Future-Proof Attribution Setup

Design attribution data collection from day one to enable any measurement model later

Austin Hay
The ultimate guide to Martech

Future-Proof Attribution Setup

"The best practice that everybody should stand up from day one is to basically design the system for MTA and then use whatever makes sense as you grow." — Austin Hay

What It Is

Future-Proof Attribution is a systematic approach to data collection that ensures you can implement any attribution model (first-touch, last-touch, multi-touch, mixed media modeling) in the future, regardless of what you use today.

Most companies start with simple attribution (first-touch or last-touch) and then want to upgrade to multi-touch attribution (MTA) or mixed media modeling (MMM) as they scale. But when they try to upgrade, they discover they don't have the historical data needed. This framework prevents that problem by collecting the right data from day one.

The key insight is that the data collection infrastructure is the same regardless of which attribution model you use—only the analysis differs. So collect everything now, analyze simply now, and upgrade your analysis later when needed.

How It Works

What You Need to Collect:

  1. Referrer URL — Where the user came from
  2. All UTM Parameters — Campaign, source, medium, content, term
  3. Ad Network Parameters — Facebook FBP/FPID, Google Click ID (GCLID), TikTok ID, Microsoft ID, etc.
  4. First-touch values — Stored when user first arrives
  5. Last-touch values — Updated on every subsequent visit

Storage Architecture:

For each user, store:

  • utm_first_campaign, utm_first_source, utm_first_medium (set once, never overwritten)
  • utm_last_campaign, utm_last_source, utm_last_medium (updated on each visit)
  • Same pattern for all ad network parameters

Event Architecture:

On every page view event, include:

  • All first-touch values (for context)
  • All last-touch values (for tracking journey changes)

This creates a complete record of the user's journey that data teams can analyze with any attribution model.

How to Apply It

Step 1: Identify All Parameters to Collect

Create a list of all UTMs and ad network parameters:

  • UTM_source, UTM_medium, UTM_campaign, UTM_content, UTM_term
  • GCLID (Google), FBCLID (Facebook), MSCLKID (Microsoft), TTCLID (TikTok)
  • Referrer URL
  • Any custom parameters your marketing team uses

Step 2: Set Up First/Last Storage

When a user arrives:

if (no first_touch values exist):
    set all first_touch values from current URL
always:
    set all last_touch values from current URL (overwrite)

Store these in:

  • Browser local storage or first-party cookies
  • User profile in your CDP/analytics tool
  • Your data warehouse

Step 3: Fire Events with Attribution Context

On every significant event (page view, signup, conversion):

  • Include all first_touch values as event properties
  • Include all last_touch values as event properties

Step 4: Let Data Team Handle Analysis

Your data warehouse will now have:

  • User profiles with first and last attribution
  • Event streams with attribution context on every event
  • The ability to reconstruct the full journey by coalescing "last" values across events

The data team can now implement any attribution model without waiting for new data collection.

Example Implementation

User Journey:

  1. Day 1: User clicks Google ad → lands on site

    • Set: first_source=google, first_medium=cpc, first_campaign=summer2024
    • Set: last_source=google, last_medium=cpc, last_campaign=summer2024

  2. Day 5: User returns via Facebook retargeting ad

    • Keep: first_source=google (unchanged)
    • Update: last_source=facebook, last_medium=paid_social

  3. Day 7: User converts via direct visit

    • Keep: first_source=google (unchanged)
    • Update: last_source=direct, last_medium=none

Analysis Options Now Available:

  • First-touch: Credit Google (100%)
  • Last-touch: Credit direct (100%)
  • Multi-touch: Credit Google (40%), Facebook (40%), Direct (20%)
  • MMM: Include all touchpoints in regression model

When to Use It

  • Startups: Set this up before you have meaningful traffic—it's much harder to retrofit
  • Before tool migrations: Ensure your new stack supports this architecture
  • When planning attribution upgrades: Verify you have the data before committing to MTA
  • During stack audits: Check if current implementation follows these principles

Common Mistakes

  • Collecting UTMs but not storing first/last separately: You can only do last-touch attribution
  • Storing on user profile but not in events: Can't reconstruct the middle of the journey
  • Not collecting ad network parameters: Limits your ability to send conversion data back to ad networks
  • Only collecting on conversion events: Missing context from the full journey

Source

  • Guest: Austin Hay
  • Episode: "The ultimate guide to Martech"
  • Key Discussion: (00:44:58) Austin walks through the technical implementation step by step
  • YouTube: Watch on YouTube

Related Frameworks

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