Calculate churn prevention break even
Calculate the absolute churn reduction an intervention must cause to break even, then test aligned baseline/treated scenarios against probability-of-positive-value and portfolio CVaR gates.
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
Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_portfolio_cvar_loss | any | Your calibration | Optional |
| minimum_probability_positive_value | number ≥ 0, ≤ 1 | Your calibration | Optional |
| scenario_probabilities | array of number ≥ 2 items | Evidence | Yes |
| segments | array of objects (7 fields) | Evidence | Yes |
| tail_probability | number ≥ 0.001, ≤ 0.5 | Your calibration | Optional |
Each segments
record
| Field | Type | Required |
|---|---|---|
| baseline_churn_probability_scenarios | array of number (≥ 2 items) | Yes |
| contribution_margin_per_retained_customer | number (> 0) | Yes |
| eligible_customers | number (> 0) | Yes |
| id | string (non-empty) | Yes |
| intervention_cost_per_customer | number (≥ 0) | Yes |
| intervention_fixed_cost | number (≥ 0) | Yes |
| treated_churn_probability_scenarios | array of number (≥ 2 items) | Yes |
{
"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 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 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 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.
Assumptions & guardrails
- Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
- Treated churn paths are causally identified or explicitly labeled forecasts, scenario columns describe the same future across segments, and margin/cost share one horizon, currency, and customer definition.
- The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
- Break-even arithmetic cannot prove the intervention reduces churn and must not be converted into customer- or employee-level targeting.
Minimum evidence
- segments: required and organization-defined
- scenario_probabilities: at least 2 rows/items
How to validate it
Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- aligned baseline and treated churn probability scenarios over one eligible population and outcome window
- retention estimand, intervention definition, eligibility, assignment integrity, maturity, segment freeze, contribution margin, complete fixed/variable cost, scenario probabilities, probability gate, and tail-loss tolerance
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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 20 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 aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.