Analysis Of Television (TV) Brand Switching Prediction In XYZ MSME Market Share Using Markov Chain

M. Ananda Dzikrul Hakul Amri; Rizky Stighfarrinata; Rio Candra Pratama

doi:10.63922/ijeter.v4i02.4460

M. Ananda Dzikrul Hakul Amri Bachelor of Industrial Engineering, Universitas Bojonegoro, Indonesia
Rizky Stighfarrinata Bachelor of Industrial Engineering, Universitas Bojonegoro, Indonesia
Rio Candra Pratama Bachelor of Industrial Engineering, Universitas Bojonegoro, Indonesia

DOI: https://doi.org/10.63922/ijeter.v4i02.4460

Keywords: Markov Chain, brand switching, market share, television, consumer behavior, steady state.

Abstract

The rapid development of digital broadcasting technology and the implementation of the Analog Switch-Off (ASO) policy have intensified competition among television brands and increased the occurrence of brand switching behavior among consumers. This condition requires business owners to understand consumer switching patterns in order to anticipate market share changes and develop more effective marketing strategies. This study aims to analyze consumer switching patterns among television brands, predict future market share, and determine the long-term equilibrium condition (steady state) using the Markov Chain method. The research was conducted at UMKM XYZ using a quantitative approach with data collected from questionnaires distributed to 50 respondents over a 90-day observation period. The analysis involved constructing a transition probability matrix, determining the initial market share vector, forecasting market share across several periods, and identifying the steady-state condition. The results indicate that the initial market shares were 18% for Aqua, 32% for LG, 24% for Polytron, and 26% for Sharp. The forecast shows that Polytron experienced the highest market share growth and became the dominant brand, while LG experienced a significant decline. The steady-state condition was reached in the fifth period, with long-term market shares of 24% for Aqua, 17% for LG, 37% for Polytron, and 23% for Sharp. These findings demonstrate that the Markov Chain method is effective for predicting brand switching behavior and market share distribution, providing valuable support for marketing decisions and inventory management in UMKM XYZ.

References

Asyrofi, M., Rahman, A., & Hidayat, T. (2023). Application of Markov chain analysis in stochastic modeling and consumer behavior prediction. Journal of Applied Statistics and Data Science, 8(2), 112–124.
Boeuf, B. (2019). Brand switching behavior in competitive consumer markets. Journal of Consumer Marketing Research, 14(3), 221–233.
Gifari, F. A., & Maulana, M. A. (2022). Analisis rantai Markov untuk mengetahui peluang perpindahan konsumen merek laptop pada mahasiswa Teknik Industri Universitas Indraprasta PGRI. Bulletin of Applied Industrial Engineering Theory, 3(1), 45–52.
Hamdani, M., & Yuliana, R. (2021). Online brand image, service standard communication, and word of mouth. Journal of Marketing Studies, 13(2), 1–7.
Herowati, E., Wahyudi, R. D., & Akbar, M. S. (2025). Pemasaran handphone Xiaomi berdasarkan analisis pangsa pasar menggunakan Markov Chain di Indonesia. Jurnal Ilmiah Teknik Industri, 9(1), 34–42.
Hillier, F. S., & Lieberman, G. J. (2021). Introduction to operations research (11th ed.). McGraw-Hill Education.
Kotler, P., & Armstrong, G. (2021). Principles of marketing (18th ed.). Pearson Education.
Latifah, S., & Astuti, Y. P. (2021). Penerapan rantai Markov dalam menganalisis persaingan jasa pengiriman barang. Jurnal Matematika dan Aplikasi Industri, 5(2), 88–97.
Lestari, L. (2022). Penerapan rantai Markov dalam menganalisis persaingan bisnis online shop. Jurnal Ilmu Komputer, Ekonomi dan Manajemen (JIKEM), 2(1), 211–215.
Novianti, R., Hidayat, M., & Putra, D. (2022). Analisis perpindahan konsumen produk songkok menggunakan metode Markov Chain. Jurnal Teknik Industri dan Sistem Manufaktur, 7(1), 55–64.
Pamungkas, A. T. (2022). Strategi bersaing stasiun jaringan NET TV Yogyakarta di era digital. Jurnal Komunikasi, 17(1), 117–136.
Rather, R. A., Hollebeek, L. D., Vo-Thanh, T., & Ramkissoon, H. (2022). Customer engagement and brand loyalty in service industries: A systematic review and future research agenda. Journal of Retailing and Consumer Services, 64, 102791. https://doi.org/10.1016/j.jretconser.2021.102791
Schiffman, L. G., & Wisenblit, J. (2023). Consumer behavior (13th ed.). Pearson Education.
Siregar, A., & Riofita, H. (2024). Consumer behavior and market share dynamics in the digital electronics industry. International Journal of Business and Marketing Research, 12(1), 45–58.
Tambunan, R., Simanjuntak, P., & Sihombing, D. (2022). Application of Markov chain in market share prediction and consumer switching analysis. International Journal of Industrial Engineering and Management, 13(4), 315–324.