SaaS Agile Marketing Mix Modeling Platform - 27/09/2025 05:06 EDT
Budget: $15 – $25 USD
Project Overview
This project aims to develop a robust agile marketing mix modeling (MMM) platform that enables high-frequency, multi-channel media optimization, incrementality experimentation, and actionable business insights for enterprise brands.
Primary Objectives
• Deliver a SaaS cloud-based MMM system with rapid, ongoing insight and forecasting for media investments. This includes user management as well as payment and invoicing.
• Integrate causal experimentation and incrementality measurement to validate and calibrate model outputs.
• Enable real-time media plan optimization and scenario forecasting for planning, spending, and ROI.
Key Deliverables
• Unified media data ingestion pipeline for cross-channel performance (e.g., TV, Facebook, Search, Social, Display).
• Agile MMM engine, using cutting-edge statistical or machine learning models (Bayesian, hierarchical regression, ad-stock modeling).
• Experimentation design and holdout support for incrementality testing across channels.
• Dynamic dashboard for weekly reporting, scenario analysis, and investment recommendations.
• Transparent model documentation, validation workflows, and scorecarding.
Data Management & Processing
• Build and QA data ETL pipelines covering spend, impressions, clicks, CRM, external factors (macroeconomic, competitive, weather, etc.).[1][4][3]
• Implement automated data harmonization and processing to enable weekly modeling refreshes.[2][9]
Modeling Engine Development
• Develop, calibrate, and validate agile MMM models supporting multiple techniques (e.g., regularized regression, Bayesian, causal inference).
• Integrate experimentation workflow to enrich models (e.g., geo holdout, A/B, cross-channel incrementality).
• Develop scenario forecast module, including diminishing returns analysis.
Experimentation & Incrementality Measurement
• Build user-friendly experimentation tools for campaign holdouts, geo-experiments, and split tests.
• Automate incrementality reporting and calibration feedback loops.
Visualization, Optimization, and Reporting Module
• Design dynamic dashboards for performance monitoring, scenario analysis, benchmark comparisons, and cross-channel reporting.
• Enable media plan optimization recommendations with actionable, weekly insights.
Validation, Transparency, & Documentation
• Develop scorecard tools for transparency on model inputs, uncertainty, and QA status.
• Document modeling decisions, limitations, and operational guidelines in a customer-facing knowledge base.
Deployment, Training, & Support
• Deliver the platform via a scalable cloud infrastructure with API access, onboarding, and ongoing support.
• Provide six months post-launch technical support and a price framework for work beyond the sixth month.
Technology & Tools Recommendations
• Scalable cloud infrastructure for data and dashboard management.
• Automated reporting, experimentation modules, and integration APIs.
Includes front-end and back-end design and development.
This project aims to develop a robust agile marketing mix modeling (MMM) platform that enables high-frequency, multi-channel media optimization, incrementality experimentation, and actionable business insights for enterprise brands.
Primary Objectives
• Deliver a SaaS cloud-based MMM system with rapid, ongoing insight and forecasting for media investments. This includes user management as well as payment and invoicing.
• Integrate causal experimentation and incrementality measurement to validate and calibrate model outputs.
• Enable real-time media plan optimization and scenario forecasting for planning, spending, and ROI.
Key Deliverables
• Unified media data ingestion pipeline for cross-channel performance (e.g., TV, Facebook, Search, Social, Display).
• Agile MMM engine, using cutting-edge statistical or machine learning models (Bayesian, hierarchical regression, ad-stock modeling).
• Experimentation design and holdout support for incrementality testing across channels.
• Dynamic dashboard for weekly reporting, scenario analysis, and investment recommendations.
• Transparent model documentation, validation workflows, and scorecarding.
Data Management & Processing
• Build and QA data ETL pipelines covering spend, impressions, clicks, CRM, external factors (macroeconomic, competitive, weather, etc.).[1][4][3]
• Implement automated data harmonization and processing to enable weekly modeling refreshes.[2][9]
Modeling Engine Development
• Develop, calibrate, and validate agile MMM models supporting multiple techniques (e.g., regularized regression, Bayesian, causal inference).
• Integrate experimentation workflow to enrich models (e.g., geo holdout, A/B, cross-channel incrementality).
• Develop scenario forecast module, including diminishing returns analysis.
Experimentation & Incrementality Measurement
• Build user-friendly experimentation tools for campaign holdouts, geo-experiments, and split tests.
• Automate incrementality reporting and calibration feedback loops.
Visualization, Optimization, and Reporting Module
• Design dynamic dashboards for performance monitoring, scenario analysis, benchmark comparisons, and cross-channel reporting.
• Enable media plan optimization recommendations with actionable, weekly insights.
Validation, Transparency, & Documentation
• Develop scorecard tools for transparency on model inputs, uncertainty, and QA status.
• Document modeling decisions, limitations, and operational guidelines in a customer-facing knowledge base.
Deployment, Training, & Support
• Deliver the platform via a scalable cloud infrastructure with API access, onboarding, and ongoing support.
• Provide six months post-launch technical support and a price framework for work beyond the sixth month.
Technology & Tools Recommendations
• Scalable cloud infrastructure for data and dashboard management.
• Automated reporting, experimentation modules, and integration APIs.
Includes front-end and back-end design and development.