Calculate analytics portfolio realized ROI

What it's for

Gives CTOs, CEOs and investors a defensible answer to whether the analytics portfolio paid back—without double-counting benefits or converting activity into invented ROI.

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.

{
  "benefit_sources": [
    {
      "allocation_by_function": {
        "completion-forecast": 0.6,
        "decision-policy": 0.4
      },
      "causal_evidence_weight": 0.8,
      "id": "delay-loss-avoided",
      "realized_incremental_value_scenarios": [
        100,
        60,
        -10
      ]
    },
    {
      "allocation_by_function": {
        "decision-policy": 0.8
      },
      "causal_evidence_weight": 1,
      "id": "incident-loss-avoided",
      "realized_incremental_value_scenarios": [
        50,
        20,
        0
      ]
    }
  ],
  "functions": [
    {
      "id": "completion-forecast",
      "implementation_cost": 20,
      "recurring_cost_scenarios": [
        10,
        10,
        10
      ]
    },
    {
      "id": "decision-policy",
      "implementation_cost": 10,
      "recurring_cost_scenarios": [
        5,
        5,

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.

{
  "benefit_source_diagnostics": [
    {
      "allocated_fraction": 1,
      "causal_evidence_weight": 0.8,
      "expected_evidence_adjusted_value": 52.8,
      "source_id": "delay-loss-avoided",
      "unallocated_fraction": 0
    },
    {
      "allocated_fraction": 0.8,
      "causal_evidence_weight": 1,
      "expected_evidence_adjusted_value": 31,
      "source_id": "incident-loss-avoided",
      "unallocated_fraction": 0.2
    }
  ],
  "configuration": {
    "maximum_tail_loss": null,
    "minimum_probability_positive_net_value": 0.7,
    "tail_probability": 0.1
  },
  "decision": "analytics_portfolio_realized_value_supported",
  "failed_gates": [],
  "function_attribution": [
    {
      "expected_allocated_benefit": 31.68,
      "expected_direct_cost": 30,
      "expected_direct_net_value_before_shared_cost": 1.68,
      "function_id": "completion-forecast"
    },
    {
      "expected_allocated_benefit": 45.92,
      "expected_direct_cost": 15,
      "expected_direct_net_value_before_shared_cost": 30.92,
      "function_id": "decision-policy"
    }
  ],
  "guardrails": [
    "Only finance-reconciled incremental benefit sources with a defensible causal evidence weight belong in realized ROI; commits, recommendations, usage and adoption are not monetary value.",
    "Each benefit source is counted once before allocation. Unallocated value remains visible, allocations cannot exceed one, and function attribution is accounting—not causal Shapley credit or employee performance.",
    "Scenario columns must describe coherent joint futures in the same currency, horizon and price basis. CVaR covers represented scenarios only and a supported portfolio result does not validate every component function."
  ],
  "method": "evidence_weighted_unique_benefit_source_scenario_roi_v1",

How it works

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

1. 1 Freeze the active function perimeter, coherent finance scenarios, complete costs and a unique benefit-source ledger whose incremental values are resolved independently of engineering activity.
2. 2 Apply the governed causal-evidence weight once per source, reject allocations above 100 percent, reconcile allocated and unallocated value, and calculate function-level accounting attribution.
3. 3 Aggregate portfolio benefit, cost, net value, ROI, positive-value probability and weighted tail loss before applying executive value and risk 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": "reconcile the analytics portfolios realized roi" }
  → finds "calculate_analytics_portfolio_realized_roi"

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

gitrevio_capability_run
  { "capability_id": "calculate_analytics_portfolio_realized_roi", "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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