Attribution Models are frameworks in Google Ads that determine how credit for a conversion is assigned across the multiple touchpoints a user interacts with before converting. As of 2026, Data-Driven Attribution is the default and recommended model, while Last Click remains available as an alternative.
Key Takeaways
- Attribution models distribute conversion credit across multiple ad interactions
- Google Ads defaulted all new conversion actions to Data-Driven Attribution starting in 2023
- Last Click, First Click, Linear, Time Decay, and Position-Based models were deprecated in 2024
- Only Data-Driven Attribution and Last Click remain as selectable options in 2026
- The model you choose directly affects how automated bidding allocates spend
What Are Attribution Models
Attribution Models solve a fundamental measurement problem: when a customer interacts with multiple ads before converting, which interaction gets the credit? A user might click a branded search ad, then a display remarketing ad, then a generic search ad before purchasing. The attribution model determines how that single conversion is counted across those three clicks.
| Model | Status (2026) | How Credit Is Assigned |
|---|---|---|
| Data-Driven | Default, recommended | Machine learning distributes credit based on actual impact |
| Last Click | Available | 100% credit to the final click before conversion |
| First Click | Deprecated | Was 100% credit to the first interaction |
| Linear | Deprecated | Was equal credit across all interactions |
| Time Decay | Deprecated | Was more credit to interactions closer to conversion |
| Position-Based | Deprecated | Was 40% first, 40% last, 20% split across middle |
How It Works
Attribution is configured at the conversion action level in Google Ads under Goals > Conversions > Settings. When you select a model:
- Google evaluates all ad interactions (clicks and engaged views) within the conversion window
- The selected model applies its rules to distribute credit
- Fractional conversions appear in campaign reports (e.g., 0.4 conversions for a campaign that assisted)
- Automated bidding strategies use the attributed conversion data to set bids
With Data-Driven Attribution, Google’s machine learning analyzes your account’s conversion paths to determine which interactions actually influenced the conversion. It compares users who converted with those who did not, identifying the touchpoints that made the difference.
Practical Example
A customer’s conversion path:
- Clicks branded search ad on Day 1
- Clicks display remarketing ad on Day 5
- Clicks generic search ad on Day 12
- Converts (purchase worth $100) on Day 12
Credit distribution by model:
| Interaction | Last Click | Data-Driven (example) |
|---|---|---|
| Branded search (Day 1) | $0 | $25 |
| Display remarketing (Day 5) | $0 | $35 |
| Generic search (Day 12) | $100 | $40 |
| Total | $100 | $100 |
Under Last Click, the generic search campaign appears to drive all revenue while branded and display appear to generate nothing. Under Data-Driven Attribution, all three campaigns receive proportional credit reflecting their actual influence.
Why It Matters
Your attribution model shapes every optimization decision in the account. A poorly chosen model can lead you to cut campaigns that are actually driving conversions (because they assist rather than close) or over-invest in last-touch campaigns that would convert regardless. Data-Driven Attribution is the strongest option because it uses your actual data rather than arbitrary rules, but it requires sufficient conversion volume to model accurately. For accounts with fewer than 300 conversions per month, the model may fall back to a simplified distribution.