WO2019153518A1 - Procédé et dispositif de poussée d'informations, dispositif informatique et support de stockage - Google Patents

Procédé et dispositif de poussée d'informations, dispositif informatique et support de stockage Download PDF

Info

Publication number
WO2019153518A1
WO2019153518A1 PCT/CN2018/084308 CN2018084308W WO2019153518A1 WO 2019153518 A1 WO2019153518 A1 WO 2019153518A1 CN 2018084308 W CN2018084308 W CN 2018084308W WO 2019153518 A1 WO2019153518 A1 WO 2019153518A1
Authority
WO
WIPO (PCT)
Prior art keywords
customer
data
preset
algorithm
sales
Prior art date
Application number
PCT/CN2018/084308
Other languages
English (en)
Chinese (zh)
Inventor
伍文岳
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2019153518A1 publication Critical patent/WO2019153518A1/fr

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0203—Market surveys; Market polls

Definitions

  • the present application relates to the field of Internet technologies, and in particular, to an information push method, apparatus, computer device, and storage medium.
  • the present application provides an information push method, device, computer device and storage medium, which are intended to provide sales efficiency and avoid waste of resources by using historical data.
  • the application provides a method for pushing information, including:
  • the application provides an information pushing device, including:
  • a data obtaining unit configured to acquire historical sales data of a preset product, where the historical sales data includes customer information data and customer behavior data;
  • a feature determining unit configured to determine a customer data feature according to the customer information data and the customer behavior data
  • a model training unit configured to perform training by using a preset algorithm to obtain a sales prediction model of the preset product based on the customer data feature
  • a result prediction unit configured to predict a current customer corresponding to the preset product based on the sales prediction model to output a prediction result
  • the information pushing unit is configured to push the customer information and the prediction result corresponding to the current customer to the salesperson corresponding to the preset product.
  • the present application further provides a computer device including a memory, a processor, and a computer program stored on the memory and operable on the processor, the processor implementing the program The information pushing method described in any one of the applications.
  • the present application also provides a storage medium, wherein the storage medium stores a computer program, the computer program comprising program instructions, the program instructions, when executed by a processor, causing the processor to execute the application
  • the embodiment of the present application obtains historical sales data of a preset product, where the historical sales data includes customer information data and customer behavior data; determining customer data characteristics according to the customer information data and customer behavior data; and based on the customer data characteristics Modeling training by a preset algorithm to obtain a sales prediction model of the preset product; predicting a current customer corresponding to the preset product based on the sales prediction model to output a prediction result; and corresponding to the current customer
  • the customer information and the predicted result are pushed to the salesperson corresponding to the preset product.
  • the method uses historical sales data to perform analytical modeling to predict a current customer's predicted result of purchasing the preset product, and sends the predicted result to a corresponding salesperson, and the corresponding salesperson performs sales according to the preset result, This can increase the sales efficiency of the preset product while saving the salesperson's time.
  • FIG. 1 is a schematic diagram of an application scenario corresponding to an information pushing method according to an embodiment of the present application
  • FIG. 2 is a schematic flowchart of an information pushing method according to an embodiment of the present application.
  • FIG. 3 is a schematic flow chart showing a sub-step of the information pushing method of FIG. 2;
  • FIG. 4 is a schematic block diagram of an information pushing apparatus according to an embodiment of the present application.
  • FIG. 5 is a schematic block diagram of a computer device according to an embodiment of the present application.
  • the embodiment of the present application provides an information pushing method, device, computer device, and storage medium.
  • the composition of the application scenario to which the information pushing method of the embodiment of the present application is applied is first introduced.
  • the application scenario includes a user terminal and a server.
  • the user terminal may be an electronic device such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device; the server may be a stand-alone server or a server cluster composed of multiple servers.
  • the information pushing method can be run in a server, and the information is pushed by the interaction between the server and the user terminal, thereby helping the salesperson to increase the sales rate and saving a lot of time.
  • FIG. 2 is a schematic flowchart of a method for pushing information according to an embodiment of the present application. Please refer to FIG. 1 at the same time.
  • the information pushing method will be introduced in conjunction with the application scenario in FIG. 1.
  • the information pushing method is run in a server for pushing information to a salesperson through a user terminal. Specifically, as shown in FIG. 2, the information pushing method includes steps S101 to S105.
  • the preset product may be a wealth management product, a fund smart investment, an old-age security management product or a fund.
  • the historical sales data refers to historical data generated when a customer pays attention to or purchases such wealth management products, funds smart investment, pension insurance management products or funds, wherein the historical sales data includes the purchase of the preset product and the purchase of the preset product. Historical data, such as whether the customer has registered the wealth management product, whether the customer has viewed the wealth management product, etc.
  • the customer information data is the basic information of the customer, and the basic information of the customer includes the customer basic attribute information, the customer product attribute information, and the customer special attribute information, the customer basic attribute information such as gender, age, region, occupation, etc.; customer product attribute information, such as The number of safety products, the number of professional companies, the degree of protection, etc.; customer special attribute information, such as bad records, blacklists, etc.
  • the customer behavior data includes data information generated when consulting or purchasing the preset product, and the customer behavior data includes customer contact information about the product, such as the number of telephone calls, the number of complaints, and the number of visits to the website in a certain period of time.
  • S102 Determine customer data characteristics according to the customer information data and customer behavior data.
  • the customer data feature refers to the data feature of the customer corresponding to the purchase of the preset product, and is a data set that affects the customer's purchase behavior, and specifically may use the feature data in the customer information data and/or the customer behavior data. To indicate, for example, gender, geography, occupation, bad record or blacklist.
  • the historical sales data corresponding to the customer who has purchased the preset product is determined according to the customer information data and the customer behavior data; and the customer information data and the customer behavior data in the historical sales data corresponding to the purchased customer are extracted.
  • Corresponding data characteristics Since the customer data feature is the data for purchasing the preset product, for example, the customer purchases the wealth management product, it needs to open an account on the website corresponding to the wealth management product to fill in the basic information of the customer, such as name, gender, age, occupation and education, etc. The information is filled in through the website of the preset product, and the website stores the basic information of the customer in the corresponding database on a customer-by-customer basis, and can be stored in a similar form, so the customer information data recorded in the form is obtained. And customer behavior data can easily extract the corresponding customer characteristic data.
  • the machine learning method is used to divide the customer data feature into a training data sample and a verification data sample, and the training data sample is trained by a preset algorithm and verified by verifying the data sample to obtain the sales of the preset product.
  • a predictive model that predicts the probability of purchase of a product for which the current customer intends to purchase the product.
  • the sales result is used as a target value and the customer characteristic data is used as a variable for training and verification to obtain a final sales prediction model, which is based on a customer who has purchased the preset product.
  • Data characteristics to predict the likelihood of a new customer's purchase, the specific form of its output is the percentage, that is, the probability of purchase.
  • the preset algorithm includes a Gradient Boosting Decision Tree (GBDT) and a Logistic Regression (LR) combination algorithm. Specifically, based on the customer data feature, the training is modeled by a gradient lifting decision tree and a logistic regression combination algorithm to obtain a sales prediction model of the preset product.
  • GBDT Gradient Boosting Decision Tree
  • LR Logistic Regression
  • the modeling training by the gradient lifting decision tree and the logistic regression combination algorithm to obtain the sales prediction model of the preset product includes a modeling training method.
  • the modeling training method is as shown in FIG. 3, that is, step S103 includes sub-steps S103a to S103c.
  • S103a generating a lifting tree model according to the gradient lifting decision tree algorithm;
  • S103b obtaining an effective combination feature based on the lifting tree model;
  • S103c setting the customer data feature and the effective combination feature to a training feature of the logistic regression algorithm Train to generate a sales forecasting model.
  • the GBDT and LR combination algorithm is a two-class combination algorithm, which first uses the original customer data feature to find a combination feature related to the target value depth through the GBDT algorithm, and then obtains the target event occurrence of all samples by using the LR algorithm. Probability.
  • Tree1 and Tree2 are two trees learned by the GBDT model, and x is an input sample. After traversing two trees, the x samples fall on the leaf nodes of the two trees, and each leaf The node corresponds to the LR one-dimensional feature, then by traversing the tree, all the LR features corresponding to the sample are obtained.
  • each path of the tree is a discriminative path that is finally segmented by minimizing the mean square error and the like, the features and feature combinations obtained according to the path are relatively distinguishable, and the effect is theoretically not inferior to the manual experience. the way. Therefore, using the combination algorithm to process the customer data features, it can be found that there are many distinguishing features and feature combinations.
  • the path of the decision tree can be directly used as the input feature of the LR algorithm, eliminating the steps of manually searching for features and feature combinations. , speed up the modeling.
  • the lifting tree model is first generated by the GBDT algorithm to find the combined features that have a significant influence on the target value.
  • age and occupation are significantly related to whether or not to buy, and the combination of age and occupation (such as post-80 + financial white-collar) will also work;
  • the original features are age and occupation, which in turn become age, occupation, and a combination of age and occupation.
  • the modeling the training by the preset algorithm to obtain the sales prediction model of the preset product further comprising: determining that the performance parameter of the sales prediction model meets a preset condition, wherein the performance parameter includes The gradient enhances the depth value of the decision tree and the regularization coefficient of the logistic regression algorithm; if the performance parameter of the sales prediction model satisfies the preset condition, the training is stopped to obtain an optimal sales preset model.
  • the GBDT and LR algorithm models are iteratively trained through parameter trials until performance has the best classification performance on the validation data set. For example, when the lifting tree consists of 50 trees with a depth of 5 and an LR regularization coefficient of 0.2, the model is optimal.
  • the sales prediction model is configured to predict a data feature of the current customer corresponding to the preset product to analyze whether it intends to purchase the preset product, wherein the preset result is used to indicate that the customer purchases the preset product.
  • the possibility of the prediction is, for example, the probability of purchase, or both the purchase and the non-purchase.
  • the prediction result is used to indicate whether the customer is a potential purchase customer
  • the customer information may specifically be a customer name, a communication method, and the like.
  • it can be sent to the user terminal used by the salesperson by means of email or product client communication. Therefore, the salesperson can carry out the sales activity according to the preset result, and is supported by the big data technology, thereby increasing the sales volume of the product.
  • the sales prediction model is used to predict the data characteristics of the current customer corresponding to the preset product to obtain the purchase probability thereof, and the customer information and the purchase corresponding to the customer whose purchase probability meets the preset condition in the current customer.
  • the probability is pushed to the salesperson corresponding to the preset product, and the purchase probability meets the preset condition that the purchase probability is greater than a preset threshold, and the preset threshold indicates that the customer is a potential purchase customer.
  • the top 1000 customers with the probability of purchase can be taken as the sales target. For example, a customer who has a purchase probability of more than 70% believes that the user has a high probability of purchasing the product.
  • the customer information can be specifically the customer name and communication method. Specifically, it can be sent to the salesperson through email and product client communication.
  • the obtaining historical sales data of the preset product includes: obtaining sales data of the product related to the preset product within a preset time period; and using the sales data in the preset time period as a preset Historical sales data for the product.
  • the method is for the case where the new product is online, and the new product has no historical sales record, and the sales data of the product similar to the product can be used for modeling.
  • the product similar to the preset product may be determined according to the product information, for example, the product information of the two wealth management products is basically the same, and the difference may be that the time of launch is different.
  • the above embodiment obtains historical sales data of a preset product, wherein the historical sales data includes customer information data and customer behavior data; determining customer data characteristics according to the customer information data and customer behavior data; and based on the customer data characteristics, Modeling training by a preset algorithm to obtain a sales prediction model of the preset product; predicting a current customer corresponding to the preset product based on the sales prediction model to output a predicted result; and corresponding to the current customer
  • the customer information and the predicted result are pushed to the salesperson corresponding to the preset product.
  • the method uses historical sales data to perform analytical modeling to predict a current customer's predicted result of purchasing the preset product, and sends the predicted result to a corresponding salesperson, and the corresponding salesperson performs sales according to the preset result, This can increase the sales efficiency of the preset product while saving the salesperson's time.
  • FIG. 4 is a schematic block diagram of an information pushing apparatus according to an embodiment of the present application.
  • the information pushing apparatus 400 can be installed in a server, wherein the server can be a stand-alone server or a server cluster composed of a plurality of servers.
  • the information pushing apparatus 400 includes a data acquiring unit 401, a feature determining unit 402, a model training unit 403, a result predicting unit 404, and an information pushing unit 405.
  • the data obtaining unit 401 is configured to acquire historical sales data of a preset product, where the historical sales data includes customer information data and customer behavior data.
  • the feature determining unit 402 is configured to determine a customer data feature according to the customer information data and the customer behavior data.
  • the model training unit 403 is configured to: according to the customer data feature, model training by using a preset algorithm to obtain a sales prediction model of the preset product; wherein the preset algorithm includes a gradient promotion decision tree and a logistic regression combination algorithm;
  • the model training unit is specifically configured to: according to the customer data feature, model training by using a gradient lifting decision tree and a logistic regression combination algorithm to obtain a sales prediction model of the preset product.
  • the model training unit 403 includes: a first model generation sub-unit 4031, a valid feature acquisition sub-unit 4032, and a second model generation sub-unit 4033.
  • the first model generating sub-unit 4031 is configured to generate a lifting tree model according to the gradient lifting decision tree algorithm;
  • the effective feature obtaining sub-unit 4032 is configured to obtain an effective combined feature based on the lifting tree model;
  • the second model generating sub-unit 4033 A training feature for setting the customer data feature and the effective combination feature to the logistic regression algorithm is trained to generate a sales prediction model.
  • the result prediction unit 404 is configured to predict a current client corresponding to the preset product based on the sales prediction model to output a prediction result.
  • the information pushing unit 405 is configured to push the customer information and the prediction result corresponding to the current customer to the salesperson corresponding to the preset product.
  • the above apparatus may be embodied in the form of a computer program that can be run on a computer device as shown in FIG.
  • FIG. 5 is a schematic block diagram of a computer device according to an embodiment of the present application.
  • the computer device 500 device can be a server.
  • the computer device 500 includes a processor 520, a memory and a network interface 550 connected by a system bus 510, wherein the memory can include a non-volatile storage medium 530 and an internal memory 540.
  • the non-volatile storage medium 530 can store an operating system 531 and a computer program 532.
  • the processor 520 can be caused to perform an information push method.
  • the processor 520 is used to provide computing and control capabilities to support the operation of the entire computer device 500.
  • the internal memory 540 provides an environment for the operation of the computer program 532 in the non-volatile storage medium 530, which when executed by the processor 520, causes the processor 520 to perform an information push method.
  • the network interface 550 is used for network communication, such as sending assigned tasks and the like. It will be understood by those skilled in the art that the structure shown in FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation of the computer device 500 to which the solution of the present application is applied, and a specific computer device. 500 may include more or fewer components than shown, or some components may be combined, or have different component arrangements.
  • the processor 520 is configured to run program code stored in the memory to implement the following steps:
  • the preset algorithm includes a gradient promotion decision tree and a logistic regression combination algorithm
  • the processor 520 performs training based on the customer data feature by using a preset algorithm to obtain the preset product.
  • the prediction model is sold, the following steps are performed: based on the customer data characteristics, the training is modeled by the gradient lifting decision tree and the logistic regression combination algorithm to obtain a sales prediction model of the preset product.
  • the processor 520 when the processor 520 performs the training based on the customer data feature and is trained by the gradient lifting decision tree and the logistic regression combination algorithm to obtain the sales prediction model of the preset product, the processor 520 further performs the following step:
  • the processor 520 when performing the training modeling by the preset algorithm to obtain the sales prediction model of the preset product, performs the following steps:
  • the prediction result includes a purchase probability
  • the processor 520 performs the following steps when performing the pushing the customer information and the prediction result corresponding to the current customer to the salesperson corresponding to the preset product: Pushing the customer information and the purchase probability corresponding to the customer whose purchase probability meets the preset condition in the current customer to the salesperson corresponding to the preset product.
  • the processor 520 may be a central processing unit (CPU), and the processor 520 may also be other general-purpose processors, a digital signal processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc.
  • the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • computer device 500 architecture illustrated in FIG. 5 does not constitute a limitation to computer device 500, may include more or fewer components than illustrated, or may combine certain components, or different components. Arrangement.
  • the disclosed information pushing apparatus and method may be implemented in other manners.
  • the information push device embodiments described above are merely illustrative.
  • the division of each unit is only a logical function division, and there may be another division manner in actual implementation.
  • multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.
  • the units in the apparatus of the embodiment of the present application may be combined, divided, and deleted according to actual needs.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, can be stored in a computer readable storage medium.
  • the technical solution of the present application may be in essence or part of the contribution to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium.
  • There are a number of instructions for causing a computer device (which may be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.
  • the computer readable storage medium may be a medium that can store program code, such as a magnetic disk, an optical disk, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disk.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un procédé et un dispositif de poussée d'informations, un dispositif informatique et un support de stockage. Le procédé consiste à : obtenir des données de ventes historiques d'un produit prédéfini ; déterminer des caractéristiques de données de client selon les données de ventes historiques ; créer un modèle et effectuer un apprentissage au moyen d'un algorithme prédéfini sur la base des caractéristiques de données de client de façon à obtenir un modèle de prédiction de ventes ; prédire un client actuel correspondant au produit prédéfini sur la base du modèle de prédiction de ventes de façon à délivrer en sortie un résultat de prédiction ; et pousser les informations de client correspondant au client actuel et le résultat de prédiction à un vendeur.
PCT/CN2018/084308 2018-02-08 2018-04-25 Procédé et dispositif de poussée d'informations, dispositif informatique et support de stockage WO2019153518A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810128050.6 2018-02-08
CN201810128050.6A CN108337316A (zh) 2018-02-08 2018-02-08 信息推送方法、装置、计算机设备及存储介质

Publications (1)

Publication Number Publication Date
WO2019153518A1 true WO2019153518A1 (fr) 2019-08-15

Family

ID=62928535

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/084308 WO2019153518A1 (fr) 2018-02-08 2018-04-25 Procédé et dispositif de poussée d'informations, dispositif informatique et support de stockage

Country Status (2)

Country Link
CN (1) CN108337316A (fr)
WO (1) WO2019153518A1 (fr)

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110827112A (zh) * 2019-09-16 2020-02-21 中国平安人寿保险股份有限公司 深度学习的商品推荐方法、装置、计算机设备及存储介质
CN111127074A (zh) * 2019-11-26 2020-05-08 杭州聚效科技有限公司 一种数据推荐方法
CN111144652A (zh) * 2019-12-26 2020-05-12 浙江力石科技股份有限公司 基于游览舒适度算法和趋势预测方法、***和装置
CN111160929A (zh) * 2019-12-26 2020-05-15 深圳前海微众银行股份有限公司 一种客户类型的确定方法及装置
CN111178949A (zh) * 2019-12-18 2020-05-19 北京文思海辉金信软件有限公司 服务资源匹配参考数据确定方法、装置、设备和存储介质
CN111445297A (zh) * 2020-04-15 2020-07-24 北京易点淘网络技术有限公司 采购量的确定方法、装置、存储介质及电子设备
CN111639967A (zh) * 2020-05-21 2020-09-08 中国工商银行股份有限公司 产品潜在客户挖掘方法及***
CN111652439A (zh) * 2020-06-04 2020-09-11 上海燕汐软件信息科技有限公司 一种派送点派件量的生成方法、装置及计算机***
CN111783810A (zh) * 2019-09-24 2020-10-16 北京沃东天骏信息技术有限公司 用于确定用户的属性信息的方法和装置
CN111815368A (zh) * 2020-07-24 2020-10-23 深圳市欢太科技有限公司 广告推送方法、装置、终端及存储介质
CN111831701A (zh) * 2020-07-06 2020-10-27 湖南赛吉智慧城市建设管理有限公司 可操作its***构建方法、装置、计算机设备及存储介质
CN111860878A (zh) * 2019-12-31 2020-10-30 北京骑胜科技有限公司 共享单车的报修方法、服务器、电子设备及存储介质
CN112102095A (zh) * 2020-09-17 2020-12-18 中国建设银行股份有限公司 一种基金产品推荐方法、装置及设备
CN112163887A (zh) * 2020-09-30 2021-01-01 深圳前海微众银行股份有限公司 电销***和电销名单管理方法、装置、设备、存储介质
CN112364919A (zh) * 2020-11-11 2021-02-12 深圳前海微众银行股份有限公司 数据处理方法、装置、设备及存储介质
CN112819258A (zh) * 2021-03-24 2021-05-18 中国工商银行股份有限公司 银行网点到店客户数量预测方法及装置
CN112862008A (zh) * 2021-03-29 2021-05-28 中信银行股份有限公司 偏好预测模型的训练方法及用户偏好的预测方法
CN112884215A (zh) * 2021-02-02 2021-06-01 国网甘肃省电力公司信息通信公司 一种基于梯度增强树人口预测模型的参数优化方法
CN112907266A (zh) * 2019-11-19 2021-06-04 马上消费金融股份有限公司 用户筛选模型的获取方法、金融信息推送方法及相关装置
CN113052689A (zh) * 2021-04-30 2021-06-29 中国银行股份有限公司 一种基于决策树的产品推荐方法及装置
CN113435918A (zh) * 2020-03-19 2021-09-24 杭州大搜车汽车服务有限公司 维护策略的确定方法、装置、电子设备及存储介质
CN113554460A (zh) * 2021-07-19 2021-10-26 北京沃东天骏信息技术有限公司 一种***的识别方法和装置
CN113592297A (zh) * 2021-07-30 2021-11-02 国家电网有限公司客户服务中心 一种电力***专属式客户管理方法及***
CN114513578A (zh) * 2022-02-15 2022-05-17 平安科技（深圳）有限公司 外呼方法、装置、计算机设备及存储介质
CN117575487A (zh) * 2024-01-19 2024-02-20 天津盛驰精工有限公司 物料清单更新方法、装置、电子设备及存储介质

Families Citing this family (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109067690B (zh) * 2018-08-07 2021-03-26 腾讯科技（深圳）有限公司 离线计算结果数据的推送方法及装置
CN109308632A (zh) * 2018-08-17 2019-02-05 中国平安人寿保险股份有限公司 基于断点的产品推送方法、装置、计算机设备和存储介质
CN109345283A (zh) * 2018-08-27 2019-02-15 平安科技（深圳）有限公司 礼品分配方法及***、终端和计算机可读存储介质
CN109492191B (zh) * 2018-09-17 2024-05-07 平安科技（深圳）有限公司 计算投保概率的方法、装置、计算机设备和存储介质
CN111062736A (zh) * 2018-10-17 2020-04-24 百度在线网络技术（北京）有限公司 模型训练、线索排序方法、装置及设备
CN109472623A (zh) * 2018-11-05 2019-03-15 海尔电器国际股份有限公司 合同管理方法
CN109711860A (zh) * 2018-11-12 2019-05-03 平安科技（深圳）有限公司 用户行为的预测方法及装置、存储介质、计算机设备
CN109214874B (zh) * 2018-11-13 2021-10-15 郑州市景安网络科技股份有限公司 Ip产品运营数据处理方法、装置、设备及可读存储介质
CN109300039A (zh) * 2018-12-05 2019-02-01 山东省城市商业银行合作联盟有限公司 基于人工智能和大数据进行智能产品推荐的方法及***
CN111598116B (zh) * 2019-02-21 2024-01-23 杭州海康威视数字技术股份有限公司 数据分类方法、装置、电子设备及可读存储介质
CN109903140A (zh) * 2019-03-07 2019-06-18 阿里巴巴集团控股有限公司 一种信用服务推荐方法、装置及设备
CN109919685B (zh) * 2019-03-18 2023-08-15 苏州大学 客户流失预测方法、装置、设备及计算机可读存储介质
CN110163655B (zh) * 2019-04-15 2024-03-05 中国平安人寿保险股份有限公司 基于梯度提升树的坐席分配方法、装置、设备及存储介质
CN110363563A (zh) * 2019-05-28 2019-10-22 成都美美臣科技有限公司 一个电子商务网站自动化营销重定向方法
CN112232612A (zh) * 2019-06-28 2021-01-15 百度在线网络技术（北京）有限公司 客户资料智能分发方法和装置
CN110502691A (zh) * 2019-07-05 2019-11-26 平安科技（深圳）有限公司 基于客户分类的产品推送方法、装置及可读存储介质
CN112445966A (zh) * 2019-08-30 2021-03-05 比亚迪股份有限公司 信息推送方法及装置，存储介质及电子设备
CN110990712A (zh) * 2019-10-14 2020-04-10 中国平安财产保险股份有限公司 产品数据推送方法、装置和计算机设备
CN110706122A (zh) * 2019-10-17 2020-01-17 易联众民生(厦门)科技有限公司 基于大数据分析提升社保代理效率方法、设备及可读介质
CN111091416A (zh) * 2019-12-10 2020-05-01 北京云迹科技有限公司 一种预测酒店购买机器人的概率的方法和装置
CN111079853A (zh) * 2019-12-27 2020-04-28 深圳市商汤科技有限公司 数据获取方法及相关装置
CN111161566B (zh) * 2019-12-30 2021-06-11 携程旅游网络技术（上海）有限公司 航班延误预警方法、***、电子设备和介质
CN113139842A (zh) * 2020-01-17 2021-07-20 阿里巴巴集团控股有限公司 一种表单处理方法、装置和***
CN111415199A (zh) * 2020-03-20 2020-07-14 重庆锐云科技有限公司 一种基于大数据的客户预测更新方法、装置及存储介质
CN111353860A (zh) * 2020-03-30 2020-06-30 中国建设银行股份有限公司 产品信息推送方法及***
CN111612632A (zh) * 2020-05-14 2020-09-01 中国工商银行股份有限公司 用户投资数据处理方法及装置
CN111667307B (zh) * 2020-05-28 2023-04-07 中国工商银行股份有限公司 一种理财产品销量的预测方法及装置
CN111861540A (zh) * 2020-06-09 2020-10-30 上海钧正网络科技有限公司 信息推送方法、装置、计算机设备和存储介质
CN111798273A (zh) * 2020-07-01 2020-10-20 中国建设银行股份有限公司 产品的购买概率预测模型的训练方法及购买概率预测方法
CN111768130B (zh) * 2020-08-06 2024-05-17 京东科技控股股份有限公司 用户分配方法、装置、电子设备及可读存储介质
CN112016961A (zh) * 2020-08-26 2020-12-01 北京字节跳动网络技术有限公司 推送方法、装置、电子设备和计算机可读存储介质
CN112214675B (zh) * 2020-10-19 2024-04-09 ***通信集团江苏有限公司 用户购机的确定方法、装置、设备及计算机存储介质
CN113781246B (zh) * 2021-09-14 2024-03-05 平安科技（深圳）有限公司 基于预设标签的策略生成方法、装置及存储介质
CN114841570B (zh) * 2022-05-07 2023-07-25 金腾科技信息(深圳)有限公司 用于客户关系管理***的数据处理方法、装置、设备及介质
CN115169757A (zh) * 2022-09-07 2022-10-11 中国民航信息网络股份有限公司 旅客升舱预测方法、旅客升舱预测模型获得方法及装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103140868A (zh) * 2010-07-28 2013-06-05 特鲁菲特公司 基于历史数据确定适用性的可能性
CN105956882A (zh) * 2016-05-09 2016-09-21 陈包容 一种获取采购需求的方法及装置
CN105956699A (zh) * 2016-04-29 2016-09-21 连云港天马网络发展有限公司 基于电商销售数据的商品分类提取及在该分类提取下的销量预测方法

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107590688A (zh) * 2017-08-24 2018-01-16 平安科技（深圳）有限公司 目标客户的识别方法及终端设备

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103140868A (zh) * 2010-07-28 2013-06-05 特鲁菲特公司 基于历史数据确定适用性的可能性
CN105956699A (zh) * 2016-04-29 2016-09-21 连云港天马网络发展有限公司 基于电商销售数据的商品分类提取及在该分类提取下的销量预测方法
CN105956882A (zh) * 2016-05-09 2016-09-21 陈包容 一种获取采购需求的方法及装置

Cited By (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110827112A (zh) * 2019-09-16 2020-02-21 中国平安人寿保险股份有限公司 深度学习的商品推荐方法、装置、计算机设备及存储介质
CN110827112B (zh) * 2019-09-16 2023-02-03 中国平安人寿保险股份有限公司 深度学习的商品推荐方法、装置、计算机设备及存储介质
CN111783810A (zh) * 2019-09-24 2020-10-16 北京沃东天骏信息技术有限公司 用于确定用户的属性信息的方法和装置
CN111783810B (zh) * 2019-09-24 2023-12-08 北京沃东天骏信息技术有限公司 用于确定用户的属性信息的方法和装置
CN112907266A (zh) * 2019-11-19 2021-06-04 马上消费金融股份有限公司 用户筛选模型的获取方法、金融信息推送方法及相关装置
CN111127074A (zh) * 2019-11-26 2020-05-08 杭州聚效科技有限公司 一种数据推荐方法
CN111127074B (zh) * 2019-11-26 2023-04-25 杭州聚效科技有限公司 一种数据推荐方法
CN111178949A (zh) * 2019-12-18 2020-05-19 北京文思海辉金信软件有限公司 服务资源匹配参考数据确定方法、装置、设备和存储介质
CN111160929B (zh) * 2019-12-26 2024-02-09 深圳前海微众银行股份有限公司 一种客户类型的确定方法及装置
CN111144652A (zh) * 2019-12-26 2020-05-12 浙江力石科技股份有限公司 基于游览舒适度算法和趋势预测方法、***和装置
CN111144652B (zh) * 2019-12-26 2023-08-08 浙江力石科技股份有限公司 基于游览舒适度算法和趋势预测方法、***和装置
CN111160929A (zh) * 2019-12-26 2020-05-15 深圳前海微众银行股份有限公司 一种客户类型的确定方法及装置
CN111860878A (zh) * 2019-12-31 2020-10-30 北京骑胜科技有限公司 共享单车的报修方法、服务器、电子设备及存储介质
CN113435918A (zh) * 2020-03-19 2021-09-24 杭州大搜车汽车服务有限公司 维护策略的确定方法、装置、电子设备及存储介质
CN111445297A (zh) * 2020-04-15 2020-07-24 北京易点淘网络技术有限公司 采购量的确定方法、装置、存储介质及电子设备
CN111639967A (zh) * 2020-05-21 2020-09-08 中国工商银行股份有限公司 产品潜在客户挖掘方法及***
CN111639967B (zh) * 2020-05-21 2023-07-25 中国工商银行股份有限公司 产品潜在客户挖掘方法及***
CN111652439B (zh) * 2020-06-04 2024-02-23 上海燕汐软件信息科技有限公司 一种派送点派件量的生成方法、装置及计算机***
CN111652439A (zh) * 2020-06-04 2020-09-11 上海燕汐软件信息科技有限公司 一种派送点派件量的生成方法、装置及计算机***
CN111831701A (zh) * 2020-07-06 2020-10-27 湖南赛吉智慧城市建设管理有限公司 可操作its***构建方法、装置、计算机设备及存储介质
CN111815368A (zh) * 2020-07-24 2020-10-23 深圳市欢太科技有限公司 广告推送方法、装置、终端及存储介质
CN111815368B (zh) * 2020-07-24 2024-05-03 深圳市欢太科技有限公司 广告推送方法、装置、终端及存储介质
CN112102095A (zh) * 2020-09-17 2020-12-18 中国建设银行股份有限公司 一种基金产品推荐方法、装置及设备
CN112163887A (zh) * 2020-09-30 2021-01-01 深圳前海微众银行股份有限公司 电销***和电销名单管理方法、装置、设备、存储介质
CN112364919A (zh) * 2020-11-11 2021-02-12 深圳前海微众银行股份有限公司 数据处理方法、装置、设备及存储介质
CN112884215A (zh) * 2021-02-02 2021-06-01 国网甘肃省电力公司信息通信公司 一种基于梯度增强树人口预测模型的参数优化方法
CN112819258A (zh) * 2021-03-24 2021-05-18 中国工商银行股份有限公司 银行网点到店客户数量预测方法及装置
CN112862008A (zh) * 2021-03-29 2021-05-28 中信银行股份有限公司 偏好预测模型的训练方法及用户偏好的预测方法
CN113052689B (zh) * 2021-04-30 2024-03-26 中国银行股份有限公司 一种基于决策树的产品推荐方法及装置
CN113052689A (zh) * 2021-04-30 2021-06-29 中国银行股份有限公司 一种基于决策树的产品推荐方法及装置
CN113554460A (zh) * 2021-07-19 2021-10-26 北京沃东天骏信息技术有限公司 一种***的识别方法和装置
CN113592297B (zh) * 2021-07-30 2023-11-24 国家电网有限公司客户服务中心 一种电力***专属式客户管理方法及***
CN113592297A (zh) * 2021-07-30 2021-11-02 国家电网有限公司客户服务中心 一种电力***专属式客户管理方法及***
CN114513578A (zh) * 2022-02-15 2022-05-17 平安科技（深圳）有限公司 外呼方法、装置、计算机设备及存储介质
CN117575487A (zh) * 2024-01-19 2024-02-20 天津盛驰精工有限公司 物料清单更新方法、装置、电子设备及存储介质

Also Published As

Publication number Publication date
CN108337316A (zh) 2018-07-27

Similar Documents

Publication Publication Date Title
WO2019153518A1 (fr) Procédé et dispositif de poussée d'informations, dispositif informatique et support de stockage
US10846643B2 (en) Method and system for predicting task completion of a time period based on task completion rates and data trend of prior time periods in view of attributes of tasks using machine learning models
US9972014B2 (en) System and method for intelligent sales engagement
JP2022548654A (ja) 機械学習モデルにおいて動的外れ値偏り低減を実装するように構成されるコンピュータベースシステム、コンピュータコンポーネント及びコンピュータオブジェクト
US11531780B2 (en) Deep learning-based identity fraud detection
JP6484730B2 (ja) 時間因子を融合させる協調フィルタリング方法、装置、サーバおよび記憶媒体
US20210112101A1 (en) Data set and algorithm validation, bias characterization, and valuation
US11954692B2 (en) Mitigating user dissatisfaction related to a product
US11907977B2 (en) Collaborative text detection and text recognition
US10678821B2 (en) Evaluating theses using tree structures
US11829455B2 (en) AI governance using tamper proof model metrics
US20230351426A1 (en) Techniques to predict and implement an amortized bill payment system
CN111179051A (zh) 金融目标客户确定方法、装置及电子设备
US11869050B2 (en) Facilitating responding to multiple product or service reviews associated with multiple sources
US20230032739A1 (en) Propensity modeling process for customer targeting
US20140214844A1 (en) Multiple classification models in a pipeline
JP7033609B2 (ja) プロセス定義およびプロセス実行トラッキングのためのユーザインターフェイスおよびランタイム環境
US20140324523A1 (en) Missing String Compensation In Capped Customer Linkage Model
US11762896B2 (en) Relationship discovery and quantification
US20170270480A1 (en) Enhancement of product or service by optimizing success factors
US11544175B2 (en) Systems and methods for continuity of dataflow operations
US20110251877A1 (en) Model for market impact analysis of part removal from complex products
US20230060245A1 (en) System and method for automated account profile scoring on customer relationship management platforms
US20230419338A1 (en) Joint learning of time-series models leveraging natural language processing
US12002099B1 (en) Systems and methods for developing convertible term products

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18904941

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 03/11/2020)

122 Ep: pct application non-entry in european phase

Ref document number: 18904941

Country of ref document: EP

Kind code of ref document: A1