Marketing attribution modelling allows credit to be allocated to the marketing channels and touchpoints that made a lead convert. With ROI Optimiser, you can gather all the data sets that have up until now been dependent on third-party 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.
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?
In this article we will explore:
Third-party cookies and attribution modelling
For more than a decade, online advertising companies have been using third-party cookies to identify internet users across different websites, a process known as cross-site tracking.
The purpose of cross-site tracking is to run behaviourally targeted advertising based on a user’s web-browsing history, conduct frequency capping, measure the performance of ad campaigns, and what our visitors check out online.
These cookies are also used to learn about what our visitors are checking out online when they aren't on owned websites. Third-party cookies - in the context of attribution modelling - allow platforms to collect information from the impressions and clicks generated by ads served from a certain advertiser (or rather, an AdTech platform).
Where the most common attribution models aim to attribute conversions across different channels, multi-touch attribution aims to attribute conversions across different web browsers and devices, as well as channels, campaigns or creatives.
To do this, attribution platforms need to collect IDs and data about the same user across different devices and channels. The most common way to collect user data in web browsers is by creating third-party cookies and storing an ID inside them.
However, the era of third-party cookies is coming to an end.
The phasing out of third-party cookies was announced by Google in February 2020, with Google announcing that they won't be tracking individuals as they browse across the web, nor will we use identifiable trackers in their products.
What does this mean for attribution modelling?
A summary of the most common attribution models, how they work, and whether they will be affected by the changes to third-party cookies are shown below:
The above table shows that the only traditional attribution model that won’t be affected by the end of third-party cookies will be pure last click. All other models will be affected because they all try to stitch the conversion to past activity, through third-party cookies.
Monitoring your digital touchpoints is essential for measuring marketing success. Attribution reveals the true impact of each touchpoint, allowing you to allocate the budget across channels with the highest return on investment. Learn more with our Simple Guide to Attribution.
What’s next? ROI Dashboard: Attribution modelling without 3rd Party Cookies
Our ROI Dashboard uses several advanced mathematical models to underscore its analyses and measure the effectiveness of channels and tactics without the need for third-party cookies.
These analyses within the dashboard are underpinned by a linear regression function. As its inputs, this function only relies on first-party data – specifically: 1, platform spend data; and 2, revenue data.
From here, the function establishes a relationship between these 2 variables and scores each channel by the extent to which its activation positively influences total sale volumes. This score is scaled from -100% to +100%: wherein a negative value indicates ineffective conversion volume when the channel was active, and a positive % value indicates an effective spend relationship. This correlation is based on emergent, calculatable patterns within the dataset, and as such; the accuracy of the model is proportional to the sample size of the datasets.
By associating the revenue data per unit of time with each channel’s spend metric – the dashboard mitigates the last-click bias of attribution by assuming each channel has a role (both great and small) in garnering total sales.
The dashboard analysis seeks to calculate the extent of this role. This calculation, and the accuracy of this calculation, is ultimately based on the variation of the 2 datasets over time and the ability of the algorithm to recognise any patterns therein.
For example, if over a 2-year timeframe, TV is activated 5 times, and total sales volumes increase by X% during the same period, a mathematical weight can be ascribed to TV as a channel concerning its influence on total spend and other channel activation levels.
Therefore, any changes to browser cookie tracking (or the “death of 3rd party cookies”) will have no impact on the effectiveness of the ROI dashboard or its underpinning algorithms.
We are here to help. If you want to be prepared for the end of third-party cookies and experience an ROI dashboard for your business, get in touch with the ROI team.
