The audience reach calculator above applies your targeting filters to a total market one at a time, shows the audience surviving each step, and then checks the result against your budget: how many impressions the money buys, what frequency that implies across the remaining audience, and what a specific frequency target would actually cost. It answers the question most media plans skip — whether the audience you have defined is the right size for the money you intend to spend on it.
Arb Digital uses this check before any paid social plan is signed off, because the most expensive mistakes in paid media happen at the targeting stage rather than in the account. An audience that is too small for its budget produces punishing frequency and rising costs no amount of optimisation can rescue, and the problem is invisible until the campaign has been running for a fortnight.
What This Audience Reach Calculator Does
Enter the size of your total addressable market and the percentage of it that survives each of three targeting layers — location, demographics, and interests or behaviours. The tool multiplies them together to give the reachable audience, shows that figure as a percentage of the market you started with, and charts the drop at each stage so you can see which filter is doing the most cutting.
It then brings budget into the picture. Your spend divided by your CPM gives the impressions you can buy, and those impressions divided by the reachable audience give the average frequency that budget implies. The final figure runs the calculation the other way: the budget required to deliver your chosen target frequency across the audience that survived the filters. If those two numbers are wildly apart, either the audience or the budget needs to change before launch.
How to Use It
- Enter your total addressable audience. Population data works for consumer markets — national statistics offices publish it freely — while B2B markets are usually sized from industry and company-size counts.
- Enter the share remaining after your location filter. If you target three cities that hold 30% of the national population, enter 30.
- Enter the share remaining after demographics — the proportion of people in those locations who fall inside your age, income or job-title constraints.
- Enter the share remaining after interest or behaviour targeting, including any exclusions such as existing customers.
- Add budget, CPM and target frequency, then click Calculate and compare the frequency your budget produces with the frequency you planned for.
The Formula / How It's Calculated
Reachable audience is a chain of multiplications: Total × Filter₁ × Filter₂ × Filter₃. With 8,000,000 people and filters of 30%, 45% and 25%, the audience falls to 2,400,000, then 1,080,000, then 270,000 — just 3.4% of where it started. That compounding is the whole reason this tool exists, because each individual filter sounds reasonable in isolation.
The budget side uses standard CPM arithmetic. Impressions are Budget ÷ CPM × 1,000, so $15,000 at a $12 CPM buys 1,250,000 impressions. Average frequency across the reachable audience is Impressions ÷ Reachable Audience, or 1,250,000 ÷ 270,000 = 4.63 exposures per person. Running it in reverse, the budget needed for a target frequency is Audience × Target Frequency × CPM ÷ 1,000, which at a target of 3.0 comes to $9,720.
For consumer market sizing, national statistics agencies are the most reliable free source of the first number — the United States Census Bureau publishes population data by geography, age and household characteristics. Platform audience estimates, such as those in the Meta Business Help Center, are useful as a cross-check but count active accounts rather than people.
Filters Multiply, and That Is Not Intuitive
Every targeting decision feels additive in a planning meeting. You keep the three biggest cities, focus on the right age band, and add an interest that matches the product — three sensible choices. But the audience does not lose three chunks; it is multiplied down three times, and the result is a fraction most people would never have agreed to if it had been proposed directly.
The practical discipline is to state the surviving percentage after every filter you add, not just at the end. A filter that leaves 60% is cheap. A filter that leaves 20% is a major decision and should be justified against evidence that the excluded 80% genuinely will not convert. In most accounts, at least one of the stacked filters is there because someone added it years ago and nobody has questioned it since.
The Independence Assumption You Are Making
The multiplication above assumes your filters are independent — that the share of people matching your demographic filter is the same inside your target locations as it is nationally. In reality, targeting criteria are usually correlated, and the correlation runs in both directions.
Sometimes the true audience is larger than the calculation suggests: if you target affluent urban postcodes and then filter on high income, the second filter removes far fewer people than its national share implies, because you already selected for it. Sometimes it is smaller: professionals in a narrow job title may be concentrated in cities you excluded. Treat this calculator's output as a planning estimate and check it against the platform's own audience size estimate before committing. Where they diverge sharply, the correlation between your filters is usually the reason.
Reachable Is Not the Same as Reached
The reachable audience is everyone eligible to see your ad, not everyone who will. Actual delivery depends on how often those people use the platform, how competitive their attention is in the auction, and how the delivery system chooses to spend your budget. A campaign will typically reach a substantial portion of a well-defined audience over a month, but it will not reach all of it, and the last portion is always the most expensive.
This matters for the frequency figure. If your budget theoretically delivers 4.6 exposures across the whole reachable audience but the campaign only reaches 70% of it, actual frequency among the people who do see the ads is closer to 6.6 — a meaningfully different experience. When you review the campaign after launch, calculate the real figure from delivered impressions and reported reach rather than assuming the plan held.
When the Answer Is to Widen, Not to Optimise
If the frequency implied by your budget is uncomfortably high, there are only four levers: spend less, reach more people, pay a lower CPM, or run for longer. Optimisation inside the account cannot fix an audience that is structurally too small, because the constraint is arithmetic rather than tactical.
Widening is often the cheapest fix and the least popular one, because narrow targeting feels precise and broad targeting feels wasteful. On platforms where delivery is algorithmic, though, a wider audience gives the system more room to find responsive people, and it frequently produces a lower cost per result than the tighter definition it replaced. Price both options before deciding — model the impression side with the CPM calculator, the resulting exposure with the ad budget calculator, and the downstream efficiency with the CPA calculator.
Arb Digital sizes audiences against real market data before a campaign launches, sets targeting that the budget can actually support, and monitors frequency so paid social does not quietly saturate the same small group.
Social Media Marketing Paid Advertising ServicesCommon Mistakes to Avoid
- Adding filters without stating the surviving percentage — three reasonable-sounding filters can leave under 5% of the market.
- Treating platform audience estimates as headcounts — they count accounts, and one person can hold several.
- Assuming full delivery — real frequency among people actually reached is always higher than the plan's average.
- Ignoring correlation between filters — overlapping criteria can make the true audience much larger or much smaller than the multiplication suggests.
- Fixing high frequency with creative alone — new creative helps, but it cannot change the arithmetic of budget divided by audience.
Related Free Tools From Arb Digital
Price impressions against your audience with the CPM calculator, plan the spend with the ad budget calculator or the marketing budget calculator, and check click economics with the CPC calculator. Judge the return with the ROAS calculator, and browse the full free online tools hub for more.
Frequently Asked Questions
For consumer markets, national statistics agencies publish population data by geography and demographic characteristics free of charge. For business markets, company counts by industry and size band are the usual starting point, filtered down to the roles that make or influence the purchase.
Platform estimates count active accounts on that platform rather than people in a market, and they already reflect correlations between your targeting criteria. Large differences usually mean your filters overlap more or less than the independent multiplication assumes.
It depends on the campaign's objective and its length. Campaigns explaining something unfamiliar generally need more exposures per person than campaigns triggering an existing intention, and the same number of exposures compressed into a shorter window feels far more intense to the audience.
No. A small audience with a strong match to your offer can be highly efficient, provided the budget is scaled to it. The problem is a large budget aimed at a small audience, which forces high frequency regardless of how well the campaign is managed.
Yes. Excluding existing customers, recent purchasers or past converters removes people from the reachable audience just as any other filter does, and those exclusions are often larger than expected in established businesses.
Not usually on a cost-per-result basis. Broader targeting gives algorithmic delivery systems more room to find responsive people and often lowers both CPM and cost per acquisition, even though it appears less precise on paper.
Length does not change the reachable audience, but it changes how the same frequency is experienced. Spreading the same impressions over a longer period lowers weekly frequency and generally reduces fatigue, at the cost of a slower build in awareness.
Figures produced by this tool are planning estimates only — audience sizes depend on data sources and platform methodologies, and actual delivery will differ from a modelled plan.
