Estimate cannibalization adjusted feature value

What it's for

Shows when an apparently successful feature mostly moves revenue from an existing product instead of creating incremental enterprise value.

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.

{
  "maximum_expected_cannibalization_rate": 0.5,
  "minimum_probability_positive_value": 0.8,
  "posterior_draws": 5000,
  "rollout_cost": 100000,
  "scenario_probabilities": [
    0.2,
    0.5,
    0.3
  ],
  "seed": 31,
  "segments": [
    {
      "displaced_legacy_contribution_per_cannibalized_adoption": 300,
      "feature_contribution_per_adoption": 500,
      "future_feature_adoption_scenarios": [
        1000,
        1500,
        2000
      ],
      "id": "small-business",
      "incremental_cost_per_feature_adoption": 50,
      "observed_cannibalized_adoptions": 50,
      "observed_feature_adoptions": 500
    }
  ]
}

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": [
    "Observed cannibalization is causally identified substitution from feature adoption rather than coincident legacy decline, with mature outcomes and stable segment definitions.",
    "Future adoption scenario columns are aligned across segments, and feature contribution, displaced legacy contribution, incremental cost, rollout cost, horizon, and currency are mutually consistent.",
    "The beta posterior covers substitution sampling only; competitive response, cross-segment migration, price changes, churn, expansion, and measurement/design error require separate scenarios or sensitivity analysis."
  ],
  "configuration": {
    "beta_prior_alpha": 1,
    "beta_prior_beta": 1,
    "confidence_level": 0.9,
    "maximum_expected_cannibalization_rate": 0.5,
    "minimum_probability_positive_value": 0.8,
    "posterior_draws": 5000,
    "scenario_count": 3,
    "seed": 31,
    "tail_probability": 0.1
  },
  "decision": "cannibalization_adjusted_feature_value_supported",
  "method": "beta_substitution_cannibalization_adjusted_feature_value_v1",
  "segment_diagnostics": [
    {
      "cannibalization_rate_interval": [
        0.0808,
        0.1255
      ],
      "expected_adjusted_feature_value_before_rollout_cost": 652177.7496,
      "expected_cannibalization_loss": 47482.2504,
      "expected_future_feature_adoptions": 1554.8,
      "expected_naive_feature_value_before_rollout_cost": 699660,
      "posterior_mean_cannibalization_rate": 0.1018,
      "probability_positive_adjusted_segment_value": 1,
      "segment_id": "small-business"
    }
  ],
  "summary": {
    "adjusted_expected_feature_value": 552177.7496,
    "adjusted_feature_value_interval": [
      317031.2725,
      747015.3039
    ],
    "cvar_adjusted_feature_value_loss": -316394.194,
    "expected_aggregate_cannibalization_rate": 0.1018,
    "expected_cannibalization_loss": 47482.2504,
    "naive_expected_feature_value": 599660,

How it works

Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.

1. 1 Freeze a causally defensible substitution label, mature feature adoptions, displaced legacy contribution, aligned future adoption scenarios, incremental feature economics, and rollout cost by non-overlapping segment.
2. 2 Draw posterior cannibalization rates, preserve common scenario identity across segments, subtract displaced legacy contribution from naive feature value, and aggregate the rollout distribution.
3. 3 Support rollout only when adjusted value clears the probability gate and expected aggregate cannibalization stays below the governed ceiling; report the naive overstatement explicitly.

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 feature value after posterior cannibalization" }
  → finds "estimate_cannibalization_adjusted_feature_value"

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

gitrevio_capability_run
  { "capability_id": "estimate_cannibalization_adjusted_feature_value", "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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