Why Did GA4 Remove Most Attribution Models?

What Google Removed—And What's Left

In late 2023, Google quietly removed four attribution models from GA4:

What remains:

This wasn't a subtle change. Google eliminated the models that marketing teams relied on for neutral baseline measurement (linear), demand gen attribution (first-touch), and e-commerce optimization (time-decay).

Google's Official Reasoning

Google's explanation was straightforward: rule-based models are "outdated" and data-driven attribution provides more accurate, property-specific insights.

The subtext: Google wants everyone using their machine learning-based approach.

How Google's Data-Driven Attribution Actually Works

Google's DDA isn't a black box of pure machine learning. It's built on a specific algorithm with a Nobel Prize lineage.

The Shapley Value Foundation

GA4's data-driven attribution uses Shapley values—a concept from cooperative game theory developed by economist Lloyd Shapley (Nobel Prize, 2012).

The core idea: How do you fairly distribute the output of a team among its members?

In attribution terms:
- The "team" = marketing touchpoints in a journey
- The "output" = the conversion
- Shapley values = fair credit distribution

What Makes It Different from Markov Chains

You might hear "data-driven attribution" and think of Markov chain models—but Google doesn't use Markov chains for GA4 DDA.

Why this matters: If you have fewer conversions, Markov chains might give you better data-driven insights than Shapley values. But Google chose Shapley—so if you need Markov-based attribution, you'll need to build it yourself or use a third-party tool.

GA4's Implementation Details

GA4's DDA adds some specific features on top of Shapley:

1. Time decay component: Recent interactions get more weight
2. 50-interaction window: Considers up to 50 touchpoints (vs 4 in Universal Analytics)
3. 90-day lookback: Attribution window extends 90 days back
4. Property-specific: Models are trained on YOUR data, not global averages

# Simplified conceptual model of Shapley calculation
class ShapleyAttribution
  def calculate_credit(journey)
    touchpoints = journey.touchpoints

    touchpoints.map do |touchpoint|
      # For each touchpoint, calculate marginal contribution
      # across all possible subsets
      subsets = all_possible_subsets(touchpoints)

      contributions = subsets.map do |subset|
        with_touchpoint = conversion_probability(subset + [touchpoint])
        without_touchpoint = conversion_probability(subset)

        with_touchpoint - without_touchpoint
      end

      {
        channel: touchpoint.channel,
        credit: contributions.sum / contributions.size  # Average marginal contribution
      }
    end
  end
end

Why the Removal Matters

The Volume Threshold Problem

GA4's DDA requires minimum thresholds to function:

If you don't meet these thresholds, GA4 falls back to last-click.

For many businesses, this means:
- Startups and SMBs → effectively stuck on last-click
- New products → no data-driven insights until volume builds
- Multiple conversion types → each needs its own volume

What You Lose Without the Removed Models

If you used linear attribution as your neutral baseline—and many sophisticated teams did—you now have no simple way to replicate it in GA4.

The Google Ecosystem Lock-In Concern

There's an elephant in the room: Google's DDA is trained on Google's data, measured in Google's tool, and may favor Google channels.

While Google states DDA is unbiased, the algorithm is a black box. You can't:
- Inspect the model weights
- Verify the Shapley calculations
- Check for channel bias
- Customize the algorithm

Your Alternatives

Option 1: Build It Yourself in BigQuery

GA4 exports raw event data to BigQuery. You can rebuild any attribution model from scratch.

First-touch in BigQuery:

-- First-touch attribution from GA4 BigQuery export
WITH journey_first_touch AS (
  SELECT
    user_pseudo_id,
    traffic_source.source AS first_source,
    traffic_source.medium AS first_medium,
    traffic_source.name AS first_campaign,
    MIN(event_timestamp) AS first_touch_time
  FROM `your_project.analytics_12345678.events_*`
  WHERE traffic_source.source IS NOT NULL
  GROUP BY user_pseudo_id, traffic_source.source, traffic_source.medium, traffic_source.name
)

SELECT
  j.first_source,
  j.first_medium,
  COUNT(DISTINCT c.user_pseudo_id) AS conversions,
  SUM(c.conversion_value) AS revenue
FROM journey_first_touch j
JOIN conversions c ON j.user_pseudo_id = c.user_pseudo_id
GROUP BY j.first_source, j.first_medium
ORDER BY conversions DESC;

Linear attribution in BigQuery:

-- Linear attribution: equal credit to all touchpoints
WITH touchpoints AS (
  SELECT
    user_pseudo_id,
    traffic_source.source,
    traffic_source.medium,
    event_timestamp,
    ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS touch_order
  FROM `your_project.analytics_12345678.events_*`
  WHERE traffic_source.source IS NOT NULL
),
journey_length AS (
  SELECT user_pseudo_id, MAX(touch_order) AS total_touches
  FROM touchpoints
  GROUP BY user_pseudo_id
)

SELECT
  t.source,
  t.medium,
  SUM(1.0 / jl.total_touches) AS linear_credit,  -- Equal share per touchpoint
  COUNT(DISTINCT t.user_pseudo_id) AS touched_users
FROM touchpoints t
JOIN journey_length jl ON t.user_pseudo_id = jl.user_pseudo_id
GROUP BY t.source, t.medium
ORDER BY linear_credit DESC;

Pros:
- Full control over methodology
- Access to all models
- Can customize for your business

Cons:
- Requires SQL expertise
- Must maintain the queries
- No real-time reporting
- Need BigQuery skills

Option 2: Third-Party Attribution Tools

Several tools provide the attribution models GA4 removed:

Option 3: Run Multiple Tools in Parallel

Many teams now run:
- GA4 for traffic analysis and last-click baseline
- Third-party tool for multi-touch attribution
- Incrementality tests to validate both

This is more work but gives you the complete picture.

Making the Best of GA4's DDA

If you're going to use GA4's data-driven attribution, here's how to get the most from it:

1. Ensure You Meet Volume Thresholds

Each conversion type needs its own volume. Here's how it plays out in practice:

Most startups and mid-market companies don't hit the threshold. If you're under 400 monthly conversions per type, GA4's "data-driven" attribution is actually just last-click with a fancier name.

2. Use DDA for Direction, Not Precision

DDA tells you which channels are likely over/undervalued relative to last-click. Use it for:
- Identifying channels that might deserve more investment
- Flagging channels that may be over-credited
- General budget allocation guidance

Don't use it for:
- Precise ROAS calculation
- Exact channel comparisons
- Replacing incrementality testing

3. Compare DDA to Last-Click Regularly

Run this comparison monthly:

Large differences reveal channel role insights.

4. Validate with Holdout Tests

DDA is still correlational. Validate with incrementality:

1. Pick a channel DDA says is valuable
2. Run a geo-holdout test
3. Compare attributed lift to incremental lift
4. Calibrate your trust in DDA accordingly

What This Means for the Future

Google's move signals a broader industry shift:

1. ML-Based Attribution Becomes Default

Expect all major platforms to push algorithmic attribution. The question isn't whether to use data-driven—it's which algorithm and whose implementation.

2. First-Party Data Becomes More Critical

As third-party cookies die, the properties with more first-party data will have better attribution. GA4's DDA will work better for larger sites.

3. Incrementality Testing Rises

As attribution becomes more opaque, incrementality testing becomes the ground truth. Teams that can run proper holdout tests will have unfair advantages.

4. Tool Fragmentation Increases

No single tool will do everything. Expect tech stacks to include:
- Analytics tool (GA4, etc.)
- Attribution tool (specialized)
- MMM tool (for long-term planning)
- Experimentation platform (for validation)

Summary

Google removed first-touch, linear, time-decay, and position-based attribution from GA4—leaving only last-click and data-driven.

Key facts:
- DDA uses Shapley values (game theory), not Markov chains
- Requires 400+ conversions per conversion type to function
- Falls back to last-click below threshold
- Is a black box you can't inspect or customize

What to do:
1. Check if you meet DDA volume thresholds (many don't)
2. For removed models, use BigQuery exports or third-party tools
3. Treat DDA as directional guidance, not precise measurement
4. Validate with incrementality testing

Bottom line: The models Google removed were useful for specific purposes. If you need them, you now need to build them yourself or buy them elsewhere.

Further Reading

On GA4's Data-Driven Attribution:
- GA4 DDA Complete Guide — Technical walkthrough of how DDA works
- Adswerve: Google's DDA Explained — Implementation details
- Arcalea: Understanding GA4 DDA — Practitioner perspective

On Shapley Values in Attribution:
- Shapley, L.S. (1953) — Original paper on the Shapley value
- OWOX: Data-Driven Attribution — Comparison of DDA approaches

On Building Your Own:
- Stacktonic: Build DDA with BigQuery and Python — Technical tutorial

Related Articles:
- What is Multi-Touch Attribution? — The complete MTA guide
- Multi-Touch Attribution Tools Compared — Find the right tool for your needs

Holly Henderson

Co-Founder, mbuzz

Holly Henderson is Co-Founder of mbuzz. With 10+ years in marketing including roles at Westpac, Avon, and Forebrite, she's obsessed with making measurement actually useful.

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