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AI Agent Retention & Churn Cost Calculator

Estimate how much customer churn, support surge, and reactivation spend cost when an AI agent delivers bad experiences.

Scenario presets

Retention inputs

Estimates are directional. Last updated: 2026-07-08. See notes.

Monthly agent-attributed churn cost

Churned users / month

Cost per failed session

Projected period cost

Lost MRR / month

Lost gross margin / month

Lost LTV / month

Support surge cost / month

Reactivation cost / month

Goodwill / refunds / month

Monthly cost breakdown

Cost line Monthly Share
Lost gross margin from churn
Support surge
Reactivation campaigns
Goodwill / refunds
Maintenance labor

Sensitivity: failure rate vs monthly churn cost

Failure rate Monthly churn cost Churned users / mo vs current

Verdict

Frequently asked questions

What does this calculator estimate?

It estimates the monthly cost of AI-agent-driven churn: lost MRR, lost LTV, extra support tickets, reactivation campaigns, goodwill credits, and ongoing maintenance. It also shows what happens if you reduce the failure rate.

How is agent-attributed churn calculated?

We estimate failed sessions per month, then apply a churn-likelihood percentage to users who experienced at least one failure. This is added to the baseline monthly churn to show the incremental churn likely caused by agent failures.

What counts as an agent failure?

Any session that ends with a wrong answer, harmful output, escalation, or user-reported dissatisfaction. Use your own support-survey or escalation rate as the failure percentage.

Does this include baseline churn?

Yes, we separate baseline churn (users who would leave anyway) from incremental churn attributed to agent failures. The headline cost is the incremental portion.

Why do I need MRR and LTV?

MRR gives the immediate monthly revenue lost. LTV shows the total long-term value destroyed when a customer churns. Both are needed for a complete business-case view.

How can I use the sensitivity table?

It shows how churn cost changes at different failure rates. Use it to justify investment in prompt engineering, guardrails, human review, or a better model.

Estimates are directional. Churn attribution is probabilistic: not every bad experience leads to churn, so we model a churn-likelihood multiplier based on the number of failed agent sessions a user sees.

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