Marketing Resource Allocation Optimizer

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Optimize budget allocation across resources using fitted response models, constraints, and scenario war-gaming to maximize marketing outcomes.

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Quick Start Guide

(click to collapse)

Upload Your Data

Provide a CSV with 3 columns: resource_id, input (spending/allocation), and output (results). Each row is one historical observation.

Example: Sales rep data with monthly spending and revenue, or ad channel data with impressions and conversions.

Fit Response Models

Choose how to model each resource's response curve. Recommended: "Auto-select BEST model for EACH resource" to let the tool pick optimal models.

What it does: Fits mathematical functions (linear, logarithmic, power, etc.) that predict output from input for each resource.

Set Constraints

Define business rules: total budget, min/max per resource, equity requirements, etc. Start simple (just budget), then add advanced constraints as needed.

Pro tip: Use "Reset All" button to quickly return to defaults when experimenting.

Optimize & Analyze

Click "Run optimization" to find the best allocation. Review results in tables and charts, compare scenarios, and export recommendations as CSV.

Key output: Optimal allocation per resource, predicted total output, ROI, and marginal returns analysis.

OVERVIEW & WHAT THIS TOOL SOLVES

Splitting a marketing budget evenly across channels, regions, or sales reps almost always leaves revenue on the table. Every resource has its own diminishing-returns curve—one channel may already sit on a flat plateau while another sits on a steep upward slope. Resource Allocation Optimizer fits response curves to each resource's historical data and shifts budget increments toward whichever resource yields the highest marginal return on the next dollar, subject to your real-world floor and ceiling constraints.

🎯 Questions It Answers

How should we divide a fixed budget across sales reps, regions, or ad channels to maximize total revenue or leads?
Which response curve (linear, log, power, S-curve, bounded ceiling) best fits each resource's diminishing returns?
How much total output do we gain by moving from our current or even split to the constrained optimal allocation?

📂 What Data You Need

Historical observations with 3 columns: resource_id (channel/rep/region), input (spend/hours), and output (revenue/conversions).
Multiple historical spend levels per resource so the tool can estimate each resource's curve shape.
A total budget plus optional per-resource minimum/maximum guardrails.

💡 What You'll Discover

Best-Fit Response Curves: Automatic per-resource model selection across 7 functional forms.
Optimal Budget Split: Dollar allocation, predicted output, average ROI, and marginal return per resource.
Scenario Comparison Table: Side-by-side lift vs. baseline or constrained alternative budgets.

🔍 Key Nuances & Common Traps to Watch For Click to expand ▾

Equalize Marginal Returns, Not Average ROI: An optimal unconstrained allocation occurs where the marginal return (the slope $f'_i(x_i)$) is equal across all funded resources. If Channel A has a higher average ROI than Channel B but is completely saturated, shifting the next dollar to Channel B increases total output.
Greedy Marginal-Returns Allocator: The optimizer steps through budget increments, assigning each increment to the resource with the steepest current marginal return while respecting min/max bounds ($L_i \le x_i \le U_i$).
Choosing Response Curve Shapes: Logarithmic and Square Root capture classic diminishing returns; Logistic (S-curve) captures threshold warm-up followed by saturation; Bounded Logarithmic enforces a known market-size ceiling.

📐 Under the Hood: Model Equations & Variable Definitions Click to expand ▾

Constrained Budget Optimization Problem Marginal Return Optimization

\[ \max_{x_1, \dots, x_n} \; Z = \sum_{i=1}^{n} f_i(x_i) \qquad \text{subject to} \quad \sum_{i=1}^{n} x_i \le B \quad \text{and} \quad L_i \le x_i \le U_i \;\;\forall i \]

💬 Read it in plain English: Choose the dollar allocation $x_i$ for each resource $i$ that maximizes the sum of predicted outputs across all $n$ resources, without exceeding total budget $B$ or violating any resource's floor $L_i$ or cap $U_i$.

Variable & Symbol Directory (Every Term Defined) Hover or focus any role badge for details

Total Predicted Portfolio Output OUTPUT ⓘ

Total expected revenue, conversions, or leads generated across all $n$ resources combined.

x_i

Resource Allocation Decision OUTPUT ⓘ

Amount of budget or effort allocated to resource $i$ (for $i = 1, \dots, n$).

f_i(x_i)

Resource Response Function ESTIMATED ⓘ

Fitted mathematical curve predicting output from allocation $x_i$ for resource $i$.

Total Available Budget YOUR DATA ⓘ

Maximum combined budget available to distribute across all $n$ resources.

L_i, U_i

Minimum & Maximum Guardrails YOUR DATA ⓘ

$L_i$ is the minimum required allocation for resource $i$; $U_i$ is the maximum allowed allocation.

Number of Resources YOUR DATA ⓘ

Total count of distinct channels, sales territories, or reps being allocated budget.

DATA SOURCE

Load a marketing use case:

Select a preset to auto-populate historical data for common resource allocation problems (sales team budgets, channel spend, regional marketing). Each scenario includes fitted models and suggested constraints.

Provide a CSV file with columns: resource_id (name/label for each resource), input (historical budget/hours), and output (historical sales/conversions/leads).

Drag & Drop historical data file (.csv, .tsv, .txt, .xls, .xlsx)

Three columns: resource_id, input, output. Each row represents one resource's historical performance. Up to 500 resources supported.

No file uploaded yet.

MODEL FITTING

Optimization Constraints

Set the rules: Constraints define the business rules and practical limits for your allocation. The optimizer will find the best allocation that respects all these constraints. Start with basic constraints (total budget, min/max per resource), then explore advanced options for more realistic scenarios.

Total budget available (total $ to allocate across all resources)

Minimum per resource (ensure every resource gets at least this much)

Maximum per resource (leave blank for no maximum)

Force integer allocations (round to whole dollars, no cents)

Allow extrapolation beyond historical range

⚠️ OVERRIDES min/max per resource: When unchecked, all allocations are strictly capped at each resource's historical range (min to max observed input), regardless of the "Maximum per resource" and "Minimum per resource" settings above. This ensures predictions stay within reliable data ranges but may conflict with budget requirements.

Cost Structure (Variable & Fixed Costs)

Variable costs: If your input units are NOT dollars (e.g., hours, impressions, people), specify the cost per unit. This converts input units to actual budget dollars (e.g., $50/hour means 100 hours = $5,000 budget).
Fixed costs: One-time or baseline costs incurred for activating each resource (e.g., salary, platform fees). These are added to the total budget calculation when a resource receives any allocation above the minimum.

Variable cost per input unit (leave 1.0 if inputs are already in dollars)

Fixed cost per active resource (e.g., $30,000 salary per rep; leave 0 if no fixed costs)

Advanced constraints

Limit number of resources funded (focus budget on top performers)

What it does: Forces the optimizer to allocate $0 to some resources. For example, max=5 means only the 5 most productive resources receive funding, all others get $0.

Apply equity constraint (prevent extreme inequality in allocations)

What it does: Enforces max_allocation ≤ ratio × min_allocation. For example, ratio=10 means if the smallest funded resource gets $1,000, the largest can't exceed $10,000. Resources with $0 are excluded from this comparison.

Limit deviation from baseline (avoid drastic changes from historical spending)

What it does: Constrains each resource's allocation to stay within ±X% of its maximum historical input. For example, 50% means if a resource's max observed input was $10,000, allocation must be between $5,000-$15,000.

VISUAL OUTPUT

Response Curves with Optimal Allocations

View:

Each curve shows a resource's predicted output for different input levels. Red diamonds indicate the optimal allocation from the current solution. Grey circles show historical data points used to fit each model.

Allocation Comparison

Compare allocated budgets across resources. Longer bars indicate higher allocations.

Marginal Returns Analysis

Shows the marginal return (additional output per additional dollar) at the optimal allocation point. In an efficient allocation, marginal returns should be equalized across funded resources.

Scenario Comparison

Compares predicted total output under different allocation strategies: (1) Historical averages, (2) Current levels (most recent data), (3) Budget-only optimization (no other constraints), and (4) Your constrained optimization (with all active constraints).

▸ Additional Info: What Each Strategy Represents

Historical Averages: Allocates the average spending level from your historical data to each resource. Represents the "typical" allocation pattern based on past behavior. This baseline shows what output you'd expect if you maintained average historical spending.

Current Levels: Uses the most recent allocation (last row of your data) for each resource. Shows the output from your current/latest spending pattern. Useful for comparing "where we are now" versus optimized strategies.

Budget-Only Optimization: Runs optimization with only the total budget constraint active—no min/max per resource, no cardinality limits, no equity requirements, no baseline maintenance, and extrapolation enabled. This represents the theoretical maximum output achievable if you had complete flexibility. The gap between this and constrained optimization shows the "cost" of your constraints.

Constrained Optimization: Your actual optimization result with all active constraints applied. Active constraints include:

Total Budget constraint

This scenario represents the realistic optimized allocation given your business rules and operational constraints.

Visual output settings

Show historical data points on response curves Max resources to display in charts:

These settings update charts immediately when changed. Use the dropdown on each chart to view individual resources.

Marketing Resource Allocation Optimizer

👨‍🏫 Professor Mode: Guided Learning Experience

Quick Start Guide

Upload Your Data

Fit Response Models

Set Constraints

Optimize & Analyze

OVERVIEW & WHAT THIS TOOL SOLVES

🎯 Questions It Answers

📂 What Data You Need

💡 What You'll Discover

DATA SOURCE

Use a Case Study

Upload Your Data

MODEL FITTING

Model Fitting Options

Model Fit Results

Optimization Constraints

OPTIMIZATION RESULTS

Allocation Summary

Resource Allocation Details

VISUAL OUTPUT

Response Curves with Optimal Allocations

Allocation Comparison

Marginal Returns Analysis

Scenario Comparison

OPTIMIZATION DIAGNOSTICS

SCENARIO COMPARISON & WAR GAMING

Saved Scenarios

Scenario Comparison Results

Sensitivity Analysis