A case study on machine learning for customer relationship management in service industry

隨著科技進步，基於機器學習技術的資料分析工具在顧客關係管理領域已被廣為使用。過去的相關研究文獻大多著重於高交易頻次、與客戶互動頻繁的產業，諸如金融、電信、零售業等，但對於具有相反產業特性的服務業等則是缺乏著墨。本研究希望透過案例研究的方式，完整呈現企業如何實際將基於機器學習技術的資料分析工具應用於顧客關係管理業務的過程，以及這些新技術如何幫助企業提升顧客關係管理的成效。本案例使用行動研究方法來歸納、分析、整理整個專案的過程與結果，文末總結本案例於作業、管理以及策略層面的管理意涵與建議。本研究使用的資料來源為台灣一間大型汽車經銷商的資訊部門與其旗下的服務廠，總共包含了約273萬筆資料。利用於微軟Azure平台上的決策樹模型分析資料，產出高購買機率的顧客推薦名單，服務廠的技師可以針對名單上的顧客推銷，不僅能有效提高推銷的成功率，節省第一線技師的時間，還能夠提升技師以及顧客的滿意度。最後本研究的結果顯示，運用機器學習技術產出的推薦顧客名單，確實能夠幫助本案例公司於顧客區隔以及顧客發展，並達成更有效的顧客關係管理。Data-mining tools and machine-learning techniques have long been used in customer relationship management (CRM), including for customer retention in the financial, retail, and telecommunications industries. However, research on machine learning for CRM in service industries remains rare. Accordingly, this paper uses action research to arrive at a holistic understanding of the process of applying machine learning-based data mining in a specific service-sector business, and whether, how, and how much these novel techniques can help it improve its customer relationships. Key areas of interest include operational, managerial and strategic decision-making processes. Based on approximately 2.73 million rows of data collected from a large car dealership’s IT department and its vehicle-maintenance plants, Microsoft Azure’s boosted decision-tree model generated lists of recommended customers. Such lists could be used by the company to increase the success rates of its promotional activities and to decrease both the overall duration and frequency of technicians’ involvement with promotion. This in turn could lead to more efficient and effective frontline operations, and increased satisfaction not only among customers but also among technicians. In short, machine learning-based recommended-customer lists helped the company achieve more effective CRM through better customer segmentation and customer development