A response curve has two purposes, forecasting responses and evaluating a campaign’s performance while the campaign is active, referred to as Projection Model or Project to Final.
Forecasting
As the diagram illustrates below, a response curve is a critical element in forecasting because it derives when responses come in over the life-cycle of a campaign given the expected responses.
Using a granular forecast (by day), a business is able to plan and staff to meet demand. Forecasting helps a company optimize operations by helping answering the following questions:
- How much staff does a call center need on hand?
- How many calls will a call center get on day X?
- How many widgets do we need to produce?
Projection Model
A Projection Model enables a campaign to be evaluated while the campaign is still active to determine how it is performing. It allows the cost to be allocated based on the percentage complete, enabling a projected cost per sale to be calculated. The project to final is used so campaign sales and cost calculations can be represented without over or under inflating the metrics.
Creating a Response Curve
To create a response curve, you use the insight gained from historical data analysis to determine the time interval and curve length. Response curves vary by media type, meaning a company will have multiple response curves. A time interval can be hours to weeks. Direct mail and direct response will typically use weeks as the time interval and hours are typically used for interactive rich media ad (TV/Radio). Once you know the length of the curve, it is a simple matter of allocating the response across the life of the campaign.