Token Cost per Lead
Definition
An operational metric assigns AI token costs to the leads processed or acquired with them.
Token costs per lead are the attributable costs for processed input and output tokens divided by the defined number of leads. Period, lead definition, and included processing steps must be consistently delimited.
Simply explained
You divide the AI text costs of a process by the number of associated contacts.
Why it matters in practice
This allows examining a specific AI-supported lead process for variable model costs.
Example from business
Hypothetical example: An operation divides the token costs of its contact qualification in the month by the leads qualified in this process.
Limits and pitfalls
- The metric does not automatically include personnel, advertising, or software costs. Without leads, the quotient cannot be meaningfully reported.
Origin & history
The metric combines usage-based model billing with classic cost-per-result considerations.
Use in mid-sized companies
- Record input and output tokens, model prices, and associated lead events within the same period.
Frequently asked questions
Is that the same as the total cost per lead?
No, it is only the accrued portion of the token costs.
Sources
– technische Grundlage zur Tokenzählung; die Kennzahl ist eine betriebliche Arbeitsdefinition.
Editorially reviewed on: