Reach

Impressions count deliveries. Reach counts people. A campaign that served 300,000 impressions to 50,000 people reached 50,000 people six times each, and those two descriptions of the same campaign lead to completely different decisions.

Frequency is simply impressions divided by reach, and it is the number that tells you which kind of campaign you are running. Low frequency across a wide audience is a prospecting campaign. High frequency across a narrow one is a retargeting campaign, whatever the brief called it.

Reporting impressions alone is the standard way this goes wrong in agency work, because impressions always go up. A client reading a rising impression count has no way to tell whether the campaign found new people or simply showed the same ones the ad again.

Every campaign has a reachable population: the people who match the targeting, are active on the platform during the flight, and are eligible to be served. Once the campaign has touched most of them, reach flattens. It is a ceiling made of audience size, and no amount of extra budget raises it.

What extra budget does after that point is buy frequency. The curve is easy to read once you know to look for it: reach bends toward horizontal while impressions carry on climbing in a straight line.

Whether that is waste depends on the goal. For a brand campaign some repetition is the point, since a single exposure rarely does anything. For a direct response campaign it usually means rising costs and falling returns, and creative fatigue shows up shortly after.

The useful move at the plateau is not a budget increase, which the client will read as growth and the data will not support. It is a new audience, a new placement or a new channel, and the plateau chart is what makes that case without an argument.

Deduplicating people is harder than it sounds. A platform sees devices, browsers, apps and accounts, and has to decide which of those belong to the same person. Someone logged in on a phone and a laptop may be recognised as one user; someone browsing logged out on a work machine may not.

So platform reach figures are estimates produced by a model, not a headcount. They are consistent enough to compare against themselves over time, which is what makes them useful for planning and for spotting a plateau.

The care needed is in how they are quoted. Presenting reach to a client as though it were a census figure invites a question nobody can answer, and it sets up a comparison against a panel-based measurement that will not agree. Saying a campaign reached approximately 50,000 people is accurate. Saying it reached 50,342 is not.

Privacy changes have widened the error bars rather than narrowed them. Reduced cross-app tracking, less durable identifiers and more logged-out browsing all make deduplication harder, so recent reach figures rest on more modelling than older ones did.

Each platform deduplicates within itself and has no view of the others. Meta does not know that the person it reached also saw the YouTube ad. So adding the two reach figures counts that person twice, and the overlap is largest exactly where it matters most, among heavy users of both.

The inflation is not small. For campaigns targeting a similar audience on two large platforms, a naive total can overstate real reach substantially, and the error grows with every platform added to the sum.

There is no free fix. Cross-platform deduplication needs either a measurement partner with a panel across both, or a media mix approach that models rather than counts. Both cost money and neither is exact.

The workable answer for most agencies is not to produce the number. Report reach per platform, report the total on impressions and spend where addition is valid, and say plainly that a combined reach figure would double count. Clients accept that far more readily than agencies expect.