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Identity & Attribution is how we deliver Complete Identity – one of the three dimensions of complete data infrastructure. Understanding the full customer journey requires connecting every touchpoint to a single user profile. Fidero’s stateful, server-first architecture automatically creates a canonical user profile that serves as the foundation for both accurate attribution and complete journey tracking.
  • Business Impact
  • How It Works

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What unified identity means for your business

Without proper identity resolution, you’re flying blind. The same user appears as multiple people across different tools, making it impossible to understand what actually drives conversions – or more importantly, what drives retention.

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The problems it solves

Identity fragmentation breaks everyone’s ability to make decisions:
  • Marketing can’t connect campaigns to customer lifetime value – which channels drive your best subscribers?
  • Product can’t see which features drive activation and retention – users appear fragmented across domains
  • Data teams can’t build reliable ML models – training data has incomplete user behavioural timelines
  • Leadership can’t understand true unit economics – customer counts and conversion rates are inflated by duplicates

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The outcomes you’ll achieve

Complete Identity means every team gets unified user journeys:
  • Marketing: Connect acquisition campaigns to LTV, retention and repeat purchase behaviour
  • Product: Track complete activation→retention funnels from first visit through app engagement
  • Data teams: Train ML models on complete user behavioural timelines (not fragmented sessions)
  • Leadership: Trustworthy user counts, accurate conversion rates, true customer lifetime value
Target: 90%+ cross-domain session linking with unified profiles from anonymous → signed-up → activated → retained.

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Real-world journey tracking

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Product team: Activation funnel tracking

The challenge: Your fitness app has multiple experiences (marketing website, web app, mobile app). Product team needs to understand: which acquisition campaigns drive users who actually activate and retain? Without Complete Identity:
  • Anonymous web visitor = one profile
  • Signed-up user = different profile
  • Mobile app user = third profile
  • Result: Product team can’t connect acquisition source to activation behaviour
With Complete Identity:
  • All three identities merged into single canonical profile
  • Activation events automatically enriched with original campaign context
  • Result: Product team sees “Users from TikTok campaign X have 45% higher activation rate”

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Marketing + Product: LTV attribution

The challenge: Marketing needs to know which campaigns drive high-LTV subscribers, not just which drive signups. Without Complete Identity:
  • Signup attributed to campaign
  • Later subscription and retention events lack campaign context
  • Result: Marketing optimises for signups, not LTV
With Complete Identity:
  • Signup, subscription, retention all enriched with original campaign context
  • Result: Marketing sees “Google Search campaign drives 2.3x higher 6-month LTV” and reallocates budget

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Data team: Churn prediction models

The challenge: Building ML models for churn prediction requires complete user behavioural timelines with attribution context. Without Complete Identity:
  • User behaviour fragmented across anonymous and known sessions
  • Missing acquisition source features in training data
  • Result: Models learn correlation without causation (“3 pageviews → churn”) missing “but users from organic search retain better”
With Complete Identity:
  • Complete user timeline from first visit through churn
  • Every event includes acquisition source, behavioural features and outcome
  • Result: Models learn “Users from paid social + low onboarding completion → high churn risk” enabling targeted intervention
Your ML models are only as good as your training data. Complete Identity ensures your models train on complete user behavioural timelines with full attribution context – not fragmented sessions missing the acquisition source features that predict retention.

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Anonymous-to-known merge strategies

Our server supports two identity merge strategies depending on your technical architecture:

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Minimal stitching (post-login merge)

How it works: When a user logs in and you send a userId, we merge their anonymous profile with any existing known profile for that userId. Best for: Simple architectures where you can reliably send userId on login. Example: 
// User just logged in
fidero.updateIdentity({
  userId: "user_abc123",
  traits: {
    email: "alex@example.com",
    plan: "premium"
  }
})

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Enhanced stitching (pre-login merge)

How it works: For GA4/Firebase users, we can stitch anonymous and known sessions even before explicit login using app_instance_id and ga_session_* tokens. Best for: Mobile apps using Firebase/GA4 where users browse extensively before logging in. Benefit: Product teams see complete pre-login behaviour connected to post-login activation and retention.

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Integration with three dimensions

Identity & Attribution enables Complete Identity, but it’s part of broader infrastructure: Complete Attribution: Campaign context preserved everywhere (see Browser State Capture) Complete Identity: User profiles unified across domains (this capability) Complete Quality: Governance enforced automatically (see Consent Enforcement) Same infrastructure, multi-team value: Marketing proves LTV attribution. Product tracks complete journeys. Data teams train models on complete behavioural timelines.