The AdTech industry is buzzing with discussions about cookie deprecation and effective strategies to tackle it. One of the commonly suggested solutions is the utilization of clean rooms alongside responsibly sourced first-party data.
Above all else, the industry recognizes the importance of respecting consumer data and complying with all privacy laws. Additionally, the industry acknowledges the need for a change in our historical practices. This shift benefits everyone involved, as consumer data is more secure than ever. Tremendous investments have been made to ensure the utmost security of consumer information.
Clean rooms are one of the tools that enable companies to use data securely, ensuring the content that you see is as relevant as possible.
Two ways the AdTech industry is addressing cookie deprecation
The days of sending data directly to partners for usage or for using only third-party data for marketing efforts are gone. Now, the emphasis is on responsibly collecting first-party data and using clean rooms to enrich first-party data to enhance marketing efforts.
First-party data
The industry is starting to lean into first-party data gained through transparent means. This valuable information provides organizations with deeper insights into their customers, allowing for more personalized and effective interactions. By embracing the power of first-party data, either on its own or enriched via partner collaboration, you can cultivate stronger relationships, build trust, and deliver tailored experiences that resonate with your customers on a deeper level.
Clean rooms
Many data lakes and warehouses offer this service, ensuring their clients can not only store their data with them but can connect it with other partners in a secure environment and extract more information through the combined data sets versus their data on its own.
Brands and their partners recognize that they need to work together, and a clean room provides a secure environment to share their first-party data without exposing their sensitive data to their partner.
So, while we’re losing third-party cookies, brands and partners can still get value from first-party data by using a clean room to generate audience insights, segmentation strategies, personalized experiences and offers, media plans, and measurement and attribution.
Three ways data clean rooms can improve
Data clean rooms are a great way to facilitate data collaboration while ensuring sensitive data is not exposed.
Data clean rooms are not yet easy to use nor are they inexpensive. They require investment, both financially and resource allocation-wise, and you are not guaranteed to yield great match results. Let’s dive into three areas for data clean room improvement.
High cost
According to the IAB’s State of Data 2023, nearly two-thirds of data clean room users spent at least $200K on the technology in 2022. In addition, one-third of data clean room users expect the price of data clean rooms to rise in 2023. The high cost of this solution can make it inaccessible to smaller companies in the advertising space.
Resource intensive
Nearly half of the companies using data clean rooms have a team of six or more dedicated to the technology, according to the IAB’s State of Data 2023, while nearly a third of companies using data clean rooms have 11 or more employees focused on the technology. Data clean rooms are not turnkey solutions.
Inefficient matching
Even if companies are using clean rooms does not mean that they are automatically going to achieve great success. Identity fragmentation, data hygiene, and differing identifiers can suppress client match rates in clean rooms, leading to significant investment and a lackluster output.
How to get the most return on your clean room investment
The finish line for data collaboration in clean rooms is not just having a relationship with a clean room. Instead, you should incorporate an identity resolution solution in your clean room. By adding an identity solution to your clean room, you can:
- Resolve and match all your identity data, regardless of the identity data that you or your partner have, giving you a larger data foundation to analyze.
- Generate more valuable insights and information, leading to a better experience for your customers.
- Join data sets to create smarter activation and targeting strategies and produce more holistic measurement.
Experian can help you get started with identity resolution and data clean rooms
If you are investing in data clean rooms, that means you are committed to the best in data practices. Experian recommends going the extra step and that you also invest in finding an identity resolution solution. By doing this, you can see better match rates.
Experian offers this capability and has existing relationships with three clean room partners, Amazon Web Services, InfoSum, and Snowflake. In addition to collaborating in clean rooms, we offer collaboration in two other secure environments.
Contact us today to discuss how we enable identity resolution in clean rooms or to chat about our other collaboration capabilities.
Latest posts
The holiday season is right around the corner and retailers are gearing up for a massive influx of customers, both new visitors and returning loyal patrons. But, organizations need to make sure they’re prepared to handle the influx of business. Data quality should be a priority for retailers at any time of year, but even more so as the holiday season approaches. During this time, it’s important for companies to provide customers with relevant shopping offers. When consumers shop for holiday gifts for their friends and families, they have countless directions to which they can turn. In order to prevail in competition, retailers need to provide customers with relevant offers and messages. Unfortunately, many businesses fall short in this regard. According to recent research from Experian QAS, organizations feel that, on average, 25 percent of their data is inaccurate. Consumers are frantically looking to find deals on products that meet their holiday needs, but loyalty offers may not be reaching consumers. One reason for the difficulty is the messy nature of companies’ customer databases. Retail businesses are working hard to gather as much information as possible about the consumers who patronize them, but all too often, information is riddled with errors. These errors have become more prevalent in recent years due to the proliferation of mobile apps, web interfaces and social media channels. There are so many different channels that companies now interact with people through an average of 3.27 channels. This poor data quality is hurting organizational bottom lines. Experian QAS found that an average of 12 percent of departmental budgets are wasted due to inaccuracies in contact data. During the 2013 holiday season, consumer spending is expected to increase by at least 11 percent. Retailers need to be certain that they improve data quality prior to the holiday season to ensure communications reach consumers and resources are maximized in order to take advantage of the expected increase. Be sure to check out our new data quality infographic.
Experian Marketing Services’ Head of Global Research Bill Tancer appeared recently on Fox Business News to talk about new holiday shopping trends, including why both retailers and consumers are starting the 2013 season earlier this year. “We are seeing the eventual extinction of Black Friday,” says Tancer. “What we see in our data is that retail is really changing. The consumer is relentlessly searching for that deal.” In fact, as Tancer notes, the hyper-connected, digital consumer is already searching for holiday deals. Hear more from Tancer in this clip. Watch the latest video at video.foxbusiness.com Tancer and other Experian Marketing Services data experts will discuss holiday marketing trends at an upcoming webinar; register for more insights and tips.
On some level, collecting data and analyzing it to find meaningful conclusions has always been part of how marketers go about connecting with consumers. Their strategies have improved dramatically over time, though. Perhaps in a previous era, marketing executives were only able to make sweeping generalizations about large swathes of the population. But, as marketers have gathered more data on individual consumers, they’ve found ways to fine-tune their searches. They’re no longer messaging to groups in vague terms. Smart Data Collective recently examined the marketing world’s transition away from broad stereotyping toward better targeted forms of data mining. Josh Brown, a member of the marketing team at business and IT consulting company Iconic Mind, argues that this era of overgeneralization is coming to an end. We now have the capability to zoom in on the specific customer. “Big data is how successful companies are building more detailed models of consumer behavior,” Brown wrote. “Instead of relying on the traditional demographic models that marketers used when we were operating in a mass consumption environment and had nothing better, big data capitalizes on developing market trends to allow businesses to become far more specific when segmenting their customers.” Brown cited Amazon.com as an example. The online superstore is notable for its targeted recommendations of products that shoppers see every time they log on to the site – the advisements are impressive because they’re usually right up the customer’s alley. Amazon doesn’t generate these ideas by making guesses based on whether the consumer is old or young, male or female – instead, the site takes in specific information about people’s buying histories and looks for similar products. This approach is quickly becoming mainstream. It’s not hard to understand why – people don’t like being reduced to profiles of their demographic characteristics. Consumers are expecting more from the companies they do business with. Thanks to the rapidly improving technologies that companies use for data collection, marketers can be more targeted and make more intelligent interactions. However, to take advantage of these new technologies, marketers need to maintain high quality data. Without a data quality strategy, customer information will be spread out across the organization, making it difficult to make intelligent marketing offers. To learn more about improving your understanding of consumers, check-out our infographic on building a single customer view.