Decoding Consumer Behavior using Agent-Based Modeling


  • CASE STUDIES
  • February 3rd, 2020
  •   1086 Views

Market players are starting to realize that historical data cannot paint a complete picture of the consumers and their future demands. Predictive analytics does not account for the complex interplay of influences, product options, and preferences which shapes the consumer choices.

So, Mu Sigma is enabling next-level consumer insights through Agent-Based Modeling (ABM) – a predictive-prescriptive technique to observe consumer behavior in a simulation of their market environment and predict demand accurately.

THE MU SIGMA APPROACH
Agent-Based Modeling simulation not only captures the overall behavior of the consumer ecosystem, but it also provides a deeper understanding of the most granular demand influencers and the interactions among them. Implementing ABM within Mu Sigma’s outcome-driven Art of Problem-solving framework, we are helping a leading Fortune 500:
• Attribute impact to each sales’ driver
• Forecast and breakdown demand
• Understand upgrade and switching behavior of consumers

Download the case study to know more

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