Information provision device, method, and program

Abstract

[Problem] To provide an information provision device, a method, and a program that make it possible to visually understand the contribution degree of evaluation factors on a customer's intent to conclude a contract or intent to recommend another party. [Solution] An information provision device comprising: a survey data acquisition means for acquiring evaluation data including evaluation values of evaluation items related to a product or service of interest, the evaluation data including evaluation values of a plurality of evaluation items pertaining to the intent to conclude a contract or intent to recommend another party; a multivariate analysis means for carrying out, on the basis of the evaluation data, a multivariate analysis in which the evaluation value of the intent to conclude a contract or intent to recommend another party serves as an objective variable and the evaluation values of the plurality of evaluation items in the evaluation data serve as a plurality of explanatory variables, and thereby calculating the contribution degree of the explanatory variables on the objective variable; an analysis result output means for causing the result of analysis by the multivariate analysis means to be displayed on an information terminal capable of communicating via a network; and a correlation analysis means for calculating the relevance between the plurality of explanatory variables.

Description

Information providing device, method and program

[0001] The present invention relates to an information providing device, method, and program for providing information that contributes to a customer's intention to enter into a contract, revisit a store, repurchase, or recommend a product to others. In this specification, "customer" includes prospective customers, and "sales" includes field sales and inside sales. In this specification, the term "intention to enter into a contract" is used to refer to a customer's intention to enter into a product (including a software product) before the purchase, and a customer's intention to continue using the product (or to renew the contract) after the purchase. The program according to the present invention is computer-executable software that can be stored on computer-readable storage media such as CD-ROMs, DVDs, USB memory sticks, HDDs, and solid-state drives (SSDs). The target stores in this specification include not only physical stores (real stores) but also virtual stores.

[0002] Conventionally, by compiling questionnaires, correlations between product or service attributes and sales performance are analyzed, and the analysis results are reflected in the development of the product or service. Barriers to customer purchasing behavior for the product or service are also analyzed. For example, Patent Literature 1 proposes an information processing device including: an expected value acquisition means for acquiring an expected value indicating the degree of a customer's intention or likelihood of purchasing the product or service; a related value acquisition means for acquiring a related value indicating factors that affect sales of the product or service; a sales value acquisition means for acquiring a sales value indicating the degree of sales of the product or service; and a coefficient calculation means for calculating an expected value coefficient indicating the degree of influence of the intention or likelihood of purchasing on sales of the product or service by operating an equation indicating that the sales value depends on the expected value and the related value weighted by the expected value coefficient.

[0003] JP 2011-215673 A

[0004] Conventional methods simply display the aggregated results of surveys or questionnaires, making it impossible to grasp the degree to which factors contribute to a customer's intention to sign a contract, revisit a store, repurchase, or recommend a product to others. Furthermore, there is a need for a display method that not only accurately calculates the contribution of evaluation factors, but also makes it easy to understand.

[0005] An object of the present invention is to provide an information providing device, method, and program that enable visual understanding of the contribution of evaluation factors to a customer's intention to make a contract, to return to the store, to repurchase, or to recommend to others.

[0006] The inventor has invented an information providing device and program that makes it possible to easily grasp effective measures to improve a customer's intention to introduce the product, to return to the store, to purchase again, or to recommend to others, by factor analyzing evaluation elements that are expected to be effective in determining a customer's intention to enter into a contract, to return to the store, to purchase again, or to recommend to others, and then collecting and statistically analyzing a statistically significant number of customer evaluations.

[0007] An information provision device according to a first aspect of the present invention comprises the following technical means: [1] A survey data acquisition means for acquiring evaluation data including evaluation values ​​of evaluation items for a target product or service, the evaluation data including evaluation values ​​of multiple evaluation items related to willingness to enter into a contract, a multivariate analysis means for performing a multivariate analysis based on the evaluation data using the evaluation value of willingness to enter into a contract as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a response variable, thereby calculating the contribution of the explanatory variables to the response variable, and an analysis result output means for displaying the analysis results obtained by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the response variable, level 2 evaluation items explaining the response variable, and level 3 evaluation items explaining the level 2 evaluation items, and the multivariate analysis means executes a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate response variable and the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the level 1 evaluation items as the response variable and the level 2 evaluation items as explanatory variables. [2] The information providing device according to [1], wherein the analysis result output means displays the plurality of explanatory variables on the information terminal using graphics of a size corresponding to the degree of contribution. [3] The information providing device according to [1] or [2], further comprising correlation analysis means for calculating the association between the plurality of explanatory variables, and wherein the analysis result output means displays on the information terminal a line connecting pairs of the explanatory variables, the line having a thickness and / or a type corresponding to the strength of the association. [4] The information providing device according to any one of [1] to [3], further comprising reference value setting means for setting a reference value for each of the plurality of explanatory variables, and wherein the analysis result output means displays the explanatory variables in different colors depending on the difference from the reference value. [5] The information providing device according to [4], wherein the reference value setting means has a function for calculating an average value of the ratings calculated for each of the plurality of explanatory variables in the same or similar industry or business type, and setting the average value as the reference value.[6] The information providing device according to [4] or [5], wherein the analysis result output means colors the explanatory variable in a first color when the explanatory variable is higher than the reference value, and colors the explanatory variable in a second color different from the first color when the explanatory variable is lower than the reference value. [7] The information providing device according to any of [1] to [6], wherein the analysis result output means displays the figures in a radial arrangement. [8] The information providing device according to any of [1] to [7], wherein the level 3 evaluation items are evaluation items that are subordinate concepts to the level 2 evaluation items.

[0008] The information providing method according to the first aspect of the present invention comprises the following technical means. [9] An information provision method in which a computer executes the following steps: acquiring evaluation data including evaluation values ​​for evaluation items for a target product or service, the evaluation data including evaluation values ​​for multiple evaluation items related to willingness to enter into a contract based on the evaluation data; calculating an evaluation value for willingness to enter into a contract based on the evaluation data; performing a multivariate analysis using the evaluation value for willingness to enter into a contract as a dependent variable and the evaluation values ​​for the multiple evaluation items in the evaluation data as a dependent variable to calculate the contribution of the explanatory variables to the dependent variable; and displaying the analysis results from the multivariate analysis on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the dependent variable, level 2 evaluation items that explain the dependent variable, and level 3 evaluation items that explain the level 2 evaluation items; and performing a first multivariate analysis in which the evaluation values ​​of the level 2 evaluation items are used as an intermediate dependent variable and the evaluation values ​​of the level 3 evaluation items are used as explanatory variables, and a second multivariate analysis in which the evaluation values ​​of the level 1 evaluation items are used as dependent variables and the evaluation values ​​of the level 2 evaluation items are used as explanatory variables. The program according to the first aspect of the present invention comprises the following technical means.

[10] A program that causes a computer to execute: means for acquiring evaluation data including evaluation values ​​for evaluation items for a target product or service, the evaluation data including evaluation values ​​for multiple evaluation items related to willingness to enter into a contract; means for calculating an evaluation value for willingness to enter into a contract based on the evaluation data; multivariate analysis means for performing multivariate analysis using the evaluation value for willingness to enter into a contract as a dependent variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a dependent variable, thereby calculating the contribution of the explanatory variables to the dependent variable; and analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal that can communicate via a network, wherein the evaluation data includes evaluation values ​​for level 1 evaluation items corresponding to the dependent variable, evaluation values ​​for level 2 evaluation items that explain the dependent variable, and evaluation values ​​for level 3 evaluation items that explain the level 2 evaluation items, and the multivariate analysis means executes a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate dependent variable and the evaluation values ​​of the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the evaluation values ​​of the level 1 evaluation items as a dependent variable and the evaluation values ​​of the level 2 evaluation items as explanatory variables.

[0009] An information provision device according to a second aspect of the present invention comprises the following technical means: [1] A survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items and evaluation attributes for a target product or service, the evaluation data including evaluation values ​​for multiple evaluation items related to intention to revisit, intention to repurchase, or intention to recommend to others; multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation values ​​for the multiple evaluation items in the evaluation data as a dependent variable and a dependent variable, and calculating the contribution of the explanatory variables to the dependent variable; and analysis result output means for displaying the analysis results obtained by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the multivariate analysis means divides the evaluation data into multiple groups based on selected evaluation attributes and calculates the contribution of the explanatory variables to the dependent variable for each selected group. [2] The information provision device described in [1], wherein the evaluation attributes include the age or generation, gender, income, and / or residential area of ​​the evaluator. [3] The information providing device according to [1] or [2], wherein the evaluation attribute includes the name of a store that provides the target service. [4] The information providing device according to any one of [1] to [3], wherein the analysis result output means displays the plurality of explanatory variables on the information terminal using a graphic of a size corresponding to the degree of contribution. [5] The information providing device according to [4], wherein the analysis result output means displays the graphic on the information terminal by arranging it radially at equal intervals. [6] The information providing device according to [4] or [5], further comprising correlation analysis means for calculating the association between the plurality of explanatory variables, and wherein the analysis result output means displays on the information terminal a line connecting pairs of the explanatory variables, the line having a thickness and / or a type corresponding to the strength of the association.[7] The information providing device according to [4], further comprising correlation analysis means for calculating the correlation between the plurality of explanatory variables, wherein the analysis result output means arranges the graphic at the vertices of a regular polygon and displays it on the information terminal, and displays a line connecting the pair of explanatory variables, the line having a thickness and / or a type corresponding to the strength of the correlation, on the information terminal. [8] The information providing device according to any of [4] to [7], wherein the graphic is a circle, a polygon, or an illustration. [9] The information providing device according to any of [4] to [8], wherein if the correlation between the pair of graphics is less than a predetermined value, the line connecting the pair of graphics is not displayed on the information terminal.

[10] The information providing device according to any of [1] to [6], wherein the multivariate analysis means calculates the contribution of the explanatory variables to the target variable in two selected groups, and the analysis result output means displays the analysis results of the two selected groups by the multivariate analysis means side by side.

[0010] An information provision method according to a second aspect of the present invention comprises the following technical means:

[11] An information provision method executed by a computer: a step of acquiring evaluation data including evaluation values ​​for evaluation items and evaluation attributes for a target product or service, the evaluation data including evaluation values ​​for multiple evaluation items related to intention to revisit, intention to repurchase, or intention to recommend to others; a multivariate analysis step of performing multivariate analysis based on the evaluation data using the evaluation values ​​for intention to revisit, intention to repurchase, or intention to recommend to others as a dependent variable and the evaluation values ​​for the multiple evaluation items in the evaluation data as a dependent variable to calculate the contribution of the explanatory variables to the dependent variable; and an analysis result output step of displaying the analysis results from the multivariate analysis step on an information terminal capable of communicating via a network, wherein the multivariate analysis step divides the evaluation data into multiple groups based on selected evaluation attributes and calculates the contribution of the explanatory variables in the selected groups to the dependent variable. A program according to the second aspect of the present invention comprises the following technical means.

[12] A program that causes a computer to execute the following: survey data acquisition means for acquiring evaluation data including evaluation values ​​and evaluation attributes for evaluation items regarding a target product or service, the evaluation data including evaluation values ​​for multiple evaluation items related to intention to revisit, intention to repurchase, or intention to recommend to others; multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation value of intention to revisit, intention to repurchase, or intention to recommend to others as a dependent variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a dependent variable, thereby calculating the contribution of the explanatory variables to the dependent variable; and analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal that can communicate via a network, wherein the multivariate analysis means divides the evaluation data into multiple groups based on selected evaluation attributes, and calculates the contribution of the explanatory variables in the selected groups to the dependent variable.

[0011] According to the present invention, it is possible to easily grasp the degree of contribution of each evaluation factor to a customer's intention to enter into a contract, intention to return to the store, intention to repurchase, or intention to recommend to others.

[0012] 11. FIG. 1 is a configuration diagram of an information provision system according to a first embodiment. FIG. 2 is a diagram illustrating functions of an information provision device according to the first embodiment. FIG. 3 is a diagram illustrating a question structure of a survey form for a questionnaire survey according to the first embodiment. FIG. 4 is a diagram illustrating an example of a survey form for a questionnaire survey according to the first embodiment. FIG. 5 is a diagram illustrating analysis results showing the contribution of each explanatory variable to "intention to introduce" in the survey results for the survey form of FIG. 4. FIG. 6 is a diagram illustrating correlation coefficients between the explanatory variables of FIG. 4. FIG. 7 is a diagram illustrating a radial display of the contribution of each explanatory variable of FIG. 4 and the correlation coefficient of each explanatory variable graphically by the analysis result output means according to the first embodiment. FIG. 8 is a diagram illustrating an example of a survey form for a monitor survey according to a second embodiment. FIG. 9 is a diagram illustrating an example of a survey form for a questionnaire survey according to the second embodiment. FIG. 10 is a diagram illustrating a multivariate analysis means according to the second embodiment. FIG. 11 is a diagram illustrating analysis results showing the contribution of each explanatory variable to intention to revisit at a specific restaurant according to the second embodiment. FIG. 12 is a diagram illustrating analysis results showing the contribution of each explanatory variable to intention to revisit at a specific beauty salon according to the second embodiment. FIG. 13 is a diagram illustrating analysis results showing the contribution of each explanatory variable to intention to revisit at a restaurant different from FIG. 11 according to the second embodiment. 15 is a diagram showing an analysis result indicating the contribution of each explanatory variable to repurchase intention for a specific beverage according to a third embodiment. FIG. 19 is a diagram showing an analysis result indicating the contribution of each explanatory variable to repurchase intention for a specific beverage according to a third embodiment. FIG. 19 is a diagram showing an analysis result indicating the contribution of each explanatory variable to repurchase intention for a specific beverage according to a third embodiment. FIG. 20 is a diagram showing an analysis result indicating the contribution of each explanatory variable (excluding fit) to repurchase intention for a specific beverage according to a third embodiment. FIG. 21 is a diagram showing an analysis result indicating the contribution of each explanatory variable (excluding fit) to repurchase intention for a specific beverage according to a third embodiment. FIG. 22 is a diagram showing an analysis result indicating the contribution of each explanatory variable (excluding fit) to repurchase intention for a specific beverage according to a third embodiment. FIG. 23 is a diagram showing an analysis result indicating the contribution of each explanatory variable (excluding fit) to repurchase intention for a specific beverage according to a third embodiment. FIG. 24 is a diagram showing an analysis result indicating the contribution of each explanatory variable (excluding fit) to repurchase intention for a specific beverage according to a third embodiment.21 is a diagram radially displaying the contribution of each explanatory variable and the correlation coefficient of each explanatory variable in FIG. 21 using the analysis result output means according to the third embodiment. It is a diagram showing the analysis results indicating the contribution of each explanatory variable (excluding fit) to repurchase intention when only survey data of people under 40 years old is extracted from FIG. 19. It is a diagram radially displaying the contribution of each explanatory variable and the correlation coefficient of each explanatory variable in FIG. 23 using the analysis result output means according to the third embodiment. It is a diagram radially displaying the contribution of each explanatory variable (excluding fit) to repurchase intention when only survey data of people 40 years old or older is extracted from FIG. 19. It is a diagram radially displaying the contribution of each explanatory variable and the correlation coefficient of each explanatory variable in FIG. 25 using the analysis result output means according to the third embodiment.

[0013] First Embodiment The information provision system 1 of the first embodiment performs multivariate analysis using the results of a questionnaire survey of customers who have received sales from a specific company, and displays evaluation factors that influence the customer's willingness to enter into a contract or recommend a product to others in numerical or graphical form, making it possible to easily understand the factors. The information provision system 1 of the first embodiment has the following configuration.

[0014] Fig. 1 is a configuration diagram of an information provision system according to this embodiment. As shown in Fig. 1, the information provision system 1 according to this embodiment is composed of an information provision device 10 functioning as a server, a management information terminal 20 functioning as a management client, an information terminal 30 used by survey respondents, a survey data collection device 40, and the Internet 50. Note that, although the example shown in Fig. 1 illustrates one each of the information provision device 10, management information terminal 20, and survey data collection device 40, the configuration is not limited to this, and two or more of each may be used.

[0015] The information providing device 10 is, for example, a PC server, and has a calculation unit 11, a memory unit 12 that stores an analysis program 121, and a communication unit 13 that enables communication with the information terminal 30 and the survey data collection device 40.

[0016] The management information terminal 20 is an information terminal, such as a personal computer, for inputting data into the information providing device 10 and the survey data collection device 40 and for executing the analysis program 121 and the survey data collection program 141. By executing the analysis program 121 (described later) from the management information terminal 20, the user can output various analysis results (for example, FIGS. 5 to 7) on the display device of the management information terminal 20.

[0017] The information terminal 30 is an information terminal used by the survey respondents and is used to input survey data into the database 122. In the example of Fig. 1, the information terminal 30 is a smartphone, but is not limited to this and can be configured as any information terminal that runs a web browser, such as a personal computer or tablet.

[0018] The survey data collection device 40 is, for example, a PC server, and includes a calculation unit 41, a storage unit 42 that stores a database 122 and an input program 123, and a communication unit 13 that enables communication with the information providing device 10 and the information terminal 30. The input program 123 includes a survey data input means that provides the database 122 with a web page that enables survey data to be input from the information terminal 30. In this embodiment, the survey data is acquired by inputting the survey data in the form of responses to a questionnaire using a web browser on the information terminal 30 using the input program 123, but it may also be configured to process survey data collected by other means, such as paper media, and upload it to the database 122 from the management information terminal 20.

[0019] 2, the calculation unit 11 of the information providing device 10 executes the analysis program 121 stored in the storage unit 12 to realize (1) a survey data acquisition means, (2) a multivariate analysis means, and (3) an analysis result output means. Each of these means will be described below.

[0020] (1) Survey Data Acquisition Means The survey data acquisition means is a means for storing survey data to be used in the multivariate analysis means in the storage unit 12. In this embodiment, a screen for inputting a query for acquiring survey data from the database 122 is provided to the management information terminal 20. The survey data in this embodiment consists of survey data from a questionnaire regarding customers' intention to introduce the product.

[0021] The questionnaire survey in this embodiment is a customer survey with predetermined evaluation items, and is composed of questions suitable for performing multivariate analysis. The questionnaire survey is conducted on multiple people (including those without the authority to conclude a contract) who belong to the company that received the sales of the target product.

[0022] FIG. 3 is a diagram illustrating the question structure of the questionnaire for the questionnaire survey of this embodiment. The level 1 evaluation item asks whether the sales pitch led to an intention to sign a contract and is a question used to calculate the score of the objective variable in multiple regression analysis. Here, the level 1 evaluation item may be targeted at companies before or after the target product has been introduced. In the former case, the intention to sign a contract in the level 1 evaluation item refers to the intention to introduce the product, while in the latter case, it refers to the intention to continue the contract (intention to renew the contract). Furthermore, a level 1 evaluation item may include a question asking whether the respondent intends to recommend the target product to others (intention to recommend to others). The level 2 evaluation item is an evaluation factor for the level 1 evaluation item and serves as an explanatory variable when multiple regression analysis is performed using the level 1 evaluation item as the dependent variable. For example, to evaluate the perceived understanding of the issues, a question is included asking whether the respondent feels that the essence of the issues facing the company to which the respondent belongs has been (renewed). To evaluate the perceived understanding of the solutions, a question is included asking whether the respondent feels that the proposed solutions will fundamentally change the situation. Level 3 evaluation items are evaluation factors of level 2 evaluation items. Level 2 questions and level 3 questions are set so that they have a relationship of superordinate and subordinate concepts. In Figure 3, questions surrounded by solid lines are, for example, multiple-choice (MA) answers for negative elements, and questions surrounded by dotted lines are, for example, multiple-choice (MA) answers for positive elements. Here, level 3 questions are not limited to the examples shown in Figure 3, and can be any combination of questions for evaluating negative and positive elements.

[0023] FIG. 4 shows an example of a questionnaire for a questionnaire survey. At the beginning of the questionnaire, there is a statement explaining that the responses will be used for the purpose of improving sales activities and services. Questions 2 and 3 ask about customer attribute elements and may be used when conducting analysis by customer segment. Here, question format SA means that the response requires selecting only one option from the answer column.

[0024] The level 1 question (item 4) asks about the intention to introduce the target product (intention to contract) and is evaluated on a multi-level scale. The product according to this embodiment is project management software. The level 2 questions (items 5 to 10) are explanatory variables for the level 1 questions and are evaluated on a multi-level scale. For example, the cost performance question asks whether the price is commensurate with the performance, and the fit question asks whether the respondent feels the product is a good fit for their company. In this embodiment, fit refers to the respondent's preference for the target, such as whether a respondent prefers option b over option a, or whether utility b is greater than utility a, expressed as a variable. By including fit, which takes into account the respondent's preferences, as an evaluation item, the multivariate analysis means described below may be provided with a function that can output the correlation between fit and clustered respondent attributes.

[0025] Level 3 questions (items 11 to 14) are questions that serve as evaluation factors for level 2 questions. In FIG. 4, the question format MA means that the answer is a format in which one or more options are selected from the answer column. It is preferable to set level 3 questions as questions that correspond to lower concepts of level 2 questions. For example, questions about the persuasiveness of the solution can include options such as the explanation being difficult to understand, there being a logical jump between the problem and the solution, and a lack of examples, while questions about the product can include options such as reliability, processing speed, and operability.

[0026] (2) Multivariate Analysis Means The multivariate analysis means of this embodiment uses multiple regression analysis to analyze how each evaluation item affects the contract intention score. Here, regression analysis is a statistical method that clarifies the relationship between numerical values ​​indicating a result (objective variable) and numerical values ​​that are factors (explanatory variables). Multiple regression analysis is a statistical method that, when explaining an objective variable, quantifies which explanatory variables influence the result to what extent, and makes future predictions. By quantifying the magnitude of the influence of the explanatory variables using multiple regression analysis, it becomes easier to plan methods for improving services. It also makes it possible to predict future changes in the objective variable due to expected changes in the explanatory variables.

[0027] FIG. 5 is a diagram showing the analysis results showing the contribution of each explanatory variable to the objective variable in the survey data related to the questionnaire in FIG. 4. The objective variable in FIG. 5 is adoption intention (intention to adopt), and the explanatory variables are each level 2 question. In the example shown in FIG. 5, it can be seen that "product" has the highest contribution to adoption intention, followed by "satisfaction of the player." Furthermore, since the VIFs of each explanatory variable are not large, it can be seen that no multicollinearity problem has occurred. Here, VIF (Variance Inflation Factor) is an index used to evaluate multicollinearity in multiple regression analysis. Multicollinearity means that there is a high correlation between explanatory variables, and if the VIF value is large, it is considered better to exclude that variable from the analysis. Furthermore, in FIG. 5, the coefficient of determination R ２ Since the value is .646, it can be seen that the analysis has sufficient explanatory power. Here, the coefficient of determination (R ２ ) is an index that represents the degree to which the predicted value of the dependent variable obtained by multiple regression analysis matches the actual value of the dependent variable. Generally, the closer the coefficient of determination is to 1, the higher the explanatory power is considered to be. Furthermore, the multivariate analysis means also has a function for calculating the difference between each explanatory variable and a base score. In FIG. 5, the difference between each explanatory variable and the average score of all customers is calculated. By looking at this difference value, it is possible to determine whether the score of the customer being analyzed for each evaluation item is lower or higher than that of all customers. Note that the base score can be the average score of any selected group; for example, the average score in the same industry can be used as the base score.

[0028] Figure 6 shows the correlation coefficients between the level 2 evaluation factors shown in Figure 5. For example, the correlation coefficient between the satisfaction of the action and the satisfaction of the task is 0.648, indicating a high correlation between the two. Therefore, when the evaluation of one increases, the evaluation of the other also increases, and conversely, when the evaluation of one decreases, the evaluation of the other also decreases. In this way, calculating the correlation coefficients between the level 2 evaluation factors makes it easier to consider the actions necessary to increase adoption intentions.

[0029] (3) Analysis Result Output Means In this embodiment, the analysis result output means not only outputs the analysis results in a table format as shown in Figures 5 and 6, but also graphically displays the degree of contribution of explanatory variables to the customer's intention to enter into a contract, allowing for visual understanding. Figure 7 is a diagram graphically displayed by the analysis result output means, showing the degree of contribution of each explanatory variable to the introduction intention (introduction intention) shown in Figure 5 and the correlation coefficient of each explanatory variable. Circles corresponding to each explanatory variable are placed at the vertices of a regular hexagon located below the intention to enter into a contract. Note that in this embodiment, circles corresponding to each explanatory variable are placed at the vertices of a regular hexagon, but the drawing method is not limited to this. They may be placed at the vertices of any regular polygon depending on the number of explanatory variables, or they may be placed at equal intervals around a circle.

[0030] In Figure 7, explanatory variables with a high contribution to adoption intention are displayed as large circles, and explanatory variables with a low contribution are displayed as small circles. In this embodiment, the size of the circle is proportional to the contribution, but if the contribution is below a certain level, it is drawn with a predetermined minimum radius and is not made smaller than the minimum radius. However, the size of the circle does not necessarily have to be proportional to the contribution value, and it may be displayed in an emphasized manner (for example, the top two explanatory variables may be displayed larger by multiplying the contribution value by a coefficient).

[0031] In FIG. 7 , the diagonal lines represent the first color, green, and the dots represent the second color, red. In this embodiment, the greater the difference between the rating of the explanatory variable and the reference rating, the darker the color is drawn. In FIG. 7 , "Sustainability of the Issue," "Product," and "Cost Performance" are colored green, so it is possible to instantly see that the ratings are higher than the reference rating. Furthermore, the objective variable "Intention to Contract" and the explanatory variables "Sustainability of the Player," "Person in Charge," and "Fit" are colored red, so it is possible to instantly see that the ratings are lower than the reference rating, and that the rating of "Fit," which is colored dark red, is particularly low.

[0032] Furthermore, explanatory variables having a specific correlation coefficient are connected and displayed by a correlation display line. That is, the analysis result output means can display the correlation display line connecting pairs of figures corresponding to explanatory variables with a line thickness and / or type corresponding to the correlation coefficient value (strength of association). In this embodiment, the correlation display lines are displayed as follows: double lines for values ​​greater than or equal to 0.7 (rounded to the nearest significant digit); thick solid lines for values ​​less than 0.7 to 0.6 or greater; solid lines of normal thickness for values ​​less than 0.6 to 0.5 or greater; thin dotted lines for values ​​less than 0.5 to 0.4 or greater; and lines less than 0.4 are not displayed. In this way, by graphically displaying the correlation coefficients of each explanatory variable with correlation display lines of different thicknesses and types, it is possible to visually easily grasp the correlation coefficients of each explanatory variable. For example, it is easy to grasp from the correlation display lines of different thicknesses and types that there is a particularly strong correlation between the player's satisfaction and the satisfaction of the task, which is then particularly strong with the product, and then a relatively strong correlation with the person in charge.

[0033] In this way, the analysis result output means can provide insights for efficiently carrying out improvement efforts by graphically visualizing and providing the magnitude of the contribution of each evaluation factor to the contract willingness score, which changes as improvement efforts are made, and the degree of correlation between each explanatory variable. In this embodiment, explanatory variables are represented by circles whose size is proportional to the contribution, but the shapes representing the contribution are not limited to this and any shape can be used, such as polygons or illustrations of food or characters. Furthermore, the arrangement order of the shapes representing explanatory variables may be changed by dragging.

[0034] As described above, the information provision system 1 according to this embodiment performs multivariate analysis of the objective variable and explanatory variables (evaluation factors), thereby enabling appropriate and easy identification of factors that influence adoption intentions (when the analysis targets a company that has already adopted the target product, it becomes possible to appropriately and easily identify factors that contribute to the formulation of customer success measures). Furthermore, by providing a graphical visualization of the analysis results obtained by the multivariate analysis, it becomes possible to instantly identify objective variables for efficient improvement efforts. Furthermore, by providing a graphical visualization of explanatory variables with high correlation coefficients, it becomes possible to easily predict the impact of improving the explanatory variables.

[0035] Second Embodiment The information provision system 1 of the second embodiment analyzes evaluation factors in a specific business format, and graphically displays the evaluation factors that influence the intention to revisit or recommend, making it possible to easily understand them. The following description will focus on the differences from the first embodiment, and will omit a description of the same parts. The information provision system 1 of the second embodiment includes an information provision device 10 that functions as a server, an administration information terminal 20 that functions as an administration client, an information terminal 30 used by customers, a survey data collection device 40, and the Internet 50, and the hardware configuration is the same as that of the first embodiment.

[0036] The survey data input means provided in the survey data collection device 40 of this embodiment inputs, as survey data, survey data relating to customer satisfaction based on responses from store users into the database 122. The database 122 of the second embodiment stores, as survey data, data from monitor surveys in addition to questionnaire survey data.

[0037] In the present embodiment, the information providing device 10 also realizes (2-1) survey data acquisition means, (2-2) multivariate analysis means, and (2-3) analysis result output means by having the calculation unit 11 execute the analysis program 121 stored in the storage unit 12. Each of these means will be described below. (2-1) Survey Data Acquisition Means The survey data acquisition means included in the information providing device 10 provides the management information terminal 20 with a screen for inputting a query for acquiring survey data from the database 122. The monitor survey in this embodiment is a survey in which a monitor (surveyor) actually uses a store's services and answers predetermined questions based on their own experience. Since the surveyor is paid a fee, the monitor survey can include a large number of questions. However, compared to a questionnaire survey, depending on the N number, it may be difficult to perform reliable multivariate analysis using the monitor survey alone.

[0038] The questionnaire survey in this embodiment is an in-store survey with predetermined evaluation items. It has less than half the number of questions compared to a monitor survey and is composed of questions suitable for multivariate analysis. On the other hand, compared to a monitor survey, the N number can be larger (for example, more than 100 surveys can be collected per store per month), making it suitable for statistical analysis. Because the N number is large, a questionnaire survey can grasp evaluation trends by customer segment categorized by evaluation attributes such as respondent gender, age, income or earnings, residential area, purpose of use, and amount spent. In this embodiment, monitor survey data and questionnaire survey data can be used together, enabling statistical analysis of the strength of the influence of key evaluation items on overall satisfaction by business type, while the monitor survey allows for identification of specific business-level issues.

[0039] The evaluation items to be recorded on each questionnaire may be, for example, items related to QSCA (Quality Service Cleanliness Atmosphere). For target restaurants in the food and beverage category, examples of items include "customer service," "service speed," "food," "cleanliness," "space / atmosphere," and "cost performance." For target restaurants in the beauty category, examples of items include "customer service," "counseling," "skill," "cleanliness," "atmosphere," and "cost performance." In either industry, questions necessary for calculating a regression with the intention to revisit the restaurant are provided. In addition, questions may be provided for calculating a regression with the intention to recommend the restaurant to others, such as whether the customer would recommend the restaurant to others. Note that the above-mentioned items are merely examples, and dozens of items may be included depending on the industry (genre), size, season, etc. of the target restaurant.

[0040] FIG. 8 is a diagram showing an example of a questionnaire for a monitor survey according to this embodiment. Evaluation attribute information of the monitor survey investigator is entered in each attribute information field. Information such as the monitor survey investigator's age, gender, and place of residence is pre-registered and therefore not entered on the questionnaire. Level 1 questions consist of willingness to recommend and willingness to revisit. Level 1 questions serve as the objective variable for the multiple regression analysis described below. The evaluation of level 1 questions is, for example, a multi-level evaluation ranging from 0 to 10 points. Level 2 questions are factors that influence the objective variable, the level 1 question. Level 3 questions are explanatory variables when multiple regression analysis is performed using the level 2 question as the dependent variable. Here, level 2 and level 3 questions are in a superordinate-subordinate relationship. For example, the subordinate concepts of "evaluation of customer service" in subcategory A of level 2 questions are "evaluation of greeting" and "evaluation of food service" in subcategory A of level 3 questions. However, level 3 questions are not provided for all subcategories of level 2 questions. Furthermore, an evaluation of fit may be provided as another question equivalent to a level 2 question. The question about fit may be a multi-level evaluation of a question such as, "Do you feel that our store suits your taste?" In this embodiment, no level 3 question is provided to explain the question about fit, but a level 3 question may be provided.

[0041] FIG. 9 illustrates an example of a questionnaire for a questionnaire survey according to this embodiment. The attribute information of the questionnaire respondent is entered in each attribute information field. Because the questionnaire respondent is not a registered respondent like the investigator in a monitor survey, evaluation attribute information such as age and gender is also required. The level 1 questions are the same as those in a monitor survey and consist of items regarding willingness to recommend and willingness to revisit. To reduce the burden on the respondent (store visitor), the questionnaire survey is designed to have a small number of questions that serve as observation variables. In this embodiment, the questionnaire survey is composed of level 2 questions and level 2 checks. Here, the level 2 questions and level 2 checks correspond to each other in the subcategory. For example, the level 2 check corresponding to "Evaluation of customer service" in the subcategory A of the level 2 questions is "Dissatisfaction with customer service (multiple choices)." From another perspective, the level 2 check can be explained as a replacement of the level 3 questions, which were previously conducted using a multi-level evaluation, with a multi-level evaluation. In the questionnaire survey, other questions equivalent to level 2 questions include evaluations of fit and satisfaction.

[0042] In this embodiment, fit refers to an individual's preference for a target, and is a variable that represents the preference structure of Person A, such as Person A preferring option b to option a, or utility b being greater than utility a. The comfort or harmony of the space and atmosphere of the survey target is used as an explanatory variable, and is positioned as a variable that incorporates lifestyle factors and reference group effects. By including fit, which takes into account the preferences of respondents to monitor surveys and questionnaire surveys, as an evaluation item, the multivariate analysis means described below may be provided with a function that enables output of the correlation between fit and clustered customer attributes.

[0043] (2-2) Multivariate Analysis Means The multivariate analysis means of this embodiment uses multiple regression analysis to analyze how each evaluation item affects the recommendation intention and revisit intention scores (hereinafter referred to as the "revisit intention score"). In this embodiment, evaluation factors that can be improved through efforts are set as explanatory variables, while evaluation factors that are difficult to improve through efforts, such as location and appearance, are not set as explanatory variables. Specifically, "customer service," "food," "cleanliness," "space / atmosphere," "cost performance," etc. were set as explanatory variables for restaurants, and monitor surveys and questionnaire surveys were conducted in which questions included evaluation items related to these explanatory variables and evaluation items for calculating the revisit intention score. Figure 9 is a diagram showing an example of the results of calculating the contribution of each explanatory variable to the revisit intention score (objective variable) using multiple regression analysis when the target store is a restaurant.

[0044] FIG. 10 is a diagram illustrating the multivariate analysis means according to this embodiment. The multivariate analysis means according to this embodiment performs (a) multiple regression analysis of levels 2 and 3, (b) simple correlation of level 3 evaluation factors, (c) multiple regression analysis of levels 1 and 2, and (d) simple correlation of level 2 evaluation factors. In this embodiment, a hierarchical multiple regression analysis can be performed to enhance the explanatory power of the influence (contribution) of level 2 evaluation factors on recommendation intention or revisit intention. In other words, when a multiple regression analysis is performed using level 3 evaluation factors as explanatory variables and the evaluation values ​​of level 1 evaluation items as objective variables, it may be difficult to accurately determine which evaluation factor is dominant. However, this embodiment solves this problem by providing level 2 evaluation factors, which are intermediate concepts.

[0045] Analysis (a) is a first multiple regression analysis using level 2 evaluation items as the objective variable (intermediate objective variable) and level 3 evaluation items as the explanatory variables. This analysis allows us to understand the level 3 evaluation items (evaluation factors) that influence the level 2 evaluation items (intermediate objective variables). For example, in Figure 3, the influence (contribution) of each of the evaluation of the food taste (B-1), the evaluation of the food selection (B-2), and the evaluation of the food quantity (B-3) on the food evaluation (B) can be quantified. Analysis (b) quantifies the correlation between level 3 evaluation factors. For example, in Figure 8, if a high correlation is found between the evaluation of the food selection (B-2) and the evaluation of the food quantity (B-3) using simple correlation analysis, it can be seen that an increase in one evaluation will also increase the other evaluation. Analysis (c) is a second multiple regression analysis using level 1 evaluation items as the objective variable and level 2 evaluation items as the explanatory variables. This analysis makes it possible to understand the level 2 evaluation items (evaluation factors) that influence the level 1 evaluation items. For example, in Figure 8, the influence (contribution) of each of the evaluation of customer service (A), evaluation of food (B), evaluation of cleanliness (C), evaluation of space / ambience (D), and evaluation of cost performance (E) on the intention to recommend or return can be quantified. Analysis (d) calculates a correlation coefficient that quantifies the correlation between level 2 evaluation factors. For example, in Figure 8, if a high correlation is found between the evaluation of food (B) and the evaluation of cost performance (E), it can be seen that an increase in one evaluation will also increase the other evaluation.

[0046] FIG. 11 is a diagram showing the analysis results showing the contribution of each level 2 explanatory variable (evaluation factor) when the level 1 revisit intention of a specific restaurant is used as the objective variable. In the example shown in FIG. 11, it can be seen that "cost performance" has the highest contribution to the revisit intention, followed by "customer service." Furthermore, since the VIFs of each explanatory variable are not large, it can be seen that there is no problem with multicollinearity. Furthermore, in FIG. 6, the coefficient of determination R２ Since the value is large at .711, it can be seen that this analysis has high explanatory power.

[0047] In addition, in this embodiment, "customer service," "counseling," "skill," "cleanliness," "atmosphere," and "cost performance" were set as level 2 explanatory variables (evaluation factors) for beauty salons, and a monitor survey was conducted in which questions were asked about the level 2 evaluation factors and level 3 evaluation items, which are sub-concepts of level 2. Figure 12 is a diagram showing an example of the results calculated by multiple regression analysis of the contribution of each level 2 explanatory variable (evaluation factor) to the customer's intention to return to the salon (objective variable) when the target store is a beauty salon. In the example shown in Figure 12, it can be seen that "customer service," "skill," and "cost performance" have a high contribution to the intention to return to the salon. Furthermore, the VIFs of each explanatory variable are not large, indicating that there is no problem with multicollinearity. In Figure 12, the coefficient of determination R ２ Since the mean is .502, it can be said that this analysis has sufficient explanatory power.

[0048] FIG. 13 is a diagram showing the analysis results showing the contribution of each explanatory variable (evaluation factor) when the intention to revisit a restaurant different from that shown in FIG. 11 is used as the objective variable. In the example shown in FIG. 13, it can be seen that "cost performance" has the highest contribution to the intention to revisit, followed by "service" and then "space and atmosphere". Furthermore, since the VIFs of each explanatory variable are not large, it can be seen that there is no problem with multicollinearity. Furthermore, in FIG. 13, the coefficient of determination R ２Since the mean score is .658, it can be seen that this analysis has sufficient explanatory power. Furthermore, in FIG. 13, the difference value between the average score, which is the standard score for the target store compared to the same business type, for each explanatory variable is also calculated. Here, the standard score can be the average score of the rating data for the same business type, or an arbitrary score that the target store aims for. Furthermore, the difference value between the standard score for a business type that is a higher-level conception of a store that serves a specific type of cuisine (e.g., a restaurant business) or another business type that shares common ingredients (e.g., a yakiniku business and a teppanyaki business) may also be used. Note that, although not shown, in FIG. 13, the difference value between the average score for the same business type for the revisit intention score was also calculated, and the difference value was -0.1.

[0049] FIG. 14 shows the analysis results of the contribution of each explanatory variable (evaluation factor) to the objective variable, the intention to revisit, when only data on respondents in their 20s was extracted from the survey data in FIG. 13 . In the example shown in FIG. 14 , it can be seen that "cost performance" contributes most to the intention to revisit, followed by "food" and then "service." This indicates that the explanatory variables with the second highest contributions are different from those of all generations shown in FIG. 13 . Furthermore, regarding the evaluation difference from the standard score, while all explanatory variables for all generations had negative values, all except two explanatory variables exceeded the standard score, indicating that the evaluation was high among people in their 20s. Although not shown in FIG. 14 , the difference value from the average score for the same business type was also calculated for the intention to revisit score, and the difference value was 5.1. In FIG. 14 , the VIFs of each explanatory variable are not large, indicating that there is no problem with multicollinearity. Furthermore, the coefficient of determination R ２ Since the value is .717, it can be said that this analysis has high explanatory power.

[0050] FIG. 15 shows the analysis results showing the contribution of each explanatory variable (evaluation factor) to the objective variable, the intention to revisit, when only respondent data in their 30s was extracted from the survey data related to FIG. 13 . In the example shown in FIG. 15 , it can be seen that "cost performance" has the highest contribution to the intention to revisit, followed by "space / atmosphere," and then "customer service." It can be seen that the overall trend is similar to the permutation of contributions for all generations shown in FIG. 13 . Furthermore, the evaluation difference from the standard score for the intention to revisit and each explanatory variable is lower (shown in dark red) than the evaluation difference for all generations shown in FIG. 8 , indicating that the evaluations for people in their 30s are particularly low. Although not shown in FIG. 15 , the difference between the revisit intention score and the average score for the same business type was also calculated, and the difference value was -2.8. In FIG. 15 , the VIFs for each explanatory variable are not large, so there is no problem of multicollinearity. Furthermore, the coefficient of determination R ２ Since the figure is .646, it can be said that this analysis has sufficient explanatory power.

[0051] (2-3) Analysis Result Output Means As in the first embodiment, the analysis result output means of this embodiment also graphically displays the contribution of explanatory variables to a customer's intention to revisit, allowing for visual understanding. In this embodiment, each explanatory variable is graphically displayed as a circle of a size proportional to its contribution to the revisit intention score. In addition, explanatory variables with ratings higher than the average rating of the same business type are colored green (diagonal lines), explanatory variables with ratings lower than the average rating of the same business type are colored red (dots), and the greater the difference between the rating of the explanatory variable and the average rating, the darker the color is drawn.

[0052] Furthermore, as in the first embodiment, explanatory variables having a specific correlation coefficient are connected and displayed by a correlation degree display line. That is, the analysis result output means of the second embodiment can also display correlation degree display lines connecting pairs of figures corresponding to explanatory variables with lines of a thickness and / or type that correspond to the value of the correlation coefficient (strength of the relationship).

[0053] Figure 16 is a diagram in which the contribution of each explanatory variable to the intention to revisit at the restaurant shown in Figure 13 and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means. In Figure 16, diagonal lines represent the first color, green, and dots represent the second color, red. In Figure 16, "customer service" and "service speed" are colored dark red, so it can be seen at a glance that "customer service" and "service speed" are particularly lower in rating than the average rating for the same business type.

[0054] In addition, in FIG. 16 , each explanatory variable is depicted as a circle of a size corresponding to the numerical value of its contribution to the revisit intention score. From the size of the circles in FIG. 16 , it can be seen that "cost performance" has the strongest influence on the revisit intention score, followed by "space / atmosphere" and "customer service." Furthermore, the negative evaluations of "service speed" and "cleanliness" suggest that standard operations are not being performed. Furthermore, considering the small size of the "food" circle and the status of the other explanatory variables, it is possible to determine from the graphical radial display in FIG. 11 that the restaurant is focusing on cost performance and not on its original value offering, "food," without examining the numerical contribution. As in the first embodiment, the arrangement and operation method of the figures representing the explanatory variables can be changed as appropriate.

[0055] Furthermore, in Figure 16, the thickness and type of the correlation display lines connecting the explanatory variables allow visual understanding of the correlation between the explanatory variables. For example, it can be seen that "food" has a strong influence on "cost performance," which is connected by a double correlation display line, that "service" has a significant influence on "cost performance," which is connected by a thick solid correlation display line, and that "service" also has a significant influence on "space / atmosphere" and "food," which are connected by solid correlation display lines. In this way, by graphically displaying the correlation coefficients of each explanatory variable using correlation display lines of different thicknesses and types, it is easy to understand that in the restaurant shown in Figure 16, food and "service" have a high correlation with "cost performance," followed by "space / atmosphere," "cleanliness," and "service." Note that the pairs of explanatory variables connected by correlation display lines are not limited to the top five pairs shown in the example; any number of correlation display lines can be displayed.

[0056] FIG. 17 is a diagram graphically displaying, by the analysis result output means, the contribution of each explanatory variable to the intention to revisit and the correlation coefficient of each explanatory variable using the data of respondents in their twenties from FIG. 14 . The arrangement of the circles representing the explanatory variables in FIG. 17 is the same as in FIG. 16 , making it easy to compare the difference in the contribution of the explanatory variables between all generations and those in their twenties. Here, FIGS. 16 and 17 may be displayed side by side on the same screen. In FIG. 17 , the diagonal lines represent the first color, green, and the dots represent the second color, red. In FIG. 17 , the explanatory variables other than "service speed" and "space / atmosphere" are colored dark green, making it possible to instantly see that the explanatory variables other than "service speed" have particularly higher ratings than the average rating for the same business type.

[0057] From the size of the circles in Figure 17, it can be visually seen that "cost performance" has the strongest influence on the revisit intention score, followed by "customer service" and "service speed." A comparison with Figure 16 reveals that "space / atmosphere," which had the third highest contribution among all generations, had a low contribution among people in their 20s. Also in Figure 17, the thickness and type of correlation display lines connecting the explanatory variables make it possible to visually grasp the correlation between the explanatory variables. For example, it can be seen that "cost performance" is strongly influenced by "service speed," which is connected by a double line, followed by "food," which is connected by a solid line, and that "customer service" is strongly influenced by "space / atmosphere," which is connected by a double line.

[0058] FIG. 18 is a diagram graphically displayed by the analysis result output means, showing the contribution of each explanatory variable to the intention to revisit and the correlation coefficient of each explanatory variable, using the data of respondents in their 30s from FIG. 15 . The arrangement of the circles representing the explanatory variables in FIG. 18 is the same as in FIG. 16 , making it easy to compare the difference in the contribution of the explanatory variables between all generations and those in their 30s. Here, FIG. 16 or FIG. 17 and FIG. 18 may be displayed side by side on the same screen. In FIG. 18 , the diagonal lines represent the first color, green, and the dots represent the second color, red. In FIG. 18 , "intention to revisit" and all explanatory variables are colored red, making it possible to instantly see that "intention to revisit" and all explanatory variables have scores lower than the average score.

[0059] From the size of the circles in Figure 18, it can be visually seen that "cost performance" has the strongest influence on the revisit intention score, followed by "customer service" and "space and atmosphere." Comparing with Figure 17, it can be seen that the contribution of "space and atmosphere" is higher for those in their 20s than for those in their 20s. Also in Figure 18, it is possible to visually understand the correlation between explanatory variables from the thickness and type of the correlation display lines connecting the explanatory variables. For example, it can be seen that "cost performance" is strongly influenced by "food," which is connected with a double line, followed by "customer service" and "space and atmosphere," which are connected with solid lines.

[0060] In this way, the analysis result output means of this embodiment can provide insights for carrying out store improvement efforts efficiently by graphically visualizing the degree of contribution of each evaluation factor to the revisit intention score, which changes as store improvement efforts are made.

[0061] As described above, the information provision system 1 according to this embodiment graphically visualizes and provides the analysis results obtained by multivariate analysis of the objective variable and explanatory variables (evaluation factors), making it possible to instantly grasp the objective variable for efficient improvement efforts. Furthermore, by graphically visualizing and providing explanatory variables with high correlation coefficients, it becomes possible to easily predict the impact of improving the explanatory variables. Furthermore, it becomes possible to visually grasp the contribution of evaluation factors to customers' intentions to revisit or recommend, by evaluation attribute.

[0062] Third Embodiment The hardware configuration of the information provision system 1 according to the third embodiment is the same as that of the first and second embodiments. The third embodiment differs from the second embodiment in that the multivariate analysis means executed in the information provision device 10 has a function of analyzing, by multiple regression analysis, how each evaluation item affects the repurchase intention score for a specific product.

[0063] FIG. 19 is a diagram showing the analysis results showing the contribution of each level 2 explanatory variable (evaluation factor) when the level 1 repurchase intention for a specific beverage (a so-called energy drink) is used as the objective variable. In the example shown in FIG. 19, it can be seen that "fit" has the highest contribution to repurchase intention, followed by "taste." Furthermore, since the VIFs of each explanatory variable are not large, it can be seen that there is no problem with multicollinearity. Furthermore, in FIG. 19, the coefficient of determination R ２ 19 also calculates the difference between the average score of each explanatory variable and the average score of the same type of beverage.

[0064] The analysis result output means of this embodiment graphically displays the degree of contribution of explanatory variables to a customer's repurchase intention, allowing for visual understanding. FIG. 20 is a diagram graphically displayed by the analysis result output means of the degree of contribution of each explanatory variable to the repurchase intention for the specific beverage shown in FIG. 19 and the correlation coefficient of each explanatory variable. As in the first and second embodiments, explanatory variables with a high degree of contribution to repurchase intention are displayed with larger circles, and explanatory variables with a low degree of contribution are displayed with smaller circles. In addition, circles corresponding to each explanatory variable are arranged at the vertices of a roughly regular heptagon. In FIG. 20, the diagonal lines represent the first color, green, and the dots represent the second color, red.

[0065] In this embodiment, as in the first and second embodiments, explanatory variables with ratings higher than the reference rating are colored green (diagonal lines), explanatory variables with ratings lower than the reference rating are colored red (dots), and the greater the difference between the rating of the explanatory variable and the reference rating, the darker the color is drawn. In FIG. 20, "Packaging" is colored dark green, so it is immediately clear that the rating is particularly higher than the reference rating. Furthermore, "Repurchase Intention" and "Effectiveness" are colored red, so it is immediately clear that the ratings are lower than the reference rating and that "Effectiveness" has a negative impact on "Repurchase Intention."

[0066] FIG. 21 shows the analysis results of extracting only male respondent data from the survey data shown in FIG. 19 and showing the contribution of each explanatory variable (evaluation factor) to the objective variable, repurchase intention. Note that in FIG. 21, "fit" was excluded from the explanatory variables shown in FIG. 19 . While "fit" was excluded in FIG. 21 , it is also possible to exclude one or more other explanatory variables for analysis (the same applies to FIGS. 23 and 25 , described below). In the example shown in FIG. 21 , it can be seen that "cost performance" contributes most to repurchase intention, followed by "taste," and then "brand attitude." It can also be seen that the evaluation differences from the standard scores for "quantity / ingredients," "packaging," and "brand attitude" are below the standard scores.

[0067] FIG. 22 is a diagram graphically displaying, by the analysis result output means, the contribution of each explanatory variable to repurchase intention and the correlation coefficient of each explanatory variable using the male respondent data of FIG. 21 . In FIG. 22 , there are six explanatory variables excluding "cost performance," and each explanatory variable is arranged at the vertex of a regular hexagon. Here, FIG. 22 and FIG. 20 may be displayed side by side on the same screen. In FIG. 22 , the diagonal lines represent the first color, green, and the dots represent the second color, red. In FIG. 22 , "amount / ingredients," "packaging," and "brand attitude" are colored red, indicating that the ratings are lower than the average rating of beverages of the same type, while the other explanatory variables and objective variables are colored green, indicating that the ratings are higher than the average rating of beverages of the same type.

[0068] From the size of the circles in Figure 2, it can be visually seen that "cost performance" has the strongest influence on the repurchase intention score, followed by "taste" and "brand." Furthermore, from the thickness and type of the correlation display lines connecting the explanatory variables, it is possible to visually grasp the degree of correlation between the explanatory variables. For example, it can be seen that "taste" and "amount / ingredients," connected by dotted lines, have an influence on "cost performance," and that "effect / benefit," connected by solid lines, has a strong influence on "amount / ingredients."

[0069] FIG. 23 shows the analysis results of extracting only data on respondents under the age of 40 from the survey data related to FIG. 19 and showing the contribution of each explanatory variable (evaluation factor) to the objective variable, repurchase intention. Note that FIG. 23 excludes "fit" from the explanatory variables of FIG. 19 for the analysis. In the example shown in FIG. 23, it can be seen that "taste" has the highest contribution to repurchase intention, followed by "brand attitude." It can also be seen that the difference in evaluation from the standard score is higher for all explanatory variables except "packaging."

[0070] FIG. 24 is a diagram graphically displaying, by the analysis result output means, the contribution of each explanatory variable to repurchase intention and the correlation coefficient of each explanatory variable using the data of respondents under 40 years of age shown in FIG. 23. In FIG. 24, there are six explanatory variables excluding "cost performance," and each explanatory variable is arranged at the vertex of a regular hexagon. Here, FIG. 24 and FIG. 20 may be displayed side by side on the same screen. In FIG. 24, the diagonal lines indicate green, which is the first color, and the dots indicate red, which is the second color. In FIG. 24, the explanatory variables and objective variables other than "packaging" are colored green, so it is possible to instantly see that the rating is higher than the average rating of the same type of beverage.

[0071] From the size of the circles in Figure 24, it can be visually understood that "taste" has the strongest influence on the repurchase intention score, followed by "brand attitude." Furthermore, from the thickness and type of the correlation display lines connecting the explanatory variables, it is possible to visually understand the degree of correlation between the explanatory variables. For example, it can be seen that "taste" is influenced by "amount / ingredients," which is connected by a dotted line.

[0072] FIG. 25 shows the analysis results of extracting only data on respondents aged 40 or older from the survey data in FIG. 19 and showing the contribution of each explanatory variable (evaluation factor) to the objective variable, repurchase intention. Note that FIG. 25 excludes "fit" from the explanatory variables in FIG. 19 for the analysis. In the example shown in FIG. 20, it can be seen that "taste" has the highest contribution to repurchase intention, followed by "brand attitude." It can also be seen that the difference in evaluation from the standard score is below the standard score for all explanatory variables except "packaging."

[0073] FIG. 26 is a diagram graphically displaying, by the analysis result output means, the contribution of each explanatory variable to repurchase intention and the correlation coefficient of each explanatory variable using the data of respondents aged 40 or over from FIG. 25 . In FIG. 26 , there are six explanatory variables, and each explanatory variable is arranged at the vertex of a regular hexagon. Here, FIG. 26 and FIG. 20 or FIG. 26 and FIG. 24 may be displayed side by side on the same screen. In FIG. 26 , diagonal lines indicate a first color, green, and dots indicate a second color, red. In FIG. 26 , explanatory variables and objective variables other than "packaging" are colored red, allowing instant recognition that the ratings are lower than the average rating for beverages of the same type. Furthermore, by comparing FIG. 24 , it can be seen that the ratings for each explanatory variable are high for respondents under 40 years old, while the ratings for each explanatory variable are low for respondents aged 40 or over.

[0074] From the size of the circles in Figure 26, it can be visually understood that "cost performance" has the strongest influence on the repurchase intention score, followed by "taste." Furthermore, from the thickness and type of the correlation display lines connecting the explanatory variables, it is possible to visually understand the degree of correlation between the explanatory variables. For example, it can be seen that "taste" is strongly influenced by "brand attitude," "amount / ingredients," and "packaging," which are connected by solid lines, followed by "effect / benefit," which is connected by dotted lines.

[0075] While the preferred embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the above-described embodiments. Various modifications and improvements can be made to the above-described embodiments, and such modifications and improvements are also included in the technical scope of the present invention.

[0076] For example, in the above-described embodiment, multiple regression analysis, a type of multivariate analysis, was used to determine the contribution of each evaluation factor to the contract intention score. However, the present invention is not limited to this configuration, and other methods, such as principal component analysis, factor analysis, correspondence analysis, multidimensional scaling, cluster analysis, path analysis, analysis of variance, conjoint analysis, regression analysis, and logistic regression analysis, may also be used. In addition to the above-described embodiment, the information providing device 10 can also be configured to collect condition information such as customer segments, usage scenarios (e.g., everyday use, anniversary use), seasons, and weather, and analyze evaluation factors contributing to contract intention or recommendation intention by taking this condition information into account. Furthermore, adding graphic attributes and psychographic attributes to the questionnaire enables analysis by customer segment. Furthermore, by linking with a system that manages point of sale (POS) and purchase history, it is possible to understand evaluation trends for each ordered item.

[0077] In the above-described embodiment, an example was described in which the explanatory power of the influence (contribution) of evaluation factors on willingness to purchase or willingness to recommend to others was enhanced by performing only multivariate analysis using level 1 evaluation factors as the objective variable and level 2 evaluation factors as the explanatory variables. However, the technical concept of the present invention can also be applied to multivariate analysis using hierarchical evaluation factors. For example, a multivariate analysis method may be constructed that performs (a) multiple regression analysis of level 2 evaluation factors and level 3 evaluation factors, (b) correlation analysis of level 2 evaluation factors and level 3 evaluation factors, (c) multiple regression analysis of level 1 evaluation factors and level 2 evaluation factors, and (d) correlation analysis between level 2 evaluation factors. Such multivariate analysis is useful because it can identify dominant evaluation factors even when it is difficult to accurately determine which evaluation factors are dominant using non-hierarchical multivariate analysis alone.

[0078] In the above-described embodiment, an example was described in which survey data targeting computer software was used, but the present invention can also be applied to industries that handle other goods or services, such as the accommodation industry, food and beverage service industry, entertainment industry, lifestyle-related service industries such as hairdressing and beauty salons and public baths, education and skills teaching industry, finance industry, insurance industry, retail industry, information and communications industry, goods rental industry, professional services industry (such as legal professionals), advertising industry, real estate industry, and transportation industry.

[0079] REFERENCE SIGNS LIST 1... Information provision system 10... Information provision device 20... Management information terminal 30... Information terminal 40... Survey data collection device 50... Internet

Claims

1. An information provision device comprising: a survey data acquisition means for acquiring evaluation data including evaluation values ​​of evaluation items for a target product or service, the evaluation data including evaluation values ​​of multiple evaluation items related to willingness to enter into a contract; a multivariate analysis means for performing a multivariate analysis based on the evaluation data using the evaluation value of willingness to enter into a contract as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a response variable, thereby calculating the contribution of the explanatory variables to the response variable; and an analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the response variable, level 2 evaluation items explaining the response variable, and level 3 evaluation items explaining the level 2 evaluation items, and the multivariate analysis means executes a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate response variable and the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the level 1 evaluation items as response variables and the level 2 evaluation items as explanatory variables.

2. The information providing device according to claim 1, characterized in that said analysis result output means causes said plurality of explanatory variables to be displayed on said information terminal in the form of a graphic having a size corresponding to said degree of contribution.

3. The information providing device according to claim 1, further comprising a correlation analysis means for calculating the correlation between the plurality of explanatory variables, and wherein the analysis result output means causes the information terminal to display lines connecting pairs of the explanatory variables, the lines having thickness and / or type corresponding to the strength of the correlation.

4. The information providing device according to claim 1, further comprising a standard value setting means for setting standard values ​​for each of the plurality of explanatory variables, and wherein the analysis result output means displays the explanatory variables in different colors depending on their differences from the standard values.

5. The information providing device according to claim 4, characterized in that the standard value setting means has a function of calculating an average value of the evaluation points calculated for each of the multiple explanatory variables in the same or similar industry or business type, and setting the average value as the standard value.

6. The information providing device according to claim 4, characterized in that the analysis result output means colors the explanatory variable in a first color when the explanatory variable is higher than the reference value, and colors the explanatory variable in a second color different from the first color when the explanatory variable is lower than the reference value.

7. The information providing device according to claim 1, wherein said analysis result output means displays said figures in a radial arrangement.

8. An information providing device according to any one of claims 1 to 7, wherein the level 3 evaluation item is a subordinate evaluation item to the level 2 evaluation item.

9. A method for providing information executed by a computer comprising the steps of: acquiring evaluation data including evaluation values ​​of evaluation items for a target product or service, the evaluation data including evaluation values ​​of multiple evaluation items related to willingness to enter into a contract based on the evaluation data; calculating an evaluation value of willingness to enter into a contract based on the evaluation data; performing a multivariate analysis using the evaluation value of the willingness to enter into a contract as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a response variable, thereby calculating the contribution of the explanatory variables to the response variable; and displaying the analysis results in the multivariate analysis on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the response variable, level 2 evaluation items explaining the response variable, and level 3 evaluation items explaining the level 2 evaluation items; and the multivariate analysis step includes performing a first multivariate analysis using the evaluation value of the level 2 evaluation items as an intermediate response variable and the evaluation value of the level 3 evaluation items as an explanatory variable, and a second multivariate analysis using the evaluation value of the level 1 evaluation items as a response variable and the evaluation value of the level 2 evaluation items as an explanatory variable.

10. A program causing a computer to execute: a means for acquiring evaluation data including evaluation values ​​of evaluation items for a target product or service, the evaluation data including evaluation values ​​of multiple evaluation items related to willingness to enter into a contract; a means for calculating an evaluation value of willingness to enter into a contract based on the evaluation data; a multivariate analysis means for performing a multivariate analysis using the evaluation value of willingness to enter into a contract as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a response variable, thereby calculating the contribution of the explanatory variables to the response variable; and an analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the evaluation data includes evaluation values ​​of level 1 evaluation items corresponding to the response variable, evaluation values ​​of level 2 evaluation items explaining the response variable, and evaluation values ​​of level 3 evaluation items explaining the level 2 evaluation items, the multivariate analysis means executing a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate response variable and the evaluation values ​​of the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the evaluation value of the level 1 evaluation items as a response variable and the evaluation values ​​of the level 2 evaluation items as explanatory variables.

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