Research And Application Of CRM Model And Data Mining For Technology Transfer Center

We have entered the information age, information technology and the rapid development of market competition intensifies the concept of customer relationship management production. The ultimate goal of customer relationship management is to improve the profitability of the enterprise, and its immediate objective is to improve customer satisfaction as well as business and customer relationship. Its main application includes customer acquisition, cross-marketing, customer maintainenance, and so on. Data mining technology is a kind of data analysis technology which is to explore undiscovered knowledge in large amounts of data. With the help of data mining technology, people can analyze customer data and find that its rule, so as to provide customer relationship management a decision-making basis.This thesis analyses the customer relationship management system application and development status, point that the current customer relationship management system a lack of customer relationship model generally. The statistical analyse of customer data a lack of effective data analysis techniques to conduct an in-depth understanding of customers. In this thesis study these two aspects, first of all, in accordance with Technology Transfer Center, this thesis presents a model of customer relationships and a customer relations exponential mathematical model, According to the mathematical model, you can use the data mining to analyses the base features of customer and frame a targeted customer marketing strategy.The thesis study the application of CRM model and association rules data mining for CRM system, and researches the classical association rules mining algorithm, applied the algorithm in cross-marketing and customer access of customer relationship management system, analyzed the apriori algorithm in detail, and then improves it. The improved algorithm is applied to analyze the technological achievements transaction records; and with the help of SQL technology, it generates the frequent aggregates, and no candidate aggregate was generated. Furthermore, the improved algorithm allowed users to choose the mining attributes, and needn't to change the table structure before association rules mining. Based on the"support-confidence"framework, the algorithm added incidence as its standard of estimating association rules, which avoided of bringing the misadvised rules. This thesis also finds that apriori algorithm can be used to characterize customers. Finally, apply the apriori algorithm to Technology Transfer Center CRM system to analyze the customer data and find data mining models.