英文摘要 |
Although customer lifetime value (CLV) is rapidly gaining acceptance as a metric to help acquire, cultivate, and retain VIP customers through customer relationship management, traditional valuation methods do not work well. Either companies have begun working with undesirable customers, or they do not know how to customize the customer’s experience to create the highest value. Consequently, the challenge that most marketing managers currently face is to bring together marketing actions and CLV. Specifically, they need to take the customer data they have collected and integrate CLV with how the firm interacts with its customers.We examine three models for the lifetime value of a cohort of customers, aggregate the lifetime value across different cohorts, and then construct models to forecast the key input to the models. In this study, we use customer data from a department store in Taiwan to illustrate the proposed framework empirically. Our main contributions are in three areas: (1) providing a better method for forecasting the future stream of sales and cash flow by using ARIMA analysis, (2) providing insights about key factors (e.g., retention rate) that can help managers improve CLV, and (3) suggesting that profits can be substantially improved when managers design resource allocation rules that improve CLV. |