Mobile terminal application program exception attribution method and device based on user experience measurement

Abstract

The embodiment of the invention provides a mobile terminal application program exception attribution method and device based on user experience measurement. The method comprises the steps that full-link data of a mobile terminal application program are collected in real time; according to the full-link data and the user experience measurement system, calculating a user experience measurement comprehensive evaluation result; whether the difference value between the comprehensive evaluation result and the comprehensive reference value is larger than or equal to a preset measurement threshold value is judged; if yes, performing streaming computation on the in-event stage data and the post-event stage data according to a user experience measurement system, and obtaining real-time evaluation results of the application program in an evaluation feedback dimension, a function definition dimension, a participation dimension and a performance and security dimension; and performing cross aggregation analysis on the real-time evaluation result and the data in the prior stage, and determining an abnormal reason of the application program of the mobile terminal. According to the method, real-time acquisition of full-link data can be realized, and based on the comprehensive evaluation result and the real-time evaluation result of each dimension, the abnormality reason is automatically positioned by backward deduction layer by layer, so that efficient abnormality solving is promoted.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of computer software development technology, and in particular to a method and device for attributing abnormalities of mobile application programs based on user experience metrics. Background Art

[0002] In recent years, with the continuous development of mobile APPs, the user experience of mobile APPs has become increasingly important in terms of customer acquisition, customer activation, customer retention, and sales promotion. Designing and developing a mobile product with excellent user experience is inseparable from accurate, comprehensive, and timely measurement and feedback. There are currently a variety of user experience measurement methods on the market. The usual solution is 1. Collect some user data, such as behavioral data and network data, 2. Obtain experience indicators of different dimensions through offline analysis and calculation, and 3. Perform weighted summation of indicators to obtain an overall measurement of user experience. Some solutions also combine user portrait expressions, questionnaire feedback and other data for comprehensive analysis.

[0003] However, the existing methods for measuring the user experience of mobile apps have the following defects: 1. The dimensions are not comprehensive enough, for example, they only involve subjective feedback or behavior analysis of users, but lack comprehensive evaluation of the loading performance, information security, user stickiness, etc. of the app; 2. Lack of real-time performance. The existing methods all use post-event offline analysis, lack real-time measurement monitoring of user experience, and the data feedback is not timely enough to meet the requirements of efficient operation; 3. The applicability is relatively narrow. Some solutions need to be combined with specific business scenarios, such as power scenarios, to measure user experience. Due to the many problems mentioned above in the existing user experience measurement of mobile apps, it is difficult to find the problems of mobile apps in a targeted manner and solve them effectively.

[0004] In view of this, the embodiments of this specification aim to provide a method, apparatus and computer device for attributing abnormalities of mobile application programs based on user experience metrics. Summary of the invention

[0005] In view of the above-mentioned problems in the prior art, the purpose of the embodiments of this specification is to provide a method and device for attributing abnormalities of mobile applications based on user experience measurement, so as to solve the problem that the user experience measurement in the prior art is not comprehensive in dimensions and has real-time overlap, making it difficult to discover abnormalities in mobile applications and solve them in a targeted manner.

[0006] In order to solve the above technical problems, the specific technical solutions of the embodiments of this specification are as follows:

[0007] In a first aspect, an embodiment of the present specification provides a method for attributing abnormalities of a mobile application based on user experience metrics, including:

[0008] Collecting the full-link data of the mobile application in real time, wherein the full-link data includes pre-stage data, in-stage data, and post-stage data;

[0009] Calculate the comprehensive evaluation result of the user experience metric of the mobile application based on the full-link data and the pre-designed user experience metric system;

[0010] Determine whether the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold;

[0011] If so, stream computing is performed on the in-process data and the post-process data according to the user experience measurement system to obtain real-time evaluation results of the mobile application in the evaluation feedback dimension, function clarity dimension, engagement dimension, and performance and security dimension respectively;

[0012] The real-time comparison result is cross-aggregated and analyzed with the prior stage data to determine the cause of the abnormality of the mobile application.

[0013] Specifically, the post-stage data includes evaluation feedback data, and obtaining the real-time evaluation results of the mobile application in the evaluation feedback dimension includes:

[0014] Using one or more combinations of questionnaires, application market evaluation components, application embedded evaluation modules, and customer complaint information, real-time collection of the evaluation feedback data of users on the mobile application during the monitoring period;

[0015] Performing text analysis on the evaluation feedback data to extract positive evaluation keywords and negative evaluation keywords;

[0016] According to the occurrence frequencies of the positive evaluation keywords and the negative evaluation keywords in the monitoring period, the real-time evaluation results of the mobile application in the evaluation feedback dimension are obtained.

[0017] Furthermore, the in-process data includes operation behavior data, and obtaining the real-time evaluation result of the mobile application in the dimension of functional clarity includes:

[0018] Collecting user operation behavior data on the mobile application in real time, wherein the operation-related data includes at least one or more combinations of penetration rate, bounce rate, operation duration, user click distribution of core pages, and thermal analysis of core pages;

[0019] According to the operation behavior data, a real-time evaluation result of the mobile application in the functional clarity dimension is obtained.

[0020] Furthermore, the in-process data also includes engagement data, and obtaining the real-time evaluation results of the mobile application in the engagement dimension includes:

[0021] Collecting user engagement data on the mobile application in real time, wherein the engagement data includes at least one or more combinations of retention rate, user stay time on core pages, session stay time, business process click-through rate, and actual conversion rate;

[0022] According to the engagement data, a real-time evaluation result of the mobile application in the functional clarity dimension is obtained.

[0023] Furthermore, the in-process data also includes performance and security data, and obtaining the real-time evaluation results of the mobile application in the performance and security dimensions includes:

[0024] Collecting performance and security data of the mobile application in real time, wherein the performance and security data at least includes one or more combinations of the startup time of the mobile application, the loading time of the core page, the CPU usage, the transaction time, the application exception data, the page exception data, and the transaction exception data;

[0025] Based on the performance and security data, obtain real-time evaluation results of the mobile application in the performance and security dimensions.

[0026] Specifically, the pre-stage data at least includes one or more combinations of user base customer group information, mobile device information, application version information, and network status information. The real-time comparison result is cross-aggregated and analyzed with the pre-stage data to determine the abnormal cause of the mobile application, including:

[0027] Determining a target business scenario according to the real-time evaluation result of the evaluation feedback dimension and a first reference value corresponding thereto;

[0028] Determining a target page according to the real-time evaluation result of the functional clarity dimension and a second reference value corresponding thereto;

[0029] Determining a target node according to the real-time evaluation result of the participation dimension and a third reference value corresponding thereto;

[0030] Determining a target checkpoint according to the real-time evaluation results of the performance and safety dimensions and a fourth reference value corresponding thereto;

[0031] One or more of the target business scenario, target page, target node, and target checkpoint are cross-aggregated and analyzed with the prior stage data to determine the cause of the abnormality.

[0032] In a second aspect, an embodiment of the present specification provides a mobile terminal application abnormality attribution device based on user experience measurement, including:

[0033] A real-time collection module, used to collect the full-link data of the mobile application in real time, wherein the full-link data includes pre-stage data, in-stage data and post-stage data;

[0034] A calculation module, used to calculate a comprehensive evaluation result of the user experience metric of the mobile application according to the full-link data and a pre-designed user experience metric system;

[0035] A judgment module, used to judge whether the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold;

[0036] An acquisition module, configured to perform streaming calculation on the in-process data and the post-process data according to the user experience measurement system when the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold, and obtain the real-time evaluation results of the mobile application in the evaluation feedback dimension, the function clarity dimension, the participation dimension, and the performance and security dimension respectively;

[0037] The attribution module is used to perform cross-aggregation analysis on the real-time evaluation results and the pre-stage data to determine the abnormal cause of the mobile application.

[0038] In a third aspect, an embodiment of the present specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided by the above technical solution when executing the computer program.

[0039] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method provided by the above technical solution is implemented.

[0040] In a fifth aspect, an embodiment of the present specification provides a computer program product, comprising at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the method provided by the above technical solution.

[0041] By adopting the above technical scheme, the embodiment of this specification provides a method and device for attributing abnormalities of mobile applications based on user experience measurement. Through automated collection technology, real-time collection of full-link data is realized, and the overall user experience measurement comprehensive evaluation results of the mobile application are obtained in real time in combination with the user experience measurement system. When the comprehensive evaluation results of the user experience measurement fluctuate, the real-time evaluation results of the mobile application on the evaluation feedback dimension, functional clarity dimension, participation dimension, and performance and security dimension can be further calculated; and cross-aggregation analysis is performed in combination with the pre-stage data of the same period, and the cause of the abnormality is automatically located layer by layer, so as to promote the efficient resolution of the abnormality.

[0042] In order to make the above and other purposes, features and advantages of the embodiments of this specification more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of the steps of a method for attributing abnormalities of a mobile application based on user experience measurement provided by an embodiment of this specification is shown;

[0045] Figure 2 A schematic diagram showing the steps of obtaining the evaluation results of the mobile application in the evaluation feedback dimension in the embodiment of this specification;

[0046] Figure 3 A schematic diagram of the steps of determining the cause of abnormality of a mobile application according to the pre-stage data and the comparison result in an embodiment of this specification is shown;

[0047] Figure 4 A schematic diagram of the structure of a mobile application abnormality attribution device based on user experience measurement provided by an embodiment of this specification is shown;

[0048] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this specification is shown.

[0049] Description of the accompanying symbols:

[0050] 401, real-time acquisition module;

[0051] 402. Calculation module;

[0052] 403, judgment module;

[0053] 404, Get module;

[0054] 405. Attribution module;

[0055] 502. Computer equipment;

[0056] 504, processor;

[0057] 506. Memory;

[0058] 508, driving mechanism;

[0059] 510, input / output module;

[0060] 512. Input devices;

[0061] 514. Output device;

[0062] 516. Presentation equipment;

[0063] 518. Graphical user interface;

[0064] 520, network interface;

[0065] 522, communication link;

[0066] 524. Communication bus. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0068] It should be noted that the terms "first", "second", etc. in this specification and claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0069] In order to solve the above problems, the embodiments of this specification provide a method, apparatus and computer device for attributing abnormalities of mobile applications based on user experience metrics. While providing a more comprehensive user experience metric for mobile applications, it can locate, analyze and solve abnormalities existing in mobile applications based on the user experience metric. Figure 1 This is a schematic diagram of the steps of a method for attributing abnormalities in a mobile application based on user experience metrics provided in an embodiment of this specification. This specification provides method operation steps as described in the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or device product is executed, it can be executed in the order or in parallel according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 1 As shown, the method may include:

[0070] S101: Collecting the full-link data of the mobile application in real time, wherein the full-link data includes pre-stage data, in-stage data, and post-stage data.

[0071] Specifically, the pre-stage data includes at least one or more combinations of user basic customer group information (including user age group, user gender, location, etc.), mobile device information, application version information, and network status information;

[0072] The data during the process at least includes operational behavior data, participation data, performance and safety data;

[0073] The post-stage data at least includes evaluation feedback data.

[0074] S102: Calculate a comprehensive evaluation result of the user experience metric of the mobile application according to the full-link data and a pre-designed user experience metric system.

[0075] S103: Determine whether the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold.

[0076] It should be noted that the comprehensive reference value used for comparison with the comprehensive evaluation result in the embodiment of this specification may be the comprehensive evaluation result of the user experience metric calculated by the mobile application in the previous monitoring cycle, that is, the comprehensive evaluation result of the user experience metric obtained in the previous monitoring cycle may be compared in a circular manner with the comprehensive evaluation result of the user experience metric in the current monitoring cycle to reflect the continuous changes in the mobile application.

[0077] S104: If yes, then streaming calculation is performed on the in-process data and the post-process data according to the user experience measurement system to obtain real-time evaluation results of the mobile application in the evaluation feedback dimension, function clarity dimension, participation dimension, and performance and security dimension.

[0078] That is, the user experience measurement system pre-designed in the embodiments of this specification includes four dimensions: evaluation feedback dimension, function clarity dimension, participation dimension, and performance and security dimension. By combining the evaluation results of these four dimensions and the weights between these four dimensions, a comprehensive evaluation result of the mobile application can be obtained. If the comprehensive evaluation result of the user experience measurement in the current monitoring cycle fluctuates significantly compared with the comprehensive evaluation result of the user experience measurement obtained in the previous monitoring cycle, the real-time evaluation results of the mobile application in the evaluation feedback dimension, function clarity dimension, participation dimension, and performance and security dimension are further obtained, and the possible reasons for the existence of the mobile application are determined by a step-by-step downward analysis method. Among them:

[0079] The evaluation feedback dimension is used to collect user evaluation feedback on the application, and then obtain indicators such as the rate of positive reviews, the rate of negative reviews, and satisfaction.

[0080] The functional clarity dimension is used to assess whether the application's functions are clear, easy to understand, and easy to use;

[0081] The engagement dimension is used to evaluate the user’s engagement with the application based on indicators such as user activity, retention rate, and usage time;

[0082] The performance and security dimensions can provide feedback on performance indicators such as the application's response time, system processing power, throughput, number of concurrent users, and whether the application has security vulnerabilities, viruses, Trojans, data leaks, and other security issues.

[0083] S105: Perform cross-aggregation analysis on the real-time evaluation results and the pre-stage data to determine the abnormal cause of the mobile application.

[0084] That is, through cross-aggregation analysis of real-time evaluation results in the evaluation feedback dimension, functional clarity dimension, participation dimension, performance and safety dimension and pre-stage data, we can automatically and quickly discover deep indicator fluctuations in subtle categories and gain deep insights into problems.

[0085] The embodiments of this specification provide a method for attributing abnormalities of mobile applications based on user experience metrics. The method realizes real-time collection of full-link data through automated collection technology, and obtains the overall user experience metric comprehensive evaluation results of the mobile application in real time in combination with the user experience metric system. When the comprehensive evaluation results of the user experience metric fluctuate, the real-time evaluation results of the mobile application on the next level of evaluation feedback dimension, function clarity dimension, participation dimension, and performance and security dimension can be calculated. The method also performs cross-aggregation analysis in combination with the prior stage data of the same period, and automatically locates the cause of the abnormality step by step, thereby promoting efficient resolution of the abnormality.

[0086] Specifically, Figure 2 As shown, in the embodiment of this specification, in step S102, obtaining the real-time evaluation result of the mobile application in the evaluation feedback dimension further includes:

[0087] S201: Using a combination of one or more of a questionnaire, an application market evaluation component, an application embedded evaluation module, and customer complaint information, the evaluation feedback data of users on the mobile application during a monitoring period is collected in real time.

[0088] Among them, the application market is the place where mobile terminals publish and download applications, and information such as ratings and reviews about the application is collected from the application market; the application embedded evaluation module is a module with interactive functions in the application itself, from which evaluation information posted by users is collected; customer complaint information is the user's evaluation of the application obtained from other channels, such as the Internet channel.

[0089] S202: Perform text analysis on the evaluation feedback data to extract positive evaluation keywords and negative evaluation keywords.

[0090] In some feasible embodiments, sentiment analysis tools such as VADER, TextBlob, etc. can be used to determine the sentiment polarity (positive, negative or neutral) of each evaluation data feedback, and then based on the sentiment polarity, extract positive evaluation keywords and negative evaluation keywords in the evaluation feedback data.

[0091] S203: Acquire a real-time evaluation result of the mobile application in the evaluation feedback dimension according to the occurrence frequencies of the positive evaluation keyword and the negative evaluation keyword in the monitoring period.

[0092] Through Figure 2 The method steps shown can quickly and comprehensively obtain user evaluation feedback on the application, which is conducive to analyzing the current drawbacks of the application from the evaluation feedback data in real time, and then formulating targeted solutions.

[0093] Furthermore, in step S102, obtaining the real-time evaluation result of the mobile application in the dimension of functional clarity includes:

[0094] Collecting user operation behavior data on the mobile application in real time, wherein the operation-related data includes at least one or more combinations of penetration rate, bounce rate, operation duration, user click distribution of core pages, and thermal analysis of core pages;

[0095] According to the operation behavior data, a real-time evaluation result of the mobile application in the functional clarity dimension is obtained.

[0096] Among them, penetration rate refers to the probability that a user will continue to click to browse the next page after browsing a certain page of the application; bounce rate is the opposite of penetration rate, that is, the probability that a user exits after browsing a certain page.

[0097] The operation duration can be the total time spent by the user in performing business processing, for example, the total time spent by the user in purchasing a product in the application from selection to settlement; it can also include the time spent in each link of the business processing.

[0098] The core pages of an application generally include the homepage / startup page, login / registration page, search page, detailed content page (such as the full text of an article, a detailed introduction to a product, details of an event, etc.), settings page, account page, payment / settlement page, and help / support page.

[0099] The user click distribution of the core page refers to the distribution of clicks on interactive controls (including time windows, input boxes, buttons, drop-down menus, etc.) in different areas of the page when users visit and browse the core page.

[0100] Core page thermal analysis uses an intuitive display method, such as color depth, to indicate the number of clicks in different areas on the core page.

[0101] In some feasible embodiments, a real-time evaluation result of the mobile application in the dimension of functional clarity may be obtained based on the above-mentioned various operation behavior data and the weights between the various operation behavior data.

[0102] Based on the collection of the above operation behavior data, it is possible to accurately and comprehensively analyze whether the functions of the mobile application are clear, thereby providing guidance for the subsequent quick and accurate analysis of possible problems in the application.

[0103] Furthermore, in step S102, obtaining the real-time evaluation result of the mobile application in the engagement dimension may include:

[0104] Collecting user engagement data on the mobile application in real time, wherein the engagement data includes at least one or more combinations of retention rate, user stay time on core pages, session stay time, business process click-through rate, and actual conversion rate;

[0105] According to the engagement data, a real-time evaluation result of the mobile application in the functional clarity dimension is obtained.

[0106] Among them, the retention rate is the proportion of users who visit or use the application again after a period of time (for example, one week, one month) after installing or using the application, such as weekly retention rate and monthly retention rate.

[0107] The length of time spent on a core page refers to the time a user spends on the core page during a browsing behavior.

[0108] Session duration is the total time a user spends during a visit to the application.

[0109] The business process click-through rate refers to the proportion of users clicking on a business process-related link or button in an application.

[0110] The actual conversion rate refers to the proportion of users who complete a certain target behavior (such as purchasing goods, registering an account, etc.) in an application.

[0111] In some feasible embodiments, the evaluation result of the mobile application in the engagement dimension can be obtained according to the above-mentioned various engagement data and the weights between the various engagement data.

[0112] The above engagement data can be used to accurately analyze the user's participation in the application, which is conducive to the subsequent timely adjustment of product direction to better meet user needs, thereby improving product quality and user satisfaction.

[0113] Furthermore, in the embodiment of the present specification, in step S102, obtaining the real-time evaluation result of the mobile application in the performance and security dimensions may include:

[0114] Collecting performance and security data of the mobile application in real time, wherein the performance and security data at least includes one or more combinations of the startup time of the mobile application, the loading time of the core page, the CPU usage, the transaction time, the application exception data, the page exception data, and the transaction exception data;

[0115] Based on the performance and security data, obtain real-time evaluation results of the mobile application in the performance and security dimensions.

[0116] The startup time of an application is the time it takes from the user clicking the application icon to the application being fully loaded and displaying the main interface.

[0117] The loading time of a core page refers to the time it takes from the time a user requests to load a core page to the time when the page is fully rendered and available for user interaction.

[0118] CPU usage refers to the proportion of CPU resources occupied by an application during its operation.

[0119] Transaction duration is usually the time it takes for a transaction to start and end in e-commerce and financial software applications, including multiple steps such as user submitting transaction request, system processing request, transaction confirmation and settlement.

[0120] Application exception data refers to exception or error data generated during the operation of the application; page exception data refers to exception or error data generated in the web page or application interface; transaction exception data refers to exception or error data generated during the transaction process.

[0121] Furthermore, if Figure 3 As shown, in the embodiment of this specification, step S105: performing cross-aggregation analysis on the real-time comparison result and the pre-stage data to determine the abnormal cause of the mobile application may include:

[0122] S301: Determine a target business scenario according to the real-time evaluation result of the evaluation feedback dimension and a first reference value corresponding thereto.

[0123] After obtaining the real-time evaluation result of the mobile application in the evaluation feedback dimension according to the positive evaluation keywords and negative evaluation keywords in the evaluation feedback data, the real-time evaluation result of the evaluation feedback dimension can be compared with the corresponding first reference value. Among them, the first reference value can be the real-time evaluation result of the evaluation feedback dimension calculated by the mobile application based on the collected evaluation feedback data during the previous monitoring cycle. If the difference between the real-time evaluation result of the evaluation feedback dimension and the first reference value is greater than or equal to the preset first threshold, the target business scenario can be determined based on the negative evaluation keywords in the evaluation feedback data.

[0124] Exemplarily, if the user's evaluation feedback data is such as "the transfer is too slow", "the verification always fails", "the face recognition function does not work", etc., then the negative evaluation keywords "slow", "failure" and "cannot be used" can be extracted therefrom. When the difference between the evaluation result of the evaluation feedback dimension and the corresponding first reference value is greater than or equal to the first threshold value, the associated business keywords are further determined based on the negative keywords extracted above. For example, in the above example, the business keyword associated with the negative keyword "slow" is "transfer", so it can be determined that the target business scenario is the transfer business. The business keyword associated with the negative keyword "failure" is "verification", then the target business scenario may be a payment settlement scenario (such as face recognition used to verify the user's identity to quickly complete the transfer), or it may be a scenario where a verification code is received for verification when registering an account or changing the account password. The business keyword associated with the negative keyword "cannot be used" is "face recognition", and the corresponding business scenario is then determined.

[0125] It should be noted that there may be one or more target business scenarios determined based on negative evaluation keywords. When there are multiple target business scenarios, the evaluation results of other dimensions may be further combined to narrow the scope of possible causes of the application's abnormality.

[0126] S302: Determine a target page according to the real-time evaluation result of the functional clarity dimension and a second reference value corresponding thereto.

[0127] Similarly, after obtaining the real-time evaluation result of the mobile application in the functional clarity dimension during the current monitoring period based on the operational behavior data, the real-time evaluation result of the functional clarity dimension in the current monitoring period can be compared with the corresponding second reference value. Among them, the second reference value can be the real-time evaluation result of the functional clarity dimension calculated by the mobile application based on the collected operational behavior data in the previous monitoring period, that is, the real-time evaluation result of the functional clarity dimension of the mobile application is compared. If the difference between the current real-time evaluation result in the functional clarity dimension and the corresponding second reference value is greater than or equal to the preset second threshold, the target page can be determined based on the operational behavior data obtained in real time.

[0128] Exemplarily, taking the transfer business scenario as an example, the various operation behavior data of each core page involved in the transfer business scenario in the current monitoring cycle are obtained. Then, the various operation behavior data of each core page in the current monitoring cycle are compared one by one with the same type of operation behavior data of the core page in the previous monitoring cycle. If there is an abnormal fluctuation, the core page with abnormal fluctuation operation behavior data is determined as the target page. For example, in the payment page in the transfer transaction scenario, if there is an abnormal fluctuation in one or more of its bounce rate, operation time, user click distribution, and thermal analysis, the payment page is determined to be the target page of the application.

[0129] Similar to the target business scenario, there may be one or more target pages.

[0130] S303: Determine a target node according to the real-time evaluation result of the participation dimension and a corresponding third reference value.

[0131] Similar to the method for determining the target page, in the embodiment of this specification, the real-time evaluation result of the engagement dimension is compared with the third reference value corresponding to the threshold, wherein the third reference value may be the real-time evaluation result of the mobile application in the functional clarity dimension calculated based on the engagement data in the previous monitoring cycle. If the difference between the real-time evaluation result of the functional clarity dimension in the current monitoring cycle and the corresponding third reference value is greater than or equal to the preset third threshold, the target node can be determined based on the engagement data collected in real time.

[0132] Exemplarily, if a certain participation data in the current monitoring period has abnormal fluctuations compared with the same type of participation data in the previous monitoring period, the link corresponding to the participation data with the abnormal fluctuations is determined as the target node.

[0133] S304: Determine a target checkpoint according to the real-time evaluation results of the performance and safety dimensions and a fourth reference value corresponding thereto.

[0134] Similarly, in the embodiments of this specification, the real-time evaluation results of the mobile application in the performance and security dimension during the current monitoring period are also compared with the corresponding fourth reference value, wherein the fourth reference value can be the real-time evaluation results of the mobile application in the performance and security dimension calculated based on the collected performance and security data during the previous monitoring period, thereby achieving a comparison of the real-time evaluation results of the performance and security dimension. If the difference between the real-time evaluation results of the mobile application in the performance and security dimension during the current monitoring period and the fourth reference value is greater than or equal to the preset fourth threshold, the target checkpoint can be determined based on the real-time collected performance and security data.

[0135] S305: Perform cross-aggregation analysis on one or more of the target business scenario, target page, target node, and target checkpoint with the prior stage data to determine the cause of the abnormality.

[0136] For example, if the business scenario is determined to be a transfer business based on negative keywords; if it is determined that there are abnormal fluctuations on the payment settlement page based on the user click distribution and thermal analysis in the operation behavior data, the target page is determined to be the payment settlement page; if it is determined that the user stays on the payment settlement page for too long based on the participation data, the target node is determined to be the payment link, etc., it can be analyzed that the user may have doubts about the payment settlement page of the transfer business scenario, for example, the user cannot find or spends a long time looking for the payment control on the page, or a clause description document that requires the user to spend time reading is inserted here. At this time, combined with the data of the prior stage, it can be analyzed whether the abnormality is concentrated in a certain type of user group. For example, whether it is concentrated in the user group with an older age, this group is unable to operate the smart mobile terminal skillfully due to their older age, resulting in a stuck point on the payment settlement page; or whether it is concentrated in the user group holding a certain brand or model of mobile devices, this group has compatibility issues between the brand or model and the application, which makes the layout of the controls on the payment settlement page bring doubts to the user in the transfer business scenario, etc. Combining the above factors, it is possible to quickly locate the abnormal problems of the application, and then facilitate the formulation of solutions.

[0137] In the embodiment of the present specification, the comprehensive evaluation result of the user experience metric of the mobile terminal application in step S102 can be calculated based on the full-link data collected in real time and the pre-designed user experience metric system. Specifically, based on the operation behavior data, participation data, performance and safety data included in the data of the in-process stage and the evaluation feedback data included in the data of the post-process stage, the evaluation results of the mobile terminal application in the four dimensions of evaluation feedback dimension, function clarity dimension, participation dimension and performance and safety dimension can be obtained, and then the comprehensive evaluation result of the user experience metric can be obtained by combining the proportions of the four dimensions of evaluation feedback dimension, function clarity dimension, participation dimension and performance and safety dimension. It should be noted that the proportions of the four dimensions of evaluation feedback dimension, function clarity dimension, participation dimension and performance and safety dimension can be adjusted according to the current optimization and upgrade goals of the application. For example, if the optimization goal of the current application is to acquire customers, the proportion of the participation dimension can be adaptively increased; if the optimization goal of the current application is to respond faster and have better performance, the proportion of the performance and safety dimension can be adaptively increased; that is, the proportions between the four dimensions can be adjusted according to actual application needs.

[0138] Therefore, the embodiments of this specification provide a comprehensive user experience measurement system for applications, which can provide comprehensive user experience measurement for applications. Compared with existing user experience measurement solutions, it has more comprehensive dimensions and a wider range of applications; it is also real-time and can promptly discover possible problems with applications and help improve and update applications.

[0139] It should be noted that in the embodiments of this specification, the pre-stage data, the in-stage data, and the post-stage data are all information that has been authorized and agreed by the user in the past. And the acquisition, storage, use, and processing of the above data in the technical solutions described in the embodiments of this specification are in compliance with the relevant provisions of national laws and regulations.

[0140] Based on the above-mentioned embodiment, a method for attributing abnormalities of mobile applications based on user experience measurement is provided. The embodiment of this specification also provides a corresponding device for attributing abnormalities of mobile applications based on user experience measurement. The device may include a system (including a distributed system), software (application), module, component, server, client, etc. using the method described in the embodiment of this specification and a device combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided in the embodiment of this specification is as described in the following embodiments. Since the implementation scheme and method of the device to solve the problem are similar, the implementation of the specific device in the embodiment of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can implement a combination of software and / or hardware for predetermined functions. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0141] like Figure 4 As shown, an abnormal attribution device for a mobile application based on user experience measurement provided by an embodiment of this specification includes:

[0142] A real-time collection module 401 is used to calculate a comprehensive evaluation result of the user experience metric of the mobile application according to the full-link data and a pre-designed user experience metric system;

[0143] A calculation module 402 is used to calculate a comprehensive evaluation result of the user experience metric of the mobile application according to the full-link data and a pre-designed user experience metric system;

[0144] The judgment module 403 is used to judge whether the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold;

[0145] The acquisition module 404 is used to perform streaming calculation on the in-process data and the post-process data according to the user experience measurement system when the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold, and obtain the real-time evaluation results of the mobile application in the evaluation feedback dimension, the function clarity dimension, the participation dimension, and the performance and safety dimension respectively;

[0146] The attribution module 405 is used to perform cross-aggregation analysis on the real-time evaluation results and the pre-stage data to determine the abnormal cause of the mobile application.

[0147] The beneficial effects obtained by the device provided in the embodiments of this specification are consistent with the beneficial effects obtained by the above method and will not be repeated here.

[0148] like Figure 5 As shown, a computer device provided in an embodiment of this specification is provided. A mobile terminal application abnormal attribution device based on user experience measurement provided in an embodiment of this specification can be a computer device in this embodiment, and the above method of this specification is executed. The computer device 502 may include one or more processors 504, such as one or more central processing units (CPUs), and each processing unit may implement one or more hardware threads. The computer device 502 may also include any memory 506, which is used to store any kind of information such as code, settings, data, etc. Non-limiting, for example, the memory 506 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 502. In one case, when the processor 504 executes an associated instruction stored in any memory or combination of memories, the computer device 502 can perform any operation of the associated instruction. The computer device 502 also includes one or more drive mechanisms 508 for interacting with any storage, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0149] The computer device 502 may also include an input / output module 510 (I / O) for receiving various inputs (via input devices 512) and for providing various outputs (via output devices 514). A specific output mechanism may include a presentation device 516 and an associated graphical user interface (GUI) 518. In other embodiments, the input / output module 510 (I / O), the input device 512, and the output device 514 may not be included, and the computer device 502 may be used as a computer device in a network. The computer device 502 may also include one or more network interfaces 520 for exchanging data with other devices via one or more communication links 522. One or more communication buses 524 couple the components described above together.

[0150] The communication link 522 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0151] Corresponding to Figures 1 to 4 In addition to the method shown, an embodiment of the specification also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0152] The embodiment of the present specification also provides a computer-readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the following Figures 1 to 4 The method shown.

[0153] The embodiments of the present specification also provide a computer program product, including at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the following Figures 1 to 4 The method shown.

[0154] It should be understood that in the various embodiments of this specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0155] It should also be understood that in the embodiments of this specification, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this specification generally indicates that the associated objects before and after are in an "or" relationship.

[0156] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0160] In addition, each functional unit in each embodiment of this specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0162] Specific embodiments are used in this specification to illustrate the principles and implementation methods of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of this specification. At the same time, for those skilled in the art, according to the ideas of this specification, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this specification.

Claims

1. A method for attributing abnormality of mobile application programs based on user experience measurement, characterized in that: include: Collecting the full-link data of the mobile application in real time, wherein the full-link data includes pre-stage data, in-stage data, and post-stage data; Calculate the comprehensive evaluation result of the user experience metric of the mobile application based on the full-link data and the pre-designed user experience metric system; Determine whether the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold; If so, stream computing is performed on the in-process data and the post-process data according to the user experience measurement system to obtain real-time evaluation results of the mobile application in the evaluation feedback dimension, function clarity dimension, engagement dimension, and performance and security dimension respectively; The real-time evaluation results are cross-analyzed with the prior stage data to determine the cause of the abnormality of the mobile application.

2. The method according to claim 1, characterized in that The post-event data includes evaluation feedback data, and obtaining the real-time evaluation results of the mobile application in the evaluation feedback dimension further includes: Using a combination of one or more of a questionnaire, an application market evaluation component, an application embedded evaluation module, and customer complaint information, real-time collection of the evaluation feedback data of the user on the mobile application during the monitoring period; Performing text analysis on the evaluation feedback data to extract positive evaluation keywords and negative evaluation keywords; According to the occurrence frequencies of the positive evaluation keywords and the negative evaluation keywords in the monitoring period, the real-time evaluation results of the mobile application in the evaluation feedback dimension are obtained.

3. The method according to claim 2, characterized in that The in-process data includes operation behavior data, and obtaining the real-time evaluation result of the mobile application in the dimension of functional clarity further includes: Collecting user operation behavior data on the mobile application in real time, wherein the operation-related data includes at least one or more combinations of penetration rate, bounce rate, operation duration, user click distribution of core pages, and thermal analysis of core pages; According to the operation behavior data, a real-time evaluation result of the mobile application in the functional clarity dimension is obtained.

4. The method according to claim 3, characterized in that The in-process data also includes engagement data, and obtaining the real-time evaluation result of the mobile application in the engagement dimension further includes: Collecting user engagement data on the mobile application in real time, wherein the engagement data includes at least one or more combinations of retention rate, core page user stay time, session stay time, business process click-through rate, and actual conversion rate; According to the engagement data, a real-time evaluation result of the mobile application in the functional clarity dimension is obtained.

5. The method according to claim 4, characterized in that The data in the interim stage also includes performance and security data, and obtaining the real-time evaluation results of the mobile application in the performance and security dimensions further includes: Collecting performance and security data of the mobile application in real time, wherein the performance and security data at least includes one or more combinations of the startup time of the mobile application, the loading time of the core page, the CPU usage, the transaction time, the application exception data, the page exception data, and the transaction exception data; Based on the performance and security data, obtain real-time evaluation results of the mobile application in the performance and security dimensions.

6. The method according to claim 5, characterized in that The pre-event data at least includes one or more of user base customer group information, mobile device information, application version information, and network status information. The real-time comparison result is cross-aggregated and analyzed with the pre-event data to determine the abnormal cause of the mobile application, further comprising: Determining a target business scenario according to the real-time evaluation result of the evaluation feedback dimension and a first reference value corresponding thereto; Determining a target page according to the real-time evaluation result of the functional clarity dimension and a second reference value corresponding thereto; Determining a target node according to the real-time evaluation result of the participation dimension and a third reference value corresponding thereto; Determining a target checkpoint according to the real-time evaluation results of the performance and safety dimensions and a fourth reference value corresponding thereto; One or more of the target business scenario, target page, target node, and target checkpoint are cross-aggregated and analyzed with the prior stage data to determine the cause of the abnormality.

7. A mobile application abnormality attribution device based on user experience measurement, characterized in that: The device comprises: A real-time collection module, used to collect the full-link data of the mobile application in real time, wherein the full-link data includes pre-stage data, in-stage data and post-stage data; A calculation module, used to calculate a comprehensive evaluation result of the user experience metric of the mobile application according to the full-link data and a pre-designed user experience metric system; A judgment module, used to judge whether the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold; An acquisition module, configured to perform streaming calculation on the in-process data and the post-process data according to the user experience measurement system when the difference between the comprehensive evaluation result and the comprehensive reference value is greater than or equal to a preset measurement threshold, and obtain the real-time evaluation results of the mobile application in the evaluation feedback dimension, the function clarity dimension, the participation dimension, and the performance and security dimension respectively; The attribution module is used to perform cross-aggregation analysis on the real-time evaluation results and the pre-stage data to determine the abnormal cause of the mobile application.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The method comprises at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the method according to any one of claims 1 to 6.

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