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Even terug: waarom doet Google dit eigenlijk?

Consent in de Google-stack is jarenlang een rommeltje geweest. Of Google Ads je data mocht gebruiken, hing tegelijk af van een paar knoppen die deels verstopt zaten. Consent Mode aan de Ads-kant, de Google Signals-toggle in Analytics en personalisatie-instellingen op verschillende niveaus in GA.

Google haalt die wirwar er nu uit. Het idee erachter is simpel: de instellingen horen thuis op de plek waar de data daadwerkelijk gebruikt wordt, niet waar die wordt verzameld. Minder dubbele knoppen betekent minder implementatiefouten en dat is op zich goed nieuws.

Wat verandert er precies?

Voor properties die je aan elkaar koppelt, ziet het er straks zo uit:

• Je Google Ads-instellingen bepalen wat er met Ads-data gebeurt, ook met de data die vanuit Analytics doorstroomt;
• Je Google Analytics-instellingen gaan alleen nog over wat er binnen Analytics zelf gebeurt voor behavioral reporting.

Concreet: tot nu toe werd het verzamelen van Google Ads-cookies en -ID’s via de GA-tag door twee dingen tegelijk gestuurd, namelijk de Google Signals-instelling in Analytics én je Consent Mode (Ads)-instellingen. Vanaf 15 juni is Consent Mode binnen Google Ads de enige knop die dit nog aanstuurt. De keuze die je gebruiker maakt via Ads Consent Mode bepaalt dan of en hoe die data wordt verzameld en gebruikt.

De Google Signals-toggle in GA verdwijnt niet, maar krijgt een veel kleinere rol. Die gaat straks alleen nog over het koppelen van GA-data aan ingelogde gebruikers voor je behavioral reporting, zoals demografische rapportages.

Google rolt dit gefaseerd uit en de eerste wijziging rond Google Signals gaat in op 15 juni. De vergelijkbare versimpeling voor ads personalization en de manier waarop versleutelde IP-adressen worden behandeld, volgt later dit jaar.

Wat betekent dit voor je meting?

Vanaf 15 juni hangt het bijna helemaal van je Consent Mode (Ads)-signalen af of Google Ads identifiers mag gebruiken, met ad_storage als signaal dat de doorslag geeft.

• Geeft een gebruiker toestemming (ad_storage = granted), dan mag Google Ads alle beschikbare advertentiesignalen gebruiken. Waar dat kan, koppelt Google het gedrag ook aan een ingelogd Google-account. Hierdoor wordt cross-device tracking en remarketing mogelijk gemaakt voor ingelogde Google gebruikers.
• Weigert een gebruiker (ad_storage = denied), dan valt Google terug op minder duurzame signalen, zoals de gclid in de URL.

De wijziging kan in de praktijk tot verrassingen leiden. Organisaties die Google Signals eerder hebben uitgeschakeld om de gegevensuitwisseling te beperken, kunnen na 15 juni alsnog meer advertentiegerelateerde data beschikbaar zien komen wanneer gebruikers toestemming geven. Een instelling die voorheen als extra rem fungeerde, verliest daarbij grotendeels zijn betekenis. Juist daarom is het verstandig om de gevolgen vooraf in kaart te brengen, in plaats van ze achteraf terug te zien in rapportages.

Waarom dit meer is dan een instellingenkwestie

Smart Bidding, attributie en je remarketing-audiences draaien allemaal op conversie- en gebruikersdata. En die data hangt straks bijna volledig aan je consent-signalen. Het werkt rechtstreeks door in je meetdata, en dus in je biedstrategie, je CPA en je ROAS-sturing.

We sturen onze campagnes steeds meer aan op basis van signalen en automatisering. Daarmee is de kwaliteit van je consent-implementatie eigenlijk de kwaliteit van je optimalisatie geworden. Eén bron van waarheid werkt prima, zolang die bron klopt. Klopt hij niet, dan stuur je door op incomplete data zonder dat je het doorhebt.

Wat moet je nu doen?

Ga er niet vanuit dat je setup klopt, maar controleer het. Concreet kun je deze stappen zetten:

1. Controleer je Consent Mode-implementatie vóór 15 juni. Vuren je update-calls correct, en weerspiegelt ad_storage echt de keuze die de gebruiker maakt?
2. Loop Consent Mode v2 helemaal na: ad_storage, ad_user_data, ad_personalization en analytics_storage. Die zijn in de EER al verplicht sinds maart 2024 als je personalisatie- en remarketingfeatures wilt blijven gebruiken, en ze worden vanaf juni nog bepalender voor je Ads-data.
3. Geef accounts waar Google Signals uitstaat extra aandacht. Juist daar kan het gedrag verschuiven.
4. Test, neem niets aan. Gebruik Tag Assistant en DebugView en controleer de Consent Mode-rapportage voordat de wijziging live gaat.
5. Meet de impact na 15 juni. Vergelijk je conversie- en sessievolumes van vóór en ná de wijziging, zodat je afwijkingen meteen ziet in plaats van pas weken later.
6. Stem af met je CMP-leverancier en je privacy- of legal-collega’s. De techniek en de juridische onderbouwing moeten op elkaar aansluiten.

Tot slot

Google maakt consent dus tegelijk eenvoudiger en strenger. De wirwar aan half verborgen knoppen verdwijnt, en daar wordt iedereen beter van. Maar met die ene centrale knop is er ook geen vangnet meer.

FAQ: Legal en nieuwe wijzigingen rond Google Signals

De DDMA Legal Helpdesk krijgt veel vragen over (de nieuwe wijzigingen rondom) Google Signals en Consent Mode V2. Hieronder gaan we in op de volgende vragen:

• Is Consent Mode hetzelfde als geldige toestemming?
• Kan ik Advanced Consent Mode inzetten zonder toestemming?
• Wat betekenen de wijzigingen vanaf 15 juni voor privacy-compliance? 
• Moet ik mijn cookiebanner en privacyverklaring aanpassen door de nieuwe wijzigingen?

Ryan Peerkhan

Directeur en performance marketeer bij Ascend Online

Isa Nieuwstad

Legal Counsel

It usually sounds something like: we’ve invested in the data, we’ve built the infrastructure, we’re running personalization, but honestly… we’re not seeing the impact we expected. Why is that?

What strikes me about this is that it’s never said with embarrassment. It comes from organizations that have done serious work. Teams that have spent years getting the foundations right. And they’re not wrong to feel frustrated, because from the outside, their setup looks solid.

So what’s going on?

The data isn’t the problem

Here’s what I’ve come to believe, after seeing this pattern repeat across a lot of different organizations: the data is rarely the problem.

Most companies at this stage have already solved the hard parts. They know what their customers are doing. They have the history, the behavior, the signals. The dashboards exist. The pipelines run. And if you ask the team what’s happening with a given customer segment, they can usually tell you something meaningful within minutes.

The gap shows up at the next step.

Once you have the insight, you need to do something with it. And that part — translating what you know into action, consistently, at the right moment — is where things break down.

What I typically see is this: insights get discussed. Use cases get identified. People agree something should happen. But then execution falls back into whatever the existing rhythm was. Campaigns go out according to a calendar. Channels operate independently of each other. And the action, when it does finally happen, is often too late to matter.

It’s not that organizations don’t care. It’s that there’s no clear, shared way to take a signal — a customer doing something meaningful right now — and turn it into a decision that happens every time, reliably, without needing someone to catch it and push it through manually. That missing piece is small on paper. In practice, it’s the difference between a data capability that produces reports and one that produces results.

The thing about timing

You only really feel the full weight of this problem in situations where timing is everything.

Think about a marketplace. Someone is browsing a category. They start watching an item. They place a low bid. Then another. The auction has six hours left and they’re checking back more frequently. Every one of those moments is a signal that something is building — interest turning into intent, intent building toward a decision.

If you can catch that and respond to it at the right moment, in the right way, you have a real chance of turning that interest into action. Wait too long and the moment’s gone. The auction closes, they move on, and whatever they were feeling has already faded. You can analyze it afterwards, but that’s all you can do with it at that point.

This is not a theoretical problem. We ran a campaign for Catawiki — an online auction platform — where the specific challenge was exactly this. Not to run better campaigns in the traditional sense, but to actually respond to what users were doing as they were doing it. To treat browsing patterns, early bids, and accelerating activity as live signals, not as data to be reviewed in next week’s meeting.

The results were striking. In a single week:

• 75,000 app installs — an all-time record for the platform
• +14% more bidders than forecast
• +8% more bidding activity during the campaign
• ROI of +6,000%, well above what was expected going in

Those numbers are worth pausing on. Not because they’re impossible, but because of what produced them. The organisation didn’t suddenly have better data than before. It had the same customers, the same behaviour, the same underlying intent. What changed was the ability to act on that behaviour while it was still relevant.

What this tells us about the real gap

Once you see this dynamic clearly, you start to notice it everywhere.

The organizations getting the best results from their customer data aren’t necessarily the ones with the most of it, or the most sophisticated models behind it. They’re the ones that have figured out how to act on what they know — consistently, at scale, without it depending on someone catching a signal and manually pushing it through.

That might sound obvious. But it’s actually quite rare. Because getting there requires more than good technology. It requires agreement, across teams and functions, on how decisions get made. What triggers what. Who’s responsible for what outcome. What the guardrails are. How you know if something is working or not.

Without that clarity, even great data ends up producing the same thing: interesting reports that inform the next planning meeting, rather than actions that happen in real time.

And this challenge looks different depending on where an organization is in its journey. For some, it starts with trust — data quality isn’t quite there yet, or different teams are working from different numbers, and nothing gets decided with real confidence as a result. For others, the foundation is solid, but execution is inconsistent. Decisions get made at the top but slow down or get reinterpreted by the time they actually reach a customer. And for more advanced organizations, it becomes a deeper question: is the operating model actually built around responding to customers, or just around sending to them?

The common thread, in every case, is not a lack of knowledge. It’s a gap between knowing and doing.

Something in the industry is quietly changing

It’s worth acknowledging that this problem has, for a long time, had a structural cause that wasn’t entirely the organization’s fault.

For years, the technical landscape made it genuinely difficult for signals and actions to live close together. Customer data sat in one system. Decisions happened in another. Execution was handled somewhere else again. Every handoff added time. And time — even a few hours — reduces relevance in ways that are hard to measure but very real.

That’s slowly starting to change. The systems organizations use to understand customer behavior are getting closer to the systems that act on it. The gap between recognizing a signal and responding to it is narrowing, technically at least.

What this means in practice is that the technical excuse is running out. If a system can detect that a customer is in the middle of making a decision, and can respond to that in real time, then the question becomes: why isn’t it?

Often the answer is that the organization hasn’t yet made the decisions it needs to make. Not about the technology itself, but about how it wants to operate. What actions should happen automatically. What still needs a human to approve. What the boundaries are and who sets them.

The technology has caught up. The hard work now is the organizational thinking.

The question worth asking yourself

If you’re leading marketing, CRM, or data at an organization, there’s one question I’d encourage you to sit with.

Not do we have enough data? Most of the time, the honest answer is yes.
Not do we have the right tools? Usually that’s not the core issue either.

The better question is this: when a customer does something that matters, what happens next and does it happen every time?

If the honest answer is it depends on who’s watching — or we’d bring it up in the next sprint — or it shows up in the weekly report — that’s the gap. Not a data gap. Not a technology gap. An execution gap.

And the encouraging thing is that it’s solvable. It doesn’t require starting over. It requires getting specific about how decisions get made, who owns them, and what consistent looks like in practice. That work is less glamorous than buying a new tool or hiring more analysts. But it’s the work that actually changes outcomes.

The organizations that compound their advantage over time aren’t doing something magical. They’re just acting on what they know, reliably, while it still counts.

So what is actually holding you back: technology, governance, or decision ownership?

This is an article by EMAS 2026 Gold Sponsor Dignify.

Just Greve

Country Director NL at Dignify

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