Compare Campaign Profitability With Consistent Assumptions

Compare campaigns by applying the same revenue, cost and attribution definitions, then calculating contribution after advertising. ROAS alone can rank campaigns differently from contribution dollars. A comparison should also state whether it measures observed results or models a future budget decision; those answer different questions.

Set the comparison rules first

Use the same currency, reporting window, refund treatment and revenue basis. Decide whether sales tax and shipping collected from customers are included, and match the associated costs. Identify whether each campaign targets new customers, existing customers or both before interpreting acquisition efficiency.

Check the value being reported by the platform. Google Ads conversion values can represent different measures of business value. A campaign reporting sales revenue cannot be compared directly with one reporting estimated lead values simply because both display a value-to-cost ratio.

Write those definitions into the campaign profitability workbook. Keep source exports alongside the working file so another reviewer can reproduce the totals rather than relying on a dashboard screenshot.

Compare two hypothetical campaigns

Campaign A spends $2,000 and records $8,000 in revenue. Campaign B spends $3,000 and records $10,000. A has a 30% contribution margin before ads, while B has 50% because its product mix leaves more revenue after variable costs. The margins are assumptions for this example, not industry benchmarks.

A has the higher ROAS. B leaves more contribution after advertising: $10,000 × 50% − $3,000 = $2,000. Neither figure includes additional fixed overhead or establishes how much revenue was incremental. The comparison describes the selected cost and attribution model.

Use the ROAS calculator to check each ratio. Then use the contribution margin calculator with consistent variable costs rather than assuming all campaign revenue has the same margin.

Stress-test the assumptions

Holding everything else constant, suppose B's realized net revenue is 20% lower than expected. At $8,000 revenue and 50% margin, contribution after its $3,000 advertising cost becomes $1,000. If its margin also falls to 35%, contribution after ads becomes $8,000 × 35% − $3,000 = −$200.

This sensitivity check identifies where the decision can reverse. It does not assign probabilities to those scenarios. Use your order and cost data to choose a plausible range, and keep a clear distinction between measured margins and estimates that still need validation.

Do not assume the next dollar performs like the last

An observed average describes the budget already spent. Increasing the budget can reach different people, change the mix of products sold or create capacity costs. Copying B's historical return onto a larger proposed budget is an explicit constant-performance assumption, not evidence that the larger campaign will deliver it.

Similarly, an apparent improvement after a campaign change may coincide with seasonality, pricing changes or reporting delays. If a causal decision matters, design an appropriate controlled comparison and examine its uncertainty. A two-row spreadsheet cannot establish incremental lift or statistical significance.

Document a decision you can revisit

Record the campaign period, source exports, revenue definition, included costs, attribution settings and the next review date. State the outcome in precise terms, such as “B left $2,000 before fixed overhead under the selected model,” rather than “B is always more profitable.”

Keep customer counts and retention evidence separate when deciding whether an acquisition campaign supports longer-term value. The CAC calculator can support that additional analysis. Download the working template from resources and preserve both the original scenario and subsequent actual results.