Information processing method and device and storage medium

Abstract

Embodiments of the invention disclose an information processing method and apparatus, and a storage medium. The method comprises the steps of determining a plurality of object level parameters corresponding to a plurality of target objects; determining a to-be-lost object from the plurality of target objects according to the plurality of object grade parameters by using an object loss prediction model; generating a target task corresponding to the to-be-lost object; transmitting the target task to a corresponding visit task claiming platform according to the object level parameter of the to-be-lost object; and a task execution end of the visiting task platform claims the target task and executes the visiting process of the target task.

Description

Technical Field

[0001] The present application relates to the field of logistics technology, and in particular to an information processing method and device, and a storage medium. Background Art

[0002] In the logistics sales business scenario, account managers promote the company's refined management on behalf of customers. However, there are relatively few account managers and a huge number of customers, making it difficult to support a large number of customer visits. Currently, the customer churn rate remains high, causing significant costs and revenue losses to the company.

[0003] In the related art, an account manager makes judgments on the customers he is responsible for, and visits the customer if he determines that the customer is at risk of churn. However, since different account managers have different prediction criteria for customer churn, inexperienced account managers are unable to accurately determine customers who are at risk of churn, resulting in a high customer churn rate. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present application hope to provide an information processing method and device, and a storage medium that can reduce customer churn rate.

[0005] The technical solution of this application is implemented as follows:

[0006] The present invention provides an information processing method, which includes:

[0007] determining a plurality of object-level parameters corresponding to the plurality of target objects;

[0008] Determine the to-be-lost object from the multiple target objects by using the object loss prediction model and according to the multiple object level parameters;

[0009] Generate target tasks corresponding to the objects to be lost;

[0010] According to the object level parameters of the object to be lost, the target task is transmitted to the corresponding visiting task claiming platform; so that the task execution end of the visiting task platform can claim the target task and execute the visiting process of the target task.

[0011] The present application provides an information processing device, the device comprising:

[0012] A determination unit, configured to determine a plurality of object level parameters corresponding to a plurality of target objects; and determine the objects to be lost from the plurality of target objects by using the object loss prediction model and according to the plurality of object level parameters;

[0013] A generating unit, used for generating a target task corresponding to the object to be lost;

[0014] The transmission unit is used to transmit the target task to the corresponding visiting task claiming platform according to the object level parameters of the object to be lost; so that the task execution end of the visiting task platform can claim the target task and execute the visiting process of the target task.

[0015] The present application provides an information processing device, the device comprising:

[0016] A memory, a processor and a communication bus, wherein the memory communicates with the processor via the communication bus, and the memory stores an information processing program executable by the processor. When the information processing program is executed, the information processing method described above is executed by the processor.

[0017] An embodiment of the present application provides a storage medium having a computer program stored thereon, which is applied to an information processing device, and is characterized in that the computer program implements the above-mentioned information processing method when executed by a processor.

[0018] The embodiments of the present application provide an information processing method, device, and storage medium. The information processing method includes: determining multiple object level parameters corresponding to multiple target objects; using an object loss prediction model and based on multiple object level parameters, determining an object to be lost from multiple target objects; generating a target task corresponding to the object to be lost; transmitting the target task to a corresponding visiting task claiming platform based on the object level parameters of the object to be lost; so that a task execution end of the visiting task platform can claim the target task and execute a visiting process for the target task. The above method is adopted to implement the scheme. The information processing device determines multiple object grade parameters corresponding to multiple target objects, that is, grades the multiple target objects; utilizes the object churn prediction model and ranks and scores the multiple target objects according to the grade classification information of the multiple target objects, thereby determining the objects to be lost from the multiple target objects. There is no need to manually determine which objects are objects with churn risks, which improves the accuracy of determining the objects to be lost. By generating target tasks corresponding to the objects to be lost, accurate target tasks are obtained. The task execution end can claim the target task on the task claiming platform and execute the target task, and visit the accurate objects to be lost according to the accurate target tasks, thereby reducing the churn risk of the objects to be lost, that is, reducing the customer churn rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of an information processing method provided in an embodiment of the present application;

[0020] Figure 2 An exemplary multiple object level parameter assessment block diagram provided for an embodiment of the present application;

[0021] Figure 3 An exemplary automatic visit and coordinated sinking schematic diagram provided for an embodiment of the present application;

[0022] Figure 4 An exemplary visit feedback analysis diagram provided in an embodiment of the present application;

[0023] Figure 5 An exemplary information processing block diagram provided for an embodiment of the present application;

[0024] Figure 6 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application Figure 1 ;

[0025] Figure 7 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application Figure 2 . DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0027] The present application provides an information processing method, which is applied to an information processing device. Figure 1 A flow chart of an information processing method provided in an embodiment of the present application, such as Figure 1 As shown, the information processing method may include:

[0028] S101: Determine multiple object level parameters corresponding to multiple target objects.

[0029] An information processing method provided in an embodiment of the present application is suitable for determining objects to be lost, and transmitting the generated target tasks corresponding to the objects to be lost to a visiting task claiming platform, so that the task execution end of the visiting task platform can claim the target tasks and execute the visiting process for the target tasks.

[0030] In the embodiments of the present application, the information processing device can be implemented in various forms. For example, the information processing device described in the present application may include devices such as mobile phones, cameras, tablet computers, laptop computers, PDAs, portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, and devices such as digital TVs, desktop computers, and servers.

[0031] In the embodiment of the present application, a plurality of target objects correspond to a plurality of object level parameters one-to-one, that is, one target object corresponds to one object level parameter.

[0032] In the embodiment of the present application, the target object may be a merchant on the e-commerce platform, or the target object may be a buyer of a certain commodity; the specific target object may be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0033] In an embodiment of the present application, multiple object level parameters corresponding to multiple target objects can be determined when a preset time is reached; or multiple object level parameters corresponding to multiple target objects can be determined when an object level parameter determination instruction is received. The specific timing of determining the multiple object level parameters corresponding to the multiple target objects can be determined based on actual conditions, and the embodiment of the present application is not limited to this.

[0034] It should be noted that the preset time can be the time information configured in the information processing device, or it can be the time information transmitted to the information processing device by other devices; the specific time can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0035] Exemplarily, the preset time may be a periodic time point, such as a certain day of a week, or a certain day of a month. The preset time may also be other time points, which may be determined based on actual conditions, and the embodiments of the present application do not limit this.

[0036] In an embodiment of the present application, the process of an information processing device determining multiple object level parameters corresponding to multiple target objects includes: when a preset time arrives, obtaining multiple current quarter average monthly income parameters of the multiple target objects in the current quarter; and determining multiple object level parameters based on the parameter values ​​of the multiple current quarter average monthly income parameters.

[0037] It should be noted that the average monthly income parameter for the current quarter is the average monthly income of the target object in the current quarter.

[0038] In the embodiment of the present application, multiple target objects correspond one-to-one to multiple current quarter average monthly revenue parameters. Specifically, one target object corresponds to one current quarter average monthly revenue parameter.

[0039] In an embodiment of the present application, the information processing device may obtain multiple current quarter average monthly income parameters from a database; or the information processing device may obtain multiple current quarter average monthly income parameters after the user inputs multiple current quarter average monthly income parameters; the information processing device may also obtain multiple current quarter average monthly income parameters from other devices; the specific way in which the information processing device obtains multiple current quarter average monthly income parameters can be determined based on actual conditions, and the embodiment of the present application is not limited to this.

[0040] In an embodiment of the present application, the process of an information processing device determining multiple object level parameters based on the parameter values ​​of multiple current quarter average monthly income parameters includes: when the parameter values ​​of multiple current quarter average monthly income parameters are less than or equal to a preset threshold, determining multiple first parameters as multiple object level parameters; when the parameter values ​​of multiple current quarter average monthly income parameters are greater than the preset threshold, determining multiple second parameters as multiple object level parameters.

[0041] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are less than or equal to a preset threshold, multiple first parameters are determined as multiple object level parameters; when the parameter value of the first revenue parameter among multiple current quarter average monthly revenue parameters is less than or equal to the preset threshold, the object level parameter of the first target object corresponding to the first revenue parameter is determined as the first parameter.

[0042] It should be noted that the first income parameter is part of multiple monthly average income parameters of the current quarter.

[0043] In the embodiment of the present application, the number of the first target objects is the same as the number of the first benefit parameters.

[0044] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are greater than a preset threshold, multiple second parameters are determined as multiple object level parameters; when the parameter value of the first revenue parameter among the multiple current quarter average monthly revenue parameters is the parameter value, the object level parameter of the first target object corresponding to the first revenue parameter is determined as the second parameter.

[0045] In an embodiment of the present application, the preset threshold may be a threshold configured in the information processing device, or the preset threshold may be a threshold transmitted from other devices to the information processing device. The specific manner in which the information processing device obtains the preset threshold may be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0046] Exemplarily, the preset threshold may be 10000, or other numerical values. The specific value of the preset threshold may be determined based on actual conditions, and the embodiments of the present application do not limit this.

[0047] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly income parameters are less than or equal to a preset threshold, the process of determining multiple first parameters as multiple object-level parameters includes: when the parameter values ​​of multiple current quarter average monthly income parameters are less than or equal to the first threshold, obtaining multiple historical quarterly average monthly income parameters of multiple target objects in the previous quarter; when the parameter values ​​of multiple historical quarterly average monthly income parameters are equal to the first threshold, determining multiple sixth-level parameters as multiple first parameters; when the parameter values ​​of multiple current quarter average monthly income parameters are greater than the first threshold and less than or equal to the second threshold, determining multiple fifth-level parameters as multiple first parameters; when the parameter values ​​of multiple current quarter average monthly income parameters are greater than the second threshold and less than or equal to the third threshold, determining multiple fourth-level parameters as multiple first parameters; and determining multiple first parameters as multiple object-level parameters.

[0048] It should be noted that the sixth-level parameter may be an F-level parameter, the fifth-level parameter may be an E-level parameter, and the fourth-level parameter may be a D-level parameter.

[0049] In the embodiment of the present application, the preset threshold includes a first threshold, a second threshold and a third threshold. The first threshold may be 0, the second threshold may be 3000, the third threshold may be 10000, the first threshold, the second threshold and the third threshold may also be other values, and the specific values ​​of the first threshold, the second threshold and the third threshold may be determined according to actual conditions, which are not limited in the embodiment of the present application.

[0050] In an embodiment of the present application, the information processing device obtains multiple historical quarterly average monthly income parameters in the same manner as the method of obtaining multiple current quarterly average monthly income parameters, and the specific embodiment of the present application is not described here.

[0051] It should be noted that multiple historical quarterly average monthly return parameters correspond one-to-one to multiple target objects, that is, one target object corresponds to one historical quarterly average monthly return parameter.

[0052] In the embodiment of the present application, the historical quarterly average monthly income parameter is the average monthly income of the target object in the previous quarter.

[0053] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are less than or equal to a first threshold, multiple historical quarterly average monthly revenue parameters of multiple target objects in the previous quarter are obtained; when the first revenue parameter among multiple current quarter average monthly revenue parameters is less than or equal to the first threshold, the first historical quarterly average monthly revenue parameter of the first target object in the previous quarter is obtained.

[0054] In an embodiment of the present application, when the parameter values ​​of multiple historical quarterly average monthly return parameters are all equal to the first threshold value, multiple sixth-level parameters are determined as multiple first parameters; when the parameter value of the first historical quarterly average monthly return parameter among the multiple historical quarterly average monthly return parameters is equal to the first threshold value, the first parameter of the second target object corresponding to the first historical quarterly average monthly return parameter is determined to be the sixth-level parameter.

[0055] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are all greater than the first threshold and are all less than or the second threshold, multiple fifth-level parameters are determined as multiple first parameters; when the parameter value of the first revenue parameter among the multiple current quarter average monthly revenue parameters is greater than the first threshold and less than or the second threshold, the first parameter of the first target object corresponding to the first revenue parameter is determined to be the fifth-level parameter.

[0056] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are all greater than the second threshold and are all less than or equal to the third threshold, multiple fourth-level parameters are determined as multiple first parameters; when the parameter value of the first revenue parameter among the multiple current quarter average monthly revenue parameters is greater than the second threshold and less than or equal to the third threshold, the first parameter of the first target object corresponding to the first revenue parameter is determined to be the fourth-level parameter.

[0057] In an embodiment of the present application, after obtaining multiple historical quarterly average monthly income parameters of multiple target objects in the previous quarter, if the parameter values ​​of the multiple historical quarterly average monthly income parameters are not equal to the first threshold, it is determined that there are no corresponding multiple object level parameters for the multiple target objects.

[0058] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly income parameters are greater than a preset threshold, the process of determining multiple second parameters as multiple object-level parameters includes: when the parameter values ​​of multiple current quarter average monthly income parameters are greater than a third threshold and less than or equal to a fourth threshold, determining multiple third-level parameters as multiple second parameters; when the parameter values ​​of multiple current quarter average monthly income parameters are greater than the fourth threshold and less than or equal to the fifth threshold, determining multiple second-level parameters as multiple second parameters; when the parameter values ​​of multiple current quarter average monthly income parameters are greater than the fifth threshold, determining multiple first-level parameters as multiple second parameters; and determining multiple second parameters as multiple object-level parameters.

[0059] It should be noted that the first-level parameter may be an A-level parameter, the second-level parameter may be a B-level parameter, and the third-level parameter may be a C-level parameter.

[0060] In the embodiment of the present application, the preset threshold value also includes a fourth threshold value and a fifth threshold value. The fourth threshold value may be 50000, the fifth threshold value may be 100000, the fourth threshold value and the fifth threshold value may also be other values, and the specific values ​​of the fourth threshold value and the fifth threshold value may be determined according to actual conditions, which is not limited in the embodiment of the present application.

[0061] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are all greater than the third threshold and are all less than or equal to the fourth threshold, multiple third-level parameters are determined as multiple second parameters; when the parameter value of the first revenue parameter among the multiple current quarter average monthly revenue parameters is greater than the third threshold and less than or equal to the fourth threshold, the second parameter of the first target object corresponding to the first revenue parameter is determined to be the third-level parameter.

[0062] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are all greater than the fourth threshold and are all less than or equal to the fifth threshold, multiple second-level parameters are determined as multiple second parameters; when the parameter value of the first revenue parameter among the multiple current quarter average monthly revenue parameters is greater than the fourth threshold and less than or equal to the fifth threshold, the second parameter of the first target object corresponding to the first revenue parameter is determined to be the second-level parameter.

[0063] In an embodiment of the present application, when the parameter values ​​of multiple current quarter average monthly revenue parameters are all greater than the fifth threshold, multiple first-level parameters are determined as multiple second parameters; when the parameter value of the first revenue parameter among the multiple current quarter average monthly revenue parameters is greater than the fifth threshold, the second parameter of the first target object corresponding to the first revenue parameter is determined to be the first-level parameter.

[0064] In the embodiments of the present application, Figure 2As shown, quarterly average monthly income (multiple current quarterly average monthly income parameters) is used as important data for customer grade (multiple object grade parameters) classification. First, the C code (customer unique code) and current quarterly average monthly income (multiple current quarterly average monthly income parameters) of all customers (multiple target objects) are obtained to determine whether the current customer's quarterly average monthly income is greater than 0. If not (when the parameter values ​​of multiple current quarterly average monthly income parameters are less than or equal to the first threshold), then determine whether any monthly income in the previous year is 0 (obtain multiple historical quarterly average monthly income parameters of multiple target objects in the previous quarter). If yes (when the parameter values ​​of multiple historical quarterly average monthly income parameters are equal to the first threshold), then determine that the customer grade is F (determine multiple sixth-level parameters as multiple first parameters); if the current customer's quarterly average monthly income is greater than 0 and less than or equal to 3,000 (when the parameter values ​​of multiple current quarterly average monthly income parameters are greater than the first threshold and less than or equal to the second threshold), then the customer grade is E (determine multiple fifth-level parameters as multiple first parameters); if the current customer If the quarterly average monthly income of the current customer is greater than 3,000 and less than or equal to 10,000 (when the parameter values ​​of multiple current quarterly average monthly income parameters are greater than the second threshold and less than or equal to the third threshold), the customer level is D (multiple fourth-level parameters are determined as multiple first parameters); if the quarterly average monthly income of the current customer is greater than 10,000 and less than or equal to 50,000 (when the parameter values ​​of multiple current quarterly average monthly income parameters are greater than the third threshold and less than or equal to the fourth threshold), the customer level is C (multiple third-level parameters are determined as multiple second parameters); if the quarterly average monthly income of the current customer is greater than 50,000 and less than or equal to 100,000 (when the parameter values ​​of multiple current quarterly average monthly income parameters are greater than the fourth threshold and less than or equal to the fifth threshold), the customer level is B (multiple second-level parameters are determined as multiple second parameters); if the quarterly average monthly income of the current customer is greater than 100,000 (when the parameter values ​​of multiple current quarterly average monthly income parameters are greater than the fifth threshold), the customer level is A (multiple first-level parameters are determined as multiple second parameters). This part of the rule logic is flexibly configured and managed in the rule engine. The output customer grade data results are saved in the MySQL database (MySQL stores customer grade assessment results). The scheduled task will also update the grade data (the scheduled worker task updates the customer data) to the customer master data (update the MySQL customer master data) for synchronization.

[0065] It is understandable that by establishing an evaluation system for customer levels, automatically processing large amounts of customer revenue data, and analyzing customer levels, powerful and important data is provided for refined customer management and follow-up visits to customers, thereby promoting and accelerating sales transactions with customers.

[0066] S102: Determine an object to be lost from multiple target objects by using an object loss prediction model and according to multiple object level parameters.

[0067] In the embodiment of the present application, after the information processing device determines multiple object level parameters corresponding to multiple target objects, it uses the object loss prediction model to determine the objects to be lost from the multiple target objects based on the multiple object level parameters.

[0068] In an embodiment of the present application, the object loss prediction model can be a model configured in the information processing device, or a model transmitted to the information processing device by other devices, or a model obtained by the information processing device in other ways. The specific way in which the information processing device obtains the object loss prediction model can be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0069] In the embodiment of the present application, the number of objects to be lost may be one or more. The specific number of objects to be lost may be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0070] It should be noted that the number of objects to be lost does not exceed the number of target objects.

[0071] In an embodiment of the present application, an information processing device uses an object churn prediction model to determine an object to be churned from multiple target objects based on multiple object level parameters, including: when a preset time arrives, obtaining multiple groups of object operation parameters corresponding to the multiple target objects; inputting multiple object level parameters and multiple groups of object operation parameters into the object churn prediction model to obtain multiple churn prediction values ​​for the multiple target objects; determining a target prediction value that is greater than or equal to a preset prediction threshold from the multiple churn prediction values; and treating the object corresponding to the target prediction value as the object to be churned.

[0072] In an embodiment of the present application, the preset prediction threshold may be a threshold configured in the information processing device, or a value transmitted to the information processing device by other devices, or a value obtained by the information processing device in other ways. The specific manner in which the information processing device obtains the preset prediction threshold may be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0073] In the embodiment of the present application, multiple groups of object operation parameters correspond one-to-one to multiple target objects, that is, one target object corresponds to one group of object operation parameters.

[0074] In an embodiment of the present application, the object operation parameters include parameters such as the customer's historical order volume, business portrait, operation quality, and competitor penetration of the target object. Among them, if the target object is a merchant on an e-commerce platform, the customer's historical order volume can be the merchant's historical sales volume; if it is a buyer who purchases a certain item, the customer's historical order volume can be the buyer's historical purchase volume. The business portrait can be the merchant's business situation of the store on the e-commerce platform. The operation quality can be the merchant's situation in terms of the operation quality of the store, and for marketing, such as the conversion rate of marketing activities, or the number of users who sell products. Competitor penetration can be information about the merchant's competitors.

[0075] In an embodiment of the present application, the information processing device can obtain multiple sets of object operation parameters from a database; the information processing device can also obtain multiple sets of object operation parameters after the user inputs multiple sets of object operation parameters; the information processing device can also obtain multiple sets of object operation parameters from other devices; the specific way in which the information processing device obtains multiple sets of object operation parameters can be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0076] In the embodiment of the present application, after the information processing device obtains multiple churn prediction values, it can also sort the multiple churn prediction values ​​from large to small according to the numerical value to obtain a churn prediction value sequence, and select the first n churn prediction values ​​from the churn prediction value sequence to obtain the target prediction value. The target prediction value can also be determined from the multiple churn prediction values ​​in other ways, and the specific implementation method can be determined according to the actual situation, and the embodiment of the present application does not limit this.

[0077] It should be noted that the value of n is less than or equal to the number of multiple churn prediction values.

[0078] S103: Generate target tasks corresponding to the objects to be lost.

[0079] In the embodiment of the present application, the information processing device uses the object loss prediction model and determines the object to be lost from multiple target objects according to multiple object level parameters, and then generates a target task corresponding to the object to be lost.

[0080] In an embodiment of the present application, the information processing device can obtain basic information of the object to be lost and operating parameters of the object to be lost, and generate target tasks for the object to be lost based on the basic information and operating parameters of the object to be lost.

[0081] In the embodiment of the present application, the number of target tasks can be one or more. The specific number of target tasks can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0082] It should be noted that the number of target tasks is the same as the number of objects to be lost, that is, one object to be lost corresponds to one target task.

[0083] S104. According to the object level parameters of the object to be lost, the target task is transmitted to the corresponding visiting task claiming platform; so that the task execution end of the visiting task platform can claim the target task and execute the visiting process for the target task.

[0084] In the embodiment of the present application, after the information processing device generates the target task corresponding to the object to be lost, it transmits the target task to the corresponding visit task claiming platform according to the object level parameters of the object to be lost.

[0085] In the embodiment of the present application, the number of platforms for claiming visiting tasks can be one or more, and the specific number can be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0086] In the embodiment of the present application, the task execution end can be a device that executes the target task, or it can be a device (such as a mobile phone) held by the sales or other personnel who execute the target task. The specific task execution end can be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0087] In an embodiment of the present application, the method for transmitting the target task to the visit task claiming platform can be to distribute the target task to the visit task claiming platform through a message queue (Message Queue, MQ), or to transmit the target task to the visit task claiming platform through other methods; the specific implementation method can be determined according to actual conditions, and the embodiment of the present application is not limited to this.

[0088] In an embodiment of the present application, the information processing device transmits the target task to the corresponding visiting task claiming platform according to the object level parameter of the object to be lost, including: when the object level parameter of the object to be lost is a first parameter, the target task is transmitted to the webmaster task claiming platform and the sales management platform; when the object level parameter of the object to be lost is a second parameter, the target task is transmitted to the district task claiming platform and the sales management platform; the district task claiming platform, the webmaster task claiming platform and the sales management platform are used as visiting task claiming platforms.

[0089] In the embodiment of the present application, the webmaster task claiming platform may be a webmaster workbench, the sales management platform may be a sales system, and the district task claiming platform may be a district workbench.

[0090] In the embodiment of the present application, the webmaster task claiming platform transmits a target task whose object level parameter is the first parameter, the district task claiming platform transmits a target task whose object level parameter is the second parameter, and the sales management platform transmits all target tasks.

[0091] In an embodiment of the present application, the information processing device can distribute the target tasks to the webmaster's task claiming platform and the sales management platform respectively through the MQ transmission method; it can also distribute the target tasks to the webmaster's task claiming platform and the sales management platform through other methods; the specific transmission method can be determined according to actual conditions, and the embodiment of the present application is not limited to this.

[0092] In an embodiment of the present application, the information processing device can distribute the target tasks to the area task claiming platform and the sales management platform respectively through the MQ transmission method; it can also distribute the target tasks to the area task claiming platform and the sales management platform through other methods; the specific transmission method can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0093] In an embodiment of the present application, the task execution end includes a first execution end, a second execution end and a third execution end; when the target task is claimed by the first execution end on the webmaster task claiming platform, the claim status of the target task is adjusted from an unclaimed state to a claimed state, and the claim status of the target task is synchronized in the sales management platform; when the target task is claimed by the second execution end on the district task claiming platform, the claim status of the target task is adjusted from an unclaimed state to a claimed state, and the claim status of the target task is synchronized in the sales management platform; when the target task is claimed by the third execution end on the sales management platform, the claim status of the target task is adjusted from an unclaimed state to a claimed state, and the claim status of the target task is synchronized in the district task claiming platform or the webmaster task claiming platform.

[0094] In the embodiment of the present application, the first execution end may be a staff member of the sales department (such as a delivery boy of the sales department) or a device of the sales department that executes the target task. The second execution end may be a device that can execute the target task for the district manager or in the district. The third execution end may be a customer manager or other device that can execute the target task.

[0095] In the embodiment of the present application, the claim status of the target task can be synchronized in the sales management platform through the synchronization method of JSF (JavaServer Faces), or in the area task claiming platform or the webmaster task claiming platform through the synchronization method of JSF. The claim status of the target task can also be synchronized in the area task claiming platform, the webmaster task claiming platform or the sales management platform through other synchronization methods; the specific synchronization method can be determined according to the actual situation, and the embodiment of the present application does not limit this.

[0096] In the embodiments of the present application, Figure 3 As shown: The customer churn warning model (object churn prediction model) algorithm covers every customer in the customer set every day, and accurately locates the target customer group in the customer group (determines the objects to be lost). The algorithm comprehensively analyzes the internal and external data of the customer (multiple groups of object operation parameters and multiple object level parameters), including customer level data, customer historical order volume, order volume fluctuation, business portrait, operation quality, competitor penetration and other factor data, and scores and ranks them according to the weight coefficient of the factor proportion through the algorithm model (object churn prediction model), and obtains the target customers that need to be accurately focused on (that is, multiple object level parameters and multiple groups of object operation parameters are input into the object churn prediction model to obtain multiple churn prediction values ​​for multiple target objects; from multiple churn prediction values, determine the target prediction value that is greater than or equal to the preset prediction threshold; and take the object corresponding to the target prediction value as the object to be lost). The target customer data is stored in the Mysql database (Mysql target customer data table). The distributed scheduled tasks (distributed scheduled creation tasks) scan these target customers to automatically create customer visit plans (automatically create visit artifacts). Then, the plans are distributed to the collaborative sinking platform (visit task claiming platform) according to the customer level through the MQ distribution method. The visit plan tasks of customers of ABC level (second parameter) are distributed to the district workbench (district task claiming platform) through MQ, and the district manager (second execution end) will claim and process the visits; the visit plan tasks of customers of DEF level (first parameter) are distributed to the webmaster workbench (webmaster task claiming platform) through MQ, and the delivery boy of the sales department (first execution end) will claim and process the visits; the visit plan tasks of customers of all levels are distributed to the sales system (sales management platform) through MQ, and the customer manager (third execution end) will claim and process the visits. The operation results of the visit plan distributed to the district workstation and webmaster workstation will be synchronized to the sales system in real time through JSF, and the account manager can see the latest status changes of the visit plan tasks; the account manager's operations on the visit plan tasks in the sales system will also be synchronized to the district workstation and webmaster workstation in real time through JSF.

[0097] It is understandable that a customer pre-churn model is built based on multi-factor data such as customer level, historical order volume, and operational quality, and customer visit plan data is automatically analyzed. The automatically created visit tasks are stored in the Mysql database, and the visit plan tasks are connected with multiple platforms through MQ. JSF is used to synchronize task status data in real time, achieving asynchronous decoupling and dynamic refresh effects, and efficient collaboration across multiple platforms and multiple roles.

[0098] In an embodiment of the present application, after the information processing device transmits the target task to the corresponding visit task claiming platform according to the object level parameters of the object to be lost, when the visit feedback information of the target task transmitted by the task execution end is obtained from the visit feedback page, the visit feedback information is verified using preset verification rules; when the verification is successful, the task execution status of the target task is adjusted to a visit success status; when the verification fails, the reason for the verification failure is obtained; and when the task execution end transmits the next visit feedback information, the reason for the verification failure is displayed on the visit feedback page.

[0099] In an embodiment of the present application, the preset verification rules may be rules configured in the information processing device, or may be rules transmitted to the information processing device from other devices, or may be rules obtained by the information processing device in other ways. The specific way in which the information processing device obtains the preset verification rules may be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0100] In the embodiment of the present application, the preset verification rule may be Drools or other verification rules. The specific preset verification rule may be determined according to the actual situation, and the embodiment of the present application does not limit this.

[0101] It should be noted that the preset verification rules may include content homogeneity check, text character length check, meaningless keyword check, etc.

[0102] In the embodiment of the present application, the method of verifying the visit feedback information using the preset verification rule can be to match the preset verification rule with the visit feedback information. If there is a match, it is determined that the verification has failed, and if there is no match, it is determined that the verification has succeeded.

[0103] In an embodiment of the present application, if a first verification rule in the preset verification rules matches the first feedback information in the visit feedback information, the first verification rule is determined as the cause of the verification failure.

[0104] In an embodiment of the present application, a visit feedback template is also provided in the information processing device. When a request for obtaining a template is received from a task execution end, the visit feedback template is displayed on a visit feedback page so that the task execution end can transmit visit feedback information to the information processing device according to the visit feedback template.

[0105] In the embodiment of the present application, when the verification is successful, the semantic detection rules can be used to perform semantic detection on the visit feedback information. When the detection is successful, the task execution status of the target task is adjusted to the visit success status.

[0106] It should be noted that the semantic detection rule may be a grammar or semantic detection rule for natural language recognition.

[0107] In an embodiment of the present application, after the information processing device verifies the visit feedback information using preset verification rules, it obtains the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end; and displays the visit task execution parameters, visit conversion parameters and visit harvest parameters.

[0108] In the embodiment of the present application, the visit task execution parameter may be the visit completion status of the task execution end. Specific visit task execution parameters include the number of tasks claimed by the visit execution end, the number of customers, the task completion rate, and suggestions for improving the quality of task completion.

[0109] In the embodiment of the present application, the visit conversion parameter can be the contract signing status of the customer (object) after the task execution end completes the visit. Specifically, the visit conversion parameter includes the number of contracted customers in each dimension, the number of customers who claim the visit plan, and the ranking of the number of contracts.

[0110] In the embodiment of the present application, the visit harvest parameter can be the personal commission (income of the visit task) obtained by the task execution end after completing the visit. Specifically, the visit harvest parameter includes the number of successful contracts signed by the customers who claimed the visit, the contract commission of the customers who claimed the visit, the commission ranking, the personal commission growth plan, etc.

[0111] In an embodiment of the present application, the information processing device can calculate the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end once the task execution end completes a target task, that is, update the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end, and store the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end in a database. The information processing device can obtain and display the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end in the database.

[0112] In an embodiment of the present application, the information processing device may also periodically calculate the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end, and update the visit task execution parameters, visit conversion parameters and visit harvest parameters in the database; the specific timing of calculating the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0113] It is understandable that through the Drools rule engine, natural language processing, and data statistical analysis technology, visit quality inspection, visit result display, personal incentives, efficiency and quality improvement suggestions, etc., sales business capabilities can be improved and customer churn rate can be reduced. It is an efficient, intelligent, and collaborative visit refinement collaborative management device that reduces customer churn rate.

[0114] In the embodiments of the present application, Figure 4 As shown: when the account manager (third execution end), district manager (second execution end), sales department stationmaster (first execution end) and others provide visit feedback for the visit plan they claim, they can choose the system-customized feedback prefabricated template (visit feedback template) and fill in the visit registration information (visit feedback information). When filling in, the Drools rule engine (preset verification rules) configured by the system checks the registration content (visit feedback information) in real time and reminds the content that does not meet the rules, such as content homogeneity check, text character length check, meaningless keyword check and other rules. The content that meets the rules will continue to automatically use the natural language recognition function for grammar check to ensure that the semantics can be understood by humans. The visit registration content that has passed the quality inspection is saved to the back-end Mysql database, and the task status is updated to visit success. Personnel of each role can view the data that the system regularly statistically analyzes on the visit dashboard, including the number of visit tasks they claim (number of claimed tasks), number of customers, and task completion rate, and summarize and analyze the visit quality inspection issues to formulate suggestions for improving the quality of the subsequent visit task completion. The customer contract distribution dashboard displays the number of contracted customers, the number of customers who claim the visit plan, and the ranking of contracted customers from the perspectives of customer level, claim time period, visit time period, etc. In the personal accrual function, each role can see the number of successful contracts signed by the customers they claim to visit, the amount of commission they can receive after the claimed visit successfully signs the contract, and the commission ranking. The system automatically formulates a later personal commission growth plan based on the commission ranking, the number of visited customers, and the contract ratio.

[0115] In the embodiments of the present application, Figure 5As shown: when the preset time arrives, the information processing device (visit refined collaborative management device) obtains multiple current quarter average monthly income parameters of multiple target objects in the current quarter; when the parameter values ​​of the multiple current quarter average monthly income parameters are less than or equal to the preset threshold, the multiple first parameters are determined as multiple object level parameters; when the parameter values ​​of the multiple current quarter average monthly income parameters are greater than the preset threshold, the multiple second parameters are determined as multiple object level parameters; the multiple object level parameters are written into the Mysql customer rating data table, the distributed scheduled update task is used to regularly update the multiple object level parameters of the multiple target objects, and the customer management module is used to manage the basic information of the multiple target objects (including relevant information such as business license, legal person, company address, contact information, etc.) and multiple object level parameters. When the preset time arrives, multiple groups of object operation parameters corresponding to multiple target objects are obtained; multiple object level parameters and multiple groups of object operation parameters are input into the object churn prediction model (customer pre-churn model) to obtain multiple churn prediction values ​​for multiple target objects; from the multiple churn prediction values, a target prediction value greater than or equal to the preset prediction threshold is determined; the object corresponding to the target prediction value is used as the object to be churned; the information of the object to be churned is stored in the Mysql target customer data table, and a distributed scheduled task is created to scan the information of the object to be churned, and the target task corresponding to the object to be churned is automatically created for the user of the visiting task to generate; when the object level parameter of the object to be churned is the first parameter (DEF level), the target task is transmitted to the webmaster task claiming platform (webmaster workbench) and the sales management platform (sales system); when the object level parameter of the object to be churned is the second parameter (ABC level), the target task is transmitted to the district task claiming platform (district workbench) and the sales management platform; the district task claiming platform, the webmaster task claiming platform and the sales management platform are used as the visiting task claiming platform; so that the task execution end of the visiting task platform can claim the target task and execute the visiting process for the target task. The task execution end includes a first execution end, a second execution end and a third execution end. When the target task is claimed by the first execution end on the webmaster task claiming platform, the claim status of the target task is adjusted from an unclaimed state to a claimed state, and the claim status of the target task is synchronized in the sales management platform through the JSF method; when the target task is claimed by the second execution end on the district task claiming platform, the claim status of the target task is adjusted from an unclaimed state to a claimed state, and the claim status of the target task is synchronized in the sales management platform through the JSF method; when the target task is claimed by the third execution end on the sales management platform, the claim status of the target task is adjusted from an unclaimed state to a claimed state, and the claim status of the target task is synchronized in the district task claiming platform or the webmaster task claiming platform through the JSF method.When the visit feedback information of the target task transmitted from the task execution end is obtained from the visit feedback page, the visit feedback information is verified using the preset verification rules (visit feedback quality inspection); the visit task execution parameters (visit completion status), visit conversion parameters (customer signing status) and visit harvest parameters (personal contributions) of the task execution end are obtained; the visit task execution parameters, visit conversion parameters and visit harvest parameters are displayed.

[0116] In the embodiment of the present application, after the salesperson logs into the system, he will see the rating results (multiple object rating parameters) of the relevant customers he is responsible for, which are automatically calculated by the system background according to the model at a fixed time. He will also see the visit plan tasks automatically created by the system for him. When the number of visited customers is large, in order to improve the timeliness of visits, the system will automatically distribute the visit plan tasks of some customers to the district workbench (district task claiming platform), the webmaster workbench (webmaster task claiming platform) and other collaborative sinking platforms according to the customer level. The district manager (second execution end), the webmaster (first execution end), the delivery boy (first execution end) and other role personnel will claim and complete these distributed visit tasks. Sales personnel (third execution end) and relevant visit processing role personnel of the collaborative sinking platform can view the visit completion status, customer contract signing status, visit feedback quality inspection, and personal accrual calculation of salary income in the visit monitoring and analysis platform.

[0117] It can be understood that by determining multiple object grade parameters corresponding to multiple target objects, that is, grading the multiple target objects; utilizing the object churn prediction model and ranking and scoring the multiple target objects according to the grading information of the multiple target objects, thereby determining the objects to be churned from the multiple target objects, there is no need to manually determine which objects are at risk of churn, thereby improving the accuracy in determining the objects to be churned, and by generating target tasks corresponding to the objects to be churned, accurate target tasks are obtained, and the task execution end can claim the target task on the task claiming platform and execute the target task, and visit the accurate objects to be churned according to the accurate target tasks, thereby reducing the churn risk of the objects to be churned, that is, reducing the customer churn rate.

[0118] Based on the same inventive concept as the above-mentioned information processing method, the embodiment of the present application provides an information processing device 1 corresponding to an information processing method; Figure 6 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application Figure 1 , the information processing device 1 may include:

[0119] A determination unit 11 is used to determine a plurality of object level parameters corresponding to a plurality of target objects; and to determine the objects to be lost from the plurality of target objects according to the plurality of object level parameters using an object loss prediction model;

[0120] A generating unit 12, configured to generate a target task corresponding to the object to be lost;

[0121] The transmission unit 13 is used to transmit the target task to the corresponding visiting task claiming platform according to the object level parameters of the object to be lost, so that the task execution end of the visiting task platform can claim the target task and execute the visiting process of the target task.

[0122] In some embodiments of the present application, the device further includes an acquisition unit and an input unit;

[0123] The acquisition unit is used to acquire multiple groups of object operation parameters corresponding to the multiple target objects when the preset time arrives;

[0124] The input unit is used to input the multiple object level parameters and the multiple groups of object operation parameters into the object churn prediction model to obtain multiple churn prediction values ​​for the multiple target objects;

[0125] The determining unit 11 is used to determine a target prediction value greater than or equal to a preset prediction threshold from the multiple churn prediction values; and to use the object corresponding to the target prediction value as the object to be churned.

[0126] In some embodiments of the present application, the input unit is used to transmit the target task to the webmaster task claiming platform and the sales management platform when the object level parameter of the object to be lost is the first parameter; and to transmit the target task to the area task claiming platform and the sales management platform when the object level parameter of the object to be lost is the second parameter;

[0127] The determining unit 11 is used to use the area task claiming platform, the webmaster task claiming platform and the sales management platform as the visit task claiming platform.

[0128] In some embodiments of the present application, the task execution end includes a first execution end, a second execution end and a third execution end; the device also includes an adjustment unit and a synchronization unit;

[0129] The adjustment unit is used to adjust the claim status of the target task from an unclaimed state to a claimed state when the target task is claimed by the first execution terminal on the webmaster task claiming platform; to adjust the claim status of the target task from an unclaimed state to a claimed state when the target task is claimed by the second execution terminal on the area task claiming platform; and to adjust the claim status of the target task from an unclaimed state to a claimed state when the target task is claimed by the third execution terminal on the sales management platform;

[0130] The synchronization unit is used to synchronize the claim status of the target task in the sales management platform; synchronize the claim status of the target task in the sales management platform; synchronize the claim status of the target task in the area task claim platform or the webmaster task claim platform.

[0131] In some embodiments of the present application, the acquisition unit is used to acquire multiple current quarterly average monthly income parameters of the multiple target objects in the current quarter when the preset time arrives;

[0132] The determining unit 11 is used to determine the multiple object level parameters according to the parameter values ​​of the multiple current quarter average monthly income parameters.

[0133] In some embodiments of the present application, the determination unit 11 is used to determine multiple first parameters as the multiple object-level parameters when the parameter values ​​of the multiple current quarter average monthly income parameters are less than or equal to a preset threshold; and to determine multiple second parameters as the multiple object-level parameters when the parameter values ​​of the multiple current quarter average monthly income parameters are greater than the preset threshold.

[0134] In some embodiments of the present application, the device further includes a verification unit and a display unit;

[0135] The verification unit is used to verify the visit feedback information of the target task transmitted by the task execution end by using a preset verification rule when the visit feedback information is obtained from the visit feedback page;

[0136] The adjustment unit is used to adjust the task execution status of the target task to a visit success status when the verification is successful;

[0137] The obtaining unit is used to obtain the reason for the verification failure when the verification fails;

[0138] The display unit is used to display the reason for the verification failure on the visit feedback page when the task execution end transmits the next visit feedback information.

[0139] In some embodiments of the present application, the acquisition unit is used to acquire the visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end;

[0140] The display unit is used to display the visit task execution parameters, visit conversion parameters and visit harvest parameters.

[0141] It should be noted that, in actual applications, the above-mentioned determination unit 11, generation unit 12 and transmission unit 13 can be implemented by a processor 14 on the information processing device 1, specifically a CPU (Central Processing Unit), an MPU (Microprocessor Unit), a DSP (Digital Signal Processing) or a field programmable gate array (FPGA); the above-mentioned data storage can be implemented by a memory 15 on the information processing device 1.

[0142] The present application also provides an information processing device 1, such as Figure 7 As shown, the information processing device 1 includes: a processor 14, a memory 15 and a communication bus 16. The memory 15 communicates with the processor 14 via the communication bus 16. The memory 15 stores a program executable by the processor 14. When the program is executed, the information processing method described above is executed by the processor 14.

[0143] In practical applications, the memory 15 may be a volatile memory, such as a random access memory (RAM); or a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD); or a combination of the above types of memory, and provide instructions and data to the processor 14.

[0144] An embodiment of the present application provides a computer-readable storage medium having a computer program thereon, and when the program is executed by the processor 14, the information processing method as described above is implemented.

[0145] It can be understood that by determining multiple object grade parameters corresponding to multiple target objects, that is, grading the multiple target objects; utilizing the object churn prediction model and ranking and scoring the multiple target objects according to the grading information of the multiple target objects, thereby determining the objects to be churned from the multiple target objects, there is no need to manually determine which objects are at risk of churn, thereby improving the accuracy in determining the objects to be churned, and by generating target tasks corresponding to the objects to be churned, accurate target tasks are obtained, and the task execution end can claim the target task on the task claiming platform and execute the target task, and visit the accurate objects to be churned according to the accurate target tasks, thereby reducing the churn risk of the objects to be churned, that is, reducing the customer churn rate.

[0146] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of hardware embodiments, software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. An information processing method, characterized in that: The method comprises: determining a plurality of object-level parameters corresponding to the plurality of target objects; Determine the to-be-lost object from the multiple target objects by using the object loss prediction model and according to the multiple object level parameters; Generate target tasks corresponding to the objects to be lost; According to the object level parameters of the object to be lost, the target task is transmitted to the corresponding visiting task claiming platform; so that the task execution end of the visiting task platform can claim the target task and execute the visiting process of the target task.

2. The method according to claim 1, characterized in that The method of using the object loss prediction model and determining the to-be-lost object from the multiple target objects according to the multiple object level parameters includes: When the preset time arrives, obtaining multiple groups of object operation parameters corresponding to the multiple target objects; Inputting the plurality of object level parameters and the plurality of sets of object operation parameters into the object churn prediction model to obtain a plurality of churn prediction values ​​for the plurality of target objects; Determine, from the plurality of churn prediction values, a target prediction value that is greater than or equal to a preset prediction threshold; The object corresponding to the target prediction value is taken as the object to be lost.

3. The method according to claim 1, characterized in that: The step of transmitting the target task to a corresponding visiting task claiming platform according to the object level parameter of the object to be lost includes: When the object level parameter of the object to be lost is the first parameter, the target task is transmitted to the webmaster task claiming platform and the sales management platform; When the object level parameter of the object to be lost is the second parameter, transmitting the target task to the area task claiming platform and the sales management platform; The area task claiming platform, the webmaster task claiming platform and the sales management platform are used as the visit task claiming platform.

4. The method according to claim 3, characterized in that The task execution end includes a first execution end, a second execution end and a third execution end; the method further includes: When the target task is claimed by the first execution terminal on the webmaster task claiming platform, adjusting the claiming status of the target task from an unclaimed state to a claimed state, and synchronizing the claiming status of the target task in the sales management platform; When the target task is claimed by the second execution terminal on the area task claiming platform, adjusting the claiming status of the target task from the unclaimed status to the claimed status, and synchronizing the claiming status of the target task in the sales management platform; When the target task is claimed by the third execution terminal on the sales management platform, the claim status of the target task is adjusted from the unclaimed state to the claimed state, and the claim status of the target task is synchronized in the area task claiming platform or the webmaster task claiming platform.

5. The method according to claim 1, characterized in that: The determining of a plurality of object level parameters corresponding to a plurality of target objects includes: When the preset time arrives, multiple current quarter monthly average income parameters of the multiple target objects in the current quarter are obtained; The plurality of object level parameters are determined according to the parameter values ​​of the plurality of current quarterly average monthly income parameters.

6. The method according to claim 5, characterized in that Determining the plurality of object level parameters according to the parameter values ​​of the plurality of current quarterly average monthly income parameters includes: In a case where the parameter values ​​of the multiple current quarter average monthly income parameters are less than or equal to a preset threshold, determining the multiple first parameters as the multiple object level parameters; When the parameter values ​​of the multiple current quarter average monthly income parameters are greater than the preset threshold, the multiple second parameters are determined as the multiple object level parameters.

7. The method according to claim 1, characterized in that After transmitting the target task to the corresponding visiting task claiming platform according to the object level parameter of the object to be lost, the method further includes: In the case where the visit feedback information of the target task transmitted by the task execution end is obtained from the visit feedback page, the visit feedback information is verified using a preset verification rule; If the verification is successful, the task execution status of the target task is adjusted to a visit success status; In the case of verification failure, the reason for the verification failure is obtained; and in the case where the task execution end transmits the next visit feedback information, the reason for the verification failure is displayed on the visit feedback page.

8. The method according to claim 7, characterized in that After verifying the visit feedback information using the preset verification rules, the method further includes: Obtaining visit task execution parameters, visit conversion parameters and visit harvest parameters of the task execution end; The visit task execution parameters, visit conversion parameters and visit harvest parameters are displayed.

9. An information processing device, characterized in that: The device comprises: A determination unit, configured to determine a plurality of object level parameters corresponding to a plurality of target objects; and determine the objects to be lost from the plurality of target objects by using the object loss prediction model and according to the plurality of object level parameters; A generating unit, used for generating a target task corresponding to the object to be lost; The transmission unit is used to transmit the target task to the corresponding visiting task claiming platform according to the object level parameters of the object to be lost; so that the task execution end of the visiting task platform can claim the target task and execute the visiting process of the target task.

10. An information processing device, characterized in that: The device comprises: A memory, a processor and a communication bus, wherein the memory communicates with the processor via the communication bus, the memory stores an information processing program executable by the processor, and when the information processing program is executed, the method according to any one of claims 1 to 8 is performed by the processor.

11. A storage medium having a computer program stored thereon, applied to an information processing device, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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