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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.
Plos One
|January 12, 2017
Summary
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.
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.
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