The measurement approach a company selects will be influenced by a number of factors including their objectives, the marketing mix of channels they are using and available budget. ID-based solutions are an option to consider for targeting and measuring online behaviour, as explored in this chapter.

ID-based solutions

To help marketers understand what an individual has done across the web, identity-based (ID) methods of measurement are being explored as an alternative to third-party cookies. Over a quarter of marketers (28%) in Econsultancy’s Future of Marketing report indicate they are already using this form of measurement, with a quarter considering it as a possible option.

ID solutions create a consumer identifier that advertisers can then use to serve targeted advertising to users and measure online behaviour. ID solutions use personal data such as an email address, phone number or login ID to track users. When a user visits a website, their personal data is collected and sent to an ID provider. The user is then matched to an existing ID, or a new ID is created, and the user’s personal information is encrypted or hashed to protect their privacy.

Benefits and challenges of ID solutions

A key advantage ID solutions have over third-party and first-party cookies is that by using an identifier such as an email address or phone number, the ID can become a universal identifier since it can be used not just across websites, but other platforms and channels, such as a retailer’s store. These IDs can then be used to understand the behaviour of individuals throughout the customer journey, making it possible to measure the effectiveness of any marketing activity they have been exposed to on that journey.

However, the main limitation of ID solutions is related to scale. They require thousands of publishers and advertisers to systematically collect and share user data, and, at present, multiple ID solutions are needed to give publishers and advertisers enough data to effectively identify users. This creates an additional challenge for companies trying to build a holistic picture of what a user has interacted with, as it requires companies being able to work together and use the same IDs.

Types of ID solutions

With the recognition that any ID solution requires scale and adoption from a number of parties across supply-side partners and platforms, there continues to be a number of ID solutions being considered.

In 2020, IAB Tech Lab launched Project Rearc to address the challenge originally posed by the deprecation of third-party cookies and other identifiers. Bringing together stakeholders from across the digital supply chain, it aims to ‘re-architect’ digital advertising by building systems and standards that preserve addressability with consumer privacy and security at its heart.^[1] Project Rearc has developed three broad categories for different types of addressability solutions. These are outlined below. “I would recommend making sure any company is well acquainted with these as it’s a really useful way to start defining what could work for your business,” suggests Chloe Nicholls, Head of Ad Tech at IAB UK. IAB UK’s website has further information about the different categories.^[2]

The IAB’s Project Rearc is ongoing, and companies are encouraged to participate. Nicholls highlights how the IAB has created a checklist of questions advertisers and agencies should be putting to prospective vendors,^[4] as well as a directory of solutions in the market to help businesses get acquainted with their options.^[5]

Any solution a company chooses needs to be compliant with all necessary regulatory and commercial rules.

User identity graphs

An identity graph is a database that stores all the identifiers which correlate with individual customers across every touchpoint. It enables a company to have a view of how users interact with their website, ads, different platforms, channels and so on. The database combines personally identifiable information (PII), such as an email address or phone number, with non-PII data, such as publisher IDs or data from first-party cookies. A company could have the same customer in their CRM, email marketing tool and ad platform. An identity graph can process the data from all the tools and the customer’s devices and stitch this together into one profile.

An identity graph can help marketers and publishers find similar customers based on their interests, intent or behaviour and group them into audiences. These audiences can then be used for targeting, upselling, remarketing and personalisation. Two methods which can be used to group data and help build cross-device customer identities are deterministic matching and probabilistic matching.

Deterministic matching finds an exact match between records, where users are identified across different screens using login details. However, while providing accurate authenticated users, the number of those which can be matched are likely to be low, amounting to only 20% of internet users as suggested in one article.^[6]

Probabilistic matching looks for a degree of similarity between two or more datasets and is based on the probability that the records are the same person. This can be done by using algorithms to score and weight the variables and inconsistencies present in the profiles.

Greater scalability can be achieved with machine learning algorithms that match datasets and identify users across different devices using data such as location, device type, IP address, operating system and browser type.

The profiles can also then be tied to universal IDs provided by solution providers. The solution provider’s technology will then search, match and refresh the profile to make sure that data is up to date and profiles are complete.^[7]

A key benefit of user identity graphs is that they enable cross-channel and cross-platform tracking and targeting, as well as the ability to deliver personalised advertising. Marketers can therefore create their own identity graphs or work with a user ID graph provider company such as LiveRamp, The Trade Desk and other DSPs who are providing access to their large datasets. Companies like Amazon and Netflix are good examples of how they are able to use their ID graphs to track user behaviour across different devices and serve them relevant product recommendations.

Digital fingerprinting

Digital fingerprinting is used to create a user fingerprint based on data snippets (such as attributes of a device IP address, OS version, language settings, browser extensions, etc.), which then enable a user to be identified across the web. Econsultancy’s Future of Marketing report revealed that 10% of marketers are using digital fingerprinting, while 31% are considering it as an alternative to third-party cookies.

However, what companies should bear in mind if they are using or considering this as an option, is how it complies with regulations. GDPR requires data that has been collected through browser fingerprinting to be necessary for the specific purpose it has been acquired for, and that companies ask users for consent, similar to the current cookies consent.

A further challenge is that this approach is also on the decline as Apple has already publicly prohibited fingerprinting. Other major browsers like Chrome and Firefox have also made changes that make the process difficult and limit the ability of companies to fingerprint users. There is also industry concern that this type of practice is just a privacy workaround that will not last for long before catching the attention of regulators.