Event Marketing Attribution 101: Understanding Conversions Across Paid Media Channels

Attribution is one of the most common and most misunderstood challenges for event marketers. When every platform reports different conversion numbers, how can you tell what’s really working?

In this session, Jack Butler, Group Director of Paid Media at Tag Digital, breaks down the mechanics behind attribution models, explores why data rarely matches across tools, and shows how to use attribution insight to make smarter campaign decisions.

Key topics covered

Why different platforms show different conversion numbers
The main attribution models: last click, first click, linear, and more
The role of view-through and click-through conversions
The impact of user privacy, cookie opt-outs and iOS tracking
Why last click attribution misses the bigger picture
Practical ways to improve attribution tracking and reporting

Why Different Platforms Show Different Conversion Numbers

Attribution data is often inconsistent across platforms. It can be difficult to reconcile the differences between what’s reported in your CRM or registration platforms versus what shows up in your paid media channels.

This is because each platform uses its own tracking methods, attribution windows, and rules for what counts as a conversion.

Marketers regularly face confusion when the numbers from a registration platform don’t align with those from Facebook, Google, or LinkedIn.

This disconnect can make it seem like some conversions are ‘missing’ or incorrectly attributed to the wrong channel.

Jack explains why this happens and why it’s important to understand these differences when evaluating campaign performance.

The Main Attribution Models: Last Click, First Click, Linear, and More

Attribution models are how we determine which touchpoints or channels should receive credit for driving a conversion. Each model approaches attribution differently, and they each have their strengths and weaknesses.

Last Click Attribution

Last click attribution assigns 100% of the credit for the conversion to the final touchpoint. This is the most common model, especially for platforms reporting registration or completion actions.

While it’s simple and gives marketers a straightforward view, it fails to show the full customer journey.

Jack explains how this model tends to overvalue high-intent touch points, such as branded search, while ignoring earlier stages of the funnel, like programmatic ads or awareness campaigns.

First Click Attribution

First click attribution, on the other hand, gives all the credit to the first interaction in the conversion path.

This model is useful for growth marketers who want to understand what channel is bringing in new prospects or kickstarting the customer journey.

However, just like last click attribution, it fails to account for the rest of the customer’s journey.

Linear Attribution

Linear attribution distributes credit equally among all touch points in the customer journey.

This gives a more balanced view of how all marketing efforts contribute to conversions. Jack shares that TAG uses this model to report on campaigns, as it enables better optimisation across channels.

However, linear attribution can lead to over-counting when multiple touch points overlap within a short timeframe.

Other Attribution Models

In addition to the common models above, Jack briefly touches on more complex attribution models, like time decay, position-based, and data-driven attribution (like the one used by Google Analytics 4).

These models are typically more advanced and often require dedicated software and more time to implement.

The Role of View-Through and Click-Through Conversions

Understanding view-through and click-through conversions is crucial to unlocking a more accurate picture of your campaign performance.

View-Through Conversions

A view-through conversion occurs when a user is shown an ad but doesn’t click it, yet later converts.

For example, someone may see a Facebook ad on Instagram but never click on it. However, they might later search for your brand on Google or visit your website directly, eventually completing a conversion.

This often causes discrepancies in reporting because, while the conversion is influenced by the ad, it wasn’t directly attributed to the platform that served it.

Jack explains how platforms like Meta assign view-through conversions within set time windows, such as 24 hours after an ad exposure.

This means that even if a user doesn’t click on the ad, it’s still attributed to the platform. While this may feel confusing at first, it helps marketers understand the broader impact of upper-funnel activities.

Click-Through Conversions

Click-through conversions are attributed when a user clicks on an ad and then converts, whether immediately or later.

Platforms often extend attribution windows for these conversions because a click is typically a stronger indicator of intent.

However, this extended window can sometimes cause confusion when comparing CRM data to platform data.

For example, if a user clicks on an Instagram ad and converts a few days later, the platform will attribute the conversion to that click, but your CRM may only capture the last-click source, like an organic search.

Jack talks through how to use these metrics together to get a full view of your campaign’s performance and how they complement each other in the journey.

The Impact of User Privacy, Cookie Opt-Outs, and iOS Tracking

In an era of increased privacy regulations and growing concerns around data security, user tracking has become a major challenge for digital marketers.

With the rollout of GDPR in Europe and Apple’s iOS 14 updates, opt-outs and cookie deprecation are making it harder to track conversions accurately.

Cookie Opt-Outs

As users become more conscious of privacy and reject cookie tracking, marketers are seeing a reduction in conversion data.

In fact, Jack shares that brands often see 20-30% fewer cookie-based conversions when users opt out. This is especially problematic for platforms relying on cookies, like Facebook, which depend on these for conversion tracking and remarketing.

iOS 14 and IDFA Opt-Outs

With iOS 14, Apple introduced an opt-out for tracking via the Identifier for Advertisers (IDFA).

Users can now block apps from tracking their activity, which can disrupt conversion data. This affects how platforms like Meta, Google, and others track user actions and tie them to conversions.

Jack explains that these shifts in user behaviour, coupled with privacy laws, mean that tracking becomes less reliable over time.

As a result, attribution models need to evolve, and marketers must adapt to these changes by using more advanced methods to understand campaign performance.

Why Last Click Attribution Misses the Bigger Picture

While last-click attribution is the default for many marketers, it often fails to provide the full picture of how a conversion actually happens.

Jack stresses that this model ignores valuable touch points that happen earlier in the journey, such as brand awareness ads or discovery-stage interactions.

When using last-click attribution, it’s easy to mistakenly assign credit only to high-intent actions like search or direct traffic, when in reality, a display ad or social media campaign may have been the initial catalyst that led the user down the path to conversion.

This is why it’s essential to use models that take the full customer journey into account.

By embracing more sophisticated attribution methods, marketers can avoid over-investing in channels that are over-represented in the last-click model, and better allocate resources to campaigns that are driving awareness and engagement.

Practical Ways to Improve Attribution Tracking and Reporting

Jack wraps up the session with actionable tips on improving attribution tracking. These strategies help optimise campaign performance and ensure that your data is as accurate and useful as possible.

Use Multiple Attribution Models

One key piece of advice is to use multiple attribution models to get a broader view of performance. While last-click and first-click have their uses, they’re limited in scope.

Using linear or time decay models can give you a better sense of how different touch points contribute to the overall conversion path.

Leverage View-Through and Click-Through Metrics

Make sure to track both view-through and click-through conversions. Understanding how both contribute to a conversion is essential for accurate campaign assessment.

This allows you to optimise your media mix, investing in channels that are influencing conversions even when the user doesn’t click on the ad.

Adapt to Changing Privacy Regulations

Finally, stay ahead of the curve by preparing for future privacy regulations. With cookie deprecation and increasing opt-out rates, it’s critical to adjust your strategy.

Focus on building robust CRM data, using first-party data, and leveraging privacy-compliant tracking methods to ensure you still get accurate insights.

Event Marketing Attribution 101: Understanding Conversions Across Paid Media Channels