Program, generation device, and generation method

Abstract

The purpose of the present invention is to provide a program capable of appropriately assisting in selection of measures by a user. A program according to the present disclosure causes a computer to execute: a measure information acquisition step for acquiring measure information including features of a measure; an evaluation index estimation step for estimating, as first and second evaluation indexes, the levels of first and second effects obtained when the measure is executed; and a map generation step for generating, on an evaluation axis corresponding to each evaluation index, an evaluation map indicating the positioning of the measure at a position corresponding to each evaluation index.

Description

Program, generation apparatus, and generation method

[0001] The present disclosure relates to a program, a generation apparatus, and a generation method.

[0002] Techniques for supporting the planning of measures for effectively promoting products and services are known. As a related technology, Patent Document 1 discloses a prediction apparatus that propagates the degree of measure effect using a graph including nodes and links, and can predict effective measures for targets for which no measure has been implemented.

[0003] Japanese Unexamined Patent Application Publication No. 2024-023848

[0004] When a plurality of measures are planned for a user, it is desirable for the user to be able to appropriately select a measure that can be expected to produce the highest advertising effect from among them. The technology disclosed in Patent Document 1 does not mention such a problem.

[0005] When a plurality of measures are presented, the expected effect (for example, sales, number of visitors, recognition degree, etc.) differs for each measure, and it is difficult to compare these on the same scale. Further, when the content of a measure is described in natural language, it is difficult to quantify and compare the characteristics of the measure, and the selection of the measure tends to depend on the experience and subjectivity of the person in charge. For this reason, there is a need for information processing technology that can appropriately support a user in selecting a measure by estimating the effect of the measure based on information on the characteristics of the measure and customer attributes, and visualizing a plurality of evaluation indices within a common framework.

[0006] An example of the object of the present disclosure is to provide a program, a generation apparatus, and a generation method that can appropriately support a user in selecting a measure, in view of the problem described above.

[0007] A program according to one aspect of the present disclosure causes a computer to execute: a measure information acquisition step of acquiring measure information including characteristics of a measure; an evaluation index estimation step of estimating the degrees of a first effect and a second effect obtained when the measure is executed as a first evaluation index and a second evaluation index; and a map generation step of generating an evaluation map that indicates the positioning of the measure at a position corresponding to each evaluation index on an evaluation axis corresponding to each evaluation index.

[0008] The generation device relating to one aspect of this disclosure comprises: a policy information acquisition unit that acquires policy information including the characteristics of the policy; an evaluation indicator estimation unit that estimates the degree of first and second effects obtained when the policy is implemented as first and second evaluation indicators; and a map generation unit that generates an evaluation map showing the position of the policy at a position corresponding to each evaluation indicator on an evaluation axis corresponding to each evaluation indicator.

[0009] A generation method relating to one aspect of this disclosure includes: a policy information acquisition step of acquiring policy information including the characteristics of the policy; an evaluation indicator estimation step of estimating the degree of first and second effects that can be obtained when the policy is implemented as first and second evaluation indicators; and a map generation step of generating an evaluation map that shows the position of the policy on an evaluation axis corresponding to each evaluation indicator, at a position corresponding to each evaluation indicator.

[0010] One example of the effects of the program, generation device, and generation method described herein is that they can appropriately support users in selecting measures.

[0011] Figure 1 is a block diagram showing the configuration of the generation device. Figure 2 is a flowchart showing the processing flow performed by the generation device. Figure 3 is a block diagram showing the overall configuration of the generation system. Figure 4 is a block diagram showing the configuration of the generation device. Figure 5 is a diagram showing an example of a user input screen used when creating a policy in the policy provision device. Figure 6 is a diagram showing an example of customer attribute information. Figure 7 is a diagram showing an example of estimation logic for estimating market size in monetary terms. Figure 8 is a diagram showing an example of estimation logic for estimating the predicted number of customers in terms of people. Figure 9 is a diagram showing an example of a policy display screen that displays policies. Figure 10 is a diagram showing an example of a policy details screen. Figure 11 is a block diagram showing the configuration of the communication terminal. Figure 12 is a block diagram showing the configuration of the customer information management device. Figure 13 is a block diagram showing the configuration of the policy provision device. Figure 14 is a flowchart showing the processing flow performed by the generation device. Figure 15 is a block diagram illustrating the hardware configuration of a computer that realizes the generation device, etc.

[0012] Embodiments of the present disclosure will be described in detail below with reference to the drawings. In each drawing, the same or corresponding elements are denoted by the same reference numerals. For clarity of explanation, redundant explanations will be omitted where necessary.

[0013] <Embodiment 1> (Generation device 100) Figure 1 is a block diagram showing the configuration of the generation device 100 according to this disclosure. The generation device 100 comprises a policy information acquisition unit 101, an evaluation indicator estimation unit 103, and a map generation unit 104.

[0014] The policy information acquisition unit 101 acquires policy information, including the characteristics of the policy. The evaluation indicator estimation unit 103 estimates the degree of the first and second effects that can be obtained when the policy is implemented, as the first and second evaluation indicators. The map generation unit 104 generates an evaluation map that shows the position of the policy at a position corresponding to each evaluation indicator on the evaluation axis corresponding to each evaluation indicator.

[0015] The generation device 100 includes a processor, memory, and storage device (not shown). The storage device stores a computer program on which the processing described herein is implemented. The processor can load the computer program from the storage device into memory and execute the computer program. In this way, the processor realizes the functions of the policy information acquisition unit 101, the evaluation indicator estimation unit 103, and the map generation unit 104.

[0016] Alternatively, the policy information acquisition unit 101, the evaluation indicator estimation unit 103, and the map generation unit 104 may each be implemented with dedicated hardware. Furthermore, some or all of each component may be implemented by general-purpose or dedicated circuits, processors, etc., or combinations thereof. These may be configured by a single chip or by multiple chips connected via a bus. Some or all of each component may be implemented by a combination of the aforementioned circuits, etc., and programs.

[0017] (Processing of the generation device 100) The processing performed by the generation device 100 will be explained with reference to Figure 2. Figure 2 is a flowchart showing the flow of processing performed by the generation device 100.

[0018] First, the policy information acquisition unit 101 acquires policy information (S1). Next, the evaluation indicator estimation unit 103 estimates the first and second evaluation indicators (S2). Then, the map generation unit 104 generates an evaluation map that shows the position of the policy at a position corresponding to each evaluation indicator on the evaluation axis corresponding to each evaluation indicator (S3).

[0019] As explained above, the generation device 100 described herein can appropriately support users in selecting measures.

[0020] Furthermore, the process of estimating the effectiveness of a measure involves acquiring measure information and customer attribute information, analyzing them, calculating estimated values ​​and response rates, and generating and displaying an evaluation map based on the calculated values. This series of processes is performed by a computer. Specifically, the acquisition of measure information, generation of measure features (e.g., measure vectors) and customer features (e.g., customer attribute vectors), estimation of response rates based on the similarity between features, estimation of first and second estimated values, calculation of first and second evaluation indicators based on these estimated values ​​and response rates, and generation of an evaluation map showing positioning according to the evaluation indicators are all specific processes performed by the device.

[0021] <Embodiment 2> Next, Embodiment 2 will be described. Embodiment 2 is a specific example of Embodiment 1 described above. Figure 3 is a block diagram showing the overall configuration of the generation system 1 according to this disclosure. The generation system 1 is a system that can appropriately support the user in selecting measures. The user is a user of the generation system 1. The user may be a person who uses the communication terminal 20.

[0022] The generation system 1 comprises a generation device 10, a communication terminal 20, a customer information management device 30, and a policy provision device 40. The generation device 10, the communication terminal 20, the customer information management device 30, and the policy provision device 40 can communicate with each other via a network N. The network N includes, for example, an internet line or a wireless communication network, but the type of communication is not limited to these. The generation device 10, the communication terminal 20, the customer information management device 30, and the policy provision device 40 each have a communication unit (not shown in Figure 3) and communicate with each other via the network N.

[0023] (Generation device 10) The generation device 10 is a device that can display the relative positions of multiple policies on a map when there are multiple policies created. In this way, the user can intuitively understand which policy to select from among the multiple policies. The generation device 10 obtains the user's desired evaluation map generation conditions from the communication terminal 20, for example, and executes predetermined processing. The generation device 10 may be composed of a server device or the like.

[0024] Figure 4 is a block diagram showing the configuration of the generation device 10. The generation device 10 is an example of the generation device 100 described above. The generation device 10 includes a policy information acquisition unit 11, a response rate estimation unit 12, an evaluation index estimation unit 13, a map generation unit 14, and a communication unit 15.

[0025] The policy information acquisition unit 11 is an example of the policy information acquisition unit 101 described above. The policy information acquisition unit 11 acquires policy information, including the characteristics of the policy. The policy information may include, for example, the content of the policy, the target product or service that is the target of marketing, the purpose of implementing the policy, the selling points, the expression format, the type of media, the expected target audience, the keywords used in the policy, or the history of past implementation.

[0026] The policy information acquisition unit 11 acquires policy information from the policy provision device 40 via the network N. The policy information may include feature quantities used to vectorize the policies. The policy information may include policy features extracted by performing natural language processing based on the policy content described in natural language. If multiple policies are provided by the policy provision device 40, the policy information acquisition unit 11 acquires policy information corresponding to each of the multiple policies.

[0027] Figure 5 shows an example of user input screen A used when creating a policy in the policy provision device 40. Input screen A is a screen for the user to input the necessary information in order to generate a customer cluster from product or service information. A customer cluster is a classification of multiple customers who could be targets of a policy into groups according to customer segment.

[0028] The policy provision device 40 transmits policy information, including the information entered on input screen A, to the generation device 10. The policy information acquisition unit 11 acquires the policy information received from the policy provision device 40.

[0029] As shown in Figure 5, input screen A includes a name field a1, a product features input field a2, a URL field a3, and an analysis start button a4. The name field a1 is for entering the name of the product or service. The product features input field a2 is for entering the product's features. The URL field a3 is for entering the URL of the webpage containing information about the target product. The analysis start button a4 is a button for starting the analysis of customer clusters.

[0030] Returning to Figure 4, the response rate estimation unit 12 estimates the customer response rate to the measure. The response rate is the proportion of customers who are expected to show interest in the measure, out of the total customer group to which the measure applies. The response rate indicates the likelihood that customers will show interest in the measure.

[0031] For example, the response rate estimation unit 12 estimates the response rate based on the similarity between the policy information and customer attribute information that indicates the attributes of the customer. Customer attribute information is information that indicates the characteristics and behavioral patterns of the customer. Customer attribute information includes the customer's preferences and interests. Customer attribute information may include, for example, the customer's age group, gender, or the categories of products and services they are interested in.

[0032] For example, the response rate estimation unit 12 may acquire customer attribute information from the customer information management device 30 via the network N. For example, the response rate estimation unit 12 acquires customer attribute information identified based on the purchase history information of each of multiple customers. The purchase history information shows a record of products that a customer has purchased in the past. The purchase history information is information that associates, for example, the date and time of purchase, the name of the purchased product, the purchase amount, and the store where the purchase was made. The purchase history information is stored in the customer information management device 30. The response rate estimation unit 12 acquires customer attribute information by acquiring the purchase history information from the customer information management device 30 and identifying customer attribute information for each customer based on the acquired purchase history information.

[0033] Figure 6 shows an example of customer attribute information. As shown in Figure 6, customer attribute information includes, for example, customer ID, age group, amount spent, number of slips, or customer attribute tags. The customer ID is information used to identify each customer. The age group is information that indicates the age range of the customer. The age group is divided into a certain range (for example, 20s). Note that age may be included as customer attribute information instead of age group.

[0034] The spending amount is information that shows the total amount of goods a customer has purchased in the past. The number of slips is information that shows the number of purchase transactions a customer has made in the past. The number of slips is related to the frequency of purchases. The number of slips is typically an integer of 1 or more. For example, each time a customer makes a purchase, the number of slips is increased by 1. Therefore, customers who make many purchase transactions will have a higher number of slips. Customer attribute tags are information that shows the customer's preferences. Examples of customer attribute tags include "beauty," "health," "luxury-oriented," and "outdoor." The scope of products that make up the customer attribute information may include all past purchase history, or it may include purchase history within a specified period from the present.

[0035] The response rate estimation unit 12 may acquire customer attribute information, including customer attribute scores weighted according to the characteristics of the purchased items included in the purchase history information. The customer attribute score is an indicator of the customer's preferences. The customer attribute score may be a numerical value calculated based on the purchase history information. For example, in the example in Figure 6, the customer attribute information of customer ID "001" includes customer attribute tags such as "Lotion_10, Brand_35, ...". The customer attribute score is the numerical part of the customer attribute tag. For example, in the case of this customer, the customer attribute score for "Lotion" is 10, and the customer attribute score for "Brand" is 35.

[0036] Customer attribute scores may be set so that the higher the frequency of product purchases, the higher the value. For example, if a customer purchases lotion once, the customer attribute score for "lotion" may be set to "1," and if the customer purchases lotion again, the customer attribute score may be set to "2."

[0037] Beyond this example, customer attribute scores may be weighted according to each characteristic. For example, if a customer purchases lotion once, the customer attribute score for "lotion" might be "1," but if they purchase lotion again, the score might increase to "3," and if they purchase it again, it might increase to "5," and so on. By changing the increase in value according to the number of purchases, the strength of interest in a particular characteristic can be appropriately reflected. In addition, the strength of interest may also be reflected according to the quantity of products purchased or the time elapsed before repeat purchases. More specifically, a higher score may be assigned to the larger the quantity of products purchased. Also, a higher score may be assigned to the shorter the time elapsed between purchases of the same or similar products as the previous purchase.

[0038] For example, the response rate estimation unit 12 vectorizes policy information and customer attribute information, and uses this vectorized information to estimate the response rate. Hereinafter, the vectorized policy information will be referred to as policy vectors, and the vectorized customer attribute information will be referred to as customer attribute vectors.

[0039] Specifically, the response rate estimation unit 12 estimates the response rate based on the similarity between a policy vector, which represents the characteristics of the policy, and a customer attribute vector, which represents the attributes of the customer. The customer attribute vector may be a feature vector generated based on the customer's gender, age, residential area, purchase history, interest categories, media contact trends, behavioral logs, or response history to past policies. The response rate estimation unit 12 may also use customer attribute vectors of customers belonging to a predetermined customer cluster as the customer attribute vector.

[0040] The response rate estimation unit 12 may estimate the similarity between the policy vector and the customer attribute vector using any method. For example, the response rate estimation unit 12 determines whether the estimated similarity exceeds a predetermined similarity threshold for each customer belonging to a predetermined customer cluster. The response rate estimation unit 12 estimates the response rate to the policy for customers belonging to a customer cluster by dividing the number of customers whose estimated similarity exceeds the predetermined similarity threshold by the total number of customers belonging to that customer cluster.

[0041] Returning to Figure 4, the evaluation indicator estimation unit 13 is an example of the evaluation indicator estimation unit 103 described above. The evaluation indicator estimation unit 13 estimates the degree of the first and second effects obtained when a measure is implemented as the first and second evaluation indicators. In this way, the evaluation indicator estimation unit 13 estimates the values ​​of the first and second evaluation indicators corresponding to each measure, and can numerically express the effect of each measure. If there are multiple measures provided by the measure provision device 40, the evaluation indicator estimation unit 13 estimates the first and second evaluation indicators corresponding to each of the multiple measures.

[0042] The effects obtained from implementing a measure are evaluated using metrics such as sales, number of visitors, brand awareness, customer satisfaction, purchase frequency, or number of inquiries. These metrics correspond to the evaluation axes used to compare and evaluate each measure in the evaluation map described later.

[0043] The evaluation index estimation unit 13 selects two of these evaluation indices and estimates each as the first and second evaluation indices. The evaluation index estimation unit 13 may accept user input to select the first and second evaluation indices, or may, for example, automatically select frequently used evaluation indices as the first and second evaluation indices.

[0044] Here, it is assumed that the evaluation index estimation unit 13 selects the first and second evaluation indices according to the user's selection. For example, the evaluation index estimation unit 13 accepts input of the first and second evaluation indices from the communication terminal 20 using an input screen (not shown). The evaluation index estimation unit 13 outputs the first and second evaluation indices to the map generation unit 14. Accordingly, the first and second evaluation indices are used as the horizontal axis (X-axis) and vertical axis (Y-axis), which are the evaluation axes of the evaluation map.

[0045] Note that the evaluation index estimation unit 13 may accept specification of units for the first and second evaluation indices. The units of the first and second evaluation indices are, for example, currency, quantity, frequency, or the like. This allows the evaluation index estimation unit 13 to estimate the first and second evaluation indices in units desired by the user.

[0046] The evaluation index estimation unit 13 may estimate the first and second evaluation indices by any method. For example, the evaluation index estimation unit 13 may estimate the first and second evaluation indices using Fermi estimation. Fermi estimation is a method for estimating the overall size by multiplying approximate values based on multiple viewpoints.

[0047] For example, the evaluation index estimation unit 13 first acquires viewpoints considered effective for performing Fermi estimation from character strings included in the policy information acquired by the policy information acquisition unit 11. For example, the evaluation index estimation unit 13 acquires viewpoints such as the population of sales destinations (viewpoint 1), market size (viewpoint 2), consumption per month (viewpoint 3), etc. The evaluation index estimation unit 13 may acquire any number of viewpoints. The evaluation index estimation unit 13 acquires information suitable for each viewpoint based on trained data of RAG (Retrieval-Augmented Generation) or LLM (Large Language Model).

[0048] Next, the evaluation index estimation unit 13 uses Fermi estimation to estimate first and second estimated values corresponding to the first and second effects, respectively, which are acquired in accordance with the acquired viewpoint. The first and second estimated values are calculated based on the content of the measure, and are pure effect estimated values that do not take into account customer reactions. The evaluation index estimation unit 13 may estimate the first and second estimated values for a plurality of measures using the same viewpoint, or may estimate the first and second estimated values using different viewpoints. For example, the evaluation index estimation unit 13 may estimate the first and second estimated values for measure P1 using viewpoints 1 to 3, and estimate the first and second estimated values for measure P2 using viewpoints 1 to 4.

[0049] Figures 7 and 8 are diagrams showing examples of estimation logic based on Fermi estimation. Figure 7 is a diagram showing an example of estimation logic for estimating a market size in monetary terms. In the example of Figure 7, the final market size is estimated based on the viewpoints of target population, proportion of people with interest, reachable proportion, proportion of people who purchase the product, unit price of the product, and average number of purchased products, which are shown as preconditions.

[0050] Furthermore, Figure 8 is a diagram showing an example of estimation logic for estimating a predicted number of attracting customers in terms of number of people. In Figure 8, the predicted number of attracting customers is estimated based on preconditions including target population, proportion of people with interest, reachable proportion, and action rate (proportion of people who actually take action), among other factors.

[0051] As described above, the evaluation index estimation unit 13 may present to the user, in a format as shown in Figures 7 and 8, based on what kind of viewpoints the first and second estimated values estimated using Fermi estimation are each derived.

[0052] Furthermore, the preconditions shown in Figures 7 and 8 may be configured so that the evaluation indicator estimation unit 13 considers the distribution of target group information (age group, gender, nationality) specified as preparation for policy formulation. Also, as shown in Figure 7, "According to data from the Ministry of Internal Affairs and Communications," the evaluation indicator estimation unit 13 may also include the source referenced when acquiring the data. In addition, each numerical value in the preconditions may be arbitrarily rewritten by the user. In that case, the evaluation indicator estimation unit 13 will estimate the evaluation indicators using the numerical values ​​rewritten by the user. Also, for example, if "market size (yen)" is estimated as an evaluation axis, the evaluation indicator estimation unit 13 may instruct the LLM to output the estimation result in monetary terms.

[0053] If the numerical values ​​of the preconditions can be rewritten by the user, the evaluation index estimation unit 13 or the map generation unit 14 may perform a validity check based on the unit or range of the input numerical values, and if an invalid input is detected, it may highlight the input field and display a message prompting the user to re-enter the value. Once a valid input is confirmed, the evaluation index estimation unit 13 re-estimates the first and second evaluation indices using the confirmed numerical values, and the map generation unit 14 updates the evaluation map to reflect the re-estimate results.

[0054] The evaluation indicator estimation unit 13 then estimates the first and second evaluation indicators based on the first and second estimated values ​​and the response rate. Specifically, the evaluation indicator estimation unit 13 estimates the first and second evaluation indicators by multiplying the first and second estimated values ​​by the response rate. By multiplying by the response rate, the evaluation indicator estimation unit 13 can estimate the first and second evaluation indicators while taking into account the customer response rate. As a result, the evaluation indicator estimation unit 13 estimates the first and second evaluation indicators as actual evaluation indicators that take customer responses into account.

[0055] The evaluation indicator estimation unit 13 may output the estimation results of the first and second evaluation indicators using relative values ​​among multiple measures. The relative values ​​among multiple measures are values ​​calculated as the estimated evaluation indicator score (e.g., standard score) for each measure. This allows users to evaluate measures based on their relative position within the overall picture, without depending on absolute values ​​such as the amount or number of people for each measure.

[0056] Furthermore, the evaluation indicator estimation unit 13 may output the estimation results of the first and second evaluation indicators using the absolute values ​​of the first and second effects for each of the multiple measures. The absolute values ​​of the first and second effects are values ​​calculated as estimated evaluation indicator values ​​for each measure, and may represent, for example, monetary amounts or the number of people. This allows users to intuitively grasp the scale of each measure.

[0057] Returning to Figure 4, the map generation unit 14 is an example of the map generation unit 104 described above. The map generation unit 14 generates an evaluation map that shows the position of each measure on the evaluation axis corresponding to each evaluation indicator. The map generation unit 14 also displays the generated evaluation map on the display unit, associating it with the measure information of multiple measures. The display unit is, for example, the display unit 22 of the communication terminal 20. This allows the user to visually associate the content of multiple measures with the evaluation map.

[0058] Figure 9 shows an example of the policy display screen B, which displays policies. The policy display screen B includes a policy list area b1, a map display area b2, and a display switching button b3. The map display area b2 includes an evaluation map M. The evaluation map M shows the positioning of nine policies on the evaluation axis corresponding to each evaluation indicator, at positions corresponding to each evaluation indicator. In Figure 9, the first evaluation indicator is the estimated sales score, and the second evaluation indicator is the estimated awareness score. The first and second evaluation indicators may be arbitrarily set by the user, etc. Also, the nine policies are indicated by circular icons on the evaluation map M.

[0059] The policy list column b1 contains text information indicating the content of multiple policies. In Figure 9, text information for three policies P1 to P3 is illustrated, but the policy list column b1 may be configured to display text information for nine policies shown in the evaluation map M through scrolling or other operations.

[0060] The map generation unit 14 generates an evaluation map M for each of the multiple measures in a display manner that corresponds to its position among the multiple measures. For example, the map generation unit 14 may generate an evaluation map M for the top-performing measures, which have high evaluation results based on the first and second evaluation indicators, in a different display manner than the measures other than the top-performing measures.

[0061] The map generation unit 14 identifies the positioning of each measure based on the evaluation results based on the first and second evaluation indicators. For example, in the example in Figure 9, suppose that measures P1 to P3 were ranked highly among the nine measures. In the following explanation, measures P1 to P3 will be used as an example to represent the nine measures. The estimated evaluation indicator scores (estimated sales score, estimated awareness score) for each measure P1 to P3 are, respectively, P1 (70, 70), P2 (63, 55), and P3 (43, 68).

[0062] The map generation unit 14 calculates the total score for each measure based on the estimated evaluation index score for each measure. The map generation unit 14 calculates the total score by summing the scores for each evaluation axis. For example, in the case of measure P1, the map generation unit 14 calculates the total score for measure P1 as 140 by (70 + 70 = 140). Similarly, the map generation unit 14 calculates the total scores for measures P2 and P3 as 118 and 111, respectively. Although omitted here, the map generation unit 14 also calculates the total score for the other six measures.

[0063] The map generation unit 14 identifies policy P1 as the most highly rated policy among the nine policies. The map generation unit 14 generates an evaluation map M for policy P1 in a different display manner than the other policies. For example, as shown in Figure 9, the map generation unit 14 displays the word "Best" near the icon for policy P1. The map generation unit 14 also generates the color and lines of the icon for policy P1 in a different display manner than the other icons. The map generation unit 14 may also make the icon for policy P1 blink. This allows the map generation unit 14 to highlight the icon for policy P1.

[0064] Furthermore, the map generation unit 14 may generate icons for the top three policies P1 to P3, in addition to the top-ranking policy P1, in a different display manner from the icons for policies P1 to P3. For example, the map generation unit 14 may display the icon for policy P1 in red, the icon for policy P2 in orange, and the icon for policy P3 in yellow, while displaying the icons for policies other than P1 to P3 in colorless. This allows the map generation unit 14 to highlight that the icons for policies P1 to P3 are high-ranking policies. The map generation unit 14 may also display text such as "2nd place" and "3rd place" near the icons for policies P2 and P3, respectively.

[0065] In the event of a tie in total scores, the map generation unit 14 may, for example, treat the policy with the highest score on the X-axis as the best policy. If there are policies tied for first place, the map generation unit 14 may highlight both of their icons.

[0066] Furthermore, the map generation unit 14 may classify multiple measures into multiple groups based on the evaluation results based on the first and second evaluation indicators, and generate an evaluation map M in a display manner corresponding to each group. For example, the map generation unit 14 may classify nine measures into two or more groups using predetermined conditions.

[0067] For example, the map generation unit 14 classifies the nine measures into three groups based on their total scores. The map generation unit 14 may also color-code each group. For example, the map generation unit 14 may color-code the icons of each measure, such as using red for 1st to 3rd place, yellow for 4th to 6th place, and blue for 7th to 9th place. This allows users to easily understand which group each measure belongs to. In addition to icons, the map generation unit 14 may also use, for example, the background area of ​​the icons to color-code them.

[0068] Furthermore, in the example shown in Figure 9, the map generation unit 14 generates the evaluation map M using estimated evaluation index scores and estimated awareness scores, which are relative values ​​of evaluation indicators among multiple measures, but it is not limited to this. The map generation unit 14 may also generate the evaluation map M using the absolute values ​​of the first and second effects for each of the multiple measures. Therefore, in the example shown in Figure 9, estimated evaluation index scores and estimated awareness scores are used as evaluation axes, but estimated market size (yen) and estimated number of visitors (people) may be used as evaluation axes. This allows users to easily grasp specific amounts and numbers.

[0069] The map generation unit 14 may generate the evaluation map M in a manner that allows switching between relative values ​​and absolute values ​​as described above. This allows the user to view the evaluation map M in a desired display manner. The map generation unit 14 may also generate the evaluation map M in a manner that allows switching to a KPI (Key Performance Indicator) display in response to the press of the display switching button b3.

[0070] The evaluation map M shown in Figure 9 is just an example, and the configuration of the evaluation map M can be changed as appropriate. For example, the map generation unit 14 may make it so that when an icon on the evaluation map M is clicked, the content and values ​​of the clicked measure are displayed in a pop-up window. The map generation unit 14 may also make it so that in the measure list section b1, the display jumps to the location of the measure that was clicked on the evaluation map M. Furthermore, the map generation unit 14 may transition to the measure details screen when the "View Details" button for each measure is pressed, or it may display the details in a pop-up window or the like.

[0071] If the display positions of multiple measures are close together within a predetermined distance on the evaluation map M, the map generation unit 14 may shift the display positions of the icons according to a predetermined rule to suppress overlap, or display the multiple close measures as a single group icon. If the group icon is selected, the map generation unit 14 may display a list of measures included in the group in a pop-up window and transition the user to a detailed screen corresponding to the selected measure.

[0072] In Figure 9, the policy list area b1 and the map display area b2 are shown on the same screen, but they may be displayed on separate screens.

[0073] Figure 10 shows an example of the policy details screen C. The details screen C displays the response rate and Fermi estimation results described above in detail. The details screen C includes a policy details section c1, a pie chart c2 showing the response rate, a section c3 for the estimated results of the first evaluation indicator, and a section c4 for the estimated results of the second evaluation indicator. By referring to this information, users can easily understand the customer response rate to the policy and the estimated results of the first and second evaluation indicators.

[0074] For example, the map generation unit 14 displays the estimation logic shown in Figure 7 or Figure 8 when the "Check Estimation Logic" button located below the estimation result field c3 for the first evaluation indicator or below the estimation result field c4 for the second evaluation indicator is pressed. In this way, the user can easily understand the estimation logic based on Fermi estimation.

[0075] If multiple measures are selected on the evaluation map M, the map generation unit 14 may extract the difference between the first and second evaluation indicators between the selected measures, as well as the breakdown of the preconditions or response rates that caused the difference, and display them in the difference display area.

[0076] Returning to Figure 4, the communication unit 15 is a communication interface for communication via wired or wireless means.

[0077] The generation device 10 caches the estimation results using evaluation axes, preconditions, and policy information as keys, and may generate an evaluation map M using the cached estimation results when the same or similar conditions are specified. If the estimation process takes a predetermined amount of time, the map generation unit 14 may display the previous evaluation map or an evaluation map of only some of the policies as a provisional display, and update the display in stages as the estimation results are acquired.

[0078] (Communication Terminal 20) Figure 11 is a block diagram showing the configuration of the communication terminal 20. The communication terminal 20 is a terminal device used by the user. The communication terminal 20 comprises an input unit 21, a display unit 22, and a communication unit 23. The communication terminal 20 may be, for example, a PC (Personal Computer), a smartphone, a tablet terminal, or a mobile phone.

[0079] The input unit 21 is an input device that receives user input. The input unit 21 may be, for example, a mouse, keyboard, touch panel, or voice input device.

[0080] The display unit 22 is a display device that displays various types of information. The display unit 22 is, for example, a display. The display unit 22 may also be a touch panel that has the functions of the input unit 21. Although not shown in the figures, the communication terminal 20 may be equipped with an audio output unit such as a speaker and output information by voice. The communication unit 23 is a communication interface for communication by wire or wireless.

[0081] (Customer Information Management Device 30) Figure 12 is a block diagram showing the configuration of the customer information management device 30. The customer information management device 30 is a device for managing customer information. The customer information management device 30 may be managed by, for example, a business that sells goods or services or a business that provides online payment services. The customer information management device 30 may manage customer information provided by multiple businesses. The customer information management device 30 includes a communication unit 31 and a storage unit 32.

[0082] The communication unit 31 is a communication interface for wired or wireless communication. The storage unit 32 stores various data and programs. At least a portion of the storage unit 32 is made of non-volatile memory so that data is retained even when the power to the customer information management device 30 is turned off. For example, the storage unit 32 may store the purchase history information described above.

[0083] (Policy Provisioning Device 40) Figure 13 is a block diagram showing the configuration of the policy provisioning device 40. The policy provisioning device 40 is a device that creates policies and provides the created policies. The policy provisioning device 40 includes a communication unit 41 and a policy creation unit 42.

[0084] The communication unit 41 is a communication interface for communication via wired or wireless means. The policy creation unit 42 creates multiple policies based on product information, customer attribute information, etc. The policy creation unit 42 transmits policy information to the generation device 10 via the communication unit 41.

[0085] The configuration of the generation system 1 has been described above. The configuration of the generation system 1 described above is merely an example and can be modified as appropriate. For example, if some or all of the components of the generation system 1 are realized by multiple information processing devices or circuits, these multiple information processing devices or circuits may be centrally located or distributed. For example, the information processing devices or circuits may be realized in a form in which each is connected via a communication network, such as a client-server system or a cloud computing system. Also, for example, the functions of the generation device 10, the customer information management device 30, or the policy provision device 40 may be provided in SaaS (Software as a Service) format.

[0086] For example, in the above description, the generation device 10 acquired policy information from the policy provision device 40, but it is not limited to this. The generation device 10 may also have the functions of a policy creation unit 42 and create policies in the generation device 10. Also, in the above description, an evaluation map was generated using an evaluation index multiplied by the response rate, but an evaluation map may be generated without using the response rate.

[0087] (Processing of the generation device 10) Next, the processing performed by the generation device 10 will be explained with reference to Figure 14. Figure 14 is a flowchart showing the flow of processing performed by the generation device 10.

[0088] First, the policy information acquisition unit 11 acquires policy information from the policy provision device 40 (S11). Next, the evaluation index estimation unit 13 accepts the selection of evaluation axes from the communication terminal 20 (S12). Subsequently, the evaluation index estimation unit 13 acquires perspectives that are considered effective for performing Fermi estimation based on the information of the evaluation axes (S13).

[0089] Next, the evaluation indicator estimation unit 13 performs Fermi estimation based on the acquired perspectives (S14). The response rate estimation unit 12 estimates the customer response rate to the measures (S15). For example, the response rate estimation unit 12 estimates the response rate based on the similarity between the measures information and customer attribute information indicating customer attributes. Subsequently, the evaluation indicator estimation unit 13 estimates the first and second evaluation indicators corresponding to the evaluation axis (S16).

[0090] The map generation unit 14 then generates an evaluation map showing the positioning of the measures (S17). The map generation unit 14 also displays the generated evaluation map in association with the measure information of multiple measures (S18). For example, the map generation unit 14 displays the evaluation map on the display unit 22 of the communication terminal 20.

[0091] The map generation unit 14 may perform display control to update the display content in response to user operations when displaying the evaluation map M on the display unit 22. For example, when the display switching button b3 is operated, an evaluation axis is changed, or a numerical value of the preconditions is edited, the map generation unit 14 re-executes the estimation process (S16) of the first and second evaluation indicators according to the content of the operation, regenerates the evaluation map M based on the estimation results, and re-displays it on the display unit 22.

[0092] As described above, in the generation system 1 relating to this disclosure, the generation device 10 acquires policy information including the characteristics of the policy, and estimates the degree of the first and second effects that can be obtained when the policy is implemented as first and second evaluation indicators. The generation device 10 generates an evaluation map that shows the position of the policy at a position corresponding to each evaluation indicator on the evaluation axis corresponding to each evaluation indicator.

[0093] With this configuration, the generation device 10 can visualize the positioning of measures through the evaluation map. This allows users to intuitively understand which measures they should adopt. Therefore, the generation device 10 can appropriately support users in selecting measures.

[0094] <Example of Hardware Configuration> Each functional component of the generation device 100, generation device 10, communication terminal 20, customer information management device 30, and policy provision device 40 (hereinafter referred to as "generation device 100, etc.") may be implemented by hardware that realizes each functional component (e.g., hardwired electronic circuits, etc.), or by a combination of hardware and software (e.g., a combination of electronic circuits and programs that control them, etc.). The case in which each functional component of the generation device 100, etc. is implemented by a combination of hardware and software will be explained further below.

[0095] Figure 15 is a block diagram illustrating the hardware configuration of a computer 900 that implements the generation device 100, etc. The computer 900 may be a dedicated computer designed to implement the generation device 100, etc., or it may be a general-purpose computer. The computer 900 may also be a portable computer such as a smartphone or tablet terminal.

[0096] For example, by installing a predetermined application on the computer 900, the computer 900 can realize various functions of the generation device 100, etc. The above application consists of a program for realizing the functional components of the generation device 100, etc.

[0097] The computer 900 includes a bus 902, a processor 904, a memory 906, a storage device 908, an input / output interface 910, and a network interface 912. The bus 902 is a data transmission path for the processor 904, memory 906, storage device 908, input / output interface 910, and network interface 912 to send and receive data to and from each other. However, the method of connecting the processor 904 and the other components to each other is not limited to bus connection.

[0098] The processor 904 is a variety of processors such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), or quantum processor (quantum computer control chip). The memory 906 is a main memory device implemented using RAM (Random Access Memory), etc. The storage device 908 is an auxiliary storage device implemented using a hard disk, SSD (Solid State Drive), memory card, or ROM (Read Only Memory), etc.

[0099] The input / output interface 910 is an interface for connecting the computer 900 with input / output devices. For example, input devices such as keyboards and output devices such as display devices are connected to the input / output interface 910.

[0100] The network interface 912 is an interface for connecting the computer 900 to a network. This network may be a LAN (Local Area Network) or a WAN (Wide Area Network).

[0101] The storage device 908 stores programs that implement each functional component of the generation device 100 (programs that implement the aforementioned applications). The processor 904 reads these programs into the memory 906 and executes them to implement each functional component of the generation device 100.

[0102] Each processor executes one or more programs containing a set of instructions for causing a computer to perform the algorithms described with reference to the drawings. These programs, when loaded into a computer, contain a set of instructions (or software code) for causing the computer to perform one or more functions described in the embodiments. The programs may be stored in various types of non-transitory computer-readable medium or tangible storage medium. Examples, but not limited to, include non-transitory computer-readable medium or tangible storage medium, such as RAM, ROM, flash memory, SSD or other memory technologies, CD-ROM, DVD (Digital Versatile Disc), Blu-ray® disc or other optical disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices. The programs may also be transmitted over various types of transient computer-readable medium or communication medium. Examples, but not limited to, include transient computer-readable medium or communication medium, such as electrical, optical, acoustic or other forms of propagating signals.

[0103] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure are possible, as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0104] Each drawing is merely illustrative to illustrate one or more embodiments. Each drawing may be associated with one or more other embodiments, rather than being associated with only one specific embodiment. As those skilled in the art will understand, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create embodiments that are not explicitly illustrated or described. Not all features or steps shown in any one drawing to illustrate an exemplary embodiment are necessarily required, and some features or steps may be omitted. The order of steps shown in any of the drawings may be changed as appropriate.

[0105] Some or all of the above embodiments may also be described as follows, but are not limited to the following: (Note 1) A program that causes a computer to execute: a policy information acquisition step of acquiring policy information including the characteristics of the policy; an evaluation indicator estimation step of estimating the degree of first and second effects obtained when the policy is implemented as first and second evaluation indicators; and a map generation step of generating an evaluation map that shows the position of the policy at a position corresponding to each evaluation indicator on an evaluation axis corresponding to each evaluation indicator. (Note 2) The program according to Note 1, further comprising a response rate estimation step of estimating the customer response rate to the policy, wherein in the evaluation indicator estimation step, the first and second evaluation indicators are estimated based on first and second estimated values ​​corresponding to each of the first and second effects and the response rate. (Note 3) The program according to Note 2, wherein in the response rate estimation step, the response rate is estimated based on the similarity between the policy information and customer attribute information indicating the attributes of the customer. (Note 4) The program according to Note 1 or 2, wherein in the measure information acquisition step, measure information corresponding to each of the multiple measures is acquired; in the evaluation indicator estimation step, the first and second evaluation indicators corresponding to each of the multiple measures are estimated; and in the map generation step, each of the multiple measures is displayed in a manner corresponding to its position among the multiple measures to generate the evaluation map. (Note 5) The program according to Note 4, wherein in the map generation step, top-performing measures with higher evaluation results based on the first and second evaluation indicators are displayed in a manner different from measures other than the top-performing measures to generate the evaluation map. (Note 6) The program according to Note 4 or 5, wherein in the map generation step, the multiple measures are classified into multiple groups based on the evaluation results based on the first and second evaluation indicators, and the evaluation map is displayed in a manner corresponding to each group. (Note 7) The program according to any one of Notes 4 to 6, wherein in the evaluation indicator estimation step, the estimation results of the first and second evaluation indicators are output using relative values ​​among the multiple measures; and in the map generation step, the evaluation map is generated using the relative values.(Note 8) The program described in Note 7, wherein in the evaluation indicator estimation step, the estimation results of the first and second evaluation indicators are output using the absolute values ​​of the first and second effects for each of the plurality of measures, and in the map generation step, the evaluation map is generated in a manner that allows switching between the relative values ​​and the absolute values. (Note 9) A generation device comprising: a measure information acquisition unit that acquires measure information including the characteristics of the measure; an evaluation indicator estimation unit that estimates the degree of the first and second effects obtained when the measure is implemented as the first and second evaluation indicators; and a map generation unit that generates an evaluation map showing the position of the measure at a position corresponding to each evaluation indicator on an evaluation axis corresponding to each evaluation indicator. (Note 10) A generation method comprising: a measure information acquisition step that acquires measure information including the characteristics of the measure; an evaluation indicator estimation step that estimates the degree of the first and second effects obtained when the measure is implemented as the first and second evaluation indicators; and a map generation step that generates an evaluation map showing the position of the measure at a position corresponding to each evaluation indicator on an evaluation axis corresponding to each evaluation indicator.

[0106] Some or all of the elements (e.g., configuration and function) described in Appendices 2 to 8 that are subordinate to Appendice 1 may also be subordinate to Appendices 9 and 10 in the same manner as those described in Appendices 2 to 8. Some or all of the elements described in any appendice may be applied to various hardware, software, recording means, systems, and methods for recording software.

[0107] This application claims priority based on Japanese Patent Application No. 2025-056422, filed on 28 March 2025, and incorporates all of its disclosures herein.

[0108] 1 Generation System 10 Generation Device 11 Policy Information Acquisition Unit 12 Response Rate Estimation Unit 13 Evaluation Indicator Estimation Unit 14 Map Generation Unit 15 Communication Unit 20 Communication Terminal 21 Input Unit 22 Display Unit 23 Communication Unit 30 Customer Information Management Device 31 Communication Unit 32 Storage Unit 40 Policy Provision Device 41 Communication Unit 42 Policy Creation Unit 100 Generation Device 101 Policy Information Acquisition Unit 103 Evaluation Indicator Estimation Unit 104 Map Generation Unit 900 Computer 902 Bus 904 Processor 906 Memory 908 Storage Device 910 Input / Output Interface 912 Network Interface A Input Screen a1 Name Field a2 Product Features Input Field a3 URL Field a4 Start Analysis Button B Policy Display Screen b1 Policy List Field b2 Map Display Area b3 Display Switch Button C Details Screen c1 Policy Details Section c2 Pie Chart Showing Response Rate c3 Estimated Results Section for First Evaluation Indicator c4 Estimated Results Section for Second Evaluation Indicator M Evaluation Map N Network P1-P3 Policy

Claims

1. A program that causes a computer to perform the following steps:

1. A process to acquire policy information including the characteristics of the policy; 2. A process to estimate the degree of the first and second effects that can be obtained when the policy is implemented, as first and second evaluation indicators; and 3. A process to generate an evaluation map that shows the position of the policy on an evaluation axis corresponding to each evaluation indicator, at a position corresponding to each evaluation indicator.

2. The program according to claim 1, further comprising a process for estimating the customer response rate to the measures, wherein the process for estimating the degree of the first and second effects estimates the first and second evaluation indicators based on the first and second estimated values ​​corresponding to the first and second effects, respectively, and the response rate.

3. The program according to claim 2, wherein the process for estimating the response rate estimates the response rate based on the similarity between the policy information and customer attribute information indicating the attributes of the customer.

4. The program according to claim 1 or 2, wherein in the process of acquiring policy information, policy information corresponding to each of the multiple policies is acquired; in the process of estimating the degree of the first and second effects, the first and second evaluation indicators corresponding to each of the multiple policies are estimated; and in the process of generating the evaluation map, each of the multiple policies is displayed in the evaluation map in a manner corresponding to its position among the multiple policies.

5. The program according to claim 4, wherein in the process of generating the evaluation map, the program generates the evaluation map in a different manner from the other measures, for top-performing measures that have higher evaluation results based on the first and second evaluation indicators.

6. The program according to claim 4, wherein the process for generating the evaluation map involves classifying the multiple measures into multiple groups based on the evaluation results based on the first and second evaluation indicators, and generating the evaluation map in a display manner corresponding to each group.

7. The program according to claim 4, wherein in the process of estimating the degree of the first and second effects, the estimated results of the first and second evaluation indicators are output using relative values ​​among the plurality of measures, and in the process of generating the evaluation map, the evaluation map is generated using the relative values.

8. The program according to claim 7, wherein in the process of estimating the degree of the first and second effects, the estimated results of the first and second evaluation indicators are output using the absolute values ​​of the first and second effects for each of the plurality of measures, and in the process of generating the evaluation map, the evaluation map is generated in a manner that allows switching between the relative values ​​and the absolute values.

9. A generating device comprising: a policy information acquisition means for acquiring policy information including the characteristics of the policy; an evaluation indicator estimation means for estimating the degree of first and second effects obtained when the policy is implemented as first and second evaluation indicators; and a map generation means for generating an evaluation map that shows the position of the policy at a position corresponding to each evaluation indicator on an evaluation axis corresponding to each evaluation indicator.

10. A method for generating an evaluation map that obtains policy information including the characteristics of the policy, estimates the degree of the first and second effects that can be obtained when the policy is implemented as first and second evaluation indicators, and shows the position of the policy on an evaluation axis corresponding to each evaluation indicator.

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