Of all the measurement approaches to choose from, data clean rooms and partnerships give marketers the option to work with other partners to aggregate, anonymise and analyse data. This chapter outlines the benefits and challenges of each.

Data clean rooms

Data clean rooms (DCRs) have been suggested as the go-to alternative to cookie-based targeting that allow companies to gain greater audience insight without compromising on privacy.^[1] A data clean room acts as a repository, storing large amounts of user data. It enables different sources of data to be aggregated and matched, such as first-party data uploaded by a marketer, sales activity data uploaded by an advertiser, or campaign exposure data uploaded by a publisher.

For example, a company such as an FMCG brand could share its insights with a company such as Walmart, Disney, Target etc. Each party places its user level data into a data clean room so they are able to see what the other already knows about audiences they have in common. This can include audience overlap, reach and frequency, purchasing behaviour, and demographics.

When matched, the uploaded information is anonymised and kept within the confines of the clean room. Personal data from each companies’ customers are therefore not revealed or shared with the other company. The outputs from the data clean room can then be used to provide aggregate level insights, e.g. the users who have performed a certain action should then be offered this. This data can then provide insights into the effectiveness of a particular marketing activity and, where data has been combined from different sources, is able to provide cross-channel insights (Figure 1).

Figure 1: Using data clean rooms for measuring digital marketing

Source: Econsultancy

Many publishers use data clean rooms, so if a brand wants to know if they can find a particular customer on a major publisher website, they can check with whichever data clean room that a publisher has shared its data with. Here datasets can be compared to identify the percentage overlap. If there is a match of, say, 95% between both datasets, the chances of reaching that particular user are high.

An example of this in practice is The Trade Desk’s clean room data integration with Disney.^[2] Disney has a clean room product which is backed by Snowflake, Habu and InfoSum. Advertisers that buy ad space through The Trade Desk can target audiences in connected TV (CTV) households by matching to Disney’s identity graph. Advertisers can then use Unified ID 2.0 (UID2) to target those audiences across Disney’s properties.^[3]

Benefits and challenges of data clean rooms

The anonymised data held within a data clean room can be used for measurement and attribution to understand the impact of a campaign on sales. It can also be used to drive marketing activities: for example, by determining reach and frequency, it can help marketers channel efforts in the right direction. It can also support audience building, provide customer insights and enable journey analysis.

The use of data clean rooms is an option that is being considered by both advertisers and publishers, often in partnership. Amazon Web Services (AWS) has launched a service to help companies and their partners customers create their own data clean rooms.^[4]

Roxane Panopoulos, Group Manager of Regional Measurement & Insights – Netherlands and Nordics at Snap, described how the company is exploring DCRs as a way of being able to share data in a privacy-safe manner and measure incrementality. “If we want to look at incrementality as a metric, often you need to have an exposed group and you need to be able to identify who’s been exposed and who hasn’t. This has been one of the bigger challenges with the privacy changes. The data clean rooms present a new kind of solution that we’re investing in with advertisers to actually build that collaborative privacy-safe dataset where we can both query.”

There is a key challenge around the scalability of data clean rooms, which need to have a significant amount of entries in order to have a good chance of matching a specific user. This requires many publishers and advertisers coordinating to share user data. However, match rates are still considered to be low, with reports of publishers only having data on 3–10% of their visitors.^[5]

A further challenge for companies exploring this option is that all data clean rooms differ, which is something the IAB Tech Lab is trying to change.^[6] Another concern is that while on the surface data clean rooms might sound an effective way for advertisers to reach audiences and track performance of their campaigns, there are still questions around the compliance of them all with privacy regulations.^[7]

A company considering using DCRs as an option should ensure they have clarity on how a data clean room is complying with privacy regulations.

Data partnerships/second-party data

Consumer-facing platforms which include the likes of Google, Facebook, Amazon and Apple, along with other social media platforms, have increased their dominance with the collection of vast amounts of first-party data and have in effect formed walled gardens, which restrict access to their data. They offer the largest pool of second-party data available to advertisers and provide a number of different tools to marketers to help measure digital campaigns. According to Econsultancy’s Future of Marketing report, close to two-thirds (62%) of respondents indicate they are using or considering this approach as an alternative means of measurement.

Benefits and challenges of data partnerships/second-party data

Working with a second party enables a company to have access to that partner’s trusted first-party data, however, due to the closed nature of walled gardens, advertisers are dependent on the tools offered by the publishers and content platforms to measure activity within them. This also creates challenges around transparency in understanding the performance of their campaigns.

Meta offers clients a range of different options including conversion and brand lift tools, commerce insights and MMM.^[8] Apple offers reporting on search ads,^[9] and through the SKAdNetwork, a way to measure the impact of marketing campaigns on app installs.^[10] Google provides reporting via Google Analytics with GA4 now offering data-driven attribution, but it is placing more focus on its Privacy Sandbox for the future of measurement.^[11]

A key challenge for companies is trying to get a cross-channel view. While they can get a view of how their marketing activity has performed across channels within their ecosystem, they are unable to attribute performance across walled gardens and other channels.

How server-side tracking can support measurement and targeting

One of the ways ad platforms are adapting to a future with less access to third-party cookies is by using server-side tracking. With client-side tracking, the user’s browser sends data directly to the tracking platform’s server. With server-side tracking, a tracking pixel or tag is used instead, and when an action is completed, the data is sent first to a company’s website server (or a server operated by a third-party on the company’s behalf) before being transferred to the advertising platform or destination system.

A key advantage of adopting this approach is that most ad blockers do not block requests sent in this way, and the process operates on a first-party data basis. From a platform’s perspective, this therefore allows them to get around cookie or tracking restrictions and is something Meta has been pushing with its cloud-based Conversion API (CAPI). Using server-side tracking such as Facebook’s CAPI also means companies have complete control over the data they send because it sits on their servers. Data can then be enriched further with a brand’s own CRM data or lower-funnel events before it is forwarded to Facebook or other platforms, resulting in more reliable data and better optimisation.

A further advantage of using server-side measurement is that it uses first-party cookies and provides businesses with the flexibility to set their own cookie expiration dates.

Other social platforms have also followed suit such as Pinterest and TikTok, allowing brands to send website or app events from their servers directly to the platforms’ servers. Snap also offers companies its Conversions API (CAPI),^[12] which allows the advertiser to directly pass web, app and offline events to Snap via a server-to-server (S2S) integration. This helps Snap’s system to optimise a company’s ad campaigns, improve their targeting and measure the conversions that resulted from their Snapchat campaigns. Roxane Panopoulos, Group Manager of Regional Measurement & Insights – Netherlands and Nordics at Snap, describes how the platform asks companies for as many data feeds as possible so that it is easier for Snap to deduce signals of who has converted on the advertiser platform.

A key note of caution for any brand utilising server-side tracking is to use this in a transparent manner. It is important for brands to ensure that they are explicit in telling users that their data is being shared with social networks.

Retail media and its ecosystem for measurement

Retail media networks (RMNs) are the advertising platforms created by retailers that give brands access to promotional inventory across owned retailer channels. Networks typically provide a range of formats, tools and methods to enable the placement of ads and campaign reporting.

The opportunity that retail media affords brands is the ability to use a retailer’s first-party data to effectively target in-market shoppers when they are close to the point of sale, and to be able to report on the performance of their marketing activity.

Econsultancy’s Retail Media Best Practice Guide highlights how one of the key benefits of retail media is the ‘closed-loop’ measurement and attribution it provides. Having access to actual sales data means that advertisers can tie campaign activity back to direct sales impact and return on investment. This can also help advertisers understand which shoppers are driving the incremental results, and this applies to advertising on retail owned media as well as campaigns that make use of off-site data.

The ISBA (Incorporated Society of British Advertisers) is working with retailers and brands such as Unilever to create a common set of measurement standards which can work towards bringing more granularity to campaign reporting on retail media networks.