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<p>Coffee shops are strongly influenced by their location, which determines customer accessibility and pedestrian traffic potential, thereby impacting sales performance. The irrevocability of location decisions further amplifies their strategic importance. Coffee shops operate in highly competitive environments oriented toward profit and sales maximization. However, existing studies have predominantly conducted static trade area analyses centered on macroeconomic physical variables such as distance, population density, and store size. Additionally, entrepreneurs often rely on intuition and experience rather than scientific analysis for site selection. We analyzed various trade area characteristics, including competitive factors, potential demand, and infrastructure, using machine learning models to predict coffee shop locations and business sustainability. By identifying key variables that significantly influence post-opening business continuity, we highlight the need to reconsider several traditional assumptions related to site selection. Furthermore, we suggest that sufficient data combined with machine learning techniques can generate meaningful insights into business phenomena.</p>

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Rival or ally? Development of a success prediction model for coffee shops using machine learning

  • 期刊论文

  • 发表日期：

    2026年4月23日

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  • YongSun Kim,
  • Jin Young Sim,
  • Min Kyung Jung,
  • Hyun Shik Yoon

摘要

Coffee shops are strongly influenced by their location, which determines customer accessibility and pedestrian traffic potential, thereby impacting sales performance. The irrevocability of location decisions further amplifies their strategic importance. Coffee shops operate in highly competitive environments oriented toward profit and sales maximization. However, existing studies have predominantly conducted static trade area analyses centered on macroeconomic physical variables such as distance, population density, and store size. Additionally, entrepreneurs often rely on intuition and experience rather than scientific analysis for site selection. We analyzed various trade area characteristics, including competitive factors, potential demand, and infrastructure, using machine learning models to predict coffee shop locations and business sustainability. By identifying key variables that significantly influence post-opening business continuity, we highlight the need to reconsider several traditional assumptions related to site selection. Furthermore, we suggest that sufficient data combined with machine learning techniques can generate meaningful insights into business phenomena.