Calculate churn prevention break even

What it's for

Inverts the usual question: instead of predicting churn, it tells you how much churn an intervention must actually prevent before it pays for itself.

Answers how much churn reduction a retention program must actually deliver before its economics become defensible, including downside risk.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "minimum_probability_positive_value": 0.8,
  "scenario_probabilities": [
    0.2,
    0.5,
    0.3
  ],
  "segments": [
    {
      "baseline_churn_probability_scenarios": [
        0.12,
        0.15,
        0.2
      ],
      "contribution_margin_per_retained_customer": 2000,
      "eligible_customers": 1000,
      "id": "mid-market",
      "intervention_cost_per_customer": 40,
      "intervention_fixed_cost": 10000,
      "treated_churn_probability_scenarios": [
        0.08,
        0.09,
        0.12
      ]
    }
  ]
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Baseline and treated churn paths are causally identified or explicitly labeled forecast scenarios over one outcome window and eligible population.",
    "Contribution margin is incremental retained-customer value after avoidable service cost; intervention cost is complete, incremental, and uses the same horizon and currency.",
    "Scenario columns encode the same future across segments; the break-even result does not establish intervention effectiveness, capacity feasibility, or customer-level targeting."
  ],
  "configuration": {
    "maximum_portfolio_cvar_loss": null,
    "minimum_probability_positive_value": 0.8,
    "scenario_count": 3,
    "tail_probability": 0.1
  },
  "decision": "churn_prevention_economics_supported",
  "method": "aligned_scenario_churn_prevention_break_even_v1",
  "segment_diagnostics": [
    {
      "break_even_absolute_churn_reduction": 0.025,
      "clears_segment_probability_gate": true,
      "cvar_net_value_loss": -30000,
      "expected_absolute_churn_reduction": 0.062,
      "expected_net_value": 74000,
      "expected_retained_customers": 62,
      "intervention_cost": 50000,
      "probability_positive_net_value": 1,
      "segment_id": "mid-market"
    }
  ],
  "summary": {
    "clears_cvar_gate": true,
    "clears_probability_gate": true,
    "expected_portfolio_net_value": 74000,
    "portfolio_cvar_net_value_loss": -30000,
    "probability_positive_portfolio_value": 1,
    "segment_count": 1
  },
  "truncated_segment_count": 0
}

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze one eligible population, outcome window, causal or explicitly forecast baseline and treated churn paths, contribution margin, and complete intervention cost for every segment.
2. 2 Compute each segment's break-even absolute churn reduction, propagate aligned scenario reductions into retained-customer contribution, and aggregate net value without assuming segment independence.
3. 3 Support the economics only when portfolio positive-value probability and the optional CVaR loss ceiling clear; keep effectiveness identification and delivery feasibility as separate gates.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "calculate the absolute churn reduction an" }
  → finds "calculate_churn_prevention_break_even"

gitrevio_capability_describe
  { "capability_id": "calculate_churn_prevention_break_even" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "calculate_churn_prevention_break_even", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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