CN113724006A - Information processing method and device for user experience journey - Google Patents
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Abstract
The embodiment of the disclosure discloses an information processing method and device for a user experience journey, which comprises the following steps: acquiring various stages of a user experience journey, behaviors included in each stage and a contact set included in each behavior; in response to receiving a notification signal that a touch point is triggered, generating a satisfaction degree scoring questionnaire containing satisfaction degree evaluation indexes in real time for the triggered touch point; acquiring actual use data of each contact and a satisfaction evaluation score corresponding to each satisfaction evaluation index for each contact in a preset time period; and determining the portrait data of each contact in a preset time period by utilizing a pre-established model based on the actual use data of each contact and the satisfaction evaluation score corresponding to each satisfaction evaluation index. By combining subjective data of a user determined based on a questionnaire with objective data during an operation process, portrait data (structured data) of a touch point is determined, which can accurately represent the user's experience at the touch point.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an information processing method for a user experience itinerary.
Background
And the user experience journey describes the experience condition of the user for using the product or receiving the service from the perspective of the user, finds pain points and satisfaction points of the user in the whole using process, and finally extracts optimized points and designed opportunity points in the product or the service.
In the related art, the generation of the portrait data of the touch points in the user journey is usually realized based on the recollection type data, and on one hand, the method cannot accurately touch the user to collect accurate data, the recollection type data of the other party has the possibility of data distortion, and the recollection type data has the data missing most of the experience of real scenes. Further, the contact portrait data cannot be truly reflected in the experience data under the contact.
Disclosure of Invention
The main purpose of the present disclosure is to provide an information processing method and apparatus for a user experience journey.
In order to achieve the above object, according to a first aspect of the present disclosure, there is provided an information processing method for a user experience journey, including: acquiring various stages of a user experience journey, behaviors included in each stage and a contact point set included in each behavior, wherein the contact point set comprises at least one contact point, and each contact point corresponds to at least one satisfaction evaluation index; responding to a received contact triggered notification signal, determining questionnaire data corresponding to a triggered contact from a pre-established questionnaire set, wherein the questionnaire data comprises satisfaction degree grading questionnaire data consisting of satisfaction degree evaluation indexes, and after a user scores the satisfaction degree of each satisfaction degree evaluation index through a user side, acquiring a satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index; acquiring actual use data of each contact and a satisfaction evaluation score corresponding to each satisfaction evaluation index for each contact in a preset time period; and determining the portrait data of each contact in a preset time period by utilizing a pre-established model based on the actual use data of each contact and the satisfaction evaluation score corresponding to each satisfaction evaluation index.
According to a second aspect of the present disclosure, there is provided an information processing apparatus for a user experience journey, comprising: the journey information acquisition unit is configured to acquire various stages of journey experienced by a user, behaviors included in each stage and a contact point set included in each behavior, wherein the contact point set comprises at least one contact point, and each contact point corresponds to at least one satisfaction evaluation index; the data acquisition unit responds to the received notification signal that the contact is triggered, and generates a satisfaction degree scoring questionnaire containing satisfaction degree evaluation indexes in real time aiming at the triggered contact, so that a user can score the satisfaction degree under each satisfaction degree evaluation index through the questionnaire to obtain the satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index; the data acquisition unit is configured to acquire actual use data of each contact point and a satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index aiming at each contact point in a preset time period; and the computing unit is configured to determine the portrait data of each contact point in a preset time period by using a pre-established model based on the actual use data of each contact point and the satisfaction evaluation score corresponding to each satisfaction evaluation index.
According to an aspect of the present disclosure, there is provided an electronic apparatus including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the method of information processing for a user experience tour as described in any of the implementations of the first aspect of the at least one processor.
The information processing method and device for the user experience journey in the embodiment of the disclosure comprise the following steps: acquiring various stages of a user experience journey, behaviors included in each stage and a contact set included in each behavior; in response to receiving a notification signal that a touch point is triggered, generating a satisfaction degree scoring questionnaire containing satisfaction degree evaluation indexes in real time for the triggered touch point; acquiring actual use data of each contact and a satisfaction evaluation score corresponding to each satisfaction evaluation index for each contact in a preset time period; and determining the portrait data of each contact in a preset time period by utilizing a pre-established model based on the actual use data of each contact and the satisfaction evaluation score corresponding to each satisfaction evaluation index. By combining subjective data of a user determined based on a questionnaire with objective data during an operation process, portrait data (structured data) of a touch point is determined, which can accurately represent the user's experience at the touch point. Further solving the problem that the contact portrait data in the correlation technique can not be truly reflected in the experience data under the contact
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In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow diagram of an information processing method for a user experience tour according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those skilled in the art, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only some embodiments of the present disclosure, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure may be described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present disclosure, there is provided an information processing method for a user experience itinerary, as shown in fig. 1, the method including steps 101 to 104 as follows:
step 101: the method comprises the steps of obtaining various stages of a user experience journey, behaviors included in each stage and a contact point set included in each behavior, wherein the contact point set comprises at least one contact point, and each contact point corresponds to at least one satisfaction evaluation index.
In this embodiment, different businesses have different user experience itineraries, for example, in the case of loan business, the user experience itinerary may include a phase m of business understanding phase-loan application-loan approval, etc., and may include at least one action n under each phase, and in the case of loan approval phase, may include a material submission action, a material approval action, a phone approval receipt action, and may include at least one contact i under each action, for example, the material submission action may include a form filling contact, a form filling consultation contact, etc. Each contact point can contain a plurality of satisfaction evaluation indexes, for example, the form filling consultation contact point can comprise an index of whether the evaluation mode is convenient or not, a consultant attitude index and the like.
The method of the present embodiment may be applied to different user experience tours.
Step 102: in response to receiving a notification signal that a touch point is triggered, determining questionnaire data corresponding to the triggered touch point from a pre-established questionnaire set, wherein the questionnaire data comprises a satisfaction degree evaluation questionnaire consisting of satisfaction degree evaluation indexes, and after a user scores the satisfaction degree of each satisfaction degree evaluation index through a user side, acquiring a satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index.
In this embodiment, data cannot be collected in real time in the related art, and there is a possibility of data distortion, instead of feedback directly submitted by a client; and the user experiences the journey analysis and misses the best data processing opportunity due to the failure to collect data in real time. Therefore, questionnaires containing different satisfaction evaluation indexes can be established in advance for different contacts, and the data of the questionnaires of all the contacts can form a questionnaire set.
And sending the questionnaire data corresponding to the contact to a user side triggering the contact every time the contact is triggered, and grading each satisfaction evaluation index through the user side by the user, wherein for example, the corresponding satisfaction evaluation score can be 9 according to whether the evaluation mode is convenient. And after the user scores, the satisfaction evaluation score can be acquired through an internet information acquisition technology. So that data collection can be performed in real time and for precise users.
It can be understood that the satisfaction evaluation index can be established according to actual requirements and further different questionnaires can be obtained according to different requirements. The scoring data can also be obtained from the questionnaire system after the contact is triggered through the connection of the execution main body of the method and the questionnaire system.
Illustratively, the content of the questionnaire may include the content of the satisfaction evaluation index and the reason of the score result of each satisfaction evaluation index, so as to obtain the reason set SiBy determining the set of causes, the cause (actuation or obstruction) that forms the current satisfaction experience score may be represented when data analysis is performed on the user experience of the touch point.
Step 103: and acquiring actual use data of each contact and a satisfaction evaluation score corresponding to each satisfaction evaluation index for each contact in a preset time period.
In this embodiment, in a preset time period, a plurality of users may trigger the contact through the user side, so that the actual usage data and the satisfaction evaluation score of each satisfaction evaluation index in the preset time period may be obtained through a preset interface. The preset time period may be a period of a preset time length, and the data of the contact point is determined every other period.
The actual use data may include the number p of people who triggered the contact in the action n at the phase m within a preset time periodmniThe total number N of the respondents in the stage m and the behavior N in the preset time periodmn. After each contact is triggered, the user scores the satisfaction evaluation indexes in the questionnaire, so that the scores of all the satisfaction evaluation indexes under each contact in the preset time period can be obtained.
Step 104: and determining the portrait data of each contact in a preset time period by utilizing a pre-established model based on the actual use data of each contact and the satisfaction evaluation score corresponding to each satisfaction evaluation index.
In this embodiment, by combining subjective data of a user determined by a questionnaire with objective data in an operation process, portrait data (structured data) of a touch point is determined, and the portrait data can accurately represent the experience of the user at the touch point. User experience data under each satisfaction evaluation index can be obtained through the portrait data, so that improvement on service is facilitated, and user experience of touch is further improved.
As an optional implementation manner of this embodiment, every preset period, the portrait data of each touch point is updated based on the actual usage data in the current period and the real-time satisfaction evaluation score in the current period.
In this alternative implementation, when the service for the touch point is improved, the touch point service improvement effect data can be obtained by continuously updating the portrait data of the touch point.
As an optional implementation manner of this embodiment, the method further includes: determining, based on the pre-established model, portrait data for each behavior using the portrait data for each contact; and/or determining the portrait data for each stage using the portrait data for each behavior based on a pre-established model.
In this optional implementation, because each behavior n includes a set of contacts, based on a pre-established model calculation strategy, after determining the portrait data for each contact, the portrait data for each behavior can be calculated; after the behavior portrait data is obtained, the portrait data of each stage m can be calculated and obtained based on the calculation strategy of the pre-established model.
As an optional implementation manner of this embodiment, the method further includes: updating the portrait data of each behavior based on the pre-established model using the updated portrait data of each contact; and/or updating the image data of each stage by using the updated image data of each behavior based on the pre-established model.
In this alternative implementation, after the portrait data of each contact is updated, the portrait data of each behavior and the portrait data of each stage may be recalculated based on the updated data by using the calculation strategy of the model, so as to establish a closed experience loop of experience-feedback-improvement-incentive for the journey.
Traditional, staged satisfaction-NPS surveys are transformed into real-time, continuous customer (user) trip experience monitoring by way of a pre-established model that is coordinated with the questionnaire system. The fixed data is changed into the mobile data, and the utilization value of the data is really realized.
As an optional implementation manner of this embodiment, determining the portrait data of each contact point by using the pre-established model based on the actual usage data of each contact point and the satisfaction evaluation score corresponding to each satisfaction evaluation index includes: after actual use data of each contact point is obtained, determining the importance score of each contact point; determining the experience satisfaction score of each contact point based on the satisfaction evaluation score corresponding to each satisfaction evaluation index; determining a second score corresponding to each satisfaction degree evaluation index based on a driving index preconfigured for each satisfaction degree evaluation index, wherein the satisfaction degree evaluation index is the driving capability of experiencing satisfaction degree score of the contact; and/or determining a target score of each satisfaction evaluation index based on the evaluation score of each satisfaction evaluation index and the second score, wherein the magnitude and the magnitude of the target score are respectively used for indicating that the satisfaction evaluation index is to be improved and the priority is low and high.
In this alternative implementation, in determining the representation data for each touch point, the scores for the touch points in the four dimensions may be determined by a pre-established model.
In one dimension, an index system of the journey contacts is established through a model, and the importance score BH (used for representing the reaching rate of the contacts) of each contact is used for measuring the contact rate between the contact and a target client (user) in a period of time on each journey. The higher the BH, the more important the contact is for the journey.
Illustratively, in determining the importance score (referred to as the heat value) for each contact, the heat value BH for the ith contact of the nth action at trip phase mCmniCan be determined by the formula:
BHCmni＝(pmni÷Nmn) X 100% determination, wherein the respective parameter definitions refer to table 1.
TABLE 1
In one dimension, a customer experience index system is established through a model, the number of users corresponding to a preset value can be determined based on the value of each satisfaction evaluation index, and the experience satisfaction score of each contact point is determined based on the number. The experience satisfaction score for each contact i may be determined by the following formula, where the definition of the various parameters is referenced in table 2:
| Emni | net satisfaction score of experience for contact i under action n of phase m |
| nhi | Number of visitors with experience satisfaction degree of contact i graded at 9 points and 10 points |
| nli | Number of visitors with experience satisfaction degree of contact i from 0 point to 6 points |
| Ni | The number of visitors who scored the experience satisfaction problem of the contact |
TABLE 2
Wherein the content of the first and second substances,
wherein 0 is the lowest score and 10 is the highest score. The calculation method of both NPS and NSS is the number of people with score of 9-10hThe number of people n who received a score of 0 to 6 is subtractedlAnd dividing by the total sample size N to obtain the ratio.
And under the other dimension, a two-dimensional index system of the driving factors is established through a model, and the driving capability of the satisfaction evaluation index for the experience satisfaction score of the contact is calculated.
The two-dimensional index is derived from a 'two-dimensional pattern of satisfaction' embodied in the KANO model, and two dimensions in the two-dimensional index are used for measuring driving forces of factors in two aspects of eliminating dissatisfaction and improving satisfaction respectively. Furthermore, for the measurement of the two-dimensional index, on the one hand the driving force for low score elimination and high score growth of the contact satisfaction; on the other hand, it is the driving force for the low-score elimination and high-score increase of NPS.
The method can be realized based on the 'nonlinear relation between product performance and user satisfaction', namely 'two-dimensional mode of satisfaction' in the KANO model. The method comprises the steps of assigning a high-score driving index S to the index corresponding to the contact point with high experience satisfaction score based on the experience satisfaction score of each contact point and the satisfaction evaluation index corresponding to each contact pointh(ii) a Assigning a low-score driving index S to the index corresponding to the contact with low experience satisfaction scorelAnd obtaining a two-dimensional index of each satisfaction evaluation index based on the following calculation formula in the model:
Shlx＝(Shx*εhx)+(Slx*εlx) (ii) a When L > 100, epsilonhx>εlx(ii) a When L is less than or equal to 100, epsilonhx<εlxThe definition of the various parameters in the formula is described in reference to table 3:
| Shlx | two-dimensional index score of x index |
| Shx | NPS high score driving index score of x index |
| Slx | NPS low score driving index score of x index |
| εhx | Index ShiWeight of (2) |
| εlx | Index ShlWeight of (2) |
| L | TGI index of brand/service/product NPS versus average |
TABLE 3
When the NPS performance is above the average level, the high-score driving is more needed to push the NPS to continue growing, and the weight of the high-score driving is increased; when the NPS is mid-downstream in the industry average, the low-score drive is more desirable to let the NPS fall off the "downstream" level as soon as possible, beyond the average, at which point the low-score drive weight is increased. The division of specific weights can be determined according to the exceeding or the failing magnitude of the NPS compared with the average level. The average level may be obtained as determined by a questionnaire.
The tgi (target Group index) index, is a common index that reflects the strengths or weaknesses of a target population within a particular research area (e.g., geographic region, demographic domain, media audience, product consumer). TGI index [ proportion of population having a certain characteristic in the target population/proportion of population having the same characteristic in the population ]. times.100. TGI index equal to 100 represents the mean level; above 100, representing that the attention degree of the users to the problems is higher than the whole level; below 100 this represents a less than global level.
In this embodiment, the dynamic relationship between the satisfaction evaluation index and the contact satisfaction score can be dynamically checked by adjusting the driving index of the index, for example, after a low-score driving index corresponding to a certain satisfaction evaluation index is reduced, whether the satisfaction score of the current contact is improved or not can be obtained. By combining the satisfaction evaluation scores of the contact points with each satisfaction evaluation index, the driving and hindering factors of each index score can be defined, and the improvement priority of each experience element can be determined.
In another dimension, the establishment of the improved target index priority system is realized through the model. User experience scoring E that may incorporate satisfaction evaluation indicators for contactsxAnd driving factor twoDimensional index score ShlxOn the basis of the result, after comprehensive calculation, the obtained improved priority score Ti. The score may be given by ExAnd ShlxAnd (4) multiplying the two to obtain the product. In order to determine the priority level to be improved for each index in the contact, the priority level can be determined by the following formula:
Tx＝Ex*Shlxin which ExRepresents each satisfaction evaluation index score, S, corresponding to each contacthlxThe index x is a two-dimensional index score.
Can be determined by comparing T of each indexxThe lower the score, the lower the priority of the improvement, ranked from low to high. Thereby obtaining an index improvement priority list. Based on the priority list, an improvement plan can be made, and after the improvement plan is made, data are obtained again for analysis, so that a set of complete closed-loop lifting action routes can be formed.
As an alternative implementation manner of this embodiment, the determining the portrait data of each behavior by using the portrait data of each contact based on the pre-established model includes: determining an importance score for each behavior based on the pre-established model using the importance score for each contact; and/or determining an experience satisfaction score for each behavior using the experience satisfaction score for each contact based on a pre-established model.
In this alternative implementation, the pre-established model defines the calculation of the importance score of the behavior, and the calculation of the experience satisfaction score of the behavior. In calculating the importance score for a behavior (used to measure the contact rate between different behaviors and the target customer (user) over a period of time on each trip), the following formula may be used:
In calculating the experience satisfaction score for a behavior, it may be determined using the following formula:
for experience net satisfaction score below phase m for action n, from its satisfaction score E for the next i contactsmniThe weighted average of (a); weight αmniIs scored by EmniCoefficients for regression analysis with NPS are formed for the normalized result in action n.
The selectable implementation mode generates the portrait data of the behavior through the portrait data of the model and the touch points, and the portrait data of the touch points shows the satisfaction degree of user experience, so that the obtained portrait data of the behavior can be used for showing the satisfaction degree of the user experience under the behavior.
As an optional implementation manner of this embodiment, determining the portrait data of each stage by using the portrait data of each behavior based on the pre-established model includes: determining the importance score of each stage based on the importance scores of the behaviors based on a pre-established model; based on the pre-established model, an experience satisfaction score for each stage is determined based on the experience satisfaction score for each behavior.
In this alternative implementation, the pre-established model defines the importance score calculation for the journey stage, and the experience satisfaction score calculation strategy for the stage. In calculating the importance scores for the phases (contact rate for different phases over a period of time), it can be determined by the following formula:
wherein, BHSmThermal value for mth trip, BHAmnThe nth action thermal value under the journey m; the weight value of the nth action at journey stage m can be determined by 'Delphi' method.
When calculating the experience satisfaction score of the phase, the following formula can be used to determine:
The representation data for each stage in the journey may be determined by the model and the representation data for the action. Because the portrait data of the touch point represents the satisfaction degree of the user experience, the portrait data of the obtained stage can be used for representing the satisfaction degree of the user experience at the stage.
As an optional implementation manner of this embodiment, the method further includes: data for mapping the user experience journey is determined based on the portrait data for each contact, the portrait data for each action, and the portrait data for each phase.
In this optional implementation, the user experience trip graph may be drawn based on all the image data obtained by the implementation, and may be generated in an exponential manner. Therefore, the final system of the journey combing, experience scoring, driving factors and improvement targets can be presented on the digitalized user experience journey graph in a systematic and visual mode.
By highly fitting the digital map with the model (called BEST model) of the application, research and consultation results can be dynamically presented, and continuous promotion of digital experience management is ensured.
Compared with the existing relevant models, the model is beneficial to enterprises to build a real-time and closed-loop digital experience management monitoring system, the implementation effect of experience management is observed through dynamic data, and a benign channel for circular promotion is entered. The system helps enterprises to realize information collection and analysis of full-aperture customers (users), and helps to realize integrated management of full-volume customers (users) and dynamic promotion of customer value. The model integrates the research model at the input end, the issuing technical rule at the implementation end, the light consultation model at the output end and the final visual digital map to a high degree, so that systematic operation of research-technology-consultation three parties is realized, and the efficiency of customer experience management is greatly improved. Each large index system of the model presents the monitoring result in an exponential form, and is beneficial to enterprises to establish a measurable datamation target for user management and development. The research model is combined with the customized internet information acquisition technology, and the acquisition efficiency and the accuracy of results are favorably improved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The embodiment of the present disclosure provides an electronic device, as shown in fig. 2, the electronic device includes one or more processors 21 and a memory 22, where one processor 21 is taken as an example in fig. 2.
The controller may further include: an input device 23 and an output device 24.
The processor 21, the memory 22, the input device 23 and the output device 24 may be connected by a bus or other means, which is exemplified in fig. 2.
The processor 21 may be a Central Processing Unit (CPU). The processor 21 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 22, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the control methods in the embodiments of the present disclosure. The processor 21 executes various functional applications of the server and data processing, i.e. the information processing method for the user experience tour, which implements the above-described method embodiments, by running non-transitory software programs, instructions and modules stored in the memory 22.
The memory 22 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of a processing device operated by the server, and the like. Further, the memory 22 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 22 may optionally include memory located remotely from the processor 21, which may be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 23 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the processing device of the server. The output device 24 may include a display device such as a display screen.
One or more modules are stored in the memory 22, which when executed by the one or more processors 21 perform the method as shown in fig. 1.
An embodiment of the present disclosure provides an information processing apparatus for a user experience journey, including: the journey information acquisition unit is configured to acquire various stages of journey experienced by a user, behaviors included in each stage and a contact point set included in each behavior, wherein the contact point set comprises at least one contact point, and each contact point corresponds to at least one satisfaction evaluation index; the data acquisition unit responds to the received notification signal that the contact is triggered, and generates a satisfaction degree scoring questionnaire containing satisfaction degree evaluation indexes in real time aiming at the triggered contact, so that a user can score the satisfaction degree under each satisfaction degree evaluation index through the questionnaire to obtain the satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index; the data acquisition unit is configured to acquire actual use data of each contact point and a satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index aiming at each contact point in a preset time period; and the computing unit is configured to determine the portrait data of each contact point in a preset time period by using a pre-established model based on the actual use data of each contact point and the satisfaction evaluation score corresponding to each satisfaction evaluation index.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the motor control methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a Random Access Memory (RAM), a flash memory (FlashMemory), a hard disk (hard disk drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. An information processing method for a user experience tour, comprising:
acquiring various stages of a user experience journey, behaviors included in each stage and a contact point set included in each behavior, wherein the contact point set comprises at least one contact point, and each contact point corresponds to at least one satisfaction evaluation index;
responding to a received contact triggered notification signal, determining questionnaire data corresponding to a triggered contact from a pre-established questionnaire set, wherein the questionnaire data comprises satisfaction degree grading questionnaire data consisting of satisfaction degree evaluation indexes, and after a user scores the satisfaction degree of each satisfaction degree evaluation index through a user side, acquiring a satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index;
acquiring actual use data of each contact and a satisfaction evaluation score corresponding to each satisfaction evaluation index for each contact in a preset time period;
and determining the portrait data of each contact in a preset time period by utilizing a pre-established model based on the actual use data of each contact and the satisfaction evaluation score corresponding to each satisfaction evaluation index.
2. The information processing method for a user experience tour of claim 1, further comprising:
and updating the portrait data of each contact point every preset period based on the actual use data in the current period and the real-time satisfaction evaluation score in the current period.
3. The information processing method for a user experience tour of claim 1, further comprising:
determining, based on the pre-established model, portrait data for each behavior using the portrait data for each contact; and/or the presence of a gas in the gas,
the image data for each stage is determined using the image data for each behavior based on the pre-established model.
4. An information processing method for a user experience tour according to claim 2, the method further comprising:
updating the portrait data of each behavior based on the pre-established model using the updated portrait data of each contact; and/or the presence of a gas in the gas,
based on the pre-established model, the updated portrait data for each behavior is used to update the portrait data for each phase.
5. The information processing method for a user experience journey according to claim 2, wherein determining the portrait data for each contact using the pre-established model based on the actual usage data for each contact and the satisfaction rating score corresponding to each satisfaction rating indicator comprises:
after actual use data of each contact point is obtained, determining the importance score of each contact point;
determining the experience satisfaction score of each contact point based on the satisfaction evaluation score corresponding to each satisfaction evaluation index;
determining a second score corresponding to each satisfaction degree evaluation index based on a driving index preconfigured for each satisfaction degree evaluation index, wherein the magnitude of the second score is used for representing the driving capability of the satisfaction degree evaluation index for experiencing the satisfaction degree score of the contact; and/or the presence of a gas in the gas,
and determining a target score of each satisfaction evaluation index based on the evaluation score of each satisfaction evaluation index and the second score, wherein the magnitude and the magnitude of the target score are respectively used for representing the high and low priority of the satisfaction evaluation index to be improved.
6. The information processing method for a user experience tour of claim 5, wherein determining portrait data for each behavior using portrait data for each contact based on a pre-established model comprises:
determining an importance score for each behavior based on the pre-established model using the importance score for each contact; and/or the presence of a gas in the gas,
based on the pre-established model, an experience satisfaction score for each behavior is determined using the experience satisfaction score for each contact.
7. The information processing method for a user experience tour of claim 6, wherein determining the portrait data for each phase using the portrait data for each behavior based on a pre-established model comprises:
determining the importance score of each stage based on the importance scores of the behaviors based on a pre-established model;
based on the pre-established model, an experience satisfaction score for each stage is determined based on the experience satisfaction score for each behavior.
8. An information processing method for a user experience tour according to claim 3, the method further comprising:
data for mapping the user experience journey is determined based on the portrait data for each contact, the portrait data for each action, and the portrait data for each phase.
9. An information processing apparatus for a user to experience a trip, comprising:
the journey information acquisition unit is configured to acquire various stages of journey experienced by a user, behaviors included in each stage and a contact point set included in each behavior, wherein the contact point set comprises at least one contact point, and each contact point corresponds to at least one satisfaction evaluation index;
the data acquisition unit is used for responding to a received notification signal that the contact is triggered, determining questionnaire data corresponding to the triggered contact from a pre-established questionnaire set, wherein the questionnaire data comprises satisfaction degree grading questionnaire data consisting of satisfaction degree evaluation indexes, and after a user scores the satisfaction degree of each satisfaction degree evaluation index through a user terminal, acquiring the satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index;
the data acquisition unit is configured to acquire actual use data of each contact point and a satisfaction degree evaluation score corresponding to each satisfaction degree evaluation index aiming at each contact point in a preset time period;
and the computing unit is configured to determine the portrait data of each contact point in a preset time period by using a pre-established model based on the actual use data of each contact point and the satisfaction evaluation score corresponding to each satisfaction evaluation index.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the method of information processing for a user experience tour of any of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151872A (en) * | 2022-11-28 | 2023-05-23 | 荣耀终端有限公司 | Product characteristic analysis method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170091810A1 (en) * | 2015-09-29 | 2017-03-30 | Michael McGovern | Brand engagement touchpoint attribution using brand engagement event weighting |
| CN106886554A (en) * | 2016-12-27 | 2017-06-23 | 苏州思杰马克丁软件有限公司 | A kind of determination method and device of article quality |
| CN109523311A (en) * | 2018-10-30 | 2019-03-26 | 广东原昇信息科技有限公司 | ' Satisfaction Index weighing computation method |
| CN110796348A (en) * | 2019-10-15 | 2020-02-14 | 支付宝(杭州)信息技术有限公司 | Satisfaction investigation method and device |
| US20200327597A1 (en) * | 2019-04-10 | 2020-10-15 | Oppobot Oy | Product evaluation system and method |
| CN112005266A (en) * | 2018-02-23 | 2020-11-27 | 蓝绿品牌有限责任公司 | System and method for customer itinerary analysis in electronic commerce and digital marketing |
-
2021
- 2021-08-30 CN CN202111006930.4A patent/CN113724006A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170091810A1 (en) * | 2015-09-29 | 2017-03-30 | Michael McGovern | Brand engagement touchpoint attribution using brand engagement event weighting |
| CN106886554A (en) * | 2016-12-27 | 2017-06-23 | 苏州思杰马克丁软件有限公司 | A kind of determination method and device of article quality |
| CN112005266A (en) * | 2018-02-23 | 2020-11-27 | 蓝绿品牌有限责任公司 | System and method for customer itinerary analysis in electronic commerce and digital marketing |
| CN109523311A (en) * | 2018-10-30 | 2019-03-26 | 广东原昇信息科技有限公司 | ' Satisfaction Index weighing computation method |
| US20200327597A1 (en) * | 2019-04-10 | 2020-10-15 | Oppobot Oy | Product evaluation system and method |
| CN110796348A (en) * | 2019-10-15 | 2020-02-14 | 支付宝(杭州)信息技术有限公司 | Satisfaction investigation method and device |
Non-Patent Citations (2)
| Title |
|---|
| 刘童 等: "基于AHP与Kano模型分析的天津水上公园满意度综合评价", 《山东农业大学学报(自然科学版)》 * |
| 陈佩琳 等: "服务设计视野下博物馆的服务创新策略", 《设计》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151872A (en) * | 2022-11-28 | 2023-05-23 | 荣耀终端有限公司 | Product characteristic analysis method and device |
| CN116151872B (en) * | 2022-11-28 | 2023-11-14 | 荣耀终端有限公司 | Product characteristic analysis method and device |
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