User-level data used to be what mobile marketers relied on wholeheartedly. In recent years, however, the surge in privacy-centric regulation and the fact that this data was rendered more elusive than a snow leopard – means that advertisers are now struggling to make data-driven decisions.
And if you thought this is just a phase, well – think again. These ecosystem-sweeping changes are only predicted to accelerate, which would further restrict access to this data, making business optimization even more challenging than it already is.
But this is not a sad story, and these changes could very much serve as a valuable opportunity for brands to cultivate their competitive edge. Forrester articulated it well when they stated that “ethical privacy practices will be the next consumer-driven, values-based source of differentiation.”
Data Clean Rooms (DCRs) stem from this very consumer privacy-centric mindset. As you may recall from our 1st blog on the topic, they’re becoming an essential tool in marketers’ tech stacks, driven by the need for privacy compliance and cross-media measurement and optimization.
According to Gartner, 80% of advertisers with substantial media budgets will utilize DCRs by 2023, estimating that there are currently between 250 to 500 DCR deployments that are either active or in various development stages.
What kind of DCR creatures are out there, you ask? Before we break it down for you, let’s freshen up your memory with a super quick crash course on what DCRs actually are.
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The DCR TLDR
Imagine a secure, isolated platform that links anonymized advertising data from multiple parties. Inside the DCR, advertisers’ first-party data and publishers’ raw data are brought together for the much-needed purpose of joint analysis. Unlike other data sharing methods, however, DCRs include detailed advertising data and strict privacy restrictions on user-level outputs.
How? Before you is the slightly technical gist of the data analysis process in a DCR environment:
- Advertisers stream their data of choosing (e.g. CRM)
- Raw data from publishers is ingested via the SRN’s API and referrer data
- Advertisers can then query business questions that are returned in aggregated form.
Here’s one use case that would get all marketers to release a unified sigh of longing: LTV analysis, which is arguably one of the most well-known use cases for DCRs.
Done using a DCR, which retains the anonymity of all involved users, marketers are able to run user-level analysis across a range of metrics, resulting in an overall assessment of customers’ value over the course of their entire relationship with your brand.
Putting a DCR to use also means that advertisers don’t have to part with valuable audience segments that could give one party a competitive edge over another. So in a sense, DCRs act as a great equalizer, ensuring all parties can share and leverage impactful insights, without imparting any side with data precedence.
And now that we got the what out of the way, let’s get down to business.
Types of DCRs – Introducing the cast and crew
All DCRs help to hide consumers in a crowd by de-identifying their user-level data and clustering them based on common attributes. But in what ways do they differ from each other?
To help you make sense of the rapidly developing DCR landscape, let’s break down the actual breeds out there, assess the relative performance of each across the value chain, and examine their unique pros and cons:
Walled Gardens – Big Tech platforms
This group consists of closed ecosystems where the tech provider has significant control over the hardware, applications, or content.
Walled gardens were first introduced by Google, Amazon, and Meta (Facebook) to safely commercialize their 1st-party data, and also capture ad spend from rivals while they’re at it.
Needless to say, nearly 70% of all ad media spend sits with these three giants — each of which allows advertisers to work within their walled garden DCRs: Google Ads Data Hub (ADH), Facebook Advanced Analytics (FAA), and Amazon Marketing Cloud (AMC).
These security-stringent environments are where the mega SRNs make the event-level data accessible for marketers to be able to make informed campaign decisions, without jeopardizing consumer privacy or the ecosystems’ defense moats.
Pros
- Supporting 1st-party data set enrichment with event-level data
Cons
- Offering raw materials for analysis – making this data readable to the common marketer will require a team of data scientists, analysts, and engineers
- Rigid architecture
- Lack of cross platform ability to generate actionable data (i.e. multi-touch attribution)
- Lack of intercompany data collaboration
- Strict query functionality
Multi platform or neutral players
This type of DCR consists of two sub-groups, each with their unique set of strengths and drawbacks:
Diversified
These are primarily legacy businesses operating in adjacent industries like marketing applications or cloud data storage, offering data collaboration mechanisms for gathering signals in a regulatory compliant way. This group includes providers such as Epsilon, Measured, BlueConic, and Merkle.
Pros
- Architectural flexibility
- Bespoke governance controls over type of data and level of analysis
Cons
- Limited access to walled garden data
- Narrow partner ecosystem
- Limited downstream integrations
- Leverages existing Customer Data Platform (CDP) / Complex Event Processing (CEP) functionality, which could lead to potential data issues
Pure players
These are your young, small- to mid-scale DCR providers, among which are Hobu, Harbr, InfoSum, and Decentriq, as well as more enterprise-focused tools such as SnowFlake.
Pros
- Architectural flexibility
- Leverages existing data piping and storage infrastructure (SnowFlake)
- Access to an ecosystem of integrated partners (SnowFlake)
Cons
- Limited 1st-party data granularity
- Often relies on 3rd-party infrastructure for data ingestion
- Narrow pool of downstream integration options
Mobile Measurement Partners (MMPs)
Ideally, an MMP is a trusted and unbiased player that enables all available user-level data to be leveraged using customers’ own business logic, and then consumed via aggregated and actionable insights.
Pros
- Cornered resource – user-level and cross channel data granularity
- Real-time conversion data
- Comprehensive analytics built for mobile apps’ business logic
- Flexible integration options
- Top-quality aggregated reporting
Cons
- Some limitations around data granularity and query-related actions could be imposed by SRNs
- Lack of existing CDP architecture
Let us show you to your room – How to choose the right DCR for your business?
Advertisers who spend meaningful dollars on data ecosystems – need to make a DCR investment now. But whether you’re implementing a brand new DCR or looking to ramp up an existing one – how do you make an informed decision on the best-fit solution for your business?
To help you decide, let’s shed more light on the competitive landscape of DCRs, where two main factors are considered:
- The volume and quality of the data – referred to as depth
- And the variety of received data – referred to as breadth
The walled garden group has the advantage of data depth – but lacks variety. The pure-play group usually offers the DCR technology alone with very little data depth or breadth. And then there are your MMPs – providing both the DCR technology, depth and breadth of data, and a variety of partner integrations.
When considering a DCR, keep in mind there are several best practices you can follow to ensure you get the most value possible:
- First, be sure to factor in your main channel (be it mobile, app, or web), business size, marketing needs, data structure, and internal resources.
- Then, begin designing your DCR with your consumers in mind. Not just for the present, but for the future. The best DCRs are set up to anticipate shifts in consumer behavior.
- Finally, start testing with a live audience. Analyzing consumer behavior in real time and getting actionable insights is nothing short of invaluable.
Here’s a head scratcher for you – Why haven’t DCRs been more widely adopted (yet)?
- Let’s get this one out of the way, folks – DCRs aren’t cheap. The mega-sized walled garden providers offer alternatives, but the logistical and operational hurdles of working with these platforms can put a strain on all parties.
- The success of DCRs is rooted in data being shared, and not all advertisers are quick to divulge detailed transactional data, mainly due to the misconception of potential privacy risks. And when half-baked data goes in – half-baked data comes out, resulting in rough measurement at best.
- Universal standards for implementation are yet to be determined. That means that pooling data that exists in multiple formats and the prep work that goes into aggregating it – could be time intensive.
- Lastly, we need to remember that user-level data is still available in some instances (e.g. Android devices and consenting iOS users), which could alleviate at least some of the urgency to implement a DCR solution.
Can these hurdles be overcome given the right technology partner, resources, and data preparation? Of course. But more on that in our next blog, which will focus on marketing use cases – so stay tuned!
Key takeaways
- Advertisers who spend meaningful dollars and resources within the data ecosystems – need to go to where the granular data is to be able to take advantage of modern measurement, segmentation, and optimization strategies. Enter DCRs.
- DCRs allow user-level data to remain anonymized throughout the process, which can then be securely used for effective campaign measurement and remarketing initiatives.
- Walled garden DCR solutions offer the advantage of data depth – but lack variety. Pure-play solutions usually offer the DCR technology alone with very little data depth or breadth. And in most cases, your MMPs provide both the DCR technology, depth and breadth of data, and a variety of partner integrations.
- DCRs are an efficient method to meet the many privacy-centric regulations required by the industry – and even state or parliament laws such as the CPRA and GDPR – by enabling a secure environment where critical data points are shared and processed in a fully privacy compliant way.
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