The Art and Science of Marketing: Building the Ideal Media Mix Model

Authors

  • Ananya Jha Partner Lead, Google and MS in Data Science, Rochester Institute of Technology, NY, USA Author
  • Ritambhara Jha Partner Lead, Google and MS in Data Science, Rochester Institute of Technology, NY, USA Author

DOI:

https://doi.org/10.47363/JMSCM/2022(1)127

Keywords:

Digital Marketing, Media Mix, Marketing Mix Modeling, Data-Driven Marketing

Abstract

In an era of fractured media landscapes and fleeting consumer attention, crafting the ideal marketing media mix has become an intricate puzzle. This paper navigates the labyrinthine path towards an effective and dynamic blend of channels, ensuring brand messages resonate with the ever-evolving digital audience. By prioritizing audience understanding, aligning with strategic goals, selecting the right channels, and implementing machine learning based modeling, brands can orchestrate a symphony of strategies that captivate consumers and drive meaningful results. The paper also emphasizes the need for data-driven decision-making and continuous optimization of the media mix in an ever-changing marketing environment

Author Biographies

  • Ananya Jha, Partner Lead, Google and MS in Data Science, Rochester Institute of Technology, NY, USA

    Partner Lead, Google and MS in Data Science, Rochester Institute of Technology, NY, USA

  • Ritambhara Jha, Partner Lead, Google and MS in Data Science, Rochester Institute of Technology, NY, USA

    Partner Lead, Google and MS in Data Science, Rochester Institute of Technology, NY, USA

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Published

2022-03-18