Proving value: SEO attribution in 2024

How marketing teams can unlock and communicate the true contribution of Organic Traffic

Attributing results accurately to marketing activities is a universally recognised challenge in digital marketing, especially regarding SEO.

The complexity of assessing SEO impact escalates when multiple channels operate in tandem. Users often engage with several touchpoints before converting, frequently undermining the role of SEO in conversions within traditional attribution models.

Extended sales cycles further complicate SEO attribution. Organic interactions frequently surpass attribution windows, unjustly skewing their perceived contribution to conversions.

Moreover, elusive touchpoints, such as offline engagements (e.g., visits to brick-and-mortar stores influenced by local SEO), can play a big part in a user's decision-making but often go unnoticed in standard tracking systems.

To overcome these hurdles, one requires inventive strategies and solid measurement techniques to unlock the full potential of SEO attribution in the ever-evolving digital landscape.

Hugo Whittaker

Senior SEO Manager

Strategies for Overcoming SEO Attribution Challenges
1. Embrace Data-Driven Attribution Models

Building precise, bespoke reporting is the first step to overcoming attribution struggles. The MarTech space is brimming with reporting platforms like Salesforce, Hubspot, and Google Analytics. Each tool employs attribution models, determining how conversion credit should be distributed among touchpoints in a user's journey.

Prominent models include:

  • Last click attribution (100% credit to the final touchpoint)

  • First click attribution (100% credit to the initial touchpoint)

  • Linear attribution (even credit distribution across touchpoints)

  • Time decay attribution (more credit to touchpoints later in the journey)

  • Position-based (U Shape) attribution (primarily credits initial and final touchpoints)

  • Data-driven attribution (machine learning discerns touchpoint influence on conversions)

Last-click and data-driven attribution (DDA) are far more commonly used, so much so that Google announced it was sunsetting the other four in GA4 and Google Ads due to not providing ‘the flexibility needed to adapt to evolving consumer journeys’.

Of the two prominent models, DDA is gaining traction for valid reasons. It leverages machine learning to allocate credit to touchpoints based on diverse factors, such as touchpoint frequency and its role in prompting user conversions. Continually evolving from incoming data, DDA offers a more insightful perspective on organic traffic's contribution than other models.

By contrast, Last-click attribution solely credits the concluding touchpoint, rendering it less insightful for businesses employing multi-channel campaigns.

DDA, however, isn't flawless. Concerns about transparency in algorithms, especially in platforms like GA4, persist.

Nonetheless, DDAs signify a progressive shift from the unequal credit distribution inherent in conventional attribution models, taking into account the full search journey and providing a more holisitc understanding of the impact and effectiveness of SEO efforts.

2. Marketing Mix Modelling

One drawback of DDA is its reliance on cookies. It’s becoming increasingly hard to track performance in a cookieless world. In 2019, Apple introduced restrictions on first-party cookies with their lifespan capped at seven days. In 2020 there was further restriction, with many third-party cookies blocked. Chrome is phasing out support for third-party cookies and proposing new functionality for cookies along with purpose-built APIs to continue supporting legitimate use cases while preserving user privacy.

Marketing Mix Modelling is a way for companies to understand online & offline marketing spend, without the need to implement sophisticated tracking technologies. Using historical data such as spend, revenue, etc, a machine learning model provides you a summary of how your marketing budget should be allocated. This analytical approach can be an alternative way to understand SEO attribution alongside DDA. However, this approach does require a lot of resource to manage the model and setup presents several challenges such as data collection, cleaning data and choosing the right model.

We'll be going deeper into marketing mix modelling in coming weeks, so do follow us on LinkedIn to stay ahead of this.

3. The Importance of Measuring Incremental Impact

A powerful way to discover the true contribution of SEO is by using incremental testing to identify its true impact on traffic and conversions. By formulating test scenarios where SEO tactics can be examined in isolation, we can better understand its true ROI.

The first step is creating a hypothesis. This should involve setting out what you hope the outcome to be and what KPIs will be used to measure success, such as sessions, revenue and/or leads.

Next, establish a comparative dataset, either from a similar page or subfolder, or historical data from target pages.

Apply your tactic and designate a data collection duration. SEO strategies usually require more time to manifest results compared to other channels, so it’s best to monitor this over a longer time frame.

Subsequently, scrutinise results for valuable insights. If the strategy yields positive outcomes, share this as a proof of concept with stakeholders. Regularly deploying such experiments highlights the influence of SEO on business KPIs, be it metadata optimisation, content refreshing, or page speed enhancement.

Another compelling case for incremental testing is the interplay between paid search ads and organic results. As cost-per-clicks (CPCs) surge in various sectors, there's growing interest in 'total search' strategies, reducing ad expenditure on keywords where a brand already has significant organic presence.

We've developed a tool for this very purpose: iX Competition. It frequently scans Google's SERPs for selected keywords within specified geolocations. Based on this data, decisions to pause or activate keywords are made, optimising efficiency and minimising PPC/organic overlap.

Using tools like iX Competition facilitates understanding the real incremental value of paid ads versus organic results, and their collective impact on online performance.

4. Don't Forget Qualitative Data

Another pivotal component in SEO attribution is understanding that solely relying on attribution models and quantitative data for SEO decision-making is flawed due to inherent limitations.

While our world becomes increasingly digital, current reporting dashboards can't capture every user journey touchpoint. Offline engagements and unclicked interactions go unnoticed. Furthermore, multi-touch models overlook existing brand relationships, making it challenging to decisively attribute a purchase to a click.

Businesses with high-ticket items or services will find it harder to attribute credit to channels like SEO. These channels often feature early in the user journey and, even with DDA, might not get adequate credit. Prolonged evaluation periods by customers might exceed the attribution window, leading to misinterpretations of 'non-converting' traffic.

While quantitative data remains vital for marketing decisions, qualitative insights from usability tests, surveys, and interviews should play a supporting role.

Exploring self-reported attribution through surveys or website forms can be insightful. Queries like 'How did you discover us?' can unearth valuable information, presenting a comprehensive view of SEO's role in the user journey.

Observing user behaviour, understanding where they face challenges or confusion, and getting verbal feedback about user experiences allows SEO professionals to address content and navigation issues, enhancing user satisfaction and conversion rates.

These insights demonstrate the broader significance of SEO, aspects often overlooked by standard attribution models.

Closing thoughts

While SEO attribution challenges are undeniable, choosing a holistic attribution model and employing a strategy that combines incremental testing and qualitative data collection, can help clarify and communicate the genuine impact of SEO initiatives on business success - while in turn helping to break down both marketing and data silos.

Interested in learning more about how SEO can drive wider marketing collaboration and deliver superior business outcomes?

Get in touch with us for a chat today.

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