User experience measurement method and device

Abstract

The embodiment of the invention discloses a user experience measurement method and device, belongs to the technical field of user experience evaluation, and is used for solving the problem of low accuracy of user experience measurement. Comprising the steps that monitoring data related to user experience are obtained, the monitoring data are coded, a behavior journey graph of a first-line seat interacting with a user is obtained, the monitoring data comprise one or more of user interview data, user observation data and operation log data, and the behavior journey graph comprises behavior contacts; according to a pre-constructed alternative index library of user experience measurement, experience indexes related to user experience are screened out, and the experience indexes comprise multiple dimensions and sub-features of the dimensions; mapping the behavior contact and the experience index to obtain a key index data set corresponding to the user; and carrying out hierarchical weighted fusion processing on the key index data set through a user experience measurement quantitative model to obtain an experience measurement score of the user.

Description

Technical Field

[0001] This application relates to the field of user experience evaluation technology, and in particular to a user experience measurement method and apparatus. Background Technology

[0002] Customer service systems have complex business scenarios and numerous built-in functions. The main users are agents in various branch centers. The quality of the hotline service provided by agents to users is positively correlated with the auxiliary capabilities provided by the customer service system. Therefore, it is of great significance to measure the user experience of the key business scenarios of the customer service system.

[0003] In related technologies, user experience measurement methods typically use page data tracking points to obtain data, resulting in a single source of experience data with few dimensions. Furthermore, most user experience measurement methods use general indicator sets, which have a coarse granularity and cannot be applied to specific products and their key business scenarios, leading to low accuracy in user experience measurement. Summary of the Invention

[0004] The purpose of this application is to provide a user experience measurement method and apparatus to solve the problem of low accuracy in user experience measurement.

[0005] To solve the above-mentioned technical problems, the embodiments of this application are implemented as follows: In a first aspect, embodiments of this application provide a user experience measurement method. The method includes: acquiring monitoring data related to user experience and encoding the monitoring data to obtain a behavioral journey map of frontline agents interacting with users. The monitoring data includes one or more of user interview data, user observation data, and operational log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built candidate indicator library for user experience measurement, experience indicators related to the user experience are selected. The experience indicators include multiple dimensions and sub-features of the dimensions. The behavioral touchpoints and experience indicators are mapped to obtain a key indicator dataset corresponding to the user. A user experience measurement quantification model is used to perform hierarchical weighted fusion processing on the key indicator dataset to obtain the user's experience measurement score. The user experience measurement quantification model is used to score the dimensions and sub-features in the key indicator dataset.

[0006] Secondly, embodiments of this application provide a user experience measurement device, comprising: an acquisition module, configured to acquire monitoring data related to user experience and encode the monitoring data to obtain a behavioral journey map of frontline agents interacting with users, wherein the monitoring data includes one or more of user interview data, user observation data, and operation log data, and the behavioral journey map includes behavioral touchpoints; a construction module, configured to filter experience indicators related to the user experience based on a pre-built candidate indicator library for user experience measurement, wherein the experience indicators include multiple dimensions and sub-features of the dimensions; a mapping module, configured to map the behavioral touchpoints and the experience indicators to obtain a key indicator dataset corresponding to the user; and a scoring module, configured to perform hierarchical weighted fusion processing on the key indicator dataset using a user experience measurement quantification model to obtain the user's experience measurement score, wherein the user experience measurement quantification model is used to score the dimensions and sub-features in the key indicator dataset.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement a user experience measurement method as described above.

[0008] Fourthly, embodiments of this application provide a readable storage medium on which a program or instruction is stored, and when the program or instruction is executed by a processor, it is a user experience measurement method as described above.

[0009] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement a user experience measurement method as described above.

[0010] Sixthly, embodiments of this application provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the user experience measurement method described above.

[0011] The technical solution of this application embodiment obtains a behavioral journey map of front-line agents interacting with users by acquiring monitoring data related to user experience and encoding the monitoring data. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built candidate indicator library for user experience metrics, experience indicators related to user experience are selected. These experience indicators include multiple dimensions and sub-features of those dimensions. The behavioral touchpoints and experience indicators are mapped to obtain a key indicator dataset corresponding to the user. Through a user experience metric quantification model, the key indicator dataset is subjected to hierarchical weighted fusion processing to obtain the user's experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset. It is evident that constructing a behavioral journey graph using monitoring data, including objective data such as operational log data and subjective data such as user interview data and user observation data, can improve the diversity and comprehensiveness of user data acquisition. Furthermore, mapping behavioral touchpoints in the behavioral journey graph to multiple dimensions and sub-features of dimensions selected from a candidate indicator library yields a key indicator dataset. Through hierarchical weighted fusion processing, a user experience metric score is obtained. This allows for the determination of the key indicator dataset to obtain the user experience metric score based on specific scenarios, thus addressing the issue of low accuracy in user experience measurement. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a user experience measurement method provided according to an embodiment of this application; Figure 2 This is a schematic diagram of the rooted analysis method for behavioral journeys provided in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the interaction between a user and a frontline agent, based on an embodiment of this application. Figure 4 This is a schematic diagram illustrating the mapping rules between user behavior types and dimensions provided in the embodiments of this application; Figure 5 This is a schematic diagram of the optimal matching set of the key indicator dataset provided in the embodiments of this application; Figure 6 This is a flowchart of the user experience measurement system provided in the embodiments of this application; Figure 7 This is a flowchart illustrating another user experience measurement method provided according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a user experience measurement device according to an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0015] The user experience measurement method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0016] Figure 1 This illustration shows an embodiment of a user experience measurement method provided by the present invention. The method can be executed by an electronic device, which may include a server and / or a terminal device, wherein the terminal device may be, for example, an in-vehicle terminal or a mobile phone terminal. In other words, the method can be executed by software or hardware installed on a user experience measurement device, and the method includes the following steps: S102, acquire monitoring data related to user experience, and encode the monitoring data to obtain a behavioral journey map of frontline agents interacting with users. The monitoring data includes one or more of the following: user interview data, user observation data, and operational log data. The behavioral journey map includes behavioral touchpoints.

[0017] User experience refers to the experience data of users during the interaction with a target system, such as how they feel about using the target system. The target system includes customer service systems. It can be seen that the user experience is different in different scenarios, and the corresponding monitoring data is also different.

[0018] The coding process includes encoding the monitoring data using grounded analysis methods, which is then used for data extraction and analysis.

[0019] Frontline agents refer to customer service personnel who directly interact with users and handle their business.

[0020] Specifically, through encoding, user behavioral characteristics are extracted, and the operational processes of frontline agents are categorized and aggregated to form a behavioral journey map covering the target business scenario. This behavioral journey map can serve as a behavioral reference framework for subsequent user experience measurement.

[0021] Behavioral touchpoints, within the behavioral journey map, represent all the behaviors or touchpoints of frontline agents during a user's interaction with a product, service, or brand. They are key nodes where frontline agent behavior occurs, typically involving the user's sensory experience, operational interactions, or emotional feedback.

[0022] User interview data includes: content data from in-depth user interviews, covering user behavior paths, pain point descriptions, and subjective experiences during actual business operations.

[0023] User observation data includes: actual records made by researchers of user actions, dwell time, and error occurrence points in real business scenarios.

[0024] The operation log data includes: system usage logs, such as user click paths in the customer service system, request response times, task completion status, and other data.

[0025] S104. Based on the pre-built candidate metric library for user experience metrics, select user experience metrics related to user experience. The user experience metrics include multiple dimensions and sub-features of the dimensions.

[0026] User experience metrics include: standardized measurement of user experience, such as the process of converting user feelings, behaviors, and system performance during product interactions into quantifiable data.

[0027] The alternative metrics library is used to store relevant metrics for user experience measurement.

[0028] Multiple dimensions include several user experience-related metrics, such as performance, efficiency, usability, and satisfaction.

[0029] Sub-features of a dimension include the features corresponding to each dimension. For example, sub-features corresponding to the efficiency dimension include: function utilization rate, task completion rate, task completion time, etc.

[0030] Based on a pre-built library of candidate metrics for user experience measurement, user experience metrics related to user experience are selected from the library. This allows for targeted selection of user experience metrics for each user, enabling the calculation of user experience metrics.

[0031] S106 maps behavioral touchpoints and experience metrics to obtain a dataset of key metrics corresponding to the user.

[0032] The behavioral touchpoints in the behavioral journey graph in S102 are mapped to the experience metrics from the pre-built alternative metric library in S104 to obtain the dimension and sub-features under each behavioral touchpoint. All user behavioral touchpoints and experience metrics are mapped to obtain the user's key metric dataset, which includes the metrics of each behavioral touchpoint under the sub-features of the corresponding dimension.

[0033] S108 uses a user experience metric quantification model to perform hierarchical weighted fusion processing on the key indicator dataset to obtain the user experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset.

[0034] The user experience measurement quantification model includes: scoring the dimensions and sub-features in the key indicator dataset based on the dimensions and sub-features corresponding to the user's behavioral touchpoints through a preset formula.

[0035] Layering refers to the first and second layers of elements in the key indicator dataset. The first layer consists of each dimension, and the second layer consists of the sub-features corresponding to each dimension.

[0036] The weighted fusion process includes weighted fusion of the scores of each dimension and sub-feature at each layer to obtain the final user experience metric score.

[0037] The technical solution of this application embodiment obtains a behavioral journey map of front-line agents by acquiring monitoring data related to user experience and encoding the monitoring data. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built candidate indicator library for user experience metrics, user experience indicators are selected. These indicators include multiple dimensions and sub-features of those dimensions. The behavioral touchpoints and experience indicators are mapped to obtain a key indicator dataset corresponding to the user. A user experience metric quantification model is used to perform hierarchical weighted fusion processing on the key indicator dataset to obtain the user's experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset. It is evident that constructing a behavioral journey graph using monitoring data, including objective data such as operational log data and subjective data such as user interview data and user observation data, can improve the diversity and comprehensiveness of user data acquisition. Furthermore, mapping behavioral touchpoints in the behavioral journey graph to multiple dimensions and sub-features of dimensions selected from a candidate indicator library yields a key indicator dataset. Through hierarchical weighted fusion processing, a user experience metric score is obtained. This allows for the determination of the key indicator dataset to obtain the user experience metric score based on specific scenarios, thus addressing the issue of low accuracy in user experience measurement.

[0038] In one embodiment, the monitoring data is encoded to obtain a behavioral journey map of frontline agents interacting with users (i.e., S102), which can be achieved by performing the following steps A1-A4: Step A1: Using grounded analysis, open coding is performed on the monitoring data to obtain initial concept labels. Based on the initial concept labels, the behavioral touchpoints of front-line agents are determined.

[0039] Grounded analysis methods include: open coding, axis coding, and selective coding.

[0040] Initial concept labels include: labeling the data after the monitoring data is broken down, which can yield initial concept labels for the monitoring data.

[0041] Specifically, the data from user interviews, user observations, and operational logs within the monitoring data are broken down and processed. For example, user actions, behaviors, and psychological expressions are analyzed to generate initial concept labels. Combining these three heterogeneous data types—user interviews, user observations, and operational logs—improves the coverage and accuracy of subsequent data extraction.

[0042] Based on the initial concept labels, identify all behaviors or touchpoints of interaction with frontline agents, and designate all such behaviors or touchpoints as the action touchpoints for frontline agents. For example, viewing customer resource usage in the marketing assistant, or confirming application usage preferences in the marketing assistant.

[0043] Step A2: Based on the similarity of each initial concept tag, aggregate the initial concept tags, determine the aggregated initial concept tags, and perform main axis encoding to obtain the behavior flow of front-line agents.

[0044] Based on the initial concept tags obtained in step A1, the similarity between each initial concept tag is calculated. Initial concept tags with high similarity are aggregated to obtain aggregated initial concept tags. The aggregated initial concept tags are then encoded to obtain the key nodes and turning points of user behavior. Based on the key nodes and turning points, front-line agents take corresponding responses to determine the behavior flow of front-line agents. Specifically, scattered behavioral touchpoints are encoded and linked together into the behavior flow of front-line agents.

[0045] Step A3 involves selectively coding the behavioral processes to determine the behavioral stages of frontline agents.

[0046] Selective coding includes: establishing a complete main line of user behavior based on the main tasks in the target business scenario, and supplementing it with peripheral branch behaviors.

[0047] Based on the behavioral flow in step A2, the behavioral stages for frontline agents are identified and summarized. These stages include: the system contacted, the actions taken within the system, and the resulting outcomes. Frontline agents refer to personnel in call centers or customer service departments who directly interact with users and are responsible for handling various user requests.

[0048] Step A4: Generate a behavioral journey map of frontline agents based on behavioral touchpoints, behavioral processes, and behavioral stages.

[0049] Based on grounded analytics, the behavioral touchpoints, processes, and stages of frontline agents are obtained, thereby generating a behavioral journey map of frontline agents, such as... Figure 2 The diagram illustrates a grounded analytics approach for behavioral journeys, which includes: acquiring user monitoring data through frontline research, such as user interview data, user observation data, and operational log data; performing grounded analytics on the monitoring data, such as open coding, axis coding, and selective coding, to obtain the behavioral touchpoints, behavioral processes, and behavioral stages of frontline agents interacting with users.

[0050] As an example, such as Figure 3 The diagram illustrates the interaction between users and frontline agents in a marketing scenario within a customer service system. The behavioral stages include: system contact—user information inquiry—marketing recommendation—business processing—processing completion. The behavioral flow includes: user information inquiry—selecting marketing products—executing marketing actions—adding target products to cart—clarifying business rules—executing processing actions—confirming processing status. Behavioral touchpoints include: viewing resource usage; adding high-value services to favorites or viewing marketing assistant recommendations—matching customer usage habits or preferences from available services; viewing marketing scripts provided by the marketing assistant or auxiliary views; adding items to favorites; searching for business rules in the knowledge base or viewing business rules in product information pop-ups; processing transactions in the marketing assistant; viewing system prompts and results in the overall customer view. No specific limitations are imposed.

[0051] This behavioral journey map not only includes operational steps, but also carries potential experience characteristics information, providing traceable basic data for subsequent indicator mapping and quantification.

[0052] In this embodiment, grounded analysis is used to process monitoring data through open coding, axis coding, and selective coding to obtain a behavioral journey map of frontline agents interacting with users. This behavioral journey map covers multiple typical business scenarios, focuses on the user's operational sequence, and ensures the objectivity, systematicity, and comprehensiveness of the source, providing a reference framework for the subsequent mapping of experience metrics to segment scenarios. Instead of relying on subjective expert experience or other single-path methods to define user processes, a systematic qualitative research approach is used to ensure the objectivity, comprehensiveness, and traceability of the behavioral journey map.

[0053] In one embodiment, before selecting user experience metrics related to user experience from a pre-built pool of candidate metrics (i.e., S104), the following steps B1-B3 may also be performed: Step B1: Obtain authoritative data in advance, extract descriptive entries related to user experience metrics from the authoritative data, and perform semantic deduplication on the descriptive entries to obtain semantic deduplication entries.

[0054] The candidate indicator library is built based on authoritative data, which are publicly known data from multiple sources, including but not limited to industry / group standards, excellent industry measurement models, industry reports and white papers, and core journal papers.

[0055] Descriptive entries include statements from authoritative sources that describe user experience.

[0056] Authoritative data is acquired in advance, parsed and processed, and the standard clauses and other documents in the authoritative data are structured and analyzed to identify and extract descriptive items with experience measurement attributes.

[0057] Semantic deduplication includes: using semantic similarity analysis to merge synonymous descriptive entries and avoid duplication and redundancy of descriptive entries. Specifically, semantic deduplication is performed on descriptive entries in authoritative materials. For descriptive entries with similar expressions in different materials, semantic deduplication is performed by calculating semantic similarity to obtain semantically deduplicated entries.

[0058] By extracting descriptive entries from authoritative sources, we can improve the comprehensiveness of user experience-related metrics. By integrating authoritative resources, we can avoid problems such as insufficient coverage and lack of authority caused by single or subjective sources.

[0059] Step B2 involves performing hierarchical processing on the semantic entries to obtain a first-level dimension of the authoritative data and a second-level sub-feature corresponding to the first-level dimension.

[0060] Semantic entries are processed in layers, and categorized and managed according to the dimension of user experience.

[0061] The semantic entries corresponding to authoritative data are divided into two layers: the first layer is a single dimension, and the second layer is a second layer of sub-features corresponding to the single dimension.

[0062] The first layer includes: a layer of authoritative data, and a layer of Quality of Experience (QoE) – the perceived elements of user experience, i.e., the user's experience dimension and the QoE dimension, including performance, efficiency, usability, and satisfaction. Here, QoE represents the user's subjective feeling or satisfaction with a product or service, and the perceived elements of user experience are the specific dimensions that influence QoE.

[0063] The second layer includes: second-layer sub-features corresponding to the first-layer dimension, that is, second-layer sub-characteristics of the first-layer QoE dimension, which refers to the quality aspects that user experience perception needs to be evaluated. For example, second-layer sub-characteristics of efficiency include: function utilization, task completion rate, task completion time, etc. Second-layer sub-characteristics can also include: descriptive statements of each second-layer sub-characteristic of the first-layer QoE dimension, making its semantics clear and its target orientation strong.

[0064] By performing multiple rounds of semantic deduplication and hierarchical classification on semantic entries and their meanings, the merging of duplicate semantic entries and the masking of fuzzy indicators are achieved, making the subsequently constructed candidate indicator library highly clear and operable.

[0065] Step B3: Based on the first-level dimension and second-level sub-features of authoritative data, construct a candidate indicator library for user experience measurement.

[0066] Based on the hierarchical processing of authoritative data, a first-level dimension and a second-level sub-feature are obtained. According to the first-level dimension and the second-level sub-feature of the authoritative data, a candidate indicator library for user experience measurement with broad coverage, clear classification and distinct logical hierarchy is output, providing comprehensive and authoritative technical support for the subsequent mapping and quantitative analysis of behavioral journey spectrum.

[0067] To filter out the experience metrics related to user experience (i.e., S104), you can perform the following steps B4-B5: Step B4: Determine user behavior types based on the behavioral journey map of frontline agents.

[0068] Based on the behavioral journey map of front-line agents in step A4, the various behavioral stages of users are determined, thereby identifying the user behavior type.

[0069] User behavior types include: reach, execution, perception, etc., which can be determined based on the user experience in different scenarios.

[0070] As an example, taking a marketing scenario in a customer service system, the overall user behavior stages are abstracted into a "reach-execution-perception" cycle based on the user's interaction with the system, which serves as the user behavior type. User behavior types are used to establish a correspondence with subsequent experience metrics.

[0071] Step B5: Based on the user behavior type, select dimensions and sub-features of the dimensions that are related to user experience from the candidate indicator library, and use the dimensions and sub-features of the dimensions that are related to user experience as experience indicators.

[0072] Based on user behavior type, dimensions and sub-features related to user experience are selected from the first-level and second-level sub-features of the candidate indicator library. Different user behavior types result in different user experience-related dimensions and sub-features selected from the candidate indicator library. These user experience-related dimensions and sub-features are used as experience indicators, and dimensions can be represented as QoE dimensions. Sub-features include descriptive statements for each dimension's sub-features, ensuring clear semantics and strong target focus.

[0073] As an example, user experience-related monitoring data is acquired within a customer service system's marketing scenarios. User behavior types include outreach, action, and perception. The mapping rules between user behavior types and dimensions in the candidate indicator library are as follows: Figure 4 As shown, user behavior types include: reach, execution, and perception; the corresponding dimensions selected from the first-level dimensions of the candidate indicator library include: consistency, page performance, usability, information validity, task efficiency, and satisfaction, which are used as dimensions in the selected experience indicators, and the second-level sub-features corresponding to these dimensions are obtained as the sub-features corresponding to the dimensions in the experience indicators.

[0074] In this embodiment, a candidate indicator library is constructed based on pre-acquired authoritative data. User behavior types are determined based on behavioral journey mapping. Then, based on these user behavior types, experience indicators related to user experience are selected from the first-level dimension and second-level sub-features of the candidate indicator library. These selected experience indicators include multiple dimensions corresponding to user behavior types. Targeted experience indicators can be selected based on the specific business user behavior types, making them suitable for measuring user experience quality in corresponding scenarios, improving the clarity of measurement objectives and adaptability to business needs.

[0075] In one embodiment, the behavioral touchpoints and experience metrics are mapped to obtain a dataset of key metrics corresponding to the user (i.e., S106), and the following steps C1-C5 can be performed: Step C1: Obtain the behavioral touchpoints of frontline agents in the behavioral journey map.

[0076] Step C2 involves performing matrix analysis on the sub-features of different dimensions in behavioral touchpoints and experience metrics to establish a multi-dimensional correspondence between behavioral touchpoints and sub-features, where a behavioral touchpoint corresponds to one or more sub-features.

[0077] Based on the behavioral touchpoints obtained in step C1, and the user experience metrics selected from the candidate metric library, a matrix analysis is used to determine the multidimensional correspondence between each behavioral touchpoint and its corresponding dimension's sub-features. The user experience metrics are essentially dimensions and their sub-features. Each mapped behavioral touchpoint can include multiple metrics, each corresponding to a unique dimension and sub-feature.

[0078] Step C3: Based on the multidimensional correspondence, determine the similarity between the behavioral touchpoints and their corresponding sub-features through semantic analysis.

[0079] Based on the multidimensional correspondence between each behavioral touchpoint and sub-feature in step C2, the semantic similarity between the behavioral touchpoint and the corresponding sub-feature is calculated using semantic analysis methods. The semantic analysis methods may include cosine similarity calculation.

[0080] Step C4: Based on similarity, determine the sub-features and corresponding dimensions of all behavioral touchpoints of frontline agents.

[0081] Based on the similarity from step C3, the target consistency of the similarity between behavioral touchpoints and their corresponding sub-features can also be calculated. Based on similarity and target consistency, through methods such as weighted fusion or expert review, the sub-features mapped to all behavioral touchpoints of frontline agents and the dimensions corresponding to these sub-features are obtained.

[0082] Step C5: Generate a key indicator dataset based on the sub-features mapped from all behavioral touchpoints of frontline agents and the dimensions corresponding to those sub-features.

[0083] Based on the sub-features mapped from all the behavioral touchpoints of frontline agents and the dimensions corresponding to the sub-features, a key indicator dataset is generated. The key indicator dataset is the optimal key experience index (KEI) index, which is the KEI index applicable to the target scenario.

[0084] In summary, based on the above mapping method of "behavioral touchpoints - experience metrics," a set of metrics covering the entire user experience journey for specific business scenarios can be formed. This set of metrics not only includes objective metrics such as efficiency, performance, and consistency, but also subjective metrics such as satisfaction and usability. Furthermore, it ensures that each metric in the key metric dataset has a clear source of experience data, guaranteeing the operability and dynamic manageability of the key metric dataset.

[0085] When the behavioral touchpoints in the business process are updated and iterated, the new behavioral touchpoints can be introduced into the analysis matrix to quickly determine the relevant mapping relationships, ensuring the dynamic adaptability of the indicator system.

[0086] As an example, such as Figure 5The diagram illustrates the optimal matching set for the key performance indicator (KPI) dataset. The dimensions in the experience metrics are represented by QoE dimensions, including: usability, which encompasses operability, learnability, and clarity. Taking usability as an example, operability's sub-features include: operational complexity, intuitive and habitual operation, fault-tolerant response, and error-prone and recoverable. Each sub-feature includes a corresponding description: operational complexity describes the overall complexity of a user completing a specified task, including the number of operation steps, page switching times, and page jumps; intuitive and habitual operation describes how the system's interaction adapts to the user's mental model and daily habits; fault-tolerant response describes the degree to which the system prevents user errors, such as the system's correction and prompting function when a frontline agent submits an application for mutually exclusive goods during business processing; and error-prone and recoverable describes the system's ability to support frontline agents in effectively recovering from errors to normal task status after operational errors or system failures occur. The behavioral touchpoints and their corresponding metrics are as follows: For querying user information, the behavioral touchpoints include: viewing resource usage, etc. The corresponding metrics include: average number of page jumps to view different types of customer information, with the sub-feature being operational complexity; and the operational intuitiveness and habitual usage score, with the sub-feature being operational intuitiveness and habitual usage. For identifying marketing products, the behavioral touchpoints include: adding high-value services to favorites in advance, or viewing services recommended by the marketing assistant, etc. The corresponding metrics include: average number of operation steps required to find the target product in the marketing assistant, average number of operation steps required to search for the target product, with the sub-feature being operational complexity; the operational intuitiveness and habitual usage score, with the sub-feature being operational intuitiveness and habitual usage; and the accuracy of the marketing assistant's pre-validation of recommended services, with the sub-feature being fault-tolerant response. For executing marketing actions, the behavioral touchpoints include: viewing marketing scripts provided by the marketing assistant, viewing marketing scripts provided by auxiliary views, etc., with no corresponding metrics. For adding target products to the cart, the behavioral touchpoints include: adding items from favorites to the cart, etc. The corresponding metric is the operational intuitiveness and habitual usage score, with the sub-feature being operational intuitiveness and habitual usage. The behavioral flow defines the behavioral touchpoints within the clearly defined business rules: searching for business rules in the knowledge base and viewing business rules in the product information pop-up. The corresponding metric is the "operation conforms to intuition and habit" score, and the corresponding sub-feature is "operation conforms to intuition and habit." The behavioral flow also defines the behavioral touchpoints within the execution of processing actions: processing business in the marketing assistant, etc. The corresponding metric is the "operation conforms to intuition and habit" score, and the corresponding sub-feature is "operation conforms to intuition and habit." Finally, the behavioral touchpoints within the confirmation of processing status: viewing the system prompt pop-up result feedback and searching for processing results in the customer's overall view, etc. The corresponding metric is the "operation conforms to intuition and habit" score, and the corresponding sub-feature is "operation conforms to intuition and habit."

[0087] In summary, the behavioral touchpoints of different behavioral processes correspond to sub-features of different dimensions, which yield corresponding indicators and serve as a key indicator dataset.

[0088] In this embodiment, matrix analysis and semantic analysis methods are used to map different dimensions of sub-features in behavioral touchpoints and experience metrics to obtain a user's key metric dataset. Through the analysis and mapping of behavioral touchpoints, the key metric dataset can freely change based on changes in behavioral touchpoints corresponding to business scenarios, and each metric in the key metric dataset is independent, without duplication.

[0089] In one embodiment, by using a user experience metric quantification model to perform hierarchical weighted fusion processing on the key indicator dataset to obtain the user experience metric score (i.e., S108), the following steps D1-D6 can be executed: Step D1: Using the user experience measurement quantification model, determine the indicator function type of the sub-features of the behavioral touchpoints in the key indicator dataset based on the indicator values ​​of each sub-feature corresponding to the behavioral touchpoints in the key indicator dataset. The indicator function types include: positive correlation indicator functions and / or negative correlation indicator functions.

[0090] The user experience measurement quantification model, based on the theoretical foundation of the law of diminishing marginal utility, unifies all sub-features corresponding to users in the key indicator dataset into two standardized function forms to simulate the non-linear characteristics of user experience as data changes.

[0091] The metrics include: data from the key metrics dataset, showing behavioral touchpoints under different corresponding sub-features. Each behavioral touchpoint includes: one or more metrics, each corresponding to a dimension and its sub-features. The metric value refers to the numerical value of the behavioral touchpoint under the corresponding sub-feature. Metrics, such as... Figure 5 The matrix contains data such as "average number of page redirects to view different types of customer information".

[0092] Specifically, the indicator function types for user sub-features in the key indicator dataset include: positively correlated indicator functions and / or negatively correlated indicator functions. First, the indicator types are standardized: based on the consistency between the pre-acquired indicator values ​​and the user experience direction, all sub-feature indicators are divided into two categories. Positively correlated indicator functions correspond to positively correlated indicators: their numerical growth has a positive impact on user experience (e.g., ratings, accuracy). This type of indicator uses concave functions for standardized mapping, and its mathematical properties can effectively simulate the pattern of "significant initial experience improvement, followed by gradual saturation of gain." Negatively correlated indicator functions correspond to negatively correlated indicators: their numerical growth has a negative impact on user experience (e.g., training time, number of operation steps). This type of indicator uses convex functions for mapping to characterize the pattern of "severe initial experience deterioration, followed by gradual convergence of negative impact." After receiving the key indicator dataset, the user experience measurement quantification model determines the indicator function type of the user sub-features in the indicator dataset to determine the data-driven measurement formula for the sub-features or their corresponding dimensions.

[0093] Step D2: Based on the importance of behavioral touchpoints in the key indicator dataset, determine the weight coefficient of each behavioral touchpoint under the corresponding sub-feature.

[0094] After the function transformation in step D1, the indicators corresponding to each sub-feature in all key indicator datasets will be normalized to the standard score of the same dimension (e.g., 0-10 points).

[0095] Weight coefficients are used to characterize the relative importance of behavioral touchpoint metrics under sub-features within the overall key metric dataset. The final overall experience score is the weighted sum of the standard score of each metric in the key metric dataset and its weight. The weight coefficients are derived through the analytic hierarchy process (AHP) and the correlation between core business metrics. Specifically, the AHP involves stratifying metrics and determining weight relationships through pairwise comparisons. For example, first determine the relative importance of dimensions, then refine to the corresponding sub-features. This allows for a more systematic and clearer understanding of weight relationships. The derivation of the correlation between core business metrics involves inversely deriving weights based on the business objectives of the scenario. For instance, if the core objective is to improve user retention, then metrics directly related to retention should have higher weights.

[0096] Step D3: Based on the positive correlation index function and / or negative correlation index function, and the weight coefficient of each behavior touchpoint, determine the data measurement formula for the sub-features corresponding to each behavior touchpoint, as well as the data measurement formula for each dimension.

[0097] Based on the index function type of each sub-feature determined in step D1, the corresponding data-driven measurement formula is adopted, and the calculation is performed in conjunction with the weight coefficients of each behavior touchpoint corresponding to the sub-feature in step D2. It should be understood that the index function type of each dimension can also be determined, and the corresponding data-driven measurement formula can be adopted in conjunction with the weight coefficients of each dimension.

[0098] As an example, the data-driven measurement formulas for sub-characteristics are shown in Table 1 below:

[0099] Table 1 in, This represents the weight coefficient of the nth action contact point; Let represent the nth behavioral touchpoint, where n represents the number of behavioral touchpoints. In the data-driven measurement formulas for the sub-characteristics of "usability, information effectiveness, and task efficiency," the indicators (sub-features) positively correlated with user experience are represented by y= The function is used for calculation, and the indicators (sub-features) that show a negative correlation are expressed through y= The function performs calculations and assigns weight coefficients based on the degree of influence of different behavioral touchpoints on sub-features under the QoE dimension.

[0100] Step D4: Determine the user's experience score for each sub-feature based on the data-driven measurement formula for each sub-feature.

[0101] Based on the data-driven measurement formulas for each sub-feature in step D3, calculate the user's experience measurement score for each sub-feature.

[0102] Furthermore, for the three QoE dimensions of "consistency, page performance, and satisfaction," experience data is primarily acquired through three methods and uniformly incorporated into the user experience measurement quantification model: First, "consistency" experience data is obtained through expert review, using a structured scoring table to manually review dimensions such as overall system style consistency, general framework consistency, and consistency of common scenarios and components, serving as a high-confidence subjective judgment basis; second, "page performance" data is obtained through page tracking and front-end monitoring, collecting objective performance indicators such as request first-screen rendering time, page request response time, and API request response time, and converting time data into statistical quantities according to quantification rules; third, "satisfaction" data is obtained through questionnaires and interviews, periodically collecting users' subjective satisfaction data through structured questionnaires and quantifiable interview items.

[0103] Step D5: Based on the data-driven measurement formulas corresponding to each dimension, the experience measurement scores of the sub-features of each dimension are weighted and fused to obtain the user's experience measurement score on the dimension corresponding to each sub-feature.

[0104] As an example, in the user experience measurement model, the data-driven measurement formula for the QoE dimension is shown in Table 2 below:

[0105] The data measurement formulas for each dimension are explained below: (1) Usability elements include sub-characteristics such as "operational complexity, intuitive and habitual operation, fault-tolerant response, easy error recovery, learning cost, effectiveness of help mechanisms, rationality of interface layout, and clarity of interface elements". The data measurement formula for usability elements is to perform weighted fusion processing on the tested KEI indicators (key indicator dataset) based on the positive and negative correlation and influence of each sub-characteristic on usability elements. The formula is as follows:

[0106] Z represents the score for the usability dimension, which is a measure of the user experience. N is the total number of all indicators in the dataset of the key indicators being tested, and k is the current number of indicators; a represents the weight percentage (0-1) of a single indicator, i.e., sub-feature. C, I, E, Y, L, H, P, and Q represent the scores (0-10) of the sub-features calculated above.

[0107] (2) Before implementing the consistency indicator system, a consistency self-checklist including "overall style, general framework, common scenarios and components" must be determined. The system consistency is then comprehensively evaluated by statistically analyzing the percentage of problematic indicators and calculating the severity scores of the problematic indicators. The data-driven measurement formula for consistency elements is as follows:

[0108] X represents the score for experience metrics on the consistency dimension; n represents the number of indicators that have encountered problems; N is the total number of all indicators in the key indicator dataset, and k is the current number of indicators; a represents the weight percentage of a single indicator (0-1); b represents the severity score (0-10) for a single indicator.

[0109] (3) The measurement of page performance elements is mainly achieved by weighting sub-features such as "first screen rendering time, page request response time, and API request response time" to obtain the total page loading time. The data-driven measurement formula for page performance elements is as follows:

[0110] M represents the score for experience metrics in the page performance dimension; N is the total number of all indicators in the dataset of the key indicators being tested, and k is the current number of indicators; a represents the weight percentage of a single indicator (0-1); b represents the score (0-10) for a single indicator.

[0111] (4) Information effectiveness includes sub-characteristics such as "fluency of information structure, completeness of information, readability of information, comprehensibility of information, and accuracy of information". The data-based measurement formula for information effectiveness elements is to perform a weighted average of the sub-features in the tested key indicator dataset based on the positive and negative correlation and influence of each sub-characteristic on the information effectiveness elements:

[0112] V represents the score for experience measurement on the information effectiveness dimension; N is the total number of all indicators in the dataset of the key indicators being tested, and k is the current number of indicators; a represents the weight percentage of a single indicator (0-1); F, B, D, S, and A are the scores (0-10) of the KEI index (sub-feature) calculated in Table 1 above.

[0113] (5) The results calculated in the task efficiency dimension, as a type of objective data, can provide direct feedback for system improvement. These include sub-characteristics such as "functional utilization rate, task completion rate, and task completion time". The data-driven measurement formula for task efficiency elements is a weighted average of the sub-features in the tested key indicator dataset based on the positive and negative correlation and influence of each sub-characteristic on the information effectiveness elements.

[0114] W represents the score for the experience metric on the task efficiency dimension; N is the total number of all indicators in the dataset of the key indicators being tested, and k is the current number of indicators; a represents the weight percentage of a single indicator (0-1); U, R, and T are the scores (0-10) of the KEI index (sub-feature) calculated in Table 1 above.

[0115] (6) The elements of the satisfaction dimension include sub-characteristics such as "performance satisfaction, interface satisfaction, function satisfaction, and information satisfaction". The data measurement formula for the satisfaction elements is to obtain the scores of the sub-characteristics through questionnaires, and then to calculate the weighted average of the sub-features in the tested key indicator dataset based on the positive and negative correlation and influence of each sub-characteristic on the information validity elements:

[0116] G represents the score for experience metrics on the satisfaction dimension; N is the total number of all indicators in the dataset of the key indicators being tested, and k is the current number of indicators; a represents the weight percentage of a single indicator (0-1); b represents the satisfaction score (0-10) for a single indicator.

[0117] (7) The user experience metric score, i.e., the total score, is calculated by weighting and fusing the scores of each QoE dimension based on their positive or negative correlation and influence on the overall experience. The data-driven formula for the user experience metric score is as follows:

[0118] Z, X, M, V, W, and G represent the scores for each QoE dimension in Table 2 above.

[0119] Step D6: Determine the user's experience metric score based on the scores of experience metrics across each dimension.

[0120] The user's experience metric score is determined by weighted fusion of the experience metrics scores from each dimension in step D5. It should be noted that the aforementioned dimensions and corresponding sub-features can be adjusted based on different user experience-related monitoring data or different business scenarios, and are not specifically limited.

[0121] In this embodiment, a user experience measurement quantification model is constructed based on a key indicator dataset, the weight coefficients of behavioral touchpoints on sub-features, and calculation rules. The user experience measurement quantification model calculates the experience measurement scores for each sub-feature using methods such as weighted averaging and hierarchical analysis. It then performs weighted fusion processing on the experience measurement scores of sub-features across each dimension to obtain the experience measurement scores for each dimension's indicators. Finally, it quantifies and aggregates these experience measurement scores to output an overall experience measurement score, thus achieving a numerical representation of the abstract experience.

[0122] In one embodiment, after obtaining the user's experience metric score (i.e., S108), the following steps E1-E2 may also be performed: Step E1: Dynamically monitor the user's experience metric score. When the user's experience metric score exceeds a preset threshold, issue a threshold alarm.

[0123] The system dynamically monitors user experience metrics scores. When an abnormal drop in score is detected or a specific sub-feature or dimension exceeds a threshold, an alarm is triggered and a problem report is generated. The experience metric scores include: the user's experience metric score, the experience metric scores for each sub-feature, and the experience metric scores for the corresponding dimension of the sub-feature.

[0124] Step E2: Based on the threshold alarm, generate and display the experience data report.

[0125] Based on threshold alarms, the system intuitively presents experience data and calculation results through dashboards, trend charts, matrix analysis charts, and other methods, supporting multi-dimensional queries and comparative analysis.

[0126] In this embodiment, various calculated scores in the user experience measurement quantification model can be dynamically monitored. When a score exceeds a preset threshold, a threshold alarm can be issued and an experience data report can be generated, enabling frontline agents to take corresponding measures to improve the user experience.

[0127] Figure 6 This is a flowchart of a user experience measurement system architecture, specifically including: Data sources for monitoring data: event tracking data, questionnaire feedback, manual annotation, and interaction data (behavior, performance, etc.) from the customer service system; a data acquisition module, which collects monitoring data from multiple sources, including system operation logs, event tracking data, questionnaire feedback, and manually annotated information; a data preprocessing module, which cleans, denoises, normalizes, and formats the raw data (i.e., monitoring data) to ensure consistency and computability of the input data; an indicator calculation module, which performs indicator processing on the collected monitoring data according to the mapping rules between a pre-built candidate indicator library and behavioral touchpoints, generating a structured key indicator dataset; a quantification model calculation module, which calls the user experience measurement quantification model to perform weighted fusion and hierarchical calculation on the indicators in the key indicator dataset, obtaining a comprehensive experience measurement score and scores for each dimension; and a monitoring and alarm module, which dynamically monitors the experience measurement score and triggers alarms and generates problem reports when an abnormal drop in the experience measurement score or a specific indicator exceeds a threshold. Visualization module: Presents experience data and calculation results intuitively through dashboards, trend charts, matrix analysis charts, etc., and supports multi-dimensional query and comparative analysis.

[0128] As can be seen, the platform applying this user experience measurement system covers multiple business scenarios, can simultaneously monitor the user experience of multiple business scenarios, and improve the overall experience perception level of the system; at the same time, it enables automated diagnosis of user experience by mapping indicators and calculating user experience measurement quantification models to automate problem localization and reduce manual intervention; in addition, it enables work order linkage by automatically generating work orders and distributing them to the responsible team when an experience is abnormal, shortening the cycle from problem discovery to resolution; and the platform monitoring is highly scalable, with the platform architecture supporting the expansion of new business scenarios, indicator systems and responsible teams to adapt to the evolution needs of different customer service systems.

[0129] Figure 7 This is a flowchart illustrating another user experience measurement method provided in an embodiment of this application, such as... Figure 7 As shown, the method includes the following steps: S701 acquires monitoring data related to user experience, uses grounded analytics to perform open coding on the monitoring data to obtain initial concept labels, and determines the behavioral touchpoints of frontline agents based on the initial concept labels.

[0130] S702, based on the similarity of each initial concept tag, aggregate the initial concept tags, determine the aggregated initial concept tags and perform main axis encoding to obtain the behavior flow of front-line agents.

[0131] S703 determines the behavioral stages of frontline agents by selectively coding the behavioral process; and generates a behavioral journey map of frontline agents based on behavioral touchpoints, behavioral processes, and behavioral stages.

[0132] S704: Obtain authoritative data in advance, extract descriptive entries related to user experience metrics from the authoritative data, and perform semantic deduplication on the descriptive entries to obtain semantic entries after semantic deduplication.

[0133] S705, the semantic entries are processed in layers to obtain the first layer dimension of the authoritative data, and the second layer sub-features corresponding to the first layer dimension.

[0134] S706 constructs a candidate indicator library for user experience measurement based on the first-level dimension and second-level sub-features of authoritative data.

[0135] S707 determines user behavior types based on the behavioral journey map of front-line agents; according to the user behavior types, it selects dimensions and sub-features of the dimensions related to user experience from the candidate indicator library, and uses the dimensions and sub-features of the dimensions related to user experience as experience indicators.

[0136] S708, acquires the behavioral touchpoints of front-line agents in the behavioral journey map.

[0137] S709 performs matrix analysis on the sub-features of different dimensions in behavioral touchpoints and experience metrics to establish a multi-dimensional correspondence between behavioral touchpoints and sub-features, where a behavioral touchpoint corresponds to one or more sub-features.

[0138] S710, based on multi-dimensional correspondence, uses semantic analysis to determine the similarity between behavioral touchpoints and their corresponding sub-features; based on the similarity, it determines the sub-features mapped to all behavioral touchpoints of front-line agents and the dimensions corresponding to the sub-features.

[0139] S711 generates a key indicator dataset based on the sub-features mapped from all behavioral touchpoints of frontline agents and the dimensions corresponding to those sub-features.

[0140] S712 uses a user experience measurement quantification model to determine the indicator function type of the sub-features of the behavioral touchpoints in the indicator dataset based on the indicator values ​​of each sub-feature corresponding to the behavioral touchpoints in the key indicator dataset. The indicator function types include: positive correlation indicator functions and / or negative correlation indicator functions.

[0141] S713, based on the importance of each behavioral touchpoint in the indicator dataset, determines the weight coefficient of each behavioral touchpoint under the corresponding sub-feature.

[0142] S714, based on the positive correlation index function and / or negative correlation index function, and the weight coefficient, determine the data measurement formula for the sub-features corresponding to each behavior touchpoint, as well as the data measurement formula for each dimension.

[0143] S715, based on the data-driven measurement formulas for each sub-feature, determine the user's experience measurement score for each sub-feature.

[0144] S716, based on the data-driven measurement formulas corresponding to each dimension, performs weighted fusion processing on the experience measurement scores of the sub-features of each dimension to obtain the user's experience measurement score on the dimension corresponding to each sub-feature.

[0145] S717 determines the user's experience metric score based on the scores of experience metrics across various dimensions.

[0146] The S718 dynamically monitors the user's experience metric score. When the user's experience metric score exceeds a preset threshold, a threshold alarm is issued. Based on the threshold alarm, an experience data report is generated and displayed.

[0147] The specific processes from S701 to S718 described above have been explained in detail in the above embodiments and will not be repeated here.

[0148] The technical solution of this application embodiment obtains a behavioral journey map of front-line agents interacting with users by acquiring monitoring data related to user experience and encoding the monitoring data. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built candidate indicator library for user experience metrics, experience indicators related to user experience are selected. These experience indicators include multiple dimensions and sub-features of those dimensions. The behavioral touchpoints and experience indicators are mapped to obtain a key indicator dataset corresponding to the user. Through a user experience metric quantification model, the key indicator dataset is subjected to hierarchical weighted fusion processing to obtain the user's experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset. It is evident that constructing a behavioral journey graph using monitoring data, including objective data such as operational log data and subjective data such as user interview data and user observation data, can improve the diversity and comprehensiveness of user data acquisition. Furthermore, mapping behavioral touchpoints in the behavioral journey graph to multiple dimensions and sub-features of dimensions selected from a candidate indicator library yields a key indicator dataset. Through hierarchical weighted fusion processing, a user experience metric score is obtained. This allows for the determination of the key indicator dataset to obtain the user experience metric score based on specific scenarios, thus addressing the issue of low accuracy in user experience measurement.

[0149] It should be noted that the user experience measurement method provided in this application embodiment can be executed by a user experience measurement device, or a control module within that user experience measurement device for executing the user experience measurement method. This application embodiment uses the execution of the user experience measurement method by a user experience measurement device as an example to illustrate the user experience measurement device provided in this application embodiment.

[0150] Figure 8 This is a schematic diagram of the structure of a user experience measurement device according to an embodiment of the present invention. Figure 8 As shown, the user experience measurement device includes: an acquisition module 81, a construction module 82, a mapping module 83, and a scoring module 84. The acquisition module 81 is used to acquire monitoring data related to user experience and encode the monitoring data to obtain a behavioral journey map of front-line agents interacting with users. The monitoring data includes one or more of the following: user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Module 82 is used to filter out user experience metrics related to user experience based on a pre-built library of candidate metrics for user experience metrics. The user experience metrics include multiple dimensions and sub-features of the dimensions. The mapping module 83 is used to map behavioral touchpoints and experience metrics to obtain a dataset of key metrics corresponding to users. The scoring module 84 is used to perform hierarchical weighted fusion processing on the key indicator dataset through the user experience measurement quantification model to obtain the user experience measurement score. The user experience measurement quantification model is used to score the dimensions and sub-features in the key indicator dataset.

[0151] In one embodiment, the acquisition module 81 is specifically used to perform open coding on the monitoring data using grounded analysis to obtain initial concept tags. Based on the initial concept tags, the behavioral touchpoints of frontline agents interacting with users are determined. The initial concept tags are aggregated according to their similarity to determine the aggregated initial concept tags and then subjected to axial coding to obtain the behavioral flow of the frontline agents. By selectively coding the behavioral flow, the behavioral stages of the frontline agents are determined. Based on the behavioral touchpoints, behavioral flow, and behavioral stages, a behavioral journey map of the frontline agents is generated.

[0152] In one embodiment, the device is further configured to pre-acquire authoritative data, extract descriptive entries related to user experience metrics from the authoritative data, and perform semantic deduplication on the descriptive entries to obtain semantically deduplicated semantic entries; perform hierarchical processing on the semantic entries to obtain a first-level dimension of the authoritative data and a second-level sub-feature corresponding to the first-level dimension; and construct a candidate indicator library for user experience metrics based on the first-level dimension and the second-level sub-feature of the authoritative data. The construction module 82 is configured to determine the user behavior type based on the behavioral journey map of frontline agents; and, based on the user behavior type, select dimensions and sub-features related to user experience from the candidate indicator library, using these dimensions and sub-features as experience metrics.

[0153] In one embodiment, the mapping module 83 is specifically used to obtain the behavioral touchpoints of frontline agents in the behavioral journey graph; perform matrix analysis on the behavioral touchpoints and sub-features of different dimensions in the experience metrics to establish a multi-dimensional correspondence between behavioral touchpoints and sub-features, wherein a behavioral touchpoint corresponds to one or more sub-features; based on the multi-dimensional correspondence, determine the similarity between the behavioral touchpoints and their corresponding sub-features through semantic analysis; calculate the sub-features mapped to all behavioral touchpoints of frontline agents and the dimensions corresponding to the sub-features based on the similarity; and generate a key metric dataset based on the sub-features mapped to all behavioral touchpoints of frontline agents and the dimensions corresponding to the sub-features.

[0154] In one embodiment, the scoring module 84 is specifically used to: quantify the user experience metrics using a quantification model; determine the indicator function type of the sub-features of the behavioral touchpoints in the key indicator dataset based on the indicator values ​​of each sub-feature corresponding to the behavioral touchpoints in the key indicator dataset; the indicator function type includes: positive correlation indicator functions and / or negative correlation indicator functions; determine the weight coefficient of each behavioral touchpoint under the corresponding sub-feature based on the importance of each behavioral touchpoint in the key indicator dataset; determine the data-based measurement formula for the sub-features corresponding to the behavioral touchpoints, and the data-based measurement formula for each dimension, based on the positive correlation indicator functions and / or negative correlation indicator functions and the weight coefficients; determine the user's experience metric score for each sub-feature based on the data-based measurement formula for each sub-feature; perform weighted fusion processing on the experience metric scores of the sub-features of each dimension based on the data-based measurement formula for each dimension, to obtain the user's experience metric score for each dimension corresponding to the sub-feature; and determine the user's experience metric score based on the experience metric scores for each dimension.

[0155] In one embodiment, the device is also used to dynamically monitor the user's experience metric score, and when the user's experience metric score is greater than a preset threshold, issue a threshold alarm; based on the threshold alarm, generate an experience data report and display it.

[0156] The technical solution of this application embodiment obtains a behavioral journey map of front-line agents interacting with users by acquiring monitoring data related to user experience and encoding the monitoring data. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built candidate indicator library for user experience metrics, experience indicators related to user experience are selected. These experience indicators include multiple dimensions and sub-features of those dimensions. The behavioral touchpoints and experience indicators are mapped to obtain a key indicator dataset corresponding to the user. Through a user experience metric quantification model, the key indicator dataset is subjected to hierarchical weighted fusion processing to obtain the user's experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset. It is evident that constructing a behavioral journey graph using monitoring data, including objective data such as operational log data and subjective data such as user interview data and user observation data, can improve the diversity and comprehensiveness of user data acquisition. Furthermore, mapping behavioral touchpoints in the behavioral journey graph to multiple dimensions and sub-features of dimensions selected from a candidate indicator library yields a key indicator dataset. Through hierarchical weighted fusion processing, a user experience metric score is obtained. This allows for the determination of the key indicator dataset to obtain the user experience metric score based on specific scenarios, thus addressing the issue of low accuracy in user experience measurement.

[0157] The user experience measurement device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0158] The user experience measurement device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0159] The user experience measurement device provided in this application embodiment can achieve... Figures 1 to 7 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0160] Based on the same technical concept, embodiments of this application also provide an electronic device for performing the above-described user experience measurement method. Figure 9 This is a schematic diagram of the structure of an electronic device to implement various embodiments of this application. The electronic device can vary significantly due to differences in configuration or performance, and may include a processor 910, a communication interface 920, a memory 930, and a communication bus 940. The processor 910, communication interface 920, and memory 930 communicate with each other via the communication bus 940. The processor 910 can call a computer program stored in the memory 930 and executable on the processor 910 to perform the following steps: Acquire monitoring data related to user experience and encode the monitoring data to obtain a behavioral journey map of front-line agents interacting with users. The monitoring data includes one or more of the following: user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built library of candidate metrics for user experience metrics, user experience metrics related to user experience are selected. These user experience metrics include multiple dimensions and sub-features of the dimensions. By mapping behavioral touchpoints and experience metrics, a dataset of key metrics corresponding to users is obtained. The user experience measurement quantification model performs hierarchical weighted fusion processing on the key indicator dataset to obtain the user experience measurement score. The user experience measurement quantification model is used to score the dimensions and sub-features in the key indicator dataset.

[0161] The technical solution of this application embodiment obtains a behavioral journey map of front-line agents interacting with users by acquiring monitoring data related to user experience and encoding the monitoring data. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built candidate indicator library for user experience metrics, experience indicators related to user experience are selected. These experience indicators include multiple dimensions and sub-features of those dimensions. The behavioral touchpoints and experience indicators are mapped to obtain a key indicator dataset corresponding to the user. Through a user experience metric quantification model, the key indicator dataset is subjected to hierarchical weighted fusion processing to obtain the user's experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset. It is evident that constructing a behavioral journey graph using monitoring data, including objective data such as operational log data and subjective data such as user interview data and user observation data, can improve the diversity and comprehensiveness of user data acquisition. Furthermore, mapping behavioral touchpoints in the behavioral journey graph to multiple dimensions and sub-features of dimensions selected from a candidate indicator library yields a key indicator dataset. Through hierarchical weighted fusion processing, a user experience metric score is obtained. This allows for the determination of the key indicator dataset to obtain the user experience metric score based on specific scenarios, thus addressing the issue of low accuracy in user experience measurement.

[0162] The specific execution steps can be found in the various steps of the above-described user experience measurement method embodiment, and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0163] It should be noted that the electronic devices in the embodiments of this application include: servers, terminals, or other devices besides terminals.

[0164] The above electronic device structure does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or arrange them differently. For example, an input unit may include a Graphics Processing Unit (GPU) and a microphone, and a display unit may use a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar display panels. User input units include at least one of a touch panel and other input devices. A touch panel is also called a touchscreen. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be elaborated further here.

[0165] Memory can be used to store software programs and various data. Memory can primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area can store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, memory can include volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0166] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and applications, while the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor.

[0167] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described user experience measurement method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0168] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0169] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described user experience measurement method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0170] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0171] This application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the processor is used to run the program or instructions to implement the various processes of the above-mentioned product recommended method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0172] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0173] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0174] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A user experience measurement method, characterized in that, The method includes: Acquire monitoring data related to user experience and encode the monitoring data to obtain a behavioral journey map of front-line agents interacting with users. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. Based on a pre-built library of candidate metrics for user experience metrics, experience metrics related to the user experience are selected, and the experience metrics include: multiple dimensions and sub-features of the dimensions; The behavioral touchpoints and the experience metrics are mapped to obtain the key metric dataset corresponding to the user. The user experience measurement quantification model is used to perform hierarchical weighted fusion processing on the key indicator dataset to obtain the user's experience measurement score. The user experience measurement quantification model is used to score the dimensions and sub-features in the key indicator dataset.

2. The method according to claim 1, characterized in that, The process of encoding the monitoring data to obtain a behavioral journey map of frontline agents interacting with users includes: Using grounded analysis, the monitoring data is processed with open coding to obtain initial concept labels. Based on the initial concept labels, the behavioral touchpoints of the front-line agents are determined. Based on the similarity of each initial concept tag, the initial concept tags are aggregated to determine the aggregated initial concept tags and perform main axis encoding to obtain the behavior flow of the front-line agent; By selectively encoding the behavioral process, the behavioral stages of the frontline agents are determined. Based on the behavioral touchpoints, the behavioral flow, and the behavioral stages, a behavioral journey map of the frontline agents is generated.

3. The method according to claim 1, characterized in that, Before selecting experience metrics related to the user experience from a pre-built pool of candidate metrics for user experience metrics, the process also includes: Authoritative data is obtained in advance, descriptive entries related to the user experience metric are extracted from the authoritative data, and semantic deduplication is performed on the descriptive entries to obtain semantic deduplication entries. The semantic entries are processed in layers to obtain a first-layer dimension of the authoritative data and a second-layer sub-feature corresponding to the first-layer dimension; Based on the first-level dimension and the second-level sub-features of the authoritative data, construct the candidate indicator library for the user experience metric; The process of filtering out experience metrics related to the user experience includes: Based on the behavioral journey map of the front-line agents, the user behavior type is determined; Based on the user behavior type, the dimension and its sub-features related to the user experience are selected from the candidate indicator library, and the dimension and its sub-features related to the user experience are used as the experience indicators.

4. The method according to claim 1, characterized in that, The process of mapping the behavioral touchpoints and the experience metrics to obtain the key metric dataset corresponding to the user includes: Obtain the behavioral touchpoints of the frontline agents in the behavioral journey map; Matrix analysis is performed on the sub-features of different dimensions in the behavioral touchpoints and the experience metrics to establish a multi-dimensional correspondence between the behavioral touchpoints and the sub-features, wherein the behavioral touchpoint corresponds to one or more of the sub-features; Based on the multidimensional correspondence, the similarity between the behavioral touchpoint and the corresponding sub-feature is determined by semantic analysis. Based on the similarity, determine the sub-features mapped to all the behavioral touchpoints of the front-line agent and the dimensions corresponding to the sub-features; The key indicator dataset is generated based on the sub-features mapped to all the behavioral touchpoints of the front-line agents and the dimensions corresponding to the sub-features.

5. The method according to claim 1, characterized in that, The process of using a user experience metric quantification model to perform hierarchical weighted fusion processing on the key indicator dataset to obtain the user's experience metric score includes: Using the user experience measurement quantification model, based on the index values ​​of each sub-feature corresponding to the behavioral touchpoint in the key index dataset, the index function type of the sub-feature of the behavioral touchpoint in the key index dataset is determined. The index function type includes: positive correlation index function and / or negative correlation index function. Based on the importance of each behavioral touchpoint in the key indicator dataset, determine the weight coefficient of each behavioral touchpoint under the corresponding sub-feature; Based on the positive correlation index function and / or the negative correlation index function, and the weight coefficient, determine the data-based measurement formula for each behavioral touchpoint corresponding to the sub-feature, and the data-based measurement formula for each dimension; Based on the data-driven measurement formula for each of the sub-features, determine the user's experience metric score for each of the sub-features; Based on the data-driven measurement formulas corresponding to each dimension, the scores of the experience metrics of the sub-features of each dimension are weighted and fused to obtain the user's score of the experience metrics on the dimensions corresponding to each sub-feature. The user's experience metric score is determined based on the ratings of the experience metrics across each dimension.

6. The method according to claim 1, characterized in that, After obtaining the user's experience metric score, the process further includes: The system dynamically monitors the user's experience metric score, and issues a threshold alarm when the user's experience metric score exceeds a preset threshold. Based on the threshold alarm, an experience data report is generated and displayed.

7. A user experience measurement device, characterized in that, include: The acquisition module is used to acquire monitoring data related to user experience and encode the monitoring data to obtain a behavioral journey map of front-line agents interacting with users. The monitoring data includes one or more of user interview data, user observation data, and operation log data. The behavioral journey map includes behavioral touchpoints. The construction module is used to filter out experience metrics related to the user experience based on a pre-built candidate metric library for user experience metrics. The experience metrics include: multiple dimensions and sub-features of the dimensions. The mapping module is used to map the behavioral touchpoints and the experience metrics to obtain the key metric dataset corresponding to the user. The scoring module is used to perform hierarchical weighted fusion processing on the key indicator dataset through a user experience metric quantification model to obtain the user's experience metric score. The user experience metric quantification model is used to score the dimensions and sub-features in the key indicator dataset.

8. An electronic device, characterized in that, The device includes a processor and a memory electrically connected to the processor, the memory storing a computer program, and the processor being configured to call and execute the computer program from the memory to implement a user experience measurement method as described in claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium is used to store a computer program that can be executed by a processor to implement a user experience measurement method as described in claims 1-6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements a user experience measurement method as described in claims 1-6.

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