A customer sees your Instagram ad on Monday. Clicks a Google ad on Wednesday. Opens your email on Friday. Buys on Saturday via a direct visit.
Which channel gets credit for the sale? The answer depends entirely on which attribution model you use — and each model gives a completely different answer.
The Six Models
1. Last Click (Google Ads Default)
100% credit to the last touchpoint before conversion.
In our example: Direct visit gets all credit. Your $5,000 in Google Ads and $3,000 in Meta Ads show zero attributed revenue.
When it’s useful: Never as the only model. It systematically undervalues awareness and consideration channels.
When it misleads: Almost always. It credits the last step of a multi-step journey, ignoring everything that brought the customer there.
2. First Click
100% credit to the first touchpoint.
Our example: Instagram ad gets all credit. Email and Google Ads show nothing.
When it’s useful: Understanding which channels drive initial awareness. Good for top-of-funnel analysis.
When it misleads: Overvalues channels that introduce but don’t convert. You might pour budget into awareness campaigns that never close deals.
3. Linear
Equal credit split across all touchpoints.
Our example: Instagram (25%), Google Ads (25%), Email (25%), Direct (25%).
When it’s useful: Fair representation when all channels contribute equally. Good baseline for comparison.
When it misleads: Assumes every touchpoint matters equally. A random Display ad impression isn’t worth the same as the retargeting email that closed the deal.
4. Time Decay
More credit to touchpoints closer to conversion. Exponentially weighted.
Our example: Direct (40%), Email (30%), Google Ads (20%), Instagram (10%).
When it’s useful: Businesses with short consideration cycles (impulse purchases, low-cost products). Rewards the channels that push people to buy.
When it misleads: Undervalues the awareness stage for high-consideration products (B2B, luxury, enterprise software) where the first touch is critical.
5. Position-Based (U-Shaped)
40% to first touch, 40% to last touch, 20% split across middle.
Our example: Instagram (40%), Direct (40%), Google Ads (10%), Email (10%).
When it’s useful: Balances awareness and conversion. Recognizes that introducing a customer and closing the deal are both important.
When it misleads: Still arbitrary. Why 40/40/20? What if the middle touchpoints are what actually convinced the customer?
6. Data-Driven (GA4 + Google Ads Default)
Uses machine learning to assign credit based on actual conversion data.
Google analyzes all converting and non-converting paths. Touchpoints that appear more frequently in converting paths get more credit.
Our example: Depends on your data. If Google Ads clicks appear in 80% of converting paths but only 30% of non-converting paths, Google Ads gets proportionally more credit.
When it’s useful: When you have enough data (300+ conversions/month). It’s the most accurate model because it’s based on YOUR customers, not a theoretical framework.
When it misleads: Black box — you can’t see the exact weighting. Requires significant conversion volume. Biased toward Google channels (Google’s model naturally sees more Google touchpoints).
The Comparison Table
| Model | Best For | Weakness | Min Data Needed |
|---|---|---|---|
| Last Click | None (legacy) | Ignores everything except final touch | Any |
| First Click | Awareness analysis | Ignores conversion drivers | Any |
| Linear | Baseline comparison | Assumes equal contribution | Any |
| Time Decay | Short sales cycles | Undervalues awareness | Any |
| Position-Based | Balanced overview | Arbitrary weighting | Any |
| Data-Driven | Mature advertisers | Black box, needs volume | 300+ conversions/month |
Which Model Should You Use?
For Google Ads Optimization
Use data-driven (it’s the default for Smart Bidding). Google’s algorithm needs conversion signals to optimize bids. Data-driven gives it the most accurate picture.
If you don’t have 300+ conversions/month, Google falls back to a simplified model. Focus on getting your conversion tracking solid first.
For Cross-Channel Budget Allocation
Use position-based or time decay in GA4 for reporting. These give reasonable credit to both acquisition and conversion channels.
Never use last click for budget allocation — it tells you to cut the channels that generate demand (social, display, content) because they don’t show up as the last click.
For Executive Reporting
Use blended ROAS (total revenue / total ad spend). Attribution models are approximations. Your finance team doesn’t care which channel “gets credit” — they care whether total revenue exceeds total spend.
Read our ROAS calculator guide for the math.
Why Platforms Disagree
Google and Meta always show different numbers. This isn’t a bug — they use different attribution:
| Platform | Default Model | Default Window |
|---|---|---|
| Google Ads | Data-driven | 30-day click, 1-day view |
| GA4 | Data-driven (cross-channel) | Session-based |
| Meta | 7-day click, 1-day view | 7-day click, 1-day view |
| TikTok | Last click | 28-day click, 1-day view |
A single purchase can be claimed by ALL platforms simultaneously. This is why platform ROAS doesn’t add up.
How to Configure Attribution in GA4
GA4 → Admin → Attribution settings:
- Reporting attribution model: Choose data-driven (default) or select a rule-based model
- Lookback window: 30 days for acquisition events, 90 days for all other events
- Paid/organic channels: Include or exclude organic touchpoints
Changes apply to reports retroactively — they recompute historical data with the new model.
The Real Answer: Use Multiple Views
No single model tells the complete story. Here’s the practical approach:
- Google Ads: Use data-driven for bidding optimization (let it ride)
- GA4 reporting: Use position-based for cross-channel comparison
- Budget decisions: Use blended ROAS as the final check
- Monthly review: Compare platform-reported conversions to actual revenue — if the gap is growing, your tracking needs attention
When Attribution Doesn’t Matter
For most businesses under $20K/month ad spend:
- You’re running 1-2 channels
- The attribution model barely changes the numbers
- Your time is better spent on creative testing and audience optimization
- Fix your conversion tracking — accurate data matters more than the model
Attribution models are a scaling problem. When you’re spending across 5+ channels and need to justify budgets, the model matters. At smaller scale, just make sure you’re tracking correctly and the ROAS is above break-even.