Campaign Profit Curve

Model the diminishing returns curve for outbound campaigns to find the optimal dial depth. Shows where additional staffing hours generate profit versus where they start losing money.

This tool models outbound campaign economics using a diminishing-returns contact rate function. As the dialer penetrates deeper into a list, the effective contact rate decays exponentially: adjusted_contact_rate = base_rate x exp(-penetration_depth x decay_rate). This reflects the real-world pattern where best numbers are reached first, and successive passes yield progressively lower connectivity. Revenue is calculated as attempts x adjusted_contact_rate x conversion_rate x revenue_per_conversion. Cost is linear: attempts / calls_per_hour x cost_per_agent_hour.

The key assumption is that contact rate decay follows an exponential function parameterized by a single decay rate coefficient. Higher decay rates model lists with concentrated reachability (e.g., cell phones with specific availability windows), while lower decay rates model more uniformly reachable populations. The model assumes conversion rate remains constant regardless of list depth — in practice, deeper-list contacts may also convert at lower rates, making the profit curve even steeper than shown.

The optimal stopping point is where marginal profit turns negative — the last 5% increment that still generates positive return. Beyond this point, every additional dial costs more than it earns. Practitioners should use the marginal profit chart (not cumulative) for operational decisions: stop dialing when the bar turns red. The ROI percentage at optimal depth tells you campaign viability. If the optimal depth is below 40%, the list quality or offer economics need improvement before scaling agent hours.