Today, metrics no longer directly reflect reality. In a world of automation and incomplete data, they are increasingly an interpretation rather than a fact. It is this shift that gives rise to a new understanding of what metrics will be in 2026.
Advertisers are no longer saved by “good numbers”
2025 brought a new norm. Reports look perfect, but business results are not so great. Green indicators, flat graphs, “everything is OK” in the offices, and at the same time, the feeling that something is not working as it should. And it’s not about bad settings or the wrong contractor. It’s just that the numbers no longer directly reflect reality.
Marketing metrics increasingly mean “well calculated” rather than “that’s how it was.” A significant portion of the results are not facts, but estimates, reconstructions, or simulations. The advertiser finds themselves between two realities:
- Platform: conversions are partially simulated or estimated, optimization is automated, reporting looks more even and “cleaner.”
- Business: sales, margins, repeat purchases, returns, LTV, and the actual quality of leads do not necessarily match the picture painted by the system.
In 2025, large advertising ecosystems stopped pretending that they see everything directly. The chain of interactions is broken, but advertising still needs to be optimized, and reports need to get rid of “holes.” The answer is modeling: some conversions are not measured but calculated statistically. Meta speaks directly about conversion modeling when data is incomplete. Google phrases it more cautiously: there are fewer “Unknown” entries in reports because the system better understands who is who.
When there are fewer signals, the approach changes not partially, but systematically. It is necessary to simultaneously maintain the controllability of advertising and the stability of reporting, even if direct measurability has declined. As a result, two things happen:
- Closing the black box — to strengthen the automation of optimization and traffic distribution by moving more decisions inside the platform.
- Cleaning up reports — compensating for measurement losses through modeling and estimation, smoothing out gaps and “Unknown” values.
In 2025, TikTok is actively pushing automation + measurement as a single package. It’s no longer about tools, but about control. Meta’s logic is the same: when conversions disappear from view, the system doesn’t wait and fills in the result using models.
Metrics that advertisers are abandoning
Most classic digital approaches were born in a world where the user was transparent for tracking, the path was linear, and the signal was transmitted without interruptions. Back then, advertising metrics directly drove decisions:
- viewed → tweaked → got results.
In 2025, this foundation failed. Visibility has become fragmented, uneven, and context-dependent. In such an environment, numbers that provide a complete picture lose their ability to guide reality. When the user’s path is no longer closed, last-click attribution, accurate conversion rates, or “pure” CPA may look convincing on paper, but they no longer have the support they used to have.
Direct attribution metrics as a source of illusions
Anything that is rigidly tied to the last click, channel, or fixed attribution window is increasingly failing to explain reality. Such figures no longer answer the question “why did this happen” — they only assign responsibility.
In an environment of modeled conversions, view-through, cross-device transitions, and incomplete signals, this logic gives a false sense of control. The advertiser sees the result in the report but cannot be sure that this particular path, channel, or click actually brought in the revenue. Control only looks clear on paper.
Performance metrics without business context
Today, it’s easy to see campaigns with “normal” CPA, stable ROAS, and accurate CR that don’t drive business. Marketing metrics look good in offices, are obediently optimized by systems, and look confident in reports. But margin, LTV, and customer quality are a completely different story.
According to this logic, effectiveness is what the platform likes. ROAS grows in the short term, CPA falls, the algorithm is satisfied, but repeat purchases and long-term customer value remain stagnant. The campaign is formally “working,” but the question “why?” hangs in the air.
Metrics that have lost their role as command levers
In the usual digital model, numbers were not discussed — they were managed. They clearly showed what to scale, what to cut, and where to push next. Everyone on the team looked at the same signals and moved in sync, without asking “what does this mean?”
In 2025, this function is gradually disappearing. When optimization is automated, traffic distribution is hidden, and a significant portion of the results are simulated, numbers cease to be a management tool. Familiar benchmarks lose their power: they still look familiar, but no longer tell you what to do next. This is where advertisers begin to abandon them — not out of ideology, but out of practicality.
The struggle for metrics that no longer work
After abandoning some of the old benchmarks, there is a natural desire not to cross everything out, but to revive something. Advertisers are trying to retain familiar marketing metrics by adapting them to the new reality: changing the window, adding a model, looking at it from a different angle. Others are let go without regret and give way to a new logic of efficiency. This is the entry point into 2026.
“Let’s tweak the attribution window, and everything will fall into place.”
This is probably the most popular reaction to shaky measurability. As soon as the numbers break down, the first thing they do is start tweaking the windows. 1 day, 7 days, click + view, a little wider, a little softer. Somewhere they added view-through, somewhere they extended attribution, somewhere they just chose the setting where ROAS looks “more plausible.”
It all seems logical. If the user thinks longer, the system needs to be given more time. Has the path become more complicated? Then we need to look broader. And at the interface level, it really works: the graphs smooth out, the CPA evens out, and the ROAS stops jumping.
But these changes have almost no effect on how the result is formed. They only affect the way the system displays it. In this context, the attribution window does not restore the cause-and-effect relationship, but sets a framework within which the result:
- is spread out over time to appear more stable;
- is redistributed between events without changing the behavior itself;
- is smoothed out where the measurement gives gaps.
More accurate figures in the report do not necessarily mean that the campaign has started to work differently. Often, it only indicates that the chosen configuration better masks measurement irregularities. User behavior, demand, and business impact may remain unchanged.
“Let’s save last-click/‘clean’ CPA”
When measurability cracks, there is a desire to roll back to simplicity. Back to an understandable world where everything was linear:
- last click → conversion → responsible party found.
The familiar patterns of 2018-2021 come into play, where last-click is presented as “objective truth” and CPA as the final argument. The logic is simple: if you remove everything unnecessary, you should be left with a clear signal, without models, embellishments, and “probably.”
The problem is that in conditions of incomplete data, the last click ceases to be an explanation and begins to play a different role — assigning blame or heroism. It explains not the cause of the event, but only the distribution of responsibility.
That is why modeling under consent mode appears in GA4 not as an alternative to last-click, but as a recognition that part of reality can no longer be seen directly. When signals disappear, the system does not “make a mistake” but statistically compensates for what is missing. In this model, marketing metrics cease to be a fact of observation and become the result of interpretation, and “pure” CPA is not the truth, but a convenient projection.
“Let’s keep ROAS/CPA, and let the business take care of itself.”
In conditions of broken measurability, the focus shifts to where the system still maintains stability. Optimization is narrowed down to a short window and clear goals. Everything looks neat in the reports. The numbers are even, there are no sharp drops, and the system looks controlled. In practice, it looks like this:
- ROAS in a short window (7d or less);
- CPA by optimization event;
- CR by the point that the algorithm “sees.”
The trap is that these metrics increasingly describe not the effectiveness of the business, but what is convenient for the platform to optimize. When measurements are smoothed out through modeling, the predicted ROAS or CPA may be the stability of the model rather than demand. As a result, the campaign formally works, but the connection with margin, repeat purchases, and real customer value is gradually blurred.
“Let’s fix the tracking accuracy with technology”
This is the reaction of people who are used to solving problems through infrastructure. When the numbers don’t add up, the first intuition is simple: there’s a tracking error somewhere. So another pixel appears, another tag, another postback, a pinch of server magic. This leads to endless patching in an attempt to piece together the full picture.
It is important to note a simple but unpleasant fact: the technical base is indeed necessary; without it, the system is blind. But the problem is not in “incorrect settings” — the reality of measurement itself has changed. Google and GA4 do not hide this: analytics are designed for a world where some data is fundamentally inaccessible due to consent, browser restrictions, and broken user paths. These losses cannot be recovered or “remedied.” They are compensated for by models. This is precisely why modeled data exists.
“Let’s bring back manual channel control”
The logic is old and understandable: if you break down the results by channel and manually tweak the weak spots, the system will become predictable again. When automation takes away transparency, there is a desire to point fingers again:
- stop the ad set;
- explain the failure by channel;
- record the result by creative.
The trap is that with the growth of automation, it is no longer a matter of managing levers, but of working with the interface. Traffic distribution, signal priorities, and optimization logic are increasingly taking place within the system, rather than at the level of individual channels or ad sets. People see the result, but they don’t see the mechanics that led to it.
What remains when numbers cannot be trusted
When it becomes clear that key metrics are not a fact but a model, a logical question arises: what should we rely on then? The answer is not to find the “best” indicator or to calculate the same thing more accurately. In 2026, the fulcrum will shift:
- “platform conversions = truth” → “business impact + incrementality + quality.”
When attribution is no longer proof
In this shift in logic, incrementality becomes decisive — not as a buzzword, but as a response to the modeled world. Attribution may remain convenient for reporting, but it is no longer proof and does not hold the decision.
Classic marketing metrics are no longer the arbiter of truth: they show numbers but do not explain impact. The focus is on approaches that do not try to “guess” the user’s path within the platform, but directly compare reality with and without advertising. This is no longer about beautiful reports, but about rough comparisons:
- regions where advertising was present and where it was not;
- audiences that saw the campaign and those that were in the holdout;
- periods before and after launch with control of external factors.
Geo-experiments, holdout groups, pre/post with control, A/B at the audience or region level look less elegant than the usual dashboards. But it is these approaches that survive in a world where measurement is fragmented and metrics are increasingly the result of modeling rather than direct observation.
Indicators after the loss of illusions
Broken attribution and simulated results have a simple effect: offices look better than the business actually performs. It becomes clear that a different foundation is needed — not metrics that depend on how the platform has pieced together the picture, but indicators that can survive any attribution chaos.
The focus is on blended logic: indicators that are tied not to a single channel, but to the business as a whole. They are rougher and less convenient for optimization in the interface, but they do not disappear when the attribution model changes.
MER (Marketing Efficiency Ratio)
This metric is not interested in who is doing well. It asks a simpler and tougher question: how much does the business earn for every hryvnia spent on marketing in general — without dividing it into channels, without trying to negotiate attribution, and without “and here’s the view-through.” MER does not guess; it looks at the overall result.
Blended CAC
This is the cost of a customer without embellishment. Not “by campaign,” not “by creative,” and not in a pretty window, but in reality. Blended CAC does not lie about how much business growth actually costs.
Contribution margin after marketing
This is where the magic of “good ROAS” ends. Only one thing is clear: is there money left after the ad has done its job? Everything else is just numbers.
These marketing metrics do not attempt to be accurate in micro-details. They do not explain which creative worked better and which channel is “to blame.” But they do what office reports can no longer do: they directly link advertising to P&L — the simple fact of whether there is money left after everything has worked.
Manage the causes, not the results
In a world where some events are simulated, the fact of a “lead” or “purchase” only records interaction with the system, but says almost nothing about the real business result. Therefore, the logic shifts from the number of conversions to what happens to them next.
The focus remains only on those conversions that pass the reality check. Metrics appear that are difficult for the platform to embellish:
- refund/return rate;
- failed payments;
- cancel rate;
- chargebacks.
Time becomes currency. The short ROAS window is increasingly turning into cosmetic optimization. The real power is shifting to where you can see what happens to the customer after the first action: LTV by cohort, retention, payback period. It is important whether the customer returns, how much they bring in, and when the advertising starts to really pay off.
Signals play an important but limited role in this logic. First-party data, server-side, consent mode, and modeled conversions do not return full measurability and do not make tracking “fair.” They provide a minimally reliable basis for training systems and connecting marketing with business. Those who have better signals and a faster feedback loop will optimize more accurately. But signals are a basis, not the truth.
Management shifts from results to causes. When outputs are partially modeled, you have to manage what generates them:
- the quality of the offer and proposal;
- creative hypotheses and message diversity;
- the speed of testing and decision-making;
- pricing and packages;
- UX and friction at checkout;
- CRM follow-up and post-conversion customer engagement.
It is at this point that the understanding of what metrics are in 2026 changes. It is no longer an attempt to accurately reproduce reality or a set of “facts from the office,” but rather benchmarks that only make sense in the context of business processes and decisions that are actually made.
Conclusion
In 2026, metrics will cease to be “facts” and become navigation in a world of incomplete data. Good reports no longer guarantee real business impact. The focus is shifting to where advertising cannot be added: to incrementality, quality, and time. This is where real control remains.