I had a meeting recently where the president of the company was struggling to understand that Google Analytics 4 (GA4) and Meta Ads attribution are not apples-to-apples comparisons. So that got me to thinking that having a chart or infographic to explain the difference would have helped to bridge the gap in understanding. That led me to prompting the popular AI models to see what I would get in return. I’ll share the prompt that I used below along with the responses that I received from ChatGPT, Claude, Gemini & Llama.
The Prompt
I need a graphic that shows the difference in how Google Analytics 4 handles attribution (Last Click / Data-Driven attribution) and how Meta Ads handles attribution (clicks + view-through). My boss doesn’t understand how these are different.
AI Test Scorecard
| AI Model | Infographic Created | Performance Rank |
| ChatGPT 4.0 | Yes | 2 |
| ChatGPT 4.5 | No | 6 |
| Claude Sonnet 3.7 | Yes | 1 |
| Gemini 2.5 Pro | No | 4 |
| Llama 4 | Yes | 7 |
| Perplexity Pro Search | Yes | 5 |
| Perplexity Deep Research | Yes | 3 |
ChatGPT 4.0
Got it! Here’s a breakdown of the graphic, and I’ll generate a clean, simple visual for you next.
Title: Attribution Differences – GA4 vs. Meta Ads
Section 1: Google Analytics 4 (GA4) Attribution
- Data-Driven Attribution (Default in GA4):
- Uses machine learning to assign credit to multiple touchpoints in the customer journey.
- Weighs each interaction based on its contribution to conversion.
- Last Click Attribution (Optional):
- 100% of credit goes to the last non-direct click before conversion.
✅ Tracks Clicks Only
🚫 Does not count view-throughs (impressions without clicks)
Section 2: Meta Ads Attribution
- Click-Through Attribution:
- Default attribution includes conversions that happen after someone clicks a Meta ad (e.g., 7-day click by default).
- View-Through Attribution:
- Also counts conversions that happen after someone views (but doesn’t click) an ad — usually within 1 day.
✅ Includes Clicks
✅ Includes Views (Impression-Based Conversions)
🎯 Meta claims credit if any ad was seen or clicked before conversion — even if other sources were involved
Bottom Summary/Callout:
Result: GA4 might underreport Meta performance compared to Meta Ads Manager, because GA4 doesn’t track view-throughs and gives different attribution weighting.
Now I’ll generate this as a clean, clear graphic for your boss. One moment.
ChatGPT 4.5
Attribution Models: Google Analytics 4 vs. Meta Ads
Google Analytics 4 (GA4)
- Last Click Attribution:
- Gives credit to the last interaction (click) a user made before converting.
- Example: If a user clicks a Google Search Ad, then later clicks a Facebook Ad, GA4 (Last Click) attributes the conversion entirely to Facebook.
- Data-Driven Attribution:
- Uses machine learning to evaluate all touchpoints a user interacts with.
- Distributes credit across multiple interactions based on how influential each interaction is toward driving a conversion.
- Example: A user clicks a Google Search Ad, later sees and clicks a YouTube Ad, and finally clicks a Facebook Ad before converting. GA4 would assign partial credit to Google Search, YouTube, and Facebook based on their respective influence.
Meta Ads Attribution (Facebook & Instagram)
- Click Attribution:
- Credits the ad only if the user clicks and converts within a defined period (e.g., 7 days).
- View-Through Attribution:
- Credits an ad if a user sees (doesn’t click) the ad but converts later within a defined window (e.g., 1 day).
- Example:
- A user sees a Facebook Ad without clicking, and later converts on the website within 24 hours. Meta attributes this conversion as a view-through.
- If a user clicks the Meta Ad and converts within 7 days, Meta attributes this as a click-based conversion.
Key Difference Summary
- GA4: Emphasizes the role of interactions (especially clicks), utilizing data-driven attribution to distribute credit across multiple channels.
- Meta Ads: Attributes conversions based on either direct clicks or ad impressions within defined windows, recognizing indirect influence through view-through attribution.
Claude Sonnet 3.7
I’ll create a graphic that clearly illustrates the difference between Google Analytics 4 attribution models and Meta Ads attribution. This will help your boss understand these different approaches.
Here’s a visual comparison of how Google Analytics 4 and Meta Ads handle attribution differently:
The key differences between these attribution models are:
Google Analytics 4:
- Last Click Attribution: Gives 100% credit to the final touchpoint before conversion. All previous interactions receive zero credit.
- Data-Driven Attribution: Distributes conversion credit across multiple touchpoints based on their impact on conversion probability. This uses machine learning to assign weighted credit to each interaction in the customer journey.
Meta Ads:
- Click-Based Attribution: Gives credit when someone clicks on a Meta ad and later converts.
- View-Through Attribution: Also gives credit when someone sees (but doesn’t click) a Meta ad and later converts through another channel.
The major distinction is that Meta’s attribution model:
- Credits both views and clicks (not just clicks)
- Can claim credit even when Meta wasn’t the last touchpoint
- Uses its own attribution window (typically 1-7 days for view-through, 1-28 days for clicks)
This creates a fundamental difference in how conversions are counted – Google Analytics 4 focuses on the actual conversion path from the user’s perspective, while Meta’s model is designed to capture Meta’s influence, even when it’s not directly linked to the final conversion action.
This is why conversion numbers often don’t match between these platforms, which can be confusing when reviewing marketing performance reports.
Gemini 2.5 Pro
Understanding Attribution Differences: GA4 vs. Meta Ads
A Visual Comparison
GA4 Attribution (Data-Driven)
- Focus: Assigns credit across all touchpoints in the customer journey.
- How it works: Utilizes machine learning to analyze user behavior and determine the true value of each touchpoint in a conversion.
- Key Features:
- Cross-channel: Considers interactions from various sources (search, social, email, etc.).
- Data-driven: Leverages historical data and user behavior patterns.
- Flexible: Can be customized to suit specific business goals.
Meta Ads Attribution (Clicks + View-Through)
- Focus: Primarily credits clicks and views within a specific timeframe.
- How it works: * Click: Assigns credit to ads that were clicked on.
- View-through: Assigns credit to ads that were simply viewed, even if not clicked.
- Key Features:
- Simplified: Easier to understand and implement.
- Limited Scope: Primarily focused on Meta ad interactions.
- Time-bound: Conversions are attributed based on a specific window (e.g., 7-day click, 1-day view).
Why the Differences Matter
- Data Discrepancies: Different attribution models can lead to varying conversion numbers and performance insights.
- Campaign Optimization: Relying solely on one platform’s attribution can lead to suboptimal campaign strategies.
- Budget Allocation: Inaccurate attribution can result in misallocation of marketing budget.
Key Takeaways:
- GA4 provides a more holistic view of customer journeys and marketing effectiveness.
- Meta Ads provides a simpler, more platform-specific view.
- Understanding these differences is crucial for accurate performance measurement and informed decision-making.
Recommendation:
- Consider using a combination of both platforms’ data for a more comprehensive understanding of campaign performance.
- Regularly review and adjust attribution models based on business objectives and campaign goals.
Note: This is a simplified explanation. Both platforms offer more advanced attribution options, and the best approach will vary depending on your specific needs and data.
I hope this graphic and explanation help your boss understand the key differences between GA4 and Meta Ads attribution!
Llama 4
Perplexity Pro Search
To help your boss understand the differences between Google Analytics 4 (GA4) and Meta Ads attribution models, here’s a clear breakdown:
Comparison of Attribution Models
| Aspect | Google Analytics 4 (GA4) | Meta Ads |
|---|---|---|
| Primary Attribution Models | Last Click and Data-Driven Attribution (DDA) | Click-through and View-through Attribution |
| Last Click Attribution | Assigns all credit for a conversion to the last touchpoint before the conversion. | Not used as a default; focuses on click-based windows. |
| Data-Driven Attribution (DDA) | Uses machine learning to distribute credit across multiple touchpoints based on their contribution to the conversion path. | Not applicable; Meta does not use multi-touch attribution. |
| Click-through Attribution | Not explicitly defined in GA4; conversions are tied to specific touchpoints based on attribution settings. | Credits conversions occurring within 1-day or 7-day after clicking an ad. |
| View-through Attribution | Not supported; GA4 does not track conversions from ad views without clicks. | Credits conversions occurring within 1 day of viewing an ad, even without clicks. |
| Engaged-view Attribution | Not available in GA4. | Tracks conversions from users who watch at least 10 seconds of a video ad and convert within 1 day. |
| Focus | Multi-touch attribution across channels using AI-driven insights. | Single-touch attribution focused on clicks and views within defined timeframes. |
Key Differences
- Multi-Touch vs Single-Touch Attribution:
- GA4’s Data-Driven Attribution uses machine learning to assign credit across all touchpoints in the conversion path, providing a holistic view of marketing impact.
- Meta Ads focuses on single-touch attribution, primarily crediting either clicks or views within specific timeframes (e.g., 7-day click or 1-day view).
- View-Based Conversions:
- Meta Ads includes view-through attribution, which credits conversions even if users only see an ad without clicking it, provided they convert within 1 day.
- GA4 does not support view-through attribution; it relies on direct interactions like clicks for attribution.
- Machine Learning vs Fixed Windows:
- GA4’s Data-Driven Attribution dynamically adjusts credit allocation based on historical data and user behavior patterns, making it adaptable to complex conversion paths.
- Meta Ads uses fixed windows (e.g., 7-day click or 1-day view), focusing on predefined timeframes for assigning credit to ads.
This graphic comparison should help clarify the fundamental differences between how these platforms approach attribution.
Perplexity Deep Research
Understanding Attribution Models: Google Analytics 4 vs. Meta Ads
The image you’ve shared effectively illustrates the fundamental differences between how Google Analytics 4 (GA4) and Meta Ads handle attribution. Attribution models determine how conversion credit is distributed across various marketing touchpoints that lead to a user’s conversion. Understanding these differences is crucial for accurate performance measurement and marketing decision-making.
Google Analytics 4 Attribution Models
GA4 offers several attribution models, with the two primary approaches shown in your graphic:
Last Click Attribution
The last click attribution model in GA4 assigns 100% of the conversion credit to the final touchpoint before conversion. As shown in the left section of your graphic, this creates a direct path from the last interaction to conversion with full credit assigned to that final touchpoint.
This model is straightforward but has significant limitations. It ignores all previous interactions in the user journey that may have influenced the conversion decision. For businesses with complex customer journeys involving multiple touchpoints, last-click attribution often overvalues bottom-of-funnel activities while undervaluing awareness and consideration stage touchpoints.
Data-Driven Attribution
The data-driven attribution model in GA4 uses machine learning algorithms to distribute conversion credit across multiple touchpoints in the conversion path. As your graphic illustrates, rather than assigning 100% credit to a single interaction, this model distributes credit based on the calculated influence of each touchpoint.
By default, GA4 uses a cross-channel data-driven model as the reporting attribution model. This sophisticated approach analyzes patterns across thousands of conversion paths to determine the relative importance of each touchpoint, providing a more nuanced view of what drives conversions.
Meta Ads Attribution
Meta Ads takes a fundamentally different approach to attribution, as shown in the right section of your graphic:
Time-Based Attribution Windows
Meta Ads uses time-based attribution windows that consider both clicks and views (impressions). The default setting is “7-day click + 1-day view”. This means:
- Any conversion that happens within 7 days after someone clicks on your ad will be attributed to that ad.
- Any conversion that happens within 1 day after someone views your ad (without clicking) will be attributed to that ad.
Your graphic correctly illustrates how both click and view interactions can lead to conversion attribution within their respective windows. This dual-path approach acknowledges that impressions (views) can influence conversion behavior even without direct engagement.
Key Differences Between GA4 and Meta Ads Attribution
Philosophical Approach
- GA4 focuses on interaction importance: Especially with data-driven attribution, GA4 attempts to determine which interactions actually influenced conversion decisions, regardless of timing.
- Meta focuses on time windows: Meta’s approach is primarily concerned with when the conversion happened relative to ad exposure, rather than the relative importance of that exposure compared to other channels.
Click vs. View Attribution
- GA4 primarily tracks clicks and direct interactions: While GA4 can track impressions, its attribution models place greater emphasis on direct engagements with content.
- Meta explicitly values both clicks and views: Meta’s attribution system formally acknowledges view-through conversions, giving credit for conversions that happen after users merely see an ad without clicking.
Customization and Flexibility
- GA4 offers multiple model options: Beyond the two shown in your graphic, GA4 also offers first click, linear, position-based, and time decay models that can be selected based on your business needs.
- Meta offers limited attribution window options: You can modify the attribution windows in Meta (such as switching to 1-day click or 7-day click only), but the fundamental approach remains time-based rather than offering different distribution methodologies.
Practical Implications for Marketing Analysis
Understanding these differences has several practical implications:
- Cross-platform discrepancies: Conversion numbers will almost always differ between GA4 and Meta Ads reports due to these fundamental attribution differences.
- Campaign optimization: Meta may show higher conversion numbers due to its inclusion of view-through conversions, which might lead to different optimization decisions than if using GA4 data alone.
- Budget allocation: Data-driven attribution in GA4 might reveal the importance of upper-funnel activities that would be undervalued in a last-click model or might not be captured at all in Meta’s attribution.
Conclusion
The graphic you’ve shared effectively illustrates the core differences between these attribution approaches. For your boss, the key takeaway should be that these systems measure success differently by design:
- GA4’s data-driven attribution provides a more holistic view of the entire customer journey across channels.
- Meta’s time-window approach with view-through attribution helps understand both direct (click) and indirect (view) impact of Meta ads specifically.
Neither approach is inherently “correct” – they simply measure different aspects of marketing performance. The best practice is to understand both systems and use them in complementary ways for comprehensive marketing measurement.
Perplexity Deep Research + Napkin AI
Now let’s try something different. I’ll pair the response from Perplexity Deep Research with Napkin AI to see what happens. Napkin AI creates charts, diagrams & flowcharts from text so this might be a suitable use-case.
While the graphics are not as simple to understand as Claude Sonnet 3.7, the Napkin AI visuals are much better than the other AI models at visualizing my prompt. This is definitely a viable tool to use for business presentations.
Summary
Claude Sonnet 3.7 was the only AI model that followed instructions. There was an issue with the chart label over-lapping but aside from that the graphic was spot-on. The text response I got was a bit lacking in terms of how to interpret the differences between the GA4 & Meta Ads attribution however.
Both ChatGPT 4.0 & Gemini 2.5 Pro did a good job at explaining the differences between attribution models. ChatGPT provided a graphic however (albeit truncated) and had a more visually-appealing text response so I gave the edge to ChatGPT here. Gemini 2.5 Pro did the best job of any AI model at explaining why the differences matter.
I was surprised that ChatGPT 4.5 gave what I’d consider to be a superficial response compared to what ChatGPT 4.0 provided. The graphic generated was completely irrelevant as well. This shows that more advanced models don’t necessarily equate to better results for every task so you’ll have to try different models for different tasks to see what works best for you.
Meta’s Llama 4 was very disappointing considering how fast the model was at generating Python code for me on my recent AI data visualization test. I don’t even know what Llama 4 produced here in the response. It was also the only AI model that gave a zero word response. What’s ironic about this is that my prompt was about Meta Ads.
Perplexity Pro Search created a nice table to highlight the differences between attribution models. I found the responses to be very literal without anything in terms of interpretation. This is probably due to Pro Search grounding the response from numerous websites.
Perplexity Deep Research provided the most complete text response as it sourced from numerous sites. Deep Research also created an interesting looking infographic though I have no idea what it’s supposed to represent.
The ideal results? If I could pair Claude’s infographic with Perplexity Pro Search’s table along with Perplexity Deep Research’s text response. Based on my recent tests, I might have to cancel my OpenAI subscription in favor of Anthropic’s Claude.