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Prediction for Private Brand Items Purchase Behavior of Hair Salons Using Bayesian Survival Analysis

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Social Computing and Social Media: Applications in Marketing, Learning, and Health (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12775))

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Abstract

In Japan, the number of beauty salons has been increased and reached over 250,000 at the end of 2018. On the other hand, there are some salon management problems such as price reduction and customer decrease. In such situations, it is an important not only to serve salon services, but to promote private brand (PB) items in addition to treatments. In this study, we analyzed purchase interval of PB items and predicted the purchase in a hair salon chain. First, we created explanatory variables using ID-POS data of the hair salon chain. Secondly, we selected explanatory variables using Cox proportional hazard model. Then, we performed Bayesian survival analysis to evaluate purchase interval considering customers’ heterogeneity. As a result, we could grasp appropriate timing when customers had highly purchase motivation and applied marketing measures to effective promotion.

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Acknowledgment

We thank a hair salon company for permission to use valuable datasets. This work was supported by JSPS KAKENHI Grant Number 19K01945.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Chuo University, Hachioji, Japan

    Yuho Katagiri

  2. Faculty of Science and Engineering, Chuo University, 1-13-27, Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Takashi Namatame

  3. School of Information and Telecommunication Engineering, Tokai University, 2-3-23, Takanawa, Minato-ku, Tokyo, 108-8619, Japan

    Kohei Otake

Authors
  1. Yuho Katagiri
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  2. Kohei Otake
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  3. Takashi Namatame
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Editors and Affiliations

  1. Department of Computer Science, Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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Katagiri, Y., Otake, K., Namatame, T. (2021). Prediction for Private Brand Items Purchase Behavior of Hair Salons Using Bayesian Survival Analysis. In: Meiselwitz, G. (eds) Social Computing and Social Media: Applications in Marketing, Learning, and Health. HCII 2021. Lecture Notes in Computer Science(), vol 12775. Springer, Cham. https://doi.org/10.1007/978-3-030-77685-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-77685-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77684-8

  • Online ISBN: 978-3-030-77685-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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