The development of the network economy has transformed business practices. A major shift occurs in the marketing efforts. Given the abundant information on customers and products, marketing responsibility has changed from managing products for sellers to managing relationships with customers, and to facilitating decision-making for customers. These marketing efforts are essentially an intermediary of two-way communication between businesses and customers, and they would require data analysis techniques. Particularly through data mining, which comprises techniques for the extraction of hidden predictive information from large databases, organisations are able to identify valuable customers, predict future behaviours, and allow firms to make proactive, knowledge driven decisions. Various techniques exist among data mining software, each method having its advantages and disadvantages for different types of marketing purposes. This paper examines the issue of applying different techniques to meet the marketing requirements in the network economy. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). Two case studies are provided to illustrate how different data mining techniques can be utilised to accomplish various marketing efforts.