Let’s be realistic – the ‘360° customer view’ is a lie

For years, digital advertising has sold the promise of connecting every touchpoint across the customer journey to deliver hyper-targeted advertising that eliminates wastage in your media budget. In today’s privacy landscape, this couldn’t be further from the truth, but it’s also nothing for marketers to shy away from.

A privacy-centric experience

Digital measurement of marketing performance is increasingly fragmented. Regulations like GDPR around the world have helped ensure that customer data is collected with explicit consent and used for limited purposes only. Major web browsers such as Safari, Firefox and Brave have now limited the lifetime of cookies (both third-party and first-party), restricting fingerprinting, redacting cross-domain information and stripping URL tracking parameters. Apple devices require prior consent to access the Identifier for Advertisers (IDFA). The list goes on and is ever-expanding. It’s clear to anyone paying attention that the direction of travel is firmly toward a more private digital experience.

A more secure, private online experience is undeniably a positive thing, despite the frustrating endless cookie banners. So why might marketers be concerned with these changes? Simply put, almost all digital advertising and analytics technologies today rely on one key concept: persistent identifiers. A persistent identifier is something that allows a data collector, such as Google, to repeatedly identify an individual, device or household when they interact with a website or mobile app. These identifiers allow an ad network to build the audiences that they sell to advertisers, or to attribute credit for a sale to a click or impression.

In the absence of a user-provided identifier, such as an email address or phone number, a random identifier is generated and stored in a cookie. When it’s not possible to create that cookie due to lack of consent, or the lifetime is cut short by the browser, the resulting data becomes fragmented and less able to accurately measure a customer journey over time. This results in a growing discrepancy between your data and reality.

The role of big tech

Google, Meta and other major ad networks have vast quantities of first-party data. Facebook has a mostly logged-in experience, making it easy for Meta to accurately identify and profile users. To help mitigate the loss of cookie tracking, these platforms have launched tools aiming to replace cookies by allowing advertisers to send a customer’s personal data alongside their usual conversion tracking. The ad network will then attempt to connect the personal data to a recent click or impression belonging to the same individual and, if a match is found, claim credit for the conversion.

It’s easy to think that conversion tracking based on personally identifiable information (PII) could be the ‘silver bullet’ that solves our conversion tracking woes. However, the unspoken truth is that the cross-channel picture remains extremely fragmented. Ad networks can protect their own interests by creating solutions that allow them to continue claiming credit for conversions, but what happens when those journeys are complex and multi-channel? Products such as Google Analytics and Adobe Analytics have long been the tools of choice for advertisers to understand cross-channel performance, but in the absence of a reliable and persistent identifier, their attribution models fall apart.

A new reality for digital marketing

The current reality of the marketing landscape is that creating a ‘360° view of the customer’ by tracking their every move across the web is not possible (and arguably never was), at least not with cookies alone. Marketers need to get comfortable with this new reality, but they also need access to new tools that make it possible to plan and execute effective cross-channel campaigns and optimize spend efficiently. We need cookieless testing and attribution capabilities. Thankfully, there are viable solutions. Combining tried-and-tested statistical methods with modern computing power gives us a path to achieve this. Incrementality testing and Market Mix Modelling (MMM) are two methods seeing a resurgence.

Both Meta and Google have recently released open-source software for MMM, a method that models aggregated media spend and other factors to predict performance. These models can then be used to understand existing performance and to propose alternative media spend to maximize efficiency without cookies. Similarly, incrementality testing is a method that can use geographic locations to create distinct groups for A/B testing media performance without cookies.

The challenge, for now, is bridging the gap between statistics and marketing skills, because these tools require a significant amount of each. Getting ahead of the curve will require a cross-discipline effort or the services of an external partner. However, with continued innovation, these tools will no doubt become increasingly accessible to non-technical marketers over time. The only question then is who will become the market leader in cookieless attribution?

For more on how the world of data-driven advertising and marketing is evolving, check out our latest Deep Dive.