Optimize multi period growth budget saturation

What it's for

Creates a board-ready growth-capital plan that prices saturation, carryover, cash pressure and downside together instead of ranking channels by fragile average ROAS.

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.

{
  "budget_by_period": [
    20,
    20
  ],
  "channels": [
    {
      "carryover_rate": 0.2,
      "evidence_verified": true,
      "half_saturation_spend": 30,
      "hill_shape": 1,
      "id": "events",
      "maximum_incremental_contribution_per_period": 80,
      "maximum_spend_per_period": 20
    },
    {
      "carryover_rate": 0.5,
      "evidence_verified": true,
      "half_saturation_spend": 20,
      "hill_shape": 1,
      "id": "paid-search",
      "maximum_incremental_contribution_per_period": 100,
      "maximum_spend_per_period": 20
    }
  ],
  "horizon_periods": 2,
  "scenarios": [
    {
      "channel_response_multipliers": {
        "events": 1,
        "paid-search": 1
      },
      "evidence_verified": true,
      "fixed_cash_flow_by_period": [
        0,
        0
      ],
      "id": "base",
      "probability": 0.7
    },
    {
      "channel_response_multipliers": {
        "events": 0.8,
        "paid-search": 0.7

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.

{
  "configuration": {
    "budget_by_period": [
      20,
      20
    ],
    "horizon_periods": 2,
    "maximum_cvar_shortfall": null,
    "maximum_liquidity_breach_probability": 1,
    "minimum_cash_buffer": 0,
    "minimum_target_probability": 0,
    "risk_aversion": 0,
    "spend_increment": 10,
    "tail_probability": 0.1,
    "target_total_incremental_contribution": 0,
    "terminal_net_value_floor": 0,
    "total_budget": 40
  },
  "decision": "growth_budget_policy_supported",
  "failed_gates": [],
  "guardrails": [
    "The schedule is aggregate and open-loop across common scenarios; it does not target or score named people or individual customers.",
    "Hill curves and carryover are organization-calibrated response models, not causal facts outside validated experimental support.",
    "Beam search has no global certificate; rerun after material response, auction, pricing, cash, or measurement changes."
  ],
  "method": "multi_period_adstock_hill_saturation_cvar_growth_budget_search",
  "scenario_diagnostics": [
    {
      "minimum_unrestricted_cash": 100,
      "probability": 0.7,
      "scenario_id": "base",
      "terminal_net_value": 80,
      "terminal_unrestricted_cash": 180,
      "total_incremental_contribution": 120
    },
    {
      "minimum_unrestricted_cash": 100,
      "probability": 0.3,
      "scenario_id": "downside",
      "terminal_net_value": 46,
      "terminal_unrestricted_cash": 146,
      "total_incremental_contribution": 86
    }
  ],

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. 1 Freeze evidence-backed local response curves, carryover, per-channel spend ceilings, common probability-weighted response/cash scenarios, horizon, spend grid, total/period budgets and board-owned liquidity, contribution and tail-risk gates.
2. 2 Enumerate every feasible channel allocation per period; use exact schedule enumeration inside the declared boundary and otherwise a deterministic beam that scores the same coherent scenarios without claiming a global certificate.
3. 3 Propagate adstock state, Hill response, incremental contribution and unrestricted cash for the same open-loop schedule in every scenario; calculate target probability and exact weighted CVaR shortfall, choose only a feasible schedule, and compare expected net value with no spend.

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": "allocate aggregate growth capital across channels" }
  → finds "optimize_multi_period_growth_budget_saturation"

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

gitrevio_capability_run
  { "capability_id": "optimize_multi_period_growth_budget_saturation", "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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