Paid Media Planning Tool

Paid Media Forecasting & Scenario Planner

Model paid media performance, test budget scaling and compare scenarios using either diminishing returns or manual funnel assumptions.

1

Choose your forecast model

Use diminishing returns for budget scaling, or manually control the full funnel for custom what-if scenarios.

Diminishing Returns ModelConversion growth slows as budget scales according to your selected Scaling Efficiency. A value of 100% is linear; lower values create progressively stronger diminishing returns.
2

Build your scenarios

Establish your baseline performance, then test up to two alternative plans.

Baseline

Current / Expected

Required
For ecommerce, this is usually AOV.
Scenario A

Alternative Plan

100% linear · 90% mild · 80% moderate · 70% stronger diminishing returns.
Scenario B

Alternative Plan

100% linear · 90% mild · 80% moderate · 70% stronger diminishing returns.

Two ways to model paid media

Diminishing ReturnsBudget scaling with a user-controlled efficiency curve

This model assumes additional budget generates progressively less incremental conversion volume when Scaling Efficiency is below 100%.

Manual FunnelBuild the funnel from your own assumptions

Set CPM, CTR and conversion rate yourself. This is useful when you already have specific assumptions for each scenario.

Diminishing returns formula

Projected Scenario ConversionsBaseline Conversions × (Scenario Budget ÷ Baseline Budget)Scaling Efficiency

Scaling Efficiency is entered as a percentage. For example, 80% uses an exponent of 0.80. At 100%, conversion volume scales linearly with budget. Below 100%, efficiency gradually deteriorates as spend increases.

Baseline ImpressionsBudget ÷ CPM × 1,000
Baseline ClicksImpressions × CTR
Baseline ConversionsClicks × CVR
CPABudget ÷ Conversions
RevenueConversions × Conversion Value
ROASRevenue ÷ Budget

What should I use scenarios for?

Use scenarios to explore questions such as how much efficiency may deteriorate when scaling spend, how improved conversion rate could change economics, or whether higher conversion value could offset increased acquisition costs.

Important:These are mathematical scenarios, not performance predictions. Paid media results can be affected by auction competition, audience saturation, creative fatigue, attribution, seasonality, platform optimization and other factors not represented by this model. Scaling Efficiency is a user-defined assumption and should not be interpreted as a universal benchmark.