IDEAS home Printed from https://ideas.repec.org/a/igg/jsds00/v14y2023i1p1-21.html
   My bibliography  Save this article

User Intent-Based Segmentation Analysis for Internet Access Services

Author

Listed:
  • Ken Nishimatsu

    (Chiba Institute of Technology, Japan)

  • Akiya Inoue

    (Shoin University, Japan)

Abstract

The Internet is available for almost all homes in Japan. During the diffusion stage of Internet access services, it was important to construct a model to evaluate the sensitivity for Internet access service attributes and estimate user service choice behavior to consider service sale strategies. However, as the demand for Internet access services reached saturation, the differences in service attributes among service providers became smaller, making it difficult to decide service sale strategies using conventional choice behavior models. Therefore, the purpose of this paper is to establish a method for extracting effective information for service sale strategies by focusing on user intentions and clarifying the differences in future intentions for current carriers or services with observable and unobservable factors concerned with user intention. The authors propose a framework for user intent-based segmentation to understand the current market structure and develop appropriate service sales strategies for each segment.

Suggested Citation

  • Ken Nishimatsu & Akiya Inoue, 2023. "User Intent-Based Segmentation Analysis for Internet Access Services," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 14(1), pages 1-21, January.
    • Handle: RePEc:igg:jsds00:v:14:y:2023:i:1:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSDS.318643
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sobolewski, Maciej & Kopczewski, Tomasz, 2017. "Estimating demand for fixed-line telecommunication bundles," Telecommunications Policy, Elsevier, vol. 41(4), pages 227-241.
    2. Mirjana Pejić Bach & Jasmina Pivar & Božidar Jaković, 2021. "Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees," JRFM, MDPI, vol. 14(11), pages 1-25, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Londhe Sanket Tanaji & Palwe Sushila, 2022. "Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach," Business Systems Research, Sciendo, vol. 13(1), pages 35-45, June.
    2. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    3. Guidon, Sergio & Wicki, Michael & Bernauer, Thomas & Axhausen, Kay, 2020. "Transportation service bundling – For whose benefit? Consumer valuation of pure bundling in the passenger transportation market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 91-106.
    4. Frank Oechsle, 2023. "Increasing the robustness of uplift modeling using additional splits and diversified leaf select," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 738-746, December.
    5. Kopczewski, Tomasz & Sobolewski, Maciej & Miernik, Ireneusz, 2018. "Bundling or unbundling? Integrated simulation model of optimal pricing strategies," International Journal of Production Economics, Elsevier, vol. 204(C), pages 328-345.
    6. Paulo Chahuara, 2018. "Preferencias y Valorizacion por Atributos del Servicio de Internet Fijo en el Peru: un primer acercamiento via experimento de eleccion discreta," Documentos de Trabajo 39, OSIPTEL.
    7. Mydyti Hyrmet & Kadriu Arbana & Pejic Bach Mirjana, 2023. "Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development," Organizacija, Sciendo, vol. 56(2), pages 138-154, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jsds00:v:14:y:2023:i:1:p:1-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.