JP6588176B2 - Computer system for analyzing evaluation results and method and program executed in the computer system - Google Patents

Description

  The present invention relates to a computer system for analyzing an evaluation result and a method and program executed in the computer system.

  Traditionally, companies have sought to improve customer satisfaction (CS). Patent Document 1 discloses an apparatus for analyzing customer satisfaction. The device of Patent Document 1 analyzes the relationship between a product and its willingness to purchase based on a quantitative questionnaire result.

  In recent years, it is not necessarily limited to “customer satisfaction” that directly affects the profitability of a company. In addition to “customer satisfaction” or in place of “customer satisfaction”, any desired other than “customer satisfaction” It has been found that improving the evaluation index also leads to improved profits of the company. In order to improve a desired evaluation index and, in turn, improve the profits of a company, the key is how to allocate limited resources by reading and interpreting customer feedback.

  A theory called Service Profit Chain is known. The theory is that employee satisfaction leads to improvement of service quality, improvement of service quality leads to customer satisfaction, and improvement of customer satisfaction leads to improvement of profits. However, a method for verifying this theory has not been established.

JP 2002-7659 A

  The present invention relates to a computer system for analyzing how a member experience or a user experience influences an evaluation result by a member or a user of a product or service provider, and a method executed in the computer system, and The purpose is to provide a program.

  A computer system according to an embodiment of the present invention includes a first receiving unit configured to receive a plurality of first evaluation results indicating a result of evaluating a product or service by a plurality of members of a provider who provides the product or service. And wherein each of the plurality of first evaluation results is associated with an evaluation by a member of each of a plurality of user experiences that a user receiving the product or service may experience. And second receiving means for receiving a plurality of second evaluation results indicating results of evaluation of the goods or services by a plurality of users who have received provision of the goods or services, Each of the evaluation results of the second receiver is associated with an evaluation by the respective users of the plurality of user experiences experienced by the respective users. If, comprising an analysis means for statistically analyzing said plurality of first evaluation result and the plurality of second evaluation result, and output means for outputting the analysis result by the analyzing means.

  In one embodiment, the analyzing means is configured to evaluate the plurality of user experiences and the plurality of user experiences based on the plurality of first evaluation results and the plurality of second evaluation results. Comparing means for comparing evaluations by the plurality of members, and generating means for generating the analysis result based on a result of comparison by the comparing means.

  In one embodiment, the computer system is a third receiving means for receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein the plurality of third evaluation results are Each of the plurality of member experiences experienced by each member further includes third receiving means associated with the evaluation by each member, and the analyzing means includes the plurality of first evaluation results. The plurality of second evaluation results and the plurality of third evaluation results are statistically analyzed.

  In one embodiment, the analyzing means is configured to evaluate the plurality of user experiences and the plurality of user experiences based on the plurality of first evaluation results and the plurality of second evaluation results. Comparing means for comparing evaluations by the plurality of members, and generating means for generating the analysis result based on a result of comparison by the comparing means.

  In one embodiment, the analyzing means correlates one of the plurality of user experiences and the plurality of member experiences based on the plurality of first evaluation results and the plurality of third evaluation results. Calculation means for calculating a relationship is further provided, and the generation means generates the analysis result further based on the correlation.

  In one embodiment, the analysis means includes a second comparison means for comparing one first evaluation result of the plurality of first evaluation results with a predetermined value, and the second comparison means. And a calculating means for calculating a correlation between one of the plurality of user experiences and the plurality of member experiences based on the comparison result by the third evaluation result and the plurality of third evaluation results. The analysis result is generated based on the comparison result by the comparison means and the correlation.

  In one embodiment, the predetermined value is one of an average value of the plurality of first evaluation results and an average value of the plurality of second evaluation results.

  In one embodiment, the generating means generates the analysis result based on the correlation and the result of the comparison, and the analysis result is a member to be improved to improve the second evaluation result. Identify the experience.

  In one embodiment, the analysis result identifies a member experience that affects the user experience.

  In one embodiment, the comparison means includes, for at least one user experience of the plurality of user experiences, an average value of evaluations by the plurality of members associated with the plurality of first evaluation results; By taking the difference from the average value of the evaluations by the plurality of users associated with the plurality of second evaluation results, the evaluation of the plurality of user experiences by the plurality of users and the plurality of user experiences Compare assessments by multiple members.

  In one embodiment, the analysis means calculates an influence degree calculating means for calculating an influence degree of the plurality of user experiences with respect to the second evaluation result based on the plurality of second evaluation results, and the influence degree calculation. Determining means for determining at least one user experience of the plurality of user experiences based on the degree of influence calculated by the means, the comparing means for the determined user experience The evaluation of the user experience by the plurality of users is compared with the evaluation of the plurality of user experiences by the plurality of members.

  In one embodiment, the analysis result identifies a user experience that should be improved to enhance the second evaluation result.

  In one embodiment, each of the first evaluation results includes a degree of recommendation indicating a degree of recommendation that each member wants to recommend the product or service to others.

  In one embodiment, each of the second evaluation results includes a recommendation intention degree indicating a degree of recommendation intention that each user wants to recommend the product or service to others.

  A computer system according to an embodiment of the present invention includes a first receiving unit configured to receive a plurality of first evaluation results indicating a result of evaluating a product or service by a plurality of members of a provider who provides the product or service. And wherein each of the plurality of first evaluation results is associated with an evaluation by a member of each of a plurality of user experiences that a user receiving the product or service may experience. And a third receiving means for receiving a plurality of third evaluation results indicating results of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a member of each of the members. A third receiving means, a plurality of the first evaluation results and a plurality of the third evaluations, which are associated with an evaluation by each member of the plurality of member experiences experienced by the person Comprising an analyzing means for statistically analyzing result, and output means for outputting the analysis result by the analyzing means.

  In one embodiment, the analyzing means correlates one of the plurality of user experiences and the plurality of member experiences based on the plurality of first evaluation results and the plurality of third evaluation results. Calculation means for calculating a relationship and generation means for generating the analysis result based on the correlation.

  In one embodiment, the analysis result identifies a member experience that affects the user experience.

  In an embodiment of the present invention, a method executed in a computer system receives a plurality of first evaluation results indicating a result of evaluation of a product or service by a plurality of members of a provider who provides the product or service. And wherein each of the plurality of first evaluation results is associated with an evaluation by a respective member of a plurality of user experiences that a user receiving the product or service may experience. Receiving a plurality of second evaluation results indicating a result of evaluating the products or services by a plurality of users who have received provision of the products or services, wherein each of the plurality of second evaluation results is Each of the plurality of user experiences experienced by each user is associated with an evaluation by each user; and Includes statistically analyzing the first evaluation result and the plurality of second evaluation result, and outputting the analysis result.

  In one embodiment, the method is to receive a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a respective configuration. The analyzing the plurality of member experiences that the member experiences are associated with an evaluation by each member, wherein the analyzing comprises the plurality of first evaluation results, the plurality of second evaluations. Statistically analyzing the results and the plurality of third evaluation results.

  In an embodiment of the present invention, a method executed in a computer system receives a plurality of first evaluation results indicating a result of evaluation of a product or service by a plurality of members of a provider who provides the product or service. And wherein each of the plurality of first evaluation results is associated with an evaluation by a respective member of a plurality of user experiences that a user receiving the product or service may experience. Receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a plurality of configurations experienced by each member. Statistically classifying the plurality of first evaluation results and the plurality of third evaluation results that are associated with the evaluation of the member experience by each member Includes that, and outputting the analysis result.

  In one embodiment of the present invention, a program executed in a computer system is provided, and the computer system includes a processor, and when the program is executed by the processor, a plurality of providers providing goods or services is provided. Receiving a plurality of first evaluation results indicating a result of evaluating the product or service, wherein each of the plurality of first evaluation results is obtained by a user who receives provision of the product or service. A plurality of user experiences associated with an evaluation by each member of a plurality of user experiences that can be experienced, and a plurality of users indicating a result of evaluation of the products or services by a plurality of users receiving the products or services; Each of the plurality of second evaluation results is received by the second evaluation result. Statistical analysis of the plurality of first evaluation results and the plurality of second evaluation results that are associated with each user's evaluation of the plurality of user experiences experienced by each user And causing the processor to perform a process including outputting an analysis result.

  In one embodiment, the processing is to receive a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, and the plurality of third evaluation results are respectively And further comprising: analyzing the plurality of member experiences experienced by the member, wherein the analyzing comprises the plurality of first evaluation results, the plurality of second experiences, Statistically analyzing the evaluation results and the plurality of third evaluation results.

  In one embodiment of the present invention, a program executed in a computer system is provided, and the computer system includes a processor, and when the program is executed by the processor, a plurality of providers providing goods or services is provided. Receiving a plurality of first evaluation results indicating a result of evaluating the product or service, wherein each of the plurality of first evaluation results is obtained by a user who receives provision of the product or service. Receiving a plurality of third evaluation results indicating that a plurality of user experiences that can be experienced are associated with an evaluation by each member and that the plurality of members have evaluated the workplace. The plurality of third evaluation results are respectively the plurality of member experiences experienced by each member. Processing associated with evaluation by a member, statistically analyzing the plurality of first evaluation results and the plurality of third evaluation results, and outputting the analysis results Let the processor do it.

  According to the present invention, a computer system for analyzing how a member experience or a user experience affects an evaluation result by a member or user of a product or service provider, and the computer system are executed. Methods and programs can be provided.

It is a figure which shows an example of the questionnaire paper 10 for the user who purchased the car in the car maker A company to evaluate the service of the car maker A company. It is a figure which shows an example of the questionnaire paper 20 for the employee of car maker A company to evaluate the service of car maker A company. It is a figure which shows an example of the additional questionnaire paper 30 for the employee of car maker A company to evaluate the service of car maker A company. It is a figure which shows an example of the questionnaire paper 40 for the employee of the car maker A company to evaluate the workplace of the car maker A company. It is a figure which shows typically that the computer system 100 of this invention provides an analysis result from the reply result of the some questionnaire papers 10, 20, 30, and 40 in which it answered. It is a block diagram which shows an example of a structure of the computer system 100 for analyzing the result of evaluation by a user, and the result of evaluation by a member. It is a flowchart which shows an example of the process 400 of the computer system 100 for analyzing the result of evaluation by a user, and the result of evaluation by a member. It is a flowchart which shows an example of the process which statistically analyzes the some 1st evaluation result and several 2nd evaluation result in step S403. It is a flowchart which shows an example of another process 600 of the computer system 100 for analyzing the result of evaluation by a user, and the result of evaluation by a member. It is a flowchart which shows an example of the process which statistically analyzes the some 1st evaluation result and several 3rd evaluation result in step S603. FIG. 10 is a flowchart illustrating an example of an alternative process 800 of process 600 of computer system 100 for analyzing the results of user evaluations and member evaluations. It is a flowchart which shows an example of the process which statistically analyzes the some 1st evaluation result and several 4th evaluation result in step S803.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

1. Analysis Figure 1A results of the evaluation by the results and employee evaluation by the user is an example of a questionnaire 10 for users who purchased a car with a car maker A company evaluating the car manufacturer A's services.

  The questionnaire sheet 10 includes a question 1, a question 2, and a question 3.

  Question 1 is a question that asks the degree of recommendation. The “recommended intention degree” in FIG. 1A is the degree of evaluation when the user evaluates the evaluation object from the viewpoint of the user wanting to recommend the evaluation object to other users (that is, the user sets the evaluation object to other users). The degree of intention to recommend). In this example, the “evaluation target” is a service of the car manufacturer A when the car of the car manufacturer A is purchased, but is not limited thereto. The “evaluation target” can be any product or service.

  The user answers the recommendation intention degree with a score of 0 to 10 in response to the question 1. For example, if the user wants to highly recommend car maker A to a close friend or acquaintance, the user can give 10 points. If the user does not want to recommend car maker A to a close friend or acquaintance at all, 0 points can be given. Can be attached.

  Question 2 is a question asking how the user experience experienced by the user in relation to the evaluation target has affected the recommended intention. The user replies to question 2 as to whether each user experience contributed positively or negatively when determining the recommended intention degree in question 1.

  Each user experience includes a plurality of types of user experiences such as a user experience related to humans, a user experience related to goods, a user experience related to money, a user experience related to time, a user experience related to information, and the like. The user experience related to humans includes, for example, store entrance greetings, guidance, product knowledge / advice, manners, friendliness, accounting support, store exit greetings, and the like. The user experience related to goods includes, for example, product type, inventory, quality, arrangement, equipment, location, and the like. The user experience regarding money includes, for example, price, presence / absence of coupons, sale frequency, and the like. The user experience related to time includes, for example, smoothness of response, speed to payment, store congestion, and the like. The user experience regarding information includes, for example, a brand image, an advertisement, a name recognition, and the like.

  For example, when the user feels comfortable at the time of entering the store and feels that the car manufacturer A company is recommended to others, the user said that “the store entrance greeting” contributed to the recommended intention “plus. Can be evaluated. For example, if the user feels uncomfortable with the accounting response and feels that he / she does not want to recommend the car manufacturer A to others, the user has “accounting response” “contributed negatively” to the recommendation intention. Can be evaluated.

  Question 3 is a question asking why the user experience of question 2 contributed positively or negatively to the recommended intention. The user answers the question 3 in a description format. For example, when the user feels that the purchase of the product has been sufficiently considered because the product description is detailed, the user can evaluate that “the product description is detailed” with respect to “product knowledge / advice”. . For example, when the user feels dissatisfied that there is no stock, the user can evaluate that “stock” was “out of stock”.

  In the above-described example, the answer to the question about the user experience in the big group of the car manufacturer A company has been explained. For example, the question about the user experience received from a specific employee of the car manufacturer A company is answered. You may do it. For example, question 2 is a user experience received from employee 1 of car manufacturer A, a user experience received from employee 2 of car manufacturer A, ... a user experience received from employee n of car manufacturer A Can be a question as to how it has influenced the degree of recommendation.

  FIG. 1B shows an example of a questionnaire paper 20 for an employee of car manufacturer A to evaluate the service of car manufacturer A.

  The questionnaire sheet 20 includes a question 1, a question 2, and a question 3.

  Question 1 is a question that asks the degree of recommendation. “Recommended intention” in FIG. 1B is the degree of evaluation when an employee evaluates the evaluation target from the viewpoint of the employee wanting to recommend the evaluation target to another person (that is, the employee recommends the evaluation target to another person) (Degree of recommendation intention) In this example, “evaluation target” is a service of car manufacturer A when purchasing a car of car manufacturer A, but is not limited to this. The “evaluation target” can be any product or service.

  The employee answers the question 1 with a recommended intention degree with a score of 0 to 10 points. For example, an employee can give 10 points if he wants to highly recommend car manufacturer A to others, and can give 0 points if he does not want car manufacturer A to be recommended to others at all. .

  Question 2 is a question asking how the user experience that the user can experience in relation to the evaluation target has affected the degree of recommendation. The employee answers whether or not each user experience that can be experienced by the user contributes positively or negatively to the question 2 in determining the recommended intention degree in the question 1.

  Each user experience includes a plurality of types of user experiences such as a user experience related to humans, a user experience related to goods, a user experience related to money, a user experience related to time, a user experience related to information, and the like. The user experience related to humans includes, for example, store entrance greetings, guidance, product knowledge / advice, manners, friendliness, accounting support, store exit greetings, and the like. The user experience related to goods includes, for example, product type, inventory, quality, arrangement, equipment, location, and the like. The user experience regarding money includes, for example, price, presence / absence of coupons, sale frequency, and the like. The user experience related to time includes, for example, smoothness of response, speed to payment, store congestion, and the like. The user experience regarding information includes, for example, a brand image, an advertisement, a name recognition, and the like.

  For example, an employee thinks that “sales greeting” is their strength, and the employee wants to recommend car manufacturer A to others because the user will feel comfortable at the entrance. When it feels, the employee can evaluate that “sales greeting” has contributed to the recommended intention. For example, an employee thinks that “accounting” is a weakness of the company, and the employee would feel uncomfortable with accounting, and would not want to recommend car manufacturer A to others. When felt, the employee can evaluate that “accounting support” “contributed negatively” to the recommendation intention.

  Question 3 is a question asking why the user experience that can be experienced by the user of Question 2 contributed to the recommended intention plus or minus. The employee answers the question 3 in a descriptive format. For example, if an employee speculates that the user will be able to fully consider purchasing a product because the product description is detailed, the employee should evaluate the product description / advance as “detailed product description”. Can do. For example, if an employee speculates that the user may feel dissatisfied with out of stock, the employee may rate “out of stock” for “inventory”.

  FIG. 1C shows an example of an additional questionnaire 30 for employees of car manufacturer A to evaluate the service of car manufacturer A.

  The questionnaire sheet 30 may be the next leaf of the questionnaire sheet 20 and includes the question 4.

  Question 4 is the employee experience that the employee experiences at the workplace when judging how the user experience that the user can experience in relation to the evaluation target in Question 2 affects the recommended intention. It is a question asking how it contributed. Employees answer to question 4 when determining the degree of contribution in question 2, whether each employee experience that the employee experiences at the workplace contributed positively or negatively. To do.

  Each employee experience includes multiple types of employee experiences, such as an employee experience related to humans, an employee experience related to goods, an employee experience related to money, an employee experience related to time, and an employee experience related to information. Employee experiences related to humans include, for example, relationships with colleagues and juniors, relationships with seniors, relationships with store managers, workplace culture, company philosophy, and training. Employee experiences related to things include, for example, work contents, social significance, future potential, growth soil, benefits, facilities, location, and the like. The employee experience related to money includes, for example, salary, compensation / bonus, overtime, etc. The employee experience related to time includes, for example, working hours, ease of taking paid leave, ease of taking maternity leave and childcare leave, and the like. The employee experience regarding information includes, for example, stability, brand image, name recognition, and the like.

  For example, if the employee thinks that the factor that the user experience of “sales greeting” has contributed positively in question 2 is that the relationship with the colleagues / junior is good, The member can evaluate that “the relationship with colleagues / juniors” has contributed to the “plus” in response to the “entry greeting”. For example, although the factor that employees evaluated in “Question 2” that the user experience of “product knowledge / advice” contributed negatively was gained knowledge through extensive training, the relationship with seniors and the relationship with store managers If the employee thinks that it is bad and that he / she does not answer the question about product information, “training” “contributed positively” to “product knowledge / advice” It can be evaluated that “Relationship with” and “Relationship with store manager” contributed to minus.

  In the example described above, answering a question about the user experience in a big group called car manufacturer A was explained. For example, from a specific employee (for example, himself or another employee) of car manufacturer A You may make it answer the question about the user experience which can be received. For example, the question 2 is a user experience that can be received from the employee 1 of the car manufacturer A company, a user experience that can be received from the employee 2 of the car manufacturer A company, ... a user experience that can be received from the employee n of the car manufacturer A company Can be a question as to how it has influenced the degree of recommendation. Thus, the question in the present invention can be a question in a 360-degree evaluation mechanism in which employees evaluate each other.

  FIG. 1D shows an example of a questionnaire paper 40 for an employee of car manufacturer A to evaluate the workplace of car manufacturer A.

  The questionnaire sheet 40 includes a question 1, a question 2, and a question 3.

  Question 1 is a question that asks the degree of recommendation. In FIG. 1D, “recommended intention” means the degree of evaluation when an employee evaluates the workplace in terms of whether the employee wants to recommend the workplace to others (that is, the recommendation that the employee wants to recommend the workplace to others) Degree of intention). Here, as generally understood, the “workplace” may include not only a physical space where an employee actually works, but also a social space to which the employee belongs.

  The employee answers the question 1 with a recommended intention degree with a score of 0 to 10 points. For example, an employee can give 10 points if he highly recommends the workplace of car manufacturer A to others, and 0 if he does not want to recommend the workplace of car manufacturer A to others. Can be attached.

  Question 2 is a question asking how the employee experience that the employee can experience in the workplace has influenced the degree of recommendation. Employees answer to question 2 whether each employee experience experienced by the employee contributed positively, negatively, or neither, when determining the degree of recommendation in question 1 .

  Each employee experience includes multiple types of employee experiences, such as an employee experience related to humans, an employee experience related to goods, an employee experience related to money, an employee experience related to time, and an employee experience related to information. Employee experiences related to humans include, for example, relationships with colleagues and juniors, relationships with seniors, relationships with store managers, workplace culture, company philosophy, and training. Employee experiences related to things include, for example, work contents, social significance, future potential, growth soil, benefits, facilities, location, and the like. The employee experience related to money includes, for example, salary, compensation / bonus, overtime, etc. The employee experience related to time includes, for example, working hours, ease of taking paid leave, ease of taking maternity leave and childcare leave, and the like. The employee experience regarding information includes, for example, stability, brand image, name recognition, and the like.

  For example, if the user feels that the relationship with the senior is good and wants to recommend the workplace of car manufacturer A to other people, the user will say that “the relationship with the senior” is a “plus” It can be evaluated that "contributed to". For example, when the user feels that the salary is dissatisfied and does not want to recommend car manufacturer A to others, the user evaluates that “salary” has contributed negatively to the recommendation intention. can do.

  Question 3 is a question asking why the employee experience experienced by the employee of Question 2 contributed positively or negatively to the recommendation intention. The employee answers the question 3 in a descriptive format. For example, when an employee feels that the workplace of car manufacturer A is good because the content of the training is substantial, the employee evaluates that “the content of the training is substantial” for “training” can do. For example, when an employee feels dissatisfied with the workplace of car manufacturer A because the senior's educational attitude is unfaithful, the employee is “senior is unfaithful” regarding “relationship with seniors” Can be evaluated.

  FIG. 2 schematically shows that the computer system 100 of the present invention provides an analysis result from the answer results of a plurality of questionnaire sheets 10, 20, 30, 40 that have been answered.

  A plurality of users who purchased a car at car manufacturer A evaluate the service of car manufacturer A on questionnaire form 10, and an employee of car manufacturer A provides the service of car manufacturer A on questionnaire forms 20 and 30. After the evaluation and the employee of the car manufacturer A evaluates the workplace of the car manufacturer A in the questionnaire sheet 40, the answer results of the plurality of questionnaire sheets 10, 20, 30, 40 are stored in the computer system 100 of the present invention. Entered. The computer system 100 of the present invention processes an input answer result and outputs an analysis result.

  Here, it does not matter how a plurality of answer results are input to the computer system 100 of the present invention. For example, a plurality of answer results may be input by scanning questionnaire sheets 10, 20, 30, 40, may be input to the computer system 100 of the present invention by manual input, or may be a plurality of answer results. May be input to the computer system 100 of the present invention via a storage medium storing the information, or may be input to the computer system 100 of the present invention via a network or the like. When a plurality of answer results are input via a network or the like, the type of network is not limited. For example, a plurality of answer results may be input via the Internet or may be input via a LAN.

  Originally, it is preferable that the evaluation of the user experience by the user and the evaluation of the user experience by the employee coincide. This is because the user experience that the employee thinks is a strength leads to user satisfaction, and the best labor effectiveness is achieved. If the user experience assessment by the user and the employee user experience assessment do not match, there is room for improvement in the work-to-effect, so such user experience should be improved or considered. By matching the user experience evaluation by the user and the user experience evaluation by the employee, the employee's feelings can be communicated to the user to the maximum extent. It can be connected.

  The analysis result provided by the computer system 100 of the present invention can identify the gap between the user experience that the user feels and the user experience that the employee is proud of. For example, the computer system 100 of the present invention identifies the degree of influence of the user experience on the recommended intention of the product or service by the user by analyzing the response results of the plurality of questionnaire sheets 10, and answers the plurality of questionnaire sheets 20 Analyzing the results identifies the impact of the user experience on the product or service's recommended intent by employees and identifies the impact gap. Thereby, it can be judged whether the user experience which the employee thinks is a strength or is proud leads to the satisfaction of the user. It can also be determined whether or not a user experience that the employee does not think is a strength or is not proud of has led to user satisfaction. A user experience that does not lead to user satisfaction despite being considered a strength is a user experience that should be improved, and a user experience that leads to user satisfaction despite not being a strength is It is a user experience that should consider the allocation of focus.

  Even if user experiences that should be improved or allocation of focus are identified, what measures are specifically taken to improve or distribute those user experiences? There are cases where it should be unknown. However, if the purpose is to improve the employee experience or to allocate a focus on the employee experience, it is easy to conceive a specific measure or to take a specific measure.

  The analysis results provided by the computer system 100 of the present invention can also identify employee experiences that have a significant impact on the user experience. For example, it is possible to identify an employee experience that has a large impact on the user experience identified as being a user experience to be improved or a user experience to be considered for allocation of focus. For example, to improve the user experience that should be improved, it can be clear which employee experience is to be improved. For example, in order to distribute the focus on the user experience for which the allocation of focus should be considered, it is possible to clarify which employee experience should be allocated the focus. In this way, it is possible to clarify what measures should be taken in order to improve the user experience or distribute the focus.

  The analysis results provided by the computer system 100 of the present invention may specify whether the impact of the employee experience that has a large impact on the user experience is a positive or negative impact.

  For example, if the analysis results indicate that a high-impact employee experience has a negative impact, the employee experience should be improved so that the impact is positive. It can also be seen that improving this employee experience preferentially over other employee experiences is effective for improving the user experience and, consequently, improving user satisfaction and increasing sales of the company.

  In the example described above, it has been described that the computer system 100 of the present invention outputs the analysis result based on the answer results of all the questionnaire sheets 10, 20, 30, 40. However, all the questionnaire sheets 10, 20, 30, 40 are described. It is not necessary to use the answer result of. For example, the computer system 100 of the present invention can output an analysis result based on the answer results of the questionnaire sheets 10, 20, and 40 without using the answer results of the questionnaire sheet 30. For example, the computer system 100 of the present invention can output the analysis result based on the answer results of the questionnaire sheets 10, 20, and 30 without using the answer results of the questionnaire sheet 40. In either case, the analysis results provided by the computer system 100 of the present invention can still identify the gap between the user experience felt by the user and the user experience that the employee is proud of, with an impact on the user experience. Identify large employee experiences. For example, the computer system 100 of the present invention may output the analysis result based on the answer results of the questionnaire sheets 20 and 40 without using the answer results of the questionnaire sheets 10 and 30. For example, the computer system 100 of the present invention may output the analysis result based on the answer results of the questionnaire sheets 20 and 30 without using the answer results of the questionnaire sheets 10 and 40. Even in this case, the analysis results provided by the computer system 100 of the present invention can still identify employee experiences that have a significant impact on the user experience. For example, the computer system 100 of the present invention may output the analysis result based on the answer results of the questionnaire sheets 10 and 20 without using the answer results of the questionnaire sheets 30 and 40. Even in this case, the analysis results provided by the computer system 100 of the present invention can still identify the gap between the user experience felt by the user and the user experience that the employee is proud of.

  In the above-described example, the computer system 100 of the present invention has described that the analysis result is output based on the answer results of the questionnaire sheets 10, 20, 30, and 40. However, the present invention is not limited to this. The computer system 100 of the present invention can output an analysis result based on an evaluation result by an arbitrary evaluation means for evaluating an evaluation object. For example, the computer system 100 of the present invention may detect a user's voice, facial expression, behavior, etc. by a sensor instead of having the user fill out a questionnaire, and use the detection result as an evaluation result. For example, an event of frowning when the user is receiving a customer is detected, and from this event, it can be considered that “experience at the time of business negotiation” contributed negatively to the recommended intention. By using such a detection result of the user's behavior as an evaluation result, the computer system 100 of the present invention can read the user's feeling (emotion) without being noticed by the user, and perform analysis based on the emotion. can do.

  In the above-described example, it has been described that the user and the employee evaluate the recommended intention degree with a score of 0 to 10 points, but the present invention is not limited to this. The recommendation intention can be evaluated in an arbitrary score range.

  In the above-described example, the user and the employee are allowed to evaluate whether each user experience or each employee experience contributes positively or negatively. By enabling such a simple answer, the burden on the respondent can be reduced. However, the present invention is not limited to this. Evaluate whether each user experience or each employee experience contributed positively or negatively in multiple stages (eg, -3, -2, -1, 0, +1, +2, +3, etc.) Also good.

  In the above-described example, the example in which the user and the employee answer the question 3 in the description format has been described, but the present invention is not limited to this. It is also within the scope of the present invention for the user and employee to answer by selecting from a pre-determined option why the user experience or employee experience of question 2 contributed positively or negatively to the recommendation intention. Question 3 may be omitted from the questionnaire sheets 10, 20, and 40.

  In the example described above, it has been described that the user and the employee are evaluated by the same method, but the present invention is not limited to this. Users and employees can be evaluated by different methods. For example, the user may evaluate the recommendation intention degree with a score of 0 to 10 points, while the employee may have the recommendation intention degree evaluated with a score of 0 to 100 points. For example, let users evaluate whether each user experience contributed positively, negatively, or neither, while employees evaluated each user experience or each employee experience positively In addition, it may be evaluated in multiple stages whether it has contributed to minus.

  The computer system 100 of the present invention can output the analysis result in any manner. For example, the computer system 100 of the present invention may output the analysis result as a line graph indicating the degree of influence of each user experience and the degree of influence of each employee experience, or the degree of influence of each user experience and each employee experience. The analysis result may be output as a bar graph indicating the degree of influence of the user, or the analysis result may be output as a curve graph indicating the degree of influence of each user experience and the degree of influence of each employee experience. For example, the computer system 100 of the present invention may output the analysis result as a numeric string or a character string.

  Further, the computer system 100 of the present invention may classify users who have evaluated the evaluation target based on attributes such as age or gender, and output an analysis result for each classified user. This is because the user experience that has an influence on the recommended intention may vary depending on the user attribute. For example, the computer system 100 of the present invention may output a result of analyzing an evaluation result by a user in their 30s, or may output a result of analyzing an evaluation result by a user in their 40s. The user experience to be improved for each user attribute can be specified based on the analysis result according to the user attribute.

  In addition, the computer system 100 of the present invention may classify employees who have evaluated the evaluation target based on attributes such as age, gender, department to which they belong, and output an analysis result for each classified employee. This is because, depending on the employee attributes, the employee experience that affects the recommended intention may differ. For example, the computer system 100 of the present invention may output a result of analyzing an evaluation result by a male employee in their 30s, or may output a result of analyzing an evaluation result by a female employee in their 40s. The employee experience that should be improved for each employee attribute can be identified based on the analysis result corresponding to the attribute of the employee.

  The computer system 100 of the present invention detects the voice, facial expression, behavior, and the like of the user and the employee by a sensor in addition to the user attribute and the employee attribute or instead of the user attribute and the employee attribute. The users and employees may be classified based on the detection results, and the analysis results may be output for each classified user and employee. This is because the user experience and the employee experience that have an influence on the recommended intention may differ depending on the behavior of the user and the employee. For example, the computer system 100 of the present invention may output a result of analyzing an evaluation result by a user who visits a store on a rainy day and a result of analyzing an evaluation result by a user who visits a store on a clear day. For example, the computer system 100 according to the present invention analyzes the result of analyzing the evaluation result by the employee who visited the user who visited the store on a rainy day, and the result of analyzing the evaluation result by the employee who served the user who visited the store on a sunny day. May be output. For example, the computer system 100 of the present invention may output a result of analyzing an evaluation result by a user who has moved to the right after visiting the store and a result of analyzing an evaluation result by a user having moved to the left after having visited the store. For example, the computer system 100 of the present invention may output a result of analyzing an evaluation result by an employee who is standing by at the time of waiting and a result of analyzing an evaluation result by an employee who is moving around at the time of waiting. The user experience and employee experience to be improved can be identified for each user and employee behavior based on the analysis results according to the user and employee behavior.

  In the above-described example, it has been described that the evaluation result by the employee of the provider of the goods or services is analyzed. However, the present invention is not limited to the employee. The subject performing the evaluation may be any member of the provider of any product or service. Hereinafter, it is simply referred to as “member”. The member may be, for example, an employee employed according to an employment contract as described above, or may be an officer who performs business execution or supervision.

  Below, the computer system 100 for analyzing the result of evaluation by a user such as a questionnaire result and the result of evaluation by a member will be described.

2. Configuration diagram of a computer system for analyzing the results of the evaluation by results and members of evaluation by the user 3, an example of the configuration of a computer system 100 for analyzing the results of the evaluation by results and members of evaluation by the user Show.

  The computer system 100 includes a receiving unit 110, a processor 120, a memory 140, and an output unit 150.

  The receiving means 110 may include a first receiving means capable of receiving a plurality of evaluation results indicating results of evaluation of goods or services by a plurality of users. For example, the first receiving unit receives a questionnaire result that a plurality of users have evaluated the evaluation target. For example, the first receiving means receives the result of the sensor detecting the behavior when a plurality of users visit a store. The form of the evaluation result received by the first receiving means is not limited. For example, the first receiving unit may receive the evaluation result in a text format, may receive the evaluation result in an image format, or may receive the evaluation result in an audio format. For example, the first receiving unit may receive a table summarizing the evaluation results. The receiving means 110 is a second receiving means that can receive a plurality of evaluation results indicating the results of evaluation of goods or services by a plurality of members, and / or a plurality of results indicating the results of evaluation of the workplace by a plurality of members. A third receiving means that can receive the evaluation result is included. The receiving means 110 may include a fourth receiving means capable of receiving a plurality of evaluation results indicating a result of evaluating at least one of a plurality of user experiences by a plurality of members. For example, the second receiving unit receives a questionnaire result obtained by evaluating a product or service by a plurality of members. For example, the third receiving means receives a questionnaire result obtained by evaluating a workplace by a plurality of members. For example, the fourth receiving means receives a questionnaire result in which a plurality of members evaluates at least one of a plurality of user experiences. For example, the second receiving means, the third receiving means, and the fourth receiving means receive the result of the sensor detecting the behavior of the plurality of members in the store. The form of the evaluation result received by the second receiving means, the third receiving means, and the fourth receiving means is not limited. For example, the second receiving unit, the third receiving unit, and the fourth receiving unit may receive the evaluation result in a text format, may receive the evaluation result in an image format, or may receive the evaluation result. It may be received in an audio format. For example, the second receiving unit, the third receiving unit, and the fourth receiving unit may receive a table that summarizes the evaluation results. The first receiving means, the second receiving means, the third receiving means, and the fourth receiving means may be constituted by the same receiving means, or may be constituted by a plurality of receiving means.

  The processor 120 executes processing of the computer system 100 and controls the operation of the entire computer system 100. The processor 120 reads a program stored in the memory 140 and executes the program. Thereby, it is possible to make the computer system 100 function as a system for executing a desired step. When the data received by the receiving unit 110 is in a format unsuitable for processing (for example, image format data or audio format data), the processor 120 performs processing to convert the data into a format suitable for processing (for example, numerical data). You may do it. The processor 120 may be implemented by a single processor or may be implemented by a plurality of processors.

  The processor 120 includes analysis means 130. The analysis unit 130 analyzes the evaluation result by processing the input evaluation result. The analysis unit 130 can perform arbitrary data processing. For example, the analysis unit 130 can perform statistical processing such as single regression analysis, multiple regression analysis, and quantification class I. For example, the analysis unit 130 can perform mathematical processing such as addition, subtraction, multiplication, and division.

  For example, the analysis unit 130 may include a comparison unit that performs comparison based on a plurality of input evaluation results. The comparison unit compares, for example, evaluations by a plurality of users of a plurality of user experiences with evaluations by a plurality of members of a plurality of user experiences based on a plurality of input evaluation results. The comparison by the comparison means can be, for example, taking a difference between the average values of the evaluations.

  The analysis unit 130 may include a second comparison unit that compares one evaluation result of a plurality of evaluation results with a predetermined value. The comparison by the second comparison unit may be, for example, a comparison between one evaluation result of a plurality of evaluation results and another evaluation result of the plurality of evaluation results, or a plurality of evaluations One of the results may be compared with an average value of a plurality of evaluation results. For example, the comparison by the second comparison unit compares each of a plurality of evaluations included in each evaluation result of the plurality of evaluation results with an average value of a plurality of evaluations included in each evaluation result of the plurality of evaluation results. It may be. For example, the second comparison unit may compare the average value of the plurality of first evaluation results with the average value of the plurality of second evaluation results. For example, the second comparison unit may take a difference between an average value of a plurality of evaluations included in the first evaluation result and an average value of a plurality of evaluations included in the second evaluation result. For example, the second comparison unit may compare one evaluation included in the first evaluation result with an average value of a plurality of evaluations included in the second evaluation result.

  For example, the analysis unit 130 may include a calculation unit that calculates a correlation based on a plurality of input evaluation results. For example, the calculating unit calculates a correlation between one of the plurality of user experiences and the plurality of member experiences based on the plurality of input evaluation results. For example, the calculation unit may calculate a correlation between one of the plurality of user experiences and the plurality of employee experiences based on the comparison result by the second comparison unit.

  The analysis unit 130 may include, for example, a generation unit that generates an analysis result based on a comparison result by the comparison unit and / or a correlation by the calculation unit. The analyzing unit 130 may include an influence degree calculating unit that calculates an influence degree as a correlation based on a plurality of input evaluation results. For example, the influence degree calculating means may calculate the influence degree based on the result of the comparison by the second comparison means. The influence degree calculated by the influence degree calculation means includes, for example, the influence degree of the user experience with respect to the recommended intention degree. The analysis unit 130 may include a determination unit that determines at least one user experience based on, for example, the degree of influence by the degree of influence calculation unit. The determination unit can determine, for example, a user experience that has the greatest influence on the recommendation intention. The determining means can determine, for example, a user experience having the greatest positive or negative influence on the recommendation intention.

  The memory 140 stores a program required for executing the processing of the computer system 100, data required for executing the program, and the like. The memory 140 is a program (for example, FIG. 4, FIG. 5, FIG. 6, FIG. 7, FIG. 8 to be described later) for causing the processor 120 to perform processing for analyzing the result of evaluation by a user and the result of evaluation by a member. , A program for realizing the processing shown in FIG. 9) may be stored. Here, it does not matter how the program is stored in the memory 140. For example, the program may be preinstalled in the memory 140. Alternatively, the program may be installed in the memory 140 by being downloaded via a network. In this case, the type of network is not limited. The memory 140 can be implemented by any storage means.

  The output unit 150 outputs the analysis result of the evaluation result to the outside of the computer system 100. The form of the analysis result output by the output unit 150 is not limited. For example, the output unit 150 may output the analysis result as a curve graph, a bar graph, a numerical string, or a character string. For example, the output unit 150 may output the analysis result by voice. It does not matter how the output means 150 outputs the analysis result. For example, when the output unit 150 includes a display screen, the analysis result may be displayed on the display screen. For example, when the output unit 150 includes a storage unit, the analysis result is stored in the storage unit, and an external device outside the computer system 100 accesses the storage unit so that the external device acquires the analysis result. May be. For example, when the output unit 150 includes a transmission unit, the transmission unit may transmit the analysis result to the outside of the computer system 100 via a network. In this case, the type of network is not limited. For example, the transmission unit of the output unit 150 may transmit the analysis result via the Internet or may transmit the analysis result via the LAN.

  Each component of the computer system 100 described above may be located in the same hardware, or may exist in a plurality of hardware. When it exists in several hardware, the aspect to which each hardware is connected is not ask | required. Each hardware may be connected wirelessly or may be connected by wire.

3. Process diagram of a computer system for analyzing the results of the evaluation by results and members of evaluation by the user 4, an example of a process 400 of computer system 100 for analyzing the results of the evaluation by results and members of evaluation by the user Indicates. By analyzing the results of the evaluation by the user and the evaluation by the members through the process 400, for example, the user experience to be improved or the user experience to be considered for allocation of focus can be identified.

  In step S <b> 401, the processor 120 receives a plurality of first evaluation results indicating results of evaluation of goods or services by a plurality of members via the receiving unit 110. The first evaluation result can be, for example, a response result by a member to the questionnaire sheet 20 shown in FIG. 1B. Each of the plurality of first evaluation results is associated with an evaluation by each member of the plurality of user experiences that the user can experience. The plurality of user experiences that the user can experience may be, for example, user experiences that the user can experience from the entire provider of the product or service, or at least one specific member of the provider of the product or service It may be a user experience that the user can experience from (for example, himself or another member). For example, an evaluation by a member of a plurality of user experiences that a user can experience indicates that the user experience that the user can experience has contributed positively when the member determines the recommended intention of the product or service. "Indicator", "Negative index" indicating that the user experience that the user can experience contributes negatively, and "Neutral index" that indicates that the user experience that the user can experience does not contribute positively or negatively Can be included.

  In step S <b> 402, the processor 120 receives a plurality of second evaluation results indicating results of evaluation of goods or services by a plurality of users via the receiving unit 110. The second evaluation result may be, for example, a response result by the user to the questionnaire sheet 10 shown in FIG. 1A. Each of the plurality of second evaluation results is associated with an evaluation by each user of the plurality of user experiences experienced by each user. The plurality of user experiences experienced by the user may be, for example, a user experience experienced by the user from the entire provider of the product or service, or the user from at least one specific member of the product or service provider It may be a user experience experienced by. For example, an evaluation by a user of a plurality of user experiences that the user has experienced is a “plus indicator” that indicates that the user experience that the user has experienced has contributed positively when determining the recommended intention of the product or service, It may include a “minus indicator” indicating that the user experience experienced by the user has contributed negatively and a “neutral indicator” indicating that the user experience experienced by the user does not contribute positively or negatively.

  In step S403, the analysis unit 130 of the processor 120 statistically analyzes the plurality of first evaluation results and the plurality of second evaluation results. Statistical analysis is, for example, multiple regression analysis. The multiple regression analysis is, for example, quantification class I. Thereby, an analysis result is generated.

  In step S404, the output unit 140 outputs the analysis result. For example, the output unit 140 may output the analysis result in a graph format or a character string.

  FIG. 5 shows an example of a process for statistically analyzing the plurality of first evaluation results and the plurality of second evaluation results in step S403.

  In step S501, the analysis unit 130 of the processor 120 evaluates a plurality of user experiences based on the plurality of first evaluation results and the plurality of second evaluation results and a plurality of configurations of the plurality of user experiences. Compare with employee evaluation. The evaluation by a plurality of users of a plurality of user experiences may be, for example, an evaluation by a plurality of users of a plurality of user experiences experienced by the user from the entire provider of the product or service, or the evaluation of the provider of the product or service It may be a plurality of user experiences experienced by the user from at least one particular member. The evaluation by a plurality of members of a plurality of user experiences may be, for example, an evaluation by a plurality of members of a plurality of user experiences that the user can experience from the entire provider of the product or service, It may be an evaluation by multiple members of multiple user experiences that a user may experience from at least one specific member of the provider (eg, himself or another member). Evaluation by multiple members of multiple user experiences can include evaluation of others. For example, the evaluation of a plurality of user experiences by a plurality of members is an evaluation of the user experience that the user can experience from the second member by the first member, and the user that the user can experience from the third member Evaluation of experience,... May include evaluation of user experience that the user can experience from the nth member. Specifically, other person evaluations by a plurality of members of a plurality of user experiences are, for example, “the second member has a good greeting greeting to the customer” or “second component The staff member is very good at greetings to customers, so I want to recommend it to others. ”

  For example, the analysis unit 130 performs preprocessing on the plurality of first evaluation results and the plurality of second evaluation results, and based on the plurality of preprocessed first evaluation results and the plurality of second evaluation results. The evaluation by a plurality of users of the plurality of user experiences experienced by the user may be compared with the evaluation by a plurality of members of the plurality of user experiences that the user can experience.

  In the preprocessing, for example, based on the plurality of first evaluation results received in step S401, an average value of evaluations by a plurality of members of a plurality of user experiences that the user can experience is calculated and received in step S402. A process of calculating an average value of evaluations by each user of the plurality of user experiences experienced by the user may be included based on the plurality of second evaluation results.

  The preprocessing can include, for example, processing for calculating the degree of influence on the recommended intention based on the plurality of first evaluation results received in step S401 and the plurality of second evaluation results received in step S402. . At this time, for example, by processing the recommended intention degree, the plus index, and the minus index for the plurality of first evaluation results received in step S401, each user experience that the user can experience A positive influence degree and a negative influence degree with respect to the recommendation intention degree are calculated, and the recommendation intention degree, the plus index, and the minus index are statistically processed for the plurality of second evaluation results received in step S402. By doing so, you may make it calculate the positive influence degree with respect to the recommendation intention degree of each user experience which the user experienced, and a negative influence degree. The statistical processing is, for example, multiple regression analysis.

  When performing multiple regression analysis, multiple regression analysis for the plus index and multiple regression analysis for the minus index are performed. In the multiple regression analysis for positive indicators, the degree of positive intention to the recommended intention of each user experience that the user can experience is calculated by performing multiple regression analysis with the recommendation intention as the objective variable and the positive indicator as the explanatory variable. The positive degree of influence on the recommended intention degree of each user experience experienced by the user is calculated. At this time, multiple regression analysis may be performed by treating indices other than the positive index (negative index and neutral index) included in the evaluation result as zero. In the multiple regression analysis for negative indicators, the negative influence on the recommended intention of each user experience that the user can experience is calculated by performing multiple regression analysis with the recommendation intention as the objective variable and the negative indicator as the explanatory variable. The negative influence degree with respect to the recommended intention degree of each user experience experienced by the user is calculated. At this time, an index other than the minus index included in the evaluation result (plus index and neutral index) may be treated as zero and the multiple regression analysis may be performed. Even when the plus index or the minus index is multistage, the multiple regression analysis can be performed in the same manner. In the multiple regression analysis for the positive index, regardless of the level of the negative index, all the negative indices and the neutral index are treated as zero and the multiple regression analysis is performed. Thereby, the positive influence degree with respect to the recommendation intention degree is calculated. In the multiple regression analysis for the minus index, regardless of the level of the plus index, all the plus indices and the neutral index are treated as zero and the multiple regression analysis is performed. Thereby, the negative influence degree with respect to the recommendation intention degree is calculated.

  In the above example, the multiple regression analysis is performed on the recommendation intention and the positive indicator, the positive influence on the recommendation intention is calculated, and the multiple regression analysis is performed on the recommendation intention and the negative indicator. The calculation of the negative influence on the recommended intention has been described, but the present invention is not limited to this. It is also within the scope of the present invention to calculate a positive influence degree and a negative influence degree by performing statistical processing such as multiple regression analysis on the recommended intention degree, the positive indicator, and the negative indicator. For example, the positive influence degree and the negative influence degree may be calculated by performing a multiple regression analysis using the recommended intention degree as an objective variable and the plus index and the minus index as explanatory variables.

  The multiple regression analysis is, for example, quantification class I. When performing quantification class I, the coefficient obtained as a result of quantification class I is compared with the analysis standard, and the coefficient that is not statistically significant without satisfying the analysis standard may be uniformly set to 0.000. . As a result, coefficients that are not statistically significant can be ignored, and the accuracy of analysis is improved. The value that is statistically significant changes according to the severity of the analysis criteria, and the number of user experiences that are “influential” on the recommendation intention change. The stricter the analysis criteria, the greater the number of user experiences with an impact of 0.000. Here, any statistical standard can be adopted as the analysis standard. For example, a significance probability (p value) may be employed, or AIC (Akaike Information Criterion) may be employed. AIC is preferable because there is a merit that reliability is statistically maintained even when there are many explanatory variables.

  In the preprocessing, further processing may be performed on the calculated influence degree. For example, the preprocessing is a process of generating a value based on a positive influence degree and a negative influence degree by mathematically processing a positive influence degree on the recommended intention degree and a negative influence degree on the recommended intention degree. Can be included. The mathematical process is, for example, addition. The first value generated by adding the positive influence degree to the recommendation intention degree and the negative influence degree to the recommendation intention degree is, for example, a positive influence or a negative influence on the recommendation intention degree. It is an influence relative value indicating whether or not there is. The mathematical process is, for example, addition of absolute values. The second value generated by adding the positive influence on the recommendation intention and the absolute value of the negative influence on the recommendation intention is, for example, recommended regardless of positive influence or negative influence. This is an absolute value of influence indicating how much the degree of intention is affected.

  In the preprocessing, further processing may be performed on the generated value. For example, the preprocessing is for identifying which user experience of the user experiences experienced by the user to improve the recommendation intention based on the first value and the second value should be improved. A process for generating a value may be included. For example, the user experience experienced by the user may be ranked in order to identify which user experience should be improved among the user experiences experienced by the user in order to improve the recommendation intention. For example, based on the calculated relative value of influence and the calculated absolute value of influence, the user experiences in descending order of the influence on the recommendation intention and the negative influence on the recommendation intention. The user experiences may be ranked and a value indicating the rank may be generated. In order to specify which user experience should be improved among the user experiences that the user can experience in order to improve the recommendation intention based on the first value and the second value, for example, the preprocessing The process of generating the value of For example, in order to identify which of the user experiences a user can experience in order to improve the degree of recommendation, the user experiences that the user can experience may be ranked, At this time, for example, based on the calculated relative value of the impact level and the calculated absolute value of the impact level, the user has the largest impact level on the recommendation intention level and the largest negative impact on the recommendation level. The user experience that can be experienced may be ranked, and a value indicating the rank may be generated.

  Note that the preprocessing is not limited to the processing described above. As long as the plurality of first evaluation results and the plurality of second evaluation results can be compared in step S501, the plurality of first evaluation results and the plurality of second evaluation results are converted to the previous one by any other process. Can be processed.

  The analysis means 130 may be based on the plurality of first evaluation results and / or the plurality of second evaluation results that have not been preprocessed, and / or the plurality of first evaluation results and / or the plurality of preprocessing results. Based on the second evaluation result, it is possible to compare the evaluation by the plurality of users of the plurality of user experiences experienced by the user with the evaluation by the plurality of members of the plurality of user experiences that the user can experience.

  For example, the analysis unit 130 performs the evaluations by the plurality of users of the plurality of user experiences included in the first evaluation result and the plurality of user experiences included in the second evaluation result without performing the above-described preprocessing. You may compare with the corresponding thing of evaluation by a plurality of members.

  For example, the analysis unit 130 may include an average value of evaluations by a plurality of members of a plurality of user experiences that can be experienced by the user calculated by the preprocessing and a plurality of user experiences that the user has calculated by the preprocessing. You may compare with the average value of evaluation by the user. At this time, the analysis unit 130 may compare the average values for all user experiences, or may compare the average values for the user experiences experienced by the user with the highest average value or the user experiences that the user can experience. Alternatively, the average values may be compared for any number of user experiences experienced by the user or the user experiences that the user may experience. Alternatively, based on the degree of influence calculated by the pre-processing, the average value may be compared for the user experience experienced by the user with the highest degree of influence or the user experience that the user can experience, or any high degree of influence The average values may be compared for a number of user experiences experienced by the user or user experiences that the user may experience.

  For example, the analysis unit 130 may evaluate each of a plurality of user experiences that a user who has not been pre-processed can experience, and each user of a plurality of user experiences that the user has calculated by the pre-processing. You may compare with the average value of evaluation by. For example, the analysis unit 130 includes a plurality of user evaluations of a plurality of user experiences experienced by a user who has not been preprocessed, and a plurality of members of a plurality of user experiences that can be experienced by the user calculated by the preprocessing. You may compare with the average value of evaluation by.

  For example, the analysis unit 130 calculates the positive influence degree and the negative influence degree with respect to the recommended intention degree of each user experience that can be experienced by the user, and the recommended intention degree of each user experience that the user has experienced, calculated by the preprocessing. You may compare the positive influence degree and the negative influence degree. At this time, as described above, the degree of influence may be compared for all user experiences, or the degree of influence may be compared for at least one user experience.

  For example, the analysis unit 130 may compare the influence degree absolute value or the influence degree relative value calculated by the preprocessing. At this time, as described above, the influence degree absolute value or the influence degree relative value may be compared for all user experiences, or the influence degree absolute value or the influence degree relative value may be compared for at least one user experience. .

  For example, the analysis unit 130 may compare values indicating ranks calculated by the preprocessing. At this time, as described above, values indicating ranks may be compared for all user experiences, or values indicating ranks may be compared for at least one user experience.

  The comparison by the analysis unit 130 may be, for example, a process for obtaining a difference between values.

  In step S502, the analysis unit 130 generates an analysis result based on the comparison result in step S501.

  The analysis result may specify, for example, a user experience in which the evaluation by a plurality of users and the evaluation by a plurality of members do not match. The analysis result may specify, for example, a user experience in which a difference between evaluation by a plurality of users and evaluation by a plurality of members exceeds a predetermined threshold. If multiple user experiences are identified, they may be ranked in order of difference.

  The analysis result may specify, for example, a user experience in which evaluation by a plurality of users is worse than evaluation by a plurality of members, or evaluation by a plurality of users is an evaluation by a plurality of members. It may identify a better user experience. For example, a user experience that is evaluated by a plurality of users is worse than an evaluation by a plurality of members. A user experience that the members think is a strength or that they are proud of does not lead to user satisfaction. And can specify that this user experience should be improved. For example, a user experience that is better evaluated by multiple users than an evaluation by multiple members indicates that the user is satisfied with a user experience that the members do not consider or are not proud of. It can be identified that this user experience should be enhanced or focus on this user experience should be considered.

  FIG. 6 shows an example of another process 600 of the computer system 100 for analyzing the results of evaluations by users and evaluations by members. By analyzing the result of the evaluation by the user and the result of the evaluation by the member through the process 600, for example, the relationship between the user experience and the member experience can be derived.

  In step S <b> 601, the processor 120 receives a plurality of first evaluation results indicating results of evaluation of goods or services by a plurality of members via the receiving unit 110. This is the same as step S401 in the process 400. Therefore, when the process 600 is performed after the process 400, step S601 can be omitted. Conversely, when the process 400 is performed after the process 600, step S401 can be omitted.

  In step S <b> 602, the processor 120 receives a plurality of third evaluation results indicating the results of evaluation of the workplace by a plurality of members via the receiving unit 110. The third evaluation result can be, for example, a response result by a member to the questionnaire sheet 40 shown in FIG. 1D. Each of the plurality of third evaluation results is associated with an evaluation by each member of the plurality of member experiences experienced by each member. The plurality of member experiences experienced by the member may be, for example, a member experience experienced by the member from the entire product or service provider, or a specific experience of at least one product or service provider. The member experience which the member experienced from the member (for example, himself or another member) may be sufficient. For example, an evaluation by a member of a plurality of member experiences experienced by a member indicates that the member experience that the member experienced contributed positively when the member determined the recommended degree of intention in the workplace. `` Indicator '', `` Negative index '' indicating that the member experience experienced by the member contributed negatively, and `` Neutral '' indicating that the member experience experienced by the member did not contribute positively or negatively "Indicator". For example, a member's evaluation of a plurality of member experiences experienced by a member is that the member experience that the member has experienced from other specific members when determining the recommended intention of the workplace. A “plus indicator” that indicates that the member contributed positively, a “minus indicator” that indicates that the member experience that the member experienced from another specific member contributed negatively, and the member that the other member specified And a “neutral index” indicating that the member experience experienced by the members of FIG. Thus, the evaluation by the members of the plurality of member experiences experienced by the members can include other person evaluations. For example, evaluation of a plurality of member experiences by a plurality of members is performed by evaluating a member experience that a second member can experience, a member experience that a third member can experience. Evaluation of the member experience that the n th member can experience. Specifically, other person evaluations of multiple member experiences by multiple members are, for example, “second member seems to be familiar with the culture of the workplace” or “second component It seems to be an evaluation by the first member that “I want to recommend other people to work together because the members look very good with my colleagues.”

  In step S603, the analysis unit 130 of the processor 120 statistically analyzes the plurality of first evaluation results and the plurality of third evaluation results.

  In step S604, the output unit 140 outputs the analysis result. For example, the output unit 140 may output the analysis result in a graph format or a character string.

  FIG. 7 shows an example of a process for statistically analyzing the plurality of first evaluation results and the plurality of third evaluation results in step S603.

  In step S701, the analysis unit 130 of the processor 120 correlates one of the plurality of user experiences and the plurality of member experiences based on the plurality of first evaluation results and the plurality of third evaluation results. calculate.

  For example, the analysis unit 130 associates one evaluation of the plurality of user experiences with the evaluation of the plurality of member experiences from the first evaluation result and the third evaluation result by the same member, and configures this. By repeating statistically processing the association between one evaluation of a plurality of user experiences and the evaluation of a plurality of member experiences, obtained for a plurality of members, among the plurality of user experiences You may make it calculate the correlation with one of several and member experience.

For example, a first evaluation result and a third evaluation by a plurality of members are obtained from a response result by a member to the questionnaire sheet 20 shown in FIG. 1B and a response result by a member to the questionnaire sheet 40 shown in FIG. 1D. Assume that the results are obtained as shown in Table 1.

  For example, for the user experience “greeting to enter the store”, the analyzing unit 130 determines that the positive index is a positive index member experience “relationship with seniors” from the first evaluation result and the third evaluation result by the first member. And “Relationship with the store manager” and the member experience of the negative indicator “work content”. From the first evaluation result and the third evaluation result by the second member, the neutral indicator is the member experience of the positive indicator “ "Relationships with colleagues and juniors" and "work content" and negative index member experience "working hours", associated with the negative evaluation from the first evaluation result and the third evaluation result by the fifth member Is associated with the member experience “training” of the positive indicator and the member experience “working time” of the negative indicator. For example, the association is as shown in Table 2 below. In this way, the analysis unit 130 associates each user experience with the positive index member experience and the negative index member experience from the first evaluation result and the third evaluation result by each member. Here, the analysis unit 130 can associate the evaluation of one user experience with the evaluation of a plurality of member experiences regardless of the types of user experiences and employee experiences such as humans, things, money, time, and information. it can.

  For example, the analysis unit 130 performs statistical processing on Table 2 to thereby positively influence each member experience on one of the plurality of user experiences (here, “entry greeting”). And negative impact. The statistical processing is, for example, multiple regression analysis. The multiple regression analysis is, for example, quantification class I.

  When performing multiple regression analysis, multiple regression analysis for the plus index and multiple regression analysis for the minus index are performed. The multiple regression analysis for the positive index uses the first evaluation result, which is one of “plus index (+1)”, “minus index (−1)”, and “neutral index (0)” as the objective variable. The positive impact of each member experience is calculated by performing multiple regression analysis using the “plus index” of the experience as an explanatory variable. At this time, an index other than the positive index included in the third evaluation result (a negative index and a neutral index) may be treated as zero, and multiple regression analysis may be performed. For example, the first evaluation result may be “neutral. The multiple regression analysis may be performed by omitting the evaluation result (for example, the evaluation result by the second member in Table 2) of the member that is “index (0)”. The multiple regression analysis for the negative index uses the first evaluation result, which is one of the “plus index (+1)”, “minus index (−1)”, and “neutral index (0)”, as the objective variable. The negative impact of each member experience is calculated by performing multiple regression analysis using the “minus index” of the experience as an explanatory variable. At this time, an index other than the minus index included in the third evaluation result (a plus index and a neutral index) may be treated as zero and a multiple regression analysis may be performed. For example, the first evaluation result may be “neutral. The multiple regression analysis may be performed by omitting the evaluation result (for example, the evaluation result by the second member in Table 2) of the member that is “index (0)”. Even when the plus index or the minus index is multistage, the multiple regression analysis can be performed in the same manner. In the multiple regression analysis for the positive index, regardless of the level of the negative index, all the negative indices and the neutral index are treated as zero and the multiple regression analysis is performed. In the multiple regression analysis for the minus index, regardless of the level of the plus index, all the plus indices and the neutral index are treated as zero and the multiple regression analysis is performed.

  In the above-described example, by performing a multiple regression analysis on the first evaluation result and the positive index of each member experience, a positive degree of influence on the first evaluation result of each member experience is calculated. Although it has been explained that the negative influence on the first evaluation result of each member experience is calculated by performing multiple regression analysis on the evaluation result of 1 and the negative index of each member experience, It is not limited to this. By performing statistical processing such as multiple regression analysis on the first evaluation result, the positive indicator of each member experience, and the negative indicator of each member experience, the positive impact of each member experience and each member experience It is also within the scope of the present invention to calculate the degree of negative influence of. For example, by performing multiple regression analysis using the first evaluation result as an objective variable and the positive indicators for each member experience and the negative indicators for each member experience as explanatory variables, the positive impact of each member experience You may make it calculate the negative influence degree of each member experience.

  The statistical process may be an averaging process, for example. The averaging process may be an arithmetic average, for example. For example, regarding the evaluation result by the member whose first evaluation result is “plus index (+1)”, the positive influence degree may be calculated by averaging the evaluation of each member experience. For example, regarding the evaluation result by the member whose first evaluation result is “minus index (−1)”, the negative influence degree may be calculated by averaging the evaluation of each member experience. .

  The above-described processing for statistically analyzing the plurality of first evaluation results and the plurality of third evaluation results is performed by, for example, evaluating the others by the plurality of first evaluation results and / or the plurality of third evaluation results. In the case of including

  For example, the question on the questionnaire form 20 shown in FIG. 1B is a question asking whether the user experience experienced by the user from the first member contributed positively or negatively. It is a case to answer. A more objective analysis can be performed by calculating the correlation of each member experience with one of a plurality of user experiences using the other person evaluation.

  In the above-described example, the correlation is calculated as the positive influence degree of each member experience and the negative influence degree of each member experience with respect to one of a plurality of user experiences. However, the present invention is not limited to this. Not. The analysis unit 130 may calculate the correlation as another arbitrary index, and thus the analysis unit 130 can calculate the correlation by other arbitrary processing.

  In the example described above, the correlation is calculated using the evaluation result itself such as the first evaluation result, but the present invention is not limited to this. For example, the correlation may be calculated after comparing the evaluation result with a predetermined value. For example, a first evaluation result (for example, “plus index (+1)”, “minus index (−1)” and “neutral index (0)” or recommended intention degree) by the target member) and a predetermined value Then, the correlation can be calculated using the difference. The predetermined value may be, for example, an average value of a plurality of first evaluation results by a plurality of members, or an average value of a plurality of second evaluation results by a plurality of users.

  For example, when the multiple regression analysis is performed on the positive index as described above, the first member by the target member who is one of “plus index (+1)”, “minus index (−1)”, and “neutral index (0)”. By performing a multiple regression analysis using the difference between the evaluation result of 1 and the average value of the plurality of first evaluation results by a plurality of members as an objective variable and the “plus index” of each member experience as an explanatory variable, The positive impact of each member experience can be calculated. The difference between the first evaluation result by the target member and the average value of the plurality of first evaluation results by a plurality of members is how much the self-evaluation by the target member differs from the evaluation by other members It can represent what you are doing. That is, the positive degree of influence of each member experience on the difference between the self-evaluation by the target member and the evaluation by other members is calculated. Here, the other members may be, for example, members of the entire provider of the product or service, or may be a part of the provider of the product or service (for example, one store, one department, etc.) It may be a member.

  Or when performing multiple regression analysis with respect to a plus index as described above, the first evaluation result by the target member that is the value of the recommended intention degree, and the average value of the plurality of first evaluation results by the plurality of members The positive effect of each member experience can be calculated by performing a multiple regression analysis using the difference between the objective variables and the “plus index” of each member experience as an explanatory variable.

For example, the difference between the first evaluation result by the target member shown in Table 3 below and the average value of the plurality of first evaluation results by a plurality of members is used as an objective variable, and each member experience is described. Multiple regression analysis can be performed as a variable.

  For example, the first member believes that the store greeting has a positive effect (“+1”), while the multiple members have a negative effect on average (“−0.5”). )), The difference between them is “+1.5”. This difference can be used as an objective variable, and multiple regression analysis can be performed using “plus index” of each member experience and / or “minus index” of each member experience as explanatory variables. Thereby, the influence degree of member experience with respect to the deviation between the evaluation by the first member and the evaluation by a plurality of members is calculated. The calculated degree of influence makes it possible to identify a member experience that should be improved in order to match the self-evaluation with the others' evaluation.

  For example, when the multiple regression analysis is performed on the positive index as described above, the first member by the target member who is one of “plus index (+1)”, “minus index (−1)”, and “neutral index (0)”. By performing multiple regression analysis using the difference between the evaluation result of 1 and the average value of a plurality of second evaluation results by a plurality of users as an objective variable and the “plus index” of each member experience as an explanatory variable, The positive influence of the member experience can be calculated. The difference between the first evaluation result by the target member and the average value of the plurality of second evaluation results by the plurality of users indicates how far the self-evaluation by the target member differs from the evaluation by the plurality of users. Can be represented. That is, the positive degree of influence of each member experience on the difference between the self-evaluation by the target member and the evaluation by a plurality of users is calculated.

  Alternatively, when multiple regression analysis is performed on the plus index as described above, the first evaluation result by the target member, which is the value of the degree of intention, and the average value of the plurality of second evaluation results by a plurality of users By performing the multiple regression analysis using the difference as an objective variable and the “plus index” of each member experience as an explanatory variable, it is also possible to calculate the positive influence of each member experience.

For example, as shown in Table 4 below, the difference between the first evaluation result by the target member and the average value of the plurality of second evaluation results by a plurality of users is used as an objective variable, and each member experience is an explanatory variable. Multiple regression analysis can be performed.

  For example, the first member considers the store greeting to have a positive effect (“+1”), while multiple users have a negative effect on average (“−0.3”). ), The difference is “+1.3”. Using this difference as an objective variable, a multiple regression analysis can be performed using the “plus index” of each member experience and / or the “minus index” of each member experience as an explanatory variable. Thereby, the influence degree of member experience with respect to the discrepancy between the evaluation by the first member and the evaluation by a plurality of users is calculated. The calculated degree of influence makes it possible to identify a member experience that should be improved to match the self-evaluation with the user evaluation.

  For example, even when the first evaluation result is a result (evaluation by others) of a plurality of other members evaluating a user experience that the user can experience from the target member (for example, the same processing as the above-described processing (for example, The correlation can be calculated by multiple regression analysis). The difference between the first evaluation result by other members and the average value of the plurality of second evaluation results by a plurality of users is evaluated by other users by other users. It can represent how far it is. That is, by using, for example, multiple regression analysis using the difference between the first evaluation result by other members and the average value of the plurality of second evaluation results by a plurality of users, The influence degree of each member experience with respect to the divergence between the other person evaluation by the member and the evaluation by a plurality of users is calculated. The calculated degree of influence makes it possible to specify a member experience that should be improved in order to match the other person evaluation with the user evaluation.

  For example, the analyzing unit 130 performs preprocessing on the plurality of first evaluation results, and calculates a correlation between one of the plurality of user experiences specified by the preprocessing and the plurality of member experiences. May be. The preprocessing can be the same processing as the processing performed in step S501, for example. One of the plurality of user experiences may be, for example, a user experience that should be improved with the highest priority in order to improve the recommendation intention.

  The analysis unit 130 calculates not only the correlation for a specific one of the plurality of user experiences but also the correlation with the plurality of member experiences for each of the plurality of user experiences. May be.

  In step S702, the analysis unit 130 generates an analysis result based on the correlation calculated in step S701.

  The analysis result may be, for example, an unprocessed value of a positive influence and a negative influence calculated as a correlation, or a value obtained by processing a positive influence and a negative influence. May be. The value obtained by processing the positive influence degree and the negative influence degree may be a value generated by, for example, mathematically processing the positive influence degree and the negative influence degree. The mathematical process is, for example, addition. The first value generated by adding the positive influence degree and the negative influence degree is, for example, relative influence degree indicating whether the user experience has a positive influence or a negative influence. Value. The mathematical process is, for example, addition of absolute values. The second value generated by adding the positive influence and the absolute value of the negative influence is, for example, how much the user experience is, regardless of whether the influence is positive or negative. This is an absolute value of influence indicating whether there is an influence. The value obtained by performing the process on the positive influence degree and the negative influence degree may be, for example, a value obtained by further processing the generated value. For example, it may be a value obtained by ranking the member experience experienced by the member based on the first value and the second value. At this time, for example, the calculated relative value of the influence degree, Based on the calculated absolute value of the degree of influence, the rank obtained by ranking the member experience in descending order of the degree of influence on the user experience and the negative influence on the user experience. May be a value indicating.

  Thus, the analysis result can identify the member experience that affects the user experience.

  Process 400 and process 600 may be performed independently of each other, or may be performed in parallel or sequentially. If performed in parallel or sequentially, the affecting member experience can be identified in process 600 for the user experience to be improved identified in process 400. Improving the member experience identified in process 600 may lead to an improvement in the user experience identified in process 400, which may lead to user satisfaction and thus increase corporate profits.

  FIG. 8 shows an example of an alternative process 800 of the process 600 of the computer system 100 for analyzing the results of user evaluations and member evaluations. By analyzing the result of the evaluation by the user and the result of the evaluation by the member through the process 800, for example, the relationship between the user experience and the member experience can be derived.

  In step S <b> 801, the processor 120 receives a plurality of first evaluation results indicating results of evaluation of goods or services by a plurality of members via the receiving unit 110. This is the same as step S401 in the process 400. Therefore, when the process 800 is performed after the process 400, step S801 can be omitted. Conversely, when the process 400 is performed after the process 800, step S401 can be omitted.

  In step S <b> 802, the processor 120 receives a plurality of fourth evaluation results indicating a result of evaluating one of a plurality of user experiences by a plurality of members via the receiving unit 110. The fourth evaluation result may be, for example, a response result by a member to the questionnaire sheet 30 shown in FIG. 1C. Each of the plurality of fourth evaluation results is associated with an evaluation by each member of the plurality of member experiences experienced by each member. For example, the evaluation by a member of a plurality of member experiences experienced by a member indicates whether the user experience that the user can experience is a “plus indicator”, “minus indicator”, or “neutral indicator”. When deciding, the “plus indicator” indicating that the member experience experienced by the member contributed positively, the “minus indicator” indicating that the member experience experienced by the member contributed negatively, and the member It may include a “neutral index” indicating that the member experience experienced by has contributed neither positively nor negatively.

  In step S803, the analysis unit 130 of the processor 120 statistically analyzes the plurality of first evaluation results and the plurality of fourth evaluation results. Statistical analysis is, for example, multiple regression analysis. The multiple regression analysis is, for example, quantification class I. Thereby, an analysis result is generated.

  In step S804, the output unit 140 outputs the analysis result. For example, the output unit 140 may output the analysis result in a graph format or a character string.

  FIG. 9 shows an example of a process for statistically analyzing the plurality of first evaluation results and the plurality of fourth evaluation results in step S803.

  In step S901, the analysis unit 130 of the processor 120 correlates one of the plurality of user experiences with the plurality of member experiences based on the plurality of first evaluation results and the plurality of fourth evaluation results. calculate.

  For example, the analysis unit 130 performs a process of calculating the degree of influence of each member experience on one of the plurality of user experiences, thereby correlating one of the plurality of user experiences with the plurality of member experiences. The relationship may be calculated. At this time, for example, with respect to the plurality of first evaluation results received in step S801 and the plurality of fourth evaluation results received in step S802, the user experience that the user can experience is “plus index”, “ Members determine whether it is a “negative indicator” or “neutral indicator” and whether the user experience that the user can experience is a “positive indicator”, “negative indicator”, or “neutral indicator” At the time, statistical processing of the “plus indicator” indicating that the member experience experienced by the member contributed positively and the “negative indicator” indicating that the member experience experienced by the member contributed negatively By doing so, you may make it calculate the positive influence degree and negative influence degree of each member experience with respect to one of several user experiences. The statistical processing is, for example, multiple regression analysis. The multiple regression analysis is, for example, quantification class I.

  When performing multiple regression analysis, multiple regression analysis for the plus index and multiple regression analysis for the minus index are performed. The multiple regression analysis for the positive index uses the objective variable to evaluate whether the user experience that the user can experience is a “plus index”, “minus index”, or “neutral index”. When a member decides whether it is a “plus indicator”, “minus indicator”, or “neutral indicator”, a “plus indicator” indicating that the member experience that the member experienced has contributed positively is an explanatory variable As a result, a positive influence of each member experience on the user experience that the user can experience is calculated. At this time, multiple regression analysis may be performed by treating indices other than the positive index (negative index and neutral index) included in the evaluation result as zero. The multiple regression analysis for negative indicators uses the objective variable to evaluate whether the user experience that users can experience is a “plus indicator”, “minus indicator”, or “neutral indicator”. When a member decides whether it is a “plus indicator”, “minus indicator”, or “neutral indicator”, the “minus indicator” indicating that the member experience experienced by the member has contributed negatively is an explanatory variable As a result, a negative influence of each member experience on the user experience that the user can experience is calculated. At this time, an index other than the minus index included in the evaluation result (plus index and neutral index) may be treated as zero and the multiple regression analysis may be performed. Even when the plus index or the minus index is multistage, the multiple regression analysis can be performed in the same manner. In the multiple regression analysis for the positive index, regardless of the level of the negative index, all the negative indices and the neutral index are treated as zero and the multiple regression analysis is performed. In the multiple regression analysis for the minus index, regardless of the level of the plus index, all the plus indices and the neutral index are treated as zero and the multiple regression analysis is performed.

  It should be noted that the user experience that the user can experience is an evaluation of “plus index”, “minus index”, or “neutral index”, and the user experience that the user can experience is “plus index”, “minus index” When a member decides whether it is a “neutral index”, the “plus indicator” indicating that the member experience experienced by the member has contributed positively, and the member experience experienced by the member is negative. It is also within the scope of the present invention to calculate a positive influence and a negative influence by performing statistical processing such as multiple regression analysis on the “minus index” indicating contribution. For example, the objective variable is the evaluation of whether the user experience that the user can experience is a “plus index”, “minus index”, or “neutral index”, and the user experience that the user can experience is “plus index”, “ When a member decides whether it is a “negative indicator” or “neutral indicator”, the “plus indicator” indicating that the member experience experienced by the member contributed positively and the member experience experienced by the member The positive influence degree and the negative influence degree may be calculated by performing a multiple regression analysis using “negative index” indicating that has contributed to negative as an explanatory variable.

  In the above-described example, the correlation is calculated as the positive influence degree and the negative influence degree of each member experience with respect to one of the plurality of user experiences, but the present invention is not limited to this. The analysis unit 130 may calculate the correlation as another arbitrary index, and thus the analysis unit 130 can calculate the correlation by other arbitrary processing.

  In the example described above, the correlation is calculated using the evaluation result itself such as the first evaluation result, but the present invention is not limited to this. For example, the correlation may be calculated using a value obtained by pre-processing the evaluation result. The pre-processing includes, for example, the first evaluation result (for example, “plus index (+1)”, “minus index (−1)” and “neutral index (0)” or recommended intention degree) by the target member) , It may be a process of obtaining a difference from a predetermined value. The predetermined value may be, for example, an average value of a plurality of first evaluation results by a plurality of members.

  For example, the analyzing unit 130 performs preprocessing on the plurality of first evaluation results, and calculates a correlation between one of the plurality of user experiences specified by the preprocessing and the plurality of member experiences. May be. The preprocessing can be the same processing as the processing performed in step S501, for example. One of the plurality of user experiences may be, for example, a user experience that should be improved with the highest priority in order to improve the recommendation intention.

  The analysis unit 130 calculates not only the correlation for a specific one of the plurality of user experiences but also the correlation with the plurality of member experiences for each of the plurality of user experiences. May be.

  In step S902, the analysis unit 130 generates an analysis result based on the correlation calculated in step S901.

  The analysis result may be, for example, an unprocessed value of a positive influence and a negative influence calculated as a correlation, or a value obtained by processing a positive influence and a negative influence. May be. The value obtained by processing the positive influence degree and the negative influence degree may be a value generated by, for example, mathematically processing the positive influence degree and the negative influence degree. The mathematical process is, for example, addition. The first value generated by adding the positive influence degree and the negative influence degree is, for example, relative influence degree indicating whether the user experience has a positive influence or a negative influence. Value. The mathematical process is, for example, addition of absolute values. The second value generated by adding the positive influence and the absolute value of the negative influence is, for example, how much the user experience is, regardless of whether the influence is positive or negative. This is an absolute value of influence indicating whether there is an influence. The value obtained by performing the process on the positive influence degree and the negative influence degree may be, for example, a value obtained by further processing the generated value. For example, it may be a value obtained by ranking the member experience experienced by the member based on the first value and the second value. At this time, for example, the calculated relative value of the influence degree, Based on the calculated absolute value of the degree of influence, the rank obtained by ranking the member experiences in descending order of the degree of influence on the user experience and the negative influence on the user experience. May be a value indicating.

  Thus, the analysis result can identify the member experience that affects the user experience.

  Process 400 and process 800 may be performed independently of each other, or may be performed in parallel or sequentially. If performed in parallel or sequentially, the affecting member experience can be identified in process 800 for the user experience to be improved identified in process 400. Improving the member experience identified in process 800 may lead to an improvement in the user experience identified in process 400, which may lead to user satisfaction and thus increase corporate profits.

  The flowchart in the above-described example is an example. The processing order of the computer system 100 of the present invention is not limited to the order shown in the flowchart. For example, in the process 400, step S402 may be performed before step S401, or step S401 and step S402 may be performed simultaneously. For example, in the process 600, step S602 may be performed before step S601, and step S601 and step S602 may be performed simultaneously. For example, in the process 800, step S802 may be performed before step S801, or step S801 and step S802 may be performed simultaneously.

  In the above-described example, it has been described that the evaluation result obtained by evaluating the product or service provided by the company by the user receiving the offer is analyzed, but the present invention is not limited to this. In the present invention, a “user” may be any entity that receives provision of goods or services.

  For example, the user may be a supplier company X that makes a transaction with the company A that provides goods or services. Analyzing the results of business partner company X evaluating the transaction between company A and business partner company X, which experience of business partner company X experiences in the transaction has an impact on the degree of recommendation Can be identified. In this case, the recommended intention degree is the degree to which the business partner company X recommends a transaction with the company A to other companies. The analysis results provided by the computer system of the present invention can identify which member experience should be improved to improve such a degree of recommendation. Increasing such a degree of recommendation can increase the number of companies that deal with companies. This can lead to improved profits for the company.

  For example, an experience when a different member is in charge of a transaction with a business partner company X (for example, an experience when a member B is in charge of a transaction with a business partner company X, By analyzing the degree of influence on the degree of recommendation for the experience of dealing with transactions and the experience of member D in charge of dealings with partner company X) It is possible to analyze whether the charge of X leads to improvement in the degree of recommendation. Further, for each member in charge, it is possible to specify which member experience should be improved in order to improve the recommendation intention. Also, for example, experience when different members are in charge of dealings with different suppliers (for example, experience when member B is in charge of transactions with supplier company X, member B is supplier company Y (Experience in case of dealings with company, experience of member C in charge of dealings with partner company X, experience in case of member C in charge of dealings with partner company Y, etc.) By analyzing the degree of influence on the degree of intention, it is possible to analyze which member is responsible for which client company leads to improvement of the degree of recommendation. Thereby, the optimal personnel assignment can be achieved.

  In the example described above, the analysis using the recommended intention degree as the evaluation index has been described, but the present invention is not limited to this. The same analysis can be performed for an arbitrary evaluation index indicating the degree of evaluation when the user evaluates the evaluation object from a predetermined viewpoint. The evaluation index may be, for example, “total satisfaction” indicating the degree to which the user is satisfied with the evaluation object when the user evaluates the evaluation object from a comprehensive viewpoint. In this case, it is possible to analyze how each of a plurality of user experiences affects the “total satisfaction”, and to identify a user experience to be improved in order to improve the “total satisfaction”. Can do. The evaluation index may be, for example, a “heat fever” indicating the degree of user frenzy for the evaluation target. In this case, it is possible to analyze how each of a plurality of user experiences affects the “heat madness”, and to identify a user experience that should be improved to improve the “heat madness”. .

  In the example described above with reference to FIGS. 4, 5, 6, 7, 8, and 9, the processing of each step shown in FIGS. 4, 5, 6, 7, 8, and 9 is performed. Has been described as being realized by the processor 120 and a program stored in the memory 140, but the present invention is not limited to this. At least one of the processes in each step shown in FIGS. 4, 5, 6, 7, 8, and 9 may be realized by a hardware configuration such as a control circuit.

  The present invention is not limited to the embodiment described above. It is understood that the scope of the present invention should be construed only by the claims. It is understood that those skilled in the art can implement an equivalent range based on the description of the present invention and the common general technical knowledge from the description of specific preferred embodiments of the present invention.

  The present invention relates to a computer system for analyzing how a member experience or a user experience influences an evaluation result by a member or a user of a product or service provider, and a method executed in the computer system, and Useful for providing programs.

10, 20, 30, 40 Questionnaire 100 Computer system 110 Receiving means 120 Processor 130 Analyzing means 140 Memory 150 Output means

Claims (20)

A plurality of first evaluation means for receiving a plurality of first evaluation results indicating a result of evaluation of the goods or services by a plurality of members of a provider who provides the goods or services; A first receiving means each comprising an evaluation by a respective member of a plurality of user experiences that a user receiving the product or service may experience;
A second receiving means for receiving a plurality of second evaluation results indicating a result of evaluating the products or services by a plurality of users who have received provision of the products or services, wherein the plurality of second evaluation results Each of the second receiving means including an evaluation by each user of the plurality of user experiences experienced by each user;
Analyzing means for statistically analyzing the plurality of first evaluation results and the plurality of second evaluation results;
Output means for outputting an analysis result by the analysis means , wherein the analysis result includes information indicating a user experience to be improved in order to improve the second evaluation result, and an output means. . The analysis means includes
Based on the plurality of first evaluation results and the plurality of second evaluation results, the evaluation of the plurality of user experiences by the plurality of users is compared with the evaluation of the plurality of user experiences by the plurality of members. Comparing means to
Based on the result of the comparison by the comparison means, a specifying means for specifying a user experience in which the evaluation by the plurality of users and the evaluation by the plurality of members do not match,
The computer system according to claim 1, further comprising: generating means for generating the analysis result based on the identified user experience . A third receiving means for receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is experienced by each member; A third receiving means including an evaluation by each member of a plurality of member experiences;
The computer system according to claim 1, wherein the analysis unit statistically analyzes the plurality of first evaluation results, the plurality of second evaluation results, and the plurality of third evaluation results. The analysis means includes
Based on the plurality of first evaluation results and the plurality of second evaluation results, the evaluation of the plurality of user experiences by the plurality of users is compared with the evaluation of the plurality of user experiences by the plurality of members. Comparing means to
The computer system according to claim 3, further comprising: generation means for generating the analysis result based on a result of comparison by the comparison means. The analysis means includes
A calculating means for calculating a correlation between one of the plurality of user experiences and the plurality of member experiences based on the plurality of first evaluation results and the plurality of third evaluation results ; The calculating means calculates, for each member of the plurality of members, one evaluation of the plurality of user experiences and the plurality of the plurality of user experiences from the first evaluation result and the third evaluation result by the same member. The correlation is calculated by associating the evaluation of the member experience of and the statistical processing of the association between the evaluation of one of the plurality of user experiences and the evaluation of the plurality of member experiences. And a calculating means ,
The generation unit generates the analysis result based on a comparison result by the comparison unit and the correlation.
The computer system according to claim 4. The analysis means includes
Second comparison means for comparing one first evaluation result of the plurality of first evaluation results with a predetermined value;
Based on the comparison result and the plurality of third evaluation result by the second comparison means, a calculating means for calculating a correlation between one of the plurality of members experience of the plurality of user experience The calculating means is configured to calculate, for each member of the plurality of members, a first evaluation result of the plurality of user experiences based on a first evaluation result and a third evaluation result by the same member. The comparison result of the two comparison means and the evaluation of the plurality of member experiences, and the comparison result of the second comparison means of one evaluation of the plurality of user experiences and the plurality of member experiences And a calculation means for calculating the correlation by statistically processing the association with the evaluation ,
The generation unit generates the analysis result based on a comparison result by the comparison unit and the correlation.
The computer system according to claim 4.   The computer system according to claim 6, wherein the predetermined value is one of an average value of the plurality of first evaluation results and an average value of the plurality of second evaluation results. The computer system according to any one of claims 4 to 7 , wherein the analysis result includes information indicating a member experience that affects the user experience. The comparison means includes, for at least one user experience among the plurality of user experiences, an average value of evaluations by the plurality of members included in the plurality of first evaluation results, and the plurality of second evaluations. Comparing the evaluation of the plurality of user experiences by the plurality of users and the evaluation of the plurality of user experiences by the plurality of members by taking a difference from the average value of the evaluations by the plurality of users included in the result The computer system according to any one of claims 2, 4 to 8 . The analysis means includes
An influence degree calculating means for calculating an influence degree of the plurality of user experiences with respect to the second evaluation result based on the plurality of second evaluation results;
Determining means for determining at least one user experience of the plurality of user experiences based on the influence degree calculated by the influence degree calculating means; and
The comparing means compares the evaluation of the plurality of user experiences by the plurality of users and the evaluation of the plurality of user experiences by the plurality of members for the determined user experience.
The computer system according to any one of claims 2, 4 to 9 . Said first respective evaluation results, each of the members including recommended intention degree indicating the degree of recommendation that he wants to recommend to others the goods or services, according to any of claims 1-10 Computer system. Wherein each of the second evaluation result, each user including recommended intention degree indicating the degree of recommendation that he wants to recommend to others the goods or services, according to any one of claims 1 to 11 computer system. A plurality of first evaluation means for receiving a plurality of first evaluation results indicating a result of evaluation of the goods or services by a plurality of members of a provider who provides the goods or services; A first receiving means each comprising an evaluation by a respective member of a plurality of user experiences that a user receiving the product or service may experience;
A third receiving means for receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is experienced by each member; A third receiving means including an evaluation by each member of a plurality of member experiences;
The first evaluation result and the third evaluation result by the same member among the plurality of members are associated with each other, and the plurality of the first evaluation results and the plurality of the third evaluation results associated with each other are statistically calculated. Analysis means to analyze
A computer system comprising: output means for outputting an analysis result by the analysis means , wherein the analysis result includes information indicating a member experience that affects the user experience . The analysis means includes
A calculating means for calculating a correlation between one of the plurality of user experiences and the plurality of member experiences based on the plurality of first evaluation results and the plurality of third evaluation results ; For each member of the plurality of members, the calculating means calculates one evaluation of the plurality of user experiences and the plurality of the plurality of user experiences from the first evaluation result and the third evaluation result by the same member. Correlating the evaluation of the member experiences and statistically processing the association of one of the plurality of user experiences with the evaluation of the plurality of member experiences. Calculating means for calculating
The computer system according to claim 13 , further comprising: generation means for generating the analysis result based on the correlation. A method performed in a computer system, the method comprising:
Receiving a plurality of first evaluation results indicating a result of evaluating the product or service by a plurality of members of a provider who provides the product or service, wherein the plurality of first evaluation results are respectively Including an assessment by each member of a plurality of user experiences that a user receiving the product or service may experience;
Receiving a plurality of second evaluation results indicating a result of evaluating the product or service by a plurality of users who have received provision of the product or service, wherein each of the plurality of second evaluation results is: Including an assessment by each user of the plurality of user experiences experienced by each user;
Statistically analyzing the plurality of first evaluation results and the plurality of second evaluation results;
Outputting an analysis result, the analysis result including information indicating a user experience to be improved in order to improve the second evaluation result . Receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a plurality of members experienced by each member; Further including evaluation of the experience by each member,
The method of claim 15 , wherein the analyzing includes statistically analyzing the plurality of first evaluation results, the plurality of second evaluation results, and the plurality of third evaluation results. A method performed in a computer system, the method comprising:
Receiving a plurality of first evaluation results indicating a result of evaluating the product or service by a plurality of members of a provider who provides the product or service, wherein the plurality of first evaluation results are respectively Including an assessment by each member of a plurality of user experiences that a user receiving the product or service may experience;
Receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a plurality of members experienced by each member; Including an evaluation of the experience by each member, and
The first evaluation result and the third evaluation result by the same member among the plurality of members are associated with each other, and the plurality of the first evaluation results and the plurality of the third evaluation results associated with each other are statistically calculated. To analyze
Outputting an analysis result, the analysis result including information indicating a member experience that affects the user experience . A program executed in a computer system, wherein the computer system includes a processor, and the program is executed by the processor.
Receiving a plurality of first evaluation results indicating a result of evaluating the product or service by a plurality of members of a provider who provides the product or service, wherein the plurality of first evaluation results are respectively Including an assessment by each member of a plurality of user experiences that a user receiving the product or service may experience;
Receiving a plurality of second evaluation results indicating a result of evaluating the product or service by a plurality of users who have received provision of the product or service, wherein each of the plurality of second evaluation results is: Including an assessment by each user of the plurality of user experiences experienced by each user;
Statistically analyzing the plurality of first evaluation results and the plurality of second evaluation results;
Outputting the analysis result, the analysis result including information indicating a user experience to be improved in order to improve the second evaluation result, and causing the processor to perform processing including: . The processing is as follows:
Receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a plurality of members experienced by each member; Further including evaluation of the experience by each member,
The program according to claim 18 , wherein the analyzing includes statistically analyzing the plurality of first evaluation results, the plurality of second evaluation results, and the plurality of third evaluation results. A program executed in a computer system, wherein the computer system includes a processor, and the program is executed by the processor.
Receiving a plurality of first evaluation results indicating a result of evaluating the product or service by a plurality of members of a provider who provides the product or service, wherein the plurality of first evaluation results are respectively Including an assessment by each member of a plurality of user experiences that a user receiving the product or service may experience;
Receiving a plurality of third evaluation results indicating a result of evaluating the workplace by the plurality of members, wherein each of the plurality of third evaluation results is a plurality of members experienced by each member; Including an evaluation of the experience by each member, and
The first evaluation result and the third evaluation result by the same member among the plurality of members are associated with each other, and the plurality of the first evaluation results and the plurality of the third evaluation results associated with each other are statistically calculated. To analyze
A program for outputting an analysis result, wherein the analysis result includes information indicating a member experience that affects the user experience .