this without custom-building validation infrastructure, modern MMPs simplify the execution. Linkrunner includes event validation tools that flag parameter mismatches, enforce required fields, and provide real-time debugging so you catch implementation issues in minutes rather than discovering them weeks later when reconciling revenue reports. The core principles above work everywhere, but platforms designed for usability reduce the manual overhead of maintaining quality event data.

When finance asks about campaign ROI, when product asks why retention dropped, when marketing asks which creative drove the most valuable users, your event taxonomy is either the foundation that makes these questions answerable in minutes, or the bottleneck that forces everyone back into spreadsheets trying to make sense of inconsistent data.

That choice gets made in the planning phase, not after implementation.

Ready to build reliable event tracking that supports confident budget decisions? Request a demo from Linkrunner to see how modern attribution platforms help teams maintain event quality without extensive manual QA processes.