In this research, we combine the BG/NBD and the Extended SMC model to simultaneously and completely incorporate the past purchase behavior of customers (X, tx, T, Zi ) to do some effective forecasts based on customer base analysis. Differed from the entire extended SMC model (based on Pareto/NBD), our research preserve and advocate the easy implementing of the BG/NBD and consider the past dollar volume spent by customers in the meanwhile through combining the Extended SMC model. Hence, our model is more suitable to be a basis to do further CLV research than the “pure” BG/NBD. We also empirically validate our model by using a database from an online VCD retailer and try to anticipate the possible purchase patterns of customers in the future both individually and collectively. And then we validate our model through 1-way MANOVA to ensure the differentiation capabilities of the important expected values. In this way we could not only use our model to do forecasting but use the model results to differentiate customers for further one-to-one marketing putting into practice.
Furthermore, based on the BG/NBD, we have derived the equation of expected active probability for a random chosen customer. It could help us to understand the individual active probability and the true customer base of a firm after summing the active probabilities of all customers.
Besides, we have transformed the worksheet of the BG/NBD to a more user-friendly form. With this new worksheet, we only should put the basic purchase history of all customers into and then we could get the expected values of interests at one time. It could save a firm a lot of time to implement this model especially when its base of customers is huge. And also we wish to improve the utility rate of our model.
5.2 Research Limitation
The biggest limitation to implement our model is that the database must be non-contractual setting. The non-contractual setting means that the transactions
occur continuously other than discretely and the time point at which a customer becomes inactive is unobserved. In this setting, the value of the expected active probability exists.
Another limitation is that the authors of these models have made assumptions that also in a non-contractual setting, the basic model assumptions, where the transaction process follows NBD and the dropout process follows BG, are satisfied.
Besides, the accuracy of our model’s forecasting ability is influenced by the future marketing activities targeted at the same group of customers. If there are many differences between present and future marketing activities, the expected values only based on past purchase histories may have some distances from the actual future conditions. Therefore while comparing the expected values with actual ones and employ a correlation analysis; we should compare the marketing activities and expenditures in advance.
5.3 Future Research Direction
There are some future research directions when using our model. Because our model has incorporated dollar volume of customers, it could be the basis to project the customer lifetime value. Furthermore our model results (three important expected values) could be the variables to help us discriminate and select customers who could bring more sales volume and profits. If the original size of the customer base is too large or very divergent within, we could utilize demographic variables or other maybe “RFM” variables to segment the customer base first and then implement our model respectively. In this way, the estimated parameters may be fitter for different segments and could improve the forecasting accuracy. If we could extend our observation time and get a database covering more than one year, we could do cross-validation to ensure and validate whether our model results could successfully predict future purchase behavior or not. Moreover, if the database applied could provide more complete information, such as demographic variables, we could not only utilize these variables to classify our customers other than the
monetary variable which combined with 80/20 rule and to have more reliable hypothesis testing results while doing model validation, but also we could combine the model results (three important expected values) with the demographic variables and we could have better understanding about the profile of every individual customer to practically implement one-to-one marketing.
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