By Biog Richard Wheaton, UK MD of data company fifty-five

If you are following the current announcements around data governance in digital marketing, you may be forgiven for thinking that digital media performance measurement is coming to an end. Europe’s GDPR and California’s CPA have set out the legal frameworks for new requirements for privacy compliance, and Apple’s ITP safeguards block the use of 3^rd party cookies on their devices. Google is committed to following this, with similar blocks for its Chrome browser by 2022.

There are going to be some online targeting tactics that you will not be able to deploy in this new world, and for users with iPhones and Apple Mac computers, those tactics are already significantly curtailed. By 2022 there will be a whole raft of use cases in targeting tools like DMPs (Data Management Platforms) that will be rendered useless on the vast majority of devices.

So, what can you do? Are we set back to the days of purely contextual targeting, in which you bought the “eyeballs” of customers of a specific financial product or a given industrial sector by advertising in their vertical trade journal? Do we just have to send the same ad to everyone, and hope that some of them are in the market for our niche products?

Moving towards the end of 3rd party cookies

For marketers of financial products and services, these are important questions because managing your audiences is important for avoiding waste and for addressing your engaged clients and prospects in targeted and effective ways. The ability to track and remarket to customers has been a mainstay of digital marketing for the past decade. This has all been enabled by the dropping of cookies – files that sit on your PC and record your browsing activity. With moves from regulators and tech companies to protect user anonymity, the cookie while not quite dead, is certainly crumbling.

The upshot is that marketers cannot rely on 3^rd party data anymore and should be wary of how any use of 3^rd party data will be viewed by the regulator. This renders even the most basic consumer journey starting with a mobile search and ending with a desktop ecommerce transaction hard to measure in many cases. But the use of anonymized 1^st party data can still yield critical insights, and this is where we need to focus our efforts.

From individual to anonymized data

So, what has fundamentally changed in digital measurement? The overarching shift is from a world where brands could access the log-file data of each individual user to a world of anonymized user behaviour, based on vast pools of data.

The ownership of these data pools and the ability to collect and model it sits with the brand that has the direct contact with the consumer. For large banks, insurance and pension companies, this is a resource for efficient and effective digital marketing, based on aggregated insights. For the adtech giants – Google, Amazon, Facebook – this will provide increased levels of targeting options within their tools.

The adtech identity graph

Google are in some ways the most advanced in delivering a scalable solution, which is worth examining. Google uses an undisclosed, supposedly huge sample of users for which they have collected the right consent first. They can accurately track across publishers, devices and even offline channels. From this they extrapolate to the entire population, applying machine learning or more traditional online polling. The end report is always aggregated and only available above a certain volume threshold. Facebook and Amazon are constantly developing similar aggregated audiences for targeting with their inventory.

These audience aggregation capabilities may appear to be similar to Nielsen’s panel-based measurement model. But this really is digital marketing on rocket fuel. It allows brands to leverage the tech brands’ enormous identity graphs and universal app tracking capabilities. And because their identity graphs are vast and data collection in real-time, the final estimate is not only accurate, but most of all, granular and actionable – the two main limitations of traditional panel-based systems.

Google launches machine-learning managed ad frequency
Practical applications of these tools are valuable to marketers. For examples, Google has recently announced the launch of a new machine-learning powered feature to manage ad frequency across its Google Ad Manager platform. It will predict how many times a user was exposed to an ad for reach and frequency analysis, based on its undisclosed sample of users who Google can track with full certainty.

This announcement comes after some similar releases across the entire stack. ‘Cross-environment estimated conversions’ will show you when customers convert on the same device, as well as when customers click a search ad on one device or browser but convert in a different environment. These are rich insights enabling you to optimize any campaigns.

Integrating the new reports into decision-making

Financial marketers will use these new features as they become available in the Google Marketing Platform, but it is also vital for these decision-makers to have their own measures of effectiveness and performance. Most of the features above are still in Beta, rarely compatible with third party systems and often difficult to export. While many media agencies are judged on ‘cost per acquisition’, this is far more difficult in a world where the conversion is estimated and therefore not auditable. This makes it absolutely essential for brand marketers to undertake their own measurement frameworks and integrate insights into their own sales and demand trackers.

The secret lies in your 1st party data given the fact that it is comparatively less impacted than 3rd party data, with the one-day notice in Safari and as-yet unaffected in Chrome. There is still a scope for projects that focus on maximizing how you capture and use your first person data, both to employ smarter segmentation of your media activation, and to obtain insights by separating out your Chrome from your ITP lines to tailor your campaign activation and measurement.

It will also be valuable for marketers in finance sectors to use their own data to build better targeting capabilities, and to monitor and validate the information they are getting from the large tech companies, whose insights are otherwise largely unauditable.

There is also a time-limit to the availability of these measurable insights, because when Google removes open-ended access to your 1st party cookies, the ability of brands to obtain these insights will reduced to almost zero.

A measurement mindset

While people might mourn the loss of the present system, in truth very few brands have had the genuine discipline or technical knowledge to make their measurement truly insightful. And in reality, even for brands that made the effort to obtain genuinely robust data, the figures in their analyses were not 100% accurate, with data discrepancies and ad fraud skewing the findings providing a false picture. Some will also be uncomfortable with the aggregation of power within the big tech companies, but the options will be increasingly few to work outside their platforms.

In the post-cookie world, what is really required is a change of mindset when measuring performance, which will be as important to you as the tools and technology that you use. We are in a new era and those who are prepared to adapt their thinking are best placed to succeed.