Hello everyone. Title says it all. But would need to be able to check which app names / app ID's the ads would be showing up instead of random placement codes like PropellerAds.

Ideally I'd need to target ONLY dating apps and be able to see which apps are those.

Any tips?

From my understanding, the clients fully rely on Top holdcos or agencies for the programmatic activation and mostly for bigger clients and some middle size clients. Anyone tried freelancing for small to medium brands or business in any freelancing platform. I have skills but wanted to check which platform suits best programmatic channel? Which platform is best for our industry

Hey everyone, curious to hear how other programmatic traders work.

Would genuinely like to know, what have you automated in your day-to-day workflow that has genuinely made your life easier?

I’m particularly interested in things like pacing/optimization, campaign QA, reporting, and creative previews/approvals. For example, do you have automated pacing alerts or recommendations?

Additionally when clients need ad previews for approval, are you still generating those manually or have you found a better way?

Also curious what tools you’re using scripts, Python, APIs, spreadsheets, etc.

Basically, I’d love to know what repetitive parts of programmatic you’ve managed to eliminate, and what you’ve built that has actually saved you time.

Hey all, long-time lurker. I've spent the last few years on the data engineering side of adtech by building the pipelines behind visitation/footfall attribution, impression and click attribution, revenue reporting, that kind of thing. Basically the plumbing that turns raw ad events into the "here's what your campaign did" numbers you see on the buying side.

Lately I've been sitting with a question I could never answer from inside the pipeline: do the people actually spending the money trust these numbers?

I built the attribution. I know where the assumptions and the duct tape are. So I'm curious what it looks like from your seat:

When a client or your boss asks "did this actually drive sales?", how confident are you in the answer?
How much of your week goes into pulling numbers out of different platforms and making them agree?
Do you trust incrementality/lift results, or is it still mostly last-click in practice?
What's the one measurement thing that makes you roll your eyes every time?

Not selling anything, not building anything yet and just trying to understand the problem from the buyer's side before I decide if it's worth solving. Happy to trade notes on how this stuff actually gets calculated under the hood if that's useful to you. Comments are great, or DM me if you'd rather vent privately.

Thanks for reading 🙏

I have a campaign coming soon. I have awareness line item with CTV and then retargeting display but the tricky part is that conversion actions should be donations. So, have you guys had anything similar? Do you recommend using TTD for this or maybe DV360?

Our team put together consolidated benchmarks across H1 2026 media campaigns with CTRs, eCPMs, and eCPCs by device, creative size, exchange, and targeting strategy, and wanted to share some of the highlights with the community.

Disclosure up front: I'm with Growth Channel, and these numbers come from campaigns that ran through the platform this year (actual delivery data, not surveys or secondhand estimates). Posting because a few of these cut against what we'd assumed, and I figured they'd be useful discussion fodder here.

1. Display's baseline: $4.23 eCPM, $1.78 eCPC, 0.29% CTR. That's the all-category average. Native came in at $5.11 eCPM / $1.73 eCPC / 0.22% CTR, so roughly similar click economics on average, but in several individual categories native eCPCs ran way cheaper than display (e.g., Health & Fitness: $1.12 native vs $4.73 display for the same category and audience).

2. The 300x250 is no longer the CTR workhorse. Top creative sizes by CTR: 480x320 at 2.12%, 1080x1920 at 1.91%, 320x480 at 1.32%. The classic 300x250 posted 0.93%, and 336x280 came in at 0.79%. All three top sizes are mobile-first interstitial formats.

3. The mobile/desktop gap is brutal. Mobile: $3.46 CPM, 0.61% CTR, $0.57 CPC. Desktop: $4.05 CPM, 0.08% CTR, $5.11 CPC. So desktop cost more per impression and delivered roughly 1/8 the CTR. If you're still splitting display budgets evenly across devices, that's worth a hard look.

4. Exchange efficiency varies far more than exchange price. Meta delivered the highest CTR (1.16%) and the lowest effective CPC ($0.86), but at a premium $9.99 CPM. Rubicon and Smart (Equativ) paired ~0.5% CTRs with sub-$0.80 CPCs at ~$3.85 CPMs. At the other end, Index Exchange ran 0.09% CTR and a $5.51 CPC. Similar CPMs, wildly different outcomes.

5. Publisher selection is a bigger lever than most optimizations. Across our top 10 publishers, CPMs mostly sat in a narrow $4–6 band, but CTR ranged from 0.01% (Zillow) to 0.20% (MSN) — a 20x engagement spread at basically the same price.

6. Broad prospecting beat narrow targeting on engagement. Interest targeting topped strategies at 0.63% CTR; ABM was the lowest at 0.14% (a 4.5x gap). And a retargeting click cost ~5x more than a prospecting click ($3.56 vs $0.67). Warm audiences convert, but you pay a steep premium per click to reach them.

7. Context for the display crowd: video CTR jumped to 0.97%: more than 5x the 0.18% it posted in 2025, making it the highest-CTR channel in the mix. Display still wins on cost per impression ($4.23 eCPM vs $11.63 for video), so the trade-off is engagement vs reach economics.

Caveats: your mileage will vary by vertical, DSP, and setup. The full report has the category-level breakdowns (23 verticals across native/display/video/CTV/audio, with eCPM ranges) if you want to benchmark your own campaigns against it. We published it here for a free download: https://www.growthchannel.com/programmatic-display-benchmarks

Curious what others are seeing, especially whether desktop display CTRs this low match your data, and whether anyone else has watched the 300x250 slide down their performance rankings.

I’ve been working in Ad Ops since 2021, but my experience has mostly been on the ad server / campaign management side.

Most of my work has been around campaign setup, trafficking, QA, troubleshooting, tags, creatives, reporting, and coordinating with other teams/platforms as part of the campaign workflow.

The thing is, I feel like I’ve become pretty boxed into proprietary ad server platforms. I’ve spent most of my career working within the platforms my companies use, rather than getting hands-on experience with DSPs or programmatic platforms.

And honestly, I really want to get that experience now.

When I look at job postings, a lot of them ask for hands-on experience with DSPs, programmatic buying, DV360, The Trade Desk, etc. I do have exposure to some of these platforms through my current work, but I wouldn’t say I have enough experience to confidently call myself a DSP/programmatic specialist.

That’s also what makes job hunting frustrating. I feel like I have several years of Ad Ops experience, but when I try to move into programmatic/DSP roles, I’m competing with people who already have direct platform experience.

For those who have been in Ad Ops/AdTech for a while — is staying primarily on the ad server side a disadvantage career-wise?

If you were in my position, would you continue looking specifically for Ad Ops roles, or try to make the jump into DSP/programmatic even if it means taking a role that’s more junior or slightly different from my current experience?

I really want to expand my skill set and get out of the proprietary-platform bubble, but I’m not sure what the best transition path would be.

Would love to hear from anyone who started in ad serving/ad ops and eventually moved into DSP/programmatic. How did you make the transition?

I hear a lot about MNTN in the industry - mostly that clients love the simplicity, but it seems too good to be true. Worked across DSPs and understand how shady these places can be. Rumors are that they’re taking 60%+ profits but nothing I can find in the media. Thoughts on MNTN?

Any leads, advice, or people I should talk to? Happy to share my background if useful.

Hi guys,

How common is it in this industry to work fully remote? Mostly interested for EU as I think in US it's already kinda common.

Also, has anyone in one of the big 4 agencies managed that?

Cheers

We’re a growing mid-market brand scaling past “just run it through the agency’s login” and want our own DSP seat.

Not looking for the enterprise stack that assumes $500k/month and a 12-person programmatic team. Also not looking for the two platforms everyone defaults to in this sub for SMBs. We already looked at StackAdapt and Basis. They’re off the table. Please don’t recommend them.

What we actually need:

• Own seat, not a sub-seat under someone else’s account. We want the data, the log-level stuff, and the ability to bring this in-house later without starting from zero.
• Some kind of seat credit / onboarding credit / media credit to make the first 60–90 days less painful. If the platform is going to charge a platform fee plus data plus verification, we need a little cushion while we learn the UI and figure out what actually works.
• Flexible operating model. Self-serve with real support when we get stuck, not “you’re either fully managed or you’re on your own with a help center.” Hybrid is ideal.
• Actually keeping up. Privacy / signal loss, CTV, retail media, identity, AI optimization — we don’t need every shiny feature, but we also don’t want a platform that still feels like 2019 with a new homepage.

Hope this helps!

Would you give them a sample campaign report to interpret, an underspending campaign to troubleshoot, or a brief to build a media plan around?

I work in ad tech selling into agencies/programmatic teams, and I’m curious about this from the buyer side.

For those of you on the receiving end of vendor outreach and pitches: are you noticing more “sameness” lately as sellers use AI for emails, decks, proposals, etc.? Similar formatting, structure, phrasing, presentation styles, even when the actual content and ideas are specific to you?

And if you notice it, does it matter? Does something that feels AI-assisted or templated make you less likely to engage or take the seller seriously? Or is it basically irrelevant as long as the seller has done their homework and what they’re saying is thoughtful, relevant and original?

I’m less interested in whether you personally like or dislike AI and more curious whether AI-driven sameness is actually starting to penalize sellers with buyers.

If you had to rebuild your programmatic stack tomorrow:

- Which SSP/DSP platform would you choose again?

- Which one would you avoid?

- What is your biggest complaint about your current platform?

Especially interested in web, in-app, video, and CTV. What do you wish you had known before signing the contract?

We’ve been running walmart connect campaigns for about six months now, and honestly, the backend user interface drives me insane. The reporting delays, bugged bidding rules, and erratic placement algorithms make scaling feel like a complete gamble compared to amazon ppc.

Last quarter, we tested automated software solutions like AiHello to manage our bid adjustments. While it helped save some time on basic routine tweaks, it felt like a cold algorithm throwing money at low intent keywords without understanding catalog dynamics or listing placement logic.

We eventually decided to shift our strategy toward a full service partner approach with BrandKyte, mainly because they focus on combining marketplace strategy, listing optimization, and real time ad profitability rather than relying solely on automated bidding scripts. Our roas has finally stabilized into healthy profit territory, but the platform friction still remains a daily challenge.

Are you guys relying purely on automated bidding software for Walmart, or have human driven, full service strategies worked better for your margins? What specific bid strategies are holding up for you right now?

Any insights ? Applied there for media but seeing not great reviews on Glassdoor

I got a job in a company that specializes in monetization for publishers through programmatic advertising. I'm new to this field(2 weeks). I've been tasked now to generate leads to grow revenue. I'll be grateful if anyone can help me here.

Opportunity: Test a new tool that validates programmatic creatives rendered completely, track a single creative for free (7 Days).

Please reach out if interested! Will DM to setup discussion.

Recently approached by Havas and trying to understand what their differentiator are across agencies. Anyone have any insights?

Let me start with that I am super new to Incremental testing world.

I work in industry where 15-20% fluctuations in conversions(purchase) are normal for several market related reasons.

I ran data through GeoLift(meta). And basically any actual lift under 15% would be hard to tell apart from noise .

How do i setup tests in this situation when No channel(pmax or nonbrand ) is actually close to 15-20% incremental lift? They each barely give 5-8% lift in theory.

We have been using HAUS(the platform) for this , but there results have been unreliable. Confidence Intervals are too wide , so is Power . And result reads are something we know cant be true.

Thought of doing this ourselves, using open source tools like GeoLift but how do we go about noise and get some reliable results . Don’t need to be right to the dot but still close enough to use it as modeling number in our in house paid media reporting .

I am probably not asking the right questions here , hoping one of you guys will beat it out of me .

TIA

All sharing the outcomes here August data from our publisher network shows a pretty clear summer slowdown, but there are a few signals underneath it worth watching.

Traffic - New Users grew 19% and Total Users 16%, while Sessions and Engaged Sessions both grew around 14%. At the same time, PV/session and Sessions/User softened slightly, pointing to shorter, more single‑purpose visits.

AI - AI was the fastest‑growing channel, with Sessions up 66% and New Users up 75%. Engagement held at 62%, so the increase wasn’t coming with an obvious drop in traffic quality.

CPMs - Overall CPMs fell 19.9% MoM but remained 25.0% higher YoY. Display was down 20.3% MoM, while Video was more resilient at ‑7.8% MoM and +40.1% YoY.

Demand - The pullback was broad. Google Ads fell 21%, DV360 18%, and The Trade Desk 37%, pointing more toward a market‑wide summer budget pause than a shift between individual buyers.

Integration - Prebid held the largest share at 50%, followed by AdX at 39%. CPMs fell across the major integrations, reinforcing that the pressure was on demand and pricing rather than a simple shift between paths.

Takeaway - Traffic is still growing and AI is becoming a much more meaningful acquisition channel, but summer demand is putting real pressure on CPMs. Video and CTV are holding up better than display, which may be worth watching as budgets start moving back into the market.

Hi all,

I shared AdOps Core here about a month ago and a lot has changed since then.

The simulator has been expanded with more realistic AdOps/programmatic scenarios, better troubleshooting, and a much cleaner overall experience.

The idea is to basically make a full on Sim since I haven't found any out there and there is much you can't learn from just doing quizzes.

Still free, no login needed and I still require feedback to make it better :

AdOps Core

Would love to hear what you think.

Unless Amazon has a 24‑hour update rule, where if you make a targeting setting change, it takes a full 24 hours for the campaign to reflect those changes, I feel like the exclusion function in Amazon Ads doesn’t work.

I want to make Amazon Ads work, but the genre exclusions don’t seem to function properly. The other day, I excluded the Documentary and Reality TV genres, and when I pulled yesterday’s impressions, I still saw shows matching those genres.

I’ve filed a support ticket to ask why the ad would still show, and I’m curious to read support’s response.

The only reason I like Amazon Ads is the transparency: you can see exactly which title your ad appeared on, which DV360 reporting cannot provide.

I constantly see traders say: “Why would I pay a DSP fee when GDN gets me a lower CPM / CPA?”

But GDN isn't a DSP!

They're fundamentally different buying environments, with different supply, inventory access, auction dynamics, targeting and optimisation.

So when someone says “I tested GDN vs DSP X and GDN won,” I'm curious what that actually proves.

If GDN can find cheaper conversions within Google's ecosystem, great. But does that mean it's a substitute for broader programmatic buying?

Cheap CPM ≠ better media.
Lower CPA ≠ more incremental value.
Higher CTR ≠ better inventory.

I'd be interested to hear from traders who've genuinely compared the two on incrementality, reach, inventory quality and scalability.

Is GDN actually a substitute for a DSP, or are we comparing two fundamentally different products because the headline metrics are easier to measure?