Operation strategy recommendation method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the technical field of operation strategy recommendation, and provides an operation strategy recommendation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining user information; determining user type information according to the user information and a preset user classification library; under the condition that the user type information represents that the user is a previous strategy promotion failure object, failure attribution information is extracted from a preset failure attribution database, the failure attribution information comprises multiple pieces of attribution factor information, and each piece of attribution factor information carries a weight value; screening out target classification factor information from the multiple pieces of classification factor information according to the weight value; and corresponding operation strategy information is obtained from a preset operation strategy system library through matching according to the target return factor information. According to the technical scheme, a refined operation strategy can be intelligently provided, so that the operation effect can be well improved.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of operation strategy recommendation, and in particular, to an operation strategy recommendation method, device, electronic device, and computer-readable storage medium. Background Art

[0002] Customer operation is of great significance for insurance promotion. The core lies in improving the ability of agents through a systematic and data-driven approach, thereby increasing the likelihood of successful insurance promotion. However, traditional operation methods mainly rely on agent experience and manual analysis, which have many defects. For example, in the operation process, it overly depends on the experience of agents. For novice agents, due to lack of experience, it is more likely to miss deals. And traditional operation methods usually adopt standardized scripts and processes, making it difficult to make refined adjustments according to the needs of different customers, resulting in poor customer experience and low conversion rates. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail in this article. This overview is not intended to limit the scope of protection of the claims.

[0004] To solve the problems mentioned in the above background art, the embodiments of the present application provide an operation strategy recommendation method, device, electronic device, and computer-readable storage medium, which can intelligently provide refined operation strategies, thereby improving the operation effect well.

[0005] In a first aspect, the embodiments of the present application provide an operation strategy recommendation method, including:

[0006] Obtain user information;

[0007] Determine user type information according to the user information and a preset user classification library;

[0008] In the case that the user type information indicates that the user is an object for which previous strategy promotion has failed, extract failure attribution information from a preset failure attribution database, where the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value;

[0009] Screen out target attribution factor information from multiple attribution factor information according to the weight value;

[0010] Match corresponding operation strategy information from a preset operation strategy system library according to the target attribution factor information.

[0011] In a second aspect, the embodiments of the present application further provide an operation strategy recommendation device, including:

[0012] An obtaining unit, configured to obtain user information;

[0013] A selection unit, configured to determine user type information according to the user information and a preset user classification library;

[0014] An extraction unit, configured to extract failure attribution information from a preset failure attribution database when the user type information indicates that the user is an object who has failed in previous strategy promotion, where the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value;

[0015] A screening unit, configured to screen out target attribution factor information from multiple attribution factor information according to the weight value;

[0016] A matching unit, configured to match corresponding operation strategy information from a preset operation strategy system library according to the target attribution factor information.

[0017] In a third aspect, an embodiment of the present application further provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the operation strategy recommendation method described in the first aspect above is implemented.

[0018] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, storing computer-executable instructions, where the computer-executable instructions are used to execute the operation strategy recommendation method described in the first aspect above.

[0019] According to the operation strategy recommendation method provided by the embodiment of the present application, it has at least the following beneficial effects: In the process of recommending an operation strategy, first obtain user information; then determine user type information according to the user information and a preset user classification library; then, when the user type information indicates that the user is an object who has failed in previous strategy promotion, extract failure attribution information from a preset failure attribution database, where the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value; then, target attribution factor information can be screened out from multiple attribution factor information according to the weight value; finally, corresponding operation strategy information is matched from a preset operation strategy system library according to the target attribution factor information. Through the above technical solutions, it is possible to automatically analyze the type of users, and then recommend relevant operation strategy information according to the type of users, and further be able to intelligently provide refined operation strategies, which can well improve the operation effect. Description of the Drawings

[0020] The drawings are used to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solutions of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0021] Figure 1It is a schematic flowchart of an operation strategy recommendation method provided by an embodiment of the present application;

[0022] Figure 2 It is Figure 1 a schematic flowchart of a specific implementation manner of step S200 in

[0023] Figure 3 It is Figure 1 a schematic flowchart of a specific implementation manner of step S300 in

[0024] Figure 4 It is Figure 1 a schematic flowchart of a specific implementation manner of step S400 in

[0025] Figure 5 It is Figure 1 a schematic flowchart of a specific implementation manner of step S500 in

[0026] Figure 6 It is Figure 5 a schematic flowchart of a specific implementation manner of step S530 in

[0027] Figure 7 It is a schematic flowchart of a specific implementation manner after executing step S500 in Figure 1

[0028] Figure 8 It is a schematic diagram of an operation strategy recommendation device provided by an embodiment of the present application;

[0029] Figure 9 It is a schematic diagram of an electronic device provided by an embodiment of the present application. Specific Embodiment

[0030] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0031] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different module division in the device or a different order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence.

[0032] It should be noted that unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0033] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0034] AI is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence; artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. The research in this field includes robots, speech recognition, image recognition, natural language processing, and expert systems, etc. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0035] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0036] Artificial intelligence is AI. AI is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0037] The server involved in artificial intelligence technology can be an independent server 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, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0038] The present application provides an operation strategy recommendation method, apparatus, electronic device and computer-readable storage medium. First, user information is obtained; then, user type information is determined according to the user information and a preset user classification library; then, in the case that the user type information indicates that the user is an object for which a previous strategy promotion failed, failure attribution information is extracted from a preset failure attribution database, where the failure attribution information includes a plurality of attribution factor information, and each attribution factor information carries a weight value; then, target attribution factor information can be screened out from the plurality of attribution factor information according to the weight value; finally, corresponding operation strategy information is matched from a preset operation strategy system library according to the target attribution factor information. Through the above technical solution, the type of the user can be automatically analyzed, and then relevant operation strategy information can be recommended according to the type of the user, and refined operation strategies can be provided intelligently, so as to improve the operation effect well.

[0039] The operation strategy recommendation method provided by the embodiments of the present application relates to the field of operation strategy recommendation. The operation strategy recommendation method provided by the embodiments of the present application can be applied to a terminal, can also be applied to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, can also be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing 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.

[0040] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, small computers, large computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0041] It should be noted that in each specific embodiment of the present application, when it comes to performing relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0042] The following further elaborates on the embodiments of the present application with reference to the accompanying drawings.

[0043] As Figure 1 shown, Figure 1 FIG. 10 is a schematic flowchart of an operation strategy recommendation method provided by an embodiment of the present application. The operation strategy recommendation method includes the following steps:

[0044] Step S100: Obtain user information.

[0045] In the operation strategy recommendation method provided by the embodiments of the present application, during the process of operation strategy recommendation, it is first necessary to obtain user information. Among them, user information is information related to the user, and user information can be used to distinguish different users. After obtaining the user information, the user type information can be determined according to the user information and the preset user classification library to distinguish different types of users. Exemplarily, in the insurance business industry, during the process of purchasing insurance, the user information of the insurance purchaser can be obtained first to prepare for subsequent operation strategy recommendation. User information can include user marking information. Different users can be distinguished based on different user marking information. Different accounts can be assigned to different users, and different user markings can be added to different users. Different user markings represent different users. For example, user marking "001" can be assigned to user A, and user marking "002" can be assigned to user B. Therefore, different users can be represented by different user markings.

[0046] Step S200: Determine the user type information according to the user information and the preset user classification library.

[0047] The operation strategy recommendation method provided by the embodiments of this application can determine the user type information based on the user information and the preset user classification library after obtaining the user information, so as to prepare for the subsequent extraction of failure attribution information. Among them, the user classification library includes multiple user classification information, and each user classification information corresponds to a marking information. Subsequently, the user marking information in the user information is matched with the marking information corresponding to the user classification information, and the corresponding user classification information can be obtained by matching. Subsequently, the user classification information can be determined as the user type information, and the failure attribution information can be determined according to the user type information subsequently, so as to prepare for the subsequent operation strategy recommendation.

[0048] It should be noted that the user classification library includes multiple user classification information, and each user classification information corresponds to a marking information. Therefore, the user marking information can be extracted from the user information; then the user marking information is matched with the marking information in the user classification library, and the corresponding target marking information can be obtained by matching. Subsequently, the user classification information corresponding to the target marking information can be determined as the user type information, and the whole process of determining the user type information is more simple, fast, accurate and reliable.

[0049] It should be noted that the user classification library includes multiple user classification information, and each user classification information corresponds to a marking information; among them, the user classification information is the classification information that has been classified previously. Based on the marking information for matching processing, the corresponding user classification information can be obtained, and the corresponding user classification information is used as the user type information.

[0050] As Figure 2 shown, the user classification library includes multiple user classification information, and each user classification information corresponds to a marking information. Determining the user type information according to the user information and the preset user classification library may include the following steps:

[0051] Step S210, extracting user marking information from the user information;

[0052] Step S220, performing matching processing on the user marking information and the marking information to obtain target marking information;

[0053] Step S230, determining the user classification information corresponding to the target marking information as the user type information.

[0054] For steps S210 to S230, in the process of determining user type information based on user information and a preset user classification library, user marker information can be extracted from the user information; then, by matching the user marker information with the marker information, the target marker information can be obtained; finally, the user classification information corresponding to the target marker information is determined as the corresponding user type information. Through the above technical solution, the determination of user type information can be made more accurate, and thus the subsequent operation strategy recommendation can be made more accurate.

[0055] It should be noted that in the process of determining user type information, first, user marker information can be extracted from the obtained user information; then, by matching the user marker information with the marker information in the user classification library, the target marker information can be obtained; finally, the user classification information corresponding to the target marker information is determined as the corresponding user type information. Exemplarily, in the field of the insurance industry, the user classification library includes multiple user classification information. For example, it includes objects of successful previous policy promotion and objects of failed previous policy promotion. Among them, the marker information of the object of successful previous policy promotion is "01", and the marker information of the object of failed previous policy promotion is "02"; if the user marker information extracted from the user information is "02", the user classification information of "object of failed previous policy promotion" can be matched from the user classification library, and subsequently, the "object of failed previous policy promotion" can be used as the final user type information. Then, subsequently, the corresponding operation strategy information can be matched based on the determined user type information, and the entire information matching process can be more accurate and reliable.

[0056] Step S300: When the user type information indicates that the user is an object of failed previous policy promotion, extract failure attribution information from a preset failure attribution database, where the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value.

[0057] For the operation strategy recommendation method provided by the embodiments of the present application, when the user type information indicates that the user is an object of failed previous policy promotion, failure attribution information can be extracted from a preset failure attribution database. The failure attribution information includes multiple attribution factor information, and each attribution factor information carries a corresponding weight value. Subsequently, the target attribution factor information can be screened from the multiple attribution factor information according to the weight value, preparing for the selection of subsequent operation strategy information.

[0058] It is understandable that the user type information indicates that the user is an object for which previous strategy promotion has failed, that is, the previous promotion of the strategy to the user was not successful. Subsequently, the failure attribution information can be extracted from the pre-set failure attribution database to prepare for subsequent operation strategy recommendations. It should be noted that when the user type information indicates that the user is not an object for which previous strategy promotion has failed, the previous operation strategy can continue to be recommended to the user, and there is no need to extract the failure attribution information from the failure attribution database, so as to well accelerate the recommendation speed of the operation strategy.

[0059] It is worth noting that the failure attribution information includes multiple sub-attribution information, and each sub-attribution information carries a weight value; according to the weight value, the corresponding target sub-attribution information can be screened from the multiple sub-attribution information to prepare for the determination of subsequent operation strategy information.

[0060] Exemplarily, in the field of insurance business, during the previous operation strategy recommendation process, the accidental insurance of Package A was recommended to the user, but the user did not accept the accidental insurance of Package A. Therefore, during the analysis and processing of the user type information, the user type information will indicate that the user is an object for which previous strategy promotion has failed, and thus the failure attribution information can be extracted from the pre-set failure attribution database.

[0061] As Figure 3 shown, the failure attribution database includes multiple user tags and the attribution analysis information corresponding to the user tags. When the user type information indicates that the user is an object for which previous strategy promotion has failed, extracting the failure attribution information from the pre-set failure attribution database may include the following steps:

[0062] Step S310, when the user type information indicates that the user is an object for which previous strategy promotion has failed, extract the target user identifier from the user type information;

[0063] Step S320, perform a matching process between the target user identifier and the user tags to obtain the target user tag;

[0064] Step S330, determine the attribution analysis information corresponding to the target user tag as the failure attribution information.

[0065] For steps S310 to S330, in the process of extracting failure attribution information from a pre-set failure attribution database, when the user type information indicates that the user is an object of previous strategy promotion failure, the target user identifier can be extracted from the user type information; then, by matching the target user identifier with the user label, the target user label can be obtained; finally, the corresponding failure attribution information can be determined based on the attribution analysis information corresponding to the target user label. Through the above technical solution, the extraction of failure attribution information can be made more accurate.

[0066] It should be noted that when the user type information indicates that the user is an object of previous strategy promotion failure, it means that the user did not accept the relevant operation strategy during the previous operation strategy promotion process, and the promotion process was not successful; subsequently, the target user identifier can be extracted from the user type information; then, by matching the target user identifier with the user label, the corresponding target user label can be obtained, and finally, the attribution analysis information corresponding to the target user label can be determined as the corresponding failure attribution information. And the failure attribution information includes multiple sub-attribution information, and each sub-attribution information carries a corresponding weight value. Subsequently, the corresponding target sub-attribution information can be screened out from the multiple sub-attribution information according to the weight value, so as to prepare for the selection of subsequent operation strategy information.

[0067] It should be noted that the failure attribution database may include multiple user labels, and each user label corresponds to an attribution analysis information; therefore, the target user identifier can be first extracted from the user type information; then, the target user identifier can be matched with the user labels in the failure attribution database, and thus the target user label can be obtained; finally, the attribution analysis information corresponding to the target user label can be used as the final target sub-attribution information, and the determination process of the entire failure attribution information can be more simple, fast and accurate.

[0068] Step S400: Screen out the target sub-attribution information from multiple sub-attribution information according to the weight value.

[0069] The operation strategy recommendation method provided by the embodiments of the present application extracts failure attribution information from a pre-set failure attribution database. Since the failure attribution information includes multiple sub-attribution information, and each sub-attribution information carries a weight value, subsequently, the corresponding target sub-attribution information can be screened out from the multiple sub-attribution information according to the weight value carried by each sub-attribution information, so that the selection of subsequent operation strategy information can be more reasonable and accurate.

[0070] It should be noted that each attribution factor information corresponds to a weight value. By comparing and processing the weight values of multiple attribution factor information, the target attribution factor information can be screened out from multiple attribution factor information, preparing for the subsequent selection of operation strategy information. Exemplarily, in the insurance business industry, the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value. For example, the failure attribution information includes the first attribution factor information, the second attribution factor information, and the third attribution factor information. The weight value carried by the first attribution factor information is 24%, the weight value carried by the second attribution factor information is 38%, and the weight value carried by the third attribution factor information is 38%. Therefore, in the process of selecting attribution factor information according to the weight size, the second attribution factor information and the third attribution factor information can be used as the target attribution factor information.

[0071] As Figure 4 shown, screening out the target attribution factor information from multiple attribution factor information according to the weight value may include the following steps:

[0072] Step S410, obtain the current influencing factor information;

[0073] Step S420, adjust and process the weight values of multiple attribution factor information according to the current influencing factor information;

[0074] Step S430, use the attribution factor information with the largest weight value as the target attribution factor information.

[0075] For steps S410 to S430, in the process of screening out the target attribution factor information from multiple attribution factor information according to the weight value, first obtain the current influencing factor information; then adjust and process the weight values of multiple attribution factor information according to the current influencing factor information; finally, the attribution factor information with the largest weight value can be used as the target attribution factor information. Through the above technical solution, the weight value of the attribution factor information can be adjusted and processed according to the change of the actual influencing factor, making the subsequent selection of operation strategy information more accurate and reasonable. Among them, the current influencing factor information may include policy information, discount information, subsidy information, etc.

[0076] Exemplarily, in the insurance business industry, in the process of screening target attribution factor information from multiple attribution factor information according to weight values, the attribution factor information includes first attribution factor information, second attribution factor information, and third attribution factor information. Among them, the first attribution factor information indicates that the user does not accept the previous operation strategy because the user feels that the preferential intensity is insufficient, the second attribution factor information indicates that the user does not accept the previous operation strategy because the user feels that the insurance price is higher than that of other companies, and the third attribution factor information indicates that the user has purchased relevant insurance and does not accept the previous operation strategy; the weight value of the previous first attribution factor information is 30%, the weight value of the second attribution factor information is 50%, and the weight value of the third attribution factor information is 20%; the current influencing factor information is preferential discount information, and there is a 20% discount for purchasing relevant insurance. Therefore, the first attribution factor information, the second attribution factor information, and the third attribution factor information can be adjusted. The weight value of the first attribution factor information is adjusted to 60%, the weight value of the second attribution factor information is adjusted to 30%, and the weight value of the third attribution factor information is adjusted to 10%; subsequently, the first attribution factor information can be determined as the target attribution factor information, and the operation strategy information obtained by subsequent screening can be closer to the current actual situation, making the recommendation of the operation strategy more reasonable and accurate.

[0077] Step S500: Match corresponding operation strategy information from the preset operation strategy system library according to the target attribution factor information.

[0078] The operation strategy recommendation method provided by the embodiments of the present application can, after obtaining the target attribution factor information, match corresponding operation strategy information from the preset operation strategy system library according to the target attribution factor information, and then can provide corresponding operation strategies for users, which can intelligently provide refined operation strategies, can well improve the operation effect, and provides great convenience for business personnel.

[0079] It should be noted that the operation strategy system library includes multiple strategy operation suggestion information, and each strategy operation suggestion information corresponds to a characteristic sub-information; matching the attribution factor characteristic information with the characteristic sub-information of the operation strategy system library can screen out multiple strategy operation suggestion information from the operation strategy system library to prepare for the subsequent selection of operation strategy information.

[0080] As Figure 5 shown, matching corresponding operation strategy information from the preset operation strategy system library according to the target attribution factor information may include the following steps:

[0081] Step S510: Perform feature extraction processing on the target attribution factor information to obtain attribution factor characteristic information;

[0082] Step S520: Screen and obtain multiple policy operation suggestion messages from the operation policy system library according to the attribution factor characteristic information;

[0083] Step S530: Determine the target policy operation suggestion message from multiple policy operation suggestion messages according to the preset quantity threshold;

[0084] Step S540: Determine the target policy operation suggestion message as the operation policy message.

[0085] For steps S510 to S540, in the process of matching and obtaining the corresponding operation policy message from the preset operation policy system library according to the target attribution factor information, first perform feature extraction processing on the target attribution factor information to obtain the attribution factor characteristic information; then screen and obtain multiple policy operation suggestion messages from the operation policy system library according to the attribution factor characteristic information; then determine the target policy operation suggestion message from multiple policy operation suggestion messages according to the preset quantity threshold; finally, determine the target policy operation suggestion message as the operation policy message, providing a suitable operation policy for the user, and can reduce the workload of business salespersons, bringing great convenience to business salespersons.

[0086] It should be noted that the quantity threshold can be set according to actual needs. In the process of determining the operation policy message, first perform feature extraction on the target attribution factor information to obtain the attribution factor characteristic information; then subsequently, multiple policy operation suggestion messages can be screened from the operation policy system library according to the attribution factor characteristic information; then determine the target policy operation suggestion message from multiple policy operation suggestion messages according to the preset quantity threshold, and finally determine the target policy operation suggestion message as the operation policy message; adjust and process the operation policy message according to the setting of the quantity threshold, so that the adjustment of the operation policy message can be more flexible and accurate.

[0087] As Figure 6 shown, determining the target policy operation suggestion message from multiple policy operation suggestion messages according to the preset quantity threshold may include the following steps:

[0088] Step S531: Screen and obtain several policy operation suggestion messages from multiple policy operation suggestion messages according to the quantity threshold;

[0089] Step S532: Perform combination processing on several policy operation suggestion messages to obtain the target policy operation suggestion message.

[0090] For steps S531 to S532, in the process of determining the target policy operation recommendation information from multiple policy operation recommendation information, first, according to the quantity threshold, several policy operation recommendation information are filtered out from the multiple policy operation recommendation information; then, the obtained several policy operation recommendation information are combined to obtain the target policy operation recommendation information; through the above technical solution, the determination of the target policy operation recommendation information can be more flexible and accurate to meet the requirements of various business promotion scenarios and improve the promotion success rate.

[0091] Exemplarily, the quantity threshold can be set to 3, and 3 policy operation recommendation information can be filtered out from the multiple policy operation recommendation information. Then, the obtained 3 policy operation recommendation information are combined to obtain the corresponding target policy operation recommendation information to meet the requirements of business promotion.

[0092] As Figure 7 shown, after matching the corresponding operation policy information from the preset operation policy system library according to the target attribution factor information, the following steps may further be included:

[0093] Step S610, perform verification processing on the operation policy information to obtain policy verification information;

[0094] Step S620, in the case that the policy verification information indicates that the policy recommendation fails, update the failure attribution database according to the policy verification information;

[0095] Step S630, in the case that the policy verification information indicates that the policy recommendation is successful, update the operation policy system library according to the policy verification information.

[0096] For steps S610 to S630, after matching the corresponding operation policy information from the preset operation policy system library according to the target attribution factor information, the operation policy information can be verified to obtain policy verification information; then, in the case that the policy verification information indicates that the policy recommendation fails, the failure attribution database can be updated according to the policy verification information to enrich the content of the failure attribution database, so as to facilitate the subsequent extraction of failure attribution information from the failure attribution database and improve the accuracy of subsequent failure attribution information extraction; finally, in the case that the policy verification information indicates that the policy recommendation is successful, the operation policy system library can be updated according to the policy verification information to enrich the data of the operation policy system library, so as to facilitate the subsequent selection of more appropriate operation policy information from the operation policy system library.

[0097] Exemplarily, in the insurance sales industry, if the user accepts the operation strategy information, the operation system library will be updated according to the strategy verification information; if the user does not accept the operation strategy information, the failure attribution database will be updated according to the strategy verification information, so that more accurate failure attribution information can be provided by using the failure attribution database subsequently.

[0098] In addition, as Figure 8 shown, an embodiment of the present application further provides an operation strategy recommendation device 10, including:

[0099] An acquisition unit 100, configured to acquire user information;

[0100] A selection unit 200, configured to determine user type information according to the user information and a preset user classification library;

[0101] An extraction unit 300, configured to extract failure attribution information from a preset failure attribution database when the user type information indicates that the user is an object of previous strategy promotion failure, where the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value;

[0102] A screening unit 400, configured to screen out target attribution factor information from multiple attribution factor information according to the weight value;

[0103] A matching unit 500, configured to match corresponding operation strategy information from a preset operation strategy system library according to the target attribution factor information.

[0104] It should be noted that in the process of operation strategy recommendation, first, user information is acquired; then, user type information is determined according to the user information and a preset user classification library; then, when the user type information indicates that the user is an object of previous strategy promotion failure, failure attribution information is extracted from a preset failure attribution database, where the failure attribution information includes multiple attribution factor information, and each attribution factor information carries a weight value; then, target attribution factor information can be screened out from multiple attribution factor information according to the weight value; finally, corresponding operation strategy information is matched from a preset operation strategy system library according to the target attribution factor information. Through the above technical solution, the type of the user can be automatically analyzed, and then relevant operation strategy information can be recommended according to the type of the user, so that refined operation strategies can be provided intelligently, and the operation effect can be improved well.

[0105] The specific implementation manner of the operation strategy recommendation device 10 is basically the same as that of the specific embodiment of the above operation strategy recommendation method, and will not be elaborated here.

[0106] In addition, as Figure 9As shown in the figure, an embodiment of the present application further provides an electronic device 700, which includes: a memory 720, a processor 710, and a computer program stored on the memory 720 and executable on the processor 710.

[0107] The processor 710 and the memory 720 can be connected through a bus or other means.

[0108] The non-transitory software program and instructions required to implement the operation strategy recommendation method of the above embodiments are stored in the memory 720, and when executed by the processor 710, they execute the operation strategy recommendation method of the above embodiments.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor 710 or a controller, for example, executed by a processor 710 in the above device embodiment, the processor 710 can be caused to execute the operation strategy recommendation method in the above embodiments.

[0111] The above embodiments can be combined for use. The modules with the same name in different embodiments may be the same or different.

[0112] The specific embodiments of the present application are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily have to be executed in the particular order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple. The relevant parts can be referred to the partial description of the method embodiments.

[0114] The apparatus, device, computer-readable storage medium, and method provided by the embodiments of the present application are corresponding. Therefore, the apparatus, device, and non-volatile computer storage medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device, and computer storage medium will not be elaborated here.

[0115] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented using a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL), and there is not just one type of HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into an integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0116] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91 SAM, Microchip PIC18F26K20, and Silicone Labs C8051 F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to make the controller implement the same function in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or the structures within the hardware component.

[0117] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0118] For the convenience of description, the above devices are described by dividing them into various units according to their functions. Of course, when implementing the embodiments of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0119] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0120] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0121] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0123] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0124] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (Flash RAM). The memory is an example of computer-readable media.

[0125] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0126] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0127] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent the cases where A exists alone, A and B exist simultaneously, and B exists alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0128] Embodiments of the present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Embodiments of the present application may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0129] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference may be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and for the relevant parts, reference may be made to the description of the method embodiments.

[0130] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for recommending an operation strategy, characterized in that: include: Get user information; Determine user type information according to the user information and a preset user classification library; In the case where the user type information indicates that the user is a target of previous strategy promotion failure, failure attribution information is extracted from a preset failure attribution database, wherein the failure attribution information includes a plurality of attribution factor information, each of which carries a weight value; Filtering target attribution factor information from the plurality of attribution factor information according to the weight value; According to the target attribution factor information, corresponding operation strategy information is matched from a preset operation strategy system library.

2. The operation strategy recommendation method according to claim 1, characterized in that: The user classification library includes a plurality of user classification information, each of which corresponds to a tag information; the user type information is determined according to the user information and the preset user classification library, including: Extracting user tag information from the user information; Matching the user tag information with the tag information to obtain target tag information; The user classification information corresponding to the target mark information is determined as the user type information.

3. The operation strategy recommendation method according to claim 1, characterized in that: The failure attribution database includes a plurality of user tags and attribution analysis information corresponding to the user tags; when the user type information indicates that the user is a failed target of previous strategy promotion, extracting failure attribution information from the preset failure attribution database includes: In the case where the user type information indicates that the user is a target of failed promotion by a previous strategy, extracting a target user identifier from the user type information; Matching the target user identifier with the user tag to obtain a target user tag; The attribution analysis information corresponding to the target user tag is determined as the failure attribution information.

4. The operation strategy recommendation method according to claim 1, characterized in that: The step of selecting target attribution factor information from the plurality of attribution factor information according to the weight value includes: Get current influencing factor information; Adjusting the weight values ​​of the plurality of attribution factor information according to the current influencing factor information; The attribution factor information with the largest weight value is used as the target attribution factor information.

5. The operation strategy recommendation method according to claim 1, characterized in that: The matching of the target attribution factor information from a preset operation strategy system library to obtain corresponding operation strategy information includes: Performing feature extraction processing on the target attribution factor information to obtain attribution factor feature information; Filtering and obtaining a plurality of strategy operation suggestion information from the operation strategy system library according to the attribute factor characteristic information; Determining target strategy operation suggestion information from the plurality of strategy operation suggestion information according to a preset quantity threshold; The target strategy operation suggestion information is determined as the operation strategy information.

6. The operation strategy recommendation method according to claim 5, characterized in that: The step of determining target strategy operation suggestion information from a plurality of strategy operation suggestion information according to a preset quantity threshold comprises: According to the quantity threshold, a plurality of the strategy operation suggestion information is screened to obtain a plurality of the strategy operation suggestion information; A plurality of the strategy operation suggestion information are combined and processed to obtain the target strategy operation suggestion information.

7. The operation strategy recommendation method according to claim 1, characterized in that: After obtaining corresponding operation strategy information from a preset operation strategy system library according to the target attribution factor information, the method further includes: Verifying the operation strategy information to obtain strategy verification information; In the case where the strategy verification information indicates that the strategy recommendation fails, updating the failure attribution database according to the strategy verification information; When the policy verification information indicates that the policy recommendation is successful, the operation policy system library is updated according to the policy verification information.

8. An operation strategy recommendation device, characterized in that: include: An acquisition unit, used for acquiring user information; A selection unit, configured to determine user type information according to the user information and a preset user classification library; an extracting unit, configured to extract failure attribution information from a preset failure attribution database when the user type information indicates that the user is a target of previous strategy promotion failure, wherein the failure attribution information includes a plurality of attribution factor information, each of which carries a weight value; A screening unit, configured to screen target attribution factor information from the plurality of attribution factor information according to the weight value; The matching unit is used to match the target attribution factor information from a preset operation strategy system library to obtain corresponding operation strategy information.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the operation strategy recommendation method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the operation strategy recommendation method described in any one of claims 1 to 7.

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