CN113449942A

CN113449942A - Customer experience evaluation method, system and management platform

Info

Publication number: CN113449942A
Application number: CN202010224569.1A
Authority: CN
Inventors: 苏晓华; 林君玉; 王莹; 陈佳
Original assignee: Beijing Yixiang Information Technology Co ltd; China Everbright Bank Co Ltd
Current assignee: Beijing Yixiang Information Technology Co ltd; China Everbright Bank Co Ltd
Priority date: 2020-03-26
Filing date: 2020-03-26
Publication date: 2021-09-28

Abstract

The invention discloses a customer experience evaluation method, a system and a management platform, wherein the customer experience evaluation method comprises the following steps: determining an evaluation index according to an evaluation object experienced by a client; obtaining a plurality of evaluation data of at least one data source based on the determined evaluation indexes, wherein the evaluation data comprises objective data and subjective data; inputting the evaluation data into a preset customer experience evaluation model, and respectively calculating evaluation values of all evaluation indexes; and obtaining an evaluation result of the customer experience according to the evaluation value of each evaluation index. According to the method, the product is evaluated by collecting the evaluation data of different data source channels, the data is more comprehensive, the customer experience problem can be found in time, the priority improvement score is lower, the product is in a medium-high level problem, and the customer experience level is obviously improved; the weight determination is carried out by combining expert assignment and factor analysis, so that the weight determination is more scientific and standard, the formulation of the scoring standard is more systematic and reasonable, and the evaluation result is more comprehensive and objective.

Description

Customer experience evaluation method, system and management platform

Technical Field

The invention relates to the technical field of customer experience evaluation, in particular to a customer experience evaluation method, a customer experience evaluation system and a customer experience evaluation management platform.

Background

With the gradual transition of the economic society to the experience economic era, not only the product itself but also more services, emotions and subjective experiences are concerned by customers. Therefore, under the condition that products and services provided by various banks are more and more homogeneous and internet finance is rapidly developed, the customer experience is emphasized, and the core competitiveness of the bank for attracting and storing customers is really achieved by taking the customers as the center.

The existing customer experience evaluation method in the financial industry is wholly divided into multiple levels of indexes, evaluation is carried out according to detailed indexes, weight subjective assignment is carried out on the detailed indexes, evaluation is subjective, and comprehensiveness and systematicness are insufficient. Specifically, there are mainly the following three problems: (1) the weight is subjective assignment, the support of objective data is lacked, and the objectivity is poor; (2) the evaluation mode is single, the adopted evaluation mode is subjective evaluation mostly, and the subjectivity is strong; (3) the evaluation range is small, at present, the evaluation range is mainly the experience evaluation of mobile phone banks, personal internet bank, enterprise internet bank and direct sales banks, and the experience evaluation of new products such as WeChat banks, intelligent counters and the like is lacked.

Disclosure of Invention

In view of the above technical problems in the prior art, embodiments of the present invention provide a method, a system and a management platform for evaluating customer experience, which aim to solve the technical problems of insufficient objectivity, comprehensiveness and systematicness in evaluation in the prior art.

In order to solve the technical problem, the embodiment of the invention adopts the following technical scheme:

a customer experience evaluating method comprises the following steps:

determining an evaluation index according to an evaluation object experienced by a client;

obtaining a plurality of evaluation data of at least one data source based on the determined evaluation indexes, wherein the evaluation data comprises objective data and subjective data;

inputting the evaluation data into a preset customer experience evaluation model, and respectively calculating evaluation values of all evaluation indexes;

and obtaining an evaluation result of the customer experience according to the evaluation value of each evaluation index.

Optionally, the evaluation index includes a multi-level evaluation index, and the multi-level evaluation index includes at least two levels of evaluation indexes classified according to a hierarchy.

Optionally, obtaining a plurality of evaluation data of at least one data source based on the determined evaluation index includes:

determining a data source channel corresponding to the evaluation index according to the determined evaluation index;

and acquiring evaluation data corresponding to the evaluation index according to the data source channel.

Optionally, the data source channel includes a data collection mode and a data type, the data collection mode includes at least one of expert evaluation data, questionnaire data, usability test data, application market evaluation data and objective data related to an evaluation object, and the data type includes qualitative data and quantitative data.

Optionally, the building of the customer experience evaluation model includes the following steps:

obtaining a sample evaluation data set corresponding to an evaluation index;

determining the weight of each evaluation index;

making a grading standard of each evaluation index and generating a corresponding grading table;

and integrating the evaluation tables of the evaluation indexes to obtain a customer experience evaluation model.

Optionally, determining the weight of each evaluation index includes:

determining the weight of the first-level index by an expert assignment method;

the weights of the other level indicators are determined by a factor analysis method.

Optionally, the scoring standard is formulated according to the type of the evaluation index, and the sample evaluation data of at least two data source channels is obtained when the scoring standard of each evaluation index is formulated.

Optionally, the step of obtaining an evaluation result of the customer experience according to the evaluation value of each evaluation index further includes:

and updating the customer experience evaluation model according to the evaluation result.

The embodiment of the invention also provides a customer experience evaluating system, which comprises:

the determining module is used for determining an evaluation index according to an evaluation object experienced by a client;

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of evaluation data of at least one data source based on a determined evaluation index, and the evaluation data comprises objective data and subjective data;

the calculation module is used for inputting the evaluation data into a preset customer experience evaluation model and respectively calculating evaluation values of all evaluation indexes;

and the evaluation module is used for obtaining an evaluation result of the customer experience according to the evaluation value of each evaluation index.

The embodiment of the present invention further provides a customer experience management platform, connected to the customer experience evaluation system, where the customer experience management platform includes:

the management module is used for managing the customer experience evaluation system;

the storage module is used for storing the management data of the customer experience evaluation system in a classified manner;

and the communication module is used for receiving and sending management data information.

The embodiment of the invention also provides a computer-readable storage medium, wherein computer-executable instructions are stored on the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the method for evaluating the customer experience is realized.

Compared with the prior art, the embodiment of the invention has the beneficial effects that:

(1) according to the customer experience evaluation method provided by the embodiment of the invention, the product is evaluated by collecting the evaluation data of different data source channels, the evaluation data is more comprehensive, the customer experience problem can be found in time, the priority improvement score is lower, and the product is in a medium-high level problem, and the customer experience score and the customer experience level can be obviously improved and the customer experience problem can be reduced in continuous optimization iteration.

(2) The weights are determined by combining expert assignment and factor analysis, so that the weights are determined more scientifically and more standard, different scoring standards are formulated according to the types of the evaluation indexes, the formulation of the scoring standards is more systematic and reasonable, and the evaluation result is more comprehensive and objective.

(3) By acquiring the evaluation data from different angles such as experts, customers and the performance of the product, the collected data is more objective, and the reliability of the evaluation result is improved.

(4) The embodiment of the invention can evaluate products in the financial fields of mobile phone banking, personal internet banking, enterprise internet banking, direct sales banking, WeChat banking, intelligent counters and the like, and the evaluation range is more comprehensive and complete.

Drawings

FIG. 1 is a flow chart of a customer experience assessment method according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating the process of obtaining customer experience profile according to an embodiment of the present invention;

FIG. 3 is a flow chart of the construction of a customer experience evaluation model according to an embodiment of the present invention;

FIG. 4 is a schematic layout diagram of a scoring table of a customer experience evaluation method according to an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a customer experience evaluation system according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a customer experience evaluation management platform according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the application.

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and, together with a general description of the application given above and the detailed description of the embodiments given below, serve to explain the principles of the application.

These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.

It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.

The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application of unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.

Fig. 1 is a flowchart of a customer experience evaluation method according to an embodiment of the present invention. As shown in fig. 1, the method for evaluating customer experience according to the embodiment of the present invention includes:

step S10: and determining an evaluation index according to the evaluation object experienced by the client.

Specifically, the evaluation object experienced by the customer is a product in the financial field such as bank, fund, insurance and the like, the bank product comprises a mobile phone bank, a personal internet bank, an enterprise internet bank, a direct sales bank, a WeChat bank, an intelligent counter and the like, and the evaluation object (evaluation range) is more comprehensive and complete. Different evaluation objects in the bank product have different evaluation indexes, for example, mobile phone banks pay more attention to the interactivity with users, internet banks pay more attention to the satisfaction of functions, that is, the completeness of function points/information points with higher user demand. In the embodiment, the mobile phone bank is used as an evaluation object to evaluate the customer experience of the mobile phone bank.

The evaluation indexes comprise multi-level evaluation indexes classified according to levels, and specifically comprise at least two levels of evaluation indexes classified according to levels. And classifying the evaluation indexes according to three dimensions of availability, usability and serviceability of the evaluation object, wherein the availability refers to whether the evaluation object can be used, the usability refers to whether the evaluation object is convenient to use, and the serviceability refers to whether a customer likes to use. Each index can be subdivided into a first-level index, a second-level index and a third-level index according to the hierarchy, each level of index at least comprises an independent index, and each evaluation index in the same level of index is independent, so that the determination of the evaluation index is more scientific and standardized. In the embodiment of the invention, three categories of indexes of availability, usability and reusability can be used as systematic indexes and are not divided into evaluation indexes of all levels. In other embodiments, the evaluation index may be further subdivided, for example, the evaluation index may be further subdivided into a fourth-level index, a fifth-level index, and the like.

In this embodiment, the primary index includes multiple dimensional indexes such as function experience, performance experience, security experience, innovation experience, operation experience, emotion experience, post-customer evaluation, and customer behavior of the mobile banking. In the first-level indexes, the functional experience, performance experience and safety experience indexes belong to availability indexes, and can be evaluated in the modes of availability test, expert evaluation, questionnaire investigation and the like. The performance experience can be used for evaluating the performance of the mobile phone bank through the automatic test platform on the basis of the evaluation mode. The operation experience index belongs to an usability index so as to be convenient for evaluating the interaction between a product and a client.

The second-level indexes under the functional experience indexes comprise functional indexes, and the third-level indexes under the functional indexes comprise functional satisfaction, functional effectiveness, flow rationality, channel cooperativity and the like.

The secondary indexes under the performance experience comprise stability, responsiveness and the like, wherein the tertiary indexes under the stability comprise function abnormity, UI abnormity, compatibility, collapse rate and the like, and the tertiary indexes under the responsiveness comprise operation fluency, starting time and the like.

The second-level indexes under the security experience comprise trust indexes, wherein the third-level indexes under the trust indexes comprise privacy protection, login security, transaction security and the like.

The second-level indexes under the operation experience comprise operation performance, transaction failure rate, interaction, vision, content and the like, wherein the third-level indexes under the operation performance comprise efficiency and efficiency, the third-level indexes under the interaction comprise convenience, fault tolerance, controllability, feedback rationality, navigation rationality and the like, the third-level indexes under the vision comprise state visibility, interface consistency, layout rationality and the like, and the third-level indexes under the content comprise content comprehensiveness, content accuracy, content perplexity, content consistency, functional innovation and the like.

The evaluation index of the customer experience can directly reflect the overall condition of the customer experience, and has important reference values in the aspects of optimizing operation, formulating market strategies, maintaining customers and the like. Therefore, when each level of indexes of the evaluation indexes are determined, the evaluation indexes are determined from multiple dimensions as comprehensively as possible, so that the evaluation indexes are finely divided, and the evaluation accuracy is improved. The evaluation indexes are refined through multiple dimensions, so that an evaluation index system constructed by using indexes at all levels is more comprehensive and systematic. Meanwhile, from the perspective of a client, an evaluation index is determined based on the actual experience condition of the client, and the real feeling of the client in using the product can be accurately evaluated.

It should be noted that, because the single index in the indexes at each level is irregular, branches of some indexes have many levels, and branches of some indexes have only a small number of levels. The excessive levels may cause that a single index in some branch levels is insufficient, and in addition, the excessively fine level division may cause that the index is excessively finely decomposed, which is not beneficial to the system evaluation of the index. In this step, the determined index level is not greater than 5.

Step S10 further includes: and initializing an evaluation index.

Specifically, according to literature research, industrial experience and the characteristics of an evaluation object, an index related to customer experience is used as an initial evaluation index, and the customer experience evaluation index is initialized.

Step S20: obtaining a plurality of evaluation data of at least one data source based on the determined evaluation indexes, wherein the evaluation data comprises objective data and subjective data.

After the evaluation indexes of the evaluation object experienced by the client are determined, evaluation data corresponding to each evaluation index needs to be collected so as to evaluate the evaluation object. The objective data is data related to customer experience obtained through detection, and the subjective data is customer experience data which is subjectively confirmed by a user.

As shown in fig. 2, step S20 includes the following steps:

step S201: and determining a data source channel corresponding to the evaluation index according to the determined evaluation index.

The data source channel comprises a data acquisition mode and a data type, specifically, the evaluation data related to each evaluation index can be collected in a mode of combining qualitative data and quantitative data, namely, the data type comprises qualitative data and quantitative data, and the data acquisition mode can comprise at least one of expert evaluation data, questionnaire survey data, availability test data, application market evaluation data, objective data related to an evaluation object and the like. Wherein, the expert evaluation data, the questionnaire survey data, the usability test data and the application market evaluation data are subjective data; the objective data related to the evaluation object comprises background technical index evaluation data, such as work order data, performance test reports, transaction failure rate reports, financial data and the like.

In the step, subjective data and objective data are collected from different data source channels, so that the evaluation data is more comprehensive, in addition, expert evaluation can provide relatively accurate expert evaluation data from a professional perspective and provide customer experience problems, questionnaires and the like can provide subjective feelings to products from a customer perspective, and the users can listen to the sounds of the users conveniently so as to provide better customer experience. Table 1 shows the data source channels for each primary index dimension.

Table 1 source of mobile banking customer experience data

As shown in table 1, the usability test data is subjective data of customer behavior, and the transaction failure rate report is objective data of customer behavior.

With the development of new technologies such as big data and cloud computing, the client experience evaluation system can be continuously improved by increasing the client behavior data in consideration of the extensibility of the evaluation content, so that the client experience problem of the product can be rapidly and sharply found from a more objective angle. Customer behavior data also includes, but is not limited to:

(1) registration conversion rate: the method refers to the ratio of the number of people who bind the first type card/the second type card to the number of registered people of the mobile phone number in the account opening process of the mobile phone bank. The attraction of the mobile phone bank to the customer can be indirectly known by collecting the number of the bound card people of the mobile phone bank.

(2) Transfer convenience: the data can be measured by the first transfer page jump, namely the number of pages jumped in the first transfer full flow is small, the flow is simple due to less pages, and the transfer is convenient.

(3) Product attractiveness: the term "financial product" refers to whether financial products such as financing, fund, insurance, etc. are attractive to customers, and is measured by a multiple investment ratio, which is the number of multiple investments/total number of purchases, and a continuous investment ratio, which is the number of continuous investments/total number of purchases.

(4) Conversion rate of product purchase: the method refers to the rate of successfully purchasing financial products such as financing, fund and insurance, and the conversion rate of key nodes can be known through funnel model analysis, and the low conversion rate is analyzed, so that the products are improved;

(5) response time of online customer service: when seeking help of online customer service, the time from sending query information to manual online customer service to the time after the customer service replies is limited, so the response time is shortened as much as possible.

The customer behavior data in the steps (1) to (5) are objective data, can be acquired in real time through background query of a mobile phone bank, and are used for daily customer experience monitoring so as to know product information at any time, and reasons can be found in time when abnormal conditions occur, so that problems are solved.

Step S202: and acquiring evaluation data corresponding to the evaluation index according to the data source channel.

Taking a questionnaire as an example, the determined evaluation index can be converted into a questionnaire topic, then the questionnaire is issued to specific or uncertain clients, and the clients are invited to answer and recover the questionnaire according to actual conditions to obtain questionnaire survey data.

Step S30: and inputting the evaluation data into a preset customer experience evaluation model, and respectively calculating the evaluation value of each evaluation index.

And before inputting the evaluation data into a preset customer experience evaluation model, performing data cleaning on the evaluation data collected in the step S20 to remove invalid data.

Specifically, part of the collected evaluation data, such as questionnaire survey data, is invalid data that is arbitrarily filled or unfilled by the user, and therefore, the invalid data needs to be removed to obtain valid data, so as to ensure reliability of evaluation data collection.

The preset customer experience evaluation model is a causal relationship model composed of a plurality of evaluation indexes, and comprises an evaluation index system constructed by using the evaluation indexes determined in the step S10 and a scoring system determined based on the weights and scoring standards of the evaluation indexes. The evaluation index system is a system formed by evaluation indexes of a first level, a second level, a third level and the like.

As shown in fig. 3, the construction of the customer experience evaluation model includes the following steps:

and S301, acquiring a sample evaluation data set corresponding to the evaluation index.

In this step, the sample evaluation data is obtained similarly to the step S20, the number of the sample evaluation data sets is as large as possible, and the evaluation data with different classification dimensions are obtained as large as possible, so that the final obtained customer experience evaluation model is more accurate.

And step S302, determining the weight of each evaluation index.

Specifically, a method combining subjective expert assignment and objective factor analysis is adopted to determine the weight corresponding to each evaluation index, and weighting and summarizing are carried out according to the weight value of each index, so that the weight and proportion of each single index in the total index are calculated.

The method for determining the weight of each evaluation index specifically comprises the following steps:

step S3021: and determining the weight of the primary index by an expert assignment method.

Experts in several fields are usually invited to assign weights of the primary indexes, and the selection rule of the experts is as follows: the method has the advantages that the user experience work is carried out for more than a certain period, the method has abundant work experience in the aspects of Internet, mobile Internet, software and hardware design, user experience and the like, and the method has professional backgrounds such as psychology, sociology, statistics, industrial design, computer science and technology and the like.

And (4) according to theoretical analysis, the experts evaluate and assign the first-level indexes by combining the working experience and the industry knowledge learned by the experts, and perform multi-round assignment through a plurality of experts, wherein the last assignment is used as the weight when the opinions of the experts tend to be consistent. For example, in this embodiment, through assignment by experts, the first-level index of the performance experience dimension may be assigned to 19%, that is, the weight of the performance experience dimension in each first-level index is 19%.

Step S3022: and determining the weights of the secondary index and the tertiary index by a factor analysis method.

The expert assignment method has strong interpretability but strong subjectivity, so that the second-level and third-level index weights of the sample evaluation data are obtained by collecting the sample evaluation data corresponding to each evaluation index relevant to customer experience and carrying out quantitative analysis on the collected sample evaluation data through a factor analysis method in the step, and the objectivity of the model is improved. Specifically, the sample evaluation data of the same evaluation index in different periods can be collected and recorded according to the respective corresponding time points, and the sample evaluation data of different evaluation indexes in the same period can also be collected and recorded.

In this embodiment, a questionnaire investigation mode may be used to collect sample evaluation data of customer experience. For example, the three-level index may be converted into a questionnaire subject form, and distributed and recycled to obtain questionnaire survey data, wherein each subject in the questionnaire may be scored in a 1-N (N >2) manner, wherein 1 represents very different agreement, 2 represents partial agreement, and … N represents very agreeable, and the collected data is subjected to factor analysis according to the user's score for each subject.

Before the factor analysis, the method also comprises the steps of carrying out data cleaning on the collected evaluation data of the customer experience sample, and deleting repeated and invalid information to obtain valid sample data.

Data analysis generally comprises reliability analysis and validity analysis (factor analysis), and the reliability analysis is an effective analysis method for measuring whether a comprehensive evaluation system has certain fixity and reliability. The reliability is an index reflecting the true degree of the tested features according to the consistency or reliability of the results obtained by the measuring tool.

An internal consistency coefficient is generally used as a measure of reliability, i.e., a kronebach α coefficient, and a larger value indicates a higher reliability. In general, the alpha coefficient of the total scale is preferably above 0.8, with an acceptable range between 0.7 and 0.8; the alpha coefficient of the sub-table is preferably above 0.7, and falls within an acceptable range between 0.6 and 0.7.

Therefore, before factor analysis, reliability analysis can be performed on the collected sample evaluation data to ensure the reliability of the data, so that the reliability of the customer experience evaluation model construction is ensured.

Validity is validity, which means the degree to which the measuring tool can accurately measure the object to be measured. Firstly, whether the collected original variables have a certain linear relation needs to be examined, and factors are analyzed and extracted by adopting the factors.

Factor analysis is a statistical method for analyzing how many common factors affect variables and govern variables and what the factors are in nature. The basic purpose of factor analysis is to use a few factors to describe the relationship between many indexes or factors, i.e. to put several closely related variables into the same class, each class of variables becomes a factor, and the most information of the original data is reflected by the fewer factors. The basic idea of factor analysis is to group the original variables according to their correlation size, so that the correlation between variables in the same group is high and the correlation between variables in different groups is low. Each group is represented by an abstract, integrated variable, called a common factor.

In this embodiment, the second-level index is a common factor to be extracted, and the third-level index is a variable under a factor-type variable classification. By means of factor analysis, irrelevant factors and/or evaluation indexes with low relevance can be eliminated, and key evaluation indexes influencing customer experience evaluation are extracted, so that the evaluation indexes are more scientific.

Further, it is necessary to determine whether the collected multidimensional sample profile is suitable for factoring before factoring.

Specifically, whether factor analysis is suitable or not is judged by a variable Butterworth sphericity test and a KMO test method. The KMO measurement criteria are as follows:

when the KMO value is more than 0.9, the KMO is very suitable for factor analysis; 0.8-0.9 means very suitable; 0.7 to 0.8 represent normal; 0.6-0.7 are marginally suitable; 0.5-0.6 means less suitable; when the ratio is less than 0.5, it means that the factor analysis is extremely inappropriate.

For example, when the observed value of the detected Bartlet sphericity test statistic is 12333.479, the corresponding probability p value is close to 0, and the KMO value is 0.944, the correlation between explanatory variables is strong, and the original variables are suitable for factor analysis.

After determining that the collected sample evaluation data is suitable for factor analysis, performing factor analysis by using SPSS software, and specifically comprising the following steps:

the method comprises the following steps: extracting factors from the collected sample evaluation data and interpreting the collected sample evaluation data.

In this embodiment, factor analysis is performed on the second-level index and the third-level index of the performance experience dimension (first-level index), where the third-level index includes 7 variables. Table 2 shows the total variance of the interpretation of the mobile banking sample evaluation data collected by the questionnaire, including the number of factors specifically extracted, and the data such as the feature value, the variance interpretation rate, and the cumulative variance interpretation rate of each factor. In this embodiment, 2 common factors are extracted from 7 variables (three-level indexes) of the performance experience dimension by using a principal component analysis method.

Total variance as explained in Table 2

In table 2, [ initial feature value ] shows the result of preliminary extraction of common factors: the column "total" is a feature value for each principal component, with a larger value indicating that the principal component is more important in accounting for variations in 7 variables; "percent variance" is listed as the percentage variation that each extraction factor can account for; the "cumulative%" column is the cumulative percentage of variability explained; the rotated eigenvalues, variance interpretations and cumulative variance interpretations are shown.

The eigenvalue of each factor represents the variance contribution of a factor to all the variable variations, reflecting the principal characteristics of the factor, which is numerically equal to the sum of the squares of the column of factor loadings. The larger the characteristic value is, the larger the explanatory power or influence of the explanatory factor on all the original variables is; the smaller the eigenvalue, the less explanatory or influence the explanatory factor has on all the original variables.

The variance interpretation rate represents the amount of information represented by a certain factor, for example when it is 30.023%, indicating that the factor can represent 30.032% of the information of the entire questionnaire. The cumulative variance interpretation rate represents the amount of information that all factors taken together can interpret the entire questionnaire. This value is not a fixed criterion, and generally greater than 60% indicates a better assay result, and 50% indicates acceptance.

The variance interpretation ratio of each factor, obtained by dividing its variance contribution by the number of variables, is shown in table 2,

the variance interpretation ratio of factor 1 is 2.899/7 100% ═ 41.41%;

the variance interpretation ratio of factor 2 is 0.916/7 100% ═ 13.08%.

As can be seen from the total variance explained in Table 2, the extracted 2 factors totally explain 54.498% of the total variance, which is greater than 50%, and it is reasonable to explain the above 2 factors, and the collected evaluation data can be better explained.

In the specific implementation, because the meaning represented by the initial factor load matrix is ambiguous, if there is no obvious difference between the load values of the factor loads in each column, it is difficult to classify the original variables and distinguish the corresponding relationship between the original variables and the common factors. Given that each variable can be made highly loaded on one factor and less loaded on the other factors, it becomes easier to group the variables and identify the common factor associated with them. Therefore, a corresponding factor rotation of the initial factor load matrix is required. The factor rotation is to make the extracted factor structure undergo mathematical transformation to make each factor be able to clearly separate, so as to facilitate the explanation and naming of the subsequent factors.

Specifically, the factor load matrix is rotated by the maximum variance method, and after the factor load matrix is rotated by the factor, the factor load matrix is arranged according to the magnitude of the factor load coefficient, and the obtained factor load matrix is shown in table 3. In table 3, 1 to 7 are three-level indexes (variables) of 7 individual performance experience dimensions, respectively.

TABLE 3 rotating composition matrix

After the factor analysis for 1 time, 2 factors are extracted and each factor can be named, the corresponding relation between the description factor and the theme is in accordance with the expectation, each theme is only dependent on one factor, and the factor load coefficient is higher than 0.4. The extracted 2 factors are secondary indexes of the performance experience dimension and are named as stability and interactivity respectively.

And (4) briefly summarizing and sorting the results obtained according to the SPSS factor analysis. Specifically, the feature values, the interpretation variance, the cumulative interpretation variance, and the main statistics such as the questionnaire questions included in each factor after the rotation of each factor are summarized and arranged as shown in table 4.

Total variance as explained in Table 4

Step two: determining the weight of the secondary index.

The weight of each secondary index in the performance experience dimension is obtained according to the feature value of each common factor in table 4, the weight of the secondary index is equal to the feature value of the factor divided by the sum of the feature values of all factors, and is specifically shown in table 5:

TABLE 5 second-level index weights

The obtained secondary index weight is the internal weight of the secondary index, and the weight of the secondary index in the total index needs to be obtained according to the primary index weight of the performance experience dimension obtained by expert assignment in step S3021 and the secondary index weight in table 5:

the weight of stability is 19%. 53.00%. 10.07%

The weight of interactive performance is 19%. 47.00%. 8.93%

The determination result of the secondary index weight shows that the secondary index weight of the performance experience dimension sequentially comprises the following steps from large to small: stability > interaction performance. When customer experience is improved, it is required to firstly ensure that the stability of the mobile phone bank is improved, namely, the compatibility of the client is improved, the breakdown rate is reduced, the function is abnormal, the UI is abnormal, and the like, and then the interaction performance of the mobile phone bank, namely, the running speed of the page, the loading time and the like, is stably improved.

Step three: and determining the weight of the three-level index.

According to the rotated factor load matrix table, the weight of a certain index is equal to the factor load coefficient of the index divided by the sum of the load coefficients of all indexes under the factor, and the specific formula is as follows: wi is Wi/Σ Wi.

For example, the index weight of the Q7 page response time is:

Wi＝Wi/∑Wi＝0.590/(0.811+0.680+0.590)＝28.35％

referring to the factor load coefficient of each index variable and the index range covered by the common factor in table 3, the three-level index weight is sequentially obtained, as shown in table 6:

table 6 mobile banking performance experience three-level index weight

The third-level index weight obtained in table 6 is the internal weight of the third-level index, and therefore, the weight of each third-level index in the total index needs to be determined according to the second-level index weight determined in step two.

If the Q7 page response time is subordinate to the "interaction performance" dimension of the secondary index, the index weight of the page response time is as follows: 8.93%. 28.35%. 2.53%.

In the embodiment of the invention, the weight of the first-level index is determined by an expert assignment method, the collected evaluation data with the customer experience is subjected to weight analysis by a factor analysis method according to the determined first-level index, and the weights of the second-level index and the third-level index are determined, so that the determination of the evaluation index weight is more scientific and standard. In addition, irrelevant factors can be eliminated through factor analysis, and key evaluation index factors are reserved, so that the determined evaluation indexes are more objective and effective. In addition, the invention determines the weight from the actual data, extracts the relevant information from the data, and can fully reflect the intention of the client.

And step S303, making a grading standard of each evaluation index and generating a corresponding grading table.

It should be noted that, the step of formulating the scoring standard of each evaluation index refers to determining the scoring standard of the second-level index and the third-level index, and the scoring standard of the first-level index is determined based on the scoring standards of the second-level index and the third-level index.

After determining the weight of each evaluation index, establishing a scoring standard of each evaluation index, and generating a scoring table according to the determined weight of each evaluation index, wherein the scoring table is a standard scoring table. In the step, the scoring standard is formulated according to the type of the evaluation index, and different evaluation indexes adopt different evaluation modes to formulate the scoring standard of each evaluation index. Specifically, the method collects the related sample evaluation data of each evaluation index by combining qualitative and quantitative modes, and works out a proper scoring standard by referring to the industry standard and analyzing the early-stage basic data of the mobile phone bank.

Further, when a scoring standard of each evaluation index is formulated, sample evaluation data of at least two data source channels are obtained.

Specifically, the sample evaluation data of at least two data source channels may be extracted from the sample evaluation data set obtained in step S301. And grading different sample evaluation data according to the types of the data source channels to determine different grading standards, summarizing the different grading standards of different evaluation indexes according to the types of the data source channels, and generating a grading table. As shown in fig. 4, the same-level index may include first to nth evaluation indexes (N >1), and the scoring criteria of each evaluation index is determined by obtaining sample evaluation data (N >1) of first to nth data source channels.

When the data source channels of the same level of evaluation indexes comprise qualitative data, the weights of the data source channels need to be determined, and the weights and the corresponding scoring standards are weighted and calculated to obtain a scoring table. The weight of each data source channel can be obtained by an expert value assigning method or determined by actual experience, and the invention is not particularly limited.

When the data source channel of the same-level evaluation index is quantitative data and the evaluation index of each evaluation index is accurately calibrated, the first-level index, the second-level index and the third-level index can be respectively and preliminarily divided into a plurality of evaluation grades (for example, four grades of high quality, good quality, qualified quality and poor quality), and a standard sample matrix of the indexes is established; and performing radar map comprehensive evaluation on the standard sample matrix to obtain the value intervals of each evaluation grade.

To better illustrate the determination of the scoring criteria, the embodiment of the present invention sequentially illustrates the determination process of the scoring criteria of each primary index listed in table 1.

(I) scoring criteria for use experience dimensions such as functional experience, security experience, innovation experience, operational experience, emotional experience, and the like

The sample evaluation data collected according to the first-level index mainly comes from two parts, namely (a) a problem list generated by expert evaluation and usability test; (b) the user's score of experience satisfaction for each dimension in the questionnaire. The weight of the question list and questionnaire each accounted for 50% when the scoring criteria were determined.

(1) Determination of question list scoring criteria

Expert evaluation mainly refers to the fact that qualitative analysis is mainly carried out on collected data through expert evaluation, and related problems of customer experience are obtained. And summarizing and tidying the usability problems found by expert evaluation and usability tests to form a problem list. Since it is mainly to find out the problem, the scoring is performed in the form of a point-and-click system.

Finding out a corresponding dimension index of each found availability problem, and carrying out importance level determination on each problem, wherein the priority of the problem is determined according to the severity, the overcoming degree and the influence range of the problem, the importance levels of the problem can be sequentially divided into 3 levels, namely a high level, a medium level and a low level, and the specific judgment standard is as follows:

the low-level problems refer to problems that cause inconvenience in user operations, but do not constitute a great influence and do not lower user satisfaction. Generally perceived by the user as "still good"; the medium-level problem is the problem that the operation efficiency of a user is reduced and the satisfaction degree of the user is influenced. It is often perceived by the user as "not too good"; advanced questions are those that directly prevent a user from discovering, identifying, or using certain key functions to complete an operation, are often perceived by the user as "very bad," and directly contribute to the user's operational errors.

After grading, the deduction scores of each grade question are the deduction base numbers and the grade deduction weight.

In order to reflect the importance of the problem with a higher level, in this embodiment, each evaluation index has a problem with an importance level of "high" twice, and the score of the index is completely deducted.

The deduction weight is a deduction weight for each importance level, and is determined according to an empirical value such as the same industry, for example, the high-level weight is 1, the medium-level weight is 0.6, and the low-level weight is 0.2.

Thus, each index score is determined as the sum of the total score of the index and the score of each grade.

After the scoring criteria for the availability issues are determined, generating an issue list scoring table, wherein the items listed in the issue list scoring table may include: problem description, suggestion, module to which the problem belongs, problem severity level, index category to which the problem belongs, remark, score and the like.

The problem description refers to usability problem points found in the expert evaluation process, and needs to be concise and easy to understand. The suggestion means that reasonable suggestions are given according to bidding analysis of excellent competitive products in the industry and evaluation experience of experts. The module to which the problem belongs is the module to which the found problem belongs, so that the problem can be conveniently searched in later retest. The problem severity level refers to the degree of influence of the problem on the customer experience, namely, three levels, high, medium and low. The index category to which the problem belongs is whether the problem belongs to a first-level index, a second-level index or a third-level index, and a specific index name is listed. Some other evaluation conditions can be recorded in the remarks, for example, the objective condition of the mobile phone system with the problem is iOS or Android and the like. From the above, the summary of the problem list is scored, and the scoring standard is the qualitative scoring standard, so the scoring table is the qualitative scoring table.

Expert's aassessment not only can evaluate same product, can also carry out the analysis of competing for goods, specifically includes: determining competitive bidding products, determining an evaluation index list of each competitive bidding product, searching and recording problem points, summarizing and sorting to form a problem list, scoring according to the problem list and checking the scoring condition. The competitive products comprise similar products, related products and reference products. The referenced products are hardware products, for example, mobile devices used by users, and customer experiences of different mobile devices applied by different mobile banking applications are different.

(2) Determination of questionnaire scoring criteria

The questionnaire survey is mainly directed to the client, and through the questionnaire survey, each question in the questionnaire survey determined based on the evaluation standard can be scored, and the score of each evaluation index can be determined by weighting and multiplying each scoring score by the weight value of each scoring standard determined in step S302.

After the scoring standard of each evaluation index is determined according to the type of the data source channel, the scoring standard of each evaluation index can be summarized according to the type and the weight of the data source channel, and a standard scoring table under the dimension is generated. The scoring table lists scoring standards of different evaluation indexes under different data source channels. Wherein, the evaluation indexes comprise all secondary indexes and tertiary indexes under the classification of the primary indexes.

(II) scoring criteria for Performance experience dimension

The sample evaluation data of the performance experience dimension mainly comes from three parts, namely (a) a performance test report; (b) a performance experience problem list; (c) the experience of the customer on the stability and the interaction performance of the mobile banking in the questionnaire is evenly divided.

Table 7 shows the scoring criteria of the three-level index of the performance experience dimension, and as shown in table 7, the scoring criteria of the performance test report is divided into four levels, i.e., excellent, good, passing, and bad, according to the industry optimal, industry average, and industry worst 3 values, with the industry average as the baseline, and the scoring weights of each level being 100%, 80%, 60%, and 0, respectively, which are lower than the industry worst and bad, which are passing between the industry worst and average, which are good between the average and the industry optimal, and which are superior than the industry optimal.

TABLE 7 three-level index Scoring criteria for Performance experience dimension

And the critical value of the grading level in each three-level index is obtained according to an empirical value or training. In particular, the empirical values may be industry means or industry optima.

The performance experience problem list is evaluated by experts to find problems of relevant dimensions, and the problems are input into the same problem list, so that the scoring standard of the performance experience problem list is the same as that of the problem list in the experience dimension, and the detailed description is omitted.

Similar to the questionnaire survey using the experience dimension, the questionnaire survey using the experience dimension collects the experience satisfaction of the customer on the mobile phone bank in the form of issuing questionnaires. The questionnaire scoring standard is determined by weighting the average of the customer experience of stability and interactive performance with the weight of each evaluation standard.

Since the sample evaluation data of the performance experience dimension is derived from three parts, when the weight is determined, the weight of the performance test report (objective data) is determined to be 50%, and the performance results (subjective data) in the performance experience problem list and the questionnaire survey respectively account for 25%.

And after the scoring standard of each evaluation index is determined, summarizing the scoring standard of each evaluation index according to the type of the data source channel and the corresponding weight, and generating a standard scoring table of the performance experience dimension.

(III) evaluation criteria for post-customer evaluation

Sample evaluation data of post-customer evaluation dimension mainly come from three parts, namely (a) customer satisfaction, use will and recommendation will in questionnaire survey; (b) the extent of complaints in the work order data; (c) the method is applied to the star-level evaluation of the customers of the mobile phone banks in the market.

The evaluation indexes are two-level indexes, wherein the customer satisfaction is the overall satisfaction of the customer to the mobile phone bank, the use intention of the customer is the intention of the customer to use or continue to use the mobile phone bank, the recommendation intention is the intention of the customer to recommend the mobile phone bank to others, and the customer cost is the time or the effort spent by the customer in solving the problems. The complaint degree is the percentage of complaints and suggestions and problematic issues of the customer due to experience problems. The client evaluates the star level, and the quality of the public praise of the client is evaluated by monitoring the mainstream android market from the application market.

The customer satisfaction, the use will and the recommendation will are average score data obtained through questionnaire survey, the complaint degree is work order data, the customer evaluation star grade obtains the score of the mobile phone bank through the score of the standard application market, and the value is the average score of the mobile phone bank in the application market.

Table 8 shows the scoring criteria of the secondary index of the post-customer evaluation dimension, which is classified into 4 grades of excellent, good, and bad as shown in table 8, and the scoring weights of each grade are 100%, 80%, 60%, 0, respectively.

TABLE 8 second-level index Scoring Standard for customer post-evaluation dimensionality

Because the scoring standards of the evaluation indexes are all quantitative scoring standards, corresponding weights do not need to be distributed to different data source channels, the scoring standards of the evaluation indexes can be directly summarized, and a standard scoring table for post-evaluation of customers is generated.

(IV) scoring criteria for customer behavior

The sample evaluation data of the customer behavior dimension mainly comes from two parts, namely (a) the customer behavior performance (efficiency and efficiency) in the usability test; (b) data in the transaction failure rate report.

And counting the task completion rate and the task completion time of each client through the availability test, and according to the formula: efficiency is the task completion rate/task completion time, and the efficiency of each client on each task is calculated. And setting a grading standard by taking the average task completion rate and the average efficiency of the client as a reference and taking an industry reference value as a basis. The tasks may include viewing accounts, transferring funds, purchasing financing, life payment, and the like.

Table 9 shows the scoring criteria of the three-level index of the customer behavior dimension, and as shown in table 9, the scoring criteria of the performance and efficiency are divided into four levels of excellent, good, passing, and bad according to the industry optimal, industry average, and industry worst 3 values with the industry mean as a baseline, and the scoring weights of each level are 100%, 80%, 60%, and 0, respectively, which are worse than the industry worst, which are passing between the industry worst and the mean, which are good between the mean and the industry optimal, and which are superior than the industry optimal.

The transaction failure rate is derived from a mobile banking transaction failure rate report, the average value and the standard deviation of the transaction failure rate report are calculated according to the total transaction number and the transaction failure rate ranking, and the scoring standard is set according to the average value and the standard deviation.

The transaction failure rate is due to data in the bank, and the industry mean value cannot be obtained, so that the mean value and the standard deviation of the bank are taken as a base line, the transaction failure rate is superior when the mean value and the standard deviation are lower than one standard deviation, the transaction failure rate is good when the mean value and the standard deviation are within one standard deviation, the transaction failure rate is good when the mean value and the standard deviation are between the mean value and the standard deviation, and the transaction failure rate is superior when the mean value and the standard deviation are higher than one standard deviation.

TABLE 9 three-level index Scoring Standard for customer behavior dimension

Similarly, the scoring standards of the evaluation indexes are quantitative scoring standards, so that the scoring standards of the evaluation indexes can be directly summarized without distributing corresponding weights to different data source channels, and a standard scoring table of customer behaviors is generated.

In some embodiments, the sample profile for the customer behavior dimension may also be derived from: the mobile phone registration conversion rate data, the transfer convenience data, the product attraction data, the product purchase conversion rate data, the online customer service response duration data and the like. And determining the grading standard of the three-level indexes such as the registration conversion rate and the like by using the data, and synthesizing the grading standard with the table 9 to obtain a more comprehensive standard grading table of the customer behavior.

In the embodiment, the scoring standard is formulated according to the type of index collection, and a set of complete and systematic scoring standard is formulated, so that the scoring standard is systematized and the evaluation is more precise. For example, the expert evaluates the found problems, and deducts and determines the problems according to the high, medium and low levels, so that the problems experienced by the client can be found in time, and the priority of the problems can be determined; the objective performance report and the effectiveness efficiency data of the usability test are divided into grading levels by taking an industry mean value as a baseline and taking an industry optimal value and a industry worst value as boundary lines, and the grading standard is determined reasonably.

And S304, integrating the evaluation tables of the evaluation indexes to obtain a customer experience evaluation model.

Specifically, the evaluation tables containing the second-level evaluation indexes and the third-level evaluation indexes and the corresponding evaluation standards are integrated to obtain the comprehensive evaluation table containing all the levels of evaluation indexes (the first-level evaluation indexes, the second-level evaluation indexes and the third-level evaluation indexes), and then the customer experience evaluation model can be obtained.

It should be noted that, the neural network model may be used, the sample evaluation data is used as the training data, the customer experience evaluation model is trained, each evaluation index and its weight are updated in real time, and the scoring standard of each evaluation index is updated, so that the accuracy of the evaluation of the customer experience evaluation model is continuously improved.

And (3) after obtaining the customer experience evaluation model, inputting the evaluation data obtained in the step (20) into the customer experience evaluation model, and respectively calculating evaluation values of each evaluation index.

Specifically, the evaluation data is input into the scoring table of each first-level index to obtain the scores of the second-level and third-level indexes (as shown in fig. 4), and then the score of the first-level index is obtained according to the scores of the second-level and third-level indexes and the weight of the first-level index.

In the scoring table of the use dimensions such as function experience, safety experience, innovation experience, operation experience, emotion experience and the like, the score of the use experience dimension in an expert evaluation mode can be automatically obtained only under the condition that the problem list entry format is correct; the evaluation score of the questionnaire (such as the average score of user experience) is input into the evaluation standard under the first-level index dimension, namely the evaluation score under the questionnaire evaluation mode can be obtained according to the weights of the second-level index and the third-level index and the evaluation standard, and the final evaluation score of each second-level index and each third-level index can be obtained by weighting the evaluation scores of the two evaluation modes.

The scoring mode of the final evaluation score of each secondary index and each tertiary index under the dimensionality of other primary indexes is similar, and is not repeated herein.

In the scoring table and the comprehensive scoring table under each level of index dimension, each level of index is distinct, and each scoring data is clearly represented. When the evaluation result is analyzed, the evaluation table under each level of index dimensionality can be checked, and the comprehensive evaluation table can also be checked.

And step S40, obtaining the evaluation result of the customer experience according to the evaluation value of each evaluation index.

And carrying out comprehensive analysis on the scores in each scoring table and the comprehensive scoring table to determine the experience problem of the client and the priority of the experience problem. And determining key indexes influencing the customer experience according to the evaluation result of the customer experience, and adjusting a customer service strategy according to the key indexes to optimize the evaluation result of the customer experience.

In some embodiments, the step S40 of obtaining the evaluation result of the customer experience according to the evaluation value of each evaluation index further includes:

Specifically, after each evaluation, the evaluation result of the evaluation can be input into the customer experience evaluation model as sample evaluation data, and the customer experience evaluation model is optimized to promote the solution of the product problem. The optimization of the customer experience evaluation model comprises optimization of evaluation indexes, optimization of weights and optimization iteration of scoring standards. And each evaluation index can be increased or decreased according to the correlation so as to improve the applicability of the customer experience evaluation model.

According to the customer experience evaluation method provided by the embodiment of the invention, the product is evaluated by collecting the evaluation data of different data source channels, the evaluation data is more comprehensive, the customer experience problem can be found in time, the priority improvement score is lower, and the product is in a medium-high level problem, and in continuous optimization iteration, the customer experience score and the customer experience level can be obviously improved, and the customer experience problem is reduced. The weights are determined by combining expert assignment and factor analysis, so that the weights are determined more scientifically and more standard, different scoring standards are formulated according to the types of the evaluation indexes, the formulation of the scoring standards is more systematic and reasonable, and the evaluation result is more comprehensive and objective.

The customer experience evaluating method provided by the embodiment of the invention can not only longitudinally track a single product of a single bank for a long time (such as the comparison of new and old versions), but also know the customer experience level and form a traceable closed loop; and the system also can transversely compare single products of multiple banks simultaneously to know the experience level of each bank client and the bank ranking, thereby making the best of the advantages and avoiding the disadvantages and driving the banking client to experience refined operation.

As shown in fig. 5, an embodiment of the present invention provides a customer experience evaluating system, and with the customer experience evaluating method according to the above embodiment, the customer experience evaluating system includes:

the determining module 501 is configured to determine an evaluation index according to an evaluation object experienced by a client;

an obtaining module 502, configured to obtain multiple evaluation data of at least one data source based on a determined evaluation index, where the evaluation data includes objective data and subjective data;

the calculating module 503 is configured to input the evaluation data into a preset customer experience evaluation model, and calculate evaluation values of each evaluation index respectively;

the evaluating module 504 is configured to obtain an evaluation result of the customer experience according to the evaluation value of each evaluation index.

The customer experience evaluating system corresponds to the customer experience evaluating method of the above embodiment, and any optional items in the customer experience evaluating method embodiment are also applicable to the embodiment, and are not detailed here.

As shown in fig. 6, an embodiment of the present invention further provides a customer experience management platform, where the customer experience management platform is connected to the customer experience evaluating system, and the customer experience management platform includes:

the management module 601 is used for managing the customer experience evaluation system;

the storage module 602 is configured to store the management data of the customer experience evaluation system in a classified manner;

the communication module 603 is configured to receive and send management data information.

Specifically, the customer experience management platform may be a cloud management platform, the customer experience evaluation system is installed on a terminal, and different customer experience evaluation systems may be managed through the customer experience management platform. For example, the customer experience evaluation system of a mobile phone bank can be managed through the customer experience management platform, and the customer experience evaluation system of an intelligent counter can also be managed.

Optionally, the customer experience management platform may further include an interaction module, configured to perform information interaction with an external system such as a bank, a financial institution, and the like, for example, the interaction module may perform interaction with a bank system to obtain and store information data of the bank, so as to perform better customer experience management.

Optionally, the customer experience management platform may further include a prompt module, configured to receive and analyze the prompt information, determine an importance level of the prompt information, and formulate a corresponding policy, so as to facilitate quick solution of the customer experience problem and optimize the customer experience evaluation system.

The embodiment of the invention also provides a computer-readable storage medium, wherein computer-executable instructions are stored on the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the method for evaluating the customer experience in the embodiment is realized.

In some embodiments, a processor executing computer-executable instructions may be a processing device including more than one general-purpose processing device, such as a microprocessor, Central Processing Unit (CPU), Graphics Processing Unit (GPU), or the like. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, processor running other instruction sets, or processors running a combination of instruction sets. The processor may also be one or more special-purpose processing devices such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like.

In some embodiments, the computer-readable storage medium may be a memory, such as a read-only memory (ROM), a random-access memory (RAM), a phase-change random-access memory (PRAM), a static random-access memory (SRAM), a dynamic random-access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random-access memory (RAM), a flash disk or other form of flash memory, a cache, a register, a static memory, a compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD) or other optical storage, a tape cartridge or other magnetic storage device, or any other potentially non-transitory medium that may be used to store information or instructions that may be accessed by a computer device, and so forth.

In some embodiments, the computer-executable instructions may be embodied in a plurality of program modules that collectively implement the customer experience profiling method according to any of the present invention.

Various operations or functions are described herein that may be implemented as or defined as software code or instructions. The display unit may be implemented as software code or modules of instructions stored on a memory, which when executed by a processor may implement the respective steps and methods.

Such content may be source code or differential code ("delta" or "patch" code) that may be executed directly ("object" or "executable" form). A software implementation of the embodiments described herein may be provided through an article of manufacture having code or instructions stored thereon, or through a method of operating a communication interface to transmit data through the communication interface. A machine or computer-readable storage medium may cause a machine to perform the functions or operations described, and includes any mechanism for storing information in a form accessible by a machine (e.g., computing device, electronic system, etc.), such as recordable/non-recordable media (e.g., Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.). A communication interface includes any mechanism for interfacing with any of a hardwired, wireless, optical, etc. medium to communicate with other devices, such as a memory bus interface, a processor bus interface, an internet connection, a disk controller, etc. The communication interface may be configured by providing configuration parameters and/or transmitting signals to prepare the communication interface to provide data signals describing the software content. The communication interface may be accessed by sending one or more commands or signals to the communication interface.

The computer-executable instructions of embodiments of the present invention may be organized into one or more computer-executable components or modules. Aspects of the invention may be implemented with any number and combination of such components or modules. For example, aspects of the invention are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments may include different computer-executable instructions or components having more or less functionality than illustrated and described herein.

The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.

Claims

1. A customer experience evaluation method is characterized by comprising the following steps:

2. The customer experience evaluation method according to claim 1, wherein the evaluation index comprises a multi-level evaluation index, and the multi-level evaluation index comprises at least two levels of evaluation indexes classified according to a hierarchy.

3. The customer experience profiling method according to claim 2, wherein obtaining a plurality of profiling data of at least one data source based on the determined profiling index comprises:

4. The method according to claim 3, wherein the data source channel comprises a data collection mode and a data type, the data collection mode comprises at least one of expert evaluation data, questionnaire data, usability test data, application market evaluation data and objective data related to an evaluation object, and the data type comprises qualitative data and quantitative data.

5. The customer experience assessment method according to claim 3, wherein the construction of said customer experience assessment model comprises the following steps:

obtaining a sample evaluation data set corresponding to an evaluation index;

determining the weight of each evaluation index;

6. The method according to claim 5, wherein the determining the weight of each evaluation index comprises:

determining the weight of the first-level index by an expert assignment method;

7. The customer experience evaluation method according to claim 5, wherein the scoring criteria is formulated according to the type of evaluation indexes, and the scoring criteria for each evaluation index is formulated to obtain sample evaluation data of at least two data source channels.

8. The customer experience evaluation method according to claim 1, wherein the step of obtaining the evaluation result of the customer experience according to the evaluation value of each evaluation index further comprises the following steps:

9. A customer experience evaluation system, comprising:

10. A customer experience management platform connected to the customer experience profiling system according to claim 9, comprising: