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Purpose -Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly-used hybrid models by classification þ classification and clustering þ classification hybrid approaches, respectively, in terms of customer value prediction. Design/methodology/approach -To construct a hybrid model, multiple techniques are usually combined in a two-stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre-process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k-means and self-organizing maps for the clustering techniques to construct six different hybrid models. Findings -The experimental results over a real case dataset show that the classification þ classification hybrid approach performs the best. In particular, combining two-stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/ 0.43 percent). Originality/value -The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.

<blockquote>Enabling undergraduate students to develop basic computing skills is an important issue in higher education. As a result, some universities have developed computer proficiency tests, which aim to assess students' computer literacy. Generally, students are required to pass such tests in order to prove that they have a certain level of computer literacy for successful graduation. This paper applies data mining techniques to make predictions about students who are going to take the computer proficiency test and fail. A national university in Taiwan is considered as the case study. Three different clustering techniques are used individually to cluster students into different groups, which are k-means, self-organising maps (SOM), and two-step clustering (i.e. BIRCH). After the best clustering result is found, the decision tree algorithm is used to extract useful rules from each of the identified clusters. These rules can be used to warn or counsel students who have higher probability of failing the test. The results can help the university identify a number of student groups who need to pay much more attention to preparing for the test, which is likely to help conserve resources. Furthermore, this study can be regarded as a guideline for future developments in assessing students' English literacy, as this is also an important graduation requirement in many universities.</blockquote>

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