Marketing Mix Modeling (MMM) Tool

Client: IT Service Organization

Category: AI/ML | Llama 3 | Jenkins | Azure | React

Date Oct,08, 2025

About the client

The client is a forward-looking marketing analytics firm aiming to optimize marketing budget allocation across multiple channels, including digital, social media, television, and print. The client’s goal is to maximize ROI and KPIs for each campaign by leveraging data-driven insights and AI/ML models for spend optimization.

Client’s requirement

The client required a marketing budget allocation and analytics tool with AI/ML-driven insights. The key requirements were:

AI/ML integration: Use machine learning to generate predictive insights and optimize marketing spend in real-time.
Budget optimization: Recommend the best allocation of marketing spend across channels to maximize acquisition, orders, and revenue.
Scenario planning: Allow users to simulate different marketing strategies and evaluate their impact on KPIs.
Channel suggestions: Provide recommendations on which channels (e.g., social media, TV, newspapers) to focus on based on product-specific audience engagement.
Data visualization: Interactive dashboards to compare historical performance, revenue, ROI, and audience response across channels.
Granularity: Enable analysis at multiple levels, including funnel, channel, and market segment.

Cannyfore Technology Solutions

Cannyfore delivered a comprehensive, AI-powered MMM tool with features for optimization, scenario planning, and visualization:

Core Functionality

Predictive Modeling & Optimization: XGBoost, LightGBM, or Random Forest for forecasting ROI, revenue, and KPI outcomes, enhanced with OpenAI GPT-4 for generating insights and optimization recommendations.
Scenario Simulation & Recommendations: PyTorch or TensorFlow for building ML models that simulate budget allocation scenarios.
Scenario planning: Users can simulate different budget scenarios and evaluate potential acquisition and revenue outcomes.
Data Analysis & Visualization: Pandas, NumPy, and Plotly / Matplotlib / Seaborn for handling data and creating interactive dashboards.
Channel performance analysis: Identify high-performing media sources and target audience engagement for better marketing decisions.

Development Approach

Collaborated with Data Science teams to integrate ML models for spend optimization
Built reusable and optimized front-end components using React, Redux-Toolkit, and Typescript
Developed responsive dashboards with MUI to present actionable insights
Ensured seamless performance and intuitive UX for decision-makers

Key Technologies Used

Predictive Modeling & Simulation: XGBoost, LightGBM, and Random Forest for forecasting ROI and KPIs; PyTorch/TensorFlow for scenario simulations and budget recommendations, powered by Llama 3 for rapid analysis and insight generation.
Data Processing & Serving: Pandas, NumPy, Plotly/Matplotlib/Seaborn for analytics and visualization; FastAPI/Flask for serving ML models; Scikit-learn for prototyping.
Frontend: React.js, Redux-Toolkit, Functional Components, Typescript, MUI, HTML, CSS.
Backend Serving: FastAPI / Flask to expose ML models as APIs.
DevOps & Cloud: GitLab, Jenkins, Azure, Yarn