Marketing Budget Allocation Simulator

A Streamlit simulator for marketing channel mix planning, CAC, ROI, payback, revenue forecasting, contribution analysis, and budget trade-off recommendations.

Live Demo

Open the Streamlit app

What It Does

• Simulates current vs recommended marketing budget allocation
• Models diminishing returns by channel using spend elasticity assumptions
• Calculates projected customers, revenue, gross profit, contribution, CAC, ROAS, contribution ROI, LTV:CAC, and payback
• Reallocates budget across channels while respecting minimum and maximum spend constraints
• Shows channel-level spend, revenue, contribution, and customer changes
• Generates an executive budget allocation memo
• Supports CSV upload for custom channel assumptions

Why This Project Matters

This project demonstrates marketing strategy and commercial planning. It shows how a growth, marketing analytics, business analyst, or consulting team can evaluate where the next marketing dollar should go before changing channel budgets.

Tech Stack

• Python
• Streamlit
• Pandas
• Standard-library tests with unittest

Run Locally

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
streamlit run app.py

Validate

python scripts/validate.py

Methodology Notes

• Channel response uses diminishing returns: projected customers scale with spend raised to each channel's elasticity.
• Allocation scores combine contribution ROI, CAC efficiency, payback speed, retention quality, and strategic priority.
• A risk adjustment reduces channel attractiveness before budget is allocated.
• Recommended spend is constrained by each channel's minimum and maximum budget limits.
• This is a planning simulator, not a substitute for incrementality testing, attribution analysis, or media-mix modeling.

Portfolio Talking Points

• Built a marketing budget simulator that connects channel spend to revenue, CAC, contribution, payback, and LTV:CAC
• Added diminishing-return response curves so budget changes do not scale unrealistically
• Created a constrained allocation model with minimum and maximum channel spend limits
• Converted the scenario output into an executive memo for marketing strategy and business planning discussions

Author

Dhruv Harlalka

MBA Finance, Middlesex University Dubai