By now we all know that Data Clean Rooms offer advertisers and publishers secure, closed-loop measurement that is fully privacy-compliant. In other words, to leverage the power of user level data without access to user level data in its raw state.
But in which instances should you put it to use? Which scenarios could benefit from analysis in a Data Clean Room environment?
Buckle up, people. In the 3rd blog of our 3-part series (part 1 – intro, part 2 – comparative analysis), we’re going to learn how Data Clean Rooms empower marketers to:
- Build more relevant audiences
- Continuously improve their customer experience
- Fuel cross platform planning and attribution
- Optimize reach and frequency measurement
- Perform deeper campaign analysis
Let’s get practical.
1 – Performance measurement
Keeping track of retention, ARPU, LTV, and ROAS are flagged as key use cases for Data Clean Rooms, and rightfully so. A Data Clean Room offers a neutral environment to analyze both the advertiser’s CRM data and the ad exposure data provided by the relevant marketing partners.
In this use case, advertisers can upload their 1st-party data into a Data Clean Room following a campaign, match up identical key identifiers, and conduct analysis across their customer data and the ad exposure data made available by the Data Clean Room provider.
Let’s say you’d like to compare your recent purchase data against Google’s ad exposure data. Google’s walled garden DCR — Ads Data Hub — will allow you to attribute the percentage of new customers to the marketing activity that took place across Google’s advertising channels.
If you’re in eCommerce, simply feed the Data Clean Room with your CRM data, unique identifiers (emails, postal addresses, mobile IDs etc.), and purchase date. Then, each media owner will include their ad exposure data and unique identifiers used to create the campaign audience.
At this point, you’ll be able to accurately measure the intersection between new customers and those exposed to the campaign across each media avenue, and then determine what percentage of new customers can be attributed to each channel.
2 – Building more granular audiences
After Apple dropped its ATT bomb, which dramatically hampered access to user-level data — granularity became marketers’ most saught-after holy grail over the past year.
A Data Clean Room enables granularity to a degree that up until recently was simply not possible. It collects data from authorized 3rd-party sources that are ingested and segmented into a range of behavioral, demographic, and location buckets, and then leveraged to enhance your internal database for more granular data enrichment and analysis.
The beauty of it all — is that rather than requiring users’ personal data to be shared in order to conduct analysis, a Data Clean Room enables multiple data sources to be virtually connected through anonymized cohorts.
This enables marketers to measure the intersection that exists between their target audience and the various media audiences. Finally, they’re able to understand the optimal route to reach their audience, plan more effective campaigns, and unlock omni-channel measurement.
How can granular audience insights supercharge your marketing efforts? Glad you asked:
Honing audience targeting
Segmenting your audiences based on fine-tuned data such as consumer behavior and shopping habits — can have a dramatic effect on your campaign strategy.
Crafting tailored content and curating engagements
When you understand the interests of each market segment, you can create more relevant content, promotional recommendations, and new ad formats specifically tailored to those interests.
Refining your messaging, formats, ad types and channels to be able to address each segment individually, speak their unique language and address their specific pain points — is so much easier when utilizing a Data Clean Room environment.
Granular segmentation use case
Say you own an eCommerce brand and your 1st-party data includes customer attributes and associated product stock keeping units (SKUs). You’d like to run a campaign targeting a prospective audience that exhibits similar attributes, and then follow up with a relevant remarketing campaign based on shopping history and frequency.
First, create your target segments. Then, upload the relevant data sets into a Data Clean Room, where your team can work with ad partners to cross analyze your 1st-party data with their 3rd-party data. This results in aggregated, actionable outputs that can help you craft targeted campaigns — without jeopardizing your users’ privacy.
3 – Optimizing reach and frequency measurement
Once you have PII-level impression data from partnered ad networks, you can understand exactly what ads are being served to which customers and how often, which — in turn — can be used to deduplicate campaign reach and frequency, minimize ad fatigue, and improve your media planning.
DCRs can also validate the assumption you’re reaching out to the right audience, which will help you tweak and hone your segmentation criteria. And, Data Clean Rooms allow you to optimize your customer journey, engaging users based on where they are in the funnel and how they interact with your ad.
4 – Incrementality measurement
Impression data from publishers, audiences, 1st-party response and conversion data can all be tied together at the user level to help you understand the incremental impact of your marketing efforts.
Think about the ability to compare between your test and mediating groups through A/B testing, or more importantly — between your exposed and unexposed groups. Pretty powerful stuff, huh?
5 – Showcasing user quality to prospective advertisers
Publishers can inject user level data into a Clean Room’s secure environment and allow advertisers to gauge customer overlap — and even users’ quality — based on various characteristics.
On the flip side, advertisers can build an audience and then test it against publisher X to assess results. It’s an ideal sandbox for both publishers and advertisers to weigh in and demonstrate the value of their acquired users.
6 – Forging 1st-party data partnerships
On the strategic side of things, two entities can agree to join and match datasets in a safeguarded and permission-only environment, cultivating new partnerships within the media ecosystem.
This secured cross-analysis can also help propel product development, and enable marketers to improve their strategic planning.
7 – Training, inference, and propensity scoring
Lastly, a Data Clean Room environment enables you to regain access to restricted granular user level data — required to successfully run training and inference models, and even propensity models, by which you can get an estimate of the likelihood that a customer will perform a specific action.
Key takeaways
- Data Clean Rooms offer marketers the ability to build more relevant audiences, continuously improve their customer experience, fuel cross platform planning and attribution, optimize reach and frequency measurement, and perform deeper campaign analysis.
- It’s a neutral environment to analyze both the advertiser’s CRM data and the partners’ ad exposure data, making it an ideal tool for performance measurement.
- Data from authorized 3rd-party sources is ingested and segmented into a range of behavioral, demographic, and location buckets, allowing you to enhance your database for more granular data enrichment and analysis.
- Once your ad networks’ PII-level impression data is safely placed in the Data Clean Room, you can draw insights around what and how often ads are being served to which users, and then deduplicate campaign reach and frequency, minimize ad fatigue, and improve your media planning.
- Impression data from publishers, audiences, 1st-party response and conversion data can all be tied together at the user level to help you understand the incremental impact of your marketing efforts.
- Publishers can inject user level data into a Clean Room’s secure environment and allow advertisers to gauge customer overlap — and even users’ quality — based on various characteristics.
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The post The practicality of Data Clean Rooms – Harnessing everyday use cases to fire up campaign measurement appeared first on AppsFlyer.