Marketing data processing method and device

Abstract

The invention provides a marketing data processing method and a marketing data processing device. The marketing data processing method comprises the steps of establishing a positive and negative sample set of a marketing training model according to marketing business basic data; establishing a marketing training model according to the positive and negative sample sets and a random forest algorithm; determining initial marketing data according to the marketing business data to be predicted and the marketing training model; updating the marketing training model by utilizing the initial marketingdata; and performing marketing data processing by using the updated marketing training model to generate marketing list data. According to the marketing data processing method provided by the invention, manual marking is changed into automatic marking, so that the marking workload of business personnel is effectively reduced. The marking accuracy is improved. The model updating strategy is changed from a single coverage updating strategy to an intelligent updating strategy. Therefore, the difficulty of setting the model parameters by the business is reduced, intelligent updating is realized,and the marketing success rate is improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a method and device for processing marketing data. Background technique [0002] Traditional marketing methods often have certain difficulties when facing a large number of customers and many products. At the same time, the pressure of competition in the financial industry is increasing day by day. Commercial banks need to use new technical tools, methods and platforms to recommend products to corporate customers more accurately and intelligently. [0003] With the continuous development and maturity of big data, artificial intelligence, cloud computing and other technologies, based on the continuous integration of bank customer portrait data, transaction data, and product data, it is possible to realize the establishment of intelligent recommendation models, effectively improve the success rate of marketing, and enhance business value. But at the same time, there are also...

AI Technical Summary

Problems solved by technology

But at the same time, there are also common problems: 1. Before marketing, in the model building stage, business participation is required to mark positive and negative samples for modeling. Marking errors will directly affect the accuracy of model training

2. In marketing, while the recommended customer data is constantly changing, if the model cannot be updated in time after it goes online, the prediction results will have a time delay, which will affect the marketing experience

3. After marketing, there will be differences between the results recommended by the model and the actual successful results. Cross-statistics need to be made, and there is a lack of real-time statistical feedback, so it is impossible to share the success or failure experience of the recommendation

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