Online marketers have started to understand that with the imminent demise of cookie tracking, Multi Touch Attribution (MTA) will never be the same again. But what exactly is happening and how can you prepare? During this era of rapid e-commerce growth and increasing use of digital marketing, it is more important than ever to understand the technologies affecting marketing measurement and data privacy. Marketing Attribution, which measures marketing to sale touch point by touch point, has implications for both topics, and the changing nature of browser technology and growing concerns over customer privacy, mean that attribution models will need to change. Marketers will need to adapt and plan for a long-term strategy that allows them to leverage first party data instead of relying on external data trackers.
So, what are the challenges and solutions? Let us make sure we understand the technology shift first, which comes down to a shift away from a long established tracking technology, the browser ‘cookie’.
What are Cookies again? And are they really going away?
The most visible application of cookies in marketing has been advertisers using cookies to target consumers with numerous ads and promotions depending on their interests, including re-targeting you after you visited their website – this happens especially if the potential customer allowed the use of ‘third-party’ cookies. But what is the difference between first-party and third-party cookies?
Third-party cookies are dead! – Long live the cookie
The key thing to understand is that only certain cookies are affected by the recent privacy motivated shifts, and that certain cookies, specifically, ‘first party’ cookies, are here to stay. First party cookies are created by the website that you are visiting at the current moment and are quite harmless from a privacy perspective. First party cookies are typically used to personalise and improve your user experience. They cannot ‘spy’ on private data about you or read any files on your computer. It is generally accepted that retailers should be allowed to cultivate automated online relationships through interactions with their customers. Indeed, it is crucial to build up trust with consumers who in return are willing to forgive brands utilising this strictly limited and anonymous data to help market their brand to different audiences.
Third-party cookies on the other hand, are created by other domains usually to harvest your data and understand the underlying information and habits, for advertisers to predict your buying and searching behaviours across different web pages. These cookies have allowed ad-tech companies to create detailed profiles of users for building highly targeted marketing strategies. This is possible because third-party cookies not only allow for tracking the user journey on a particular website domain but across multiple domains, for any domain which shares the third party code, For example, if you visit a healthy eating website and accept the third-party cookie, other websites can be given access to that cookie, and you will likely receive a load of health and food related ads wherever you go online. Although this common use is usually fairly harmless, if sometimes annoying, it is the technical potential of the third party cookie to endlessly accumulate detailed information about a person from site to site. This has caused a concern among data protection advocates and privacy conscious consumers.
In the end, technology which relies on third-party cookies has lost this battle and they are being phased out. Indeed if you are reading this article in 2023 or later, you will be reading about a technology that used to exist. What started as a tracking blocking feature of less well used browsers like Safari and Firefox in 2013 has now become the norm, notably since Google announced in 2020 they would phase out third party cookies from Chrome which is used by over 50% of users. Google’s new ‘privacy first web’ will change the digital marketing space forever.
As cookies are being phased out new tracking technologies are growing in importance, such as user cohorting (e.g. Google’s ‘FLOC’) and anonymous IDs (e.g. Unified ID 2.0). These are interesting but we think will be limited in terms of scale and no doubt will attract privacy concerns as well.
The End of ‘View-Through’ Attribution
The ‘view-through’ metric was never the best way to measure online ads. The idea was simple enough – when someone clicks on an online ad the performance can be measured by counting the clicks that convert for ‘click-through’ conversions, and so why not also count the ad views that lead to conversions as well, and call them ‘view-through’ conversions!? The only problem was how to count ad views when customers never actually interact with the ad before making a purchase. Enter once again the third party cookie, which could be dropped by the ad-server domain through the ad placement itself and then updated again later in the online conversion for that advertiser, in order to count each view-through sale.
What seemed like a clever way to understand how ad impressions drive value for advertisers, the ‘view-though’ rapidly became a major source of mis-attribution. This is because the sale could happen many days after the user is shown the ad in which time many other ads and influences both offline and online may have occurred. The concept of measured ‘viewability’ helped this a little, but over attribution remained. One of the huge challenges has been targeting itself: a well targeted ad in a high reach campaign is almost bound to generate a ‘view through’ sale since all the ad needs to do is get loaded into a users browser at some point in the days before purchase. In this way display ads, in particular, have been rewarded for just showing up sometime before a sale with the actual incremental impact of the advertising unknown. With a click, the consumer is usually making a conscious choice to visit the advertiser, whereas most ‘views’ just happen because the consumer is online a lot.
However, as explained, the view-through metric depends on the third-party cookie which is history. View based measurement has to connect a person buying something online to an ad seen earlier on a different website, which means you need the cross domain tracking capability of third-party cookies.
Just as the third party cookie will not be missed by most consumers, likewise many serious measurement professionals will be glad to see the end of view-throughs. Before they celebrate though, it is worth noting that major sites like Facebook, Google and Amazon, can still link ad views to sales when they handle both the ad delivery and the sale on their platform, which of course they uniquely have the reach and power to do. So there may yet be an afterlife for the view-through metric.
Multi-Touch Attribution Moves to Click or Visitor Only
Third-party cookies made it possible to track both ad impressions and clicks in attribution models, but now it is not possible to collect data in this way. The ad view (impression) touchpoints are dropping out of the picture, and only direct clicks remain within the measurable customer journey. What people do on the web, including what ads they get exposed to, is now hidden by stronger privacy controls. However, an attribution which is based on clicks to the advertiser site can still be picked up by first party cookies and web analytics. Therefore online sellers can only tell if their ads are being noticed when a potential customer shows up by clicking on one.
How will Marketing Effectiveness Change?
The gap left for online ad view measurement means upper funnel brand activity including display, video and social media all become harder to measure. Performance journeys, such as affiliate and search clicks and including some mid funnel consideration activity like generic search and email, can all continue to be measured using data driven attribution methods. So this type of click attribution must now be combined with other methods to evaluate ad views.
The topic of alternative methods for measuring display and social ad impact is huge in itself, so we will just take a brief tour of the three main options: (1) New measurement solutions offered by the media tech giants (2) AB Tests/ Randomised Control Trials (3) Econometric methods.
First off, the tech giants are rolling out new measurement systems which promise to track how ad exposure has changed people’s behaviour without the possibility of identifying any particular individuals. Google has posted an explanation of their approach centred on their Privacy Sandbox, which includes solutions for targeting in a post-cookie world. Google’s FLOC and FLEDGE technologies both get around user ID tracking by aggregating users together in anonymous groups at browser level, with tightly defined criteria for the group definitions which prevent drill down or cross referencing to pick out individuals. FLOC (Federated Cohorts of Learning) groups users by affinity and interest for in-market audience targeting which can then be targeted by advertisers whilst retaining user privacy, while FLEDGE (First Locally-Executed Decision over Groups Experiment) focuses on enabling automated display retargeting at non-user level where interactions with advertisers are stored within the browser and then sold by Google as retargetable segments. Google says it will use these aggregation type methods for anonymous measurement through to conversion. The benefit to users of increased privacy protection also leads to increased advertiser reliance on Google to accurately measure performance with no way for independent measurement or verification.
A second approach to consider for any kind of ad measurement is to run an experimental AB test, or to be precise, a Randomised Control Trial. This is where a group of people who are not exposed to the ad are compared to a well matched group who are exposed to the ad (control vs treatment groups). Done properly, nothing beats an experiment for accurate measurement of incremental impact. Targeting can help with experiments, helping in ensuring the two groups are well matched and therefore suitable for comparison to make ad impact estimations. The problem now is that targeting is increasingly dependent on the big tech media owners i.e. with Facebook and also with Google FLOC, you can target only within their networks and on their terms. The measurement challenge increases when you want to combine or compare media impacts across more than one media network e.g. a campaign on both Google and Facebook combined. Without 3rd party cookies cross media AB testing is technically very difficult since you cannot be sure if someone in the non-exposed control group on one site might nonetheless be exposed to an ad on another one. Solving this requires some kind of common targeting framework such as matched location geo-targeting (a good option, but not without challenges) or otherwise using an opt-in consumer research panel that can work across multiple networks (requiring research company and media owner support).
The third type of solution is old measurement tech – Econometrics, or Market Mix Modelling. These are statistical methods which represent the earliest forms of classical machine learning and grew in popularity back in the 1990s as computational power made it possible to estimate a wider variety of more complex models with relatively small amounts of data. The idea is to use several years of observations of ads and sales, as well as data on economic, seasonal and business drivers, to build a ‘best fit’ model. This model measures how the drivers work together to generate sales, in particular estimating a coefficient (a multiplier) for each media channel to estimate how many sales it generates. The great thing about econometrics is that the data is readily available and can be increased in scope by sampling across multiple locations. Given the fragmentation and complexity of online data, it is not surprising that Econometrics is making something of a comeback. The downside is that it remains a tool of very imprecise high level directional estimation, and requires significant expertise to get right. If attribution is a microscope examining the user marketing journey closeup, econometrics sees it through a telescope in the gloom of a cloudy day.
The marketing analyst is left with these imperfect tools to try and evaluate the now hidden user journey from ad exposure to sales. A combination of methods will usually work best – for example using a one off experiment to help validate an econometric estimation.
What do these changes mean for businesses and how do you adapt?
At the highest levels, the big shift going on here is that big tech companies such as Google and Facebook increasingly keep the data about user interactions with ads within their own walled gardens. This increases the dependency that brands have on these giants especially for targeting and measurement. This suggests the long term strategy for marketers should involve growing their own first party consumer data using sources like web analytics, transactions data, customer data and CRM, rather than relying on third-party data collectors. Likewise, a marketing strategy will have to include mechanisms collecting data from their own data points such as logins, subscriptions, email forms and call centres.
Investing in resources to build brand awareness and capitalising the pull approach of the PPC techniques and keywords targeting will be vital. This allows companies to ‘pull’ potential customers from the web as they are already interested in the product or service as they are browsing. Advertising partners will continue to be used to reach new audiences and grow the customer base, but ad effectiveness measurement of less tangible brand and impression based impacts will increasingly rely on aggregated data methods which group customers together to protect their privacy. Relying on the media platforms to measure how your customers respond to your campaigns will only increase your dependency on them and provides no independent means of validation on your media spend.
With the increased focus on first party data it is essential to build a strategy around customer relationship and trust. Since businesses will be focused on their own data collection it will be imperative to build up trust with your consumer, who will, in turn, allow you to use their data. This can be accomplished by following strict data privacy policies and using data for the consumer benefits such as user experience enhancement and content personalisation.
It will also be important to understand the aggregated data, as well as first party individual data, grouping consumers based on their common buying behaviours and search habits. Google’s FLOC and FLEDGE should prove helpful for audience analysis, allowing marketers to choose a target audience, segmented by common buying behaviours and interests. Targeting by the audience and contextual targeting will grow in importance as cookie based targeting falls away.
In conclusion, utilising a data-driven approach for click based attribution modelling will continue to enable accurate insights for commercial decision making and performance ROI, leveraging first party data. Companies will have to rely heavily on their own data collected web analytics and other customer facing systems in order to understand the true value behind each touchpoint in the customer journey. For data around how customers passively interact – for example, ad exposure effects – aggregated data is the only way forwards. At Metageni we only use anonymised first party data for click attribution and then use econometric approaches to understand the branding potential of display, social as well as all offline media channels. While businesses still have time to adjust to the new reality, the digital marketing space has always been subject to change. The question for brands is are they getting closer to their customers or relying on the tech companies to do it for them?
Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. To get in touch with us: firstname.lastname@example.org or click here.
Metageni.com – You can read the full article also here.