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Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.

Tomoaki Chiba1, Hideitsu Hino2, Shotaro Akaho3

  • 1Department of Electrical Engineering and Bioscience, Waseda University, Shinjuku, Tokyo, Japan.

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Summary
This summary is machine-generated.

This study introduces a new method to track how product market shares change over time using Markov processes. The approach reveals dynamic shifts in market competition, offering insights into consumer behavior and brand transitions.

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Area of Science:

  • Economics
  • Quantitative Marketing
  • Time Series Analysis

Background:

  • Product and stock markets feature intense competition among similar offerings.
  • Understanding dynamic market share transitions is crucial for strategic decision-making.
  • Existing methods may not fully capture the time-varying nature of market dynamics.

Purpose of the Study:

  • To propose a novel method for estimating time-varying transition matrices of product share.
  • To analyze the underlying Markov processes governing market share dynamics.
  • To provide a tool for understanding competitive shifts in real-world markets.

Main Methods:

  • Utilizing multivariate time series of product share data.
  • Assuming observed shares represent stationary distributions of underlying Markov processes.
  • Estimating time-varying transition probability matrices for each observation point.

Main Results:

  • The proposed method successfully estimates intrinsic transitions of product shares.
  • Analysis of an automobile market dataset revealed significant market share flow changes.
  • Specific shifts between TOYOTA and GM groups were identified, correlating with sales performance.

Conclusions:

  • The developed method effectively captures dynamic market share evolution.
  • The findings offer valuable insights into competitive dynamics and consumer behavior.
  • This approach provides a robust framework for analyzing time-varying market structures.