Case Study: Leading CPG Company decodes consumer behavior with Mu Sigma’s Agent-Based Modeling

Leading CPG Company - Customer Case study

Leading CPG Company worked with Mu Sigma to tackle a complex consumer profiling challenge: forecasting baseline demand, attributing demand drivers, and understanding consumer upgrade and switching behavior in a highly competitive CPG market. The company needed a better way to see how pricing, promotions, brand awareness, competitor actions, and other factors interact at the consumer level beyond traditional predictive modeling.

Mu Sigma implemented an Agent-Based Modeling (ABM) solution, using a simulated “real games” environment to model consumers, competitors, clients, and influencers as interacting agents. The approach combined data fusion, Bayesian decision logic, and what-if scenario analysis to support demand forecasting, multi-touch attribution, and upgrade/switching insights for planning across marketing, sales, revenue growth, and brand strategy. The case study highlights a more granular and prescriptive view of demand, but it does not provide specific quantified business results.