Making sense of attribution models

Here we delve into the importance of attribution in digital marketing and how it helps map out the customer journey. We outline key attribution models, compare Google Analytics and Google Ads, and addresses challenges like data gaps from opt-in tracking.

Attribution in digital marketing refers to the process of identifying and assigning credit to the various touchpoints in a customer’s journey that lead to a desired outcome or key event, such as a purchase, form submission, call or email.

This modelling helps you determine which interactions – whether they come via paid ads, organic search, email or other – have the most significant impact on conversions and business objectives.

Understanding attribution models in digital marketing

By understanding attribution, us marketers can better evaluate the effectiveness of campaigns and make informed decisions about where to invest resources. There are several attribution models, each with unique advantages and limitations:

• Last-click attribution assigns all credit to the final interaction before a key event. While this model is straightforward, it overlooks earlier user interactions that may have initiated or nurtured the journey
• First-click attribution, on the other hand, captures the initial touchpoint but misses later actions that lead to a conversion
• Linear attribution, a more balanced method, distributes credit evenly across all touchpoints, providing a holistic view while sacrificing some specifics
• Time-decay attribution gives more weight to interactions closer to the conversion. This is typically used on campaigns with shorter sales cycles
• Position-based models strike a middle ground by prioritising both the first and last interactions while still acknowledging mid-journey contributions
• Data-driven attribution, available in the likes of Google Ads and Google Analytics, uses machine learning to assign credit based on real performance data, tailoring insights to actual customer behaviour

Data-driven attribution continues to be prioritised and pushed by Google as the most effective and widely used attribution model, as it provides a comprehensive and accurate view of the customer journey. Unlike rule-based models, such as first-click or last-click methods, data-driven attribution uses machine learning to analyse patterns in user behaviour and conversion data.

This approach ensures that credit is assigned based on the actual impact of each touchpoint, rather than relying on assumptions or predefined rules.

Data-driven attribution: a football analogy

To help explain data-driven attribution, we’ll use a football analogy. In traditional models like last-click attribution, the goalscorer gets all the credit, ignoring the contributions of other players. Like digital marketing, football is of course a team sport, with many factors beyond the goalscorer contributing to the conversion. A goalkeeper might make first make save, a defender might intercept the ball, or a midfielder might assist the striker.

Data-driven attribution operates like a coach who recognises the value of every player and backroom assistant. Each touchpoint in the customer journey is evaluated and credited for its contribution, whether it’s generating awareness, building interest or closing the deal. By acknowledging the collective effort and not focusing on one single element, data-driven attribution ensures a more accurate and fair assessment of performance.

Photo by Abigail Keenan on Unsplash

Google Analytics vs. Google Ads attribution

Google Analytics (GA4) and Google Ads are both essential tools for digital marketers, yet they have distinct purposes and offer different attribution capabilities. GA4 excels in providing a holistic view of the customer journey, tracking user interactions across multiple platforms and devices. Its data-driven attribution model ensures that credit is allocated based on patterns observed in actual user behaviour, giving marketers a comprehensive understanding of how different channels contribute to conversions.

Google Ads, however, is more focused. It emphasises interactions within its own advertising ecosystem, which makes it invaluable for evaluating the performance of paid campaigns. However, this narrower focus means it may not fully account for the influence of other touchpoints in a customer’s journey.

For instance, a display ad might not seal the deal, but instead capture a buyer’s initial interest before they return to complete a conversion via another channel like direct or organic. These gaps highlight the importance of using GA4 in tandem with Google Ads to gain a more complete picture.

Addressing attribution gaps and opt-in tracking

Tighter rules around privacy, particularly with opt-in requirements for cookies, have introduced challenges for attribution. When users decline cookies, their interactions remain untracked, creating gaps in data and affecting averages and totals. While these non-consenting users may well engage with the site and even convert, we have no oversight of those interactions. This “invisible” traffic complicates efforts to evaluate campaign effectiveness and allocate resources accurately.

Traffic drops as a result of this opting out can vary by site, device and even month to month. As such, it’s important to note that a drop in visible traffic may not actually mean less users are visiting your site, but potentially that less users are accepting cookies.

The focus should be on educating stakeholders on these limitations and relying on additional, relevant datastreams to give a bigger-picture view of performance.

Reporting on live leads without a CRM

Without a CRM to track lead generation and conversion, accurate reporting can be difficult. Our focus at Wriggle Marketing in these instances is custom reporting that tracks first-party leads data each month – even in the absence of an official CRM system, this is possible.

This first-party approach provides immediate insights and, crucially, provides oversight of conversions even when cookies have not been accepted – as it relies on calls, emails, contact form submissions at source. While not as robust as a multi-touch attribution model, this method serves as a practical alternative for understanding lead generation performance in real time.

Strategic takeaways 

Attribution is not just about assigning credit but about telling the story of how customers engage with your brand across channels, and how that can be mapped. It means looking beyond surface metrics and understanding the broader web of possible touchpoints.

Clear communication about the reality of attribution models is essential, along with reporting that acknowledges wider metrics to give as much oversight as possible.

Whether using site performance insights from GA4, evaluating paid ads performance with Google Ads, or relying on last-click attribution for live lead reporting, the goal remains the same: to build a cohesive and effective marketing strategy that aligns with both performance data and business objectives.

Get in touch with the team at Wriggle to find out how effective your current model is and how we can help, across organic search, paid search and more.