Multi-dimensional user experience monitoring method and device, equipment and storage medium

Abstract

The invention discloses a multi-dimensional user experience monitoring method, device and equipment and a storage medium, and is applied to the field of financial science and technology, and the method comprises the steps: collecting user data of each experience index under a plate product, and obtaining multi-dimensional user experience data; performing diagnosis analysis on the multi-dimensional user experience data under each plate product according to a preset data analysis period to obtain a multi-dimensional experience diagnosis result; and updating the experience index data table according to the multi-dimensional experience diagnosis result, and performing visual monitoring by adopting a specified chart based on the updated experience index data table. Multi-dimensional user experience data collection is carried out from different experience indexes for plate products, so that one-sidedness of single-dimensional data experience monitoring is avoided, a full-automatic path of data collection, analysis and display is realized, the problem of experience monitoring timeliness lag is solved, collection and diagnosis results are visually displayed, and user experience is improved. And a targeted strategy suggestion is given.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology, and in particular to a multi-dimensional user experience monitoring method, device, equipment and storage medium. BACKGROUND

[0002] With the weakening of the Internet traffic bonus, the industry development enters the stock customer deepening stage, and it has become an inevitable development trend to change from product-centered to customer-centered and from function to experience. The demand and complexity of user experience management will be greatly improved.

[0003] At present, single means such as user point data tracking and user voice analysis for user experience monitoring and management have been widely used in practice, but there are problems such as single evaluation dimension, evaluation timeliness lag, and evaluation results cannot directly guide practice, which cannot meet the needs of various roles for user experience monitoring and management. SUMMARY

[0004] The present application provides a multi-dimensional user experience monitoring method to monitor the multi-dimensional experience of users for block products.

[0005] According to a first aspect of the present application, a multi-dimensional user experience monitoring method is provided, comprising: obtaining an experience index data table, wherein the experience index data table includes the correspondence between block products and different types of experience indexes;

[0006] Collecting user data of each experience index under the block product to obtain multi-dimensional user experience data;

[0007] Diagnosing and analyzing the multi-dimensional user experience data under each block product according to a preset data analysis period to obtain multi-dimensional experience diagnosis results;

[0008] Updating the experience index data table according to the multi-dimensional experience diagnosis results, and visualizing monitoring by using a specified graph based on the updated experience index data table, wherein the specified graph includes an experience overview dashboard, a business experience monitoring dashboard and a product experience monitoring dashboard.

[0009] According to another aspect of the present application, a multi-dimensional user experience monitoring device is provided, comprising:

[0010] An experience index data table acquisition module is configured to obtain an experience index data table, wherein the experience index data table includes the correspondence between block products and different types of experience indexes;

[0011] A multi-dimensional user experience data acquisition module is configured to collect user data of each experience index under the block product to obtain multi-dimensional user experience data;

[0012] A multi-dimensional experience diagnosis result acquisition module is configured to acquire multi-dimensional experience diagnosis results by diagnosing and analyzing the multi-dimensional user experience data under each product of the board according to a preset data analysis period.

[0013] A visual monitoring module is configured to update the experience index data table according to the multi-dimensional experience diagnosis results, and to perform visual monitoring based on the updated experience index data table by using a specified chart, wherein the specified chart includes an experience overview dashboard, a business experience monitoring dashboard and a product experience monitoring dashboard.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor; and

[0016] a memory connected to the at least one processor in communication; wherein

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the method according to any one of the embodiments of the present application when executed by the processor.

[0019] According to another aspect of the present application, a computer program product is provided, which comprises a computer program for enabling a processor to perform the method according to any one of the embodiments of the present application when executed by the processor.

[0020] The present application has the beneficial technical effects that the multi-dimensional user experience data is collected from different experience indexes for the product of the board, thereby avoiding the one-sidedness of the single-dimensional data experience monitoring, realizing the full-automatic channel of data collection, analysis and display, solving the problem of experience monitoring timeliness lag, and giving targeted strategy suggestions by visualizing the collection and diagnosis results, thereby providing practical guidance for the user experience improvement direction.

[0021] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0023] Figure 1 is a flow chart of a multi-dimensional user experience monitoring method according to the first embodiment of the present application;

[0024] Figure 2 is a schematic diagram of an experience overview board according to the first embodiment of the present application;

[0025] Figure 3 is a schematic diagram of a service experience monitoring board according to the first embodiment of the present application;

[0026] Figure 4 is a schematic diagram of a product experience monitoring board according to the first embodiment of the present application;

[0027] Figure 5 is a flow chart of a multi-dimensional user experience monitoring method according to the second embodiment of the present application;

[0028] Figure 6 is a structural schematic diagram of a multi-dimensional user experience monitoring device according to the third embodiment of the present application;

[0029] Figure 7 is a structural schematic diagram of an electronic device according to the fourth embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the technical personnel in the art better understand the present application scheme, the following will combine the drawings in the embodiments of the present application, and the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and in the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. In addition, the collected information is information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards of relevant countries and regions, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for user to choose authorization or refusal

[0032] Embodiment one

[0033] Figure 1 A flowchart of a multi-dimensional user experience monitoring method is provided for the first embodiment of the application. The present embodiment can be applicable to comprehensive monitoring of user experience for product plateaus. The method can be executed by a multi-dimensional user experience monitoring device, which can be realized in the form of hardware and / or software. As shown in the figure, the method comprises the following steps. Figure 1

[0034] Step S101: Obtain an experience index data table.

[0035] Optionally, obtaining the experience index data table comprises: receiving a business system setting instruction based on an enterprise organizational structure and product form, and determining the products of each business plate and subordinate business plate under the enterprise according to the business system setting instruction; determining the experience index name under different types of experience indexes for each product under the business plate from the experience index list, wherein the types of experience indexes include user attitude, user behavior, user characteristics and user voice; and generating the experience index data table according to the business plate, the product and the experience index under the enterprise.

[0036] Optionally, determining the experience index name under different types of experience indexes for each product under the business plate from the experience index list comprises: determining the mandatory experience index name under different types of experience indexes for each product under the business plate from the experience index list according to the monitoring requirements; and selecting the optional experience index name under different types of experience indexes for each product under the business plate from the experience index list according to the selection instruction. ​

[0037] Specifically, in this embodiment, the system provides a human-computer interaction interface for operators to configure the business system. Operators input their business structure into the online system based on the enterprise's organizational structure and product type. This business system includes two levels: business segments and products. An enterprise can contain multiple business segments, and a business segment can contain multiple products. This embodiment does not limit the specific number of enterprise segments and products set by the operator. Furthermore, once the business system is configured, a list of experience metrics as shown in Table 1 will be displayed.

[0038] Table 1

[0039]

[0040]

[0041] This implementation method includes four types of experience indicators: user attitude, user behavior, user feedback, and user characteristics. Each type of experience indicator includes multiple indicator names. In this implementation method, operators can use a human-computer interaction interface to determine the names of different experience indicators for each product under each business segment within the online system. Furthermore, this implementation method allows for pre-setting mandatory options for each experience indicator type based on data collection needs. For example, for user attitude, the Experience Tracking System (ETS) score and the Experience Tracking System Change (ETSC) score change trend are mandatory options; for user behavior, the Page View (PV) and Page View Change (PVC) change trends are mandatory options. These mandatory options are determined according to monitoring requirements and are not limited in this implementation method. Additionally, this implementation method allows operators to select from the optional experience indicator names in the experience indicator list for each product under each business segment, based on the selection instructions entered by the operator. In this embodiment, the online system will generate an experience metric data table based on the business system and selected experience metrics set above. Table 2 below shows an example of an experience metric data table:

[0042] Table 2

[0043]

[0044]

[0045] Since the unit of measurement and icon style are pre-configured for each experience indicator name, the unit of measurement and icon style can be determined once the experience indicator name is determined. The specific chart name can be a combination of the selected experience indicator type and the experience indicator name. Of course, this embodiment is only an example and does not limit the specific method of determining the chart name. Therefore, the experience indicator data table in this embodiment includes the correspondence between the product section and different types of experience indicators.

[0046] Step S102: Collect user data for each experience indicator under the product segment to obtain multi-dimensional user experience data.

[0047] Optionally, user data for various experience metrics under the product segment can be collected to obtain multi-dimensional user experience data, including: collecting user attitude data through questionnaires; collecting user feedback data through on-page comments; collecting user behavior data through page probes; and collecting user characteristic data through database mining. User attitude data, user feedback data, user behavior data, and user characteristic data are then used as multi-dimensional user experience data.

[0048] Specifically, in this embodiment, after obtaining the experience indicator data table, user data for each experience indicator under the product section in the data table will be collected. The data collected for experience indicators of user attitude type will be saved to the user attitude data table, the data collected for experience indicators of user behavior type will be saved to the user behavior data table, the data collected for experience indicators of user voice type will be saved to the user voice data table, and the data collected for experience indicators of user characteristics type will be saved to the user characteristic data table. The user attitude data table, user behavior data table, user voice data table, and user characteristic data table are saved in document form. Of course, this embodiment is only an example and does not limit the specific storage location of different types of data. As long as they are saved independently, they are all within the protection scope of this application.

[0049] Step S103: Perform diagnostic analysis on the multi-dimensional user experience data of each product segment according to the preset data analysis cycle to obtain multi-dimensional experience diagnostic results.

[0050] Optionally, diagnostic analysis is performed on the multi-dimensional user experience data of each product segment according to a preset data analysis cycle to obtain multi-dimensional experience diagnostic results. This includes: cleaning the multi-dimensional user data of each product segment according to a preset data analysis cycle to obtain cleaned multi-dimensional user experience data, wherein the data analysis cycle includes daily, weekly, monthly, quarterly, and yearly; and performing diagnostic analysis on the cleaned multi-dimensional user experience data to obtain multi-dimensional experience diagnostic results.

[0051] Optionally, the multidimensional user data under each product segment is cleaned according to a preset data analysis cycle to obtain cleaned multidimensional user experience data, including: cleaning user attitude data and user voice data under each product segment according to a preset data analysis cycle to obtain cleaned user attitude data and cleaned user voice data; and using the cleaned user attitude data, cleaned user voice data, user behavior data and user characteristic data as cleaned multidimensional user experience data.

[0052] Specifically, in this embodiment, multi-dimensional user experience data is collected in real time, but data analysis can be performed periodically. The specific analysis period can be set by the operator through the human-computer interaction interface on the online system. The data analysis period can be daily, weekly, monthly, quarterly, or yearly, and the operator can choose according to the granularity of the data analysis. When the data analysis period is determined, before data analysis, the multi-dimensional user data under each product segment needs to be cleaned according to the preset data analysis period to obtain cleaned multi-dimensional user data, removing noise and other interference from the collected data. During the cleaning of multi-dimensional user data, since user attitude data and user voice data are submitted by users through questionnaires, there may be a large amount of blank questionnaires and other invalid information. User behavior data and user characteristic data, on the other hand, are obtained by the system through probes or database queries, with less or no invalid information. Therefore, this embodiment mainly focuses on cleaning user attitude data and user voice data.

[0053] It should be noted that in this embodiment, a questionnaire data cleaning model can be used to clean user attitude data, and a user voice validity model can be used to clean user voice data. The voice validity model cleaning process mainly includes two parts: regular expressions and a machine learning model. First, invalid data is removed using regular expressions such as those for text repetition and text length. Then, the machine learning model is used to identify semantically invalid user voice data after invalid data removal, and this semantically invalid data is then deleted. Thus, the cleaned user attitude data, cleaned user voice data, and the collected user behavior data and user feature data constitute the cleaned multidimensional user experience data.

[0054] Optionally, diagnostic analysis is performed on the cleaned multidimensional user experience data to obtain multidimensional experience diagnostic results, including: obtaining a list of experience indicator analysis algorithms, wherein the experience indicator analysis algorithm list includes the algorithm corresponding to each experience indicator name; analyzing the cleaned multidimensional user experience data based on the experience indicator analysis algorithm list to obtain user data analysis results; and performing diagnosis based on the user data analysis results to obtain functional classification, multidimensional positioning, and strategy suggestions for the product segment.

[0055] In this embodiment, after cleaning the collected multidimensional user experience data, diagnostic analysis can be performed on the cleaned multidimensional user experience data according to the period selected by the operator to obtain multidimensional experience diagnostic results. The diagnostic analysis mainly involves two parts: diagnosis and analysis. The analysis phase involves analyzing the user data according to the analysis algorithms corresponding to each experience indicator to obtain user data analysis results. The diagnosis phase involves using the collected data and user data analysis results to perform a panoramic multidimensional diagnostic analysis of the product, focusing on the products that should be given priority, and providing experience improvement strategy suggestions based on the actual situation of the product. For the analysis phase, a list of experience indicator analysis algorithms needs to be obtained. This list includes the algorithms corresponding to each experience indicator name, as shown in Table 3 below as an example of an experience indicator analysis algorithm list.

[0056] Table 3

[0057]

[0058]

[0059]

[0060] After the multidimensional user data is analyzed according to the algorithm in Table 3 above to obtain the user data analysis results, a diagnosis is performed based on the collected data and the user data analysis results to obtain the functional classification, multidimensional positioning and strategy suggestions of the product segment. The product is diagnosed and analyzed from a panoramic multidimensional perspective, focusing on the products that should be given the highest priority, and based on the actual situation of the product, suggestions for experience improvement strategies are given. The multidimensional experience diagnosis approach is shown in Table 4 below:

[0061] Table 4

[0062]

[0063]

[0064] As shown in Table 4, the multi-dimensional experience diagnosis approach in this implementation method, through M2's multi-dimensional positioning, covers multiple dimensions of data, including user attitudes, user behavior, user characteristics, and user feedback. This avoids the one-sidedness of user experience evaluation during analysis and diagnosis. Furthermore, by reducing the dimensionality of the data, four-dimensional data are integrated into a single-dimensional data with a unified dimension that can be directly compared, thereby improving the accuracy and comparability of the analysis and diagnosis. Additionally, the strategy suggestions in M3 demonstrate that this implementation method combines four-dimensional data (ETS, ETSC, PV, PVC) and attention value data. It can automatically generate a current status summary and experience improvement strategy suggestions for each product, bridging the gap between experience monitoring and experience improvement decision-making and execution, significantly enhancing the practical value of experience monitoring. For example, the final strategy recommendation given in this implementation method could be: "Both satisfaction and traffic are high and rising significantly. The current situation is good. It is recommended to summarize the experience and share traffic," or "Traffic is high and rising significantly, but satisfaction is low and declining. The current situation is average. It is recommended to continuously monitor the declining trend of satisfaction, analyze the reasons, identify key experience dimensions, and carry out experience optimization and improvement." Of course, this implementation method is only an example and does not limit the specific content of the strategy recommendation given.

[0065] Step S104: Update the experience index data table based on the multidimensional experience diagnosis results, and use a specified chart for visualization monitoring based on the updated experience index data table.

[0066] Optionally, the updated experience metric data table can be visualized using specified charts, including: displaying the updated experience metric data table using an experience overview dashboard, which includes an overview of business segment experiences and an overview of product experiences; displaying the updated experience metric data table using a business experience monitoring dashboard, which includes segment details; and displaying the updated experience metric data table using a product experience monitoring dashboard, which includes product details.

[0067] Specifically, after obtaining the multi-dimensional experience diagnosis results, this implementation method updates the experience index data table shown in Table 1 above. When it is determined that the analysis is performed monthly, the updated experience index data table is shown in Table 5 below:

[0068] Table 5

[0069]

[0070]

[0071] After obtaining the updated experience metric data table, the system will visualize the updated experience metric data. This visualization can be achieved using... Figure 2 The experience overview dashboard shown is visualized, including an overview of the experience across business segments and an overview of the experience for each product. This can be achieved using methods such as... Figure 3 The business experience monitoring dashboard shown is visualized, including detailed information on each section; additionally, it uses methods such as... Figure 4 The diagram illustrates a product experience monitoring dashboard, which includes detailed product information. Therefore, management can gain a general understanding of the overall user experience across enterprise segments and products based on the experience overview dashboard, while the execution level can gain a detailed understanding of the user experience within specific segments based on the business experience monitoring dashboard, and the execution level can gain a detailed understanding of the user experience within specific products based on the product experience monitoring dashboard. This visualized experience monitoring dashboard integrates and presents a large amount of experience monitoring data in a drill-down structure from experience overview to business experience to product experience, meeting the actual needs of different roles from management to execution levels for experience monitoring management and demonstrating significant practical value.

[0072] In this application, multi-dimensional user experience data is collected for different experience indicators for segmented products, thereby avoiding the one-sidedness of single-dimensional data experience monitoring, realizing a fully automated path for data collection, analysis, and display, solving the problem of delayed timeliness in experience monitoring, and providing targeted strategic suggestions by visualizing the collection and diagnostic results, thus providing practical guidance for improving user experience.

[0073] Example 2

[0074] Figure 5 This is a flowchart of a multi-dimensional user experience monitoring method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment specifically describes the collection of user data for various experience indicators under a product segment to obtain multi-dimensional user experience data. For example... Figure 5 As shown, the method includes:

[0075] Step S201: Obtain the experience index data table.

[0076] Optionally, the process of obtaining the experience metric data table includes: receiving business system setting instructions based on the enterprise's organizational structure and product form, and determining the various business segments under the enterprise and the products belonging to the subordinate business segments according to the business system setting instructions; determining the experience metric names under different types of experience metrics from the experience metric list for each product under the business segment, wherein the types of experience metrics include user attitude, user behavior, user characteristics, and user feedback; and generating the experience metric data table based on the business segments, products, and experience metrics under the enterprise.

[0077] Optionally, for each product under the business segment, determine the names of experience metrics under different types of experience metrics from the experience metric list, including: for each product under the business segment, determine the mandatory experience metric names under different types of experience metrics from the experience metric list according to monitoring requirements; for each product under the business segment, select the optional experience metric names under different types of experience metrics from the experience metric list according to selection instructions.

[0078] Step S202: Collect user attitude data by conducting a questionnaire survey on user attitudes under the product category.

[0079] In this implementation method, different methods are used to collect user data corresponding to different types of experience indicators. For user attitudes within a product segment, a questionnaire survey is used. Specifically, the questionnaire link is pushed to the online channels of the business segment and product through online system integration. After users click to fill in and submit the questionnaire, the system stores the user's data in a structured format to form user attitude data, and saves the formed user attitude data to a user attitude data table.

[0080] Step S203: Collect user feedback data from the product section using the page feedback method.

[0081] Specifically, user feedback for products within a given product segment will be collected via a web-based feedback system. The device generates a user feedback collection page based on the business segment and product entered by the operator, and displays it on the online channels for the corresponding business segment and product. Users select the business segment and product on the user feedback collection page and fill in their feedback text. The device then structures and stores the collected text information as user feedback data and saves the resulting user feedback data to a user feedback data table.

[0082] Step S204: Collect user behavior data by using page probes to track user behavior within the product category.

[0083] Specifically, user behavior data is collected using page probe tracking for each product segment. The device uses probe tracking technology to display the front-end pages of products and services related to user behavior metrics under each business segment and product. Operators select each behavior metric on the page, and the device records the user behavior metric tracking data based on the selection results. The device stores the tracking data in a structured manner as user behavior data and saves the resulting user behavior data to the user behavior data table.

[0084] Step S205: User characteristic data is collected from the database by means of database mining for user characteristics under the product segment.

[0085] Among them, the user characteristics of the product segment are collected by database mining. Specifically, the device uses data mining technology to mine user characteristic data in the database according to the user characteristic indicators selected by the operator, and stores it in a structured manner as user characteristic data, and saves the formed user characteristic data to the user characteristic data table.

[0086] Step S206: User attitude data, user voice data, user behavior data, and user characteristic data are used as multi-dimensional user experience data.

[0087] Step S207: Perform diagnostic analysis on the multi-dimensional user experience data of each product segment according to the preset data analysis cycle to obtain multi-dimensional experience diagnostic results.

[0088] Optionally, diagnostic analysis is performed on the multi-dimensional user experience data of each product segment according to a preset data analysis cycle to obtain multi-dimensional experience diagnostic results. This includes: cleaning the multi-dimensional user data of each product segment according to a preset data analysis cycle to obtain cleaned multi-dimensional user experience data, wherein the data analysis cycle includes daily, weekly, monthly, quarterly, and yearly; and performing diagnostic analysis on the cleaned multi-dimensional user experience data to obtain multi-dimensional experience diagnostic results.

[0089] Optionally, the multidimensional user data under each product segment is cleaned according to a preset data analysis cycle to obtain cleaned multidimensional user experience data, including: cleaning user attitude data and user voice data under each product segment according to a preset data analysis cycle to obtain cleaned user attitude data and cleaned user voice data; and using the cleaned user attitude data, cleaned user voice data, user behavior data and user characteristic data as cleaned multidimensional user experience data.

[0090] Optionally, diagnostic analysis is performed on the cleaned multidimensional user experience data to obtain multidimensional experience diagnostic results, including: obtaining a list of experience indicator analysis algorithms, wherein the experience indicator analysis algorithm list includes the algorithm corresponding to each experience indicator name; analyzing the cleaned multidimensional user experience data based on the experience indicator analysis algorithm list to obtain user data analysis results; and performing diagnosis based on the user data analysis results to obtain functional classification, multidimensional positioning, and strategy suggestions for the product segment.

[0091] Step S208: Update the experience index data table based on the multi-dimensional experience diagnosis results, and use a specified chart for visualization monitoring based on the updated experience index data table.

[0092] Optionally, the updated experience metric data table can be visualized using specified charts, including: displaying the updated experience metric data table using an experience overview dashboard, which includes an overview of business segment experiences and an overview of product experiences; displaying the updated experience metric data table using a business experience monitoring dashboard, which includes segment details; and displaying the updated experience metric data table using a product experience monitoring dashboard, which includes product details.

[0093] In this application, multi-dimensional user experience data is collected for different experience indicators for segmented products, thereby avoiding the one-sidedness of single-dimensional data experience monitoring, realizing a fully automated path for data collection, analysis, and display, solving the problem of delayed timeliness in experience monitoring, and providing targeted strategic suggestions by visualizing the collection and diagnostic results, thus providing practical guidance for improving user experience.

[0094] Example 3

[0095] Figure 6 This is a schematic diagram of the structure of a multi-dimensional user experience monitoring device provided in Embodiment 3 of the present invention. Figure 6 As shown, the device includes: an experience index data table acquisition module 310, a multi-dimensional user experience data acquisition module 320, a multi-dimensional experience diagnostic result acquisition module 330, and a visualization monitoring module 340.

[0096] Among them, the experience indicator data table acquisition module 310 is used to acquire the experience indicator data table, which includes the correspondence between the product segment and different types of experience indicators.

[0097] The multi-dimensional user experience data acquisition module 320 is used to collect user data on various experience indicators under the product segment to obtain multi-dimensional user experience data.

[0098] The multi-dimensional experience diagnostic results acquisition module 330 is used to perform diagnostic analysis on the multi-dimensional user experience data of each product segment according to a preset data analysis cycle to obtain multi-dimensional experience diagnostic results.

[0099] The visualization monitoring module 340 is used to update the experience indicator data table based on the multi-dimensional experience diagnosis results, and to perform visualization monitoring using specified charts based on the updated experience indicator data table. The specified charts include the experience overview dashboard, the business experience monitoring dashboard, and the product experience monitoring dashboard.

[0100] Optionally, the experience indicator data table acquisition module is used to receive business system setting instructions based on the enterprise's organizational structure and product form, and to determine the various business segments under the enterprise and the products of the subordinate business segments according to the business system setting instructions.

[0101] For each product under the business segment, determine the names of the experience metrics under different types from the experience metric list. The types of experience metrics include user attitude, user behavior, user characteristics, and user voice.

[0102] Generate an experience metric data table based on the company's business segments, products, and experience metrics.

[0103] Optional, the Experience Metrics Data Table Acquisition Module is used to determine the required experience metric names for different types of experience metrics from the experience metric list for each product under the business segment according to monitoring requirements;

[0104] For each product under the business segment, select the name of the available experience metric under different types from the list of experience metrics according to the selection instructions.

[0105] Optional, a multi-dimensional user experience data acquisition module is used to collect user attitude data by means of questionnaires for the products in the segment;

[0106] User feedback data is collected from product sections using on-page feedback methods.

[0107] User behavior data is collected by using page probes to track user behavior within product categories.

[0108] User characteristic data for each product segment is collected by mining data from a database.

[0109] User attitude data, user voice data, user behavior data, and user characteristic data are used as multi-dimensional user experience data.

[0110] Optionally, the multidimensional experience diagnostic results acquisition module includes a data cleaning unit, which is used to clean the multidimensional user data under each product segment according to a preset data analysis cycle to obtain cleaned multidimensional user experience data. The data analysis cycle includes daily, weekly, monthly, quarterly, and yearly data.

[0111] The multidimensional experience diagnostic result acquisition unit is used to perform diagnostic analysis on the cleaned multidimensional user experience data to obtain multidimensional experience diagnostic results.

[0112] Optionally, a data cleaning unit is used to clean the user attitude data and user voice data under each product segment according to a preset data analysis cycle, and obtain the cleaned user attitude data and cleaned user voice data.

[0113] The cleaned user attitude data, cleaned user voice data, user behavior data, and user characteristic data are used as cleaned multidimensional user experience data.

[0114] Optionally, a multi-dimensional experience diagnostic result acquisition unit is used to acquire a list of experience indicator analysis algorithms, wherein the list of experience indicator analysis algorithms includes the algorithm corresponding to each experience indicator name;

[0115] The user data analysis results are obtained by analyzing the cleaned multidimensional user experience data based on the list of experience index analysis algorithms.

[0116] Based on user data analysis results, diagnostics are conducted to obtain functional classifications, multi-dimensional positioning, and strategic recommendations for product segments.

[0117] Optional, a visualization monitoring module is used to display the updated experience metric data table using an experience overview dashboard, which includes an experience overview of business segments and an experience overview of products.

[0118] The updated experience metrics data table is displayed using a business experience monitoring dashboard, which includes detailed breakdowns of different sections.

[0119] The updated experience metrics data is displayed using a product experience monitoring dashboard, which includes detailed product information.

[0120] The multi-dimensional user experience monitoring device provided in this embodiment of the invention can execute the multi-dimensional user experience monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0121] Example 4

[0122] Figure 7 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0123] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0125] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as multi-dimensional user experience monitoring methods.

[0126] In some embodiments, the multidimensional user experience monitoring method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multidimensional user experience monitoring method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the multidimensional user experience monitoring method by any other suitable means (e.g., by means of firmware).

[0127] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0132] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0133] Example 5

[0134] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the multidimensional user experience monitoring method provided in any embodiment of this application.

[0135] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0137] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method of multidimensional user experience monitoring, characterized by, The method comprises the following steps: obtaining an experience index data table, wherein the experience index data table comprises a corresponding relationship between a product and different types of experience indexes; collecting user data of each experience index of the product to obtain multi-dimensional user experience data; diagnosing and analyzing the multi-dimensional user experience data of each product according to a preset data analysis period to obtain a multi-dimensional experience diagnosis result; updating the experience index data table according to the multi-dimensional experience diagnosis result, and visualizing monitoring by using a specified chart based on the updated experience index data table, wherein the specified chart comprises an experience overview dashboard, a business experience monitoring dashboard and a product experience monitoring dashboard.

2. The method of claim 1, wherein, The method of obtaining the experience index data table comprises the following steps: receiving a business system setting instruction based on an enterprise organizational structure and a product form, and determining each business board under the enterprise and the products subordinate to the business board according to the business system setting instruction; determining experience index names of different types of experience indexes for each product under the business board from an experience index list, wherein the types of experience indexes comprise user attitude, user behavior, user characteristics and user voice; generating the experience index data table according to the business board, the products and the experience indexes under the enterprise.

3. The method of claim 2, wherein, The method of determining the experience index names of different types of experience indexes for each product under the business board from the experience index list comprises the following steps: determining mandatory experience index names of different types of experience indexes from the experience index list according to monitoring requirements for each product under the business board; selecting optional experience index names of different types of experience indexes from the experience index list according to a selection instruction for each product under the business board.

4. The method of claim 2, wherein, The method of collecting user data of each experience index of the product to obtain multi-dimensional user experience data comprises the following steps: collecting user attitude data by using a questionnaire survey for the user attitude of the product; collecting user voice data by using a page opinion feedback for the user voice of the product; collecting user behavior data by using a page probe burying for the user behavior of the product; collecting user characteristic data by using a database mining for the user characteristics of the product; collecting the user attitude data, the user voice data, the user behavior data and the user characteristic data as the multi-dimensional user experience data.

5. The method of claim 4, wherein, The method of diagnosing and analyzing the multi-dimensional user experience data of each product according to a preset data analysis period to obtain a multi-dimensional experience diagnosis result comprises the following steps: cleaning the multi-dimensional user data of each product according to a preset data analysis period to obtain cleaned multi-dimensional user experience data, wherein the data analysis period comprises day, week, month, season and year; diagnosing and analyzing the cleaned multi-dimensional user experience data to obtain the multi-dimensional experience diagnosis result.

6. The method of claim 5, wherein, The multi-dimensional user experience data after cleaning is obtained by cleaning the multi-dimensional user data under each block product according to the preset data analysis period, including: The user attitude data and the user voice data under each block product are cleaned according to the preset data analysis period, and the cleaned user attitude data and the cleaned user voice data are obtained; The cleaned user attitude data, the cleaned user voice data, the user behavior data and the user feature data are used as the cleaned multi-dimensional user experience data.

7. The method of claim 5, wherein, The multi-dimensional experience diagnosis result is obtained by diagnosing and analyzing the cleaned multi-dimensional user experience data, including: An experience index analysis algorithm list is obtained, wherein the experience index analysis algorithm list includes algorithms corresponding to each experience index name; The cleaned multi-dimensional user experience data is analyzed based on the experience index analysis algorithm list to obtain a user data analysis result; The function classification, multi-dimensional positioning and strategy suggestion of the block product are obtained by diagnosing according to the user data analysis result.

8. The method of claim 1, wherein, The updated experience index data table is visualized and monitored by using a specified chart, including: The updated experience index data table is displayed by using an experience overview dashboard, wherein the experience overview dashboard includes business block experience overview and product experience overview; The updated experience index data table is displayed by using a business experience monitoring dashboard, wherein the business experience monitoring dashboard includes block expansion details; The updated experience index data table is displayed by using a product experience monitoring dashboard, wherein the product experience monitoring dashboard includes product expansion details.

9. A multi-dimensional user experience monitoring apparatus, characterized by, The device includes: An experience index data table acquisition module is configured to acquire an experience index data table, wherein the experience index data table includes a correspondence between block products and different types of experience indexes; A multi-dimensional user experience data acquisition module is configured to collect user data of each experience index under the block product to obtain multi-dimensional user experience data; A multi-dimensional experience diagnosis result acquisition module is configured to diagnose and analyze the multi-dimensional user experience data under each block product according to a preset data analysis period to obtain a multi-dimensional experience diagnosis result; A visual monitoring module is configured to update the experience index data table according to the multi-dimensional experience diagnosis result, and to visualize and monitor the updated experience index data table by using a specified chart, wherein the specified chart includes an experience overview dashboard, a business experience monitoring dashboard and a product experience monitoring dashboard.

10. An electronic device, comprising: The electronic device includes: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method of any one of claims 1-8.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the method of any one of claims 1-8 when executed.

12. A computer program product, characterised in that, A computer program comprising computer program elements which, when executed by a processor, perform the method of any one of claims 1-8. A computer program comprising computer program elements which, when executed by a processor, perform the method of any one of claims 1-8.

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