Smart precision marketing system for big data

Abstract

The invention discloses a smart precision marketing system for big data. The system comprises a big data platform, a data fusion unit, a model analysis unit, a model optimization unit and a model application unit, wherein the data fusion unit is used for collecting and cleaning multi-source heterogeneous data and performing data correlation analysis; the model analysis unit is used for deeply analyzing a data center and making a corresponding analysis model according to a business scenario; the model optimization unit is used for acquiring a model selection optimization scheme and a characteristic change scheme through monitoring, comparison and analysis for the analysis model; the model application unit is used for realizing fusion of the trained model or processed data with tools and systems of an e-commerce platform. Travellers are precisely differentiated and the special analysis models are established with a distributed deep learning algorithm through collection and fusion of traveller data on the basis of a big data technology, and the smart precision marketing system is constructed by tracking the model effect and optimizing the model continuously.

Description

technical field [0001] The invention relates to the technical field of big data processing, in particular to a big data intelligent precision marketing system. Background technique [0002] With the continuous development of aviation e-commerce and the general trend of Internet +, it is necessary to promote e-commerce to realize the O2O model, open up online and offline resources, and improve the marketing and circulation chain of enterprises. The entire closed loop of online and offline marketing requires a more complete marketing platform support and Improve the electronic marketing channels and function construction day by day to meet the growing needs of users for personalized services. [0003] As far as the traditional electronic marketing system is concerned, its main problems are: [0004] 1) Using artificial experience to model user behavior characteristics is limited to a certain extent by the experience of analysts. At the same time, business and user behaviors a...

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Problems solved by technology

[0004] 1) Using artificial experience to model user behavior characteristics is limited to a certain extent by the experience of analysts. At the same time, business and user behaviors are constantly changing, and artificial experience cannot be adapted in time. analyze

[0005] 2) The traditional e-marketing system is just a tool for automatically configuring features and activity strategies. The feedback mechanism for marketing effects is not fully constructed, and there is no corresponding optimization mechanism, which makes the understanding of marketing conclusions not in place. The effect is not clear enough, there is a certain blindness and reliance on traditional experience in the formulation of marketing activities

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