Information providing apparatus, method and program

Abstract

To provide an information providing apparatus, method and program for visually identifying a degree of contribution of an evaluation factor with respect to re-purchase intension or recommendation intension of a customer.SOLUTION: An information providing apparatus includes: investigation data acquisition means which acquires evaluation data including evaluation values of evaluation items for a target commodity or a service, the evaluation data including evaluation values of multiple evaluation items related to re-visit intention, re-purchase intension or other person recommendation intention; multivariable analysis means which performs multivariable analysis using, as objective variables, evaluation values of re-visit intention, re-purchase intension, or other person recommendation intention based on the evaluation data, and using, as explanatory variables, the evaluation values of the multiple evaluation items in the evaluation data, to calculate a degree of contribution of the explanatory variables to the objective variables; and analysis result output means which causes a network-connected information terminal to display a result analyzed by the multivariable analysis means. The analysis result output means causes the information terminal to display the explanatory variables using figures of sizes corresponding to the degree of contribution.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present invention relates to an information providing device, method, and program for providing information that contributes to a customer's intention to repeat purchase or recommend. In the present invention, the concept of repeat purchase intention includes not only the intention to repurchase a product or service, but also the intention to revisit a store. Note that the target stores in the present invention include not only physical stores (real stores) but also stores in virtual space. [Background technology]

[0002] Conventionally, by collating the results of monitor surveys and in-store questionnaires, customer evaluations of a target store are analyzed and used to improve the service of the target store. Patent Document 1 discloses a technology that displays a display showing the psychological state of customers toward the target store (for example, whether their satisfaction is high or low) superimposed on map information of the target store. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2013-089035 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional methods including Patent Document 1 simply display the survey results or questionnaire results, and it is not possible to grasp the degree to which factors contribute to the customer's intention to repeat purchase (or recommendation intention). In addition, there is a demand for a display means that not only calculates the contribution of evaluation factors with high accuracy, but also allows easy understanding.

[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 repeat purchase or recommend a product. [Means for solving the problem]

[0006] In the service industry, guidance is given on measures to improve operation quality in order to increase the rate of repeat visits (intention to return), but the proposals do not correspond to the operation quality of the store or the expectations of the customers, and there is a problem that the improvement effect is limited. The inventor hypothesized that the background to such problems is that the operation quality of the store and the expectations of the customers change depending on the contents handled by the store, the status of the improvement efforts on the store side, the customer segment, etc. In product sales, it is important to accurately grasp the evaluation factors that affect the intention to repeat purchases and use them to improve the product and the creative of promotions in order to increase the repurchase rate (intention to repeat purchases). Therefore, the inventor invented an information providing device and a program that make it possible to easily grasp effective measures to increase the repeat rate by factor analyzing the evaluation factors expected to be effective in customers' repeat purchases, collecting the number of repeat customer evaluations that are statistically significant, and visually displaying the results of the statistical analysis in a diagram.

[0007] The information providing device of the present invention comprises: 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 intention to revisit the store, intention to repurchase, or intention to recommend to others; a multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation value of intention to revisit the store, 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 multiple explanatory variables, thereby calculating the contribution of the explanatory variables to the dependent variable; and an analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communication via a network, and the analysis result output means can be configured to display the multiple explanatory variables on the information terminal using figures of a size corresponding to the contribution degree. In the above information providing device, the analysis result output means may be configured to display the objective variable and the plurality of explanatory variables connected by lines of different thicknesses and / or types according to the degree of contribution. The above-mentioned information providing device may further include a correlation analysis means for calculating an association between the plurality of explanatory variables, and the analysis result output means may be configured to display 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. The above-mentioned information providing device may further comprise a reference value setting means for setting a reference value for each of the plurality of explanatory variables, and the analysis result output means may display the explanatory variables in different colors according to their differences from the reference values. In the above information providing device, the standard value setting means may be configured to have a function of calculating an average value of the scores in the same or similar industry / business type calculated for each of the plurality of explanatory variables, and setting the average value as the standard value. In the above information providing device, the analysis result output means can be configured to color the explanatory variable in a first color when the explanatory variable is higher than the reference value, and to color the explanatory variable in a second color different from the first color when the explanatory variable is lower than the reference value. In the above information providing device, the analysis result output means may be configured to display the figures in a radial arrangement. In the above information providing device, the evaluation data can be configured to include level 1 evaluation items corresponding to the objective variable, level 2 evaluation items explaining the objective variable, and level 3 evaluation items explaining the level 2 evaluation items, and the multivariate analysis means can be configured to execute a first multivariate analysis in which the evaluation values ​​of the level 2 evaluation items are intermediate objective variables and the level 3 evaluation items are explanatory variables, and a second multivariate analysis in which the level 1 evaluation items are objective variables and the level 2 evaluation items are explanatory variables. In the above information providing device, the level 3 evaluation items may be evaluation items that are subordinate concepts to the level 2 evaluation items.

[0008] The information provision method of the present invention is an information provision method executed by a computer, which includes the steps of: 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 intention to revisit the store, intention to repurchase, or intention to recommend to others; calculating an evaluation value for intention to revisit the store, intention to repurchase, or intention to recommend to others based on the evaluation data; performing a multivariate analysis using the evaluation value for intention to revisit the store, 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 multiple explanatory variables to calculate the contribution of the explanatory variables to the dependent variable; and displaying the analysis results in the multivariate analysis step on an information terminal capable of communicating via a network, wherein the analysis result output step is characterized in that the multiple explanatory variables are displayed on the information terminal using a figure of a size corresponding to the contribution degree in the analysis result output step.

[0009] The program of the present invention causes a computer to execute: 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 intention to revisit the store, intention to repurchase, or intention to recommend to others; means for calculating an evaluation value of intention to revisit the store, intention to repurchase, or intention to recommend to others based on the evaluation data; multivariate analysis means for calculating the contribution of the explanatory variables to the objective variable by performing multivariate analysis using the evaluation value of the intention to revisit the store, intention to repurchase, or intention to recommend to others as an objective variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as multiple explanatory variables; and analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communication via a network, wherein the analysis result output means displays the multiple explanatory variables on the information terminal using a figure of a size corresponding to the contribution degree. Effect of the Invention

[0010] According to the present invention, it is possible to easily grasp the contribution of each evaluation factor to a customer's intention to repurchase, revisit a store, or recommend a product. [Brief description of the drawings]

[0011] [Figure 1] 1 is a configuration diagram of an information providing system according to a first embodiment. [Diagram 2] FIG. 2 is a diagram illustrating functions of the information providing device according to the first embodiment. [Diagram 3] FIG. 2 is a diagram showing an example of a questionnaire for a monitor survey of the present embodiment. [Figure 4] FIG. 2 is a diagram showing an example of a questionnaire for a questionnaire survey according to the present embodiment. [Diagram 5] FIG. 2 is a diagram illustrating a multivariate analysis means according to the first embodiment. [Figure 6] FIG. 13 is a diagram showing the analysis results showing the contribution of each explanatory variable to the intention to revisit a ramen restaurant in the ramen restaurant format according to the first embodiment. [Figure 7] FIG. 13 is a diagram showing the analysis results showing the contribution of each explanatory variable to the intention to revisit an izakaya restaurant in the first embodiment. [Figure 8] FIG. 7 is a diagram in which the contribution degree of each explanatory variable in FIG. 6 and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means according to the first embodiment. [Figure 9] FIG. 8 is a diagram in which the contribution degree of each explanatory variable in FIG. 7 and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means according to the first embodiment. [Figure 10] FIG. 11 is a modified example in which the contribution of each explanatory variable to the intention to return and the correlation coefficient of each explanatory variable are graphically compared radially using the analysis result output means, where (A) is a diagram for a ramen restaurant business and (B) is a diagram for an izakaya restaurant business. [Figure 11] FIG. 13 is a diagram showing the analysis results showing the contribution of each explanatory variable to the intention to revisit in a yakiniku restaurant according to the second embodiment. [Figure 12] FIG. 12 is a diagram in which the contribution degree of each explanatory variable in FIG. 11 and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means according to the second embodiment. [Figure 13] FIG. 13 is a diagram showing the analysis results showing the contribution of each explanatory variable to the intention to revisit a target store according to the third embodiment. [Figure 14] FIG. 14 is a diagram in which the contribution degree of each explanatory variable in FIG. 13 and the correlation coefficient of each explanatory variable are graphically displayed in a radial manner by the analysis result output means according to the third embodiment. [Figure 15] FIG. 13 is a diagram showing analysis results showing the contribution of each explanatory variable to repurchase intention for a beverage according to the fourth embodiment. [Figure 16] FIG. 16 is a diagram in which the contribution degree of each explanatory variable in FIG. 15 and the correlation coefficient of each explanatory variable are graphically displayed in a radial pattern by the analysis result output means according to the fourth embodiment. [Figure 17] FIG. 13 is a diagram in which the contribution of each explanatory variable in a cosmetic product and the correlation coefficient of each explanatory variable are graphically displayed in a radial pattern by the analysis result output means according to the fourth embodiment. [Figure 18] FIG. 13 is a diagram in which the contribution of each explanatory variable in toothpaste and the correlation coefficient of each explanatory variable are graphically displayed in a radial pattern by the analysis result output means according to the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] First Embodiment The information provision system 1 of the first embodiment analyzes evaluation factors in a specific business format, allowing customers to easily understand the evaluation factors that influence the intention to revisit or recommend a store by graphically displaying them, and has the configuration described below.

[0013] 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 form respondents, a survey data collection device 40, and the Internet 50. Note that, in the example shown in Fig. 1, one each of the information provision device 10, management information terminal 20, and survey data collection device 40 is illustrated, but the present invention is not limited to this configuration, and two or more of each may be used.

[0014] The information providing device 10 is, for example, a PC server, and has a calculation unit 11, a storage 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.

[0015] The management information terminal 20 is an information terminal, for example 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. 6 to 18) on the display device of the management information terminal 20.

[0016] The information terminal 30 is an information terminal used by the survey respondent, 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.

[0017] The survey data collection device 40 is, for example, a PC server, and has 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 data using the input program 123 in the form of store customers answering a questionnaire via a web browser on the information terminal 30, but it is also possible to process the survey data collected from the questionnaire respondents via another means, such as paper media, and upload it from the management information terminal 20 to the database 122.

[0018] 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.

[0019] (1) Method of collecting survey data 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 is data on customer satisfaction based on responses from customers of the store, and consists of two types of data: monitor survey data and questionnaire survey data.

[0020] The monitor survey in this embodiment is a survey in which a monitor (surveyor) actually uses the services of a store and answers certain questions based on his or her own experience. Since the surveyor is paid a fee, the monitor survey can have a large number of questions. On the other hand, compared to a questionnaire survey, it may be difficult to perform a reliable multivariate analysis using only the monitor survey, depending on the N number.

[0021] The questionnaire survey in this embodiment is an in-store survey with predetermined evaluation items, the number of questions is less than half that of a monitor survey, and the questions are suitable for multivariate analysis. On the other hand, the number of subjects can be larger than that of a monitor survey (for example, more than 100 cases can be collected per store per month), making it suitable for statistical analysis. Since the number of subjects is large, the questionnaire survey makes it possible to grasp evaluation trends by customer segments classified by attributes such as gender, age, and region. In this embodiment, it is also possible to use monitor survey data and questionnaire survey data together, making it possible to perform a statistical analysis of the strength of the influence of key evaluation items on overall satisfaction by business type, while also making it possible to grasp issues at a specific business level through the monitor survey.

[0022] The evaluation items to be written on each questionnaire are, for example, items related to QSCA (Quality Service Cleanliness Atmosphere). For target restaurants in the food and beverage category, items such as "customer service," "service speed," "food," "cleanliness," "space and atmosphere," and "cost performance" are examples. For target restaurants in the beauty category, items such as "customer service," "counseling," "skill," "cleanliness," "atmosphere," and "cost performance" are examples. In any business type, questions necessary for calculating a regression with the intention to revisit the store are set. In addition, questions for calculating a regression with the intention to recommend to others, such as whether the customer would like to use the target store to others, may be set. The above-mentioned items are just an example, and there may be several tens of items depending on the type (genre), size, season, etc. of the target store.

[0023] FIG. 3 is a diagram showing an example of a survey form for a monitor survey of this embodiment. In each attribute information field, enter attribute information for the survey staff who will be conducting the monitor survey. Note that the age, gender, place of residence, and other information for the survey staff is registered in advance, so this information does not need to be written on the survey form. The level 1 questions consist of a recommendation intention and a revisit intention. The level 1 questions are the objective variables of the multiple regression analysis described later. The evaluation of the level 1 questions is, for example, a multi-level evaluation of 0 to 10 points. The level 2 questions are factors that affect the level 1 questions, which are the objective variables. The level 3 questions are explanatory variables when performing multiple regression analysis with the level 2 questions as the dependent variables. Here, the level 2 questions and the level 3 questions are in a relationship of a higher concept and a lower concept. For example, the lower concept of "evaluation of customer service" in the subcategory A of the level 2 questions is "evaluation of greeting" and "evaluation of food serving" in the subcategory A of the level 3 questions. However, level 3 questions are not provided for all the subcategories of the level 2 questions. In addition, an evaluation of fit may be provided as another question equivalent to the level 2 questions. The question about fit is, for example, a multi-level evaluation for a question such as "Do you feel that our restaurant suits your taste?" In this embodiment, a level 3 question explaining the question about fit is not provided, but a level 3 question may be provided.

[0024] FIG. 4 is a diagram showing an example of a questionnaire for the questionnaire survey of this embodiment. In each attribute information field, the attribute information of the survey respondent is entered. Since the survey respondent is not a registered respondent like the survey investigator for a monitor survey, the respondent is also asked to enter information such as age and gender. The level 1 questions are the same as those in the monitor survey, and consist of items on willingness to recommend and willingness to revisit. In order to reduce the burden on respondents, the questionnaire survey is set with a small number of questions that serve as observation variables, and in this embodiment, it is composed of level 2 questions and level 2 checks. Here, the level 2 questions and the level 2 checks correspond to each other in terms of subcategories. For example, the level 2 check corresponding to "evaluation of customer service" in 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 conducted in a multi-level evaluation, with a multi-level evaluation. In the questionnaire survey, other questions equivalent to the level 2 questions include evaluations of fit and satisfaction.

[0025] The sense of fit in this embodiment is a variable representing the preference structure of A, such as an individual's preference for a target, that an individual A feels that option b is more preferable than option a, or that utility b is 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 includes lifestyle factors and reference group effects. By including the sense of fit that takes into account the tastes of the respondents to the monitor survey and questionnaire survey as an evaluation item, a function that enables the output of the correlation between the sense of fit and clustered customer attributes may be provided in the multivariate analysis means described later.

[0026] (2) Multivariate analysis methods The multivariate analysis means of this embodiment uses multiple regression analysis to analyze how each evaluation item influences the score of recommendation intention and revisit intention (hereinafter referred to as "revisit intention score"). Here, regression analysis is a statistical method that clarifies the relationship between a numerical value indicating a result (objective variable) and a numerical value that is a factor (explanatory variable), and multiple regression analysis is a statistical method that, when explaining an objective variable, quantifies to what extent each explanatory variable influences the result and makes a future prediction. By quantifying the influence of explanatory variables using multiple regression analysis, it becomes easier to plan ways to improve services. It also makes it possible to predict future changes in the objective variable that will accompany expected changes in explanatory variables.

[0027] In this embodiment, evaluation factors that can be improved by efforts are set as explanatory variables, and evaluation factors that are difficult to improve by efforts such as location and appearance are not set as explanatory variables. Specifically, "service," "food," "cleanliness," "space / atmosphere," "cost performance," etc. are set as explanatory variables for restaurants, and monitor surveys and questionnaire surveys are conducted in which the questions are evaluation items related to the explanatory variables and evaluation items for calculating the revisit intention score. Figure 6 is a diagram showing an example of the results of calculating the contribution of each explanatory variable to the revisit intention score (objective variable) by multiple regression analysis when the target store is a ramen restaurant. Here, VIF (Variance Inflation Factor) is an index for evaluating multicollinearity in multiple regression analysis. Multicollinearity means that there is a high correlation between explanatory variables, and it is considered better to remove the variable from the analysis when the VIF value is large. Here, the coefficient of determination (R 2 ) is an index that shows how closely the predicted values ​​of the dependent variable obtained by multiple regression analysis match the actual values ​​of the dependent variable. In general, the closer the coefficient of determination is to 1, the higher the explanatory power.

[0028] FIG. 5 is a diagram for explaining the multivariate analysis means according to this embodiment. The multivariate analysis means of this embodiment analyzes (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, by performing a hierarchical multiple regression analysis, it is possible to improve the explanatory power of the influence (contribution) of the level 2 evaluation factors on the will to recommend or the will to revisit the store. In other words, when performing a multiple regression analysis with the level 3 evaluation factors as explanatory variables and the evaluation values ​​of the level 1 evaluation items as objective variables, it may not be possible to accurately grasp which evaluation factor is dominant, but this embodiment solves this problem by providing level 2 evaluation factors, which are intermediate concepts.

[0029] The analysis (a) is a first multiple regression analysis in which the level 2 evaluation items are the objective variables (intermediate objective variables) and the level 3 evaluation items are the explanatory variables. This analysis makes it possible to understand the level 3 evaluation items (evaluation factors) that have an influence on the level 2 evaluation items (intermediate objective variables). For example, in Figure 3, it is possible to quantify the influence (degree of contribution) of each of the evaluation of the taste of the food (B-1), the evaluation of the variety of food (B-2), and the evaluation of the amount of food (B-3) on the evaluation of the food (B). Analysis (b) quantifies the correlation between evaluation factors at level 3. For example, in Figure 3, if simple correlation analysis reveals a high correlation between the evaluation of the variety of food (B-2) and the evaluation of the amount of food (B-3), it can be seen that an increase in the evaluation of one will also increase the evaluation of the other. Analysis (c) is a second multiple regression analysis with the level 1 evaluation items as the objective variables and the level 2 evaluation items as the explanatory variables. This analysis makes it possible to understand the level 2 evaluation items (evaluation factors) that have an influence on the level 1 evaluation items. For example, in Figure 3, the influence (contribution) of each of the evaluations of customer service (A), food (B), cleanliness (C), space and atmosphere (D), and cost performance (E) on the intention to recommend or revisit the restaurant can be quantified. Analysis (d) calculates a correlation coefficient that quantifies the correlation between evaluation factors at level 2. For example, in Figure 3, if simple correlation analysis reveals a high correlation 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.

[0030] Figure 6 is a diagram showing the analysis results showing the contribution of each explanatory variable (evaluation factor) at level 2 when the intention to revisit at level 1 in a ramen restaurant is used as the objective variable. In the example shown in Figure 6, it can be seen that "food" has the highest contribution to the intention to revisit, followed by "cost performance." Furthermore, the VIF of each explanatory variable is not a large value, which shows that no problems with multicollinearity have occurred. Also, in Figure 6, the coefficient of determination R 2 Since the mean is .583, it can be seen that the analysis has sufficient explanatory power.

[0031] Figure 7 is a diagram showing the analysis results showing the contribution of each explanatory variable (evaluation factor) at level 2 when the intention to revisit at level 1 in an izakaya establishment is used as the objective variable. In the example shown in Figure 7, it can be seen that "cost performance" has the highest contribution to the intention to revisit, followed by "service" and "food". In addition, the VIF of each explanatory variable is not a large value, which shows that there is no problem with multicollinearity.

[0032] (3) Analysis result output means The analysis result output means of this embodiment not only outputs the analysis results in a table format as shown in Figures 6 to 7, 11, 13, and 15, but also makes it possible to visually grasp the contribution of explanatory variables to customers' intention to revisit by graphically displaying the contribution (see Figures 8 to 10, 12, 14, 16 to 18). FIG. 8 is a diagram in which the contribution of each explanatory variable to the intention to revisit and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means in the ramen business shown in FIG. 6. Explanatory variables with a high contribution to the intention to revisit 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 when the contribution is below a certain level, it is drawn with a predetermined minimum radius so that it will not be smaller than the minimum radius. However, the size of the circle does not necessarily need to be proportional to the contribution value, and it may be highlighted (for example, the top two explanatory variables may be displayed large by multiplying the contribution value by a coefficient).

[0033] In addition, the contribution display lines connecting the explanatory variables, which are the objective variable, the intention to revisit, are drawn with thickness and type according to the contribution. That is, the lines connecting the explanatory variables and the objective variables that have a high contribution to the intention to revisit are displayed thick, the lines connecting the explanatory variables and the objective variables that have a medium contribution are displayed thin, and the lines connecting the explanatory variables and the objective variables with low contribution are drawn as dotted lines. In this way, by changing the thickness and type of the contribution display lines depending on the magnitude of the contribution, it is possible to visually grasp the contribution of each explanatory variable to the objective variable.

[0034] Also, explanatory variables having a specific correlation coefficient can be connected and displayed by a correlation display line. In the example of FIG. 8, the two pairs of explanatory variables with the highest correlation coefficients are connected by a correlation display line, and the correlation coefficient value is displayed. Any rule can be set for the method of displaying the correlation display line and the correlation coefficient, and for example, the top five pairs with a value of 0.4 or more after rounding off the least significant digit may be displayed. In addition, the correlation display line of this embodiment adopts a rule that a value of 0.7 or more after rounding off the least significant digit is displayed as a double line, a value less than 0.7 to 0.6 or more is displayed as a thick solid line, a value less than 0.6 to 0.5 or more is displayed as a solid line of normal thickness, a value less than 0.5 to 0.4 or more is displayed as a thin dotted line, and a value less than 0.4 is 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 easily grasp that in ramen restaurants, there is a high correlation between food and cost performance, followed by a high correlation between space / atmosphere and cleanliness. The explanatory variables connected by the correlation degree display line are not limited to the top two pairs, and any number of correlation degree display lines can be displayed.

[0035] In addition, although the circles and lines are displayed in a single color in the example of Fig. 8, they may be displayed in a color. For example, the circles of the two explanatory variables with the highest contributions may be colored in a different color (e.g., green), and the circles of the two explanatory variables with the lowest contributions may be colored in a color different from the highest (e.g., red). Similarly, the contribution display line and correlation display line may be displayed in different colors depending on the magnitude of the contribution and correlation.

[0036] Figure 9 is a diagram in which the contribution of each explanatory variable to the intention to revisit and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means in the izakaya establishment shown in Figure 7. As in Figure 8, the size of the circle allows the user to visually grasp the magnitude of the contribution to the intention to revisit.

[0037] As in Figure 8, the contribution line between the objective variable (revisit intention) and the explanatory variables is drawn with a thickness according to the contribution. As in Figure 8, the two pairs of explanatory variables with the highest correlation coefficient are connected with a correlation line to display the value of the correlation coefficient.

[0038] FIG. 10 is a diagram of a modified example in which the contribution of each explanatory variable to the intention to revisit and the correlation coefficient of each explanatory variable are graphically compared and displayed radially by the analysis result output means, (A) being a diagram of the ramen business shown in FIG. 6, and (B) being a diagram of the izakaya business shown in FIG. 7. In FIG. 10, circles representing each explanatory variable are arranged at the vertices of a regular hexagon. In FIG. 10 as well, the size of the circle represents the magnitude of the contribution to the intention to revisit, which is the objective variable. In FIG. 10 as well, the two explanatory variables with the highest correlation coefficients are connected and drawn with a contribution display line. The analysis result means of this embodiment is capable of displaying the analysis results for two different business types side by side, as exemplified in FIG. 10. In this case, it is preferable to display the explanatory variables in symmetrical locations in each business type, which makes it easier to compare the revisit structure by business type.

[0039] In this way, the analysis result output means can provide insight for efficiently carrying out store improvement efforts by graphically visualizing and providing the magnitude of the contribution of each evaluation factor to the revisit intention score, which changes with store improvement efforts. Note that in this embodiment, the explanatory variables are represented by circles whose size is proportional to the contribution, but the figures representing the contribution are not limited to this and any figure can be used, for example, a polygon or an illustration of food or a character. Also, the arrangement order of the figures representing the explanatory variables may be configured to be interchangeable by a drag operation.

[0040] As described above, according to the information provision system 1 of this embodiment, the analysis results obtained by multivariate analysis of dependent variables and explanatory variables (evaluation factors) are graphically visualized and provided, making it possible to instantly grasp the dependent variables for efficiently carrying out improvement efforts. In addition, 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.

[0041] The information provision system 1 according to the present embodiment can also provide a graphical visualization of the results of a multivariate analysis performed using a repurchase intention or a recommendation intention of a specific product as a response variable. Application to a specific product will be described later in the fourth embodiment.

[0042] <Second embodiment> The information provision system 1 according to the second embodiment includes 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 customers, a survey data collection device 40, and the Internet 50, and the hardware configuration is the same as that of the first embodiment. The second embodiment differs from the first embodiment in that the analysis result output means of the analysis program 121 executed by the information provision device 10 includes a coloring function by evaluation value.

[0043] Figure 11 shows the analysis results showing the contribution of each explanatory variable (evaluation factor) at level 2 when the intention to revisit at level 1 in a yakiniku restaurant is used as the objective variable. In the example shown in Figure 11, it can be seen that "cost performance" has the highest contribution to the intention to revisit, followed by "space and atmosphere", and then "food". In addition, the VIF of each explanatory variable is not a large value, which shows that there is no problem with multicollinearity. Also, in Figure 11, the coefficient of determination R 2 Since the difference is .801, it can be seen that this is a highly explanatory analysis. Furthermore, in FIG. 11, the difference value from the standard score in the yakiniku business format is also calculated for each explanatory variable. The standard score can be the average score of the score data in the yakiniku business format (same business format), or any score that the target store aims for. The average score of the score data in similar business formats can also be used, and for example, the difference value from the standard score in a similar business format that has ingredients in common, such as a business format that is a higher conceptualization of the yakiniku business format, such as a restaurant business format, or a teppanyaki business format, can be used. Although not shown in the figure, the difference value from the standard score in the yakiniku business format for the revisit intention score was also calculated, and the difference value was -6.0.

[0044] Fig. 12 is a diagram in which the contribution of each explanatory variable in Fig. 11 and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means. In Fig. 12, the diagonal lines indicate the first color, green, and the dots indicate the second color, red. In the example shown in FIG. 12, explanatory variables with a higher rating than the average rating of the same business type are colored green (diagonal lines), and explanatory variables with a lower rating than the average rating of the same business type are colored red (dots). In this embodiment, the greater the difference between the rating of the explanatory variable and the average rating, the darker the color is drawn. Unlike the example of FIG. 12, numbers indicating the difference value may be displayed near the objective variable and each explanatory variable. In FIG. 12, only "food" is colored green, so it can be seen instantly that only "food" is higher in rating than the average rating of the same business type, and each explanatory variable and objective variable colored red is lower in rating than the average rating of the same business type. In addition, since the intention to revisit, customer service, and space and atmosphere are colored dark, it can be seen that they are significantly lower in rating than the average rating.

[0045] In addition, two explanatory variables are connected by a correlation display line whose thickness and type correspond to the value of the correlation coefficient. In the second embodiment, a rule is adopted to display pairs whose correlation coefficient is 0.4 or more, and the result is that all explanatory variables are connected to at least one correlation display line. The correlation display lines are drawn as double lines, thick solid lines, normal solid lines, and thin dotted lines according to the same criteria as in the first embodiment.

[0046] From Figure 12, we can see that while the evaluation of the deliciousness of the food is not bad, the low evaluation of the space, atmosphere, and customer service are a drag and affect the cost performance. Furthermore, among the explanatory variables that have a high contribution to the objective variable, it is possible to identify explanatory variables that have a lower evaluation than the average evaluation of the same business type, which makes it possible to efficiently plan improvement measures. Here, a function can be added to sort the explanatory variables in ascending or descending order by contribution, or by ascending or descending order by evaluation.

[0047] According to the information providing system 1 of the second embodiment described above, it is possible to visually grasp the contribution of the explanatory variables to the objective variable, the correlation coefficient between the objective variables, and the level of the evaluation score relative to the reference score, so that improvement work can be carried out efficiently.

[0048] <Third embodiment> The information provision system 1 according to the third embodiment includes an information provision device 10 functioning as a server, an administration information terminal 20 functioning 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. The third embodiment differs from the first embodiment in that the analysis result output means of the analysis program 121 executed by the information provision device 10 has a function of radially arranging and displaying the contribution of each explanatory variable to the intention to revisit and the correlation coefficient of each explanatory variable.

[0049] Figure 13 shows the analysis results indicating the contribution of each explanatory variable (evaluation factor) when the intention to revisit a target store is used as the objective variable. In the example shown in Figure 13, it can be seen that "cost performance" has the highest contribution to the intention to revisit, followed by "customer service" and then "space and atmosphere". Furthermore, the VIF of each explanatory variable is not a large value, which indicates that no problem of multicollinearity has occurred. Also, in Figure 13, the coefficient of determination R 2 Since the mean score is .658, it can be seen that this analysis has sufficient explanatory power. Furthermore, in Figure 13, the difference between the average score for each explanatory variable and the average score for the same business format in the target store is also calculated. Although not shown, the difference between the mean score for the revisit intention score and the average score for the same business format was also calculated, and the difference was -0.1.

[0050] Fig. 14 is a diagram in which the contribution of each explanatory variable in Fig. 13 and the correlation coefficient of each explanatory variable are graphically displayed radially by the analysis result output means. In Fig. 14, the diagonal lines indicate the first color, green, and the dots indicate the second color, red. In the third embodiment, explanatory variables with a higher rating than the average rating of the same business type are colored green (diagonal lines), and explanatory variables with a lower rating than the average rating of the same business type are colored red (dots). In this embodiment, the greater the difference between the rating of the explanatory variable and the average rating, the darker the color is drawn. In FIG. 14, "service" and "service speed" are colored dark red, so it can be seen instantly that "service" and "service speed" are particularly lower than the average rating of the same business type. It can also be seen that "food" has a strong influence on "cost performance" connected with a double correlation display line, that "service" has a large influence on "cost performance" connected with a thick solid correlation display line, and that "service" also has a significant influence on "space / atmosphere" and "food" connected with solid correlation display lines.

[0051] In addition, in FIG. 14, each explanatory variable is drawn by a circle of a size according to the numerical value of the contribution degree to the revisit intention score. From the size of the circle, it can be seen that "cost performance" has the strongest influence on the revisit intention score, and "food" connected to "cost performance" by a double line has a very strong influence on "cost performance". In addition, since the scores of "service speed" and "cleanliness" are negative, it can be inferred that normal operation is not being performed. And, considering the small size of the "food" circle and the situation of the other explanatory variables, it is possible to judge from the graphical radial display in FIG. 14 that the evaluation is that the attention is not paid to "food", which is the original value provided, and the cost performance is the main focus, without examining the numerical value of the contribution degree. In this embodiment, the circles corresponding to each explanatory variable are arranged at the vertices of a regular hexagon, but the drawing method is not limited to this, and the explanatory variables may be arranged at the vertices of any regular polygon according to the number of explanatory variables, or may be arranged at equal intervals on the circumference. Also, the arrangement order of the figures indicating the explanatory variables may be changed by dragging.

[0052] According to the information provision system 1 of the third embodiment described above, the explanatory variables that affect the target variable are arranged radially and have good visibility, making it possible to carry out improvement work efficiently.

[0053] <Fourth embodiment> The information provision system 1 according to the fourth embodiment includes an information provision device 10 functioning as a server, an administration information terminal 20 functioning 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. The fourth embodiment differs from the first embodiment in that the multivariate analysis means executed by the information provision device 10 has a function of analyzing, by multiple regression analysis, how each evaluation item affects the score of repurchase intention for a specific product, and the analysis result output means has a function of radially arranging and displaying the contribution of each explanatory variable to repurchase intention and the correlation coefficient of each explanatory variable.

[0054] FIG. 15 is a diagram showing the analysis results showing the contribution of each level 2 explanatory variable (evaluation factor) when 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. 15, it can be seen that "fit" has the highest contribution to repurchase intention, followed by "flavor." In addition, the VIF of each explanatory variable is not a large value, which shows that no problem of multicollinearity has occurred. Also, in FIG. 15, the coefficient of determination R 2 Since the mean score is .565, it can be seen that the analysis has sufficient explanatory power. Furthermore, in Figure 15, the difference value between the average score and the same type of beverage is also calculated for each explanatory variable.

[0055] The analysis result output means of the present embodiment makes it possible to visually grasp the contribution of explanatory variables to a customer's repurchase intention by graphically displaying the contribution. FIG. 16 is a diagram in which the contribution of each explanatory variable to the repurchase intention for the specific beverage shown in FIG. 15 and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means. As in the third embodiment, explanatory variables with a high contribution to the repurchase intention are displayed as large circles, and explanatory variables with a low contribution are displayed as small circles. Also, circles corresponding to each explanatory variable are arranged at the vertices of a substantially regular heptagon. In FIG. 16, the diagonal lines indicate the first color, green, and the dots indicate the second color, red.

[0056] In this embodiment, as in the third embodiment, explanatory variables with a higher rating than the standard rating are colored green (diagonal lines), explanatory variables with a lower rating than the standard rating are colored red (dots), and the greater the difference between the rating of the explanatory variable and the standard rating, the darker the color is drawn. In FIG. 16, "Package" is colored dark green, so it is immediately visible that it is a rating that is particularly higher than the standard rating. Also, "Repurchase Intention" and "Effectiveness" are colored red, so it is immediately visible that they are lower than the standard rating and that "Effectiveness" has an effect on "Repurchase Intention".

[0057] Also, as in the first and third embodiments, explanatory variables having a specific correlation coefficient can be connected and displayed by a correlation degree display line. The correlation degree display line of this embodiment adopts a rule that a double line is displayed when the least significant digit is rounded off to 0.7 or more, a thick solid line is displayed when it is less than 0.7 to 0.6 or more, a solid line of normal thickness is displayed when it is less than 0.6 to 0.5 or more, and a thin dotted line is displayed when it is less than 0.5 to 0.4 or more, and less than 0.4 is not displayed. In this way, by graphically displaying the correlation coefficient of each explanatory variable with correlation degree display lines of different thicknesses and types, it is possible to easily grasp, for example, that the fit of the target product is highly correlated with the taste, package, and brand, and that the taste of the target product is highly correlated with the fit, package, amount / ingredients, and cost performance.

[0058] FIG. 17 is a diagram in which the contribution of each explanatory variable to the repurchase intention for a particular cosmetic product and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means. The explanatory variables are "usability," "amount / ingredients," "effect / efficacy," "packaging," "fit," and "brand." As in FIG. 16, explanatory variables with high contributions to the repurchase intention are displayed as large circles, and explanatory variables with low contributions are displayed as small circles. In addition, circles corresponding to each explanatory variable are placed at the vertices of a regular hexagon. The diagonal lines indicate the first color, green, and the dots indicate the second color, red. The display rules for the correlation display line are the same as those in FIG. 16. In FIG. 17, the objective variable and all explanatory variables are colored green, making it possible to instantly grasp that they are higher than the reference score.

[0059] From the size of the circles in Figure 17, we can see that "fit" and "usage" have a high degree of contribution to repurchase intention. Also, from the fact that "packaging" and "usage" are colored dark green, we can see that they are rated higher than the standard scores. Furthermore, from the correlation line, it is easy to see that there is a high correlation between the evaluation of the target product's "usage" and its "effects / efficacy," "fit," and "amount / ingredients," as well as a high correlation between the evaluation of the target product's "fit" and its "brand," "usage," and "packaging."

[0060] Figure 18 is a diagram in which the contribution of each explanatory variable to the repurchase intention for a particular toothpaste and the correlation coefficient of each explanatory variable are graphically displayed by the analysis result output means. The explanatory variables are "usability," "effects and efficacy," "SNS sharing," "packaging," "brand," and "fit." As in Figures 16 and 17, explanatory variables with high contributions to the repurchase intention are displayed with large circles, and explanatory variables with low contributions are displayed with small circles. In addition, circles corresponding to each explanatory variable are placed at the vertices of a regular hexagon. The diagonal lines indicate the first color, green, and the dots indicate the second color, red. The display rules for the correlation display lines are the same as those in Figures 16 and 17. In Figure 18, it is possible to instantly grasp that only "repurchase intention" and "effects and efficacy," which are colored red, are lower than the standard score.

[0061] From the size of the circles in Figure 18, we can see that "effect / efficacy" and "usage" have a high degree of contribution to repurchase intention. Also, because "repurchase intention" and "effect / efficacy" are colored dark red, it is immediately clear that they are rated lower than the standard score and that "effect / efficacy" has a negative impact on "repurchase intention." Furthermore, from the correlation line, it is easy to see that there is a high correlation between the evaluation of the target product's "effect / efficacy" and its "usage," "fit," and "SNS shares," as well as a high correlation between the target product's "brand" and "packaging."

[0062] According to the information provision system 1 of the fourth embodiment described above, explanatory variables that affect the objective variable are radially arranged and have good visibility, so that product improvement work can be performed efficiently. Note that the target of multivariate analysis by the information provision system 1 of the fourth embodiment is not limited to the exemplified products, and any product can be the target.

[0063] Although the preferred embodiment of the present invention has been described above, the technical scope of the present invention is not limited to the description of the above embodiment. Various modifications and improvements can be made to the above embodiment, and such modifications and improvements are also included in the technical scope of the present invention.

[0064] For example, in the above-described embodiment, a configuration was exemplified in which multiple regression analysis, which is a type of multivariate analysis, was used to determine the contribution of each evaluation factor to the revisit intention score, but the present invention is not limited to this configuration, and principal component analysis, factor analysis, correspondence analysis, multidimensional scaling, cluster analysis, path analysis, analysis of variance, conjoint analysis, regression analysis, logistics regression analysis, etc. 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 scenes (daily use, anniversary use, etc.), seasons, and weather, and to analyze evaluation factors that contribute to repeat purchase or recommendation intentions by taking these condition information into consideration. Also, by adding graphic attributes and psychographic attributes to the questionnaire, analysis by customer segment is possible. Also, by linking with a system that manages POS (Point of Sales) and purchase history, it is possible to grasp evaluation trends by ordered product.

[0065] Furthermore, in the above-mentioned first to third embodiments regarding the service industry, examples have been described using survey data targeting restaurants, but the present invention can also be applied to other industries, 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 service industries (professionals, etc.), advertising industry, real estate industry, and transportation industry. In the above-described embodiment, an example was described in which a hierarchical multiple regression analysis was performed to enhance the explanatory power of the influence (contribution) of evaluation factors on recommendation intention or repeat purchase intention, but the technical concept of the present invention is also applicable to multivariate analysis using non-hierarchical evaluation factors. [Explanation of symbols]

[0066] 1. Information provision system 10...Information provision device 20...Administrative information terminal 30…Information terminal 40...Survey data collection device 50…Internet

Claims

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 a plurality of evaluation items related to a willingness to revisit a store, a willingness to repurchase, or a willingness to recommend to others; a multivariate analysis means for performing a multivariate analysis using an evaluation value of a store revisit intention, a repurchase intention, or an intention to recommend to others based on the evaluation data as a response variable and using evaluation values ​​of a plurality of evaluation items in the evaluation data as a plurality of explanatory variables to calculate a contribution of the explanatory variables to the response variable; and an analysis result output means for displaying the analysis result by the multivariate analysis means on an information terminal capable of communicating via a network, The information providing device is characterized in that the analysis result output means displays the plurality of explanatory variables on the information terminal in the form of a graphic having a size corresponding to the degree of contribution.

2. 2. The information providing device according to claim 1, wherein the analysis result output means displays the objective variable and the plurality of explanatory variables connected by lines of different thicknesses and / or types according to the degree of contribution.

3. Further, a correlation analysis means for calculating a correlation between the plurality of explanatory variables is provided, The information providing device according to claim 1 or 2, characterized in that the analysis result output means displays on the information terminal a line connecting pairs of explanatory variables, the line having a thickness and / or a type corresponding to the strength of the association.

4. Further, a reference value setting means for setting a reference value for each of the plurality of explanatory variables is provided, 3. The information providing apparatus according to claim 1, wherein the analysis result output means displays the explanatory variables in different colors according to the differences from the reference 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 scores 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. 5. 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 has a value higher than the reference value, and colors the explanatory variable in a second color different from the first color when the explanatory variable has a value lower than the reference value.

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

8. the evaluation data includes level 1 evaluation items corresponding to the objective variable, level 2 evaluation items explaining the objective variable, and level 3 evaluation items explaining the level 2 evaluation items, 3. The information providing device according to claim 1, wherein the multivariate analysis means executes a first multivariate analysis in which the evaluation values ​​of the level 2 evaluation items are used as intermediate objective variables and the level 3 evaluation items are used as explanatory variables, and a second multivariate analysis in which the level 1 evaluation items are used as objective variables and the level 2 evaluation items are used as explanatory variables.

9. 9. The information providing device according to claim 8, wherein the level 3 evaluation item is a subordinate concept evaluation item of the level 2 evaluation item.

10. acquiring evaluation data including evaluation values ​​for evaluation items regarding the target product or service, the evaluation data including evaluation values ​​for a plurality of evaluation items related to intention to revisit the store, intention to repurchase, or intention to recommend to others; A step of calculating an evaluation value of intention to revisit the store, intention to repurchase, or intention to recommend to others based on the evaluation data; a multivariate analysis step of calculating the contribution of the explanatory variables to the objective variable by performing a multivariate analysis using the evaluation values ​​of the revisit intention, repurchase intention, or recommendation intention as an objective variable and the evaluation values ​​of the plurality of evaluation items in the evaluation data as a plurality of explanatory variables; An information providing method, comprising: an analysis result output step of displaying the analysis result in the multivariate analysis step on an information terminal capable of communicating via a network, the information providing method being executed by a computer, The information providing method, wherein in the analysis result output step, the plurality of explanatory variables are displayed on the information terminal using a graphic having a size corresponding to the degree of contribution.

11. On the computer, A means for acquiring evaluation data including evaluation values ​​for evaluation items regarding a target product or service, the evaluation data including evaluation values ​​for a plurality of evaluation items related to intention to revisit a store, intention to repurchase, or intention to recommend to others; A means for calculating an evaluation value of a store's intention to revisit, repurchase, or recommend to others based on the evaluation data; a multivariate analysis means for performing a multivariate analysis using the evaluation value of the revisit intention, repurchase intention, or recommendation intention as a response variable and the evaluation values ​​of a plurality of evaluation items in the evaluation data as a plurality of explanatory variables to calculate the contribution of the explanatory variables to the response variable; and an analysis result output means for displaying the analysis result by the multivariate analysis means on an information terminal capable of communicating via a network, The program, wherein the analysis result output means displays the plurality of explanatory variables on the information terminal using a graphic having a size corresponding to the degree of contribution.

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