Operational analysis methods, devices, and electronic equipment applicable to different operational activities

Abstract

This application relates to the field of data analysis technology, and discloses an operational analysis method, device, and electronic device applicable to different operational activities. Based on a lifecycle model, different operational activities are promoted using corresponding promotion methods, and feedback information under each promotion method is obtained. According to predefined raw indicator data, the raw indicator data corresponding to each operational activity is extracted from the feedback information. According to predefined operational performance indicator data, the raw indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data. In response to an operational performance viewing command, the operational performance indicator data of the operational activity is retrieved from the database and displayed in a visual form on a display interface. This solves the problem of the lack of unified and systematic operational data analysis in existing operational activities.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to an operational analysis method, apparatus, and electronic device applicable to different operational activities. Background Technology

[0002] Data analysis of operational activities is the most crucial step in evaluating the effectiveness of these activities. Different stages in the business process correspond to different user lifecycles. For example, the AARRR model achieves five metrics for user growth: Acquisition, Activation, Retention, Revenue, and Referral. The RARRA model highlights the importance of user retention and achieves five metrics for user growth: Retention, Activation, Revenue, Referral, and Acquisition.

[0003] Two types of data are generated during operational activities: user behavior data and user business data. For example, in a payment activity, the actions a user takes to make a payment are user behavior data, while the number of users who ultimately make a payment is user business data. In the current technology, user behavior data generated by the business party through different promotional platforms (not its own) is stored in the databases of each promotional platform. The data from the promotional platforms cannot be shared with the business party's own business system, resulting in the separation of the two types of data. In addition, user behavior data also exists in the business party's own system, but it is relatively scattered. Each business activity focuses on different indicators, some focusing on user retention and others on user activation. There are no unified indicators for statistics, resulting in a lack of unified and systematic operational data analysis. Summary of the Invention

[0004] The purpose of this application is to provide an operational analysis device and electronic equipment applicable to different operational activities, in order to solve the problem of the lack of unified and systematic operational data analysis in existing operational activities.

[0005] In a first aspect, embodiments of this application provide an operational analysis method applicable to different operational activities, the method comprising:

[0006] Based on the lifecycle model, different operational activities are promoted using corresponding promotion methods, and feedback information is obtained under each promotion method.

[0007] Based on predefined raw indicator data, extract the raw indicator data corresponding to each operational activity from the feedback information;

[0008] Based on predefined operational performance indicator data, the original indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data for each operational activity, thereby completing the operational analysis of the operational activities.

[0009] Wherein: the predefined raw indicator data includes basic raw indicator data uniformly defined for different operational activities;

[0010] The predefined operational performance metrics data include basic operational performance metrics data that are uniformly defined for different operational activities.

[0011] In some possible embodiments, in response to an instruction to view the operational effectiveness of an operational activity, operational effectiveness indicator data of the operational activity is obtained and displayed in a visual form on the display interface.

[0012] In some possible embodiments,

[0013] The process involves parsing the raw indicator data of each operational activity based on predefined operational performance indicator data to obtain the corresponding operational performance indicator data for each activity, including:

[0014] Based on the predefined operational performance indicator data and the parsing rules obtained from parsing the original indicator data, the original indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data for each operational activity.

[0015] The predefined raw indicator data also includes personalized raw indicator data defined for different operational activities;

[0016] The predefined operational performance metrics data also include personalized operational performance metrics data defined for different operational activities.

[0017] In some possible embodiments, obtaining feedback information under each promotion method includes:

[0018] Obtain user behavior data and user business data from various promotion methods;

[0019] Extract the corresponding primary raw indicator data from user behavior data, parse the primary raw indicator data to obtain the primary operational performance indicator data for each operational activity;

[0020] Extract the corresponding second raw indicator data from the user business data, parse the second raw indicator data, and obtain the second operational performance indicator data corresponding to each operational activity.

[0021] In some possible embodiments, the method further includes:

[0022] In response to the operation effect comparison viewing command, the comparison method selected by the operation account is determined according to the comparison viewing command, wherein different comparison methods use different comparison conditions to filter comparison objects;

[0023] Based on the selected comparison method, filter the target operational performance indicator data of the target operation plan and operation activities that meet the corresponding comparison conditions;

[0024] The target operation plan and the target operation effect index data of the operation activities are compared, and the comparison results are displayed in a visual form on the display interface.

[0025] In some possible embodiments, the comparison conditions include any of the following:

[0026] Operational performance metrics for the same campaign promoted using different methods;

[0027] Performance metrics for different operational activities using the same promotional method;

[0028] Performance metrics data for different operational activities with the same business objective.

[0029] In some possible implementations, operational performance metrics are defined for different operational activities in the following ways:

[0030] Based on the user lifecycle stage defined by the lifecycle model, and the types of operational strategies and feedback information executed for users at different lifecycle stages, the original indicator data and operational performance indicator data for each lifecycle stage are defined.

[0031] In some possible embodiments, defining the raw indicator data and operational performance indicator data for each lifecycle stage includes at least one of the following:

[0032] Data related to retention metrics at all stages of the product lifecycle;

[0033] Data related to activation metrics during the beginner phase of the product lifecycle;

[0034] Data related to the monetization metrics at the maturity and decline stages of the product lifecycle;

[0035] Data related to the self-propagation metrics and effects at the novice, growth, and maturity stages of the product lifecycle;

[0036] Data related to the effectiveness of acquisition metrics during the potential phase of the lifecycle.

[0037] In some possible embodiments, the basic raw metric data defined for different operational activities includes at least one of the following:

[0038] The feedback information is provided to the recipient of the feedback; the time of the feedback; relevant indicators for business access channels, traffic distribution versions, and business occurrences; the business results of the feedback; and the location of the recipient of the feedback.

[0039] Secondly, embodiments of this application provide an operational analysis device suitable for different operational activities, the device comprising:

[0040] The acquisition module is used to promote different operational activities using corresponding promotion methods based on the lifecycle model and obtain feedback information under each promotion method.

[0041] The module for extracting raw indicator data is used to extract raw indicator data corresponding to each operational activity from the feedback information based on predefined raw indicator data. The predefined raw indicator data includes basic raw indicator data uniformly defined for different operational activities.

[0042] The raw indicator data parsing module is used to parse the raw indicator data of each operational activity based on predefined operational performance indicator data to obtain the operational performance indicator data corresponding to each operational activity. The predefined operational performance indicator data includes basic operational performance indicator data uniformly defined for different operational activities.

[0043] The module for obtaining operational performance metrics data is used to respond to the command to view the operational performance of an operational activity, retrieve operational performance metrics data from the database, and display them in a visual form on the display interface.

[0044] Thirdly, embodiments of this application provide an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the operational analysis method for different operational activities provided in the first aspect above.

[0045] Fourthly, embodiments of this application provide a computer program product, including computer program instructions, which, when executed by a processor, implement the method described in the first aspect above.

[0046] This application's embodiments aim to address the limitations of related technologies where business entities cannot access user data from non-proprietary channels, making it impossible to combine and analyze data from both channels for unified analysis. Furthermore, data from proprietary channels is often fragmented; for example, Activity A focuses on user acquisition, tracking click-through rates and new user numbers, while Activity B focuses on retention—different activities have different metrics. Similarly, when both Activities A and B advertise on third-party platforms like Toutiao, targeting the same audience, conversion rates may differ. The fragmented data within proprietary systems hinders systematic analysis and optimization of operational activities. This approach improves the efficiency of operations personnel in tracking campaign performance, enabling timely adjustments to advertising strategies and campaign page design.

[0047] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of an operational analysis process applicable to different operational activities according to one embodiment of this application;

[0050] Figure 2 This is a schematic diagram of a UI interface for displaying all indicator data in an operational analysis applicable to different operational activities according to an embodiment of this application.

[0051] Figure 3 This is a schematic diagram illustrating data collection in operational analysis applicable to different operational activities according to one embodiment of this application;

[0052] Figure 4 This is a schematic diagram of an operation analysis device applicable to different operational activities according to an embodiment of this application;

[0053] Figure 5 This is a schematic diagram of an electronic device structure according to an embodiment of this application. Detailed Implementation

[0054] The technical solutions in the embodiments of this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0055] In the description of the embodiments of this application, unless otherwise stated, the term "multiple" refers to two or more, and other quantifiers are similarly understood. The preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0056] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on conventional or non-inventive effort. For steps that do not logically have a necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the control device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0057] Given that in related technologies, business entities acquire user data through different channels, making it impossible to combine and analyze data from these channels (such as non-proprietary channels) in a unified manner, and that data from proprietary channels is often fragmented—for example, campaign A focuses on user acquisition, tracking click-through rates and the number of new users, while campaign B focuses on retention—different campaigns have different metrics. Or, even if campaigns A and B both run ads on third-party platforms like Toutiao, targeting the same audience, their conversion rates may differ. Because the data in proprietary systems is fragmented, systematic analysis of how to optimize operational activities is impossible. This application provides an operational analysis method applicable to different operational activities, improving the efficiency of operational personnel in tracking campaign performance and enabling timely adjustments to advertising strategies and campaign page design.

[0058] In view of this, the inventive concept of this application is as follows: data obtained from the promotion of business activities in the business party's own business system or different third-party platforms are uniformly aggregated into the business party's own database. Based on predefined operational performance indicator data, the original indicator data of each operational activity are parsed to obtain the operational performance indicator data corresponding to each operational activity. The predefined operational performance indicator data includes basic operational performance indicator data uniformly defined for different operational activities. In response to the operational performance viewing command of the operational activity, the operational performance indicator data of the operational activity is obtained from the database and displayed in a visual form on the display interface.

[0059] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0060] The operational analysis applicable to different operational activities in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0061] Figure 1 This application illustrates an embodiment of an operational analysis process applicable to different operational activities, including:

[0062] Step 101: Based on the lifecycle model, promote different operational activities using corresponding promotion methods and obtain feedback information under each promotion method.

[0063] In related technologies, the lifecycle model analyzes user data to determine the user's lifecycle stage. It divides the process from a user's initial awareness of a business system's app to their departure into six lifecycle stages: potential, new user, growth, maturity, decline, and dormancy. Five metrics are defined for each lifecycle stage a user is in when participating in operational activities. Corresponding operational strategies are then employed to achieve growth in these five metrics at each lifecycle stage, as detailed below:

[0064] Customer acquisition occurs during the potential phase, guiding potential users to enter the platform.

[0065] Activation occurs during the initial user period, when users enter the platform and are guided to become familiar with the app;

[0066] Retention is crucial from the moment a user enters the app until they leave, in order to maximize the user lifecycle.

[0067] Revenue mainly occurs after users have used the app for a period of time, namely the maturity and decline phases, in which users pay the platform.

[0068] Self-propagation occurs during the novice, growth, and maturity stages, when the brand gains user recognition and users recommend the app to their friends, thus acquiring high-quality new users.

[0069] Current life cycle models include the AARRR funnel model and the RARRA model;

[0070] The AARRR funnel model explains the five metrics for achieving user growth: Acquisition, Activation, Retention, Revenue, and Referral.

[0071] The RARRA model stands for Retention, Activation, Revenue, Referral, and Acquisition.

[0072] The RARRA model highlights the importance of user retention.

[0073] Currently, there are many ways to promote operational activities. For example, the operational activities can be advertised on third-party platforms such as Toutiao and Weibo. Users can see the promotional ads for the operational activities when browsing the third-party platforms and choose whether to participate in the operational activities.

[0074] Feedback information refers to the feedback information obtained after a user interacts with them through a lifecycle model based on their different lifecycle stages, after they have taken corresponding actions to participate in the operation activities and become users of the business system. Alternatively, it refers to the feedback information obtained after a user does not intend to participate in the operation activities and refuses to participate, such as when a user starts registering a new user in the business system or when a user starts handling related business in the business system. The server aggregates and obtains various feedback information under each promotion method.

[0075] Step 102: Extract the original indicator data corresponding to each operational activity from the feedback information based on the predefined original indicator data.

[0076] The predefined raw indicator data includes basic raw indicator data that are uniformly defined for different operational activities.

[0077] Regardless of the specific operational activity or the promotional method employed, this embodiment uniformly defines the basic original indicator data.

[0078] As an optional implementation method, the basic raw indicator data defined for different operational activities includes at least one of the following:

[0079] The feedback information is provided to the recipient of the feedback; the time of the feedback; relevant indicators for business access channels, traffic distribution versions, and business occurrences; the business results of the feedback; and the location of the recipient of the feedback.

[0080] Specifically, the basic raw indicator data is defined from the above five perspectives. The objects of the feedback information include, for example, user ID and device name; the time of the feedback information includes, for example, the time of business occurrence related to the operation activity; indicators related to business access channels, traffic distribution versions, and business occurrences, such as different payment methods for business occurrences; the business results of the feedback information include, for example, order amount, number of user clicks, and the duration of user stay on the activity page; and the location of the objects in the feedback information includes, for example, GPS, IP address, country, city, and public physical information indicators (operating system, application version, device manufacturer, device model, network environment, operator, MAC address).

[0081] Step 103: Based on the predefined operational performance indicator data, parse the original indicator data of each operational activity to obtain the corresponding operational performance indicator data for each operational activity, so as to complete the operational analysis of the operational activities.

[0082] The predefined operational performance metrics data include basic operational performance metrics data that are uniformly defined for different operational activities.

[0083] Regardless of the specific operational activity or promotional method used, this embodiment uniformly defines operational performance metrics data. As an optional implementation, the operational performance metrics data are defined in the following manner for different operational activities:

[0084] Based on the user lifecycle stage defined by the lifecycle model, and the types of operational strategies and feedback information executed for users at different lifecycle stages, the original indicator data and operational performance indicator data for each lifecycle stage are defined.

[0085] Specifically, this application achieves a unified set of operational performance indicators in the business unit's own system by defining unified operational performance indicators for different operational activities. This unified set of operational performance indicators avoids the fragmentation of different operational performance data for different operational activities.

[0086] As an optional implementation method, the original indicator data and operational performance indicator data for each lifecycle stage include at least one of the following:

[0087] Data related to retention metrics at all stages of the product lifecycle;

[0088] Data related to activation metrics during the beginner phase of the product lifecycle;

[0089] Data related to the monetization metrics at the maturity and decline stages of the product lifecycle;

[0090] Data related to the effectiveness of self-propagation metrics at the novice, growth, and maturity stages of the life cycle;

[0091] Data related to the effectiveness of acquisition metrics during the potential phase of the lifecycle.

[0092] Specifically, the performance metrics for each stage of the lifecycle are defined as different types of data. For example, retention metrics include data on participating users such as the number of potential participants and their addresses; activation metrics include data on activated users such as the number of potential activated users and their activation time; monetization metrics include data on monetized users such as the amount of money monetized and the monetization method; self-propagation metrics include data on self-propagation users such as the propagation channels and propagation time; and acquisition metrics include data on acquired users such as the acquisition time, acquisition address, and acquisition method.

[0093] Retention metric data can also include data collected at different activity times, such as daily, weekly, and monthly activity data, i.e., data on participating users of an activity on a specific day, week, or month.

[0094] The performance indicators for each stage of the lifecycle can be predefined based on actual conditions, allowing for flexible definition of performance indicators according to actual needs.

[0095] As an optional implementation, in response to the command to view the operational effectiveness of an operational activity, the operational effectiveness indicator data of the operational activity is obtained and displayed in a visual form on the display interface.

[0096] Specifically, operations personnel send a command to the server to view operational performance. The server responds to this command by displaying operational performance metrics data according to different stages: potential phase, novice phase, growth phase, maturity phase, decline phase, and dormant phase. The corresponding UI interface illustration for displaying all metrics data is shown below. Figure 2As shown, operators can click the corresponding option boxes for the operational performance indicators on the display interface to obtain the final operational results, and adjust the advertising and promotion strategies and the creation strategies for promotional campaign pages in a timely manner based on the obtained operational results.

[0097] This application's embodiments implement systematic data analysis for each operational activity, conducted according to a unified model. All operational activity analyses are performed under the operational model, allowing operational personnel to directly view the performance metrics they wish to monitor. All data is visualized using a unified model, resulting in more consistent evaluation of operational activity effectiveness. This improves the efficiency with which operational personnel can track the effectiveness of operational activities and promptly adjust their advertising and promotional campaign strategies.

[0098] As an optional implementation, the predefined raw indicator data also includes personalized raw indicator data defined for different operational activities, and the predefined operational performance indicator data also includes personalized operational performance indicator data defined for different operational activities.

[0099] Specifically, when each operational plan's promotional activities are launched on different platforms, at least basic raw indicator data should be collected based on the feedback received. Additionally, individually defined personalized indicator data specific to the characteristics of this operational activity should also be collected. Regardless of whether the business objectives are the same or different, this application can conduct a comprehensive analysis. Basic raw indicator data is necessary for every operational activity, but in addition to basic raw indicator data, personalized indicators are also included, such as personalized raw indicator data and personalized operational performance indicator data. For example, in the user retention phase of Activity A, the focus is on user retention time, while in the user retention phase of Activity B, the focus is on the user retention address. In other words, the detailed indicator data in the retention data differs for each operational activity.

[0100] It is also meaningful to put the definitions of personalized raw indicator data together, as activities of the same type gradually tend to share data, enabling personalized raw indicator data to be reused to a certain extent.

[0101] The process involves parsing the raw indicator data of each operational activity based on predefined operational performance indicator data to obtain the corresponding operational performance indicator data for each activity, including:

[0102] Based on the predefined basic operational performance indicator data and the parsing rules for obtaining basic operational performance indicator data by parsing the original indicator data, the original indicator data of each operational activity is parsed to obtain the basic operational performance indicator data corresponding to each operational activity. The specific parsing rules can be defined according to the specific basic operational performance indicator data, which will not be detailed here.

[0103] Based on predefined personalized operational performance indicator data and the parsing rules for obtaining personalized operational performance indicator data by parsing the original indicator data, the original indicator data of each operational activity is parsed to obtain the personalized operational performance indicator data corresponding to each operational activity. The specific parsing rules can be defined according to the specific personalized operational performance indicator data, which will not be detailed here.

[0104] As an optional implementation, obtaining feedback information under each promotion method includes:

[0105] Obtain user behavior data and user business data from various promotion methods;

[0106] Extract the corresponding primary raw indicator data from user behavior data, parse the primary raw indicator data to obtain the primary operational performance indicator data for each operational activity;

[0107] Extract the corresponding second raw indicator data from the user business data, parse the second raw indicator data, and obtain the second operational performance indicator data corresponding to each operational activity.

[0108] Specifically, user behavior data and user business data from various promotion methods are collected and stored in a distributed publish-subscribe messaging system such as Kafka. User business data from various promotion methods is collected and stored in a relational database such as MySQL. User business data is extracted from MySQL and transferred to Kafka through a distributed log collection, aggregation and transmission system such as Flume.

[0109] Using the distributed storage system HDFS, the first original indicator data is extracted from the user behavior data stored in Kafka based on the original indicator data defined for different operational activities. The first original indicator data of each operational activity is parsed to obtain the first operational performance indicator data corresponding to each operational activity, and stored in the non-relational database Redis.

[0110] Using open-source stream processing frameworks such as Flink, based on the original indicator data defined for different operational activities, the corresponding second original indicator data is extracted from the user business data stored in Kafka. The second original indicator data of each operational activity is parsed to obtain the second operational performance indicator data corresponding to each operational activity, and stored in the non-relational database Redis.

[0111] User business data and user behavior data. User business data includes, for example, the number of users who participated in the operational activities, the business-related data obtained from the business services obtained from participating in the activities, and the amount of revenue generated from participating in the operational activities. User behavior data includes feedback information from users to the server during the participation in operational activities, such as users clicking to participate in this operational activity, users clicking to register, and users clicking to pay.

[0112] Specifically, after defining the indicator data, during the data collection process, see [link to relevant documentation]. Figure 3 For example, some of the user business data resides in the business's MySQL database. Flume is used to extract the user business data from the MySQL database to Kafka, while some user business data goes directly into Kafka. User behavior data is stored directly in Kafka.

[0113] As an optional implementation, the method further includes: responding to a performance comparison and viewing instruction, determining the comparison method selected by the operating account according to the comparison and viewing instruction, wherein different comparison methods use different comparison conditions to filter comparison objects;

[0114] Based on the selected comparison method, filter the target operational performance indicator data of the target operation plan and operation activities that meet the corresponding comparison conditions;

[0115] The target operational activities and their target operational performance indicators are compared, and the comparison results are displayed in a visual form on the display interface.

[0116] It should be noted that the solution provided in this application embodiment can obtain two dimensions of operational performance indicator data. One dimension is the operational performance indicator data corresponding to different promotion methods of the same operational activity. The other dimension is the sum of the same operational performance indicator data obtained after promoting the same operational activity using all promotion methods, thus obtaining the operational performance indicator data corresponding to the operational activity.

[0117] Specifically, comparison methods can be defined from different dimensions, and corresponding comparison conditions can be defined for each comparison method. As an optional implementation, the comparison conditions include any one of the following:

[0118] Operational performance metrics for the same campaign promoted using different methods;

[0119] Performance metrics for different operational activities using the same promotional method;

[0120] The operational performance metrics data for different operational activities with the same business objective can be understood as the summarized operational performance metrics data mentioned above.

[0121] Specifically, users first select a comparison method and then obtain target operational performance metrics data for that comparison method.

[0122] When comparing the performance metrics of the same campaign promoted using different methods—for example, a 20% discount on credit card activation—and placing related advertisements on the Toutiao app and Weibo app respectively, the comparison of the target performance metrics shows that the campaign is more effective on the Toutiao app. This result is then displayed visually to the operations team. Consequently, for similar credit card activation campaigns in the future, the operations team will prioritize placing more advertisements on the Toutiao app.

[0123] When users select different operational activities and promote them using the same method to analyze performance metrics, for example, if there are two operational activities: a 20% discount on credit card activation and a prize for payment, and both promotional ads are placed on the Toutiao app, a comparison of the target operational performance metrics shows that the 20% discount on credit card activation acquires more new users on the Toutiao app. In other words, the 20% discount on credit card activation is more effective than the prize for payment promotion on the Toutiao app platform. Therefore, the operations team will prioritize placing more promotional ads on the Toutiao app for similar credit card activation activities in the future.

[0124] In general, comparison rules can be preset in advance according to the different needs of operators. For example, the comparison of the promotion effect of different operation activities with the same business purpose on the same promotion platform, or the comparison of the promotion effect of different operation activities with different business purposes on the same promotion platform, etc.

[0125] This application focuses on operational analysis of user growth. It creates an activity page for a specific operational campaign, integrates data collected from the business's own channels and data obtained from the promotion open platform, summarizes and analyzes the data, and finally obtains the operational performance indicators of the campaign from the database and displays them to the operations personnel in a visual form on the display interface.

[0126] Based on the same inventive concept, this application also provides an operational analysis device 400 suitable for different operational activities, such as... Figure 4 As shown, the device includes:

[0127] The acquisition module 401 is used to promote different operational activities using corresponding promotion methods based on the lifecycle model and obtain feedback information under each promotion method.

[0128] The raw indicator data extraction module 402 is used to extract the raw indicator data corresponding to each operational activity from the feedback information based on the predefined raw indicator data.

[0129] The raw indicator data parsing module 403 is used to parse the raw indicator data of each operational activity based on the predefined operational performance indicator data, so as to obtain the corresponding operational performance indicator data of each operational activity and complete the operational analysis of the operational activities.

[0130] The predefined raw indicator data includes basic raw indicator data that are uniformly defined for different operational activities;

[0131] The predefined operational performance metrics data include basic operational performance metrics data that are uniformly defined for different operational activities.

[0132] Optionally, the device further includes an operational performance indicator data acquisition module 404, which is used to acquire operational performance indicator data of the operational activity from the database in response to an operational performance viewing command, and display it on the display interface in a visual form.

[0133] Optionally,

[0134] The raw indicator data parsing module 403 is specifically used for:

[0135] Based on the predefined operational performance indicator data and the parsing rules obtained from parsing the original indicator data, the original indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data for each operational activity.

[0136] The predefined raw indicator data also includes personalized raw indicator data defined for different operational activities;

[0137] The predefined operational performance metrics data also include personalized operational performance metrics data defined for different operational activities.

[0138] Optionally, the acquisition module 401 is specifically used to acquire user behavior data and user business data fed back under each promotion method and extract them into the distributed publish-subscribe messaging system;

[0139] By using a distributed storage system to extract the corresponding original indicator data from the user behavior data stored in the distributed publish-subscribe messaging system based on the original indicator data defined for different operational activities, the original indicator data of each operational activity is parsed to obtain the operational performance indicator data corresponding to each operational activity.

[0140] By using an open-source stream processing framework, the corresponding raw indicator data is extracted from the user business data stored in the distributed publish-subscribe messaging system based on the raw indicator data defined for different operational activities. The raw indicator data of each operational activity is then parsed to obtain the corresponding operational performance indicator data for each operational activity.

[0141] Optionally, the device further includes a selection module 405, which is used to respond to a comparison and viewing instruction for operating effect, and determine the comparison method selected by the operating account according to the comparison and viewing instruction, wherein different comparison methods use different comparison conditions to filter comparison objects;

[0142] Based on the selected comparison method, filter the target operational performance indicator data of the target operation plan and operation activities that meet the corresponding comparison conditions;

[0143] The target operation plan and the target operation effect index data of the operation activities are compared, and the comparison results are displayed in a visual form on the display interface.

[0144] Optionally, the comparison conditions include any of the following:

[0145] Operational performance metrics for the same campaign promoted using different methods;

[0146] Performance metrics for different operational activities using the same promotional method;

[0147] Performance metrics data for different operational activities with the same business objective.

[0148] Optionally, for different operational activities, the operational performance metrics data can be defined in the following ways:

[0149] Based on the user lifecycle stage defined by the lifecycle model, and the types of operational strategies and feedback information executed for users at different lifecycle stages, the original indicator data and operational performance indicator data for each lifecycle stage are defined.

[0150] Optionally, the raw indicator data and operational performance indicator data for each lifecycle stage may include at least one of the following:

[0151] Data related to retention metrics at all stages of the product lifecycle;

[0152] Data related to activation metrics during the beginner phase of the product lifecycle;

[0153] Data related to the monetization metrics at the maturity and decline stages of the product lifecycle;

[0154] Data related to the self-propagation metrics and effects at the novice, growth, and maturity stages of the product lifecycle;

[0155] Data related to the effectiveness of acquisition metrics during the potential phase of the lifecycle.

[0156] Optionally, the basic raw indicator data defined for different operational activities includes at least one of the following:

[0157] The feedback information is provided to the recipient of the feedback; the time of the feedback; relevant indicators for business access channels, traffic distribution versions, and business occurrences; the business results of the feedback; and the location of the recipient of the feedback.

[0158] Having described the methods and apparatus for operational analysis applicable to different operational activities according to exemplary embodiments of this application, we will now describe an electronic device according to another exemplary embodiment of this application.

[0159] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0160] In some possible implementations, the electronic device according to this application may include at least one processor and at least one memory. The memory stores program code that, when executed by the processor, causes the processor to perform the steps described above in the operational analysis applicable to different operational activities according to various exemplary embodiments of this application.

[0161] The following reference Figure 5 To describe the electronic device 130 according to this embodiment of the present application, namely the above-described operation analysis device applicable to different operational activities. Figure 5 The electronic device 130 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0162] like Figure 5As shown, the electronic device 130 is presented in the form of a general-purpose electronic device. The components of the electronic device 130 may include, but are not limited to: at least one processor 131, at least one memory 132, and a bus 133 connecting different system components (including memory 132 and processor 131).

[0163] Bus 133 represents one or more of several bus structures, including a memory bus or memory controller, peripheral bus, processor, or local bus using any of the various bus structures.

[0164] The memory 132 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 1321 and / or cache memory 1322, and may further include read-only memory (ROM) 1323.

[0165] The memory 132 may also include a program / utility 1325 having a set (at least one) of program modules 1324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0166] Electronic device 130 can also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), and with one or more devices that enable a user to interact with electronic device 130, and / or with any device that enables electronic device 130 to communicate with one or more other electronic devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 135. Furthermore, electronic device 130 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 136. As shown, network adapter 136 communicates with other modules used in electronic device 130 via bus 133. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 130, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0167] In some possible implementations, the various aspects of operational analysis applicable to different operational activities provided in this application can also be implemented in the form of a program product, including computer program instructions that are executed by a processor to perform the steps of operational analysis applicable to different operational activities according to the various exemplary embodiments of this application described above.

[0168] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0169] The monitoring program product of the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on an electronic device. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0170] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0171] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0172] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's electronic device, partially on the user's device, as a standalone software package, partially on the user's electronic device and partially on a remote electronic device, or entirely on a remote electronic device or server. In cases involving remote electronic devices, the remote electronic device can be connected to the user's electronic device via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external electronic device (e.g., via the Internet using an Internet service provider).

[0173] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0174] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0175] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This application is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block and / or block in the flowchart illustrations and block diagrams, as well as combinations of blocks and processes in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0177] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0179] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0180] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An operational analysis method applicable to different operational activities, characterized in that, The method includes: Based on the lifecycle model, different operational activities are promoted using corresponding promotion methods. User behavior data and user business data from each promotion method are collected and stored in a distributed publish-subscribe messaging system. User business data from each promotion method is also collected and stored in a relational database. User business data is extracted from the relational database and transferred to the distributed publish-subscribe messaging system through a distributed log collection, aggregation, and transmission system. Feedback information from each promotion method is obtained from the distributed publish-subscribe messaging system. The feedback information includes actions taken by users who intend to participate in operational activities, feedback obtained after users become users of the business system by interacting with them using corresponding operational strategies based on different lifecycle stages according to the lifecycle model, and feedback information obtained after users refuse to participate in operational activities due to a lack of intention to participate. Based on predefined raw indicator data, extract raw indicator data corresponding to each operational activity from the feedback information; wherein: the predefined raw indicator data includes basic raw indicator data uniformly defined for different operational activities; the predefined operational performance indicator data includes basic operational performance indicator data uniformly defined for different operational activities; Based on predefined operational performance indicator data, the original indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data for each operational activity, thereby completing the operational analysis of the operational activities. The target operational performance metrics data for each comparison method selected by the user are obtained. For each comparison method, corresponding comparison conditions are defined. These conditions include any one of the following: operational performance metrics data for different operational activities using the same promotion method; comparison of promotional performance for different operational activities with the same business objective on the same promotion platform; comparison of promotional performance for different business objectives and different operational activities on the same promotion platform. The original indicator data and operational performance indicator data for each lifecycle stage should include at least one of the following: Retention metrics performance data at all stages of the lifecycle; retention metrics performance data, including data related to participating users, includes the number of potential participants and the addresses of participating users; Data related to activation metrics effectiveness during the novice phase of the user lifecycle; Activation metrics effectiveness data, defined data related to activated users, includes the number of potentially activated users and the activation time of activated users; Data related to the effectiveness of monetization metrics during the maturity and decline phases of the product lifecycle; data related to the effectiveness of monetization metrics, including the amount of money monetized by each user and the monetization method used by each user; Data related to the effectiveness of self-propagation metrics at the novice, growth, and maturity stages of the product lifecycle; data related to the effectiveness of self-propagation metrics, including defined user-related data such as the user's propagation channels and the propagation time. Acquisition metrics related to the potential phase of the user lifecycle include: acquisition metrics related to user acquisition, which includes the acquisition time, acquisition address, and acquisition method of new users.

2. The method according to claim 1, characterized in that, The method further includes: In response to the command to view the operational effectiveness of an operational activity, the system retrieves operational effectiveness metrics data and displays them in a visual format on the interface.

3. The method according to claim 1, characterized in that, The process involves parsing the raw indicator data of each operational activity based on predefined operational performance indicator data to obtain the corresponding operational performance indicator data for each activity, including: Based on the predefined operational performance indicator data and the parsing rules obtained from parsing the original indicator data, the original indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data for each operational activity. The predefined raw indicator data also includes personalized raw indicator data defined for different operational activities; The predefined operational performance metrics data also include personalized operational performance metrics data defined for different operational activities.

4. The method according to any one of claims 1-3, characterized in that, The process of obtaining feedback information under each promotion method includes: Obtain user behavior data and user business data from various promotion methods; Extract the corresponding primary raw indicator data from user behavior data, parse the primary raw indicator data to obtain the primary operational performance indicator data for each operational activity; Extract the corresponding second raw indicator data from the user business data, parse the second raw indicator data, and obtain the second operational performance indicator data corresponding to each operational activity.

5. The method according to any one of claims 1-3, characterized in that, Also includes: In response to the operation effect comparison viewing command, the comparison method selected by the operation account is determined according to the comparison viewing command, wherein different comparison methods use different comparison conditions to filter comparison objects; Based on the selected comparison method, filter the target operational performance indicator data of the target operation plan and operation activities that meet the corresponding comparison conditions; The target operation plan and the target operation effect index data of the operation activities are compared, and the comparison results are displayed in a visual form on the display interface.

6. The method according to claim 1, characterized in that, The basic raw indicator data defined for different operational activities includes at least one of the following: The feedback information is provided to the recipient of the feedback; the time of the feedback; relevant indicators for business access channels, traffic distribution versions, and business occurrences; the business results of the feedback; and the location of the recipient of the feedback.

7. An operational analysis device suitable for different operational activities, characterized in that, The device includes: The acquisition module is used to promote different operational activities using corresponding promotion methods based on a lifecycle model. It acquires user behavior data and user business data from each promotion method and stores them in a distributed publish-subscribe messaging system. It also acquires user business data from each promotion method and stores it in a relational database. Through a distributed log collection and aggregation transmission system, it extracts user business data from the relational database into the distributed publish-subscribe messaging system. It then acquires feedback information from the distributed publish-subscribe messaging system for each promotion method. This feedback information includes actions taken by users indicating their intention to participate in operational activities, feedback obtained after users become users of the business system and the corresponding operational strategies are applied based on the user's different lifecycle stages according to the lifecycle model, and feedback information obtained after users refuse to participate in operational activities. The raw indicator data extraction module is used to extract raw indicator data corresponding to each operational activity from the feedback information based on predefined raw indicator data; wherein: the predefined raw indicator data includes basic raw indicator data uniformly defined for different operational activities; the predefined operational performance indicator data includes basic operational performance indicator data uniformly defined for different operational activities; The raw indicator data parsing module is used to parse the raw indicator data of each operational activity based on predefined operational performance indicator data, thereby obtaining the corresponding operational performance indicator data for each operational activity to complete the operational analysis of the operational activities; it also obtains the target operational performance indicator data for the comparison method selected by the user; wherein, corresponding comparison conditions are defined for each comparison method; the comparison conditions include any one of the following: operational performance indicator data of different operational activities promoted using the same promotion method; comparison of the promotion effect of different operational activities with the same business purpose on the same promotion platform; comparison of the promotion effect of different operational activities with different business purposes on the same promotion platform; The original indicator data and operational performance indicator data for each lifecycle stage should include at least one of the following: Retention metrics performance data at all stages of the lifecycle; retention metrics performance data, including data related to participating users, includes the number of potential participants and the addresses of participating users; Data related to activation metrics effectiveness during the novice phase of the user lifecycle; Activation metrics effectiveness data, defined data related to activated users, includes the number of potentially activated users and the activation time of activated users; Data related to the effectiveness of monetization metrics during the maturity and decline phases of the product lifecycle; data related to the effectiveness of monetization metrics, including the amount of money monetized by each user and the monetization method used by each user; Data related to the effectiveness of self-propagation metrics at the novice, growth, and maturity stages of the product lifecycle; data related to the effectiveness of self-propagation metrics, including defined user-related data such as the user's propagation channels and the propagation time. Acquisition metrics related to the potential phase of the user lifecycle include: acquisition metrics related to user acquisition, which includes the acquisition time, acquisition address, and acquisition method of new users.

8. An electronic device, characterized in that, The method includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-6.

9. A computer program product comprising computer program instructions, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-6.

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