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.

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.