JP2023171289A - Computer system, method and program for analyzing investor's evaluation result of investment object - Google Patents

Abstract

To provide a computer system, etc. for analyzing an influence of investors' experience on an evaluation index (for example, investors' satisfaction degree).SOLUTION: A computer system for analyzing investors' evaluation results of an investment object, comprises: receiving means for receiving a plurality of evaluation results representing evaluation results of the investment object by a plurality of investors, each of the plurality of evaluation results including an evaluation index representing a degree of evaluation when an investor evaluates the investment object from a prescribed point of view and an evaluation relating to a plurality of investor experiences that the investor can experience; analysis means for analyzing the plurality of evaluation results so as to identify the investor experience to be improved for improving the evaluation index; and output means for outputting an analysis result by the analysis means, the analysis result including information representing the investor experience to be improved for improving the evaluation index.SELECTED DRAWING: Figure 2

Description

The present invention relates to a computer system, method, and program for analyzing the results of an investor's evaluation of an investment target.

Traditionally, companies have strived to improve "customer satisfaction (CS)." Patent Document 1 discloses an apparatus for analyzing customer satisfaction. The device disclosed in Patent Document 1 analyzes the relationship between a product and its purchase intention based on quantitative questionnaire results.

In recent years, it has become clear that what is directly linked to improving a company's profits is not necessarily limited to "customer satisfaction," and that desired factors other than "customer satisfaction" can be used in addition to or in place of "customer satisfaction." It has become clear that improving evaluation indicators also leads to increased profits for companies. In order to improve desired evaluation indicators and, ultimately, increase corporate profits, the key is to decipher customer feedback and how to allocate limited resources.

Japanese Patent Application Publication No. 2002-7659

The inventor of the present invention believed that the satisfaction level of investors (for example, shareholders) is important for improving corporate profits. When investors are satisfied, their motivation to invest in the company increases, and they increase their investment in the company (for example, they hold the stock for a longer period of time, and their intention to own the stock becomes stronger). This is because companies should be able to use the capital increased by investors to improve profits. Therefore, the inventor of the present invention discovered the importance of establishing a method for verifying evaluation indicators (for example, investor satisfaction) by investors.

The present invention has been made in view of the above idea, and an object of the present invention is to provide a computer system etc. for analyzing the influence of investor experience on evaluation indicators (for example, investor satisfaction). shall be.

The present invention provides a computer system for analyzing the results of an investor's evaluation of an investment target. More specifically, the present invention provides, for example, the following items.
(Item 1)
A computer system for analyzing the results of investors' evaluation of investment targets,
A receiving means for receiving a plurality of evaluation results indicating the results of evaluations of an investment target by a plurality of investors, each of the plurality of evaluation results being a result of evaluation of the investment target by an investor from a predetermined viewpoint. a receiving means including an evaluation index indicating the degree of evaluation and evaluations regarding a plurality of investor experiences that the investor may experience;
an analysis means for analyzing the plurality of evaluation results to identify an investor experience that should be improved in order to improve the evaluation index;
An output means for outputting a result of analysis by the analysis means, the result of the analysis including information indicating an investor experience that should be improved in order to improve the evaluation index.
(Item 2)
The computer system according to the above item, wherein the plurality of investor experiences include an investor experience related to business activities by the investment target and an investor experience related to IR activities by the investment target.
(Item 3)
At least one of the investor experiences related to the business activity is correlated with an evaluation of the product or service by a user who receives the product or service from the investment target, and/or
at least one of the investor experiences related to the business activity is correlated with a workplace evaluation by a member of the investment target;
The at least one investor experience that correlates with the user's evaluation of the product or service is different from the at least one investor experience that correlates with the member's evaluation of the workplace. The computer system according to paragraph 1.
(Item 4)
further comprising a second receiving means for receiving a plurality of second evaluation results indicating the results of evaluations of the product or service by a plurality of users who received the product or service,
The analysis means identifies a user experience that should be improved in order to improve the evaluation index by analyzing the plurality of evaluation results and the plurality of second evaluation results,
The computer system according to any one of the above items, wherein the analysis result includes information indicating a user experience that should be improved in order to improve the evaluation index.
(Item 5)
Each of the plurality of second evaluation results includes a second evaluation index indicating the degree of evaluation when the user evaluates the product or service from a predetermined viewpoint, and a plurality of user experiences that the user may experience. , and ratings by respective users.
The analysis means identifies a user experience that should be improved in order to improve the second evaluation index,
The analysis means specifies a user experience that should be improved in order to improve the evaluation index based on the identified investor experience, the identified user experience, and the correlation. A computer system according to any one of the items.
(Item 6)
Further comprising third receiving means for receiving a plurality of third evaluation results indicating the results of evaluations of the workplace by the plurality of members of the investment target,
The analysis means identifies a member experience that should be improved in order to improve the evaluation index by analyzing the plurality of evaluation results and the plurality of third evaluation results,
The computer system according to any one of the above items, wherein the result of the analysis includes information indicating a member experience that should be improved in order to improve the evaluation index.
(Item 7)
Each of the plurality of third evaluation results includes a third evaluation index indicating the degree of evaluation when the member evaluates the workplace from a predetermined viewpoint, and a plurality of member experiences that the member may experience. including an evaluation by each member of the
The analysis means identifies a member experience that should be improved in order to improve the third evaluation index,
The analysis means identifies a member experience that should be improved in order to improve the evaluation index based on the identified investor experience, the identified member experience, and the correlation. A computer system according to any one of the items.
(Item 8)
The evaluation regarding the plurality of investor experiences includes a positive index indicating that the investor experience experienced by the investor made a positive contribution when the investor determined the evaluation index; and a negative indicator indicating that the investor experience experienced by the investor contributed negatively when making the decision,
The analysis means includes:
By statistically processing the evaluation index, the positive index, and the negative index for the plurality of evaluation results, the degree of positive influence of each investor's experience on the evaluation index and each investment on the evaluation index are determined. a calculation means for calculating the degree of negative influence of the home experience;
generating means for generating the results of the analysis based on the degree of positive influence of each investor experience on the evaluation index and the degree of negative influence of each investor experience on the evaluation index, the generation means comprising: generating a first value by adding a positive influence of each investor experience on the evaluation index and a negative influence of each investor experience on the evaluation index;
The generating means generates a second value by adding the positive influence degree of each investor experience on the evaluation index and the absolute value of the negative influence degree of each investor experience on the evaluation index,
The computer system according to any one of the above items, wherein the output means outputs the first value and the second value.
(Item 9)
The analysis means calculates the degree of influence of each investor experience on the evaluation index by performing regression analysis using the evaluation index as an objective variable and evaluations regarding the plurality of investor experiences as explanatory variables. A computer system according to any one of the items.
(Item 10)
The computer system according to any one of the above items, wherein the evaluation index is a degree of recommendation intention indicating a degree of recommendation intention of the investor to recommend the investment target to other people.
(Item 11)
A method for analyzing the results of an investor's evaluation of an investment target, the method comprising:
Receiving a plurality of evaluation results indicating the results of evaluations of the investment target by a plurality of investors, each of the plurality of evaluation results being an evaluation when the investor evaluated the investment target from a predetermined viewpoint. including an evaluation index indicating the degree of the investor's experience, and an evaluation regarding multiple investor experiences that the investor may experience;
analyzing the plurality of evaluation results to identify investor experience that should be improved to improve the evaluation metric;
A method comprising: outputting a result of an analysis by the analysis means, the result of the analysis including information indicating an investor experience that should be improved in order to improve the evaluation index.
(Item 11A)
12. A method according to item 11, comprising the features described in one or more of the above items.
(Item 12)
A program for analyzing the results of an evaluation of an investment target by an investor, the program being executed in a computer system including a processor unit, the program comprising:
Receiving a plurality of evaluation results indicating the results of evaluations of the investment target by a plurality of investors, each of the plurality of evaluation results being an evaluation when the investor evaluated the investment target from a predetermined viewpoint. including an evaluation index indicating the degree of the investor's experience, and an evaluation regarding multiple investor experiences that the investor may experience;
analyzing the plurality of evaluation results to identify investor experience that should be improved to improve the evaluation metric;
outputting a result of the analysis by the analysis means, the result of the analysis including information indicating an investor experience that should be improved in order to improve the evaluation index; A program that causes
(Item 12A)
13. The program according to item 12, comprising the features described in one or more of the above items.
(Item 13)
A computer system for analyzing the results of investors' evaluation of investment targets,
a first receiving means for receiving a plurality of first evaluation results indicating results of evaluations of investment targets by a plurality of investors, each of the plurality of first evaluation results being an evaluation result of an investor from a predetermined viewpoint; a first receiving means that includes a first evaluation index indicating the degree of evaluation when evaluating the investment target; and evaluations regarding a plurality of investor experiences that the investor may experience;
a second receiving means for receiving a plurality of second evaluation results indicating the results of evaluations of the product or service by a plurality of users who have received the product or service from the investment target; Each of the evaluation results includes a second evaluation index indicating the degree of evaluation when the user evaluates the product or service from a predetermined viewpoint, and a second evaluation index indicating the degree of evaluation by the user of the plurality of user experiences that the user may experience. a second receiving means comprising an evaluation;
a third receiving means for receiving a plurality of third evaluation results indicating the results of evaluations of the workplace by a plurality of members of the investment target, each of the plurality of third evaluation results having a predetermined value. A third evaluation index indicating the degree of evaluation when the member evaluates the workplace from the viewpoint, and an evaluation by each member of a plurality of member experiences that the member may experience. a receiving means,
An analysis means for analyzing the plurality of first evaluation results, the plurality of second evaluation results, and the third evaluation result, wherein the analysis means analyzes the plurality of first evaluation results. The analysis means identifies an investor experience that should be improved in order to improve the first evaluation index, and the analysis means analyzes the plurality of first evaluation results and the plurality of second evaluation results. By doing so, the user experience to be improved in order to improve the first evaluation index is identified, and the analysis means analyzes the plurality of first evaluation results and the plurality of third evaluation results. an analysis means for identifying a member experience that should be improved in order to improve the first evaluation index;
An output means for outputting a result of analysis by the analysis means, the result of the analysis includes information indicating an investor experience that should be improved in order to improve the first evaluation index, and the first evaluation index. output means, the computer system comprising: information indicating a user experience that should be improved to improve the first evaluation index; and information indicating a member experience that should be improved to improve the first evaluation index.
(Item 13A)
14. A computer system according to item 13, comprising the features described in one or more of the above items.

According to the present invention, it is possible to provide a computer system for analyzing the results of an investor's evaluation of an investment target. The impact can be analyzed.

Diagram showing an example of questionnaire form 10 for investors who own shares of ○○○ Co., Ltd. to evaluate ○○○ Co., Ltd. A diagram schematically showing that the computer system 100 of the present invention provides an analysis result 50 from the answer results of a plurality of answered questionnaire forms 10. A diagram showing an example of a questionnaire form 20 for users who purchased a car from ○○○ Co., Ltd. to evaluate the services of ○○○ Co., Ltd. Diagram showing an example of a questionnaire form 30 for employees of ○○○ Co., Ltd. to evaluate the workplace of ○○○ Co., Ltd. A diagram schematically showing that the computer system 100 of the present invention provides analysis results from the answer results of a plurality of answered questionnaire forms 10, 20, and 30. A diagram showing an example of the configuration of a computer system 100 for analyzing the results of evaluation of investment targets by investors. A flowchart showing an example of a process 600 in the computer system 100 for analyzing the results of an investor's evaluation of an investment target. Flowchart illustrating an example of processing for analyzing multiple evaluation results in step S602 A flowchart illustrating an example of a process in step S6021 for calculating a positive influence degree on the recommendation intention degree and a negative influence degree on the recommendation intention degree for a plurality of evaluation results. A flowchart illustrating an example of a process of generating analysis results of a plurality of evaluation results based on the positive influence degree and the negative influence degree in step S6022. Flowchart illustrating an example of a process 800 for identifying a user experience that should be improved in order to improve investors' recommendation intention. Flowchart showing an example of processing in step S803 Flowchart illustrating an example of a process 900 for identifying member experiences that should be improved to improve investor recommendation intention. Flowchart showing an example of processing in step S903

Embodiments of the present invention will be described below with reference to the drawings.

1. Analysis of Results of Evaluation of Investment Targets by Investors FIG. 1A shows an example of a questionnaire form 10 for investors who own shares of ○○○ Corporation to evaluate ○○○ Corporation.

The questionnaire form 10 includes question 1, question 2, and question 3.

Question 1 is a question asking about the degree of recommendation intention. “Recommendation intention level” in Figure 1A is the degree of evaluation when an investor evaluates an investment target from the perspective of whether the investor wants to recommend other people to invest in the investment target (i.e., the investor degree of recommendation intention to recommend other people to invest in the investment target). In this example, the "investment target" is a stock company, ○○○ Corporation, but is not limited to this. The "investment target" may be, for example, a corporation, a natural person, or a national or local government. An "investment" may be an investment by any means, typically an investment in stocks, but may also be an investment in bonds, for example. When the "investment target" is a "stock company," the "investor" can be called a "shareholder."

Investors respond to Question 1 with a score of 0 to 10 regarding their recommendation intention. For example, an investor can give a score of 10 if he or she would highly recommend investing in ○○○ Co., Ltd. to a close friend or acquaintance. If you do not want to recommend it at all, you can give it a score of 0.

Question 2 is a question asking how the investor experience experienced by the investor affected the degree of recommendation intention regarding the business activities of the investment target. In response to Question 2, investors evaluate each investor's experience regarding the business activity when determining the degree of recommendation intention in Question 1. Investors can rate themselves on a five-point scale: dissatisfied, somewhat dissatisfied, neutral, somewhat satisfied, and satisfied. The investor experience may be, for example, an experience that involves external action, or an experience that does not involve external action. Experiences that do not involve external actions typically include the experience of acquiring information, receiving benefits, having emotions, and the like.

Investor experience related to business activities includes, for example, experience related to "market environment", experience related to "products/services/technology", experience related to "management philosophy", experience related to "management judgment/risk response", and "business growth potential". This includes, but is not limited to, experience related to "profitability," "financial stability," and "utilization of human capital and contribution to local communities." The investor experience related to business activities can be any investor experience related to business activities. Investor experience related to business activities may be related to shareholder return activities, for example, and may include experience related to "dividend policy" and experience related to "shareholder benefits." Investor experience related to business activities may be, for example, related to ESG activities, such as experience related to "contribution to environmental issues," experience related to "utilization of human capital and contribution to local communities," experience related to "corporate governance initiatives," etc. This may include experiences related to "posture".

For example, if an investor is impressed with the profitability of ○○○ Corporation and feels that he/she would like to recommend ○○○ Corporation to others, the investor would consider Can answer that they are ``satisfied'' with their experiences related to ``sexuality.'' For example, if an investor feels that he/she does not want to recommend ○○○ Co., Ltd. to others because the shareholder benefits received from ○○○ Co., Ltd. are poor, the investor may When evaluating, it is possible to answer that you are "unsatisfied" with your experience regarding "shareholder benefits."

Question 3 is a question asking how the investor experience experienced by the investor affected the recommendation intention level regarding IR (Investor Relations) activities by the investment target. In response to Question 3, investors evaluate each investor's experience regarding IR activities when determining their recommendation intention in Question 1. Investors can rate themselves on a five-point scale: dissatisfied, somewhat dissatisfied, neutral, somewhat satisfied, and satisfied.

Investor experiences related to IR activities include, for example, experiences related to the frequency and content of information disclosure, experiences related to IR site content, experiences related to ``explanation of financial performance,'' and experiences related to ``explanation of non-financial items.'' including but not limited to. The investor experience related to IR activities can be any investor experience related to IR activities. Investor experiences related to IR activities may be related to information dissemination activities, for example, such as experiences related to "timing and content of financial results announcements," experiences related to "frequency and content of financial results briefings," and "contents of general meetings of shareholders." This may include experiences related to, "exposure to information media," etc. Investor experience related to IR activities may be, for example, related to IR materials, such as experience related to "explanation regarding business content", experience related to "explanation related to budget content", and experience related to "explanation regarding medium- to long-term business strategy". may be included.

For example, if an investor is impressed with the content of the general meeting of shareholders of ○○○ Co., Ltd. and feels that he or she would like to recommend ○○○ Co., Ltd. to others, the investor will Respondents can respond that they are "satisfied" with their experience regarding "the content of the general meeting of shareholders." For example, if an investor feels that they do not want to recommend ○○○ Co., Ltd. to others because the explanation regarding the status of business activities disclosed by ○○○ Co., Ltd. is unclear and difficult to understand. , investors can respond that they are "unsatisfied" with their experience regarding "explanation regarding the status of business activities" when evaluating their recommendation intention.

In addition to Questions 1 to 3, the questionnaire form 10 may include a question asking the reason for the answer to Question 2 and/or the reason for the answer to Question 3. Investors can answer this question in written form.

FIG. 2 schematically shows that the computer system 100 of the present invention provides an analysis result 50 from the response results of a plurality of completed questionnaire forms 10.

After a plurality of investors who have invested in ○○○ Corporation evaluate ○○○ Corporation on the questionnaire form 10, the response results of the plurality of questionnaire forms 10 are input into the computer system 100 of the present invention. The computer system 100 of the present invention processes the input answer results and outputs an analysis result 50.

Here, it does not matter how the plurality of answer results are input into the computer system 100 of the present invention. For example, the plurality of response results may be input by scanning the questionnaire form 10, may be input into the computer system 100 of the present invention by manual input, or the plurality of response results may be input into the computer system 100 of the present invention. The information may be input to the computer system 100 of the present invention via a medium, or may be input to the computer system 100 of the present invention via a network or the like. If multiple answer results are input via a network, etc., the type of network does not matter. For example, a plurality of answer results may be input via the Internet or via a LAN.

The analysis result 50 is output as a graph including two curves. The horizontal axis shows each investor's experience. The plurality of investor experiences are classified into investor experiences related to business activities and investor experiences related to IR activities. A first curve 21 drawn with a broken line indicates the absolute value of the degree of influence on the degree of recommendation intention. The absolute value of the degree of influence is a value indicating how much influence there is on the degree of recommendation intention, regardless of whether the influence is positive or negative. The vertical axis of the broken line is the axis of the absolute value of the degree of influence, where 0 indicates no influence on the degree of recommendation intention, and the closer it is to 100, the greater the influence on the degree of recommendation intention. A second curve 22 drawn as a solid line indicates the relative value of the degree of influence on the degree of recommendation intention. The influence degree relative value is a value indicating the degree of positive influence or negative influence on the recommendation intention degree. The vertical axis of the solid line is the axis of relative influence value, where a negative value indicates a negative influence on the recommendation intention, and a value closer to -100 indicates a greater negative influence on the recommendation intention. , 0 indicates that there is no influence on the recommendation intention level or that the positive and negative effects are the same, positive indicates that there is a positive influence on the recommendation intention level, and 100 indicates that there is a positive influence on the recommendation intention level. The closer it is, the greater the positive influence on the recommendation intention level. The analysis results 50 can be said to express the movement of investors' emotions in a curved line for each investor experience.

For example, in the analysis result 50, the first curve 21 represents experience 5 related to business activities (for example, experience related to "profitability") and experience 10 related to IR activities (for example, "experience related to shareholder meeting") among multiple investor experiences. Experience 11 related to IR activities (e.g., experience related to ``IR site content''), and Experience 12 related to IR activities (e.g. experience related to ``explanation of non-financial items'') had a significant influence on recommendation intention. It shows that it is high. However, using only the first curve 21, even if it becomes clear that the investor experience has an influence on the degree of recommendation intention, it is not clear whether the influence is positive or negative. Therefore, it is unclear whether the investor experience should be improved or strengthened in order to increase recommendation intention. Therefore, by referring to the second curve 22, it can be seen whether each investor's experience has a positive or negative influence on the degree of recommendation intention. Analysis result 50 shows that each investor experience that has a high impact on recommendation intention all has a negative impact, so in order to increase recommendation intention, these investor experiences should be improved. can be seen visually. Furthermore, it can be seen that improving these investor experiences preferentially over other investor experiences is effective in increasing investors' recommendation intentions and ultimately improving company sales.

The computer system 100 of the present invention can also analyze the results of evaluations by multiple types of subjects. Here, the multiple types of entities are three entities: investors who invest in the investment target, users who receive products or services from the investment target, and employees who work at the investment target.

FIG. 3A shows an example of a questionnaire form 20 for users who have purchased a car from ○○○ Corporation to evaluate the services of ○○○ Corporation.

The questionnaire form 20 includes Question 1, Question 2, and Question 3.

Question 1 is a question asking about the degree of recommendation intention. The “recommendation intention level” in FIG. 3A is the degree of evaluation when a user evaluates an evaluation target from the viewpoint of whether the user wants to recommend the evaluation target to other users (i.e., the degree of evaluation when the user evaluates the evaluation target from the viewpoint of whether the user wants to recommend the evaluation target to other users degree of recommendation intention). In this example, the "evaluation target" is the service provided by ○○○ Corporation when purchasing a car from ○○○ Corporation, but is not limited thereto. The "evaluation target" can be any product or service.

The user responds to Question 1 with a score of 0 to 10 regarding the degree of recommendation intention. For example, a user can give a score of 10 if he/she wants to highly recommend ○○○ Co., Ltd. to his or her close friends or acquaintances, and if he or she does not want to recommend ○○○ Co., Ltd. to his or her close friends or acquaintances at all. You can give 0 points.

Question 2 is a question asking how the user experience experienced by the user related to the evaluation target influenced the recommendation intention level. In response to Question 2, the user answers whether each user experience contributed positively, negatively, or neither in determining the degree of recommendation intention in Question 1.

Each user experience includes a plurality of types of user experiences, such as a user experience related to people, a user experience related to things, a user experience related to money, a user experience related to time, and a user experience related to information. User experiences related to humans include, for example, greetings upon entering the store, guidance, product knowledge/advice, manners, friendliness, transaction handling, greetings upon leaving the store, and the like. The user experience regarding things includes, for example, product type, inventory, quality, arrangement, equipment, location, and the like. The user experience related to money includes, for example, price, presence or absence of coupons, frequency of sales, and the like. User experiences related to time include, for example, smoothness of customer service, speed of checkout, degree of store congestion, and the like. User experience regarding information includes, for example, brand image, advertising, popularity, and the like.

For example, if a user feels comfortable with the greeting upon entering a store and feels comfortable recommending ○○○ Co., Ltd. to others, the user may feel that the "greeting upon entering the store" has a "positive contribution" to the degree of recommendation intention. You can make an evaluation by saying, “I did it.” For example, if a user feels that they do not want to recommend ○○○ Co., Ltd. to others because they feel uncomfortable with the accounting process, the user may feel that the 'accounting process' has a 'negative contribution' to the recommendation intention. ” can be evaluated.

Question 3 is a question asking why the user experience in Question 2 contributed positively or negatively to the recommendation intention level. The user answers question 3 in written form. For example, if a user feels that they were able to fully consider purchasing a product because the product description was detailed, the user can rate "product knowledge/advice" as "the product description was detailed". . For example, when a user is dissatisfied with the fact that something is out of stock, the user can evaluate "inventory" as "out of stock."

In the example above, we explained that we would be answering questions about the user experience of a large company called ○○○ Corporation. You may also choose to answer. For example, question 2 is a user experience received from employee 1 of ○○○ Co., Ltd., a user experience received from employee 2 of ○○○ Co., Ltd., a user experience received from employee n of ○○○ Co., Ltd. This question may ask how the user experience affected the recommendation intention level.

FIG. 3B shows an example of a questionnaire form 30 for employees of ○○○ Corporation to evaluate the workplace of ○○○ Corporation.

The questionnaire form 40 includes Question 1, Question 2, and Question 3.

Question 1 is a question asking about the degree of recommendation intention. In Figure 3B, "recommendation intention level" refers to the degree to which employees evaluate the workplace from the perspective of whether they would recommend the workplace to others (i.e., the degree of recommendation that the employee would recommend the workplace to others). degree of intention). As generally understood, "workplace" here includes not only the physical space where employees actually work, but also the social spaces to which employees belong.

Employees respond to Question 1 with a score of 0 to 10 regarding their recommendation intention. For example, an employee can give a score of 10 if he/she would highly recommend the workplace of ○○○ Co., Ltd. to others, and a 0 if he/she does not want to recommend the workplace of ○○○ Co., Ltd. to others at all. You can add points.

Question 2 is a question asking how the employee experience that employees may experience in the workplace influences their recommendation intention. In response to question 2, employees should answer whether each employee experience they experienced contributed positively, negatively, or neither in determining the degree of recommendation intention in question 1. .

Each employee experience includes multiple types of employee experiences, such as employee experience related to people, employee experience related to things, employee experience related to money, employee experience related to time, and employee experience related to information. Employee experiences related to people include, for example, relationships with colleagues and juniors, relationships with seniors, relationships with store managers, workplace culture, company philosophy, training, etc. Employee experiences related to things include, for example, job content, social significance, future potential, growth soil, welfare benefits, equipment, location, etc. Employee experiences related to money include, for example, salaries, compensation/bonuses, overtime pay, etc. Employee experiences related to time include, for example, working hours, ease of taking paid leave, ease of taking maternity/childcare leave, etc. Employee experience regarding information includes, for example, stability, brand image, name recognition, and the like.

For example, if a user feels that he or she would like to recommend the workplace of ○○○ Co., Ltd. to others because he/she feels that the relationship with his or her seniors is good, then the user would consider that the "relationship with seniors" would be higher than the recommendation intention level. They can be evaluated as having made a positive contribution. For example, if a user feels dissatisfied with the salary and does not want to recommend ○○○ Co., Ltd. to others, the user may think that the salary has "negatively contributed" to the recommendation intention. can be evaluated.

Question 3 is a question asking why the employee experience experienced by the employee in Question 2 contributed positively or negatively to the recommendation intention. The employee answers Question 3 in written form. For example, if an employee feels that the workplace at ○○○ Co., Ltd. is good because the training content is fulfilling, the employee may say that the training content is fulfilling. can be evaluated. For example, when an employee feels dissatisfied with the workplace at ○○○ Co., Ltd. because a senior's attitude towards education is not serious, the employee may ask, ``My senior is not serious about my relationship with my senior.'' ” can be evaluated.

FIG. 4 schematically shows that the computer system 100 of the present invention provides analysis results from the answers to the plurality of completed questionnaire forms 10, 20, and 30.

Multiple investors who invested in ○○○ Co., Ltd. evaluated ○○○ Co., Ltd. in questionnaire form 10, and multiple users who purchased cars from ○○○ Co., Ltd. evaluated ○○ Co., Ltd. in questionnaire form 20. After evaluating the service of ○ and having the employees working at ○○○ Co., Ltd. evaluate the workplace of ○○○ Co., Ltd. in questionnaire form 30, the results of responses to multiple questionnaire forms 10, 20, and 30 are based on the present invention. is input into the computer system 100 of. The computer system 100 of the present invention processes input answer results and outputs analysis results.

The computer system 100 of the present invention can analyze the degree of influence of each user experience on the user's recommendation intention level in a manner similar to the manner described above with reference to FIG. This makes it possible to know which user experience out of a plurality of user experiences has a large influence on the user's recommendation intention, and which user experience should be improved or strengthened.

For example, at least one of the plurality of investor experiences may have a correlation with the user's recommendation intention level. That is, at least one of the plurality of investor experiences can be improved as the user's recommendation intention increases, and can be decreased as the user's recommendation intention decreases. , is influenced by the user's recommendation intention. The at least one investor experience that has a correlation with the user's recommendation intention level may be an investor experience found in advance or an investor experience identified through analysis by the computer system 100 of the present invention. It's okay. The at least one investor experience that has a correlation with the user's recommendation intention level may be at least one investor experience among experiences related to the business activities of the investment target.

The computer system 100 of the present invention identifies an investor experience that should be improved or strengthened in order to improve the investor's recommendation intention, and the user experience that should be improved or enhanced in order to improve the user's recommendation intention. is identified, and there can be a correlation between the investor experience and the user's recommendation intention. Therefore, the analysis results of the computer system 100 of the present invention can be improved or improved in order to improve the investor's recommendation intention. It can represent the user experience that should be enhanced. In other words, improving or enhancing the user experience will improve the user's recommendation intention, which will in turn improve or enhance the investor experience, which correlates with the user's recommendation intention, thereby increasing the investor's recommendation intention. will improve.

For example, even if an investor experience that should be improved or enhanced is identified, it may be unclear what specific measures should be taken to improve or enhance that investor experience. However, if the purpose is to improve or strengthen the user experience rather than the investor experience, it may be easier to come up with or take specific measures.

In this way, the computer system 100 of the present invention provides a solution for more easily increasing investor recommendation intention (i.e., not only an investor experience that should be improved or enhanced, but also a user experience that should be improved or enhanced). ) can be presented.

Furthermore, the computer system 100 of the present invention can analyze the degree of influence of each employee's experience on the employee's recommendation intention level in a manner similar to the manner described above with reference to FIG. This makes it possible to find out which employee experiences out of multiple employee experiences have a large impact on employees' recommendation intentions, and which employee experiences should be improved or strengthened.

For example, at least one of the plurality of investor experiences may have a correlation with the employee's recommendation intention. In other words, when the employee's intention to recommend at least one of the plurality of investor experiences increases, the evaluation of the at least one investor experience can improve, and when the employee's intention to recommend it decreases, the evaluation of the at least one investor experience increases. At the very least, it is affected by employee recommendation intentions, so that ratings of investor experience may be lowered. The at least one investor experience that has a correlation with the employee's recommendation intention level may be an investor experience found in advance, or an investor experience identified through analysis by the computer system 100 of the present invention. There may be. The at least one investor experience that has a correlation with the employee's recommendation intention level may be at least one investor experience among experiences related to the business activities of the investment target. The at least one investor experience that correlates with the user's recommendation intention may be different from the at least one investor experience that correlates with the employee's recommendation intention. For example, investor experience related to "products/services/technology" may be correlated with user's recommendation intention, while investor experience related to "human capital utilization" may be correlated with employee recommendation intention. may have.

The computer system 100 of the present invention identifies an investor experience that should be improved or strengthened in order to improve the investor's recommendation intention, and an employee experience that should be improved or strengthened in order to improve the employee's recommendation intention. Since the employee experience is identified and there can be a correlation between the investor experience and the employee's recommendation intention, the analysis results of the computer system 100 of the present invention are useful for improving the investor's recommendation intention. can represent an employee experience that should be improved or enhanced. That is, improving or enhancing that employee experience will improve employee recommendation intent, which in turn improves or enhances the investor experience, which correlates with employee recommendation intent, which in turn will improve investor recommendation intent. This improves the recommendation intention.

For example, even if an investor experience that should be improved or enhanced is identified, it may be unclear what specific measures should be taken to improve or enhance that investor experience. However, if the purpose is to improve or strengthen the employee experience rather than the investor experience, it may be easier to come up with or take specific measures.

In this way, the computer system 100 of the present invention provides a solution for more easily increasing investor recommendation intent (i.e., not only the investor experience that should be improved or enhanced, but also the employee experience that should be improved or enhanced). experience).

In the example described above, it has been explained that the computer system 100 of the present invention outputs the analysis result 50 based on the response results of the questionnaire form 10, but the present invention is not limited to this. The computer system 100 of the present invention can output an analysis result 50 based on an evaluation result by any evaluation means for evaluating an investment target. For example, instead of having the investor fill out a questionnaire, the computer system 100 of the present invention may detect the investor's voice, facial expression, behavior, etc. using a sensor, and use the detection result as the evaluation result. . For example, it is possible to detect an event in which an investor grimaces while receiving an explanation at a general meeting of shareholders, and from this event, it can be assumed that the investor has evaluated the experience regarding the "general meeting of shareholders" as unsatisfactory. By using such detection results of investor behavior as evaluation results, the computer system 100 of the present invention can read the investor's emotions without being noticed by the investor, and can make decisions based on the emotions. and analysis. This applies to users and employees alike.

In the example described above, it has been explained that the computer system 100 of the present invention outputs the analysis results based on the response results of all the questionnaire forms 10, 20, and 30. There is no need to use it. For example, the computer system 100 of the present invention can output analysis results based on the response results of the questionnaire forms 10 and 20 without using the response results of the questionnaire form 30. For example, the computer system 100 of the present invention can output analysis results based on the answers to the questionnaire forms 10 and 30 without using the answers to the questionnaire form 20.

In the above example, it has been explained that investors, users, and employees evaluate their respective recommendation intentions using scores from 0 to 10, but the present invention is not limited to this. The degree of recommendation intention can be evaluated using an arbitrary score range.

In the above example, investors were asked to rate each investor's experience on a five-point scale: dissatisfied, somewhat dissatisfied, neutral, somewhat satisfied, and satisfied. However, the present invention is not limited thereto. For example, simpler answers such as dissatisfied, undecided, or satisfied may be acceptable. By allowing such simple answers, the burden on respondents can be reduced. For example, whether each investor's experience contributed positively or negatively may be evaluated in multiple stages (for example, -3, -2, -1, 0, +1, +2, +3, etc.) .

In the example described above, users and employees were asked to rate whether each user experience or each employee experience contributed positively, negatively, or neither. By allowing such simple answers, the burden on respondents can be reduced. However, the present invention is not limited thereto. Evaluate whether each user experience or each employee experience contributed positively or negatively on a multi-level scale (for example, -3, -2, -1, 0, +1, +2, +3, etc.). Good too.

In the above example, an example was explained in which an investor answers a question asking the reason in a descriptive format, and an example in which a user and an employee answers Question 3 in a descriptive format. Not limited. It is also within the scope of the present invention for investors, users, and employees to respond by selecting a reason from a predefined selection.

In the example described above, it has been explained that the analysis result 50 is output as a graph including two curves, but the present invention is not limited to this. The computer system 100 of the present invention can output the analysis results 50 in any manner. For example, the computer system 100 of the present invention may output the analysis result 50 as a bar graph or as a line graph. For example, the computer system 100 of the present invention may output the analysis results as a string of numbers or as a string of characters.

Further, the computer system 100 of the present invention may classify investors based on attributes such as age, gender, annual income, etc., and output analysis results for each classified investor. This is because the investor experience that influences the degree of recommendation intention may differ depending on the investor's attributes. For example, the computer system 100 of the present invention may output the results of analyzing the evaluation results of a male investor in his 30s, or output the results of analyzing the evaluation results of a female investor in his 40s with an annual income of 20 million yen. It's okay. Based on the analysis results according to investor attributes, it is possible to identify the investor experience that should be improved or strengthened for each investor attribute.

Further, the computer system 100 of the present invention may classify users based on attributes such as age or gender, and output analysis results for each classified user. This is because the user experience that influences the recommendation intention level may differ depending on the user's attributes. For example, the computer system 100 of the present invention may output the results of analyzing the evaluation results by a male user in his 30s, or may output the result of analyzing the evaluation results by a female user in his 40s. Based on the analysis results according to user attributes, it is possible to identify user experiences that should be improved or strengthened for each user attribute.

Further, the computer system 100 of the present invention may classify employees based on attributes such as age, gender, department, etc., and output analysis results for each classified employee. This is because the employee experience that influences the degree of recommendation intention may differ depending on the employee's attributes. For example, the computer system 100 of the present invention may output the results of analyzing the evaluation results by a male employee in his 30s, or may output the results of analyzing the evaluation results by a female sales department employee in his 40s. good. Based on the analysis results according to employee attributes, it is possible to identify employee experiences that should be improved or strengthened for each employee attribute.

In the above example, it has been explained that the results of the evaluation by the employees of the investment target (○○○ Corporation) are analyzed, but the present invention is not limited to employees. The entity performing the evaluation may be any member of the investment target. Hereinafter, they will simply be referred to as "members." The members may be, for example, employees employed by the investment target company under an employment contract, or officers who execute or supervise business operations at the investment target company.

The computer system 100 of the present invention may generate a report including the analysis results 50, for example. The report may include, for example, in addition to the analysis results 50, the analysis results of the questionnaire form 20 and the analysis results of the questionnaire form 30. Alternatively, the report may include other analysis results. The report may be, for example, an electronic report or a physical report (eg, a report printed on paper). For example, the computer system 100 of the present invention may provide a generated report through a network, may provide a generated report through a storage medium, or may provide a generated report by distributing the generated report. It's okay. The report may be provided, for example, to an investment target (for example, "○○○ Co., Ltd." in the above example). As a result, the investment target will refer to the report and take measures to improve the recommendation intention level.

In the following, a computer system 100 for analyzing the results of investors' evaluation of investment targets based on questionnaire results and the like will be described.

Note that in the following description and claims, the term "improvement" includes not only the meaning of improving a bad point but also the meaning of improving the current state/level. That is, the above-mentioned "strengthening" is included in "improvement."

2. Configuration of a computer system for analyzing the results of an investor's evaluation of an investment target FIG. 5 shows an example of the configuration of a computer system 100 for analyzing the result of an investor's evaluation of an investment target.

Computer system 100 includes receiving means 110, processor 120, memory 140, and output means 150.

The receiving means 110 is configured to receive a plurality of evaluation results indicating the results of evaluations of investment targets by a plurality of investors. For example, the receiving means 110 receives the results of a questionnaire in which a plurality of investors evaluated the investment target (for example, the results of responses to the questionnaire form 10 shown in FIG. 1). For example, the receiving means 110 receives the results of a sensor detecting the behavior of a plurality of investors. The form of the evaluation result received by the receiving means 110 does not matter. For example, the receiving means 110 may receive the evaluation results in text format, the evaluation results in image format, or the evaluation results in audio format. For example, the receiving means 110 may receive a table summarizing the evaluation results.

Each of the plurality of evaluation results can include a recommendation intention level indicating the degree to which the investor wants to recommend the investment target to others, and evaluations regarding the plurality of investor experiences that the investor may experience. Evaluations regarding multiple investor experiences include positive indicators that indicate that the investor experience that the investor experienced made a positive contribution when determining the investor's recommendation intention, and and a negative indicator indicating that the investor experience experienced by the home has contributed negatively. The evaluation regarding multiple investor experiences may further include a neutral indicator indicating that the investor experience experienced by the investor does not contribute positively or negatively when determining the investor's recommendation intention.

The receiving means 110 receives, in addition to a plurality of evaluation results showing the results of evaluations of the investment object by a plurality of investors, the results of evaluations of the evaluation object (for example, products or services provided by the investment object) by a plurality of users. You may receive a plurality of evaluation results indicating the results of evaluations of the workplace by a plurality of members and/or a plurality of evaluation results indicating the results of evaluations of the workplace by a plurality of members. At this time, the receiving means 110 includes a first receiving means that receives a plurality of first evaluation results indicating the results of evaluations of the investment target by a plurality of investors, and a first receiving unit that receives a plurality of first evaluation results indicating the results of evaluations of the evaluation target by a plurality of users. A second receiving means for receiving a plurality of second evaluation results, and/or a third receiving means for receiving a plurality of third evaluation results indicating the results of evaluations of the workplace by a plurality of members. be able to. For example, the second receiving means receives the results of a questionnaire in which a plurality of users evaluated the product or service (for example, the results of responses to the questionnaire form 20 shown in FIG. 3A). For example, the third receiving means receives the results of a questionnaire in which a plurality of members evaluated the workplace (for example, the results of responses to the questionnaire form 30 shown in FIG. 3B). For example, the second receiving means receives the results of the sensors detecting the behaviors of a plurality of users. The form of the evaluation results received by the second receiving means and the third receiving means does not matter. For example, the second receiving means and the third receiving means may receive the evaluation results in text format, may receive the evaluation results in image format, or may receive the evaluation results in audio format. Good too. For example, the second receiving means and the third receiving means may receive a table summarizing the evaluation results. The first receiving means, the second receiving means, and the third receiving means may be constituted by the same receiving means, or may be constituted by a plurality of receiving means.

Each of the plurality of second evaluation results can include a recommendation intention level indicating the degree to which the user wants to recommend the evaluation target to others, and evaluations regarding a plurality of user experiences that the user may experience. Evaluations regarding multiple user experiences include positive indicators indicating that the user experience that the user experienced made a positive contribution when determining the user's recommendation intention level, and the user experience that the user experienced when determining the user's recommendation intention level. can include a negative indicator indicating that the negative contribution has been made. The evaluation regarding the plurality of user experiences may further include a neutral index indicating that the user experience experienced by the user does not contribute positively or negatively when the user determines the recommendation intention level.

Each of the plurality of third evaluation results can include a recommendation intention level indicating the degree to which the member wants to recommend the workplace to others, and evaluations regarding a plurality of member experiences that the member may experience. Evaluations regarding multiple member experiences include positive indicators that indicate that the member experience that the member experienced made a positive contribution when determining the member's recommendation intention, and a and a negative indicator indicating that the member experience experienced by the member has contributed negatively. The evaluation regarding the plurality of member experiences may further include a neutral indicator indicating that the member experience experienced by the member does not contribute positively or negatively when the member determines the recommendation intention level.

The plurality of evaluation results received by the receiving means 110 are passed to the processor 120, and the processor 120 can receive them.

Processor 120 executes processing of computer system 100 and controls the overall operation of computer system 100 . Processor 120 reads a program stored in memory 140 and executes the program. This allows the computer system 100 to function as a system that executes desired steps. If the data received by the receiving means 110 is in a format unsuitable for processing (for example, image format data, audio format data), the processor 120 performs processing to convert it into a format suitable for processing (for example, numerical data). You can do it like this. Processor 120 may be implemented by a single processor or multiple processors.

Processor 120 comprises analysis means 130 . The analysis means 130 analyzes the evaluation results by processing the input evaluation results. The analysis means 130 can perform arbitrary data processing. For example, the analysis means 130 can perform statistical processing such as simple regression analysis, multiple regression analysis, and quantification type I. For example, the analysis means 130 can perform mathematical operations such as addition, subtraction, multiplication, and division.

The analysis means 130 can analyze the plurality of evaluation results to identify investor experiences that should be improved in order to improve the investor's recommendation intention. For example, the analysis means 130 calculates the degree of influence of each investor experience on the degree of recommendation intention, and identifies investor experiences that should be improved in order to improve the degree of recommendation intention, based on the magnitude of each degree of influence. be able to. For example, the investor experience with the highest degree of influence may be identified as the investor experience that should be improved in order to improve the degree of recommendation intention, or the investor experience with the degree of influence greater than a predetermined threshold may be identified as the investor experience that should be improved in order to improve the recommendation intention. It may also be specified as an investor experience that should be improved in order to improve the recommendation intention level.

The analysis means 130 calculates the degree of influence of each investor experience on the recommendation intention, for example, by performing a regression analysis using the investor's recommendation intention as an objective variable and evaluations regarding a plurality of investor experiences as an explanatory variable. be able to.

In a specific embodiment, the analysis means 130 may include a calculation means and a generation means. A plurality of evaluation results received by the receiving means 110 are input to the analyzing means 130 . The multiple evaluation results are based on the investor's recommendation intention, a "positive indicator" indicating that the investor experience that the investor experienced made a positive contribution when determining the investor's recommendation intention, and the investor's recommendation intention. There is a "negative indicator" that indicates that the investor experience experienced by the investor has contributed negatively when determining the level of intention to recommend, and a "negative indicator" that indicates that the investor experience experienced by the investor has contributed positively when determining the level of recommendation intention. It also includes a "neutral indicator" that indicates that it does not contribute negatively.

The calculation method calculates the positive influence of each investor's experience on the recommendation intention and the recommendation intention by statistically processing the recommendation intention, positive indicators, and negative indicators for multiple input evaluation results. Calculate the degree of negative impact of each investor experience on the The statistical processing performed by the calculation means is, for example, multiple regression analysis. The multiple regression analysis performed by the calculation means is, for example, quantification type I.

When the calculation means performs multiple regression analysis, the calculation means may include a plus index multiple regression analysis section and a minus index multiple regression analysis section. The positive index multiple regression analysis unit calculates the degree of positive influence of each investor's experience on the recommendation intention by performing multiple regression analysis using the recommendation intention as an objective variable and the positive index as an explanatory variable. At this time, the plus indicator multiple regression analysis unit performs multiple regression analysis, treating indicators other than the plus indicators (minus indicators and neutral indicators) included in the evaluation results as zero. The negative indicator multiple regression analysis unit calculates the degree of negative influence of each investor's experience on the recommendation intention by performing multiple regression analysis using the recommendation intention as an objective variable and the negative indicator as an explanatory variable. At this time, the negative indicator multiple regression analysis unit performs multiple regression analysis by treating the indicators other than the negative indicators (positive indicators and neutral indicators) included in the evaluation results as zero. Even when the plus index or the minus index has multiple levels, the plus index multiple regression analysis section and the minus index multiple regression analysis section can similarly perform multiple regression analysis. The plus indicator multiple regression analysis unit performs multiple regression analysis by treating all minus indicators and neutral indicators as zero, regardless of the stage of the minus indicators. This calculates the degree of positive influence of each investor's experience on the degree of recommendation intention. The negative indicator multiple regression analysis unit performs multiple regression analysis by treating all positive indicators and neutral indicators as zero, regardless of the stage of the positive indicators. This calculates the degree of negative influence of each investor's experience on the degree of recommendation intention.

The generation means generates analysis results by processing the input values. The generating means generates an analysis result of the plurality of evaluation results based on the input positive influence degree of each investor experience on the recommendation intention degree and the negative influence degree of each investor experience on the recommendation intention degree. For example, the generation means mathematically processes the positive influence of each investor experience on the recommendation intention and the negative influence of each investor experience on the recommendation intention. Generate a value based on the impact level. For example, the mathematical process performed by the generating means is addition. For example, the mathematical process performed by the generating means is addition of absolute values. For example, the generation means may generate a value for identifying which of the investor experiences should be improved in order to improve the recommendation intention level from values based on the positive influence degree and the negative influence degree. generate.

The generating means may include, for example, a first value generating section, a second value generating section, and a specifying section.

The first value generation unit generates the first value by adding the positive influence degree of each investor experience on the recommendation intention level and the negative influence degree of each investor experience on the recommendation intention degree. For example, the first value is an influence degree relative value indicating whether there is a positive influence or a negative influence on the recommendation intention degree.

The second value generation unit generates a second value by adding the positive influence degree of each investor experience on the recommendation intention level and the absolute value of the negative influence degree of each investor experience on the recommendation intention degree. do. For example, the second value is an absolute value of the degree of influence indicating the degree of influence on the degree of recommendation intention, regardless of whether the influence is positive or negative.

The identification unit generates a value for identifying which investor experience among the investor experiences should be improved in order to improve the recommendation intention level. For example, the specific unit generates an investor's experience based on the first value calculated by the first value generating unit and the second value calculated by the second value generating unit in order to improve the recommendation intention level. Generate values to identify which of the investor experiences should be improved. The identifying unit may rank the investor experiences, for example, in order to identify which of the investor experiences should be improved in order to improve the recommendation intention level. For example, the specifying unit determines that the degree of influence on the recommendation intention level is large based on the relative influence value calculated by the first value generation unit and the absolute value of influence calculated by the second value generation unit, and Investor experiences are ranked in order of their negative impact on recommendation intention, and a value indicating the rank is generated.

The analysis means 130 analyzes a plurality of second evaluation results indicating the results of evaluation of the evaluation target by the plurality of users and/or a plurality of third evaluation results indicating the results of the evaluation of the workplace by the plurality of members. Even if a plurality of investors evaluate an investment target, the same analysis as described above can be performed on a plurality of evaluation results indicating the results of evaluations of investment targets by a plurality of investors. Thereby, the analysis means 130 can identify the user experience that should be improved in order to improve the user's recommendation intention, and/or identify the user experience that should be improved in order to improve the recommendation intention by the members. Be able to identify experiences.

The plurality of investor experiences may include an investor experience regarding business activities by the investment target and an investor experience regarding IR activities by the investment target.

Among these, at least one of the investor experiences related to business activities by the investment target may have a correlation with the user's recommendation intention level. When the user's recommendation intention level increases, the evaluation of at least one of the investor experiences related to business activities may also improve, and when the user's recommendation intention level decreases, the evaluation of at least one of the investor experiences related to business activities may also improve. Ratings for a single investor experience can also decline. For example, among investor experiences related to business activities, investor experiences related to "products/services/technologies" may have a correlation with a user's recommendation intention level. Such a correlation may be found empirically or may be derived by the computer system 100.

Furthermore, at least one of the investor experiences related to business activities by the investment target may have a correlation with the recommendation intention level by the members. When the recommendation intention level of the members increases, the evaluation of at least one of the investor experiences related to business activities may also improve, and when the recommendation intention level of the members decreases, the evaluation of at least one of the investor experiences related to business activities may also improve. Investor experience ratings of at least one of the following may also decline. For example, among investor experiences related to business activities, investor experiences related to "utilization of human capital" may have a correlation with the recommendation intention level by members. Such a correlation may be found empirically or may be derived by the computer system 100. The at least one investor experience that correlates with the user's recommendation intention is different from the at least one investor experience that correlates with the employee's recommendation intention. Therefore, multiple investor experiences (investor experience related to business activities and investor experience related to IR activities) are correlated with user's recommendation intention level (investor experience related to business activities), and employee experience Investor experience that has a correlation with the recommendation intention level (investor experience related to business activities) and investor experience that has no correlation with the recommendation intention level by users and the recommendation intention level by employees (investor experience related to IR activities) It will be composed of.

Based on these correlations, the analysis means 130 generates a plurality of first evaluation results indicating the results of evaluations of the investment target by a plurality of investors, and a plurality of first evaluation results indicating the results of evaluations of the evaluation target by a plurality of users. It is possible to perform a composite analysis of the second evaluation results and/or a plurality of third evaluation results indicating the results of evaluations of the workplace by a plurality of members.

For example, by analyzing the plurality of first evaluation results and the plurality of second evaluation results, the analysis means 130 can identify user experiences that should be improved in order to improve investors' recommendation intention. . For this purpose, the analysis means 130
(1) Identifying the investor experience that should be improved in order to improve investors' recommendation intention;
(2) Identifying user experiences that should be improved in order to improve the user's recommendation intention;
(3) Based on the identified investor experience, the identified user experience, and the correlation between the investor experience and the user's recommendation intention, improvements should be made to improve the recommendation intention by investors. You can specify the user experience. For example, if the investor experience that should be improved in order to improve investors' recommendation intention does not have a correlation with the user's recommendation intention, then Since the user experience does not exist, the analysis means 130 cannot determine this. Therefore, the analysis means 130 determines whether or not the identified investor experience has a correlation with the user's recommendation intention, and determines whether the identified investor experience has a correlation with the user's recommendation intention. When the determination is made, the user experience that should be improved may be specified in order to improve the investor's recommendation intention.

For example, by analyzing the plurality of first evaluation results and the plurality of third evaluation results, the analysis means 130 can identify the member experience that should be improved in order to improve the investor's recommendation intention. can. For this purpose, the analysis means 130
(1) Identifying the investor experience that should be improved in order to improve investors' recommendation intention;
(2) Identifying member experiences that should be improved in order to improve the recommendation intention level by members;
(3) Improvements to improve the recommendation intention level by investors based on the identified investor experience, the identified member experience, and the correlation between the investor experience and the recommendation intention level by the members. be able to identify relevant constituent experiences; For example, if the investor experience that should be improved in order to improve investors' recommendation intention does not have a correlation with the recommendation intention of members, then Since there is no relevant member experience, the analysis means 130 cannot identify this. For this reason, the analysis means 130 determines whether or not the identified investor experience has a correlation with the member's recommendation intention, and determines whether the identified investor experience has a correlation with the member's recommendation intention. When it is determined that there is a member experience that should be improved in order to improve the investor's recommendation intention level, it may be possible to specify the member experience that should be improved.

The memory 140 stores programs required to execute the processing of the computer system 100, data required to execute the programs, and the like. The memory 140 includes programs (for example, FIGS. 6, 7A, 7B, 7C, 8A, and 8B that will be described later) that cause the processor 120 to perform processing for analyzing the results of evaluations of investment targets by investors. , a program that implements the processing shown in FIGS. 9A and 9B). Here, it does not matter how the program is stored in the memory 140. For example, the program may be preinstalled in memory 140. Alternatively, the program may be installed in memory 140 by being downloaded via a network. In this case, the type of network does not matter. The program may be stored on a non-transitory computer-readable storage medium or may be installed into memory 140 by reading the program from the non-transitory computer-readable storage medium. good. Memory 140 may be implemented by any storage means.

The output means 150 outputs the analysis results of the evaluation results to the outside of the computer system 100. The format of the analysis results output by the output means 150 does not matter. For example, the output means 150 may output the analysis results in a curve graph, a bar graph, a numerical string, or a character string. For example, the output means 150 may output the analysis results in audio. It does not matter how the output means 150 outputs the analysis results. For example, if the output means 150 includes a display screen, the analysis results may be displayed on the display screen. For example, when the output means 150 is equipped with a storage means, the analysis results are stored in the storage means, and the external device outside the computer system 100 accesses the storage section so that the external device obtains the analysis results. It's okay. For example, if the output means 150 includes a transmitter, the transmitter may transmit the analysis results to the outside of the computer system 100 via a network. In this case, the type of network does not matter. For example, the transmitter of the output means 150 may transmit the analysis results via the Internet or may transmit the analysis results via the LAN.

The analysis results output from the output means 150 can include at least information indicating the investor experience that should be improved in order to improve the investor's recommendation intention. For example, the analysis result may further include information indicating user experience that should be improved in order to improve investors' recommendation intention. Alternatively, or in addition to this, the analysis results may further include information indicating the member experience that should be improved in order to increase the investor's recommendation intention, for example.

Further, the analysis results can further include information indicating user experience to be improved to improve at least one investor experience that is correlated with the user's recommendation intention. Alternatively, or in addition, the analysis results may further include information indicating a constituent experience that should be improved to improve at least one investor experience that correlates with constituent recommendation intent. .

Furthermore, the analysis results may include information indicating investor experience regarding IR activities that should be improved in order to improve investors' recommendation intention. For example, even if the user experience is improved in order to improve at least one investor experience, if the improvement and the effect are not communicated to investors, it may not lead to an increase in investors' recommendation intention. For example, even if the member experience is improved in order to improve at least one investor experience, if the improvement and the effect are not communicated to investors, it may not lead to an increase in investors' recommendation intention. Therefore, in addition to the information indicating the user experience that should be improved in order to improve at least one investor experience that is correlated with the user's recommendation intention level, the analysis result may also include information indicating the user experience that should be improved in order to improve at least one investor experience that is correlated with the user's recommendation intention level, or the analysis results should be improved to improve at least one investor experience that correlates with constituent recommendation intention. In addition to information showing member experience, including information showing investor experience related to IR activities that should be improved (for example, experience related to "explanation regarding non-financial items") will surely improve investors' recommendation intention. You will be able to connect to.

Note that each component of the computer system 100 described above may be located within the same hardware, or may exist within multiple pieces of hardware. If it exists in multiple pieces of hardware, it does not matter how each piece of hardware is connected. Each piece of hardware may be connected via a LAN, wirelessly, or wired, for example. Computer system 100 is not limited to any particular hardware configuration. For example, it is within the scope of the present invention to configure the processor with analog circuits rather than digital circuits. The configuration of computer system 100 is not limited to that described above as long as its functions can be realized.

3. Processing in a computer system for analyzing the results of an investor's evaluation of an investment target FIG. 6 shows an example of a process 600 in the computer system 100 for analyzing the result of an investor's evaluation of an investment target.

In step S601, the receiving means 110 receives a plurality of evaluation results. The multiple evaluation results are the results of multiple investors evaluating the same investment target. The plurality of evaluation results include a recommendation intention level, a positive index, and a negative index. The plurality of evaluation results may also include a neutral index. The plurality of evaluation results received by the receiving means 110 are passed to the processor 120, and the processor 120 receives them.

In step S602, the analysis means 130 of the processor 120 analyzes a plurality of evaluation results. The analysis means 130 analyzes the plurality of evaluation results received by the reception means 110 in step S601. This identifies the investor experience that should be improved to improve recommendation intention. The results of the analysis by the analysis means 130 are output to the output means 140.

In step S603, the output means 140 outputs the results of the analysis. The output means 140 outputs the result analyzed by the analysis means 130 in step S602. For example, the output means 140 can output the analysis results in a graph format, as shown in FIG. 2, for example.

FIG. 7A shows an example of a process of analyzing a plurality of evaluation results in step S602.

In step S6021, the calculating means 131 calculates the positive influence degree of each investor experience on the recommendation intention level and the negative influence degree of each investor experience on the recommendation intention degree with respect to the plurality of evaluation results.

In step S6022, the generation means 132 generates an analysis result of a plurality of evaluation results based on the degree of positive influence of each investor experience on the degree of recommendation intention and the degree of negative influence of each investor experience on the degree of recommendation intention. do.

FIG. 7B shows an example of a process in step S6021 of calculating the positive influence degree on the recommendation intention degree and the negative influence degree on the recommendation intention degree for a plurality of evaluation results.

In step S60211, the plus index multiple regression analysis unit of the calculation means 131 calculates the degree of positive influence on the recommendation intention by performing multiple regression analysis using the recommendation intention as an objective variable and the plus index as an explanatory variable. The plus indicator multiple regression analysis unit may perform multiple regression analysis by treating indicators other than the plus indicators (minus indicators and neutral indicators) as zero. When the plus index is an index of one level, such as +1, the plus index multiple regression analysis unit may perform quantification type I.

In step S60212, the negative index multiple regression analysis unit of the calculation means 131 calculates the degree of negative influence on the recommendation intention by performing multiple regression analysis using the recommendation intention as an objective variable and the negative index as an explanatory variable. The negative indicator multiple regression analysis unit may perform multiple regression analysis by treating indicators other than negative indicators (positive indicators and neutral indicators) as zero. If the negative indicator is an indicator of one level, such as -1, the negative indicator multiple regression analysis unit may perform quantification type I.

In the above example, the degree of positive influence on the degree of recommendation intention is calculated by performing multiple regression analysis on the degree of recommendation intention and positive indicators, and the degree of positive influence on the degree of recommendation intention and negative indicators is calculated by performing multiple regression analysis on the degree of recommendation intention and negative indicators. Although the calculation of the degree of negative influence on the degree of recommendation intention has been described, the present invention is not limited thereto. It is also within the scope of the present invention to calculate the positive influence degree and the negative influence degree by performing statistical processing such as multiple regression analysis on the recommendation intention degree, the positive index, and the negative index. For example, the positive influence degree and the negative influence degree may be calculated by performing multiple regression analysis using the recommendation intention degree as the objective variable and the positive index and the negative index as explanatory variables.

FIG. 7C shows an example of a process of generating an analysis result of a plurality of evaluation results based on the positive influence degree and the negative influence degree in step S6022.

In step S60221, the first value generating unit of the generating means 132 adds the positive influence of the investor experience on the recommendation intention and the negative influence of the investor experience on the recommendation intention. Generate the value of .
(first value) = (positive influence) + (negative influence)

For example, the first value is an influence degree relative value indicating whether there is a positive influence or a negative influence on the recommendation intention degree.

In step S60222, the second value generating unit of the generating means 132 adds the positive influence degree of the investor experience on the recommendation intention level and the absolute value of the negative influence degree of the investor experience on the recommendation intention degree. A second value is generated by .
(Second value) = (Positive influence) + | (Negative influence) |

For example, the second value is an absolute value of the degree of influence indicating the degree of influence on the degree of recommendation intention, regardless of whether the influence is positive or negative.

In step S60223, the specifying unit of the generating means 132 generates a value for specifying which of the plurality of investor experiences should be improved in order to improve the investor's recommendation intention. The specific part ranks each of the investor experiences in order to identify which of the investor experiences should be improved in order to improve the recommendation intention level. It is also possible to generate a value indicating the rank of.

In this way, the computer system 100 determines which of the plurality of investor experiences should be improved in order to improve the investor's recommendation intention based on the plurality of evaluation results by the investors. can be specified.

The above-described processing can be similarly applied to a plurality of second evaluation results by a plurality of users, and thereby, in order to improve the user's recommendation intention, Identify which user experiences should be improved. The above-described processing can be similarly applied to a plurality of third evaluation results by a plurality of members, and thereby, in order to improve the recommendation intention level by a plurality of members, Identify which of your constituent experiences should be improved.

For example, even if an investor experience that should be improved is identified through the above-described process, it may be unclear what specific measures should be taken to improve the investor experience. However, if the purpose is to improve user experience or member experience, it may be easier to come up with or take specific measures. For this purpose, the computer system 100 can identify the user experience or member experience that should be improved in order to improve the investor's recommendation intention through processing that will be described later.

FIG. 8A shows an example of a process 800 for identifying user experiences that should be improved to improve investor recommendation intent.

In step S801, the first receiving means of the receiving means 110 receives a plurality of first evaluation results. Step S801 is the same process as step S601 described above. The plurality of first evaluation results received by the first receiving means are passed to the processor 120, and the processor 120 receives them.

In step S802, the second receiving means of the receiving means 110 receives the plurality of second evaluation results. The plurality of second evaluation results include the user's recommendation intention level, a positive index, and a negative index. The plurality of second evaluation results may also include a neutral index. The plurality of second evaluation results received by the second receiving means are passed to the processor 120, and the processor 120 receives them.

In step S803, the analysis means 130 of the processor 120 analyzes the plurality of first evaluation results and the plurality of second evaluation results. The analysis means 130 can identify the user experience that should be improved in order to improve the investor's recommendation intention through processing that will be described later with reference to FIG. 8B.

In step S804, the output means 140 outputs the results of the analysis. The output means 140 outputs the result analyzed by the analysis means 130 in step S803. The analyzed results can include information indicating user experience that should be improved in order to increase the investor's recommendation intention.

FIG. 8B shows an example of the process in step S803.

In step S8031, the analysis means 130 analyzes the plurality of first evaluation results. This is a process similar to step S602 described above. This identifies the investor experience that should be improved in order to improve investors' recommendation intention.

In step S8032, the analysis means 130 analyzes the plurality of second evaluation results. This is performed by applying the same process as step S602 described above to the plurality of second evaluation results. This identifies the user experience that should be improved in order to increase the user's recommendation intention.

In step S8033, the analysis means 130 determines whether the investor experience identified in step S8031 has a correlation with the user's recommendation intention level. If it is determined that the investor experience identified in step S8031 has a correlation with the user's recommendation intention level, the process advances to step S8034. If it is determined in step S8031 that the investor experience specified has no correlation with the user's recommendation intention level, the process advances to step S8035 and ends. If the investor experience identified in step S8031 has no correlation with the user's recommendation intention, there is no user experience that should be improved in order to improve the investor's recommendation intention. This is because it cannot be specified.

In step S8034, the analysis means 130 determines the user experience identified in step S8032 as a user experience that should be improved in order to improve investors' recommendation intention. The user experience identified in step S8032 can improve the user's recommendation intention, and the investor experience identified in step S8031 can improve the investor's recommendation intention. Yes, and if the user's recommendation intention level is improved by improving the user experience identified in step S8032, the investor experience identified in step S8031, which has a correlation with the user's recommendation intention level, will also be improved, and as a result, This is because the user's recommendation intention level also improves.

Through the processes shown in FIGS. 8A and 8B, it is possible to identify user experiences that should be improved in order to improve the investor's recommendation intention. As a result, even if you are unsure of what specific measures should be taken to improve the investor experience, you will be able to come up with specific measures and take them. .

FIG. 9A illustrates an example process 900 for identifying member experiences that should be improved to increase recommendation intention by investors.

In step S901, the first receiving means of the receiving means 110 receives a plurality of first evaluation results. Step S901 is the same process as step S601 described above. The plurality of first evaluation results received by the first receiving means are passed to the processor 120, and the processor 120 receives them.

In step S902, the third receiving means of the receiving means 110 receives a plurality of third evaluation results. The plurality of third evaluation results include the recommendation intention level by the members, a positive index, and a negative index. The plurality of third evaluation results may also include a neutral index. The plurality of third evaluation results received by the third receiving means are passed to the processor 120, and the processor 120 receives them.

In step S903, the analysis means 130 of the processor 120 analyzes the plurality of first evaluation results and the plurality of third evaluation results. The analysis means 130 can identify the member experience that should be improved in order to improve the investor's recommendation intention through processing that will be described later with reference to FIG. 9B.

In step S904, the output means 140 outputs the results of the analysis. The output means 140 outputs the result analyzed by the analysis means 130 in step S903. The analyzed results can include information indicating the constituent experience that should be improved in order to improve the investor's recommendation intention.

FIG. 9B shows an example of the process in step S903.

In step S9031, the analysis means 130 analyzes the plurality of first evaluation results. This is a process similar to step S602 described above. This identifies the investor experience that should be improved in order to improve investors' recommendation intention.

In step S9032, the analysis means 130 analyzes the plurality of third evaluation results. This is performed by applying the same process as step S602 described above to the plurality of third evaluation results. This identifies the member experience that should be improved in order to improve the recommendation intention level by the members.

In step S9033, the analysis means 130 determines whether the investor experience identified in step S9031 has a correlation with the recommendation intention level by the members. If it is determined that the investor experience identified in step S9031 has a correlation with the recommendation intention level by the members, the process advances to step S9034. If it is determined in step S9031 that the investor experience identified has no correlation with the member's recommendation intention level, the process advances to step S9035 and ends. If the investor experience identified in step S9031 does not have a correlation with the recommendation intention level by the members, there is no member experience that should be improved in order to improve the investor recommendation intention level, so the analysis This is because the means 130 cannot specify this.

In step S9034, the analysis means 130 determines the member experience identified in step S9032 as a member experience that should be improved in order to improve the investor's recommendation intention. The member experience identified in step S9032 can improve the recommendation intention of the members, and the investor experience identified in step S9031 can improve the investor's recommendation intention. Therefore, if the recommendation intention level by the members is improved by improving the member experience identified in step S9032, the investor experience identified in step S9031, which has a correlation with the recommendation intention level by the members, will also be improved. This is because the recommendation level of the members increases as a result.

Through the processes shown in FIGS. 9A and 9B, it is possible to identify member experiences that should be improved in order to improve the investor's recommendation intention. As a result, even if you are unsure of what specific measures should be taken to improve the investor experience, you will be able to come up with specific measures and take them. .

Process 800 and process 900 may be performed independently of each other, in parallel, or sequentially. When performed in parallel or consecutively, either step S801 or step S901 and either step S901 or step S904 may be omitted. By performing processing 800 and processing 900 in parallel or sequentially, an analysis result including information indicating investor experience, user experience, and member experience that should be improved in order to improve recommendation intention by investors can be output. Using this as a reference, it is encouraged to take multifaceted measures to improve investors' recommendation intentions.

In the example described above, it has been explained that each step is performed in a specific order, but the order of each step is not limited to that described. Each step can be processed in any logically possible order. Alternatively, several steps can be performed simultaneously. For example, step S60222 can be performed before step S60221. For example, step S802 can be performed before step S801 or simultaneously with step S801. For example, step S902 can be performed before step S901 or simultaneously with step S901.

In the example described above, the processing of each step shown in FIGS. 6, 7A, 7B, 7C, 8A, 8B, 9A, and 9B is realized by the processor 120 and the program stored in the memory 140. However, the present invention is not limited thereto. At least one of the processes in each step shown in FIGS. 6, 7A, 7B, 7C, 8A, 8B, 9A, and 9B may be realized by a hardware configuration such as a control circuit. good.

In the above-mentioned example, an analysis using the investor's recommendation intention level as an evaluation index has been described, but the present invention is not limited to this. Similar analysis can be performed on any evaluation index that indicates the degree of evaluation when an investor evaluates an investment target from a predetermined viewpoint. The evaluation index may be, for example, a "total satisfaction level" that indicates the degree to which the investor is satisfied with the investment target when the investor evaluates the investment target from a comprehensive perspective. In this case, it is possible to analyze how each of the multiple investor experiences affects "overall satisfaction," and identify investor experiences that should be improved to improve "overall satisfaction." can do. The evaluation index may be, for example, "enthusiasm level" indicating the degree of investor enthusiasm for the investment target. In this case, it is possible to analyze how each of the multiple investor experiences affects the "enthusiasm level" and identify the investor experience that should be improved to increase the "enthusiasm level". Can be done. The same applies to the user's recommendation intention level and the member's recommendation intention level, and the present invention performs a similar analysis on any evaluation index that indicates the degree of evaluation when the user evaluates the evaluation target from a predetermined viewpoint. It is possible to perform a similar analysis on any evaluation index that indicates the degree of evaluation when a member evaluates an evaluation target from a predetermined viewpoint.

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

INDUSTRIAL APPLICABILITY The present invention is useful in providing a computer system for analyzing the results of evaluations of investment targets by investors.

10, 20, 30 questionnaire form 100 computer system 110 receiving means 120 processor 130 analysis means 140 memory 150 output means

Claims (1)

The invention described herein.