Manual reporting is costing ad ops teams more than time with Chris Quinn of ProOps Consulting
BY ROB BEELER, BEELER.TECH + CHRIS QUINN, CO-FOUNDER OF PROOPS CONSULTING
Most ad ops teams know manual reporting is a grind.
What they do not always recognize is how much of the job has been shaped around that grind. Pull the GAM reports. Check pacing. Scan revenue. Look across the SSPs. Stitch it together in a spreadsheet.
And then you have to do it all again tomorrow.
That’s what I wanted to talk through with Chris Quinn, co-founder of ProOps Consulting. Chris has spent a lot of time inside the daily reality of ad ops teams, and he has a useful way of separating the work that requires judgment from the work that simply consumes attention. Manual reporting may keep teams close to the numbers, but it also asks experienced people to spend their best hours searching for problems instead of solving them.
In this conversation, we get into why those workflows have persisted, where the low-value work tends to concentrate, and what publishers lose when detection depends on a reporting cycle.
Some of those costs are easy to see, like hours lost to pulling reports. Others are harder to measure but more damaging: make-goods, missed optimization, advertiser trust, and the quiet retention risk that comes when good operators spend too much of the day on busywork.
The bigger question here is whether an ad ops workflow is helping a team stay ahead or simply helping them keep up. Chris puts it plainly: did you find the problems this morning, or did the problems find you? That’s a useful test for any publisher still relying on manual checks as the first line of defense.
Rob: Most ad ops teams know manual reporting is tedious, but many still treat it as a normal part of the job. Why has that workflow persisted for so long?
Chris: Because it works just well enough to never become anyone’s priority to fix. That’s the honest answer. Manual reporting isn’t broken in a way that sets off alarms – it’s broken in a way that quietly eats an hour every morning.
There’s also a culture piece. A lot of ad ops folks came up doing the manual pull. You learned the platform by living in the reports, so there’s a quiet belief that doing it by hand keeps you close to the data. I understand that instinct – I had it too. But “close to the data” and “manually re-pulling the same numbers every day” aren’t the same thing. One is judgment. The other is just busywork.
And nobody really owns the decision to change it. The person doing the work is too buried to step back, and the person who could approve a different approach doesn’t feel the pain directly. So it persists – not because anyone defends it, but because no one’s been handed the room to question it.
Rob: When you look at a typical ad ops day, where do you see the biggest concentration of low-value, repetitive work?
Chris: The first 60 to 90 minutes. Almost every team I work with starts the day the same way – log into Google Ad Manager, pull the delivery reports, scan pacing across the active line items, glance at revenue, eyeball the inventory. Then do a version of that again across the SSPs, and stitch it together in a spreadsheet.
Here’s the thing that makes it so sneaky: most mornings, nothing’s wrong. So the ritual feels productive – you checked, everything’s fine, move on. But you’ve just spent your sharpest hour of the day confirming a negative. You didn’t improve anything. You didn’t catch anything. You verified that the building wasn’t on fire, which it usually isn’t.
That’s the concentration point. It’s not one big inefficient task – it’s a daily tax paid by your most experienced people, at the exact time of day when their attention is worth the most. Multiply it by a small team and you’re losing 4 to 6 hours per person, every week, to checking.
Rob: You describe a lot of ad ops work as uncovering issues rather than solving them. How much time do teams actually spend searching for problems versus optimizing performance?
Chris: In the teams I work with, it’s often more than half of the so-called “analytical” time – and that’s just my observation from the field, not a figure I’d put in stone. But the pattern is consistent enough that I’d stand behind it.
The distinction matters more than it sounds. Searching for a problem and solving it are two completely different kinds of work. Searching is repetitive, low-judgment, and honestly a machine should do it. Solving is the work you actually hired an ad ops person for – the yield call, the targeting fix, the conversation with sales about a deal that’s structured to fail.
When most of the day goes to finding, the fixing gets whatever’s left, which is usually the back half of the afternoon when everyone’s already tired. So you’ve got skilled people spending their best hours on detection and their worst hours on the decisions that actually move revenue. We’ve got it exactly backwards, and we’ve normalized it.
Rob: Many publishers think about efficiency in terms of time saved. But what are the costs that don’t show up neatly on a spreadsheet?
Chris: Time saved is the easy number, and it’s the smallest one. The costs that don’t show up are bigger.
Start with the make-good. A direct-sold campaign under-delivers because nobody caught it in time, and now you’re serving free inventory to fix it – which means the cost isn’t just the lost revenue, it’s the inventory you could have sold to someone else. Then there’s the trust cost: an advertiser who gets made-good twice in a quarter starts padding their next insertion order or quietly moving budget elsewhere. That erosion never appears on a time sheet, but it shows up at renewal.
Then there’s the human cost, which I think gets dismissed too quickly. Your most experienced person spending their morning on busywork is a retention risk. Good ad ops people don’t leave because the work is hard – they leave because the work is boring and they can see it. And the opportunity cost underneath all of it: every hour spent confirming nothing’s wrong is an hour not spent on the optimization that would have grown the number.
You can’t put a clean dollar figure on the test you never ran.
Rob: A common theme in ad operations is firefighting. Why do so many teams still find themselves reacting to problems instead of catching them earlier?
Chris: Because detection is tied to the reporting cycle, and the reporting cycle is slower than the problem.
That’s the whole mechanism. If you look at delivery on a weekly report, you can only catch problems weekly – which means a campaign can drift for six days before anyone sees it. A PMP can stop on a Wednesday and you won’t notice until the next cycle, three days later, by which point the revenue’s just gone. You’re not reacting because your team is careless. You’re reacting because the structure of how you see your data guarantees you’ll always find out after the fact.
Firefighting feels like a discipline problem – “we need to be more proactive.” It isn’t. It’s a timing problem. You cannot be proactive about something you only see on a schedule.
Proactivity isn’t a mindset you can will into existence; it’s a function of how quickly information reaches you. Shorten the time between “something changed” and “someone knows,” and the firefighting mostly disappears on its own. Leave that gap wide and no amount of hustle closes it.
Rob: As publishers grow, manual processes tend to scale with them. At what point does a workflow that feels manageable become a genuine operational risk?
Chris: There’s a fairly clean inflection point: it’s when the number of things you need to check every morning exceeds what one person can reliably scan before the day actually starts.
Below that line, manual works. One person, a manageable volume, they know every campaign by name – fine. Above it, two things break at once. First, the checking stops being thorough, because no one can truly eyeball eighty line items, three SSPs, and inventory health every single morning – so they start skimming, and skimming is where things slip through. Second, you’ve created a single point of failure. The whole monitoring function now lives in one person’s routine, and the day they’re sick, on vacation, or just slammed, your early-warning system is simply off.
That’s the moment manageable becomes risk – usually right alongside revenue growth, which is the cruel part. The busier and more successful you get, the more the manual approach quietly turns into your biggest exposure, and it happens gradually enough that nobody notices the line being crossed.
Rob: You talk about surfacing issues before they become problems. What are some of the most common issues publishers don’t realize they’re discovering too late?
Chris: Three come up again and again. The direct-sold campaign quietly under-delivering toward a make-good. The PMP or programmatic deal that stops mid-week, days before anyone would have looked. And the ad unit that goes dark after a routine site or dev release – nobody connects the deploy to the lost revenue until weeks later when someone asks why that placement stopped earning.
What they have in common is that none of them announce themselves. They’re not errors that throw a flag. They’re silent, and they sit in your numbers waiting for the reporting cycle to find them.
Honestly, this is the pattern that led us to build ProOps Ads Tracker. We kept watching capable teams discover these days late – not through any failing of their own, just because the timing of manual checks left a gap. So we built a tool that reads your GAM network every morning, through a read-only connection, and flags those three (and more) while they’re still a quick fix and not a make-good.
The point was never to replace the ad ops person’s judgment – it was to stop wasting it on the search, and hand them the list of things that actually need attention that day. One publisher caught an $8,500 under-delivery on day two of their trial. That’s not a software win. That’s a margin that would have quietly walked out the door.
Rob: When teams finally see their GAM network monitored every morning, what tends to surprise them most about what was slipping through?
Chris: Two things, and they land back to back.
The first is the volume. Teams expect to catch the occasional big problem. What gets them is how much small stuff was quietly slipping by – a deal pacing a little behind here, an ad unit earning less than it should there. None of it dramatic enough to trigger a manual look, all of it adding up. The reaction I hear most often isn’t “good catch” – it’s “wait, how long has that been happening?”
That’s the second surprise, and it’s the uncomfortable one. The issues were never hidden. They were sitting right there in the data the whole time, completely findable – just not on a day anyone happened to look. Once you see a week of mornings laid out side by side, you realize the problem was never that your team missed things. It’s that nobody can manually eyeball everything, every day, and still catch the quiet stuff before it costs you.
And honestly, that reframe is the valuable part. People walk in assuming they’ve got a diligence problem and walk out realizing they had a visibility problem. Those need very different fixes – and only one of them is solvable by telling your team to try harder.
Rob: If a publisher wanted to pressure-test whether their ad ops workflow is helping them stay ahead or simply helping them keep up, what’s the one question they should ask themselves?
Chris: Ask: “This morning, did we find the problems – or did the problems find us?”
If your honest answer is that you mostly learn about issues when an advertiser emails, when finance flags a discrepancy, or when the weekly report finally surfaces it – you’re keeping up. You’re running a workflow designed to confirm what already happened. It’s competent, it’s busy, and it’s permanently one step behind.
If the answer is that you tend to know first – that the gap between something changing and your team seeing it is measured in hours, not reporting cycles – you’re staying ahead. That’s the whole difference, and it has almost nothing to do with how hard your team works. The hardest-working team in the world will still lose the race if their information arrives late.
So that’s the test. Not “are we busy” – everyone’s busy. The question is whether your busyness is buying you a head start or just helping you tread water. If you’re not sure which, that uncertainty is usually the answer.
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This is content created in paid partnership with ProOps Consulting. We only feature partners who we believe bring real value to the publisher community.