Google Ads: from last click to DDA, how to consolidate data and not get lost in attribution

Until recently, measuring in Google Ads was quite simple: a user clicked on an ad, made a purchase, and… done, attributed. But the digital ecosystem changed radically. The progressive disappearance of third-party cookies, the rise of privacy as a priority, and the multi-channel nature of user journeys made measurement much more complex.

In addition, with the arrival of GA4, many companies had to relearn how to measure, compare, and consolidate data. And here comes the challenge: how to truly understand which channel or campaign drives conversions when the old models no longer exist?

That’s why in 2024 Google simplified the rules of the game: goodbye to models like linear, time decay, or position-based, and hello to just two options: Last Click or Data-Driven Attribution (DDA).

TABLE OF CONTENTS

• How attribution works in Google Ads today

• GA4 and its attribution model

• Why numbers don’t match between Google Ads and GA4

• Key tools you should definitely use
• How to get the most out of DDA
• Best Practices for consolidating attribution in Google Ads

How attribution works in Google Ads today

Attribution in Google Ads has been reduced to two main models:

1. Last Click
   
   
   • All the credit goes to the last click before the conversion.
   • It’s a model that’s very easy to understand and explain, but unfair to campaigns that operate at the top or middle of the funnel.
   • Example: if a user first clicks on a Display ad, then on a generic Search ad, and finally converts after clicking on a brand ad, all the credit goes to the latter.
     
     
2. Data-Driven Attribution (DDA)
   
   
   • Distributes conversion credit among the different touchpoints.
   • Based on machine learning and uses signals such as device, interaction type, number of steps in the journey, and thousands of other datapoints.
   • The goal is to reflect the real contribution of each channel, not just “who was last”.

👉 Key advantage of DDA: it allows you to see the real value of awareness or consideration campaigns that were undervalued with last click.

And if you’re unsure which one to use, Google Ads provides the Model Comparison tool, within the attribution section. With it you can apply different models to your conversions and see how the results shift, helping you make more informed budget decisions.

Quick example: Last Click or DDA?

Imagine you want to buy a pair of sneakers:

1. You see an ad on YouTube → you don’t click.
2. You notice a Display ad → you click to browse.
3. You search on generic Google: “running sneakers” → you click.
4. Finally, you enter through a brand ad → you purchase.

• Last Click: only the brand ad gets all the credit 🏆.
• DDA (Data-Driven Attribution): distributes credit across all steps of the journey.

GA4 and its attribution model

GA4 works differently from Google Ads, and that’s where the discrepancies begin.

• By default, GA4 uses the last non-direct click model. This means that credit goes to the last non-direct click (excluding direct visits to the site).
• This seems fairer than classic last click, but still leaves out the value of earlier interactions.
• GA4 also allows you to apply the DDA model, but with limitations: although you can compare models in reports, the internal conversion metric is still based on last non-direct click.
  
  

👉 Translation: even if you enable DDA in GA4, the numbers will probably never match 100% with Google Ads.

Why numbers don’t match between Google Ads and GA4

If you’ve ever been frustrated that your reports don’t match, here’s the explanation:

• Different default models
  
  
  • Google Ads usually uses DDA (if you have enough conversion volume).
  • GA4 always starts with last non-direct click.
    
    
• Different conversion dates
  
  
  • Ads attributes the conversion to the day the click happened.
  • GA4 attributes it to the day the conversion event occurred.
  • Result: peaks and drops appear on different days in each platform.
    
    
• Reporting speed
  
  
  • Ads usually shows conversions in less than 24h.
  • GA4 can take 24–72h to register and display them.
    
    
• Conversion counting
  
  
  • Ads can count multiple conversions per click if configured.
  • GA4 usually counts only one per session/event.
    
    
• View-through conversions
  
  
  • Ads includes them by default (especially in Display and Video campaigns).
  • In GA4, you need to configure them manually and they are not always available.
    
    

👉 In short: neither of the two is wrong, they just use different methodologies.

Scheme: Why Ads and GA4 don’t match

The most common mistakes when working with attribution in Google Ads

Blindly trusting Last Click: it overvalues brand and short-term performance campaigns, and undervalues awareness.

Not aligning models between Ads and GA4: comparing apples to oranges leads to internal debates and wrong decisions.

Forgetting offline data: if you don’t connect CRM or in-store sales, your view will always be partial.

Key tools you should definitely use

• Model Comparison (Ads): compare last click vs. DDA and see how attribution changes.
• Conversion Value Rules (Ads): assign more value to conversions with higher business impact (e.g. repeat customers, in-store sales, priority regions).
• Attribution Reports (GA4): cross-channel view to understand how channels interact together.
• Adsmurai Marketing Platform (AMP): integrates data from different sources (Google Ads, GA4, CRM, offline sales…) into a single dashboard to compare metrics, attribution and results in real time.

How to get the most out of DDA

Volume: make sure you have enough conversions to train the model.

First-party data: connect CRM, offline sales and your own signals to enrich attribution.

Periodic review: compare models quarterly to detect changes in the customer journey.

Budget adjustment: reallocate budget to the campaigns that truly add value, not just the last click.

Centralize everything in AMP: control results and cross-channel attribution without opening 5 different tabs.

The future of attribution in Google Ads

• AI in charge: DDA will keep improving with more advanced machine learning and signals.
• Integration with GA4: expected to reduce discrepancies between Ads and Analytics.
• Privacy first: the future won’t be exact 1:1 attribution, but probabilistic models predicting behaviors.

Case study

A fashion eCommerce worked with the Last Click model and saw that 80% of sales came from brand campaigns. However, after activating DDA and consolidating data from Ads, GA4 and CRM:

• They discovered that generic Search campaigns had a 30% greater impact on the purchase journey than it seemed.
• They reallocated budget and increased investment in generic campaigns.
• Result: a +22% ROAS in just a few weeks.
  
  

👉 Lesson: it’s not always about spending more, but about attributing better.

✅ Best Practices for consolidating attribution in Google Ads

🛠 Synchronize attribution models

• In Ads, if eligible, activate Data-Driven Attribution and make sure GA4 also reports under comparable models (using the comparison report).
• For comparisons, use the DDA model in both platforms.

🕒 Align windows and dates

• Set the same conversion and lookback window (30/60/90 days) in GA4 and Google Ads.
• When comparing specific dates, check click date vs. event date to reconcile differences.

✔️ Unify counting

• In Google Ads, adjust whether to count only one conversion per click or multiple.
• In GA4, review the counting policy per event/session.

📈 Register view-through and cross-device

• In Google Ads, make sure view-through conversions are enabled if you use Display/Video.
• In GA4, activate and configure Google Signals to capture cross-device conversions.

✅ Review tags and configuration

• Enable Enhanced Conversions and Consent Mode in Google Ads and verify the conversion tag is installed correctly.
• Check that GA4 event names for conversions don’t exceed 40 characters (otherwise they won’t register as conversions).

🧪 Run A/B tests

• Create duplicate conversions (e.g. GA4 imported vs. native Ads) and measure them as primary/secondary to compare without double counting.

💻 Create a custom channel view

• Create a custom channel view in GA4 to indicate attribution priority among channels, as well as the parameters it should consider to identify conversion sources.

2. Recommendations for interpreting the data

• Use Model Comparison reports in Google Ads to assess which keywords or campaigns get more credit under DDA vs. Last-Click.
• In GA4, review attribution reports selecting Paid & Organic last-click to compare them with Ads-compatible models.
• Keep in mind that GA4 provides a broader multichannel view, useful for understanding complete user paths; Ads focuses exclusively on direct advertising performance.

From chaos to order

Attribution is not an absolute truth, it’s a way of looking at the data. That’s why the key is not to “believe” Google Ads or GA4, but to understand their differences, consolidate sources, and make contextualized decisions.

In a world without cookies, that’s the difference between optimizing with clarity or chasing numbers that never add up.