CPG manufacturers need a fusion of retailer data, advanced analytics, and tailored revenue growth management capabilities to address heuristic decision-making and organizational complexity for effective growth.

Specialty

Manufacturer

Solution Category

Revenue Management

The Challenge

A leading food sector Consumer Packaged Goods (CPG) giant grappled with understanding the key drivers behind its sales performance, hindering effective revenue management. They lacked reliable data to make informed decisions about trade promotions, including optimal expenditure allocation, effective strategies, and profitable investments.

Mu Sigma’s Approach

Leveraging proprietary tools muUniverse and muPDNA (Problem DNA), we identified core issues, including fragmented data silos and varying trade outcomes across retail partners. Drawing from our extensive experience with CPG brands and retailers, we developed a comprehensive Strategic Revenue Management (SRM) framework to address these challenges.

The solution revolved around an SRM platform with a solid foundation in both data and analytics.

Data Foundation: Integrated multiple data sources into a cloud-based data lake, enabling the acquisition, integration, analysis, and storage of over two million data rows.

Analytical Foundation: Implemented machine learning-based models for thorough sales driver analysis and an optimization and scenario builder, facilitating optimal promotional planning and scenario modification for enhanced decision-making.

Insight Consumption: Deployed a suite of user-friendly tools for flexible planning, analysis, visualization, and automation of insights, including support for third-party tools and analysis.

Business Impact

  • $15M

    Yearly Profit Increase

  • Better Decisions

    For revenue management

Mu Sigma is the world's largest pure-play big data analytics and decision sciences company. Through our Art of Problem-Solving, we strive to scale and systematize better decision-making through business intelligence services. Mu Sigma works across the entire decision support ecosystem (data sciences, data engineering, and decision sciences) with Fortune 1000 companies worldwide, including 140+ Fortune 500 companies.