A new standard for programmatic advertising

The programmatic landscape is becoming increasingly complex. With the rise of AI-generated content and the growing number of Made-for-Advertising sites, maintaining control over advertising environments is more challenging than ever. 

To tackle that challenge, Draft Digital became the first agency in the Netherlands to introduce Brand Standards — an AI-driven solution from Scope3 that evaluates content based on nuance and context, rather than relying on simple keyword blocking.

Instead of using a standard brand safety blocklist, an LLM agent (large language model) determines in real time whether a webpage is suitable to place a brand alongside. It evaluates context, brand values, fraud prevention, CO₂ emissions, and attention metrics.

“We are proud that Draft Digital was the first Dutch partner to implement Brand Standards. It shows how innovation and sustainability can go hand in hand.”
— Kate Moeller, Director  of Partnerships Europe, Scope3

Strategy, implementation and guidance by Draft Digital

Draft Digital oversaw the entire process: from the strategic development of the prompt to the technical implementation and A/B testing to make the impact measurable.
Together with BridgeFund, a Brand Standards prompt was developed that precisely defined which content is ‘aligned’ and ‘unaligned’ with the brand. The following, among others, was excluded:

• content related to violence, fraud, war, or sexually explicit material
  
• economic crises or negative policies affecting vulnerable groups
  
• puzzle, gaming, and radio sites
• In addition, preference was given to high-quality content in specific languages, including Dutch, Turkish, Arabic, French, German, Italian, Spanish, and English.

The prompt was then tested on 183,000 Dutch articles. With feedback from the team, the AI agent was continuously refined— a strong example of the ‘Human in the Loop’ process that Draft Digital prefers in its AI-driven approach.

Measurable impact on brand metrics and performancee

The pilot, carried out by Draft Digital in collaboration with BridgeFund, delivered compelling results:

• +2% higher viewability (from 84% to 86%)
  
• –30% lower CPA
  
• +3% more ad exposure >10 seconds (from 36.87% to 39.61%)
  
• –50% decrease in Cost per Lead in an A/B test between two line items

The insights confirm that brand safety does not have to come at the expense of performance. By using context more intelligently, greater impact was achieved with less advertising waste.

“With this approach, we were able to define much more precisely where our ads did and did not belong. That gave us greater control and noticeably better results.”
— Mark Hoekx, Performance Marketeer, BridgeFund

The next step: moving toward agentic advertising

Following the success of the pilot, Draft Digital is now working on the structural integration of Brand Standards across its full programmatic approach. BridgeFund is one of the first partners to also apply this structurally within its campaigns.
The next step? Testing with Brand Stories — an advanced LLM solution that personalizes ads in real time based on audience segmentation.

“Agentic advertising is the future. With this technology, brands like BridgeFund can advertise at scale, internationally, and with meaningful relevance. We’re excited for what comes next.”
— Aliks Roling, Digital Marketing Consultant, Draft Digital

Let’s put things into perspective.

What you need to know

1. Gradual rollout – The change started on September 1, but it hasn’t reached all devices yet. No need for major panic.
   
2. Workarounds exist – Click IDs are being blocked in the standard way, but there are simple methods to keep them available in ad platforms. Your data is still usable.
   
3. Alternatives on the horizon – Google and others are already developing new solutions. This cat-and-mouse game has been ongoing since GDPR came into force in 2018.
   
4. Limited scope – Safari’s market share is around 18–19% according to Statcounter. Relevant, but not dominant.
   

What you can do

• Replace Autotagging with manual UTMs in Google Analytics. This was already a smart idea, since Click IDs had been disappearing in private browsers. Use macros to avoid errors.
  
• Improve your tracking setups in ad platforms, e.g. with Enhanced Conversions or server-side tracking through CAPI.
  
• Share Click IDs in alternative ways, either via Client-Side or Server-Side tracking.
  

Draft Digital’s perspective

At Draft Digital, we don’t believe in relying solely on ad platforms’ attribution. We often see duplicate conversions in reporting and notice that GA4 or backend data provide an incomplete picture.

That’s why we’ve built independent attribution models, using regression analysis and data-driven approaches. This enables our clients to:

• Understand the true value of each channel
  
• Allocate budgets more intelligently
  
• Stay resilient and flexible when Big Tech shifts the rules
  

Of course, feeding ad platforms with data remains essential for algorithm performance. That’s why we advocate for a hybrid approach: independent measurement for reliable insights, combined with smart data feedback to optimize campaigns.

This leads not only to better results, but also to more privacy-conscious marketing.

Conclusion

Apple’s update is significant, but it’s no reason to panic. With the right preparations and independent attribution, marketers can remain in control.

At Draft Digital, we focus on clarity, responsibility, and sustainable strategies. That’s how we grow together with our clients, no matter how the digital landscape shifts.

Geen paniek. Wél perspectief.

Mad Science is an international organization that offers franchise licenses in various countries. The company’s headquarters are in Canada, where, among other things, the global website is managed and maintained. In every country, the same landing page is used; however, the Dutch organization localized the global products. As a result, new pages had to be created. In the current situation, it was not possible to do this on the main domain, so the Dutch organization created additional (sub)domains. This choice led to a proliferation of different domains, which in turn created several challenges.

• Consent Management: Because multiple domains were used during the customer journey, users were shown a cookie notification multiple times. In addition, consent had to be requested separately for each domain.
  • The user enters via Google on https://nederland.madscience.org/ and then proceeds to apply via https://werken.mad-science.nl/. As a result, the user sees the notification twice.
• Tracking: Because multiple third‑party sites were linked together, it became very difficult to clearly understand both what was happening on the websites and the impact of other marketing channels. As a result, Mad Science could not demonstrate that its marketing efforts were contributing to results.
  • Example: Because different sites referred traffic to each other, GA4 frequently recorded these as “referral” clicks. As a result, it became difficult, if not impossible, to identify the original marketing channel from which the click originated.

To solve the consent issue, the websites would need to be merged. However, this was not a short-term project. For that reason, it was important to find an alternative solution to ensure the measurability of the websites. The current GA4 module was not sufficient to provide the necessary insights into Mad Science’s marketing efforts.

For that reason, the following aspects were considered:

Customized Analytics: While GA4 is built in a fairly uniform way, it was important for Mad Science to be able to introduce certain nuances in the data. For example, it should be possible to exclude certain referral clicks from a customer journey.

Attribution: In the tools where Mad Science advertises (Meta, Google Ads, TikTok), conversions were generally not attributed because tracking was not set up correctly due to the use of multiple domains. As a result, it was not possible to assign value to the different channels. In addition, it is important to be able to evaluate the effectiveness of the campaigns across different media channels.

From GA4, insufficient insights were provided, attribution was often limited to last-click, and it was not possible to implement custom solutions. For this reason, a tool that could provide these capabilities was explored.

The replacement and solution turned out to be Billy Grace. Billy Grace is a Dutch scale-up that describes itself as a “next-level” analytics platform. Through its own first-party pixel, its session stitching model, and the ability to customize the tool according to specific needs, it proved to be an excellent solution for Mad Science.

• The advantages of a first-party pixel
  • More privacy-friendly and future-proof: First-party pixels and cookies are placed directly by the website owner, making them compliant with stricter regulations. This also makes them less vulnerable to blocking by browsers such as Safari and Firefox, which actively restrict third-party cookies.
  • Better data quality: Because first-party data goes directly from the user to the website owner, it is more reliable and less prone to errors or loss during the process. This results in a more accurate representation of user behavior.
  • Data ownership: The data collected through a first-party pixel or cookie belongs exclusively to the website owner, giving them control over how this information is used and stored. This helps in building a more robust customer database.
• The benefits of session stitching:
  • Even though a first-party pixel helps in collecting data, it is still possible for these cookies to be deleted. For this reason, Billy Grace has also developed another model to connect different sessions. It does this by storing many signals from the user, such as screen brightness, Wi-Fi network, and other device-related information. By comparing thousands of these signals, a unique anonymous profile can be created. 

Image 1: Channel Grouping – This feature makes it possible to exclude UTMs from attribution or assign UTMs to Channel Groups. This allows you to retroactively improve your analytics. 

By using Billy Grace, it became possible to get a clear picture of Mad Science’s customer journey. However, Billy Grace still received a lot of referral traffic. Since this tool also offers the option to create specific exclusions, this issue could be resolved. In this case, all the different (sub)domains were excluded from each other. This made it possible to see an overview of all sites as well as where each session originated. This included the mentioned marketing channels, allowing the impact of the relevant ads to be analyzed. 

By implementing the tool, Mad Science can now: 

• Analyze user behavior across the different Mad Science domains, enabling them to improve conversion rates. 
• Analyze the impact of marketing channels, allowing it to now deploy controlled budget for recruitment campaigns, after-school projects and in-class projects. 

This way, Mad Science can continue working toward fulfilling its mission: “Inspiring children to explore the world through science and technology.” This is the first step toward better measuring the impact of online advertising efforts. The next step could be integrating offline data or developing a business case for creating one unified domain where consent management is properly implemented. By doing so, the Dutch market will set a leading example worldwide.

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.

Google’s New Philosophy

Since 2020, Google has been working to phase out third-party cookies. Its effort to build a “Privacy Sandbox” was intended to create a new ecosystem without relying on cookies. However, after various tests, Google appears to have realized that traditional cookie-based remarketing performs better than the new Privacy Sandbox model. This effect was particularly noticeable in retargeting campaigns, but even standard conversion campaigns showed that the Privacy Sandbox did not yet match the effectiveness of the current approach.

The Impact on Publishers

Additionally, we see that publishers experience a decline in revenue when third‑party cookies are removed and when they do not enable the Privacy Sandbox. Publishers using Google Ad Manager saw programmatic ad revenue drop by 34% when selling ads without third‑party cookies and without implementing the Privacy Sandbox. Even with Privacy Sandbox implemented, there was still a decline: programmatic ad revenue fell by 20% for publishers selling cookieless ads via Google Ad Manager within the Privacy Sandbox. In short, both Google and publishers generate less revenue without third‑party cookies.

What’s next for advertisers?

For advertisers, this decision means that the online advertising landscape won’t undergo the drastic changes that were once expected. Cookies have been essential for targeted advertising, enabling precise ad personalization and effective retargeting. The continued support of cookies allows advertisers to maintain their current strategies without needing immediate overhaul. However, Google already accounts for about 50% of cases where measurement is poor. Often, we see that Google (and Meta) apply modeling to link data to unmeasurable inventory, which is something advertisers should keep a close eye on.

Draft Digital’s Perspective

At Draft Digital, we believe companies cannot continue to rely solely on Google and its third‑party cookies. Consumer expectations are evolving, with growing demands for privacy and personalized experiences. Google’s dominance in the browser market doesn’t cover all devices and browsers, Apple is leading the way in phasing out third‑party cookies.

We believe every advertiser needs to move away from third‑party cookies and focus on alternative tracking methods, especially new ways to engage consumers safely and effectively. Ultimately, it should be a balanced mix of performance and truly listening to the consumer. Advertisers must be willing to invest time and effort in understanding their audience to deliver real value.

The Future of Privacy and Advertising

Although Google will continue to support cookies, it remains committed to developing the Privacy Sandbox. The introduction of new privacy controls, such as IP protection in Chrome’s incognito mode, shows that Google intends to make progress. Nevertheless, these measures fall far short of what was initially expected.

Final Thoughts:

Google’s reversal on phasing out cookies highlights the delicate balance between privacy and functionality in online advertising. At Draft Digital, we emphasize the importance of responsibility, integrity, and growing together. As the digital landscape continues to evolve, we are committed to helping our clients navigate these changes with confidence and continual improvement. Want to learn more? Feel free to contact us via this link. (link).

Additional Resources:

• Ad Age
• Ad Age

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 .

Tags play an important role in data collection and performance measurement. They enable marketers to gather information about interactions and behavior on websites. There are two ways to implement tags: server side tagging and client side tagging. Client Side Tagging was commonly used in the past. However, due to increasing data protection and the limitations of third-party cookies, it has become increasingly difficult to use. That’s why marketers have sought alternative methods to collect data and target ads. In this blog, we clarify the differences between server side and client side tagging. Want to know more about our data & analytics services? Check out this page.

What Do the Different Data Flows Look Like in Server and Client Side Tagging?

Client Side Tagging 

Starting with client side tagging: in this case, external platforms place pixels in the ad tag manager. These third-party pixels fire on parts of the website based on the tag manager’s settings. Because these pixels are registered as third parties, they are highly susceptible to ad blockers and new restrictions like ITP. 

Server Side Tagging

In server side tagging, only a few first-party pixels are placed that send data to a server. This server stores the various data and then forwards it to the respective platforms. Because this involves first-party data, restrictions from ITP and ad blockers are significantly reduced. 

What Are the Privacy Differences?

Privacy is a critical concern in today’s digital world. Server side tagging means that tags are activated on the website’s server before the page is sent to the user. This means all data collection and processing happens server-side, reducing the risk of exposing personal information to external parties. It provides greater control over which data is collected and with whom it is shared, thereby enhancing privacy protection.

On the other hand, client side tagging uses tags that run directly in the user’s browser. This can lead to greater exposure of personal data, as external parties may gain access to this information. This raises concerns about user privacy and the ethical implications of data collection without explicit consent.

Why Does This Tagging Method Improve Customer Experience?

Besides privacy, the effectiveness of tagging methods also plays a role. Server side tagging can offer better performance in terms of speed and page load times. Since tags are executed server-side, the load on the user’s browser is reduced, resulting in faster page loads and an improved user experience. This is especially important for mobile pages, which often experience longer load times.

Why Is Server Side Tracking Particularly Relevant Now? 

Met de opkomst van privacyregelgeving hebben webbrowsers en platformen zoals Google en Apple maatregelen genomen om de trackingmogelijkheden van 3rd party cookies te beperken. Binnen Apple worden 3rd parties al veelal geblokkeerd, terwijl Google hier in 2024 mee zal gaan experimenteren. Dit heeft directe gevolgen voor client side tagging aangezien het afhankelijk is van het plaatsen en uitlezen van cookies in de browser van de gebruiker. Zonder trackinggegevens worden de inzichten en metingen die worden verkregen via client side tagging minder nauwkeurig, waardoor marketeers uitdagingen ondervinden bij het effectief targeten en personaliseren van advertenties.

With the rise of privacy regulations, web browsers and platforms like Google and Apple have taken measures to limit the tracking capabilities of third-party cookies. Apple already blocks most third-party cookies, and Google plans to begin experimenting with this in 2024. This has direct implications for client side tagging, as it relies on placing and reading cookies in the user’s browser. Without tracking data, the insights and measurements obtained through client side tagging become less accurate, causing marketers to face challenges in effectively targeting and personalizing ads.

How Can Server Side Tagging Help Improve Your Personalization Strategy?

With server side tagging, marketers have the ability to handle collected data more advancedly. Because data collection and processing take place on the server, marketers gain greater flexibility and opportunities to perform complex analyses.

One advantage of server side tagging is that marketers can combine data from multiple sources. By integrating information from different sources such as CRM systems, customer databases, and other internal data, marketers can gain a more complete view of their target audience. This combined data allows them to obtain deeper insights and better understand who their customers are, what they want, and how they behave. For retailers, this could mean adding offline data to their marketing campaigns. 

Additionally, server side tagging enables marketers to implement advanced targeting and personalization strategies. By collecting and analyzing detailed data, marketers can better segment their audiences and deliver more relevant ads and offers. They can use demographic information, interests, browsing history, and behavioral data to develop targeted campaigns tailored to the individual needs and preferences of consumers.

With server side tagging, marketers can also gain more accurate insights into the effectiveness of their campaigns. By analyzing data at the server level, they obtain deeper understanding of user behavior and the impact of their ads. For example, they can measure which ads drive conversions, which channels are most effective, and how users interact with various touchpoints in their customer journey. These insights enable marketers to optimize campaigns, allocate marketing budgets more effectively, and achieve better results.

What Are Concrete Examples of Using Server Side Tagging to Improve Media Performance?

• By sending more data to the various ad platforms, there is greater room for optimization and less reliance on modeling. As a result, fewer assumptions are made. 
• In retail, it is possible to send offline store sales data back through the server to various tools like Google Campaign Manager and Meta. This enables optimization of omnichannel performance.
• Many automotive brands optimize based on test drive requests. By using server side tagging to send back whether the requested test drive 1) was attended and 2) resulted in a conversion, platform algorithms can be better optimized.
• Server Side Tagging enables the linking of data to specific individuals over an extended period. This allows advertisers to deliver relevant offers for a longer duration, which is particularly valuable for advertisers with longer customer journeys, such as in B2B or travel sectors. 

Conclusion

In short: Server side tagging is a strong alternative to client side tagging and offers the following benefits:

• Improved data privacy and security: With server side tagging, tags are implemented directly on the server, ensuring data collection and processing take place in a trusted and secure environment. This reduces the exposure of sensitive data to external parties and strengthens data privacy for users.

• Reduced dependency on browsers: Client side tagging relies on cookies stored in the user’s browser. However, browsers are becoming increasingly strict about blocking third-party cookies. Server side tagging bypasses this issue by not depending on the user’s browser, allowing marketers to collect more reliable and consistent data.

• Betere gegevenskwaliteit en consistentie: Omdat server side tagging plaatsvindt op de server, kunnen marketeers meer controle uitoefenen over de gegevensverzameling en -verwerking. Dit resulteert in een betere gegevenskwaliteit en consistentie, omdat de verzamelde gegevens minder gevoelig zijn voor fouten of beperkingen van individuele browsers.

• Mogelijkheid voor geavanceerde dataverwerking: Doordat server side tagging plaatsvindt op de server, hebben marketeers toegang tot geavanceerde dataverwerkingstechnieken. Ze kunnen complexere analyses uitvoeren, data combineren met andere bronnen en geavanceerde targeting- en personalisatiestrategieën implementeren. Dit stelt marketeers in staat om nauwkeurigere inzichten te verkrijgen en gepersonaliseerde advertenties op maat te leveren.

If you want to learn more about Server Side Tagging, how it can add value to your organization, or how it can be implemented, please get in touch with us .

Data Management Platform (DMP)

A Data Management Platform is designed to collect, store, and analyze data from various sources. Its main purpose is to enable detailed audience segmentation and targeting for advertising campaigns. DMPs primarily focus on anonymous and aggregated data. They gather and process information such as demographic data, browsing behavior, and online interactions to identify audiences and optimize ads based on those segments. DMPs are commonly used in programmatic advertising to target specific audiences across digital channels.

Key Features of a DMP:

• Collects and analyzes anonymous data from multiple sources
• Segments audiences based on demographics, behavior, and interests
• Optimizes ad campaigns by targeting specific audience segments
• Focused on online ads and programmatic advertising
• Limited focus on individual customer profiles and customer journeys

Customer Data Platform (CDP)

A Customer Data Platform focuses on collecting, unifying, and managing customer data from multiple channels and sources. The goal of a CDP is to create a 360-degree view of individual customers, integrating both online and offline data. A CDP emphasizes understanding individual customer behavior, identifying patterns, and delivering personalized experiences throughout the entire customer journey. It enables marketers to define customer segments, automate marketing campaigns, and deliver personalized communication based on individual profiles.

Key Features of a CDP:

• Collects and unifies customer data from various channels and sources
• Creates individual customer profiles for a 360-degree customer view
• Provides insights into customer behavior and patterns for improved segmentation and personalization
• Supports personalized marketing campaigns and cross-channel communication
• Focuses on the full customer journey and customer lifetime value

Although it is true that DMPs have played an important role in the past, there are several reasons why CDPs are now more relevant and valuable for marketers. 

Why CDPs Are More Relevant Today

DMPs mainly collect and analyze aggregated anonymous data to identify audience segments for campaigns, making it difficult to provide personalized experiences. CDPs create detailed, integrated customer profiles, allowing marketers to gain deep insights into individuals and create hyper-personalized campaigns to increase engagement.

Digital Marketing

DMPs focus on online ads and programmatic buying, collecting and segmenting data for optimizing campaigns across digital channels. CDPs integrate data from online and offline channels, providing a full picture of customer interactions and preferences, enabling seamless omnichannel experiences.

Tracking and Privacy

With increasing privacy regulations and the decline of third-party cookies, DMPs relying on client-side tagging face challenges. CDPs are built with privacy compliance in mind, mainly using first-party data, allowing marketers to manage data in line with legal requirements, offering transparency and control over data usage.

Getting Started with a CDP

Popular CDPs include Segment, Tealium AudienceStream, BlueConic or Salesforce CDP. These tools can be costly, so considering alternatives like Google Cloud may be worthwhile. While Google Cloud is not an out-of-the-box CDP, it offers scalable, flexible tools to build a custom solution:

Scalability & Flexibility

Use services like Google Cloud Storage, BigQuery, and Dataflow to collect, store, process, and analyze customer data at scale, tailored to your needs and growth potential.

Pay-as-you-go Model

You pay only for the resources and services you use, which is cost-effective for smaller advertisers and scalable for growth.

Data Integration

Integrate multiple data sources such as websites, mobile apps, and external databases using tools like Google Cloud Pub/Sub and Data Fusion for a unified customer view.

Advanced Analytics & Machine Learning

Use Google Cloud AI Platform and AutoML to derive valuable insights, identify patterns, segment audiences, and build predictive models for personalized marketing.

Media Integration

Connect your CDP with paid ad platforms like DV360, Google Ads, Meta, and Snapchat to use customer data for targeted, effective advertising campaigns.

Conclusion

Google Cloud is a cost-efficient foundation to build a customized CDP that empowers advertisers to collect, manage, analyze customer data, and execute personalized marketing campaigns while benefiting from scalability, flexibility, and affordability.

To fully leverage your existing customer base, implementing a CDP is highly recommended. It’s essential to choose the right type of CDP based on your needs. At Draft, we have extensive experience with CDPs and are happy to advise you (link) or learn more here..

The main goal of lead scoring is to prioritize leads that are most likely to convert into customers. By assigning scores, marketers can identify high-quality leads and focus their resources more efficiently and effectively.

Industries Where Lead Scoring Is Especially Useful

Lead scoring is commonly applied in industries with longer customer journeys such as travel, automotive, or B2B. With extended sales cycles, measuring advertising impact becomes challenging. For example, when buying a car, the process may include:

• Needing a new car
• Being inspired by a social media video about a car
• Searching for the brand on Google and using a car configurator
• Calling the local dealer to book a test drive
• Purchasing the car in person at the dealership

Since these steps happen across multiple channels, predicting and optimizing the impact of each interaction is difficult. Lead scoring helps assign value to each touchpoint, improving marketing efficiency.

Limited Conversion Data

Often, brands in these verticals have insufficient conversion data for platforms like Google and Meta to optimize properly. Algorithms usually require over 500 conversions per month to perform well. For smaller companies or those with expensive conversions, this threshold may be lower.

The difference between lead scoring and standard attribution is clear: traditional attribution looks at single actions like clicks, leads, or purchases. Lead scoring uses historical data to assign value to different actions combined, calculating an expected customer value. This enables better campaign optimization aligned with business goals. 

Step-by-Step Guide to Implement Lead Scoring

Hieronder vind je een aantal stappen om te beginnen met lead scoring: 

• Identify Funnel Steps: Define the key steps in your customer funnel that contribute to value creation.
  
• Analyze Data: Assess the importance of each funnel stage based on available data; the last step may not always have the highest value.
  
• Assign Values: Attribute scores to funnel steps to clearly communicate their relative importance. 

External Platforms

Once you understand the value of each step, it’s vital to share this data with platforms like Google, Meta, and Snapchat for optimization: 

• Set triggers to update lead scores when users perform key actions, e.g., form submissions or purchases.

• Implement custom tracking codes on your website’s relevant pages to monitor user behavior and update scores accordingly here.

• Integrate score data with tools such as Google Ads Conversion Tracking to use scores in bidding strategies and reporting.

• Optimize campaigns by targeting or bidding higher for leads with better scores.

DV360

Most platforms require technical expertise to enable lead scoring, but DV360 simplifies this through its interface. You can assign values to floodlight activities and let the algorithm optimize based on these scores. Running experiments helps determine the added value of lead scoring.

Conclusion

Although lead scoring can significantly improve marketing ROI, it is still underused by many companies, resulting in missed opportunities. If you want assistance implementing lead scoring, feel free to get in touch with us , we’re happy to help!

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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!