Marketing Mix Modeling with Fount

This notebook walks you through building a causal Marketing Mix Model (MMM) using Fount's API. Unlike traditional regression-based MMMs that confuse correlation with causation, Fount discovers the structural causal relationships between your marketing channels and business outcomes. You'll learn how to upload channel spend data, train a causal model that accounts for saturation effects and carry-over, and generate counterfactual predictions to measure the true incremental impact of each channel.

What You'll Accomplish

Measure the true incremental ROI of each marketing channel by isolating causal impact from seasonal co-movement
Optimize budget allocation across channels by simulating counterfactual spend scenarios
Detect channel interactions and synergies that traditional last-click models miss entirely
Forecast the impact of budget changes before committing real dollars

Prerequisites

Python 3.8+Fount API keyChannel-level spend and conversion data (CSV)

Code Preview

Marketing-Mix-Modeling-with-Fount.py

# Function for nth Fibonacci number 

def Fibonacci(n): 
	if n<0: 
		print("Incorrect input") 
	# First Fibonacci number is 0 
	elif n==1: 
		return 0
	# Second Fibonacci number is 1 
	elif n==2: 
		return 1
	else: 
		return Fibonacci(n-1)+Fibonacci(n-2) 

# Driver Program 

print(Fibonacci(9))

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