Which Ad Should Get Credit for the Conversion?
Understanding how conversion attribution works will help you better interpret the numbers reported by each platform and make more informed decisions about your campaigns.
If you've ever run ad campaigns on Meta, Google, TikTok, or other platforms, you've probably asked yourself: how many conversions are my campaigns/ads generating? And consequently, are these campaigns really worth it?
The answer can become more complex depending on factors like the number of active campaigns, the channels being used, and the attribution window (the rule set on platforms to count a conversion after a user interacts with an ad), among others.
By the time you finish reading this article, you'll have more tools to answer this and other questions to evaluate your campaigns' effectiveness.
Table of Contents
What Does Attribution Mean in Marketing?
Put simply, attribution—or the attribution model—is the specific interaction that impacted a user's decision to make a conversion.
Imagine you run an online store with an active Search campaign in Google Ads. A user sees your ad on Google, clicks it, and lands on your product page. However, they decide not to buy right then. The next day, they search again, ANOTHER ad from your campaign appears, and they click it. Unlike the previous day, today they make a purchase.
Now, in a Last Click attribution model, the second ad receives credit for the sale—meaning the last ad the buyer clicked before converting.
On the other hand, in a First Click attribution model, the ad from the previous day gets the credit because it was the user's first interaction. These are just two of many attribution models available.
If you had only one campaign active on a single channel (Google Ads, in the example above), attribution is straightforward. But if your paid strategy includes multiple channels, things get a bit more complicated. Plus, as mentioned earlier, there are many attribution models you might encounter. Here is an easy-to-understand table published by Adobe for Business on their blog.
Single-Touch Attribution Models
Model | Description | Pros | Cons |
First-Touch | Assigns 100% of the credit to the first marketing interaction. | Helps identify which channels attract new prospects. | Ignores all subsequent interactions that may influence the final decision. |
Last-Touch | Assigns 100% of the credit to the final interaction before conversion. | Helps identify what drives the final action. | Ignores earlier touchpoints that helped build brand awareness or trust. |
Credit: Adobe for Business
Multi-Touch Attribution Models
Model | Description | Pros | Cons | Example |
Linear | Gives equal credit to every touchpoint. | Simple and equitable for multi-channel journeys. | Does not distinguish which touchpoints had the greatest impact. | A prospect clicks a Google ad, attends a webinar, downloads an eBook, and schedules a demo. Each interaction gets 25% of the credit. |
Position-Based (U-Shaped) | Assigns 40% of the credit to the first and last touchpoints, distributing the remaining 20% across middle interactions. | Emphasizes key entry and conversion points. | Assumes first and last touchpoints are always the most important. | Display ad (40%), email (10%), product page (10%), and signup page (40%). |
W-Shaped | Assigns 30% to first touch, 30% to lead creation, and 30% to opportunity creation. The remaining 10% is shared among other touchpoints. | Ideal for B2B buyer journeys with multiple critical stages. | May undervalue actions taken during later funnel stages. | Guide download (30%), contact form (30%), demo booking (30%), and minor touchpoints (10%). |
Time Decay | Gives more credit to touchpoints that occur closer to the conversion time. | Highlights the influence of more recent interactions. | Undervalues initial top-of-funnel actions. | Organic visit (low credit), display ad (medium credit), and retargeting email (high credit). |
Credit: Adobe for Business
Overwhelming? A bit, but I'm not sharing all these models for you to memorize them or become a paid ads expert overnight. The takeaway is that the answer to your original question changes depending on how you choose to distribute conversion credit.
The Customer Journey Is Usually Multi-Channel
Building on the Google Ads example, imagine you also run campaigns on Instagram and an email retargeting workflow for shoppers who abandoned their cart. The picture gets more complex.
And surprisingly, this "more complex" scenario is still incomplete because it leaves out digital touchpoints like social media and organic search, as well as offline touchpoints like word-of-mouth recommendations, print ads, OOH advertising, and many others.
The key takeaway is that a conversion can result from multiple touchpoints across various channels. As Wavetec's article explains, different touchpoints don't all serve the same purpose. Some channels drive discovery, others foster consideration, and others push conversion or retention. An effective multi-channel strategy recognizes that your marketing efforts shouldn't compete against each other—evaluating them solely on "how many conversions" each generates can lead to misleading conclusions.
Looking back at the attribution tables shared earlier, the logic behind these diverse models likely makes more sense now. Different models allow you to analyze and credit touchpoints from distinct angles.
Why Don't Conversion Numbers Match Across Platforms?
Despite all the theory—which has practical applications—you've likely encountered this issue: Meta Ads shows one conversion number, Google Ads shows another, and your e-commerce platform (Shopify, Google Analytics, etc.) shows something completely different. In short, the math doesn't seem to add up. Suppose in one month your Shopify store records 100 purchases. When checking your ad platforms, Meta Ads reports 60 conversions and Google Ads reports 70. Added together, that's 130 conversions. This doesn't mean there are 30 "phantom" purchases or that a platform is reporting wrong data; rather, the same conversion is being viewed through different lens (attribution rules).
One major factor driving discrepancies is the attribution window. Simply put, this is the timeframe during which a platform can attribute a conversion to an ad interaction. If Meta Ads uses a 7-day attribution window and Google Ads uses a 5-day window, and a user interacts with ads on both platforms before purchasing on day 4, both Meta and Google will claim credit for that single sale.
Your e-commerce platform answers "how many purchases occurred" (100), while Meta and Google answer a different question: "How many conversions can be tied to interactions with my ads under my rules?"
Note: This doesn't mean platform reports are useless or deceptive. But because each operates on different rules, comparing them directly to judge channel or ad efficiency can be like comparing apples to oranges. Here is how you can use these reports effectively.
So, Which Ad Deserves Credit for the Conversion?
While platform reports shouldn't be treated as absolute truths for your entire ad performance, they remain valuable for decision-making.
If you run three campaigns in Meta Ads under identical tracking conditions and one consistently reports far more attributed conversions than the others, that insight lets you compare campaign performance to evaluate whether audiences, ad creatives (images/videos), or ad copy drive the gap. The same applies to Google Ads, LinkedIn, X, Bing, and other networks.
However, decision-making shouldn't stop inside individual platforms. You need a way to compare channels against each other. Reconciling discrepancies isn't about getting every platform to output identical numbers; it's about establishing a consistent baseline for comparison.
Fortunately, you can implement a "neutral third party" in your digital ecosystem for a bird's-eye view. Depending on your business model, this could be a CRM, direct e-commerce data, or an analytics solution. Google Analytics is widely used for this purpose because it applies a unified methodology across all channels (provided it is properly integrated and configured).
By combining insights from each ad platform, a central tool like Google Analytics, and actual sales recorded in your e-commerce platform or site, cross-channel evaluation and decision-making become far more accurate and straightforward.
Finally, keep in mind that conversion volume alone doesn't determine ad effectiveness. Key metrics like CTR, CPC, LTV, CPA, and ROAS should also inform your evaluation.
In summary: don't try to force Meta, Google, LinkedIn, and Shopify to report identical figures. Instead, use a single source of truth to evaluate channels under uniform criteria and connect performance directly to total investment.
References:
FAQ
1. What is an attribution model in digital marketing?
It is a set of rules determining how credit for conversions is assigned to touchpoints (clicks, impressions, or visits) along a user's conversion path.
2. Why don't conversion numbers match between Meta Ads, Google Ads, and my online store?
Discrepancies don't mean conversions are duplicate or invalid. Each platform relies on its own attribution rules and windows (the timeframe after an interaction during which conversions are tracked). As a result, a single user who clicks a Meta ad and then a Google ad before buying may be counted as a conversion by both platforms.
3. What is the difference between First-Touch and Last-Touch attribution?
First-Touch attributes 100% of the credit to the initial channel that introduced the prospect, disregarding later steps. Last-Touch assigns 100% of the credit to the final touchpoint right before the purchase, omitting earlier interactions that built interest and trust.
4. How can I reconcile reporting discrepancies across different ad platforms?
The best practice is to implement a neutral third-party analytics platform (such as Google Analytics or a CRM) that tracks user journeys under standardized methodology, then compare those metrics against actual sales recorded in your e-commerce backend.
About the author
David Santos
Digital Marketing Specialist at Werko Marketing Solutions. He has over 10 years of experience in digital marketing. His primary focus is data monitoring and analysis for the management and optimization of digital advertising campaigns, websites, and e-commerce stores.
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