ChatGPT Ads in Europe: My Early Hands-On Assessment as a Performance Marketer

 

Where Does It Fit in the Media Mix and Customer Journey? | How Do You Actually Run and Measure Campaigns? | What Are the Opportunities and Limitations?

 

ChatGPT Ads has arrived in Europe, and for me the interesting question is not simply whether advertisers now have another place to buy impressions or clicks.

The bigger question is what role this channel can realistically play in a media plan and across the customer journey.

People can use ChatGPT while discovering something, researching a problem, narrowing their requirements, comparing alternatives and moving closer to a decision. That makes the advertising environment particularly interesting because relevance can develop through the context of a conversation rather than through a single isolated action.

I have started exploring ChatGPT Ads hands-on in Europe, looking at it from two perspectives at the same time:

Media Strategy → Customer Journey → Business Model → Campaign Execution → Measurement → Optimization

And then, at a much more operational level:

Campaign → Ad Group → Ad → Bid → Context → Audience → Creative → Conversion Event → Reporting

A note before I start

ChatGPT Ads and Ads Manager are still at an early stage and evolving quickly.

This is therefore an early hands-on assessment, not a performance case study or industry benchmark. Where I use hypothetical advertisers, campaigns or customer journeys, they are examples designed to explain how I would think about the platform in a real media plan.

1. Where Could ChatGPT Ads Fit in the Media Mix?

I would not start by asking which existing channel should lose budget to fund ChatGPT Ads.

I would start with a different question:

What role could ChatGPT play in this customer's decision journey?

Take a hypothetical travel advertiser.

A conversation could develop like this:

“Where should I go in Portugal?”

→ “Which areas are good for couples?”

→ “Somewhere near the beach but not extremely touristy.”

→ “What could I do around Lagos for five days?”

→ “Which hotels would fit a €1,500 budget?”

The commercial relevance of that conversation changes as the user progresses.

At the beginning, it is largely discovery.

Later, preferences and constraints appear.

Eventually, the user may be evaluating actual options.

That makes me think about ChatGPT Ads across:

Discovery → Consideration → Evaluation → Action

For media planning, I would therefore initially treat ChatGPT Ads as an incremental channel that needs to establish its own role and prove its business contribution, rather than automatically assigning it budget because it is new.

2. The Biggest Strategic Limitation: Not Every ChatGPT User Is Reachable

This is one of the first things I would put into a media plan.

Advertisers cannot currently reach the entire ChatGPT user base. Ads are limited to ad-supported experiences, while paid ad-free subscribers are outside the available advertising audience.

That distinction matters.

Take a hypothetical consumer electronics retailer.

Someone asks:

“I need a laptop for video editing and travelling. I use Adobe applications heavily and have a budget of €1,500 to €2,000. What should I consider?”

That could represent commercially valuable intent.

But if that conversation belongs to a user on an ad-free paid plan, the advertiser cannot participate through ChatGPT Ads.

I would not assume that paid subscribers are automatically wealthier, more valuable or more likely to purchase. There is no need to make that assumption.

The strategic limitation is straightforward:

Potentially relevant conversations exist within a part of the ChatGPT audience that advertisers cannot currently reach.

For me, that needs to be considered when evaluating the addressable opportunity and potential scale of the channel.

Europe adds another layer because personalized advertising is currently restricted within the EEA and Switzerland. That affects how I would think about audiences and personalization in a European media plan.

3. From Media Strategy to an Actual Campaign

Once inside Ads Manager, the structure becomes much more familiar:

Campaign → Ad Group → Ad

But the interesting part is what sits at each level.

At campaign level, I am thinking about:

Business Objective → Campaign Type → Objective → Geography → Platform → Audience → Budget → Schedule

Then at ad-group level:

Bid Strategy → Destination → Context Hints

And finally:

Ad → Headline → Description → Creative → Landing Experience

This matters because ChatGPT Ads should not be planned backwards from an ad.

The process should still begin with the business problem.

For example:

Need: Generate incremental product discovery for a fashion retailer.

↓

Customer Journey: Discovery and consideration.

↓

Campaign Objective: Selected according to the desired outcome.

↓

Audience + Context: Define where the advertiser could genuinely be relevant.

↓

Creative: Communicate the proposition.

↓

Measurement: Determine whether those interactions eventually create business value.

4. Objectives Need to Connect to the Full Funnel

ChatGPT Ads already allows advertisers to think beyond a single campaign outcome.

The important strategic distinction is between campaigns designed around reach, traffic and downstream conversion activity.

For a hypothetical D2C skincare brand, I might think about the funnel like this:

Customer stage	Advertising objective
Discovery	Build visibility
Product exploration	Generate qualified traffic
Consideration	Bring relevant users to product/category experiences
Conversion	Optimize toward a meaningful downstream event
Business evaluation	Measure customers, orders, revenue and acquisition economics

 

The key for me is not simply whether an objective exists in the interface.

It is whether I have the measurement infrastructure and sufficient data to evaluate what happens after the ad interaction.

That leads directly to one of the most important parts of the platform.

5. Full-Funnel Measurement: The Pixel Matters

ChatGPT Ads is not limited to:

Impression → Click

The conversion setup introduces a much more useful framework:

Data Source → Conversion Event → Event Activity → Campaign Measurement

For an eCommerce advertiser, I would want to understand the journey much further down the funnel:

Ad

↓

Click

↓

Product View

↓

Add to Basket

↓

Checkout

↓

Purchase

↓

Revenue / Business Outcome

That distinction is critical.

A campaign producing inexpensive clicks is not automatically a good campaign.

If the traffic does not progress through the funnel, convert or create commercially valuable customers, the CPC itself tells me very little.

This is why I would establish the measurement framework before trying to scale media investment.

6. Targeting: Conversation Context Is the Interesting Part

This is where ChatGPT Ads starts becoming different from a conventional campaign setup.

Advertisers can define geography and supported platform environments such as:

iOS App | Android App | Web

But the element I find more interesting is context hints.

Instead of thinking only in terms of a list of keywords, I can think about the kinds of conversations and situations where a product or service might genuinely be useful.

Take a hypothetical luggage retailer.

Instead of thinking only:

“Suitcase”

I could think about situations such as:

→ Planning a two-week European holiday
→ Travelling with cabin baggage only
→ Looking for lightweight luggage
→ Comparing hard-shell and soft-shell options
→ Preparing for frequent business travel

That changes the strategic exercise from:

“Which words do I want?”

to:

“In which customer situations could my product actually be useful?”

The advertiser provides the context, but the hints are not guarantees that an ad will appear whenever a particular phrase is mentioned.

That makes relevance extremely important.

It also raises a question I will be watching closely as the platform develops:

How much insight will advertisers eventually receive into the conversational contexts that are actually creating performance?

7. Audience Strategy Looks Different in Europe

Audience creation and first-party data are important parts of any serious paid-media operation.

But European advertisers need to evaluate the capabilities available in the European environment, rather than assuming every globally available audience capability works identically here.

My current European setup makes the EEA personalization restriction particularly visible.

That affects how I would think about:

Prospecting → Known Customers → Exclusions → Retargeting → Personalization

The media-planning question therefore becomes:

What audience strategy can I actually activate in this market?

rather than:

What does the global feature list say the platform can theoretically do?

8. Budget, Bidding and Delivery: The Details Matter

This is where hands-on platform use starts revealing things that a launch announcement never will.

Campaign budgeting currently gives me two broad approaches:

Campaign Total Budget

or

Daily Budget

But a daily budget should not automatically be interpreted as an absolute daily spending ceiling.

For example, in the interface I am currently working with:

€65 Daily Budget

can permit substantially higher spend on an individual day while the system manages spending across the broader period.

That matters operationally.

Then there is pacing.

I currently do not see an explicit campaign pacing control that lets me deliberately define how I want spend distributed through a campaign period.

Consider a hypothetical retailer with:

€30,000 Budget → 10-Day Promotion

There is a difference between:

“Do not spend more than €30,000.”

and:

“Here is how I want that €30,000 distributed across those ten days.”

For large or tightly controlled promotional budgets, that distinction can matter.

Bidding also needs careful interpretation

Bidding happens within the ad-group structure, and the available approach depends on the campaign objective.

In the European interface I am currently using, €1.95 is the minimum maximum-CPC bid I can enter for the setup I am exploring.

That is important wording.

It does not mean:

“My CPC is €1.95.”

It does not mean:

“ChatGPT Ads CPC in Europe is €1.95.”

It means:

The interface is currently preventing me from entering a maximum CPC below €1.95 for that setup.

Performance is a completely different question.

9. Creative: Simple Inputs, Increasing Automation

The creative workflow is relatively compact.

The interface currently gives me tight headline and description limits, along with the destination and creative experience.

AI also enters the workflow through suggested ads and text customization.

That could be useful for scaling creative development, but it introduces another consideration:

Automation → Scale → Variation → Brand Governance

Imagine a hypothetical premium fashion retailer.

Generating more variations is useful only if those variations remain:

Accurate → Relevant → On Brand → Commercially Appropriate

For me, AI-assisted creative is therefore not simply a productivity feature.

It is also a governance question.

10. Product Feeds Make Retail and eCommerce Particularly Interesting

One of the areas I want to explore much further is product-feed advertising.

For retail and eCommerce, this potentially connects conversational discovery with structured product information.

Imagine a fashion retailer with 50,000 products.

Manually building advertising around every:

Shoe → Dress → Jacket → Bag → Accessory

would obviously be impractical.

A feed-based structure potentially changes that:

Product Catalog → Feed → Eligible Products → Relevant Conversation → Product Advertising Opportunity

Now consider this hypothetical conversation:

“I need black leather ankle boots under €180 that are smart enough for the office but not too formal.”

The user has communicated:

Product Category → Material → Price Ceiling → Use Case → Style Preference

That is exactly why I find the intersection of conversation + commerce data particularly interesting.

Whether it ultimately produces incremental, profitable sales is a completely separate question that requires actual performance evidence.

11. Reporting: I Need More Than “What Happened?”

The current reporting environment gives advertisers the fundamental campaign performance view.

But as a performance marketer, my requirement eventually goes beyond:

What happened?

I need:

Why did it happen?

Suppose performance suddenly deteriorates.

I want to investigate:

Campaign

↓

Ad Group

↓

Bid

↓

Audience

↓

Context

↓

Creative

↓

Platform

↓

Landing Experience

↓

Conversion Behaviour

The deeper the platform allows advertisers to diagnose those relationships, the easier it becomes to make meaningful optimization decisions.

This is one area where I want more hands-on time before forming a strong conclusion.

12. The Small Operational Details Matter Too

This is probably the least glamorous part of evaluating an advertising platform, but one of the most important when you actually manage campaigns.

I am looking at things such as:

Navigation → Inline Editing → Bulk Upload → Filters → Change History → Help Text → Tooltips → Error Messages → Diagnostics → Campaign Status → Editing Workflow

Even something as small as contextual help matters.

When an advertiser sees an unfamiliar bidding or targeting option, can they understand what it means without leaving the workflow?

When something is not delivering, does the platform explain why?

When several people work on an account, can I understand what changed?

When I need to modify hundreds of objects, can I do it efficiently?

None of these features creates demand or revenue directly.

But collectively, they determine how efficiently an advertiser can operate at scale.

13. Putting Everything Together: A Hypothetical German Fashion Retailer

Let me bring the strategy and execution together.

Assume a German multi-brand fashion eCommerce retailer wants to test ChatGPT Ads as an incremental paid-media channel.

The business sells:

Fashion | Footwear | Bags | Accessories

The objective is not simply:

“Run ChatGPT Ads.”

It is:

Identify where ChatGPT could contribute to product discovery and customer acquisition, then measure whether that contribution creates incremental business value.

A hypothetical journey could begin with:

“I need something smart-casual for a September wedding in Munich. Budget around €300 and I don't want anything too formal.”

From an advertising perspective, that conversation contains useful context:

Occasion → Location → Budget → Style Preference

The campaign framework could then look like:

Layer	Hypothetical approach
Business Goal	Incremental customer acquisition and revenue
Customer Journey	Discovery → Consideration → Purchase
Campaign	Align objective with the required outcome
Context	Define situations where the retailer is genuinely relevant
Products	Use appropriate catalog/feed infrastructure
Creative	Match proposition to the customer situation
Bidding	Align bidding with the campaign objective
Measurement	Track meaningful post-click behaviour
Business Evaluation	Orders, revenue, acquisition economics and customer quality
Optimization	Scale only where evidence supports it

 

This is hypothetical.

There are no invented results, because the purpose is to demonstrate how I would connect the individual capabilities into a coherent advertising strategy.

14. Where Could It Fit Across Different Business Models?

I would not evaluate ChatGPT Ads equally for every business.

Business model	Hypothetical opportunity
eCommerce	Product discovery, comparison and measurable purchase journeys
D2C	Connecting specific consumer needs with differentiated products
Retail	Product discovery across large catalogs and feed-based advertising
B2C Services	Considered decisions where consumers naturally ask multiple questions
Subscription	Explaining value and helping users evaluate alternatives
Marketplace	Connecting detailed requirements with relevant supply
Travel	Destination, budget, timing and preference-rich conversations
B2B	Potential relevance for considered technology or service decisions

 

B2B is certainly part of the opportunity, but it is not where I would centre my assessment of the channel.

The pattern that interests me more is:

How much consideration happens before the customer acts?

A €15 impulse purchase and a €1,800 laptop involve completely different decision journeys.

The more a consumer needs to:

Explore → Understand → Compare → Refine → Decide

the more interesting the conversational environment potentially becomes.

15. Which Verticals Look Interesting?

From that perspective, several verticals immediately become interesting to explore:

Fashion & Retail | Beauty | Travel | Consumer Electronics | Automotive | Home & Lifestyle | Education | Technology | Selected Consumer Services

But the business model, customer journey and regulatory environment matter as much as the vertical name itself.

A conversational use case that looks strategically attractive does not automatically mean every advertiser, product or targeting approach within that vertical will be eligible or appropriate.

16. What I Like So Far

Several things genuinely interest me.

Conversational relevance

The opportunity to think about customer situations rather than only predefined targeting inputs is strategically interesting.

Full-funnel infrastructure

The presence of conversion events and measurement means I can eventually evaluate something more meaningful than clicks.

Product feeds

The combination of conversational product discovery and structured commerce data could become particularly interesting for retail and eCommerce.

Platform-level targeting

Being able to think separately about web, iOS and Android creates another useful planning dimension.

AI-assisted creative

Potentially useful for scale and localization, provided advertisers maintain appropriate control.

A recognizable campaign hierarchy

Campaign → Ad Group → Ad makes the operating model relatively easy to understand.

17. What Concerns Me or Feels Limited

The biggest issue for me remains addressable reach.

An advertiser cannot currently reach the entire ChatGPT audience because ad-free paid subscribers sit outside the advertising inventory.

Then there are European audience and personalization restrictions.

I also see areas where I would like greater operational maturity, including things such as:

Pacing Control → Diagnostic Depth → Contextual Reporting → Audience Flexibility → Workflow Guidance → Optimization Transparency

The importance of each limitation will vary by advertiser.

A small advertiser running a €1,000 test has different operational requirements from an international retailer managing substantial budgets across multiple markets.

18. What I Would Like to See Develop

My wishlist is not about turning Ads Manager into an unnecessarily complicated platform.

I would rather see deeper capability where it genuinely helps advertisers make better decisions:

More control over budget delivery

→ better ability to manage time-sensitive investment.

Deeper contextual insights

→ better understanding of where relevance is actually producing value.

Richer reporting and diagnostics

→ move faster from “performance changed” to “this is why.”

Stronger audience capabilities where permitted

→ more sophisticated acquisition and customer strategies.

Continued development of product-feed advertising

→ particularly important for retail and eCommerce.

More consistent contextual guidance

→ make unfamiliar controls easier to understand directly within the workflow.

The platform does not need complexity for the sake of complexity.

It needs useful control and useful transparency.

19. What I Am Not Ready to Judge

This is where I think early commentary around any new advertising platform needs discipline.

A few days of access cannot tell me enough about:

Customer Acquisition Cost

Return on Advertising Spend

Conversion Quality

Incrementality

Customer Quality

Scalability

Audience Saturation

Performance Stability

Long-Term Optimization

And a CPC number certainly cannot answer those questions.

Ultimately, for an eCommerce advertiser, my evaluation would look closer to:

Media Investment

↓

Qualified Traffic

↓

On-Site Behaviour

↓

Conversion Rate

↓

Orders

↓

Revenue

↓

Customer Acquisition Cost

↓

Customer Quality

↓

Incremental Business Value

That takes time and sufficient data.

20. My Early Assessment

ChatGPT Ads is interesting to me because conversation creates a different advertising context.

A person can explain what they want, why they want it, their constraints, their preferences and the trade-offs they are considering.

That creates potentially meaningful opportunities across:

Discovery → Consideration → Evaluation → Action

At the same time, this is still an early advertising platform.

The inability to reach ad-free paid subscribers is a significant strategic limitation for me. European restrictions affect audience strategy. Some campaign-management and diagnostic capabilities still have room to mature. And there simply has not been enough time to responsibly judge long-term commercial performance.

So I would neither build a media plan around the assumption that ChatGPT Ads has already proven itself nor dismiss it because the platform is still developing.

My approach would be:

Understand the Customer Journey

↓

Identify a Genuine Conversational Use Case

↓

Define Its Role in the Media Mix

↓

Build Full-Funnel Measurement

↓

Start With Controlled Investment

↓

Evaluate Traffic and Customer Quality

↓

Measure Business Value

↓

Scale Only When the Evidence Supports It

For me, that is where ChatGPT Ads currently stands in Europe:

Interesting enough to test. Developed enough to take seriously. Early enough to keep questioning.