u/Upbeat_Quit7362

Meta and Google have solid retargeting infrastructure but I am curious about what works outside those two.

Display networks, native, push, has anyone built retargeting on these channels in a way that actually performed? The challenge I keep hitting is minimum audience sizes and match rates being too low for meaningful delivery.

Are there specific verticals where non-Meta retargeting really shines or is it always a complementary play rather than a primary one?

The disconnect between performance data and creative teams is something I run into constantly.

Performance side knows what needs to change based on data. Creative side gets vague direction like make it more engaging and produces work that looks great but does not perform. Then the blame loop starts.

What does your actual creative brief contain when it is driven by performance data? And how do you communicate what the numbers say in a way that leads to better output rather than defensiveness?

I know the theory. IOS users tend to have higher purchasing power. Android gives more volume at lower CPC. But has anyone actually run controlled tests where splitting by OS led to a material improvement in overall ROAS rather than just cleaner data?

Curious about the magnitude of the effect and whether it varies significantly by vertical. Also whether anyone has found cases where Android actually outperformed iOS on conversion quality outside of gaming.

Most frequency guidance I find comes from platforms with an obvious incentive to recommend higher exposure.

Has anyone seen actual third party research or done internal brand lift studies that show optimal frequency ranges by ad format and objective?

The recommended ranges I see vary wildly from three impressions per week to numbers that seem designed to maximise platform revenue rather than advertiser outcomes. Trying to find something I can actually use to set caps with confidence.

This is the decision I struggle with most in paid advertising. The sunk cost fallacy is real and I have kept campaigns alive longer than I should have hoping one more change would turn it around. But I have also killed things too early.

What framework do people use for this call? Is it purely a cost threshold or do you factor in qualitative signals like whether engagement suggests the offer resonates even if it is not converting yet?

Looking for something more systematic than gut feel.

Beyond bounce rate and time on site what metrics and patterns do you actually look at to decide if traffic from a network is quality or not.

I have had situations where engagement metrics looked fine but backend conversion rates were consistently terrible and I could never cleanly isolate whether it was the landing page, the offer, or the traffic source itself.

Does anyone have a systematic process for doing a proper traffic quality audit before scaling spend on a new source?

I keep seeing view-through attribution used in ways that feel like overclaiming.

If someone saw a display banner and then converted three hours later through an organic search is that really the display ad earning the credit?

I understand the argument that the impression contributed to recall but the practical problem is you have no way to distinguish a genuine view-through assist from a coincidence.

Where does the community actually draw the line between useful attribution signal and noise? And is anyone using view-through data in a way they genuinely trust?

Classic problem. Platform wants historical data to optimise bids but you need to run the campaign to get the data.

My current approach is manual CPC to start, bid based on max CPA worked backwards from target margins, collect two or three weeks of data, then consider moving to target CPA once there are enough conversions.

But I feel like I am leaving performance on the table in the learning phase. What do others actually do in the first 30 days and is there a smarter cold start approach than this?

Not looking for the textbook answer. I mean the actual workflow

How many variants do you test at once? What variable do you isolate first? How many impressions before you call a test? How do you document learnings so they compound instead of getting forgotten?

I have a process I have cobbled together but it feels inconsistent. Would genuinely appreciate seeing what this looks like for people running it systematically at scale rather than winging it campaign by campaign.

I used to always split mobile and desktop for cleaner data and separate bid logic. But on smaller accounts I keep questioning whether the complexity is worth it when the algorithm needs consolidated budget to learn.

For accounts doing 10k plus a month splitting feels like a no-brainer. Under that threshold I wonder if consolidation wins even if the data is messier.

What threshold have people landed on and is anyone finding that consolidated campaigns actually outperform splits on limited budgets?

It has been a few years since ATT and I am curious where people actually land now.
My modelled conversions on Meta have never fully matched backend numbers even after proper aggregated event measurement setup and switching to server-side events. The gap has narrowed but it is still there. Some people moved to MMPs for attribution. Others treat Meta data as directional only. How much confidence does anyone here actually have in their reported ROAS right now? And has anyone found a setup that gets you consistently close to accurate?

30 days gets thrown around as the default retargeting window but it clearly does not fit every vertical.

For eCommerce with a short consideration cycle 30 days might even be too long. For B2B software where the sales cycle runs months you probably need 90 to 180 days.

Has anyone actually tested window length systematically and found meaningful performance differences? Or is everyone just using whatever the platform defaults to and calling it a day?

Looking for real data points not best practice generalities.

I have mostly worked in one vertical and I am thinking about whether diversifying across offer categories would reduce overall risk or just add complexity.

For anyone who has crossed verticals: how much of your existing knowledge transferred and how much did you have to relearn? Did you use the same traffic sources or find that different categories work better on different networks? And how long before you had a real read on whether the new category was worth continuing?

Also curious whether you approached it as a formal test with a defined budget and success criteria or more exploratory at the start. Trying to figure out the right level of structure for something genuinely new.

Looking back I can identify a few things that would have saved real money and frustration if I had understood them earlier.

The biggest one: the goal in the early phase is not to be profitable. It is to collect clean data and understand your funnel. Expecting profitability before understanding what is actually happening leads to pausing tests too early and never building the foundation that consistent results require.

Second: get your tracking right before anything else. I spent months optimising on incomplete pixel data without knowing it. The gap between what I was seeing and what was actually happening was significant.

Third: change one variable at a time. Already knew this in theory. Did not practice it until the damage from ignoring it was obvious.

What operational thing took you longer than it should have to figure out?