Information analysis system supporting fine customer value management of stockbroking industry

Abstract

The invention relates to an information analysis system supporting the fine customer value management of the stockbroking industry. By adopting the data ETL (extracting, transforming and loading) process, the EDW comprising a large amount of data, the DDW comprising the mildly accumulated historical departmental data and meeting the needs of a certain department of the specific enterprise, the CUBE for quickly realizing upwards and downwards drilling, rotary examining by fluoroscopy and slice and block cutting and the OLAP for making the analyzer, the manager or executor to access information quickly, consistently and interactively from multiple aspects, a kind of software technology for further knowing data can be obtained. By applying the EDW, DDW and OLAP technologies comprehensively and integrating the data provided by multiple service systems of the securities company, a comprehensive data analysis system for the securities users is formed so that the customer needs, potential and service states can be known more effectively, the implementation progress and effect of various service means can be known and the customer analysis performance and the daily work efficiency of all service departments of the securities company can be improved.

Description

technical field [0001] The present invention designs an information analysis system and method that supports refined customer value management in the securities brokerage industry Background technique [0002] In recent years, with the opening of the domestic securities market, the securities brokerage industry is increasingly facing a competitive industry with fierce party struggles at home and abroad. Improve industry competitiveness. Emphasize customer value management and rely on refined management to achieve industrial upgrading, which is highly valued by the government and the industry. Supporting the construction of refined information infrastructure with the goal of improving customer value has become a key construction object supported by the state and the securities industry. At present, there are many types of securities brokerage information analysis products in the domestic and foreign markets, but most of them have defects: for example, in terms of design posi...

AI Technical Summary

Problems solved by technology

At present, there are many types of securities brokerage information analysis products in the domestic and foreign markets, but most of them have defects: for example, in terms of design positioning, there is no clear goal of value enhancement, and most of them only provide broker salary calculations, operating results reports, and a small amount. The information provided by this type of system is limited to understanding the operating results and status quo under the existing business model, and the amount of information provided is very limited in terms of refined operation planning for business optimization. Meet the actual needs of refined operation management and enhance customer value; in terms of indicator system, this type of system lacks systematic and refined processing for converting basic data into key information, and has not formed a key indicator group that can express differences in customer characteristics. The lack of detailed processing of some important and complex indicators will lead to large data errors and affect the user's trust in the information provided by the system; in terms of the underlying data model, most of them have not formed a basic layer and a summary layer. Hierarchical model design system makes it difficult for the system to adapt to the needs of business management in terms of robustness, scalability, and operational efficiency; in terms of processing technology from data to information, the processing methods used are relatively single, which is not helpful Data mining and other technologies based on regular discovery make the ability to control key information such as natural customer grouping and customer churn tendency weak

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