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Personalized recommendation method and system based on automobile industry after-sales scene

A recommendation system and industry technology, applied in business, equipment, sales/lease transactions, etc., can solve problems such as lack of comprehensive customer cognition, low recommendation success rate, single recommendation scenario, etc., to improve output value and service satisfaction, Improve the success rate of recommendation and improve the effect of service experience

Pending Publication Date: 2020-11-10
上海数策软件股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] (1) Mainly rely on the experience of service consultants or maintenance manuals for recommendations, lack of comprehensive understanding of customers, and the success rate of recommendations is not high
[0005] (2) Recommendations are made based on the delivery process and workshop inspection results, and customers' needs cannot be predicted in advance, so passive recommendations
[0006] (3) Make popular recommendations based on mileage, car series or promotional activities, and the recommendation scenarios are relatively single
[0007] Compared with mature industries such as the Internet, a large amount of offline behavior data of traditional OEMs is not standardized and standard, and it is difficult to meet the data standards required by the recommendation algorithm and system

Method used

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  • Personalized recommendation method and system based on automobile industry after-sales scene
  • Personalized recommendation method and system based on automobile industry after-sales scene
  • Personalized recommendation method and system based on automobile industry after-sales scene

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] According to the present invention, a personalized recommendation system based on the after-sales scene of the automobile industry includes:

[0075] Module M1: Build a recall algorithm model by using internal data combined with external data to obtain a list of items to be recommended for each customer;

[0076] Module M2: Build a ranking algorithm model by using sales data, channel activity data and lead feedback data;

[0077] Module M3: According to the sorting algorithm model, select items that match customer needs from the list of items to be recommended;

[0078] Module M4: According to the sorting algorithm model, select items that match customer needs, combine different online channels and different business departments of offline dealers, build an intelligent clue distribution business model, and follow up clues intelligently;

[0079] The recall algorithm model includes selecting items that meet customer needs among the accessories and services to be selecte...

Embodiment 2

[0149] Embodiment 2 is a modification of embodiment 1

[0150] This system is applied in the automotive after-sales industry, in the recommended service scenario where customers enter the store:

[0151] (1) Before the customer enters the 4S store, the recommendation system passes through the recall and sorting module, outputs a list of clues for the accessories and services that the customer needs next time, and prompts them on the recommendation page of the customer's App and applet. Customers can choose to accept or not to receive certain accessories and services. Customers can also make appointments on customer apps and programs.

[0152] (2) When the customer clicks to accept or make an appointment, the model monitoring module will collect this information. At the same time, the dealer's service consultant will call the customer to confirm the recommendation information and confirm the time when the customer enters the store.

[0153] (3) When the customer enters the 4...

Embodiment 3

[0156] Embodiment 3 is a modification of embodiment 1 and / or embodiment 2

[0157] This system is used in the after-sales recommendation business scenario of the automotive industry.

[0158] 1) By using after-sales work orders, spare parts delivery, financial and Internet of Vehicles data, combined with external data, a recall algorithm model is constructed to obtain a list of items to be recommended for each customer.

[0159] 2) Build a ranking algorithm model by using sales data, channel activity data and lead feedback data. From the list of items to be recommended, items with a high degree of matching with customer needs are screened out.

[0160] 3) Combining different online channels and different business departments of offline dealers, build an intelligent lead distribution business model to help dealers reach customers effectively.

[0161] Through the recall and sorting algorithm model in this system, it can help dealers discover the potential needs of customers a...

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Abstract

The invention provides a personalized recommendation system and method based on an automobile industry after-sales scene, and the method comprises the steps: building a recall algorithm model throughemploying internal data and external data, and obtaining a to-be-recommended article list of each customer; constructing a sorting algorithm model by using the sales data, the channel active data andthe clue feedback data; screening out articles matching customer requirements from the list of to-be-recommended articles according to a sorting algorithm model; screening out articles matching customer requirements according to the sorting algorithm model, constructing an intelligent clue method distribution service model by combining different online channels and different offline dealer servicedepartments, and intelligently carrying out clue following; The sorting algorithm model of the to-be-recommended articles and the users is constructed by adopting business rules and machine learningand combining client online CTR and offline ATR effect feedback data, so that the problems that dealers are difficult to select from massive to-be-recommended articles and the like are solved, the client requirements are accurately predicted, and the recommendation success rate is increased.

Description

technical field [0001] The present invention relates to the field of after-sales recommendation in the automobile industry, in particular to a personalized recommendation method and system based on an after-sales scene in the automobile industry. Background technique [0002] Today, Chinese society is in the mature stage of the mobile Internet era. Representatives of the traditional automotive after-sales service industry and traditional after-sales service providers represented by the after-sales service system of automobile dealers will face the price, technology and operation brought by the Internet industry after the automobile aftersales. conceptual shock. The automotive after-sales industry urgently needs to build a deep-rooted industry-based feature and make full use of the latecomer advantages in the field of recommendation systems to better match car owners and provide after-sales services for car owners. [0003] When traditional dealers make customer referrals, t...

Claims

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Application Information

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IPC IPC(8): G06Q30/00G06Q30/06G06Q50/10
CPCG06Q30/016G06Q30/0631G06Q50/10
Inventor 程振陈洋洋胡聪慧邬凯乐李红明张椿琳
Owner 上海数策软件股份有限公司
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