Product and customer matching method and system
A matching method and customer technology, applied in the financial field, can solve problems such as empirical rules and methods that are difficult to meet market demand, bank financial product market positioning and customer individual demand mismatch, etc., to achieve the effect of meeting market positioning and improving matching accuracy
Pending Publication Date: 2021-09-21
CHINA CONSTRUCTION BANK
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Problems solved by technology
[0003] At present, banks mainly use rule engines based on human experience and traditional machine learning methods for product marketing. However, with the increasing number of bank financial products, data dimensions, and data volume, the empirical rules and methods adopted by marketing are becoming more and more difficult to satisfy. Market demand, resulting in a mismatch between the market positioning of banking financial products and the individual needs of customers
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[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0066] The embodiment of the present invention discloses a product-customer matching method and system, which acquires customer information of all customers in the bank, extracts customer characteristic information corresponding to each customer information, and divides customers with the same customer characteristic information into the same customer group , based on the product information held by each customer in each customer group, determine the product co...
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The invention discloses a product and customer matching method and system. The method comprises the steps: dividing customers with the same customer feature information in all customers into the same customer group, determining the product concentration of to-be-recommended products in each customer group based on the information of the products held by each customer in each customer group, determining a recommendation index representing the preference matching degree between each customer and the to-be-recommended product by taking the product concentration of the to-be-recommended product held by each customer group as a reference and combining the holding condition of the to-be-recommended product by each customer and the characteristics of the to-be-recommended product. According to the method, the customer groups are divided, and the preference matching degree between each customer and the to-be-recommended product is determined by taking the product concentration of the to-be-recommended product held by each customer group as a reference and combining the holding condition of each customer on the to-be-recommended product and the characteristics of the to-be-recommended product; the matched customer group is determined according to the product, the matched product is determined according to the customer group, and the matching precision between the product and the customer is improved.
Description
technical field [0001] The invention relates to the field of financial technology, and more specifically, relates to a method and system for matching products and customers. Background technique [0002] This year, people's recognition, trust and demand for banking financial products are getting higher and higher. Different from the singleness of banking financial products in the past, the current banking products have the characteristics of rich variety, high selectivity, and low purchase threshold. Banking products have changed from financial products that a small number of people could buy to one of the asset appreciation channels that the public can participate in. Therefore, how to better tap the potential of customers with financial products of banks has become an important demand for personalized and accurate recommendations for customers. [0003] At present, banks mainly use rule engines based on human experience and traditional machine learning methods for product...
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IPC IPC(8): G06Q40/06G06Q30/06G06F16/9535
CPCG06Q40/06G06F16/9535G06Q30/0631
Inventor 季然邓飞飏朱笑涛
Owner CHINA CONSTRUCTION BANK