隨著商業競爭加劇，企業不再單純依靠產品本身差異以維持競爭力，進而將焦點轉向個人化之服務，然而在顧客數眾多的情況下，如何評估個別顧客為企業帶來的終身價值 (customer lifetime value, 簡稱CLV或LTV) 已儼然成為重要的課題。若企業可明確知道顧客流失時點則稱為契約型關係 (contractual relations)，反之則稱為非契約型關係 (non-contractual relations)。本論文探討的是非契約型關係，考慮顧客在企業中存續時間為不可觀測之下，分別建構交易次數與顧客存續時間模型及交易金額模型之後，再依據CLV的計算公式，以預測個別顧客的CLV。不少實證研究顯示，交易次數相較於卜瓦松分配有過度離散 (overdispersion) 或不足離散 (underdispersion) 的現象，本論文乃延續 Mzoughia et al. (2018) 的做法，以Conway-Maxwell-Poisson (CMP) 分配為交易次數之分配，但修正Mzoughia et al. (2018) 的公式，納入顧客間交易次數離散現象之異質性，並進一步推導及計算出兩種CLV估計值，可分別評估顧客未來於一定期間內及至其流失為止的價值。

As business competition intensifies, companies no longer rely solely on the superior products to maintain their competitive edge. Instead, they turn their focuses to personalized services. When having thousands of customers, how to evaluate individual customer’s customer lifetime value (CLV or LTV) is undoubtedly a significant issue. If a company can observe exactly the time of customer dropout, then it belongs to “contractual relations”. Otherwise, it belongs to “non-contractual relations”. This thesis discusses the non-contractual relationship. Considering that the customer's lifetime in the business is unobservable, models for the number of transactions, the customer’s lifetime and the transaction amount are constructed separately, and then the CLV formula is applied to predict the CLV of each individual customer. Several empirical studies have already shown that the numbers of transactions are sometimes being overdispersion or underdispersion compared to Poisson distribution. This thesis continues the work of Mzoughia et al. (2018) and constructs the model of number of transactions by Conway-Maxwell-Poisson (CMP) distribution, but modifies the formula of Mzoughia et al. (2018), and considers heterogeneous dispersion of the number of transactions among customers. Moreover, we derive and compute two CLV estimates, which can be used to evaluate each individual customer’s future value within a certain period and until customer dropout.

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