marketing-mix-model

Allocates marketing investment across channels using incrementality, saturation, and lag modeling.

Updated Aug 22, 2026
One-click install
npx skills add https://github.com/fritzgeraldz/Vibe-Managing --skill marketing-mix-model-fritzgeraldz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marketing-mix-model
Source: https://github.com/fritzgeraldz/Vibe-Managing/tree/main/skills/marketing/marketing-mix-model
Command: npx skills add https://github.com/fritzgeraldz/Vibe-Managing --skill marketing-mix-model-fritzgeraldz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Deciding how to split a marketing budget across channels is difficult because channel effects overlap, saturate, and lag over time. This Skill produces an evidence-backed marketing mix allocation decision and operating plan grounded in incrementality, margin, and strategic effects rather than generic benchmarks. ## Core Features & Use Cases - Channel Response Modeling: Separates base demand, estimates incremental response, and models lag and saturation per channel. - Constrained Optimization: Ranks allocation options by risk-adjusted value, confidence, and hard-constraint compliance, with downside and stress-case simulation. - Experiment Validation: Recommends the cheapest decision-relevant test when evidence could reverse the recommendation. - Use Case: A founder asks how to reallocate a quarterly budget across paid search, social, and email without exceeding cash limits. The Skill loads company context, compares feasible options against the counterfactual, and returns a ranked plan with owners, KPIs, and monitoring thresholds. ## Quick Start Use the marketing mix model skill to recommend a budget allocation across our marketing channels given our current spend, margins, and cash constraints.

Frequently Asked Questions about marketing-mix-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a marketing mix model for budget allocation?▼

Normalize channel data, separate base demand, estimate incremental response per channel, then model lag and saturation effects. Finally optimize spend under hard constraints and validate the recommendation with the cheapest decision-relevant experiment before committing budget.

What is incrementality in marketing mix modeling?▼

Incrementality is the portion of demand caused by a channel rather than baseline demand that would occur anyway. The model separates base demand first, then estimates each channel's incremental response so budget is allocated on causal effect, not correlation.

When should I not use a marketing mix model?▼

Do not use it before benchmark calibration establishes comparability, during an active crisis outside the incident command structure, or for regulated determinations requiring a licensed specialist. Also stop if a missing fact could reverse the decision.

How does the model handle diminishing returns and adstock lag?▼

The analysis framework explicitly models lag and saturation for each channel after estimating incremental response. These curves feed the constrained optimization so marginal spend is directed where incremental return remains highest.

What KPIs measure marketing mix model performance?▼

The Skill tracks incremental ROAS, marginal CAC, and contribution after marketing, each with baseline, target, actual, trend, confidence, and owner. It also tracks recommendation calibration by comparing expected versus actual results.