顧客關係管理是企業營運中重要的一環，企業透過顧客關係管理來穩定的獲取新顧客，同時提高舊顧客的忠誠度，最終提高企業的營運績效。為因應數位時代虛實整合之顧客關係管理需求，本研究提出一「新顧客關係管理模式」，以更全面的管理顧客與品牌的互動關係。在眾多的顧客關係管理活動中，本研究針對顧客價值管理提出一「顧客價值預測方法」，以在快速變動的顧客需求與顧客關係變化中，更即時的預測顧客價值變化並提供個人化行銷策略。本研究以全方位品牌體驗歷程為基礎，串聯顧客消費旅程中，接觸、認識、購買、參與、推薦等與品牌互動的一系列活動，再透過收集雙方互動之資料進行分析，以全面、深入的了解顧客，掌握顧客與品牌之關係與其對品牌之價值。
在顧客個人資料受到取得和利用的限制之際，本研究選擇使用合成資料取代真實資料的收集，利用統計方法生成具有利用彈性且有助於模型訓練的合成資料。本研究以Y食品業者真實銷售資料、會員資料、社群媒體資料為基礎，根據其統計特徵，並模擬特定統計分佈模式，生成完整的顧客屬性、顧客基本資料，以及顧客與品牌一系列之互動過程中，可能產生的互動行為記錄，最終將這些合成資料作為分析與實作之用。
本研究之研究項目包含新顧客關係管理模式設計、應用資料分析、顧客價值預測模型開發以及模型驗證與評量等。對顧客價值預測模型之準確性驗證，本研究對不同類型的顧客進行價值預測，結果顯示平均絕對百分比誤差皆低於20%，表示模型的預測效能具有一定的準確性與穩定性，可適應不同類型的客群資料。針對預測方法效能評量，實驗結果表示，透過本研究所設計之預測方法所得到的預測誤差低於傳統價值預測方法，證明本研究方法的優越性。

Customer relationship management (CRM) plays a crucial role in business operations. It enables businesses to consistently acquire new customers, enhance loyalty among existing customers, and ultimately boost revenue. relying solely on historical consumption records for customer value evaluation may not suffice in addressing the dynamic Internet ecosystem.
In response to the demand for integrated virtual and physical CRM in the digital age, this study proposes a novel customer relationship management model to comprehensively manage the interaction between customers and enterprises. Rooted in a comprehensive brand experience process, the research establishes connections between diverse customer-brand interactions throughout the customer journey, encompassing touchpoints, awareness, purchase, engagement, and referrals. By collecting and analyzing data on customer interactions with brands, it is possible to gain a deeper understanding of customers, thereby better grasping the relationship between customers and brands and their impact on brand value.
To accomplish the aforementioned research objectives, this study encompasses multiple research tasks, including the design of a new customer relationship management model, the application of data analysis, the development of a customer value prediction model, and the subsequent validation and evaluation of the model. The study validates the customer value prediction model through two rounds of verification using Y Food Company’s sales data, membership data, social media data, and data generated by data generation technology. The method proposed in this study exhibits a lower error rate and outperforms traditional methods.

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