Revenue Attribution◾️ Dreamdata◾️ Sales leader with a crush on marketing

👩🏻‍💻 Professionally :
- Sales leader with a flavour for Marketing 🤝 
- Sales Pathfinder
- Doubling my LinkedIn followers base each year through content creation here.
I generate all my sales leads inbound from Linkedin Social Selling.
How do I know that Linkedin delivers Revenue?
Follow my Linkedin course on how to measure Linkedin Social Selling to Revenue: 

https://www.linkedin.com/learning/how-to-measure-linkedin-social-selling-to-pipeline/[…]g-and-adjusting-strategies-based-on-performance?


💃 Personally:
- Interested in B2B Influencer marketing.
- Morning 5K runner
- Appointed as an upcoming talent in Denmark u34
https://www.berlingske.dk/karriere/til-kamp-for-kvinder-i-it

Currently testing out the hypothesis: 
If you want to work in the past - join an enterprise.
If you want to work in the future - join a startup.

Fun fact: I was a weekend radio DJ for 2 years in my teen years 🎧

Laura Erdem

We are hiring! 
I am hiring! 

Account Executives in New York!

You will love to sell Dreamdata
- Amazing team
- Market winning product
- Inbounds that are not just qualified, but close within the quarter! 
- Competitive pay

Ping me!

B2B Attribution & Activation◾️ Dreamdata◾️ Sales director with a crush on marketing

🟠 - Marketing
⚪️ - Sales 

Organic Search and Direct are the least favourite sources for Inbounds. Both means "something is working" but we can't put the finger on it due to:
- No proper website tracking
- Deals not joined to Linkedin engagement
- Buyers just not clicking on the sponsored Google link you pay for.

As soon as you see an overview over a full customer journey, it gets so much easier to aggregate the data and understand which channels help educate the prospects and which of those are helping them to convert. 

This specific deal came through Organic - Straight to the pricing page! 
Seems like those LinkedIn targeted ads have worked out after all!

Please, you don’t need more intent signals. 
You need the right ones, the kind that actually lead to revenue.

That’s what we’ve been solving with Dreamdata Signals.

"Show me which Signals became Closed Won opps?"

Since launch, I’ve been watching how teams use it to sharpen their go-to-market. And the difference is wild.

Here’s how I’ve seen B2B teams use it best:
- Build precise audiences around signals and instantly sync them to ad platforms
- Create customized engagement scores to define what “intent” means for your business
- Trigger notifications to sales (on Slack/Teams) when an account shows intent 
Use AI to scan all your Go-To-Market data to uncover hidden intent signals that actually correlate with pipeline and revenue. 

Now you have a loop of success

I’ve spent 20 years mostly in SaaS and B2B marketing and somehow ended up in the weird and wonderful intersection of AI, fashion, and e-commerce. 

As CMO at Pixelz, I’m helping build the future of fashion content. The kind that’s fast, scalable, and still looks really, really good. We’re talking AI-generated models, hybrid production workflows, and enough acronyms to make your head spin. It’s nerdy and creative and chaotic and I love it.

My background is a mix of SaaS, e-com, and tech across startups, scaleups, and companies that had way too many Slack channels. I’ve led teams, launched campaigns, fixed broken funnels, rewritten brand stories, and gently explained to stakeholders why we can’t just “make it viral.”

I believe in marketing that works, not just marketing that looks good in a keynote. And I believe saying no is wildly underrated.

Outside of work, I run a lot, post a little too honestly on LinkedIn, and have strong opinions about AI in fashion.

If you’re also trying to figure out how to scale content without losing your mind or your soul, we should probably talk.

Katrine Rasmussen

What makes this Mango imagery interesting is not the AI, but the fact that it’s PDP product imagery, and not campaign or lifestyle imagery.

A couple of weeks ago, I shared my predictions for AI in fashion in 2026, and one of them was much less glamorous than the campaign-heavy experimentation that usually dominates this space.

I wrote that PDP imagery is where AI will eventually prove its value, not because it looks impressive, but because it solves a very real operational problem for fashion brands.

And here we are, only weeks into the new year, and Mango is already putting that prediction into practice.

This is PDP imagery. I pulled it from the Mango website, and it's 100% GenAI.

To be clear, this isn't the first time Mango has experimented with AI beyond campaign or lifestyle visuals, but the quality this time around feels noticeably stronger. That matters because most brands that have gone all-in on GenAI imagery have done so in campaigns, where there is simply more time and budget to invest, and where the production model allows for a much higher level of iteration than is realistic for high-volume PDP content.

PDP product imagery plays by very different rules.

I think high-volume product imagery is where GenAI has the biggest long-term upside. In reality, it's also where most AI tools still struggle. AI doesn't naturally slot into a finely tuned, high-volume studio workflow, and integrating it into that machine remains difficult. Quality control is still non-negotiable, color accuracy continues to be fragile, and most tools do not (yet?) deliver a truly finished, PDP-ready asset without human intervention.

That kind of setup is manageable when you're producing a single campaign or a limited test. It becomes far more complex when you're talking about hundreds of product images that need to go live every single week without breaking timelines, teams, or sanity.

Right now, a hybrid workflow is still unavoidable, with humans and technology working side by side, it's still the only way this works in practice. What is interesting is that the effort required to produce AI-generated campaign imagery and GenAI PDP imagery is still surprisingly similar, even though the business logic and investment behind the two couldn't be more different.

But the gap is starting to close.

We are getting closer to workflows where PDP imagery no longer demands campaign-level investment, and when that shift happens, the impact will not be loud or flashy, but structural.

I'm curious to see if and how Mango plans to scale this beyond controlled rollouts, because so far it still feels like smaller tests rather than high-volume. But if there is a brand that has consistently shown it can move from experimentation to execution without losing control of quality or brand trust, it's Mango.

This isn't the most glamorous version of AI in fashion, but it's the one that will eventually change how the industry works.

Mildly obsessed with AI and fashion tech, CMO at Pixelz, unapologetic Swiftie with a suitcase that’s permanently half-packed

To label or not to label GenAI imagery in e-com? Probably the wrong question!

The real one is what transparency actually looks like for your brand, and what fits within your policies and values.

This came up during our recent E-commerce Content Creation Week session, and I've been thinking about it since,  because Thomas made a point that changed how I think about the whole thing.

The question isn’t really whether you slap an AI label on an image or not. That’s the easy part. 

The bigger question is what kind of brand you actually want to be.

At Pixelz, I think we all like the idea lof abeling AI imagery. There’s something about being upfront that feels grounding. Like saying, yes, we did this on purpose, and yes, we stand by it.

But consumers might not care - at all.

If the image is good enough, if it looks right, feels right, and does the job, most people aren’t zooming in to ask how it was made. They’re deciding in seconds whether they trust it, like it, or scroll past it. And that trust seems to be driven far more by quality than by process.

Which raises another thought we don’t talk about enough. How much PDP content is already out there that’s AI-generated, not labeled, and so good that we don’t even question it anymore? Chances are, a lot. Probably more than anyone wants to admit.

So when brands debate labeling, what they’re really debating is their own policy on transparency, control, and identity. How much do we want to explain ourselves, how much do we want to stand out, and on what terms.

Labeling isn’t a moral checkbox -  it’s a brand choice. (At least until August 2026...)

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Laura Erdem

Katrine Rasmussen

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taplio

The Battle

Laura Erdem

Katrine Rasmussen

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Laura Erdem's LinkedIn Performance

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Performance ScoreLaura Erdem's LinkedIn Performance