Optimize retention interventions by principal strata

What it's for

Identifies who a retention offer can actually change the mind of — not merely who looks likely to leave — and allocates limited budget to that group.

Moves retention recommendations beyond risk ranking: Gitrevio can distinguish people likely to stay anyway, people this specific intervention may help, and people for whom the intervention lacks evidence.

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.

{
  "as_of_ms": 1000,
  "current_candidates": [
    {
      "audit_group_id": "group-b",
      "audit_group_use_authorized": true,
      "data_cutoff_ms": 900,
      "evidence_verified": true,
      "features": {
        "support_gap": 0
      },
      "id": "candidate-0",
      "intervention_cost": 10,
      "intervention_eligible": true,
      "retained_value": 1000,
      "role_id": "engineer",
      "supportive_use_authorized": true
    },
    {
      "audit_group_id": "group-a",
      "audit_group_use_authorized": true,
      "data_cutoff_ms": 900,
      "evidence_verified": true,
      "features": {
        "support_gap": 0
      },
      "id": "candidate-1",
      "intervention_cost": 10,
      "intervention_eligible": true,
      "retained_value": 1000,
      "role_id": "engineer",
      "supportive_use_authorized": true
    },
    {
      "audit_group_id": "group-b",
      "audit_group_use_authorized": true,
      "data_cutoff_ms": 900,
      "evidence_verified": true,
      "features": {
        "support_gap": 0
      },
      "id": "candidate-2",
      "intervention_cost": 10,
      "intervention_eligible": true,

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.

{
  "allocation": {
    "fairness_constraint_satisfied": true,
    "maximum_observed_group_selection_rate_gap": 0.25,
    "optimizer_exact": true
  },
  "candidate_diagnostics": [
    {
      "always_leave_probability": {
        "median": 0.3528,
        "p05": 0.269,
        "p95": 0.4398
      },
      "always_stay_probability": {
        "median": 0.3571,
        "p05": 0.3034,
        "p95": 0.4259
      },
      "conservative_incremental_retention_probability": 0.174,
      "conservative_net_value": 164,
      "harm_sensitivity_bound": 0.02,
      "helped_probability": {
        "median": 0.2842,
        "p05": 0.194,
        "p95": 0.3663
      },
      "intervention_cost": 10,
      "retained_value": 1000,
      "selected": true,
      "subject_ref": "candidate-1"
    },
    {
      "always_leave_probability": {
        "median": 0.3528,
        "p05": 0.269,
        "p95": 0.4398
      },
      "always_stay_probability": {
        "median": 0.3571,
        "p05": 0.3034,
        "p95": 0.4259
      },
      "conservative_incremental_retention_probability": 0.174,
      "conservative_net_value": 164,

How it works

Causal inference & experiment design — Separate what a change caused from what merely moved alongside it.

1. 1 Use only mature randomized trials with overlap, verified assignment, no interference and a plausible exclusion restriction; reserve the latest trials for untouched evaluation.
2. 2 Fit conditional always-stay, helped and always-leave stratum probabilities by EM under monotonicity and require better later observed-outcome likelihood than unconditional trial rates.
3. 3 Subtract a governed harmed-stratum sensitivity bound from the lower helped probability, value only incremental retention, and optimize positive conservative value under intervention budget, capacity and authorized group-rate constraints.

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": "estimate who an optional retention intervention" }
  → finds "optimize_retention_interventions_by_principal_strata"

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

gitrevio_capability_run
  { "capability_id": "optimize_retention_interventions_by_principal_strata", "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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