Business opportunity extraction method, device, medium and electronic equipment

Abstract

The present application belongs to the field of computer technology, and specifically relates to a business opportunity extraction method, device, computer-readable medium and electronic device. The method includes: obtaining user operation data in real time, and obtaining user portrait data of the user corresponding to the user operation data; tailoring the preset business opportunity rules according to the user portrait data to obtain the tailored business opportunity rules; matching the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules; classifying the user business opportunity data according to the user portrait data and the user business opportunity data to obtain the data type corresponding to the user business opportunity data; distributing the user business opportunity data to the corresponding business endpoint according to the data type. The present application can improve the accuracy of business opportunity extraction and ensure the timeliness of business opportunity push, so as to efficiently manage business opportunity information.

Description

Background Art

[0002] Customers are important assets of an enterprise. The salesperson of an enterprise needs to develop new customers, retain old customers, and convert existing customers into loyal customers through effective communication with customers, so as to expand the enterprise market. To achieve this goal, the salesperson of an enterprise needs to enter customer data information into the enterprise's customer management system, manually fill in the business opportunity information related to the customer based on the customer data information, and contact the customers in the customer management system regularly or irregularly; however, the volume of customer data information of an enterprise is huge, and the salesperson needs to sort out the customer data information and manually fill in the business opportunity information corresponding to the customer data information, which takes a long time and consumes a lot of energy, and cannot take into account the communication with the customers corresponding to the business opportunity information.

[0003] In the related technologies, the collection time of business opportunity information is long. Business opportunity information obtained based on the acquired customer data information often takes several days to reach the company's salesperson. At this time, the user may have been lost, affecting the conversion rate of business opportunity information. Therefore, how to efficiently manage business opportunity information is a technical problem that needs to be solved urgently.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present application is to provide a business opportunity extraction method, a business opportunity extraction device, a computer-readable medium and an electronic device, which at least to a certain extent overcome the technical problem of how to efficiently manage business opportunity information in the related art.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0007] According to one aspect of an embodiment of the present application, a business opportunity extraction method is provided, the business opportunity extraction method comprising:

[0008] Acquire user operation data in real time, and acquire user portrait data of the user corresponding to the user operation data, wherein the user operation data is browsing operation data of the user on the web page;

[0009] Tailoring the preset business opportunity rules according to the user portrait data to obtain tailored business opportunity rules;

[0010] Matching the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules, wherein the business opportunity data includes the user operation data used to indicate the user's purchasing tendency;

[0011] Classifying the user business opportunity data according to the user portrait data and the user business opportunity data to obtain a data type corresponding to the user business opportunity data;

[0012] The user business opportunity data is distributed to corresponding service endpoints according to the data type.

[0013] According to one aspect of an embodiment of the present application, a business opportunity extraction device is provided, the business opportunity extraction device comprising:

[0014] A user data acquisition module is configured to acquire user operation data and user portrait data of the user corresponding to the user operation data in real time, wherein the user operation data is browsing operation data of the user on a web page;

[0015] A business opportunity rule cutting module is configured to cut preset business opportunity rules according to the user portrait data to obtain cut business opportunity rules;

[0016] A user business opportunity data acquisition module is configured to match the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules, wherein the business opportunity data is user operation data used to indicate the user's purchasing tendency;

[0017] A data type determination module is configured to match the user business opportunity data with a preset data type based on the user portrait data and the user business opportunity data to obtain a data type corresponding to the user business opportunity data;

[0018] The business opportunity data distribution module is configured to distribute the user business opportunity data corresponding to the data type to the corresponding service endpoint according to the data type.

[0019] In some embodiments of the present application, based on the above technical solution, the business opportunity rule tailoring module includes:

[0020] A user type determination unit, configured to determine a user type corresponding to the user according to the user portrait data;

[0021] a tailoring rule acquisition unit, configured to query a preset database according to the user type to obtain a tailoring rule corresponding to the user type, wherein the preset database is used to store the tailoring rules and a mapping relationship between the user type and the tailoring rules, and the tailoring rules are used to represent a tailoring mode for modifying parameters of one or more preset business opportunity rules;

[0022] The business opportunity rule acquisition unit performs tailoring processing on the preset business opportunity rules according to the tailoring rules corresponding to the user type to obtain the tailored business opportunity rules, wherein the tailoring processing is to customize and modify parameters of one or more preset business opportunity rules.

[0023] In some embodiments of the present application, based on the above technical solution, the data type determination module includes:

[0024] A rule engine building unit, configured to build a preset rule engine according to preset rules;

[0025] The data type determination unit is configured to input the user portrait data and the user business opportunity data into a preset rule engine to obtain the data type corresponding to the user business opportunity data.

[0026] In some embodiments of the present application, based on the above technical solution, the business opportunity data distribution module includes:

[0027] A user business opportunity data pushing unit is configured to push the user business opportunity data and the data type corresponding to the user business opportunity data to a distribution center, the distribution center includes a mapping relationship between the data type and the business module, and the user business opportunity data includes one or more associated business endpoints of the user;

[0028] The user business opportunity data distribution unit is configured to distribute the user business opportunity data to associated business endpoints in the business module having a mapping relationship with the data type through the distribution center.

[0029] In some embodiments of the present application, based on the above technical solution, the user data acquisition module includes:

[0030] A user operation raw data acquisition unit, configured to acquire user operation raw data in real time;

[0031] The filtering unit is configured to filter the original user operation data according to a preset crawling rule to obtain the user operation data under the crawling rule.

[0032] In some embodiments of the present application, based on the above technical solution, the user operation raw data acquisition unit includes:

[0033] A point-of-sale data collection unit is configured to collect point-of-sale data of user operations according to the type of the triggered point-of-sale when the point-of-sale in the page is triggered by the browser, and store the collected data in a preset cache area, wherein the data stored in the preset cache area becomes invalid after exceeding a preset time period;

[0034] The user operation original data acquisition subunit is configured to use the non-expired buried point data of the user in the preset cache area as the user operation original data.

[0035] In some embodiments of the present application, based on the above technical solution, the user data acquisition module further includes:

[0036] A code acquisition unit, configured to acquire the local area code of the user and acquire the channel code of the user;

[0037] A global code acquisition unit, configured to combine the local code and the channel code to generate a global code of the user;

[0038] The user portrait data acquisition unit is configured to acquire the user portrait data of the user according to the global code of the user.

[0039] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute a business opportunity extraction method as in the above technical solution by executing the executable instructions.

[0040] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the business opportunity extraction method in the above technical solution is implemented.

[0041] According to one aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the business opportunity extraction method in the above technical solution.

[0042] In the technical solution provided in the embodiment of the present application, user operation data is obtained in real time, and user portrait data of the user corresponding to the user operation data is obtained; the preset business opportunity rules are tailored according to the user portrait data to obtain the tailored business opportunity rules; the user operation data is matched with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules; the user business opportunity data is classified according to the user portrait data and the user business opportunity data to obtain the data type corresponding to the user business opportunity data; the user business opportunity data is distributed to the corresponding business endpoint according to the data type; thereby, the user operation data can be extracted in real time according to the user portrait data and the preset business opportunity rules to obtain the user business opportunity data, thereby improving the accuracy of business opportunity extraction, and then the user business opportunity data is classified to distribute the user business opportunity data to the corresponding business endpoint according to the data type, and the timeliness of business opportunity push is guaranteed, so that business opportunity information can be managed efficiently.

[0043] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A schematic diagram of the system architecture of a business opportunity extraction method in some embodiments of the present application is schematically shown.

[0046] Figure 2 The flowchart schematically shows the steps of the business opportunity extraction method of certain embodiments of the present application.

[0047] Figure 3 The flowchart schematically shows the steps of obtaining user operation data in real time in a certain embodiment of the present application.

[0048] Figure 4 The flowchart schematically shows the steps of obtaining the original data of user operation in real time in a certain embodiment of the present application.

[0049] Figure 5 A flowchart schematically illustrates the steps of obtaining user portrait data of a user corresponding to user operation data in a certain embodiment of the present application.

[0050] Figure 6 The flowchart schematically shows the steps of tailoring preset business opportunity rules according to user portrait data to obtain tailored business opportunity rules in a certain embodiment of the present application.

[0051] Figure 7 A flowchart schematically illustrates the steps of classifying user business opportunity data according to user portrait data and user business opportunity data to obtain the data type corresponding to the user business opportunity data in an embodiment of the present application.

[0052] Figure 8 The flowchart schematically shows the steps of distributing user business opportunity data to corresponding service endpoints according to data types in a certain embodiment of the present application.

[0053] Fig. 9 The structural block diagram of the business opportunity extraction device provided in the embodiment of the present application is schematically shown.

[0054] Fig.10 The structural diagram of the electronic device provided in the embodiment of the present application is schematically shown.

[0055] Fig.11 The composition diagram of the program product provided by the embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0056] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0057] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0058] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0059] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0060] Figure 1 The system architecture diagram of a business opportunity extraction method in some embodiments of the present application is schematically shown. Figure 1 As shown, the system architecture includes a personal computer 110, a server 120 and a database 130. The personal computer 110 and the server 120, and the server 120 and the database 130 are connected via a communication link, which can be used to send or receive data. The personal computer 110 and the server 120 are both implementation terminals in this embodiment. The following description takes the server 120 as an example of the implementation terminal in this embodiment. When a business opportunity extraction method provided by the present disclosure is applied to Figure 1In the system architecture shown, a process may be as follows: first, the server 120 obtains the user operation data in real time, and obtains the user portrait data of the user corresponding to the user operation data; then, the server 120 tailors the preset business opportunity rules according to the user portrait data to obtain the tailored business opportunity rules; next, the server 120 matches the user operation data with the tailored business opportunity rules to obtain the user business opportunity data that matches the tailored business opportunity rules; then, the server 120 classifies the user business opportunity data according to the user portrait data and the user business opportunity data to obtain the data type corresponding to the user business opportunity data; finally, the server 120 distributes the user business opportunity data to the corresponding business endpoint according to the data type. Business endpoints can be deployed at Figure 1 A personal computer 110 in the system.

[0061] Based on the business opportunity extraction method of the implementation mode of the present application, the user operation data is extracted in real time according to the user portrait data and the preset business opportunity rules to obtain the user business opportunity data, so as to improve the accuracy of business opportunity extraction, and then the user business opportunity data is classified to distribute the user business opportunity data to the corresponding business endpoint according to the data type, and the timeliness of the business opportunity push is guaranteed, so that the business opportunity information can be managed efficiently.

[0062] It can be understood that due to the large amount of information data in user operation data, not all user operation data can generate corresponding business opportunities in a targeted manner. This application matches the user operation data with the tailored business opportunity rules, and can accurately extract business opportunities for different users in a personalized manner, thereby improving the accuracy of business opportunity extraction.

[0063] It is worth mentioning that Figure 1 This is only one embodiment of the present disclosure. Although the implementation terminal in this embodiment is a personal computer 110 or a server 120, in other embodiments, the implementation terminal may be various terminals or devices as described above. The present disclosure does not make any limitation on this, and the protection scope of the present disclosure should not be limited by this. In some embodiments, the implementation terminal in this embodiment may be a business opportunity extraction platform equipped with a server 120.

[0064] The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application.

[0065] The business opportunity extraction method provided in this application is described in detail below in conjunction with specific implementation methods.

[0066] Figure 2 The flowchart schematically shows the steps of the business opportunity extraction method of certain embodiments of the present application. The execution subject of the business opportunity extraction method can be a terminal device or a server, etc., and the present application does not limit this. Figure 2 As shown, the business opportunity extraction method may mainly include the following steps S210 to S250.

[0067] S210. Obtain user operation data in real time, and obtain user portrait data of the user corresponding to the user operation data. The user operation data is the browsing operation data of the user on the web page.

[0068] Specifically, the user operation data can be obtained in real time through the message queue, and the user portrait data of the user corresponding to the user operation data can be obtained through the user ID in the user operation data.

[0069] Figure 3 The following schematically shows a flow chart of the steps for obtaining user operation data in real time in a certain embodiment of the present application. Figure 3 As shown, based on the above embodiment, the real-time acquisition of user operation data in step S210 may further include the following steps S310 to S320.

[0070] S310. Real-time acquisition of user operation raw data;

[0071] S320. Filter the original user operation data according to the preset crawling rules to obtain the user operation data under the crawling rules.

[0072] Specifically, the preset crawling rules can be pre-set on the business opportunity extraction platform by the user of the business opportunity extraction platform, such as a certain enterprise or a certain merchant. For example, the preset crawling rules may include: information about products browsed by users, types of products browsed by users, statistics on the cumulative number of views of products by users, records of purchase operations of users, single browsing time of products by users, cumulative browsing time of products by users, single stay time and cumulative stay time of users on product pages, and the number of views of users on information related to a certain type of products.

[0073] Therefore, the original user operation data is filtered according to the preset crawling rules to obtain the user operation data under the crawling rules, which can accurately extract the user operation data so that the user business opportunity data can be accurately extracted based on the user operation data in the future, thereby improving the accuracy of business opportunity extraction.

[0074] Specifically, the commodities browsed by the user may include physical commodities, virtual commodities, knowledge commodities or financial commodities, etc., and this application does not impose any restrictions on this.

[0075] Figure 4 The flowchart of the steps of obtaining the original data of user operation in real time in a certain embodiment of the present application is schematically shown. Figure 4 As shown, based on the above embodiment, the real-time acquisition of user operation raw data in step S310 may further include the following steps S410 to S420.

[0076] S410. When the browser's embedding point in the page is triggered, the embedding point data of the user operation is collected according to the type of the triggered embedding point, and the collected data is stored in a preset cache area. The data stored in the preset cache area becomes invalid after exceeding the preset length of time;

[0077] S420. Use the user's non-expired buried point data in the preset cache area as the user operation original data.

[0078] Specifically, the original data of user operations can be pushed in real time through the Kafka message system to the business opportunity extraction platform for implementing the business opportunity extraction method of the present application. The embedding point of the browser in the page can be a embedding point in a preset product page, and the embedding point can be specifically set when the page is opened, when the product details are expanded, and when the page stay time exceeds the preset minimum time.

[0079] It is understandable that in the related technologies, the business opportunity information obtained based on the acquired customer data information often takes several days to reach the company's salesperson. The entire process has a long operation cycle, and the final business opportunity data takes several days. When the salesperson contacts the customer based on the business opportunity data from several days ago, a large number of users have already been lost, which seriously affects the timeliness of the business opportunity information and the conversion rate of business opportunities.

[0080] In the embodiment of the present application, when a user browses related commodity products, the original user operation data obtained by collecting data on the user operation can be pushed to the business opportunity extraction platform in near real time through the Kafka message system, which is conducive to the timely push of business opportunities and the business opportunities can be pushed to the salesperson more quickly, so that the salesperson can reach the user faster and more timely, and can provide the user with timely commodity consulting services.

[0081] Figure 5 The following is a schematic flow chart showing the steps of obtaining the user portrait data of the user corresponding to the user operation data in a certain embodiment of the present application. Figure 5 As shown, based on the above embodiments, the step S210 of obtaining the user portrait data of the user corresponding to the user operation data may further include the following steps S510 to S530.

[0082] S510. Obtain the user's local area code and the user's channel code;

[0083] S520. The local code and the channel code are combined to generate the user's global code;

[0084] S530. Obtain the user portrait data of the user according to the user's global code.

[0085] Therefore, the user's identity data can be completed by generating the user's global code by combining the local code and the channel code, so that the user's user portrait data can be obtained based on the user's global code, so that the preset business opportunity rules can be subsequently tailored according to the user portrait data to obtain personalized user business opportunity data, which can improve the accuracy of user business opportunity data extraction.

[0086] S220. Tailor the preset business opportunity rules according to the user portrait data to obtain tailored business opportunity rules.

[0087] Specifically, the preset business opportunity rules may include: extracting user operation data for preset types of goods; extracting statistics on goods whose cumulative user views are greater than a preset number; extracting statistics on goods whose single browsing time is greater than a preset time; extracting users' browsing footprints of preset pages within a preset number of days; extracting operation records of users staying on relevant web pages of certain product information for more than a preset time; extracting the cumulative number of users' views of relevant web pages of preset type of product information; extracting product information for which the cumulative number of users' views of relevant web pages of product information is greater than a preset number; extracting product information for which the user has more than the first number of views within the preset number of days but has not generated any purchase behavior; extracting product information for which the user has browsed a product for a preset time but has not generated any purchase behavior for the product, etc.

[0088] The preset business opportunity rules are tailored according to the user portrait data. For example, when the user portrait data shows that the user's frequently searched website is a preset website, the operation record of the user staying on the relevant web page of certain product information on the preset website for a period of time exceeding the preset time can be extracted; or, for example, when the user portrait data shows that the user is a heavy user of Internet web pages, the default preset number of times is increased, and then the product information of the user's cumulative browsing times of the relevant web page of product information is extracted after the user's cumulative browsing times of the relevant web page of product information is greater than the preset number of times; or, for example, when the user portrait data shows that the user is a light user of Internet web pages, the default preset number of times is reduced, and then the product information of the user's cumulative browsing times of the relevant web page of product information is extracted after the user's cumulative browsing times of the relevant web page of product information is greater than the preset number of times. Or, for example, when the user portrait data shows that the user is a hesitant purchase user, the first browsing number is increased first, and then the product information of the user having browsed more than the first number of times within the preset number of days but not generating a purchase behavior is extracted; when the user portrait data shows that the user is a decisive purchase user, the first browsing number is reduced first, and then the product information of the user having browsed more than the first number of times within the preset number of days but not generating a purchase behavior is extracted.

[0089] It is understandable that when a user is a heavy user of Internet web pages, he or she is more likely to browse the relevant web pages of product information for reasons other than purchasing. At this time, the preset business opportunity rules are tailored according to the user portrait data, and after increasing the default preset number of times, the product information whose cumulative number of users browsing the relevant web pages of product information is greater than the preset number of times is extracted. The business opportunity rules can be customized for each user with different characteristics, so that the corresponding business opportunity data can be extracted in a targeted manner according to the behavioral characteristics of different users, avoiding the misextraction of business opportunities, and thus improving the conversion rate of the extracted business opportunities.

[0090] Therefore, by tailoring the preset business opportunity rules according to the user portrait data, it is possible to customize the business opportunity rules for each user with different user portrait data to obtain the tailored business opportunity rules, and then match the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules. It is possible to customize the extraction of corresponding business opportunity data for users with different behavioral characteristics under different user portrait data, thereby avoiding the occurrence of misjudgment of business opportunities under a unified business opportunity extraction standard, and further improving the accuracy of business opportunity extraction.

[0091] Figure 6 The flowchart schematically shows the steps of tailoring the preset business opportunity rules according to the user portrait data to obtain the tailored business opportunity rules in an embodiment of the present application. Figure 6As shown, based on the above embodiments, step S220 of tailoring the preset business opportunity rules according to the user portrait data to obtain the tailored business opportunity rules may further include the following steps S610 to S630.

[0092] S610. Determine the user type corresponding to the user according to the user portrait data;

[0093] S620. Query the preset database according to the user type to obtain the tailoring rules corresponding to the user type, wherein the preset database is used to store the tailoring rules and the mapping relationship between the user type and the tailoring rules, and the tailoring rules are used to represent the tailoring mode for modifying the parameters of one or more preset business opportunity rules;

[0094] S630. Tailor the preset business opportunity rules according to the tailoring rules corresponding to the user type to obtain tailored business opportunity rules. The tailoring process is to customize and modify parameters of one or more preset business opportunity rules.

[0095] Specifically, the parameters of one or more preset business opportunity rules can be customized and modified, such as the preset number of times, preset quantity, preset number of days, preset duration, and preset page. In this way, the corresponding business opportunity data can be customized and extracted for users with different behavioral characteristics under different user portrait data, thereby avoiding the occurrence of misjudgment of business opportunities under the unified business opportunity extraction standard, and further improving the accuracy of business opportunity extraction.

[0096] S230. Match the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules. The business opportunity data includes user operation data used to indicate the user's purchasing tendency.

[0097] Specifically, the real-time stream computing service deployed on Flink SQL can be used to match the real-time user operation data with the tailored business opportunity rules in real time, which can facilitate the timely extraction of user business opportunity data, thereby ensuring the timeliness of business opportunity push and enabling efficient management of business opportunity information.

[0098] Specifically, the preset business opportunity rules and the tailored business opportunity rules can be configured in the data table of the data engine phoenix to communicate with Flink in real time, realize real-time matching processing of real-time acquired user operation data with the tailored business opportunity rules, and output the corresponding user real-time business opportunity data.

[0099] The entire user business opportunity data extraction process can be handled through message flow rather than batch processing. In this way, processing user business opportunity data one by one can improve the timeliness of business opportunity push, thereby achieving efficient management of business opportunity information.

[0100] S240. Classify the user business opportunity data according to the user portrait data and the user business opportunity data to obtain the data type corresponding to the user business opportunity data.

[0101] In some implementations, data types may include channel type, region type, product type of business opportunity mapping, user willingness strength type, user portrait personality type, etc. Channel type is used to indicate the type of source channel of user business opportunities, and channel types may include web pages, apps, offline, etc. Region type is used to indicate the user's frequently used region.

[0102] Figure 7 The following schematically shows a flow chart of the steps of classifying user business opportunity data according to user portrait data and user business opportunity data to obtain the data type corresponding to the user business opportunity data in an embodiment of the present application. Figure 7 As shown, based on the above embodiments, step S240 classifies the user business opportunity data according to the user portrait data and the user business opportunity data to obtain the data type corresponding to the user business opportunity data, which may further include the following steps S710 to S720.

[0103] S710. Build a preset rule engine according to the preset rules;

[0104] S720. Input the user portrait data and the user business opportunity data into a preset rule engine to obtain the data type corresponding to the user business opportunity data.

[0105] Specifically, enterprises can input preset rules through the interactive interface to build a preset rule engine in a customized manner. Thus, a preset rule engine is built according to the preset rules, and then user portrait data and user business opportunity data are input into the preset rule engine to obtain the data type corresponding to the user business opportunity data, so that users of the business opportunity extraction platform can configure different business endpoints for different types of user business opportunity data, thereby improving the conversion rate of business opportunities, which is conducive to enterprises implementing different business opportunity distribution systems according to their own product characteristics, and can improve the user experience of the business opportunity extraction platform.

[0106] A rule engine is a component embedded in an application that receives data input and matches the input data against business rules to make business decisions. The rule engine can separate business rules from application code and write business decisions using predefined semantic modules. The device that hosts the rule engine is called a rule engine server.

[0107] One way to build a rule engine is to pre-set business rules corresponding to the business on the rule engine server; the business rules are a combination of a series of rule conditions corresponding to the business. After the rule engine server receives the input parameters corresponding to the rule conditions in the business rules, it calls the business rules to perform logical judgment on the input parameters to obtain the rule results (i.e., business decisions). Specifically, the business rules can be rules for representing the impact of user portrait data and user business opportunity data on the determination results of data types, and the rule results are the determination results of the data types corresponding to the user business opportunity data.

[0108] S250. Distribute user business opportunity data to corresponding business endpoints according to data type.

[0109] Therefore, user business opportunity data is classified according to user portrait data and user business opportunity data, and the data type corresponding to the user business opportunity data is obtained. Then, the user business opportunity data is distributed to the corresponding business endpoint according to the data type, which can achieve accurate push of user business opportunity data to business endpoints, improve the conversion rate of business opportunities, and thus facilitate the efficient management and utilization of business opportunity information.

[0110] Figure 8 The flowchart schematically shows the steps of distributing user business opportunity data to corresponding service endpoints according to data types in an embodiment of the present application. Figure 8 As shown, based on the above embodiment, step S250 of distributing user business opportunity data to corresponding service endpoints according to data types may further include the following steps S810 to S820.

[0111] S810. Pushing the user business opportunity data and the data type corresponding to the user business opportunity data to the distribution center, the distribution center includes the mapping relationship between the data type and the business module, and the user business opportunity data includes one or more associated business endpoints of the user;

[0112] S820. Distribute the user business opportunity data to the associated business endpoints in the business modules that have a mapping relationship with the data types through the distribution center.

[0113] Specifically, when a user has communicated or contacted with a certain business endpoint, the business endpoint can be used as the user's associated business endpoint. Therefore, the user's business opportunity data and the data type corresponding to the user's business opportunity data are first pushed to the distribution center, and then the user's business opportunity data is distributed to the associated business endpoint in the business module that has a mapping relationship with the data type through the distribution center. The data type and the associated business endpoint can be used to locate the most suitable business endpoint for the business opportunity at the same time, which is conducive to accurately pushing the user's business opportunity data to the associated business endpoint of the business module that has a mapping relationship with the data type, which is conducive to improving the conversion rate of business opportunities, so that business opportunity data can be effectively managed and used.

[0114] In summary, the business opportunity extraction method based on the implementation mode of the present application can extract user operation data in real time according to user portrait data and preset business opportunity rules to obtain user business opportunity data, thereby improving the accuracy of business opportunity extraction, and then classify the user business opportunity data to distribute the user business opportunity data to the corresponding business endpoint according to the data type, and ensure the timeliness of business opportunity push, so that business opportunity information can be managed efficiently.

[0115] It should be noted that although the steps of the method in the present application are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0116] According to the third aspect of the present disclosure, the following introduces an apparatus embodiment of the present application, which can be used to execute the business opportunity extraction method in the above-mentioned embodiment of the present application. Fig. 9 The structure block diagram of the business opportunity extraction device provided in the embodiment of the present application is schematically shown. Fig. 9 As shown, the business opportunity extraction device 900 includes:

[0117] The user data acquisition module 910 is configured to acquire user operation data and user portrait data of the user corresponding to the user operation data in real time, wherein the user operation data is the browsing operation data of the user on the web page;

[0118] The business opportunity rule cutting module 920 is configured to cut the preset business opportunity rules according to the user portrait data to obtain the cut business opportunity rules;

[0119] The user business opportunity data acquisition module 930 is configured to match the user operation data with the tailored business opportunity rules to obtain the user business opportunity data that matches the tailored business opportunity rules. The business opportunity data is the user operation data used to indicate the user's purchasing tendency.

[0120] The data type determination module 940 is configured to match the user business opportunity data with a preset data type according to the user portrait data and the user business opportunity data, and obtain the data type corresponding to the user business opportunity data;

[0121] The business opportunity data distribution module 950 is configured to distribute the user business opportunity data corresponding to the data type to the corresponding service endpoint according to the data type.

[0122] In some embodiments of the present application, based on the above embodiments, the business opportunity rule tailoring module includes:

[0123] A user type determination unit, configured to determine a user type corresponding to the user according to the user portrait data;

[0124] A tailoring rule acquisition unit is configured to query a preset database according to the user type to obtain a tailoring rule corresponding to the user type, wherein the preset database is used to store the tailoring rules and the mapping relationship between the user type and the tailoring rules, and the tailoring rules are used to represent a tailoring mode for customizing and modifying parameters of one or more preset business opportunity rules;

[0125] The business opportunity rule acquisition unit performs tailoring processing on the preset business opportunity rules according to the tailoring rules corresponding to the user type to obtain the tailored business opportunity rules. The tailoring processing is to customize and modify parameters of one or more preset business opportunity rules.

[0126] In some embodiments of the present application, based on the above embodiments, the data type determination module includes:

[0127] A rule engine building unit, configured to build a preset rule engine according to preset rules;

[0128] The data type determination unit is configured to input the user portrait data and the user business opportunity data into a preset rule engine to obtain the data type corresponding to the user business opportunity data.

[0129] In some embodiments of the present application, based on the above embodiments, the business opportunity data distribution module includes:

[0130] A user business opportunity data pushing unit is configured to push user business opportunity data and data types corresponding to the user business opportunity data to a distribution center, the distribution center includes a mapping relationship between the data type and the business module, and the user business opportunity data includes one or more associated business endpoints of the user;

[0131] The user business opportunity data distribution unit is configured to distribute the user business opportunity data to the associated business endpoints in the business module having a mapping relationship with the data type through the distribution center.

[0132] In some embodiments of the present application, based on the above embodiments, the user data acquisition module includes:

[0133] A user operation raw data acquisition unit, configured to acquire user operation raw data in real time;

[0134] The filtering unit is configured to filter the original user operation data according to the preset crawling rules to obtain the user operation data under the crawling rules.

[0135] In some embodiments of the present application, based on the above embodiments, the user operation raw data acquisition unit includes:

[0136] The embedding data collection unit is configured to collect embedding data of user operations according to the type of the triggered embedding when the embedding in the page of the browser is triggered, and store the collected data in a preset cache area, and the data stored in the preset cache area becomes invalid after exceeding a preset time;

[0137] The user operation raw data acquisition subunit is configured to use the non-expired buried point data of the user in the preset cache area as the user operation raw data.

[0138] In some embodiments of the present application, based on the above embodiments, the user data acquisition module further includes:

[0139] A code acquisition unit, configured to acquire a local area code of a user and acquire a channel code of the user;

[0140] A global code acquisition unit, configured to combine the local code and the channel code to generate a global code of the user;

[0141] The user portrait data acquisition unit is configured to acquire the user portrait data of the user according to the user's global code.

[0142] The specific details of the business opportunity extraction device provided in each embodiment of the present application have been described in detail in the corresponding method embodiments and will not be repeated here.

[0143] According to a third aspect of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0144] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0145] The electronic device may include a processor and a memory. The memory is used to store executable instructions of the processor. The processor is configured to execute the business opportunity extraction method in the above technical solution by executing the executable instructions.

[0146] Refer to the following Fig.10 The electronic device 1000 according to this embodiment of the present invention will be described. Fig.10 The structural diagram of the electronic device provided in the embodiment of the present application is schematically shown. Fig.10 The electronic device 1000 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0147] like Fig.10As shown, the electronic device 1000 is in the form of a general computing device. The components of the electronic device 1000 may include but are not limited to: the at least one processing unit 1010, the at least one storage unit 1020, and a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010).

[0148] The storage unit stores program codes, which can be executed by the processing unit 1010, so that the processing unit 1010 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0149] The storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 1021 and / or a cache memory unit 1022 , and may further include a read-only memory unit (ROM) 1023 .

[0150] The storage unit 1020 may also include a program / utility 1024 having a set (at least one) of program modules 1025, such program modules 1025 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0151] Bus 1030 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0152] The electronic device 1000 may also communicate with one or more external devices 1200 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1000, and / or communicate with any device that enables the electronic device 1000 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1050, such as communicating with a display unit 1040. Furthermore, the electronic device 1000 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 1060. As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 via a bus 1030. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0153] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0154] According to a fourth aspect of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary implementations of the present invention described in the above "Exemplary Method" section of the present specification.

[0155] The computer program product or computer program includes computer instructions, which are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the business opportunity extraction method in the above technical solution.

[0156] Fig.11 The following is a schematic diagram showing the composition of the program product provided by the embodiment of the present application. Fig.11 As shown, a program product 1100 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0157] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0158] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0159] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0160] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0161] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0162] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be performed without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A business opportunity extraction method, characterized in that: The method comprises: When the browser's embedding point in the page is triggered, embedding point data is collected for the user operation according to the type of the triggered embedding point, and the collected data is stored in a preset cache area. The data stored in the preset cache area becomes invalid after exceeding a preset time. The non-expired buried point data of the user in the preset cache area is used as the original data of the user operation; Filter the original user operation data according to the preset crawling rules to obtain the user operation data under the crawling rules, and obtain the user portrait data of the user corresponding to the user operation data, wherein the user operation data is the browsing operation data of the user on the web page; Determining a user type corresponding to the user according to the user portrait data; Querying a preset database according to the user type to obtain a tailoring rule corresponding to the user type, wherein the preset database is used to store the tailoring rules and a mapping relationship between the user type and the tailoring rules, and the tailoring rules are used to represent a tailoring mode for customizing and modifying parameters of one or more preset business opportunity rules; Tailoring the preset business opportunity rules according to the tailoring rules corresponding to the user type to obtain tailored business opportunity rules, wherein the tailoring is to customize and modify parameters of one or more preset business opportunity rules; Matching the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules, wherein the business opportunity data includes the user operation data used to indicate the user's purchasing tendency; Classifying the user business opportunity data according to the user portrait data and the user business opportunity data to obtain a data type corresponding to the user business opportunity data; The user business opportunity data is distributed to corresponding service endpoints according to the data type.

2. The business opportunity extraction method according to claim 1, characterized in that: The classifying the user business opportunity data according to the user portrait data and the user business opportunity data to obtain the data type corresponding to the user business opportunity data includes: Build a pre-set rule engine based on pre-set rules; The user portrait data and the user business opportunity data are input into a preset rule engine to obtain the data type corresponding to the user business opportunity data.

3. The business opportunity extraction method according to claim 1, characterized in that: The distributing the user business opportunity data to the corresponding business endpoint according to the data type includes: Pushing the user business opportunity data and the data type corresponding to the user business opportunity data to a distribution center, wherein the distribution center includes a mapping relationship between the data type and the business module, and the user business opportunity data includes one or more associated business endpoints of the user; The user business opportunity data is distributed to associated business endpoints in a business module having a mapping relationship with the data type through the distribution center.

4. The business opportunity extraction method according to claim 1, characterized in that: The obtaining of user portrait data of the user corresponding to the user operation data includes: Obtaining the local area code of the user and obtaining the channel code of the user; Combining the local code and the channel code to generate a global code for the user; The user portrait data of the user is obtained according to the global code of the user.

5. A business opportunity extraction device, characterized in that: include: The user data acquisition module is configured to collect the buried point data of the user operation according to the type of the triggered buried point when the buried point of the browser in the page is triggered, and store the collected data in a preset cache area, and the data stored in the preset cache area will become invalid after exceeding a preset time; The non-expired buried point data of the user in the preset cache area is used as the original data of user operation; the original data of user operation is filtered according to the preset crawling rules to obtain the user operation data under the crawling rules; the user portrait data of the user corresponding to the user operation data is obtained, and the user operation data is the browsing operation data of the user on the network page; A business opportunity rule tailoring module is configured to determine a user type corresponding to the user according to the user portrait data; Querying a preset database according to the user type to obtain a tailoring rule corresponding to the user type, wherein the preset database is used to store the tailoring rules and a mapping relationship between the user type and the tailoring rules, and the tailoring rules are used to represent a tailoring mode for customizing and modifying parameters of one or more preset business opportunity rules; performing tailoring processing on the preset business opportunity rules according to the tailoring rules corresponding to the user type to obtain tailored business opportunity rules, wherein the tailoring processing is customizing and modifying parameters of one or more preset business opportunity rules; A user business opportunity data acquisition module is configured to match the user operation data with the tailored business opportunity rules to obtain user business opportunity data that matches the tailored business opportunity rules, wherein the business opportunity data is user operation data used to indicate the user's purchasing tendency; A data type determination module is configured to match the user business opportunity data with a preset data type based on the user portrait data and the user business opportunity data to obtain a data type corresponding to the user business opportunity data; The business opportunity data distribution module is configured to distribute the user business opportunity data corresponding to the data type to the corresponding service endpoint according to the data type.

6. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the business opportunity extraction method described in any one of claims 1 to 4 is implemented.

7. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the business opportunity extraction method according to any one of claims 1 to 4 by executing the executable instructions.

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