CN113537759B - Weight self-adaption-based user experience measurement model - Google Patents
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Abstract
The invention relates to a weight self-adaption-based user experience measurement model, which comprises the following steps: the user experience module is preset with five types of indexes of each user experience, and the five indexes are respectively: satisfaction, task completion, participation, retention, and acceptance; the weight strategy module comprises a subjective weighting unit and an objective weighting unit, wherein the subjective weighting unit is used for weighting each index, the subjective weighting unit is used for assigning fixed weight assignment, and the weighting mode of the objective weighting unit is an entropy value method; combining the fixed weight assignment, and calculating to obtain a final weight value of each index through an entropy method; and the user experience measurement module is used for carrying out weighted summation on the index scores and the calculated final weight values of the indexes to obtain measurement scores of the indexes, and obtaining the experience measurement total score of each user according to the measurement scores of the indexes. The invention self-adapts each index weight by combining subjective weighting and objective weighting, thereby intuitively obtaining the overall experience condition of the system user.
Description
Technical Field
The invention relates to the technical field of system scoring, in particular to a weight self-adaption-based user experience measurement model.
Background
At present, the embedding points of each system are mostly dependent on business side decisions and embedding points of a third party platform, so that excessive and scattered indexes are often caused easily, and a decision maker cannot optimize all indexes at the same time and cannot see the overall user experience condition of the system from the indexes.
Based on the problem that the overall user experience condition of the system is difficult to see, a patent CN110059232B, a data visualization method based on user experience measurement, is proposed in the prior art, a measurement model of system data visualization narrative experience is designed, an eye movement experiment and an electrocardiograph experiment are designed, data are collected based on a parallel nested hybrid method, the analyzed data are visually displayed through a statistical method, and a final user experience measurement model is determined through example operation. However, the following disadvantages remain in this patent: first, eye movement experiments and electrocardiographic experiments require specialized equipment, and are high in implementation cost and require specialized personnel to operate. Secondly, the eye movement experiment and the electrocardiographic experiment need a plurality of groups of testees with different characteristics, and the requirement on the testees is high. Third, the user experience metric model is completely dependent on the processing of the usability test result, and the importance of the system objective data and the user true evaluation in the user experience metric is ignored. Fourth, the user experience metrics generated by different systems are different and cannot be quantitatively compared.
Therefore, it is necessary to provide a weight-adaptive user experience measurement model, which adapts to each index weight by combining subjective weighting and objective weighting, so as to intuitively obtain the overall condition of the user experience of the system.
Disclosure of Invention
The invention aims to provide a weight-adaptive-based user experience measurement model, which is used for adaptively obtaining the weight of each index by a method of combining subjective weighting and objective weighting so as to intuitively obtain the overall condition of user experience of a system.
In order to solve the problems in the prior art, the invention provides a weight self-adaption-based user experience measurement model, which comprises the following steps:
the user experience module is preset with five types of indexes of each user experience, and the five indexes are respectively: satisfaction, task completion, participation, retention, and acceptance;
the weight strategy module comprises a subjective weighting unit and an objective weighting unit, wherein the subjective weighting unit is used for weighting each index, the subjective weighting unit is used for assigning fixed weight assignment, and the weighting mode of the objective weighting unit is an entropy value method; combining the fixed weight assignment, and calculating to obtain a final weight value of each index through the entropy method;
and the user experience measurement module is used for carrying out weighted summation on the index scores and the calculated final weight values of the indexes to obtain measurement scores of the indexes, and obtaining the experience measurement total score of each user according to the measurement scores of the indexes.
Optionally, in the weight-based adaptive user experience metrics model,
satisfaction includes three subclasses of indicators: subjective satisfaction of users, performance data and expert walks; the subjective satisfaction degree of the user comprises the following specific indexes: net recommendation value, system availability scale and security experience value; the performance data comprises the following specific indexes: flash back rate, stuck rate, first start time and non-first start time; the expert walks to check the specific indexes including: availability, aesthetics, and safety;
the task completion level includes three subclasses of indicators: total completion, efficiency, and error; the total completion degree comprises the following specific indexes: the completion rate of the key task; the specific indexes of the efficiency are as follows: operating steps, task time and lost degree; the specific indexes of the errors are as follows: the incidence rate and error-reporting friendliness of the staged major event;
the engagement includes two subclasses of indicators: effective user conversion rate and user usage frequency; the effective user conversion rate comprises the following specific indexes: visitor and newly added active users; the specific indexes included in the user using frequency are as follows: the time length of the user's daily participation and the number of times of the user's daily participation;
the retention comprises the following specific indexes: a new user retention;
the acceptance degree comprises newly-increased user indexes, wherein the newly-increased user indexes comprise a daily newly-increased account opening number index and a daily newly-increased installation quantity index.
Optionally, in the weight-based adaptive user experience metric model, the entropy method includes the following steps:
step one: the index is standardized, namely the absolute value of the index is converted into a relative value, and the specific calculation formula is as follows:
for the forward index, the relative value is calculated by the following steps:
for the negative index, the relative value is calculated by the following steps:
wherein x' ij Representing the relative value of the jth index at the ith day, x ij Values representing the j-th index at i-th day, i=1, 2,3,..n, j=1, 2,3,..m, for n days, m indices;
step two: calculating an average value and a standard deviation of the relative values of the indexes, and calculating the variation coefficient of each index according to the average value and the standard deviation, wherein the specific calculation formula is as follows:
the calculation formula of the average value is:
the calculation formula of the standard deviation is as follows:
the calculation formula of the variation coefficient of the index is as follows:
wherein CV j 、S j 、The variation coefficient, standard deviation and average value of the j index are respectively represented;
step three: adjusting and adopting an entropy method and calculating a plurality of weight values of each index according to the variation coefficient of each index, wherein the calculation formula of the entropy method is as follows:
wherein w is j For the weight value of the j-th index, when n takes a plurality of values, a plurality of solutions w are provided j1 ,w j2 ,w j3 ,w j4 …；
Step four: the method comprises the steps of preprocessing a plurality of weight values of each index, and screening the weight values of each index according to errors;
step five: and carrying out local optimization adjustment by combining the fixed weight assignment and the multiple weight values of each index obtained by the entropy method to obtain the final weight value of each index.
Optionally, in the weight-based adaptive user experience metric model, the step of locally optimizing and adjusting is as follows:
assigning z to a fixed weight j Defining the obtained multiple weight values of each index as a cost function to form a weight set { z ] of the j-th index as a constraint condition j ,w j1 ,w j2 ,w j3 ,w j4 …}；
Setting a central value z j The central value is any one of the weight sets, and the distance between the central value and any weight value in the weight set is calculated by the following calculation method: d, d j1 ＝|z j -z j '|，d j2 ＝|w j1 -z j '|，d j3 ＝|w j2 -z j '|，d j4 ＝|w j3 -z j '|，d j5 ＝|w j4 -z j ' I, …, multiple sets of different distance values d are obtained according to different values of the center value j1 ,d j2 ,d j3 ,d j4 ,d j5 …；
Calculating final weight value, when |Z j -z j Z is less than or equal to 0.1 j ＝min{d j1 +d j2 +d j3 +..}, when |z @ j -z j When the I is more than 0.1, Z j ＝z j Wherein Z is j The final weight value of the j index.
Optionally, in the weight-based adaptive user experience metrics model,
if the positive index is the positive index, the relative value of the positive index is taken in the second step; and if the index is the negative index, taking the relative value of the negative index in the second step.
Optionally, in the weight-adaptive user experience metric model, in one calculation, the number of parameters n in each index is equal, and the value of n in each index is the same.
Optionally, in the weight-based adaptive user experience metric model, the parameter n in each index is taken for 7 days, 14 days, 30 days and 60 days.
Optionally, in the weight-adaptive user experience metric model, the original data of each index is subjected to scoring mapping between full partitions to obtain the score of each index.
Optionally, in the weight-based adaptive user experience metric model, a manner of obtaining an experience metric total score of each user according to the metric score of each index is as follows: the metric scores of all the indicators of each user are summed to obtain a total experience metric score for each user.
Compared with the prior art, the invention has the following advantages:
(1) According to the invention, the relation between the whole system and each index is reflected by the self-adaptive index weight of the method combining subjective and objective weights, so that a decision maker can quickly locate the main user experience problem, intuitively acquire the whole system user experience condition, and can perform staged optimization according to the importance degree of each index on the whole product, thereby realizing the maximum value of user experience improvement in a limited time, greatly increasing the readability of each index and effectively improving the decision efficiency.
(2) The index weight is determined in a self-adaptive way through an algorithm, so that the method can be applied to different systems in different industries, and the floor range is wide. For the same type of system or different versions of the same system, quantitative comparison can be performed due to fixed weight, and the user experience of the whole system and user experience evaluation under different versions of iteration are effectively reflected by calculating the total score of the user experience measurement.
(3) The user experience index in the invention covers three dimensions of objective data of the system, real evaluation of the user and usability test, and can provide product optimization schemes and iterative suggestions under various dimensions.
(4) The total score of experience measurement of each user in the system can be calculated through system burial points, expert walking and availability tests without professional equipment, the total score is divided into a plurality of dimension addition results, data expression in each dimension can be further obtained, and an optimization scheme of the system can be formulated through comparing the dimension results.
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FIG. 1 is a flowchart illustrating a calculation of a user experience metric model according to an embodiment of the present invention;
fig. 2 is a schematic diagram of indexes of each user according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention will be described in more detail below with reference to the drawings. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for convenience and clarity in aiding in the description of embodiments of the invention.
If the method described herein comprises a series of steps, the order of the steps presented herein is not necessarily the only order in which the steps may be performed, and some of the described steps may be omitted and/or some other steps not described herein may be added to the method.
The invention provides a weight self-adaption-based user experience measurement model, which comprises the following steps:
the user experience module is preset with five types of indexes of each user experience, and the five indexes are respectively: satisfaction, task completion, participation, retention, and acceptance;
the weight strategy module comprises a subjective weighting unit and an objective weighting unit, wherein the subjective weighting unit is used for weighting each index, the subjective weighting unit is used for assigning fixed weight assignment, and the weighting mode of the objective weighting unit is an entropy value method; combining the fixed weight assignment, and calculating to obtain a final weight value of each index through the entropy method;
and the user experience measurement module is used for carrying out weighted summation on the index scores and the calculated final weight values of the indexes to obtain measurement scores of the indexes, and obtaining the experience measurement total score of each user according to the measurement scores of the indexes.
Specifically, the user experience measurement model covers three dimensions of objective data of the system, user true evaluation and usability test. The five types of indexes of the user experience can be specifically displayed as follows:
satisfaction includes three subclasses of indicators: subjective satisfaction of users, performance data and expert walks; the subjective satisfaction degree of the user comprises the following specific indexes: net recommendation value (NPS, net Promoter Score), system availability scale (SUS, system Usability Scale), and security feel value; the performance data comprises the following specific indexes: flash back rate, stuck rate, first start time and non-first start time; the expert walks to check the specific indexes including: availability, aesthetics, and safety;
the task completion level includes three subclasses of indicators: total completion, efficiency, and error; the total completion degree comprises the following specific indexes: the completion rate of the key task; the specific indexes of the efficiency are as follows: operating steps, task time and lost degree; the specific indexes of the errors are as follows: the incidence rate and error-reporting friendliness of the staged major event;
the engagement includes two subclasses of indicators: effective user conversion rate and user usage frequency; the effective user conversion rate comprises the following specific indexes: visitor and newly added active users (visitor index can be changed into user index under certain condition), newly added active users can be changed into newly added user index under certain condition); the specific indexes included in the user using frequency are as follows: the time length of the user's daily participation and the number of times of the user's daily participation;
the retention comprises the following specific indexes: the new user retention is judged by judging whether the new user is retained for 7 days;
the acceptance degree comprises newly-increased user indexes, wherein the newly-increased user indexes comprise a daily newly-increased account opening number index and a daily newly-increased installation quantity index.
Optionally, in the weight-based adaptive user experience metric model, the entropy method includes the following steps:
step one: because the measurement units of the indexes are not uniform, before the final weight value is calculated by using the indexes, the indexes are subjected to standardized treatment, namely the absolute values of the indexes are converted into relative values, and the specific calculation formula is as follows:
for the forward index, the higher the expression value, the better, the calculation mode of the relative value is as follows:
for the negative index, the lower the expression value is, the better the expression value is, and the calculation mode of the relative value is as follows:
wherein x' ij Representing the relative value of the jth index at the ith day, x ij Values representing the j-th index at i-th day, i=1, 2,3,..n, j=1, 2,3,..m, for n days, m indices; preferably, in one calculation, the number of parameters n in each index is equal, and the values of n in each index are the same; for example, the parameter n in each index is taken for 7 days, 14 days, 30 days, and 60 days.
Step two: calculating an average value and a standard deviation of the relative values of the indexes, and calculating the variation coefficient of each index according to the average value and the standard deviation, wherein the specific calculation formula is as follows:
the calculation formula of the average value is:
the calculation formula of the standard deviation is as follows:
the calculation formula of the variation coefficient of the index is as follows:
wherein CV j 、S j 、The variation coefficient, standard deviation and average value of the j index are respectively represented; if the index is the forward index, taking the relative value of the forward index when calculating the average value; if the index is a negative index, taking the relative value of the negative index when calculating the average value;
step three: adjusting and adopting an entropy method and calculating a plurality of weight values of each index according to the variation coefficient of each index, wherein the calculation formula of the entropy method is as follows:
wherein w is j For the weight value of the j-th index, when n takes a plurality of values, there are a plurality of solutions, for example, as follows: w (w) j1 ,w j2 ,w j3 ,w j4 …；
Step four: the multiple weight values of each index obtained through preprocessing are screened according to the errors, for example, the weight value with larger error is removed;
step five: and carrying out local optimization adjustment by combining the fixed weight assignment and the multiple weight values of each index obtained by the entropy method to obtain the final weight value of each index. Preferably, the fixed weight assignment can be obtained by adopting an expert consultation method (Delphi method), and the specific operation is that the importance degree of the formulated index is evaluated by ten industry experts, the evaluation result is tidied, generalized and counted, anonymously fed back to each expert, the comments are solicited again, and the comments are concentrated and fed back again until the consistent comments are obtained to preliminarily determine the expert weight assignment of each index. The expert consultation method (Delphi method) makes full use of resources, has wide representativeness, is simple and easy to implement, and has objectivity of comprehensive opinions.
Further, the steps of local optimization adjustment are as follows:
assigning z to a fixed weight j Defining the obtained multiple weight values of each index as a cost function to form a weight set { z ] of the j-th index as a constraint condition j ,w j1 ,w j2 ,w j3 ,w j4 …}；
Setting a central value z j The central value is any one of the weight sets, and the distance between the central value and any weight value in the weight set is calculated by the following calculation method: d, d j1 ＝|z j -z j '|，d j2 ＝|w j1 -z j '|，d j3 ＝|w j2 -z j '|，d j4 ＝|w j3 -z j '|，d j5 ＝|w j4 -z j ' I, …, multiple sets of different distance values d are obtained according to different values of the center value j1 ,d j2 ,d j3 ,d j4 ,d j5 …；
Calculating final weight value, when |Z j -z j Z is less than or equal to 0.1 j ＝min{d j1 +d j2 +d j3 +..}, when |z @ j -z j When the I is more than 0.1, Z j ＝z j Wherein Z is j The final weight value of the j index. According to the method, the final weight value of each index is calculated in turn.
In one embodiment, the method for obtaining the score of each index is to perform score mapping between full partitions on the original data of each index.
In one embodiment, the manner of obtaining the experience metric total score of each user according to the metric score of each index may be: the metric scores of all the indicators of each user are summed to obtain a total experience metric score for each user. Of course, if other mean values, standard deviations and other modes are adopted, the method can be used for calculating the total score of the experience measurement of each user as long as the experience degree of each user can be distinguished.
Compared with the prior art, the invention has the following advantages:
(1) According to the invention, the relation between the whole system and each index is reflected by the self-adaptive index weight of the method combining subjective and objective weights, so that a decision maker can quickly locate the main user experience problem, intuitively acquire the whole system user experience condition, and can perform staged optimization according to the importance degree of each index on the whole product, thereby realizing the maximum value of user experience improvement in a limited time, greatly increasing the readability of each index and effectively improving the decision efficiency.
(2) The index weight is determined in a self-adaptive way through an algorithm, so that the method can be applied to different systems in different industries, and the floor range is wide. For the same type of system or different versions of the same system, quantitative comparison can be performed due to fixed weight, and the user experience of the whole system and user experience evaluation under different versions of iteration are effectively reflected by calculating the total score of the user experience measurement.
(3) The user experience index in the invention covers three dimensions of objective data of the system, real evaluation of the user and usability test, and can provide product optimization schemes and iterative suggestions under various dimensions.
(4) The total score of experience measurement of each user in the system can be calculated through system burial points, expert walking and availability tests without professional equipment, the total score is divided into a plurality of dimension addition results, data expression in each dimension can be further obtained, and an optimization scheme of the system can be formulated through comparing the dimension results.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any person skilled in the art will make any equivalent substitution or modification to the technical solution and technical content disclosed in the invention without departing from the scope of the technical solution of the invention, and the technical solution of the invention is not departing from the scope of the invention.
Claims (7)
1. A weight-adaptive based user experience metrics model, comprising:
the user experience module is preset with five types of indexes of each user experience, and the five indexes are respectively: satisfaction, task completion, participation, retention, and acceptance;
the weight strategy module comprises a subjective weighting unit and an objective weighting unit, wherein the subjective weighting unit is used for weighting each index, the subjective weighting unit is used for assigning fixed weight assignment, and the weighting mode of the objective weighting unit is an entropy value method; combining the fixed weight assignment, and calculating to obtain a final weight value of each index through the entropy method;
the entropy method comprises the following calculation steps:
step one: the index is standardized, namely the absolute value of the index is converted into a relative value, and the specific calculation formula is as follows:
for the forward index, the relative value is calculated by the following steps:
for the negative index, the relative value is calculated by the following steps:
wherein x is i ′ j Representing the relative value of the jth index at the ith day, x ij Values representing the j-th index at i-th day, i=1, 2,3,..n, j=1, 2,3,..m, for n days, m indices;
step two: calculating an average value and a standard deviation of the relative values of the indexes, and calculating the variation coefficient of each index according to the average value and the standard deviation, wherein the specific calculation formula is as follows:
the calculation formula of the average value is:
the calculation formula of the standard deviation is as follows:
the calculation formula of the variation coefficient of the index is as follows:
wherein CV j 、S j 、The variation coefficient, standard deviation and average value of the j index are respectively represented;
step three: adjusting and adopting an entropy method and calculating a plurality of weight values of each index according to the variation coefficient of each index, wherein the calculation formula of the entropy method is as follows:
wherein w is j For the weight value of the j-th index, when n takes a plurality of values, a plurality of solutions w are provided j1 ,w j2 ,w j3 ,w j4 …；
Step four: the method comprises the steps of preprocessing a plurality of weight values of each index, and screening the weight values of each index according to errors;
step five: combining the fixed weight assignment and the multiple weight values of each index obtained by the entropy method, and performing local optimization adjustment to obtain the final weight value of each index;
the local optimization adjustment comprises the following steps:
assigning z to a fixed weight j Defining the obtained multiple weight values of each index as a cost function to form a weight set { z ] of the j-th index as a constraint condition j ,w j1 ,w j2 ,w j3 ,w j4 …}；
Setting a central value z j '，The central value is any one of the weight sets, and the distance between the central value and any one of the weight sets is calculated by the following calculation method: d, d j1 ＝|z j -z j '|，d j2 ＝|w j1 -z j '|，d j3 ＝|w j2 -z j '|，d j4 ＝|w j3 -z j '|，d j5 ＝|w j4 -z j ' I, …, multiple sets of different distance values d are obtained according to different values of the center value j1 ,d j2 ,d j3 ,d j4 ,d j5 …；
Calculating final weight value, when |Z j -z j Z is less than or equal to 0.1 j ＝min{d j1 +d j2 +d j3 +..}, when |z @ j -z j When the I is more than 0.1, Z j ＝z j Wherein Z is j The final weight value of the j index;
and the user experience measurement module is used for carrying out weighted summation on the index scores and the calculated final weight values of the indexes to obtain measurement scores of the indexes, and obtaining the experience measurement total score of each user according to the measurement scores of the indexes.
2. The weight-based adaptive user experience metrics model of claim 1,
satisfaction includes three subclasses of indicators: subjective satisfaction of users, performance data and expert walks; the subjective satisfaction degree of the user comprises the following specific indexes: net recommendation value, system availability scale and security experience value; the performance data comprises the following specific indexes: flash back rate, stuck rate, first start time and non-first start time; the expert walks to check the specific indexes including: availability, aesthetics, and safety;
the task completion level includes three subclasses of indicators: total completion, efficiency, and error; the total completion degree comprises the following specific indexes: the completion rate of the key task; the specific indexes of the efficiency are as follows: operating steps, task time and lost degree; the specific indexes of the errors are as follows: the incidence rate and error-reporting friendliness of the staged major event;
the engagement includes two subclasses of indicators: effective user conversion rate and user usage frequency; the effective user conversion rate comprises the following specific indexes: visitor and newly added active users; the specific indexes included in the user using frequency are as follows: the time length of the user's daily participation and the number of times of the user's daily participation;
the retention comprises the following specific indexes: a new user retention;
the acceptance degree comprises newly-increased user indexes, wherein the newly-increased user indexes comprise a daily newly-increased account opening number index and a daily newly-increased installation quantity index.
3. The weight-based adaptive user experience metrics model of claim 1,
if the positive index is the positive index, the relative value of the positive index is taken in the second step; and if the index is the negative index, taking the relative value of the negative index in the second step.
4. The weight-based adaptive user experience metric model of claim 1, wherein in one calculation, the number of parameters n in each index is equal, and the value of n in each index is the same.
5. The weight-based adaptive user experience metric model of claim 4, wherein the parameter n in each index is taken for 7 days, 14 days, 30 days, and 60 days.
6. The weight-adaptive based user experience metric model of claim 1, wherein the raw data for each index is subjected to a scoring mapping between full partitions to obtain each index score.
7. The weight-based adaptive user experience metric model of claim 1, wherein the total score of the experience metrics for each user is obtained from the metric scores for each index by: the metric scores of all the indicators of each user are summed to obtain a total experience metric score for each user.
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| US7362766B2 (en) * | 2005-06-24 | 2008-04-22 | Hewlett-Packard Development Company, L.P. | System and method for dynamically controlling weights assigned to consumers competing for a shared resource |
| KR102078782B1 (en) * | 2011-06-30 | 2020-02-19 | 헴펠 에이/에스 | Fouling control coating compositions |
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