Information processing device, verification method, and program

Abstract

Provided are an information processing device, a verification method, and a program that shorten the time required for effect verification of measures for influencing sales of business. The information processing device comprises: a measure acquisition unit that acquires measures for influencing sales of target business; and a verification unit that verifies the effect of the measures on the basis of at least one of people flow data of a facility related to the target business or people flow data around the facility.

Description

Information Processing Apparatus, Verification Method, and Program

[0001] The present disclosure relates to an information processing apparatus, a verification method, and a program.

[0002] In recent years, technologies related to attempts to analyze customer consumption behavior data and utilize it for marketing have been developed. For example, Patent Document 1 discloses a technique for detecting a deviation between the actual value and the predicted value of events (the number of visitors and environmental (precipitation, temperature, etc.) data) that are factors in sales changes, and appropriately correcting the sales prediction of products.

[0003] International Publication No. 2018 / 061136

[0004] In order to verify that the sales prediction in Patent Document 1 is correct, it is necessary to actually aggregate the sales amount, and there is a problem that it takes time to verify the effect.

[0005] An object of the present disclosure is to provide an information processing apparatus, a verification method, and a program that shorten the time required to verify the effect of measures for affecting the sales of a business, in view of the above-described problems.

[0006] The information processing apparatus according to the present disclosure includes: a measure acquisition unit that acquires a measure for affecting the sales of a target business; and a verification unit that verifies the effect of the measure based on at least one of the pedestrian flow data of a facility related to the target business or the pedestrian flow data around the facility.

[0007] The verification method according to the present disclosure includes: acquiring a measure for affecting the sales of a target business; and verifying the effect of the measure based on at least one of the pedestrian flow data of a facility related to the target business or the pedestrian flow data around the facility.

[0008] The program according to the present disclosure causes a computer to execute: a process of acquiring a measure for affecting the sales of a target business; and a process of verifying the effect of the measure based on at least one of the pedestrian flow data of a facility related to the target business or the pedestrian flow data around the facility.

[0009] This disclosure provides an information processing device, a verification method, and a program that shorten the time required to verify the effectiveness of measures that affect business sales.

[0010] This is a block diagram illustrating the configuration of the information processing device related to this disclosure. This is a flowchart illustrating an example of the flow of the verification method related to this disclosure. This is a block diagram illustrating the configuration of a system including the information processing device related to this disclosure. This is a block diagram illustrating the configuration of a business terminal related to this disclosure. This is a schematic diagram illustrating a network diagram. This is a schematic diagram explaining a specific example of a method for verifying the effectiveness of a measure. This is a flowchart illustrating an example of the operation of the information processing device related to this disclosure. This is a block diagram illustrating the hardware configuration of the information processing device related to this disclosure.

[0011] Embodiment 1 Hereinafter, an example of the configuration of the information processing device 10 will be described with reference to Figure 1. The information processing device 10 may be a computer device that operates by a processor executing a program stored in memory. The information processing device 10 may be, for example, a server device. The information processing device 10 may also be composed of multiple computer devices. In this case, the components or functions that constitute the information processing device 10 may be distributed among the multiple computer devices. The multiple computers may be connected via a network, or they may be directly connected via cables or the like.

[0012] The information processing device 10 includes a policy acquisition unit 11 and a verification unit 12. The policy acquisition unit 11 and the verification unit 12 may be software or modules whose processing is performed by the processor executing a program stored in memory. Alternatively, the policy acquisition unit 11 and the verification unit 12 may be hardware such as a circuit or chip.

[0013] The policy acquisition unit 11 acquires policies that will affect the sales of the target business. The policies may be policies that increase the sales of the target business, or they may be policies that maintain the sales of the target business. The policy acquisition unit 11 may generate policies based on the business data of the target business using, for example, generative AI (Artificial Intelligence) such as a large-scale language model.

[0014] Here, generative AI is implemented, for example, by a neural network. A neural network contains multiple artificial neurons, each with synapses connecting them. Each synapse has a weight. When such a neural network receives an input, it performs calculations using the weights associated with each synapse and produces an output corresponding to the input.

[0015] A model representing the connection relationships between neurons and synapses may be stored in memory, for example, in software format. Alternatively, the model may be implemented as a dedicated circuit. Similarly, the weights of each synapse may also be stored in memory in software format. Alternatively, a circuit representing the weights may be implemented in a dedicated circuit. Note that when constructing a generative AI using multiple models, it is not necessarily required that all models be stored in the same memory. There are many different types of neural network models, and generative AI may be realized by adopting and substituting a wide variety of models such as Transformer, convolutional neural networks (CNN), and recurrent neural networks (RNN).

[0016] The verification unit 12 verifies the effectiveness of the measures based on at least one of the following: pedestrian flow data of facilities related to the target business or pedestrian flow data around the facilities. The verification unit 12 may, for example, confirm that the number of customers located at and around the facilities increased during the implementation of the measures. The area around a facility includes a predetermined range from a certain facility. The predetermined range may be set in advance by the user or automatically by the system. For example, the predetermined range may be changed depending on the size, scale, and type of business of the store. More specifically, the predetermined range may be made larger for larger stores or larger in scale. The predetermined range may also be adjusted by a value that specifies the accuracy of the measurement. When measuring with high accuracy, the predetermined range may be set to be narrower. The predetermined range may be defined, for example, by a circle centered on the store or by a polygon. The predetermined range may be adjusted as appropriate, such as to include surrounding roads, taking geographical information into consideration. Note that people located around the facility may also include people located inside the facility.

[0017] Next, an example of the verification method relating to this disclosure will be explained with reference to Figure 2. First, the policy acquisition unit 11 acquires policies that will affect the sales of the target business (step S11). Next, the verification unit 12 verifies the effectiveness of the policies based on at least one of the following: pedestrian flow data of facilities related to the target business or pedestrian flow data around the facilities (step S12).

[0018] Embodiment 1 uses pedestrian flow data to verify the effectiveness of measures, thus allowing for verification of the effectiveness of measures without calculating actual sales. Therefore, Embodiment 1 can shorten the time required to verify the effectiveness of measures that affect business sales.

[0019] Embodiment 2 Embodiment 2 is a specific example of Embodiment 1. Figure 3 is a block diagram illustrating the configuration of a system including the information processing device 300 according to the present disclosure. The system shown in Figure 3 comprises a business terminal 100, a customer terminal 200, and an information processing device 300. The business terminal 100, the customer terminal 200, and the information processing device 300 are connected to each other via a network N. Here, the network N is a wired or wireless communication line. The network N may be a network accessible only to some information devices, or it may be a network that can be used by an unspecified number of people, such as the Internet.

[0020] The business operator terminal 100 is an information processing device that requests the information processing device 300 to generate measures for the target business. For example, it is owned by the user requesting the measure generation, i.e., the business operator conducting the target business. The business operator terminal 100 transmits the business data and objectives of the target business for which it wants to generate measures to the information processing device 300, and receives at least one measure from the information processing device 300. Note that the user and the worker using the business operator terminal 100 do not need to be the same person. For example, a worker who has interviewed the user about the objectives, etc., may perform the various operations.

[0021] Figure 4 is a block diagram showing an example configuration of a business terminal 100. The business terminal 100 may be a computer device that operates by having a processor execute a program stored in memory.

[0022] The business terminal 100 includes a business data transmission unit 110, a target transmission unit 120, and a policy receiving unit 130. The business terminal 100 may also include a database (not shown) that stores business data for the target business. The database may store business data as text data, or in other formats such as graphs or photographs. Business data may include, for example, sales data, customer data, records of promotional events, and customer survey results from a retail store operated by the user. Customer data may include at least one word associated with the customer. Customer data may also include purchase history.

[0023] The business data transmission unit 110 is a communication means for transmitting business data specified by the user to the information processing device 300. If the business data is stored in a format other than text, the business data transmission unit 110 may use an external service to convert the business data into text data and transmit it to the information processing device 300. Here, the external service is a service that converts data such as graphs into text data, and may be provided as a cloud service, for example. Business data stored in a format other than text, such as graphs, is usually larger in size than business data stored as text data. Therefore, converting business data stored in a format other than text into text data before transmission can reduce the amount of data transmitted. Note that the process of converting business data stored in a format other than text into text data may be performed on the information processing device 300 side. In this case, the business data transmission unit 110 transmits business data stored in a format other than text to the information processing device 300 without converting it to text.

[0024] The target transmission unit 120 is a communication means that transmits the target for the target business, entered by the user, to the information processing device 300. The target may be a sentence such as "Increase sales at XX store," or it may be one or more words such as "Increase sales at XX store." The target may also include numerical targets such as the target amount of sales or the growth rate. When the information processing device 300 receives the business data and the target, it is set to generate measures. The measure receiving unit 130 is a communication means that receives measures from the information processing device 300.

[0025] When verifying the effectiveness of the measures, the business terminal 100 may transmit information indicating the name and implementation date of the implemented measures to the information processing device.

[0026] Referring again to Figure 3, the customer terminal 200 is a communication terminal owned by a customer of the target business. The customer terminal 200 may also be a computer device that operates by having a processor execute a program stored in memory.

[0027] The customer terminal 200 may be, for example, a mobile phone such as a smartphone, or a personal computer. The customer terminal 200 acquires information about its current location (also referred to as location information) and transmits this location information, along with the ID of the customer who possesses the customer terminal (also referred to as the customer ID), to the information processing device 300. The customer ID and the customer's attributes may be associated in the operator terminal 100 or the information processing device 300.

[0028] Location information may be obtained, for example, by GNSS (Global Navigation Satellite System) positioning. GNSS may be GPS (Global Positioning System) or QZSS (Quasi-Zenith Satellite System). Alternatively, location information may be obtained, for example, by Wi-Fi® positioning.

[0029] Furthermore, when the pedestrian flow data acquisition unit 340 acquires pedestrian flow data based on captured images and payment results, the customer terminal 200 does not need to have a function to transmit location information to the information processing device 300. The customer terminal 200 may have a payment application installed. Also, when the pedestrian flow data acquisition unit 340 acquires pedestrian flow data based on captured images, the customer does not need to possess the customer terminal 200.

[0030] The information processing device 300 is a specific example of the information processing device 10. The information processing device 300 may also be a computer device that operates by having a processor execute a program stored in memory.

[0031] The information processing device 300 comprises a reception unit 310, a classification unit 320, a policy acquisition unit 330, a human flow data acquisition unit 340, and a verification unit 350. Each component constituting the information processing device 300 may be software or a module whose processing is executed by a processor executing a program stored in memory. Alternatively, each component constituting the information processing device 300 may be hardware such as a circuit or a chip. The policy acquisition unit 11 in the information processing device 10 corresponds to the policy acquisition unit 330 in the information processing device 300. The verification unit 12 in the information processing device 10 corresponds to the verification unit 350 in the information processing device 300.

[0032] The reception unit 310 receives business data and targets from the business terminal 100. The targets may include the names of retail stores, etc.

[0033] The classification unit 320 extracts customer attribute data from business data and classifies customers into multiple clusters based on the customer attribute data. The policy acquisition unit 330 may assign textual information to each cluster indicating what kind of attributes, i.e., the person profile, each cluster represents. The classification unit 320 may also determine the psychographic attributes of each customer based on their purchase history. For example, if a customer purchases pet supplies, words such as "animal lover" and "family-friendly" may be determined as attributes of that customer.

[0034] The method for classifying customers into clusters is not particularly limited, but for example, the classification unit 320 may summarize customer attributes into multiple words using a large-scale language model and classify customers with similar words into the same cluster. Data such as total spending amount and number of people may be associated with each cluster. This makes it possible to determine which clusters are most likely to be targeted by a campaign.

[0035] The policy acquisition unit 330 determines the target attributes for the policy based on the classification results and objectives from the classification unit 320. The target attributes may include demographic attributes such as age, gender, and family structure, and may also include psychographic attributes such as hobbies and preferences. Demographic attributes are demographic attributes and may specifically include age, gender, household size, family life cycle, income, occupation, and educational background. Psychographic attributes are psychological attributes and may specifically represent psychological characteristics such as values, lifestyle, personality, and preferences. Alternatively, a user operating the business terminal 100 may determine the target attributes by referring to the classification results from the classification unit 320 and transmit the target attributes to the information processing device 300.

[0036] The policy acquisition unit 330 generates policies from the determined target attributes using a large-scale language model or the like. The policy acquisition unit 330 may output a sentence such as "Hold a ○○ Fair," or it may output a word such as "○○ Fair." In addition, the policies may include information indicating the timing of the policy's implementation, such as "Hold a ○○ Fair in the middle of ○○."

[0037] The policy acquisition unit 330 may generate text data representing the policies, or it may generate a network diagram as shown in Figure 5. The user can refer to the text data or network diagram to decide which policies to implement.

[0038] The policy acquisition unit 330 can, for example, search for relevant information related to both target attributes and objectives, and generate policies based on that relevant information. For example, the policy acquisition unit 330 can search for a list of products that customers with target attributes frequently purchase at stores included in the objectives, and generate policies based on the search results.

[0039] Referring to Figure 5, when displaying a network diagram, multiple icons are shown, and related icons are connected by lines. When the cursor is hovered over each icon, the content of that icon may be displayed as text information. Icon 800 is an icon that indicates a goal. Icons 810, 820, and 830, which are connected to icon 800, are icons that represent the names of each policy.

[0040] Among the icons representing the names of each policy, a prominent icon may be used for policies that the information processing device 300 has determined to be particularly effective. In the example shown in Figure 5, icons 810, 820, and 830 are used, decreasing in size in that order, with icon 810 being the most prominent. Methods for making an icon stand out include increasing its size, using a bright color, or changing its shape from others. Icons 814 and 815, which are connected to icon 811, represent related words linked to the policy name shown in icon 811.

[0041] Referring again to Figure 3, the processing of the policy acquisition unit 330 is not limited to the process of generating policies by inputting prompts to the generation AI. The policy acquisition unit 330 can also generate policies by using one template selected from several pre-prepared templates, or by using a combination of multiple templates. The policy acquisition unit 330 may also select policies that have been previously generated for other users, policies that have been previously generated by other users, or policies that have been modified based on past policies.

[0042] The pedestrian flow data acquisition unit 340 acquires pedestrian flow data around facilities related to the target business (also referred to as target facilities) based on the location information of the customer terminal 200. Facilities are, for example, stores where a promotional campaign (e.g., a XX fair) is implemented.

[0043] The pedestrian flow data acquisition unit 340 may acquire pedestrian flow data by analyzing the captured images of the vicinity of the store or the interior of the store. The pedestrian flow data acquisition unit 340 may further analyze the captured images and estimate the attributes of customers from the appearance of the customers shown in the captured images. The pedestrian flow data acquisition unit 340 may identify the customers shown in the captured images and acquire the attributes of the customers from the database.

[0044] Also, the pedestrian flow data acquisition unit 340 may acquire pedestrian flow data based on the settlement results at the settlement terminals of the store. As an example, the pedestrian flow data acquisition unit 340 acquires pedestrian flow data when short-range wireless communication is performed between the settlement terminal and the customer terminal 200 held by the customer. The pedestrian flow data acquisition unit 340 can measure pedestrian flow data by acquiring information such as customer data and purchased items from the settlement application. The pedestrian flow data acquisition unit 340 may simply acquire pedestrian flow data based on the settlement history of the settlement terminal.

[0045] In addition to this, pedestrian flow data may be acquired using the results measured by means such as an infrared sensor or means for collecting position information such as GPS.

[0046] The verification unit 350 verifies the effect of the measure based on the pedestrian flow data. Specifically, the verification unit 350 measures, based on the pedestrian flow data, the number of customers who have the attributes of the customers targeted by the measure (target attributes) and who were located around the target facility during the execution of the measure. When the customer ID and the customer attributes are associated, the verification unit 350 can measure the number of customers with specific attributes from the pedestrian flow data including the customer ID and the position information. Information indicating the execution period of the measure may be included in the measure or may be acquired from the operator terminal 100.

[0047] Then, the verification unit 350 verifies the effect of the measure based on the measurement results. Note that the verification unit 350 can also verify the effect of the measure simply based on the pedestrian flow data around the facility without considering the target attributes. For example, the verification unit 350 may determine that the measure has an effect when the number of customers with the target attributes located around the target facility and its vicinity has increased during the execution of the measure.

[0048] The graph shown in FIG. 6 shows an example of people flow data. The horizontal axis indicates the date and time. The vertical axis indicates the number of customers who had the target attribute and were located in and around the store where the measure was implemented. T1 represents the date and time when the implementation of the measure started, and T2 represents the date and time when the implementation of the measure ended. The upward arrow represents the number of customers who increased between T1 and T2. The verification unit 350 can verify the presence or absence and the magnitude of the effect of the measure based on the increase rate of the number of customers. As an example, the information processing device 300 can cause an output device such as a display to output a graph as shown in FIG. 6. Also, when new people flow data is acquired, the drawing of the currently displayed graph may be automatically updated.

[0049] Referring again to FIG. 3, when it is confirmed by the verification unit 350 that the effect of the measure is low, the measure acquisition unit 330 may generate a new measure using the generation AI. The new measure may be a measure for which it has not been confirmed by the verification unit 350 that the effect is low. The measure acquisition unit 330 may display, for example, the icon representing the measure for which it has been confirmed that the effect is low in a non - prominent manner, such as small, in the network diagram.

[0050] FIG. 7 is a flowchart illustrating the operation of the information processing device 300. First, the reception unit 310 of the information processing device 300 receives the business data and goals of the target business (step S101). Next, the measure acquisition unit 330 of the information processing device 300 determines the target attribute and acquires measures based on the target attribute (step S102). Next, the people flow data acquisition unit 340 of the information processing device 300 acquires the people flow data of the store where the measure is implemented and its surroundings (step S103). Next, the verification unit 350 of the information processing device 300 verifies the effect of the measure based on the acquired people flow data (step S104).

[0051] Embodiment 2 allows for rapid verification of the effectiveness of a measure without using sales data from the stores where the measure was implemented. According to the information processing device 300 of Embodiment 2, pedestrian flow can be measured using sensors, and the effectiveness of the measure output via AI can be verified based on the measurement results. More specifically, by analyzing captured images acquired by an imaging device having an image sensor such as a camera, it is possible to measure whether there has been a change in people's movement. Similarly, by analyzing the increase or decrease in communication with payment terminals, it is possible to measure whether there has been a change in people's movement. This makes it possible to verify whether the implemented measure was effective.

[0052] Furthermore, the measures are not limited to measures that increase the sales of the target business, but may also be measures that maintain the sales of the target business. In this case, the verification unit 350 may determine that the measures were effective if the number of customers with the target attributes located around the target facility has not decreased during the implementation of the measures.

[0053] The information processing device 300 may also be configured to accept information for determining whether the measures are being implemented. The information for determining whether the measures are being implemented may be linked with the data acquired from each sensor and stored in a database. Furthermore, if the information processing device 300 accepts a setting indicating that the measures are being implemented, the data acquired by each sensor during that period may be automatically stored in the database in a way that makes it possible to determine that the data was acquired during the period in which the measures were being implemented.

[0054] In addition, the information processing device 300 may be linked with other devices used to implement the measures, such as digital signage, movable display shelves (also called movable shelves), and POS terminals. The information processing device 300 may be communicatively connected to devices such as digital signage, movable shelves, and POS terminals. The digital signage may display information related to the measures to customers (e.g., information that the measures will be implemented). When implementing the measures, the movable shelves on which the products targeted by the measures are displayed may be moved. The movable shelves may include actuators. The POS terminal manages sales information of the products. The POS terminal may offer discounts on the products targeted by the measures or award points when the products targeted by the measures are sold. Also, if the POS terminal is equipped with a display, the POS terminal may display information related to the measures to customers. For example, if the information processing device 300 obtains information from the digital signage indicating that information related to the measures is being displayed, it may manage the pedestrian flow data obtained during that period as pedestrian flow data for the period during which the measures were implemented. The information processing device 300 may, for example, acquire information on the position of the movable shelves displaying the products targeted by the measure from the movable shelves, and manage the pedestrian flow data acquired during the period when the movable shelves were in a different position from their original position as pedestrian flow data for the period of the measure's implementation. The information processing device 300 may, for example, acquire sales information of the products targeted by the measure, and manage the pedestrian flow data acquired during the period when the products targeted by the measure were discounted as pedestrian flow data for the period of the measure's implementation.

[0055] In another embodiment, if no change is observed in pedestrian flow data during the period in which the measures are being implemented, the information processing device 300 may control the settings of signage, movable shelves, POS terminals, etc. For example, the information processing device 300 may control the frequency, size, and manner (e.g., color) of information displayed on signage, change the position of movable shelves, or change the amount of discounts or points. No change in pedestrian flow data includes, for example, cases where the increase in pedestrian flow does not exceed a threshold when compared with pedestrian flow data acquired during periods when the measures are not being implemented. Furthermore, even if pedestrian flow decreases as a result of implementing the measures, the information processing device may control the settings of signage, movable shelves, etc.

[0056] Figure 8 is a block diagram showing an example configuration of the information processing devices 10 and 300 (hereinafter referred to as "information processing devices 10, etc.").

[0057] Referring to Figure 8, the information processing device 10 includes a network interface 1201, a processor 1202, and a memory 1203. The network interface 1201 is used to communicate with other network node devices that constitute the communication system. The network interface 1201 may also be used for wireless communication. For example, the network interface 1201 may be used for wireless LAN communication as defined in the IEEE 802.11 series, or for mobile communication as defined in 3GPP (registered trademark) (3rd Generation Partnership Project). Alternatively, the network interface 1201 may include, for example, a network interface card (NIC) compliant with the IEEE 802.3 series.

[0058] The processor 1202 reads and executes software (computer programs) from the memory 1203, thereby performing the processing of the information processing device 10, etc., as described using a flowchart or sequence in the above embodiment. The processor 1202 may be, for example, a microprocessor, an MPU (Micro Processing Unit), a CPU (Central Processing Unit), or a GPU (Graphics Processing Unit). The processor 1202 may include multiple processors.

[0059] Memory 1203 is composed of a combination of volatile and non-volatile memory. Memory 1203 may include storage located away from the processor 1202. In this case, the processor 1202 may access memory 1203 via an I / O interface (not shown).

[0060] In the example shown in Figure 8, memory 1203 is used to store a group of software modules. The processor 1202 can perform the processes in steps S11 to S12 and S101 to S104 by reading these software modules from memory 1203 and executing them.

[0061] As explained using Figure 8, each processor in the information processing device 10, etc., executes one or more programs that include a set of instructions for causing the computer to perform the algorithm described in the diagram.

[0062] 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 can be made 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.

[0063] 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, for example, to create embodiments not explicitly shown 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 described in any of the drawings may be changed as appropriate.

[0064] In the examples described above, the program includes a set of instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more of the functions described in the embodiments. The program may be stored on a non-temporary computer-readable medium or a physical storage medium. Examples, but not limited to, include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disc (DVD), Blu-ray® disc or other optical disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices. The program may be transmitted over a temporary computer-readable medium or a communication medium. Examples, but not limited to, include temporary computer-readable medium or a communication medium that includes electrical, optical, acoustic or other forms of propagating signals.

[0065] Some or all of the above embodiments may also be described as follows, but are not limited to the following:

[0066] 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.

[0067] (Note 1) An information processing device comprising: a measure acquisition unit for acquiring measures to affect the sales of a target business; and a verification unit for verifying the effectiveness of the measures based on at least one of pedestrian flow data of a facility related to the target business or pedestrian flow data around the facility. (Note 2) The information processing device according to Note 1, wherein the verification unit has target attributes which are the attributes of the customers targeted by the measures, and measures the number of customers located in and around the facility during the execution of the measures based on the pedestrian flow data, and verifies the effectiveness of the measures based on the measurement results. (Note 3) The information processing device according to Note 2, wherein the measure acquisition unit determines the target attributes based on customer data of the target business and generates the measures based on the determined target attributes. (Note 4) The information processing device according to Note 3, comprising a classification unit for classifying customers of the target business into a plurality of clusters, wherein the measure acquisition unit determines the target attributes based on the classification results by the classification unit. (Note 5) The information processing device according to any one of Notes 2 to 4, wherein the target attributes include psychographic attributes or demographic attributes. (Note 6) An information processing device according to Note 1 or 2, comprising a human flow data acquisition unit that acquires human flow data based on the location information of each of a plurality of customer terminals. (Note 7) An information processing device according to Note 1 or 2, wherein the measure acquisition unit generates a new measure using generating AI (Artificial Intelligence) when the verification unit confirms that the effect of the measure is low. (Note 8) An information processing device according to Note 1 or 2, wherein the measure acquisition unit generates text data or a network diagram showing the measure. (Note 9) A verification method for acquiring measures to affect the sales of a target business and verifying the effect of the measures based on at least one of human flow data of facilities related to the target business or human flow data around the facilities. (Note 10) A program that causes a computer to perform a process of acquiring measures to affect the sales of a target business and a process of verifying the effect of the measures based on at least one of human flow data of facilities related to the target business or human flow data around the facilities.

[0068] Although the present invention has been described above with reference to embodiments, the present invention is not limited thereto. Various modifications to the structure and details of the present invention can be made that are understandable to those skilled in the art within the scope of the invention.

[0069] This application claims priority based on Japanese Patent Application No. 2024-170295, filed on 30 September 2024, and incorporates all of its disclosures herein.

[0070] 10, 300 Information Processing Unit 11, 330 Policy Acquisition Unit 12, 350 Verification Unit 100 Business Terminal 110 Business Data Transmission Unit 120 Target Transmission Unit 130 Policy Reception Unit 200 Customer Terminal 310 Reception Unit 320 Classification Unit 340 Human Flow Data Acquisition Unit 800, 810-817, 820-823, 830-836 Icon 1201 Network Interface 1202 Processor 1203 Memory N Network

Claims

1. An information processing device comprising: a means for acquiring measures to affect the sales of a target business; and a means for verifying the effectiveness of the measures based on at least one of the following: pedestrian flow data of a facility related to the target business or pedestrian flow data of the area surrounding the facility.

2. The information processing apparatus according to claim 1, wherein the verification means has target attributes which are the attributes of customers targeted by the measure, and measures the number of customers located in and around the facility during the implementation of the measure based on the pedestrian flow data, and verifies the effectiveness of the measure based on the measurement results.

3. The information processing apparatus according to claim 2, wherein the means for acquiring the measures determines the target attributes based on customer data of the target business and generates the measures based on the determined target attributes.

4. The information processing apparatus according to claim 3, comprising a classification means for classifying customers of the target business into multiple clusters, wherein the measure acquisition means determines the target attributes based on the classification results by the classification means.

5. The information processing apparatus according to any one of claims 2 to 4, wherein the target attribute includes psychographic attributes or demographic attributes.

6. The information processing apparatus according to claim 1 or 2, further comprising means for acquiring pedestrian flow data based on the location information of each of a plurality of customer terminals.

7. The information processing apparatus according to claim 1 or 2, wherein the measure acquisition means generates a new measure using Artificial Intelligence (Generative AI) when the verification means confirms that the measure is ineffective.

8. The information processing apparatus according to claim 1 or 2, wherein the means for acquiring the measures generates text data or a network diagram showing the measures.

9. A verification method for obtaining measures to affect the sales of a target business and verifying the effectiveness of those measures based on at least one of the following: pedestrian flow data of facilities related to the target business or pedestrian flow data of the area surrounding those facilities.

10. A program that causes a computer to perform the following processes: acquiring measures to affect the sales of the target business; and verifying the effectiveness of the measures based on at least one of the following: pedestrian flow data of facilities related to the target business or pedestrian flow data around the facilities.

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