r/adtech

How are you actually using MMM outputs to justify brand investment when the model keeps pointing you toward performance?

Working on a paid media strategy where I’m trying to balance Reach and Frequency for long-term brand building against short-term ROI. The MMM keeps optimising toward what converts, which means brand-heavy investment looks terrible on paper.

Curious how others are handling this in practice. Are you presenting MMM outputs alongside something separate, like brand tracking data or a Binet/Field split, to make the case for upper funnel spend? Or are you finding ways to frame the MMM results so the brand investment doesn’t just look like wasted budget to whoever is approving spend?

Not asking whether MMM is the right tool for this job. I know it has limits with adstock and long-term equity. I’m asking how you’re actually navigating the conversation with stakeholders when the model is telling one story and your instincts are telling another.

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Why is GPID so important in the adtech world? What is it? And how is it consumed by the DSPs if this is sent as a signal to Dsp in a bid request?

I've noticed that in CM there is a safety area: is tis relevant if you apply list and blick on DV?

Dailymotion constantly appear in domain reports. TBH for me daily motion in America is not widely used. I saw that dailymotion people, pay keynotes at Possible conference to sell their platform. Any good expeiences or recommendations? For me its just another MFA site

One downside of behavioral targeting that does not get talked about much is how quietly reach shrinks over time.

You keep chasing the same users based on past actions, while missing people who are in the right moment but do not have the “right” history yet.

That is one of the key differences when looking at contextual targeting vs behavioral targeting. Relevance is not only about who you reach. It is also about the relevant demand you never see.

Curious how others think about this:

• Have you hit reach ceilings with behavior-based strategies?
• Does expanding beyond past behavior change scale or quality for you?

If you’re looking for roles in AdTech, in my experience, you end up checking:

• company career pages (SSPs, DSPs, ad networks)
• LinkedIn (noisy, hard to filter)
• random job boards with poor categorization

There is no real single place where AdTech jobs are properly aggregated and structured.

Given how niche and interconnected the industry is, I would have expected something more centralized by now.

I am currently looking to run billboard and digital screen campaigns for a product launch this year. Need something that makes buying DOOH as easy as buying digital ads online instead of calling a million vendors.

hi

To leverage first-party data, is it possible to set a campaign objective that gathers CRM data and optimizes campaigns based on ROAS?

Basically I want to understand how to better use Enanched conversion for leads data into Gads

Been tracking AdTech job postings lately and one thing that surprised me is how often companies still explicitly ask for Prebid.js + GAM experience.

Even with all the AI and automation talk, a huge amount of the ecosystem still seems heavily dependent on people who understand about header bidding and browser rendering, ad performance, measurement and auction mechanics are really that important skills?

Curious if others are seeing the same in hiring.

Contextual advertising was a real improvement for the industry.

It shifted targeting away from tracking people and toward understanding content, improving both privacy and relevance in many use cases.

Where contextual can still fall short is that content alone doesn’t fully explain how people are engaging in a given moment. Two pages can cover the same topic but trigger very different mindsets.

The move to Neuro-Contextual isn’t about new placements or formats. It’s about adding signals like interest, emotion, and intent to better understand mindset, not just subject matter.

At Seedtag, this has been the direction of our work: keeping a privacy-first approach, while grounding targeting in how attention and decisions actually form in real environments.

Curious how others here see this shift. Is understanding the page enough, or do we also need to understand the mindset around it?

Have you ever apllied brand safety filters on meta?

What about publisher blocklist?

Do you have any experience or evidence on how to improve brand sutability on meta?

right now, we're seeing ChatGPT ads are being treated as the next big surface performance channel but the fundamentals still feel rough - ~$200K minimum spends, reporting limited to weekly CSVs (no real‑time dashboards), limited control. In that setup, expecting ROAS is premature.

The pixel rollout is progress sure but even with that, targeting clarity and conversion paths are missing. I mean there’s still a pretty big gap between “can measure” and “can confidently optimize”

plus a observation- a lot of this feels like AI talking to AI. Not even sure how much of the ad is actually landing with a human vs getting filtered/reshaped along the way.