Estimate pricing experiment value

What it's for

Finds pricing changes that improve contribution after costs without silently accepting an unacceptable conversion decline, and quantifies whether more information could matter.

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.

{
  "arms": [
    {
      "conversions": 300,
      "exposures": 2000,
      "id": "baseline",
      "price": 100,
      "variable_cost_per_conversion": 20
    },
    {
      "conversions": 275,
      "exposures": 2000,
      "id": "higher-price",
      "price": 125,
      "variable_cost_per_conversion": 20
    },
    {
      "conversions": 190,
      "exposures": 2000,
      "id": "too-high",
      "price": 170,
      "variable_cost_per_conversion": 20
    }
  ],
  "baseline_arm_id": "baseline",
  "future_eligible_customers": 40000,
  "implementation_cost": 50000,
  "maximum_conversion_decline": 0.03,
  "maximum_conversion_harm_probability": 0.2,
  "minimum_profit_superiority_probability": 0.75,
  "posterior_draws": 5000,
  "seed": 23
}

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.

{
  "arm_diagnostics": [
    {
      "arm_id": "higher-price",
      "clears_profit_and_conversion_gates": true,
      "expected_future_contribution": 529091.6565,
      "posterior_mean_conversion_rate": 0.1379,
      "probability_conversion_harm": 0.0592,
      "probability_profit_superior_to_baseline": 0.8792
    },
    {
      "arm_id": "too-high",
      "clears_profit_and_conversion_gates": false,
      "expected_future_contribution": 522625.298,
      "posterior_mean_conversion_rate": 0.0954,
      "probability_conversion_harm": 0.992,
      "probability_profit_superior_to_baseline": 0.8076
    },
    {
      "arm_id": "baseline",
      "clears_profit_and_conversion_gates": false,
      "expected_future_contribution": 430873.9636,
      "posterior_mean_conversion_rate": 0.1503,
      "probability_conversion_harm": 0,
      "probability_profit_superior_to_baseline": 0.5
    }
  ],
  "assumptions": [
    "Arm assignment is randomized or otherwise causally identified, exposure and conversion windows are mature, interference is negligible, and price presentation/product/segment are held stable except for governed treatment differences.",
    "Price minus variable cost is the correct incremental contribution boundary; refunds, taxes, churn, cannibalization, expansion, support, and implementation effects are included or separately guarded.",
    "Perfect-information value is a model-conditional upper bound on more learning, not the value of any specific experiment design or permission to continue experimentation indefinitely."
  ],
  "configuration": {
    "implementation_cost": 50000,
    "maximum_conversion_decline": 0.03,
    "maximum_conversion_harm_probability": 0.2,
    "minimum_profit_superiority_probability": 0.75,
    "posterior_draws": 5000,
    "seed": 23
  },
  "decision": "pricing_arm_supported",
  "method": "bayesian_pricing_contribution_and_information_value_v1",
  "summary": {
    "baseline_arm_id": "baseline",

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 randomized pricing arms, exposure and mature conversion counts, net price and variable cost, future eligible customers, baseline arm, implementation cost, and acceptable conversion harm.
2. 2 Draw beta-binomial conversion rates, translate every arm into future contribution, compare alternatives with the no-change baseline after implementation cost, and apply profit-superiority plus conversion-harm gates.
3. 3 Return the supported highest-contribution arm and the expected value of perfect information as an upper bound on additional learning, then separately stress churn, refunds, taxes, cannibalization, and competitive response.

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": "choose pricing experiment arms by posterior" }
  → finds "estimate_pricing_experiment_value"

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

gitrevio_capability_run
  { "capability_id": "estimate_pricing_experiment_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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