Design And Implementation Of Multimedia MMS System Based On

With the rapid development of information technology, the traditional media industry is facing the huge impact of the digital new media, especially newspapers, because of its static text picture messages could easily be replaced by digitized information, the decline in circulation and advertising revenue due to the impact of the new media is especially obvious. In this case, the traditional newspaper groups have put forward the new media transformation slogan, and began looking for the entry point in the field of new media. Which, based on The MMS media, which based on the MMS technology, became a breakthrough in new media transformation because of its simple technical architecture, easy content migration and high concentrated channel. At present, the domestic MMS media users has surpassed more than300million.But the general usage of IT is still at a low level. The MMS media lacks technical solution to boost the customer satisfaction and product competitiveness.Response to this phenomenon, the paper first analyzes the the MMS media’s operational processes, to establish analytical CRM systems to customer demand as the center and around the CRM system to reconstruct the MMS media operational processes. The demand of MMS media operators mainly reflects in customer feedbacks to MMS content and advertising through a variety of interactive channels, collecting aggregate dynamic product operations presented in the form of data and charts and achieving through an improved K-mean method clustering, classification of the customer base to make a more detailed and precise positioning. The CRM system through data extraction and content clustering, from customer service phone records and text messages from upstream data normalized integration, build a support OLAP data warehouse, data customer data through improved K-Means algorithm and Apriori algorithm mining, while B/S architecture allows content editors and advertising planners can be applied to the results of the data analysis in decision-making.