CN110796348A - Satisfaction investigation method and device - Google Patents

Satisfaction investigation method and device Download PDF

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CN110796348A
CN110796348A CN201910978992.8A CN201910978992A CN110796348A CN 110796348 A CN110796348 A CN 110796348A CN 201910978992 A CN201910978992 A CN 201910978992A CN 110796348 A CN110796348 A CN 110796348A
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蒋博赟
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Alipay Hangzhou Information Technology Co Ltd
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Abstract

The utility model relates to a satisfaction survey method, which comprises the steps of carrying out statistical analysis on a user data set, and resolving the user satisfaction into one or more main service scenes which influence the user satisfaction; based on the one or more primary business scenarios affecting user satisfaction, providing a questionnaire comprising questions of overall satisfaction and distributed satisfaction with respect to the one or more primary business scenarios; analyzing the one or more recovered questionnaires to determine demographic distribution information of the user who submitted the questionnaire; determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on an analysis of the one or more recovered questionnaire; and determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios. The disclosure also relates to a corresponding apparatus and a computer-readable storage medium.

Description

Satisfaction investigation method and device
Technical Field
The present disclosure relates to internet technology, and more particularly, to user satisfaction surveys.
Background
In the internet era of rapid development, an important dimension for knowing the quality of internet products or services is to calculate the satisfaction degree of users on the products.
Conventional user satisfaction survey methods may include issuing questionnaires via telephone, text message, mail, or online page. These questionnaires typically provide a number of questions for the user to select. For example, in a satisfaction questionnaire, the options for each question may typically be, for example: "how do you rate their satisfaction with a certain product and service? Score 5 was satisfactory, score 4 was substantially satisfactory, score 3 was more satisfactory, score 2 was less satisfactory, and score 1 was unsatisfactory ". However, since the questionnaire is a fixed form set in advance, the questions are too rigid to understand and often selected at will by the user, resulting in that no real answers can be collected.
Moreover, for the questionnaire of the internet channel, the distribution of the population filling the questionnaire and the distribution of the whole users are not necessarily the same, for example, the questionnaire is usually a high-activity user of the product, and the proportion of the low-activity user participating in the satisfaction survey is obviously low. These factors may result in an unrepresentative questionnaire result being collected.
On the other hand, the user satisfaction survey scheme of the related art has no way of knowing why the satisfaction is high or low even if the result of the satisfaction of the user is given, so that it is difficult to determine an improvement scheme.
Therefore, the conventional user satisfaction survey methods have low survey accuracy and do not provide information or solutions that are valuable enough in improving internet products or services.
Disclosure of Invention
One aspect of the present disclosure relates to a method of satisfaction survey, comprising performing statistical analysis on a user data set, and disaggregating user satisfaction into one or more primary business scenarios that affect user satisfaction; based on the one or more primary business scenarios affecting user satisfaction, providing a questionnaire comprising questions of overall satisfaction and distributed satisfaction with respect to the one or more primary business scenarios; analyzing the one or more recovered questionnaires to determine demographic distribution information of the user who submitted the questionnaire; determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on an analysis of the one or more recovered questionnaire; and determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios.
According to an exemplary embodiment, the method further comprises providing at least one of the overall satisfaction and a revised satisfaction of the one or more primary business scenarios.
According to an exemplary embodiment, the method further comprises distributing the provided questionnaire to one or more users; and retrieving the questionnaire submitted by the one or more users.
According to an exemplary embodiment, the user data set comprises at least a user ID, a communication category and a behavior type, and the statistical analysis is performed on the user data set, and the disaggregation of the user satisfaction into one or more main business scenarios affecting the user satisfaction comprises determining the number and/or the proportion of the communication category and the behavior type based on the user data set; and determining one or more primary business scenarios that affect user satisfaction based on the quantity and/or the fraction of communication categories and behavior types.
According to an exemplary embodiment, analyzing the one or more retrieved questionnaires to determine demographic distribution information of the user who submitted the questionnaire comprises comparing the demographic distribution of the one or more retrieved questionnaires to the overall user distribution; and determining one or more significant difference dimensions that are significantly different from and independent of the overall user distribution based on the analysis.
According to a further exemplary embodiment, determining revised satisfaction of the one or more primary business scenarios by reverting the crowd distribution of users who submitted the questionnaire to the overall user distribution based on the analysis of the retrieved one or more questionnaires comprises, for each primary business scenario, revising the satisfaction of the primary business scenario by reverting the crowd distribution of users who submitted the questionnaire to the overall user distribution in each significant difference dimension.
According to another exemplary embodiment, determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on the analysis of the retrieved one or more questionnaires comprises, for each primary business scenario, cross-combining the values of the one or more significant difference dimensions, determining satisfaction of each cross-combination based on the retrieved one or more questionnaires; and determining a revised satisfaction level for the primary business scenario by reverting the distribution of each cross-portfolio among the population of users who submitted the questionnaire to the distribution of the cross-portfolio among the overall users.
According to an exemplary embodiment, determining the overall satisfaction based on the revised satisfaction of the one or more primary business scenarios comprises determining the dissatisfaction of each primary business scenario based on the revised satisfaction of the primary business scenario; determining the integral dissatisfaction probability of the user under each main service scene on the premise of dissatisfaction based on the recovered questionnaire or questionnaires; and combining the dissatisfaction degree of each main service scene with the overall dissatisfaction probability under the condition that the user is dissatisfied under the main service scene to determine the overall satisfaction probability.
According to a further exemplary embodiment, the method further comprises assigning a weight to each primary traffic scenario, and the overall dissatisfaction probability under each primary traffic scenario, if the user is dissatisfied, is further determined based on said weight.
Other aspects of the disclosure relate to corresponding apparatuses and computer-readable storage media.
Drawings
FIG. 1 illustrates a system architecture diagram of a user satisfaction survey system in accordance with an aspect of the present disclosure.
Fig. 2 illustrates a block diagram of a data analysis module in accordance with an aspect of the present disclosure.
FIG. 3 illustrates a block diagram of a questionnaire design module in accordance with an aspect of the present disclosure.
FIG. 4 illustrates a block diagram of a questionnaire distribution and recovery module in accordance with an aspect of the present disclosure.
Fig. 5 illustrates a flow diagram of a method of satisfaction survey in accordance with an aspect of the present disclosure.
Detailed Description
For better understanding of the technical solutions of the present invention, the following detailed description of the embodiments of the present application is provided with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application and are not a listing of all embodiments. All other variations that can be made by one skilled in the art without undue experimentation based on the embodiments described in the present disclosure are within the scope of the present application.
Fig. 1 illustrates a system architecture diagram of a user satisfaction survey system 100 in accordance with an aspect of the present disclosure. As shown in fig. 1, a user data set 101 is input to a data analysis module 102. According to an exemplary embodiment, the user data sets 101 may include, but are not limited to, user communication channels, specific communication content, and the like.
The data analysis module 102 performs statistics and analysis on the user data set 101 to determine the main business scenarios that affect user satisfaction. User satisfaction may include one or more aspects including, but not limited to, such as security satisfaction, payment satisfaction, pre-sale service satisfaction, post-sale service satisfaction, and the like.
According to an example, the primary business scenarios that affect user security satisfaction may include, but are not limited to: worry about stolen fund, worry about the password is broken, worry about the cell-phone is lost, worry about user privacy, etc.
According to another example, the primary business scenarios that affect user payment satisfaction may include, but are not limited to: too few payment channels, complicated payment procedure opening, too much payment procedure fee and the like.
Data analysis module 102 provides the analysis results to questionnaire design module 104. The questionnaire design module 104 designs or obtains corresponding questions designed for overall satisfaction and for satisfaction of one or more business scenarios based on the analysis results for collecting the user's satisfaction score.
Questionnaire distribution module 106 distributes the satisfaction questionnaire designed by questionnaire design module 104 to user 107 through a channel. Questionnaire recovery module 108 recovers the satisfaction questionnaire from user 107 and provides it to data analysis module 102.
The data analysis module 102 analyzes the retrieved satisfaction questionnaire. Specifically, the data analysis module 102 may compare the crowd distribution of the retrieved questionnaire with the distribution of the whole user to obtain dimensions, such as activity level, gender, and old and new users, which are different from the distribution of the whole user and are independent of each other. The data analysis module 102 may also perform reduction according to the distribution of the whole user for each dimension, so as to obtain the end user satisfaction of each aspect. The data analysis module 102 may determine the overall satisfaction of the product or service based on the end user satisfaction with each satisfaction aspect. Based on this, the data analysis module 102 may output satisfaction data 110. The satisfaction data 110 may include overall satisfaction of a product or service and/or satisfaction of one or more aspects and/or satisfaction of one or more business scenarios, etc.
The operation and function of the user satisfaction survey system of the present disclosure are described in further detail below with reference to the accompanying drawings. Fig. 2 illustrates a block diagram of a data analysis module 200 in accordance with an aspect of the present disclosure. According to an example, the data analysis module 200 may include or constitute the data analysis module 102 of the user satisfaction survey system 100 described in connection with fig. 1.
As shown in fig. 2, the data analysis module 200 may include, but is not limited to, for example, a data statistics sub-module 202, a business scenario determination sub-module 204, a crowd distribution analysis sub-module 206, a significant difference dimension determination sub-module 208, a user distribution reduction sub-module 210, and a satisfaction determination and output sub-module 212.
According to an exemplary embodiment, the data statistics sub-module 202 may receive a user data set. According to an exemplary embodiment, the user data set 101 may include, but is not limited to, user communication channels, communication categories, behavior types, and the like. The user data set may include data for one or more users. For example, table 1 below shows an exemplary typical user data set.
User ID Communication channel Communication category Type of behavior
A Complaints of incoming calls Capital Fund stolen
B Incoming call consultation Payment How to open payment channel
C On-line complaints Privacy Privacy disclosure
D Online consultation Payment How to reduce or avoid payment handling fee
E On-line complaints Capital Capital safety
F Incoming call consultation Account number Mobile phone changing and binding
G Complaints of incoming calls Capital Fund stolen
TABLE 1
As can be appreciated, the user data set is not limited to the form, fields, and/or content illustrated in table 1.
The data statistics submodule 202 performs statistics on the user data set, determines a communication category and a behavior type, which account for a large number/proportion, as statistical results, and provides the statistical results to the service scenario determination module 204. The larger number/proportion of communication categories and behavior types may refer to, for example, those of the highest magnitude, or those of the largest proportion, etc. The present disclosure is not limited in this respect. The determination of a greater number/ratio may be based on a threshold, an ordering, etc.
The business scenario determination sub-module 204 receives the statistical results and determines the primary business scenarios that affect user satisfaction based on the statistical results.
For example, user satisfaction may include one or more aspects including, but not limited to, such as security satisfaction, payment satisfaction, pre-sale service satisfaction, post-sale service satisfaction, and the like.
According to an example, the primary business scenarios that affect user security satisfaction may include, but are not limited to: worry about stolen fund, worry about the password is broken, worry about the cell-phone is lost, worry about user privacy, etc.
According to another example, the primary business scenarios that affect user payment satisfaction may include, but are not limited to: too few payment channels, complicated payment procedure opening, too much payment procedure fee and the like.
After determining the primary business scenarios that affect user satisfaction, the business scenario determination sub-module 204 may output them (e.g., to the questionnaire design module 104 as described in fig. 1 for designing a satisfaction questionnaire). On the other hand, the business scenario determination sub-module 204 may provide the determined primary business scenarios affecting user satisfaction to other sub-modules in the data analysis module 200 for use, as will be described below.
On the other hand, the crowd distribution analysis sub-module 206 receives a satisfaction questionnaire. The satisfaction survey sub-questionnaire may be, for example, retrieved by questionnaire retrieval module 108 in fig. 1, or the like, and provided to data analysis module 200.
The crowd distribution analysis sub-module 206 compares the crowd distribution of the collected questionnaire with the overall user distribution for analysis. According to an example, the overall user distribution may be an independent source of statistics. According to other examples, the overall user distribution may also be statistically derived through other channels. The population distribution analysis sub-module 206 then provides the alignment analysis results to the significant difference dimension determination sub-module 208.
Based on the comparison analysis result, the significant difference dimension determination submodule 208 may obtain dimensions that are different from the overall user distribution and are independent of each other from the population distribution of the questionnaire.
The dimension with large difference can mean that the distribution of the population who answers the questionnaire and the whole user is obviously different in the dimension. For example, assuming that the overall user distribution is 50% for men and women, respectively, and the population who answers the questionnaire is 30% for men and 70% for women, in terms of the age dimension in a certain example, the dimension of age is a significant difference dimension. For another example, assuming that the overall user distribution is 50% for each of men and women, and the population who answers the questionnaire is 51% for men and 49% for women, the dimension of age may not be a significantly different dimension. The determination of the significantly different dimension may be based on a threshold or other metric associated with the dimension.
An independent dimension may mean that the dimension is not dependent on or significantly associated with other dimensions. For example, assuming that in some example, the age dimension is significantly associated with the activity dimension, the age dimension and the activity dimension need not both be considered. The independence of the dimensions may be judged by probability statistics and/or experience, etc.
The significant difference dimension determination submodule 208 provides the determined significant difference dimension (e.g., without limitation, activity level, gender, new and old users, etc. … …) to the user distribution reduction submodule 210. The user distribution reduction sub-module 210 may also receive primary business scenarios from the business scenario determination sub-module 204 that affect user satisfaction.
The significant difference dimension determination sub-module 208 also provides the determined significant difference dimension to the data statistics sub-module 202. On the other hand, questionnaire data is also input to the data statistics sub-module 202, and the overall satisfaction of the user, the user satisfaction of each aspect (such as security, payment, pre-sale, post-sale, etc.), the user satisfaction of each dimension, the user distribution of each dimension, and the like are counted by the data statistics sub-module 202. The data statistics sub-module 202 may provide the statistics to the user distribution reduction sub-module 210.
On this basis, in each satisfaction or service scenario, the user distribution restoration submodule 210 may restore the satisfaction according to the overall user distribution for each significant difference dimension. For example, for the "protection of user privacy", the overall satisfaction of the collected questionnaire is 87% according to the statistics of the data statistics sub-module 202, wherein in the gender dimension, the male satisfaction is 90% and the female satisfaction is 80%. On the other hand, the collected questionnaires contained 70% for males and 30% for females. However, the proportion of men and women among the entire users is 50% each.
Thus, the user distribution reduction submodule 210 may reduce the gender dimension to the whole user, and obtain the satisfaction degree after the gender dimension reduction is 90% by 50% + 80% by 50% — 85%. Similarly, the user distribution reduction sub-module 210 may perform reduction processing on other dimensions (such as activity level, new and old users, etc.) one by one, and obtain a final satisfaction degree in terms of "protecting user privacy".
According to another embodiment, each value in each dimension can be subjected to cross combination, the satisfaction degree of each combination is calculated, and then the distribution of the whole user is restored according to the distribution of each combination in the whole user.
The user distribution reduction sub-module 210 may then perform the same calculation for other satisfaction aspects and/or service scenarios to obtain the reduced satisfaction of each satisfaction aspect and/or service scenario and provide it to the satisfaction determination and output sub-module 212.
The satisfaction determination and output sub-module 212 may obtain an overall satisfaction of the product and/or service based on the respective satisfaction aspects and/or the restored satisfaction of the business scenario. The overall satisfaction can be obtained, for example, by combining individual satisfaction aspects and/or restored satisfaction of the business scenario.
According to an example, if the restored satisfaction of n service scenarios is obtained, the dissatisfaction x of the n service scenarios may be obtained accordingly, for example1～xn(e.g., dissatisfaction ≦ 1-satisfaction), where 0 ≦ xi≤1。
For each business scenario, the satisfaction determination and output submodule 212 also calculates the dissatisfaction probability y of the entire product/service when the user is dissatisfied under that business scenario1～ynWherein 0 is less than or equal to yiLess than or equal to 1. The probability of dissatisfaction may be determined based on questionnaire data. E.g. yi＝P(utotal|ui) Wherein u isiIs an event that the user is not satisfied under the ith service scenario, utotalIs an event where the user is not satisfied with the entire product/service.
Subsequently, the satisfaction determination and output submodule 212 calculates
Figure BDA0002234576320000081
On this basis, the satisfaction determination and output submodule 212 may calculate the overall satisfaction of the product/service as, for example
Figure BDA0002234576320000082
Satisfaction determination and output submodelBlock 212 may then output the satisfaction data. The satisfaction data may include overall satisfaction of a product or service and/or satisfaction of one or more aspects and/or satisfaction of one or more business scenarios, etc.
According to an alternative embodiment, an expert scoring method can be adopted to give weight to each service scene, and a method of weighting and summing can be adopted. For example, the aforementioned n service scenarios may be given a weight w1～wn. Satisfaction determination and output submodule 212 calculatesOn this basis, the satisfaction determination and output submodule 212 may calculate the overall satisfaction of the product/service as, for example
Figure BDA0002234576320000084
Fig. 3 illustrates a block diagram of a questionnaire design module 300 in accordance with an aspect of the present disclosure. According to an example, questionnaire design module 300 may include or constitute questionnaire design module 104 of user satisfaction survey system 100 described in connection with fig. 1.
Questionnaire design module 300 may include a questionnaire question acquisition sub-module 302, a questionnaire database 304, and a questionnaire organization sub-module 306.
According to an exemplary embodiment, questionnaire question acquisition sub-module 302 acquires information of major business scenarios that affect user satisfaction. This information may be received, for example, from data analysis module 102 of fig. 1 and/or data analysis module 200 of fig. 2.
Questionnaire questions acquisition sub-module 302 may then retrieve questionnaire questions associated with the respective business scenario from questionnaire database 304 based on the primary business scenario affecting user satisfaction and provide them to questionnaire organization sub-module 306.
The questionnaire organization sub-module 306 may combine the questionnaire questions associated with the corresponding business scenario with conventional questions and the like, organize and output the questionnaire. The questionnaire can be output to, for example, questionnaire distribution module 106 as described in fig. 1, and so on.
FIG. 4 illustrates a block diagram of a questionnaire distribution and recovery module 400 in accordance with an aspect of the present disclosure. Questionnaires can be distributed to users for a variety of reasons. For example, a questionnaire can be specifically issued to a user when a user complaint, consultation, or the like is received. As another example, questionnaires can be issued to users randomly when the users make normal purchases, browsing, etc.
As shown in FIG. 4, questionnaire distribution and recovery module 400 can include questionnaire distribution sub-module 402 and questionnaire recovery sub-module 404. Questionnaire distribution sub-module 402 may obtain the questionnaire in response to a request from a user and/or a trigger from the system. The questionnaire may be obtained, for example, from questionnaire design module 104 described in conjunction with fig. 1 and/or questionnaire design module 300 described in conjunction with fig. 3.
Questionnaire distribution sub-module 402 distributes the questionnaire to the users. The questionnaire may be sent to the user's computer, cell phone, tablet and/or other terminal over a network, or may be distributed to the user over the telephone in an AI session.
After the user answers the questionnaire, the questionnaire recycling sub-module 408 recycles the questionnaire. According to an example, questionnaire recovery sub-module 408 may provide the recovered questionnaire to other modules, such as data analysis module 102 described in conjunction with fig. 1 and/or data analysis module 200 described in conjunction with fig. 2, and so on.
Fig. 5 illustrates a flow diagram of a method 500 of satisfaction survey in accordance with an aspect of the present disclosure. The method 500 of FIG. 5 may include, at block 502, performing a statistical analysis on the user data set to disaggregate user satisfaction into one or more primary business scenarios that affect user satisfaction.
At block 504, the method 500 may include providing a questionnaire including questions for overall satisfaction and for distributing satisfaction with the one or more primary business scenarios based on the one or more primary business scenarios affecting user satisfaction.
At block 506, method 500 may include distributing the questionnaire to one or more users.
At block 508, the method 500 may include retrieving questionnaires answered by the users and analyzing the retrieved questionnaire or questionnaires to determine demographic distribution information of the users who submitted the questionnaires.
At block 510, based on the analysis of the questionnaire, a revised satisfaction level for the one or more primary business scenarios is determined by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution.
At block 512, an overall satisfaction is determined based on the revised satisfaction of the one or more primary business scenarios.
As can be appreciated, the above functional division and naming of the respective modules are only for facilitating understanding of the technical spirit of the present disclosure, and do not constitute any limitation to the present disclosure. For example, the functionality of any two or more modules may be implemented by a single module. As another example, the functionality of a single module may be split to be implemented by two or more modules, and so on.
The questionnaire designed by the satisfaction survey method and system disclosed by the invention is interactive and friendly, and the user understanding deviation is avoided because the abstract satisfaction problem is decomposed into the specific business problem. By restoring the user distribution, the obtained satisfaction result accords with the integral user distribution, and the evaluation is more objective and effective. In addition, the method and the device for obtaining the user satisfaction not only obtain the overall satisfaction, but also provide the user satisfaction under each specific service scene. This provides interpretability, facilitates locating causes of satisfaction fluctuations, and determines improvement solutions.
In other aspects, the methods of the present disclosure may be implemented by various means. The various modules of such an apparatus may be implemented as hardware, such as logic blocks, circuit modules, general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, gate or transistor logic, hardware components, and the like, or any combinations thereof. In a further aspect, the various modules of such an apparatus may also be implemented as software, or a combination of hardware and software (such as firmware). The present disclosure is not limited in this respect.
Those of ordinary skill in the art appreciate that the benefits of the invention are not realized in full in any single embodiment. Various combinations, modifications, and alternatives will be apparent to one skilled in the art in light of this disclosure.
Furthermore, unless specifically stated otherwise, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from context, the phrase "X employs A or B" or similar phrases is intended to mean any of the natural inclusive permutations. That is, the phrase "X employs a or B" is satisfied by any of the following examples: x is A; x is B; x employs both A and B. The terms "connected" and "coupled" may mean the same meaning, i.e., the direct coupling between two components or the indirect coupling via one or more intervening components. In addition, the articles "a" and "an" as used in this application and the appended claims should generally be construed to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form.
Various aspects or features are presented in terms of systems that may include a number of devices, components, modules, and the like. It is to be understood that the various systems may include additional devices, components, modules, and the like, and/or may not include all of the devices, components, modules, and the like in the embodiments discussed.
The various illustrative logics, logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, gate or transistor logic, or a hardware component. But, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The embodiments described above in connection with the method may be implemented by a processor and a memory coupled thereto, wherein the processor may be configured to perform any of the steps of any of the methods described above, or a combination thereof.
The steps and/or actions of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. For example, the embodiments described above in connection with the various methods may be implemented by a computer readable medium having stored thereon computer program code which, when executed by a processor/computer, performs any of the steps of any of the methods described above, or any combination thereof.
All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are intended to be encompassed by this disclosure. Moreover, nothing herein is intended to be dedicated to the public regardless of whether such disclosure is recited in the claims.

Claims (20)

1. A method of satisfaction survey, comprising:
performing statistical analysis on the user data set, and resolving the user satisfaction into one or more main service scenes influencing the user satisfaction;
based on the one or more primary business scenarios affecting user satisfaction, providing a questionnaire comprising questions of overall satisfaction and distributed satisfaction with respect to the one or more primary business scenarios;
analyzing the one or more recovered questionnaires to determine demographic distribution information of the user who submitted the questionnaire;
determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on an analysis of the one or more recovered questionnaire; and
determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios.
2. The method of claim 1, further comprising:
providing at least one of the overall satisfaction and a revised satisfaction of the one or more primary business scenarios.
3. The method of claim 1, further comprising:
distributing the provided questionnaire to one or more users; and
retrieving the questionnaire submitted by the one or more users.
4. The method of claim 1, wherein the user data set includes at least a user ID, a communication category, and a behavior type, and wherein statistically analyzing the user data set to resolve user satisfaction into one or more primary business scenarios that affect user satisfaction comprises:
determining the number and/or the proportion of communication categories and behavior types based on the user data set; and
determining one or more primary business scenarios that affect user satisfaction based on the quantity and/or the fraction of communication categories and behavior types.
5. The method of claim 1, wherein analyzing the one or more questionnaires retrieved to determine demographic distribution information of the user who submitted the questionnaire comprises:
comparing and analyzing the crowd distribution and the whole user distribution of the one or more recovered questionnaires; and
based on the analysis, one or more significant difference dimensions that are significantly different from and independent of the overall user distribution are determined.
6. The method of claim 5, wherein determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of users who submitted the questionnaire to the overall user distribution based on analysis of the one or more recovered questionnaires comprises:
for each primary business scenario, the satisfaction of that primary business scenario is revised by reverting the crowd distribution of users who submitted the questionnaire to the overall user distribution in each significant difference dimension.
7. The method of claim 5, wherein determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of users who submitted the questionnaire to the overall user distribution based on analysis of the one or more recovered questionnaires comprises:
for each main business scenario, cross-combining the values of the one or more significant difference dimensions, and determining the satisfaction degree of each cross-combination based on the one or more recovered questionnaires; and
the revised satisfaction of the primary business scenario is determined by reverting the distribution of each cross-portfolio among the population of users who submitted the questionnaire to the distribution of the cross-portfolio among the entire users.
8. The method of claim 1, wherein determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios comprises:
determining a dissatisfaction of each primary business scenario based on the revised satisfaction of the primary business scenario;
determining the integral dissatisfaction probability of the user under each main service scene on the premise of dissatisfaction based on the recovered questionnaire or questionnaires; and
and combining the dissatisfaction degree of each main service scene with the overall dissatisfaction probability under the condition that the user is dissatisfied under the main service scene to determine the overall satisfaction probability.
9. The method of claim 8, further comprising assigning a weight to each primary traffic scenario, and wherein the overall probability of dissatisfaction if a user is dissatisfied for each primary traffic scenario is further determined based on the weight.
10. An apparatus for satisfaction survey, comprising:
the data analysis module is used for carrying out statistical analysis on the user data set and resolving the user satisfaction into one or more main service scenes influencing the user satisfaction;
a questionnaire design module for providing a questionnaire comprising questions about overall satisfaction and about distributing satisfaction with respect to the one or more primary business scenarios based on the one or more primary business scenarios affecting user satisfaction; wherein
The data analysis module is further to determine revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on an analysis of the one or more retrieved questionnaires; and determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios.
11. The apparatus of claim 10, wherein the data analysis module is further for providing at least one of the overall satisfaction and a revised satisfaction of the one or more primary business scenarios.
12. The apparatus of claim 10, further comprising:
a questionnaire distribution module for distributing the provided questionnaire to one or more users; and
and the questionnaire recovery module is used for recovering the questionnaires submitted by the one or more users.
13. The apparatus of claim 10, wherein the user data set includes at least a user ID, a communication category, and a behavior type, and the data analysis module further comprises:
the data statistics submodule is used for determining the number and/or the proportion of the communication categories and the behavior types based on the user data set; and
a business scenario determination submodule for determining one or more main business scenarios affecting the user satisfaction based on the number and/or the fraction of the communication categories and the behavior types.
14. The apparatus of claim 10, wherein the data analysis module further comprises:
the crowd distribution analysis submodule is used for comparing and analyzing the crowd distribution of the one or more recovered questionnaires with the whole user distribution; and
and the significant difference dimension determining submodule is used for determining one or more significant difference dimensions which are significant in difference with the overall user distribution and are independent of each other based on the analysis.
15. The apparatus of claim 14, wherein the data analysis module further comprises:
and the user distribution reduction submodule is used for correcting the satisfaction degree of each main business scene by reducing the crowd distribution of the users submitting the questionnaire to the whole user distribution in each significant difference dimension.
16. The apparatus of claim 14, wherein the data analysis module further comprises:
a user distribution reduction submodule, configured to determine, for each main service scenario, a satisfaction degree of each cross combination based on one or more recovered questionnaires, by cross combination of values of the one or more significant difference dimensions; and determining a revised satisfaction level for the primary business scenario by reverting the distribution of each cross-portfolio among the population of users who submitted the questionnaire to the distribution of the cross-portfolio among the overall users.
17. The apparatus of claim 10, wherein the data analysis module further comprises:
a satisfaction determination and output sub-module for determining the dissatisfaction of each primary service scenario based on the revised satisfaction of the primary service scenario; determining the integral dissatisfaction probability of the user under each main service scene on the premise of dissatisfaction based on the recovered questionnaire or questionnaires; and combining the dissatisfaction degree of each main service scene with the overall dissatisfaction probability under the condition that the user is dissatisfied under the main service scene to determine the overall satisfaction probability.
18. The apparatus of claim 17, wherein the satisfaction determination and output sub-module is further for assigning a weight to each primary traffic scenario, and wherein the overall probability of dissatisfaction if the user is dissatisfied for each primary traffic scenario is further determined based on the weight.
19. An apparatus for satisfaction survey, comprising:
a memory; and
a processor coupled to the memory and configured to:
performing statistical analysis on the user data set, and resolving the user satisfaction into one or more main service scenes influencing the user satisfaction;
based on the one or more primary business scenarios affecting user satisfaction, providing a questionnaire comprising questions of overall satisfaction and distributed satisfaction with respect to the one or more primary business scenarios;
analyzing the one or more recovered questionnaires to determine demographic distribution information of the user who submitted the questionnaire;
determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on an analysis of the one or more recovered questionnaire;
determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios.
20. A computer-readable storage medium having stored thereon processor-executable instructions for conducting satisfaction surveys, the processor-executable instructions, when executed by a processor:
performing statistical analysis on the user data set, and resolving the user satisfaction into one or more main service scenes influencing the user satisfaction;
based on the one or more primary business scenarios affecting user satisfaction, providing a questionnaire comprising questions of overall satisfaction and distributed satisfaction with respect to the one or more primary business scenarios;
analyzing the one or more recovered questionnaires to determine demographic distribution information of the user who submitted the questionnaire;
determining revised satisfaction of the one or more primary business scenarios by reverting the demographic distribution of the users who submitted the questionnaire to the overall user distribution based on an analysis of the one or more recovered questionnaire;
determining an overall satisfaction based on the revised satisfaction of the one or more primary business scenarios.
CN201910978992.8A 2019-10-15 2019-10-15 Satisfaction investigation method and device Pending CN110796348A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111652775A (en) * 2020-05-07 2020-09-11 上海奥珩企业管理有限公司 Method for constructing household service process management system model
CN113674031A (en) * 2021-08-30 2021-11-19 广州快决测信息科技有限公司 System and method for analyzing net recommended value questionnaire data
CN113724006A (en) * 2021-08-30 2021-11-30 苏州众言网络科技股份有限公司 Information processing method and device for user experience journey
CN113761333A (en) * 2020-11-10 2021-12-07 北京沃东天骏信息技术有限公司 Information processing method, device and storage medium

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111652775A (en) * 2020-05-07 2020-09-11 上海奥珩企业管理有限公司 Method for constructing household service process management system model
CN111652775B (en) * 2020-05-07 2023-09-26 颐家(上海)医疗养老服务有限公司 Intelligent supervision method for household medical and nutritional service process
CN113761333A (en) * 2020-11-10 2021-12-07 北京沃东天骏信息技术有限公司 Information processing method, device and storage medium
CN113674031A (en) * 2021-08-30 2021-11-19 广州快决测信息科技有限公司 System and method for analyzing net recommended value questionnaire data
CN113724006A (en) * 2021-08-30 2021-11-30 苏州众言网络科技股份有限公司 Information processing method and device for user experience journey

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