Market Mix Modeling (MMM) Analyst

Requirements

Market Mix Modeling (MMM) Analyst

Key Responsibilities:

Market Mix Modeling Development

• Design, develop, and deploy both short-term and long-term MMM models that measure the effectiveness of marketing channels and campaigns.
• Use statistical techniques such as regression modeling, time-series analysis, and Bayesian methods to quantify media impact.
• Conduct model diagnostics and validation to ensure reliability and robustness.
  

Data Handling and Processing

• Leverage SQL and Python to access, clean, and prepare large datasets from multiple sources (CRM, media, sales, digital platforms).
• Work with business teams to gather relevant KPIs, marketing investment data, and control variables needed for accurate modeling.
  

Insight Generation and Reporting

• Translate complex analytical outputs into actionable marketing insights and strategic recommendations.
• Visualize data trends, model results, and optimization scenarios using Python-based visualization libraries (e.g., Matplotlib, Seaborn, Plotly).
• Present insights to stakeholders in a clear and concise manner that informs business decisions.
  

Media Planning & Optimization

• Support marketing and media planning teams in optimizing spend across channels using model-driven insights.
• Provide budget allocation guidance using optimization techniques to maximize ROI and impact.
• Track and compare modeled vs. actual performance to improve predictive accuracy and campaign outcomes.
  

Cross-functional Collaboration

• Partner with marketing, finance, data engineering, and analytics teams to align on data pipelines, business objectives, and outcome expectations.
• Integrate MMM outputs with broader business intelligence tools and dashboards.
  

Qualifications and Skills:

• 5-8 years of relevant experience in marketing analytics with a focus on MMM.
• Proficient in Python for data processing, statistical modeling, and visualization.
• Strong command of SQL for data extraction and transformation.
• Deep understanding of econometric techniques and statistical best practices.
• Experience working with media mix optimization tools and budget allocation frameworks.
• Strong knowledge of digital and offline marketing channels, CRM systems, and campaign metrics.
• Excellent problem-solving abilities, attention to detail, and communication skills.
• Experience with AI/ML techniques is a plus, but not mandatory.