Game Industry Market Segmentation Based on Clustering Algorithms: Evidence from VALORANT and Counter-Strike 2

DOI

10.2991/978-94-6239-652-4_28How to use a DOI?

Keywords

Market segmentation; Target market; FPS games; Player motivation; Business model

Abstract

The present research identifies VALORANT and Counter-Strike 2 (CS2) as stand-alike cases in terms of tactical FPS games. Basing on market segmentation and target market strategy, this study employs a comparative-case-study method to systematically investigate discrepancies between the two games in terms of market positioning, player structure and business approach. We found that while they shared the same core gameplay genre, VALORANT and CS2 had a stark contrast in terms of their strategic preferences in choosing player targets and market structure. VALORANT appeals primarily to a Generation Z and more inclusive population with highly visualized look and feel, agent driven skill mechanics, and socio-moral gameplay approach: Its free-to-play model and cosmetics based monetization strongly complements players’ consumption motivations and encourages greater player engagement and lifetime value. CS2 on the other hand has traditional FPS type character with realistic shooting strategies and high competitive skill ceiling, for core users who care about technical depth and fair competition. Its Steam-based virtual item trading model also reinforces its consumer logic based on the value of collection and exchange. This study emphasizes the distinct segmentation paths found in the FPS market and that FPS products may coexist through competitive positioning strategies, rather than complete substitution. The findings offer pragmatic insights for long-term product positioning and monetization strategy to game developer and operator.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wuyou Xie
PY  - 2026
DA  - 2026/04/19
TI  - Game Industry Market Segmentation Based on Clustering Algorithms: Evidence from VALORANT and Counter-Strike 2
BT  - Proceedings of the  2026 5th International Conference on Engineering Management and Information Science (EMIS 2026)
PB  - Atlantis Press
SP  - 288
EP  - 297
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-652-4_28
DO  - 10.2991/978-94-6239-652-4_28
ID  - Xie2026
ER  -