The customer journey in a world post-cookies

With new platforms, channels and features emerging so rapidly, it can be difficult for a business investing in their digital marketing to know where, and how, to get the most value out of their money, writes Jack Huntley, National Head of Data & Insights at Resolution Digital

Digital attribution has always been a method for marketers to better understand the customer journey, leveraging data to inform which channels and campaigns should be assigned value.

Traditionally, this relied on third-party cookies to measure and understand the effects of digital campaigns. However, with increasing privacy regulations, ongoing tracking restrictions, and changes to browsers across devices, the ability to implement a successful digital attribution model is now more challenging.

So, how does cookie deprecation impact multi-touch digital attribution and how can digital marketers best manage the customer journey in a world post-cookies?

The challenges and opportunities post-Cookie

With the onset of tracking limitations and user privacy regulations, multi-touch attribution is becoming less accurate. While cookie-based digital attribution remains an important part of attribution measurement, it is recommended to use it in conjunction with alternative cookieless solutions.

Additionally, there are several ongoing challenges with digital measurement, such a walled gardens, attention tracking, and accurate fraud detection.

Cookies previously enabled marketers to measure and understand the effect of digital campaigns. Yet now, cookies and Mobile Advertising ID (MAID) based tracking are strongly impacted by the ongoing tracking restrictions. To manage this, marketers can explore new tools and approaches as well as consider combining multiple attribution/measurement techniques to achieve greater accuracy.

Innovations on the horizon

Calculating the contribution of every click interaction based on data from a user’s account, data-driven attribution in Google Analytics 4 (GA4) works within each conversion event to allocate conversion credit. This approach evaluates both converting and non-converting paths through machine learning (ML) algorithms in attribution, establishing how different touchpoints impact conversion outcomes.

This data-driven model incorporates factors such as:

• Time from conversion
• Device type
• Number of ad interactions
• The order of ad exposure
• The type of creative assets

To determine which touchpoints are most likely to drive conversions, data-driven attribution uses a counterfactual approach, contrasting what happened with what could have occurred. From this statistical analysis, the data-driven model assigns credit to each touchpoint.

The future of attribution

Several new forms of measurement and technological solutions can maximise positive customer journeys. These include:

1. Regression-Based Attribution (RBA)   

• RBA uses a methodology similar to MMM (Marketing Mix Modelling) but focuses on digital media touchpoints.
• Digital media data ingestion can be mostly automated via application programming interface (API) resulting in an RBA that can be run more frequently than MMM, delivering more granular results.

2. Server-Side Tagging

Meta Conversion API (previously known as Facebook CAPI) and Google Enhanced conversion tracking are examples of privacy-centric measurement solutions which connect personally identifiable information (PII) data to user behavior.

3. Data Clean Rooms

• Data clean rooms empower marketers to get the most out of their first-party data and unlock media efficiency measurement, which is reliant on matching to partners’ second-party data.

Attribution measurement helps marketers to understand the value of their marketing efforts and gain insights into the marketing funnel. By understanding the effectiveness of channels and touchpoints, marketers can efficiently allocate a marketing budget, increase ROI, boost conversions, and optimise their omnichannel strategies.

Until recently, the majority of attribution measurement techniques have been heavily reliant on third-party cookies and advertising identifiers. Recent privacy-centric measures have problematised the operation of user-level tracking and attribution. However, by implementing other measurement methods and tools, such as regression-based attribution, server-side tagging, data clean rooms, and the data-driven attribution model in Google Analytics 4 (GA4), marketers can still successfully run effective attribution models in this new era of digital analytics.

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