Lot of Happiness believes in making the world a better place for everyone. Charities, both big and small, play a pivotal role, and they all deserve the support that Lot of Happiness provides. That’s why Lot of Happiness is the lottery where your voice counts, your impact is significant, and you have the chance to win great prizes, like a million euros in the bank or a new electric cargo bike at your door.

The Challenge for Lot of Happiness

Lot of Happiness initially relied on traditional client‑side tagging to measure user behavior. However, tighter restrictions introduced by browsers and platforms, like Apple’s Intelligent Tracking Prevention (ITP), started making this method less reliable. The resulting data gaps impacted both data quality and the optimization of marketing campaigns.

Although Lot of Happiness had implemented both client‑side and server‑side tracking across most platforms, it hadn’t yet been enabled for their AI tool, Billy Grace, an automation platform that allocates budgets across Google Ads, Bing, Meta, Snap, and TikTok. Since Billy Grace depends on complete and accurate data, improving its measurement infrastructure became crucial for achieving precise optimizations. 

The Solution by Draft Digital

To future‑proof their measurement approach, Lot of Happiness became the first advertiser within Billy Grace to pilot Server‑Side Tagging (SST). This technique allows data to be collected via a dedicated server environment, making it more robust and resilient to ad blockers and browser restrictions.

Rather than replacing client‑side tagging, a hybrid model was implemented, allowing both methods to operate in parallel. This approach provides a more complete picture of customer behavior by leveraging the strengths of both methods, making direct performance comparisons possible. 

Results for Lot of Happiness

The results confirmed that the hybrid approach significantly improves data reliability. Interestingly, client‑side tagging captured more unique events, yielding slightly higher error margins: 

→ 2.04% more server‑side hits were measured compared to client‑side tagging, indicating that server‑side is more likely to register an event. 

We also analyzed the number of unique events:

→ 3.26% of web events could not be matched with server‑side tagging.

→ 1.17% of server‑side events could not be matched with client‑side data.

This shows that client‑side tracking captures more unique events, making it more accurate for Billy Grace. 

“We often hear in the market that server‑side tagging is the ultimate replacement for client‑side tagging. However, these tests show that both methods actually complement each other very well. It’s vital to validate different tracking methods against each other, but, more importantly, to combine them. This is how you get as close as possible to the truth.”

— Aliks Röling, Digital Marketing Consultant at Draft Digital

The improved tracking allows Billy Grace to operate with greater precision, leading to a more effective allocation of marketing budgets across channels such as Google, Meta, Bing, and Snap.

“Thanks to the combination of server‑side and client‑side tagging, along with Billy Grace’s intelligent optimization, we have a much sharper understanding of the value of each marketing channel. We can clearly see where the returns are and make adjustments more quickly.”

– Leo Nijs, Online Marketer at Lot of Happiness

Next steps for Lot of Happiness

The implementation of server‑side tagging has been an important milestone, but the optimization process is still ongoing. In the next phase, the HubSpot CRM will be connected to Billy Grace, allowing backend data, such as customer value, lead status, and conversion cycles, to be automatically fed back into the AI model.

This creates a fully integrated ecosystem where data, automation, and marketing strategy work together, allowing Lot of Happiness to unlock its full growth potential.

Data has become a valuable asset for businesses. Collecting and analyzing data can provide valuable insights that lead to improved performance and decision-making. This is especially true in digital marketing and programmatic advertising, where data has been a crucial component of success. Historically, third-party cookies were used to track users across the internet, allowing advertisers to serve the right content at the right time. However, third-party cookies are set to disappear permanently in the near future, and their reliability is already far from optimal. Many browsers have already begun to largely block them.

For this reason, companies are seeking solutions to continue personalized advertising. In many cases, server-side tagging or customer match are used. However, these solutions are typically accessible to large tech companies like Google and Meta, making collaboration between Dutch publishers such as DPG Media or Marktplaats more challenging. To address this, various parties have explored ways to work together with these publishers, and the solution appears to be Clean Rooms.

What Are Clean Rooms?

Data bunkers are an emerging technology that enables secure data sharing and aggregation without exchanging the underlying data itself. Through Clean Rooms, companies can gain valuable insights and make data-driven decisions without compromising user privacy. Here are some ways data bunkers operate. 

Data Aggregation in Clean Rooms

Data bunkers enable the integration of data from multiple sources without sharing the underlying data itself. Instead, aggregated and anonymized statistics and patterns are exchanged, allowing companies to gain valuable insights into trends and behaviors without exposing individual information. Moreover, only Personally Identifiable Information (PII) is used, eliminating dependence on third‑ or first‑party cookies. 

Cross-Reference Analysis via a Clean Room

With data bunkers, companies can compare and analyze data across different datasets without sharing the underlying data itself. This enables businesses to gain insights into customer behavior, market trends, and other valuable information without exposing sensitive data to risk. For example, you could compare your own customer base with that of a partner, allowing you to strategically target potential new customers. 

Data Security in Clean Rooms

Clean Rooms use advanced security protocols to ensure the confidentiality and integrity of data. This includes encryption, strict access controls, and monitoring of data access to guarantee that only authorized users can access the information.

Data bunkers like Infosum (www.infosum.com) & EDM (www.edm.nl) play an important role in finding a balance between privacy and performance. Data bunkers provide a secure environment to store and process data, while enabling companies to gain valuable insights without having to share the underlying data itself. By using these technologies, businesses can safeguard privacy while still benefiting from data analysis and insights. 

So, there is a way to securely share data between different parties, but how does this work in practice, and how could it be used? 

Example of How to Use a Clean Room

In this example, we’re working with the company Draft, which wants to purchase ads on NU.nl. 

• Both Draft and NU.nl upload personal data into their own clean rooms.
  • Draft only uploads the data they want to target into the clean room. For example, people who have previously made a purchase.
  • NU.nl uploads all their data into the clean room to increase the chances of finding matches. Additionally, NU.nl must provide an ID that is used within their advertising platforms.
• It’s important to regularly coordinate how the data is structured. For example, if matching is based on postal codes, it’s crucial that the data is formatted consistently.
• Once the data is in the data bunkers, it is matched and sent back, including IDs, to NU.nl’s advertising platform.
• Draft can now purchase inventory based on these IDs through NU.nl and re-engage the target audience.

We can conclude that data bunkers offer a solution to use your own data on external platforms, but what exactly can you do with them?

How Can You Use Clean Rooms to Improve Digital Performance?

Targeting

In addition to being able to find users again, it can also be used to exclude certain customers. Look-alike audiences can also be leveraged. Since the external platform can see what these users do within their own environment, it can search for people who resemble the initial group. This allows you to deliver more relevant ads. 

Data Enrichment

By matching data with external platforms, it’s also possible to learn from this data. If you share data with Marktplaats, for example, you can discover which categories your target audience frequently browses or uses. If your audience is interested in interior design, this insight can serve as a valuable hook for other marketing efforts.

Channels

In principle, data bunkers can be utilized across all channels where CRM data is matched. Currently, this is mostly applied in display and video advertising in collaboration with partners such as DPG Media, Mediahuis, and Marktplaats. However, it should also be feasible on more traditional channels like TV. TV platforms use set-top boxes that store personal data. Although addressable advertising on TV is not yet common practice in the Netherlands, it is expected to become possible in the near future: https://www.adformatie.nl/media/adverteerders-kunnen-tv-commercials-bij-talpa-en-rtl-richten

From then on, more and more opportunities for personalization on TV will emerge. In Belgium, this is already possible. 

Retail Media and Clean Rooms

Naast reguliere advertising zien we ook een trend op het gebied van retail media. Grote partijen zoals Albert Heijn, Jumbo, MediaMarkt en Amazon zijn hiermee aan het experimenteren in Nederland. Doordat deze retailers over zeer veel first party data beschikken kan een databunker ook de connectie zijn tussen de data van retailpartner en adverteerder. Hierdoor zijn ze niet afhankelijk van reguliere customer match oplossing.

Conclusion

Alongside traditional advertising, we’re also seeing a growing trend in retail media. Major players like Albert Heijn, Jumbo, MediaMarkt, and Amazon are experimenting with this in the Netherlands. Since these retailers possess extensive first-party data, a data bunker can serve as the bridge between the retail partner’s and the advertiser’s data. This means they aren’t reliant on standard customer match solutions.

Thanks to data bunkers, companies can collaborate with external partners, such as publishers, to deliver personalized ads to the right audience without directly sharing first-party data. This opens new opportunities for advertising across various channels, including digital platforms and, in the future, even traditional channels like television.

Using data bunkers requires close coordination between the involved parties, such as the company sharing the data and the platform receiving it. Ensuring proper data structuring and agreement on the identifiers used is crucial to guarantee successful matching.

Contact

All in all, data bunkers offer a promising solution to balance privacy with personalized advertising. Through secure data sharing and advanced analytics, companies can gain valuable insights and deliver targeted ads while protecting individual user privacy. It’s a positive step forward for the future of data-driven marketing. If you’d like to receive more information about data bunkers, please contact us at get in touch with us .

Mitch Voskuilen, CEO and founder of Billy Grace, is excited about the new partnership. “With Billy Grace, we provide marketing teams access to AI, enabling them to better automate and optimize the effectiveness of online marketing within and across channels. This is a perfect fit for a brand like Pricewise, which, with its digital comparison platform, aims to serve more customers in the marketing funnel and convert them into clients. We look forward to offering the comparison site data-driven insights and supporting their online growth.” 

Merel Dekker, Head of Marketing at Pricewise: “At Pricewise, we had various tools for mapping attribution, but they didn’t provide the clarity we needed for optimization. With the AI tool Billy Grace, all campaigns come together, allowing us to measure effectiveness and create more synergy. Additionally, with the help of Billy Grace, we can better plan and optimize our campaigns to further grow Pricewise.” 

Winning team: people and machine

Lars Postmus, one of the founders of Draft Digital, says the following about the collaboration with Billy Grace: “We are always looking for innovative solutions to existing problems. AI is an indispensable part of that, as it provides us with independent arguments for decisions about marketing efforts. The smart tools from Billy Grace help us make better choices. We believe in the combination of smart software and experienced colleagues. The winning team is the combination of people and machine.”

About Billy Grace 

The Billy Grace platform enables marketers to collaborate with self-learning algorithms (AI). Through the platform, marketers can better track, attribute, analyze, automate, and optimize the effectiveness of online marketing within and across channels. This way, the startup empowers marketers to free up more time for tasks where people truly make a difference. 

About Pricewise

Pricewise is the independent comparison site for energy, insurance, internet & TV, SIM-only plans, and loans. Pricewise helps consumers make choices and save on their fixed costs such as energy, insurance, internet, TV, mobile, and borrowing. Pricewise aims to assist consumers by making complex contracts simple, clear, and understandable. With just a few clicks, you can easily compare and save tens or even hundreds of euros. Pricewise has experts who lay everything out clearly for consumers, highlight the fine print, and expose hidden pitfalls.

Would you like to know what we think about this collaboration? 

Do you want to create a win-win situation as an advertiser, where your brand grows and performance improves? Then get in touch with us!