Geeks Digital 
Every ad platform grades its own homework. Google Ads reports the conversions Google wants credit for. Meta reports the conversions Meta wants credit for. Add the platform numbers together and you will routinely find more revenue claimed than your bank account received. Teams that budget from those dashboards end up scaling channels that look efficient and starving the ones that quietly do the work.
The problem with last click and platform attribution
Last click attribution hands full credit to the final touchpoint, which systematically flatters bottom funnel campaigns, especially brand search. Your own brand keywords convert beautifully because the buyer had already decided; the ad just collected the click on the way in. Meanwhile the prospecting campaign that created the demand three weeks earlier reports a mediocre return and gets cut.
Platform attribution has the opposite bias: each platform uses view through windows and modeled conversions that expand its own claimed contribution. Neither view is lying exactly. Both are answering a narrow question that is not the question your business needs answered.
The question that matters
The real question is simple to state: for each incremental dollar of spend, how much contribution margin comes back, and how fast? Answering it requires three numbers most dashboards never show.
First, true contribution per order or customer: revenue minus cost of goods, payment fees, shipping or delivery cost, and any variable service cost. A 4x ROAS on a product with thin margins can lose money; a 2x ROAS on a high margin service can be excellent.
Second, blended acquisition cost: total marketing spend divided by total new customers across all channels, tracked as a trend. Blended CAC cannot be gamed by attribution because it ignores attribution entirely.
Third, payback period: how many weeks or months until the contribution from a cohort of customers covers what you paid to acquire them. Payback determines how fast you can safely reinvest, which for most growing companies matters more than any ratio.
A practical operating model
You do not need a data science team to run this. Build a simple monthly model: spend by channel, new customers, blended CAC, average contribution per customer, and payback based on your real repeat or retention pattern. For subscription and service businesses, add cohort revenue over time. This single sheet, kept honestly, will settle more budget arguments than any attribution tool.
Then use platform metrics for what they are good at: comparing campaigns against each other inside the same platform. Platform ROAS is a fine relative signal and a poor absolute one. Let it rank your ad sets; never let it size your budget.
Testing incrementality without a lab
The cleanest way to learn what a channel truly adds is to vary it and watch blended results. Geo holdouts, where a channel runs in some regions and not others, are practical for most businesses. So are budget step tests: raise or lower a channel meaningfully for three or four weeks and watch blended CAC and total new customers, not the platform’s self report. Brand search is the classic first experiment, and many companies discover a large share of that spend was collecting clicks they would have received anyway.
What changes when you operate this way
Creative and offer testing gets funded properly, because prospecting is finally judged on the demand it creates rather than the last clicks it happens to catch. Budget shifts stop being political, because everyone argues from the same blended sheet. And scaling decisions get calmer: you raise spend when payback holds at the margin, and pause when it stretches, regardless of what any dashboard celebrates.
Our Google Ads and Meta Ads engagements are run against exactly these numbers, and the funnel work that improves contribution per visitor makes every channel’s math better at once. The dashboards can keep flattering themselves. Your budget should listen to the unit economics.
