Overview
Response curves show how expected outcomes change as spend changes. They help answer:If we spend more or less in this channel, what is likely to happen?
Why response curves matter
Media channels do not scale in a straight line forever. At low spend levels, adding budget may create strong incremental gains. At higher spend levels, the same additional dollar may produce less impact. This is called diminishing returns or saturation. Response curves help Provalytics identify:- Channels with room to grow
- Channels approaching saturation
- Channels that may need budget reductions
- Channels that should be protected by guardrails
How response curves are used
Provalytics uses response curves in planning and optimization features such as:- Budget Recommendations
- Scenario Planner
- Spend Headroom
How to interpret a response curve
A response curve is not a guarantee. It is a modeled estimate based on observed data, channel behavior, and historical performance. Use it as a planning guide, not as a promise. The most useful questions are:- Is the channel still efficient at higher spend?
- Is the next dollar likely to perform above or below target?
- Is the recommendation within a spend range where we have enough historical evidence?
- Are there business reasons to override the pure efficiency recommendation?