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Deep learning model of customer lifecycle value for customer classification and multi-entity matching

A life cycle, customer-oriented technology, applied in biological neural network models, neural learning methods, clustering/classification of other databases, etc., can solve unpredictable and complex problems

Pending Publication Date: 2021-04-23
罗科仕科技(北京)股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these marketing strategies are either highly individual dependent on skilled agents, or are too complex and unpredictable for existing rule-based technical systems to function effectively

Method used

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  • Deep learning model of customer lifecycle value for customer classification and multi-entity matching
  • Deep learning model of customer lifecycle value for customer classification and multi-entity matching
  • Deep learning model of customer lifecycle value for customer classification and multi-entity matching

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Embodiment Construction

[0016] Reference will now be made in detail to some embodiments of the invention, examples of which are illustrated in the accompanying drawings.

[0017] A successful insurance company should provide a holistic solution that focuses on the entire financial situation of the customer in an ecosystem with multiple players (even with third-party players), thereby providing customers with the best user experience and making customers feel confident Feel comfortable with a support team in your financial situation. The ecosystem can include insurance companies, agents, advisors and coaches (to train clients to think and understand their financial situation), as well as other professionals such as lawyers (legal advice, living trusts, wills, etc.), financial planners, accountants, banks, mortgage lender), etc. Companies should also implement flexible processes for both front-end customers and back-end operations, especially in claims management. Companies that can connect back-end ...

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Abstract

The invention provides a deep learning integrated model method and system for customer classification and multi-entity matching strategies. In one novel aspect, a deep learning model (DNN) based on customer lifecycle value (CLV) uses an aggregate of data mining and recurrent neural network (RNN)-convolutional neural network (CNN) to identify potential prospects from potential customers, predict churn / retention, predict next purchase, recommend policies to maintain and enhance existing customer relationships, and provides n-ary matching between potential customers / customers, agents, products, and delivery policies. In one embodiment, a CLV system obtains a CLV profile of a customer, generates a CLV-based output for the customer using a DNN model, selects an n-ary match for the customer according to the CLV-based output, and collects feedback for the n-ary match to update the n-ary match until one or more exit conditions are met.

Description

technical field [0001] The present invention relates generally to deep learning models, and more particularly, to deep learning models for customer lifetime value (CLV) for customer classification and multi-entity matching strategies. Background technique [0002] The insurance industry has been an early adopter of information technology. In recent years, the industry has adopted artificial intelligence (AI) to improve the efficiency of its operations and reduce costs. From online direct to consumer direct call centers to support online and telemarketing, and personal to personal selling, the industry is under full-scale frontal attack. There is a misconception that millennials are all online and digital. It turns out that millennials are the generation that wants digital first, but not only digital. This makes it all the more important to nurture these future customers by combining a great online digital user experience and complementing it with face-to-face counseling a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q40/08G06F16/906G06N3/04G06N3/08
CPCG06Q30/0255G06Q30/0252G06Q30/0271G06Q40/08G06F16/906G06N3/08G06N3/044G06N3/045G06Q30/0204G06Q30/016G06Q10/06375G06Q30/0276G06Q40/02
Inventor 黃宏灿李明桦
Owner 罗科仕科技(北京)股份有限公司