Analysis systems, analysis methods, and analysis programs

Abstract

We provide an environment that supports easy and highly effective analysis regarding the distribution of printed materials. [Solution] An analysis system characterized in that a processor performs a distribution record acquisition process to acquire a predetermined printed material, the distributed areas where the printed material has been distributed, and the undistributed areas where the printed material has not been distributed, and a next distribution area proposal process to propose the distribution areas to be targeted when the predetermined printed material is distributed in the future, using the predetermined printed material, the distributed areas, and the undistributed areas.

Description

Technical Field

[0006]

[0001] The present invention relates to an analysis system, an analysis method, and an analysis program.

Background Art

[0002] Regarding posting support, various improvements have been made conventionally. In recent years, with the improvement of the performance of smartphones and the enhancement of communication infrastructure, it has become possible to easily request posting from posting operators. The posting business basically refers to the business of distributing paper media such as flyers to posts in houses or apartment houses where residents in the area live.

[0003] For example, the information collection device disclosed in Patent Document 1 is an information collection device for posting used to distribute flyers displaying predetermined advertisement information to potential customers for sales promotion, and displays a barcode capable of displaying identification information for classifying or identifying each of the flyers on the flyers, and based on the identification information collected by reading the barcode of the flyers brought by the potential customers, it is characterized in that customer information regarding the potential customers can be made into a database.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] For example, according to the information collection device as described above, it is possible to collect the responses for each classification of potential customers regarding the distributed flyers, but it is not possible to collect the responses for those who are not the distribution targets, and accurate effect prediction cannot be made without repeating distribution and information collection.

[0006] The present invention has been made in view of the above points, and aims to provide an environment that supports simple and highly effective analysis regarding the distribution of printed materials. [Means for solving the problem]

[0007] The present invention includes several means for solving at least some of the above problems, and an example thereof is as follows: An analysis system according to one aspect of the present invention is characterized in that a processor performs a distribution record acquisition process that acquires a predetermined printed material, a distributed area where the printed material has been distributed, and an undistributed area where the printed material has not been distributed, and a next distribution area proposal process that uses the predetermined printed material, the distributed area, and the undistributed area to propose a distribution area to be distributed when the predetermined printed material is distributed in the future.

[0008] Furthermore, in the analysis system described above, the next distribution area proposal process may involve selecting and proposing the distribution area from the previously undistributed areas.

[0009] Furthermore, the above analysis system may also perform a distribution method proposal process that suggests improvements to the distribution method of the predetermined printed materials for each distribution area.

[0010] Furthermore, the above analysis system may also perform an advertising content suggestion process that proposes improvements to the content of the predetermined printed materials for each distribution area.

[0011] Furthermore, the above analysis system may also perform an advertising match determination process that presents the degree of match between the predetermined printed material and the distributed area for each distributed area.

[0012] Furthermore, in the above-described analysis system, the ad match determination process may calculate and present the match using statistical information relating to the economy of the distributed area.

[0013] Furthermore, the above analysis system may include a predetermined generating AI that has previously learned at least the economic statistical information of the distributed area, and in the advertising match determination process, the printed material and the distributed area are input, and the generating AI calculates and presents the match score.

[0014] Furthermore, in the above analysis system, the ad match rate determination process may calculate and present the match rate using statistical information regarding the population composition of the distributed area.

[0015] Furthermore, the above analysis system may include a predetermined generating AI that has previously learned statistical information regarding the population composition of the distribution area, and in the advertising match determination process, the printed material and the distribution area are input, and the generating AI calculates and presents the match degree.

[0016] Furthermore, in the above analysis system, the ad match rate determination process may calculate and present the match rate using text information obtained by web crawling of the distributed area.

[0017] Furthermore, the above analysis system may include a predetermined generating AI that utilizes text information obtained by web crawling of at least the distributed areas as supplementary information, and in the advertising match degree determination process, the printed material and the distributed areas are input, and the generating AI calculates and presents the match degree.

[0018] Furthermore, an analysis system according to another aspect of the present invention is characterized in that the processor performs a request acceptance process that accepts input of attribute information of a predetermined printed material and a predetermined market area where the distribution of the printed material is being considered, and an initial distribution area proposal process that uses the attribute information and the market area to propose a distribution area to be used when the predetermined printed material is distributed thereafter.

[0019] Further, an analysis method according to another aspect of the present invention is an analysis method using a computer system, wherein a processor obtains a predetermined printed matter, a distributed area where the printed matter has been distributed, and an undistributed area where the printed matter has not been distributed, and a distribution result acquisition process, and uses the predetermined printed matter, the distributed area, and the undistributed area to propose a distribution area to be distributed at the time of distribution of the subsequent predetermined printed matter, a next distribution area proposal process.

[0020] Further, an analysis program according to another aspect of the present invention causes a processor to perform a distribution result acquisition process of obtaining a predetermined printed matter, a distributed area where the printed matter has been distributed, and an undistributed area where the printed matter has not been distributed, and a next distribution area proposal process of proposing a distribution area to be distributed at the time of distribution of the subsequent predetermined printed matter using the predetermined printed matter, the distributed area, and the undistributed area.

Advantages of the Invention

[0021] According to the present invention, it is possible to provide an environment that easily supports highly effective analysis regarding the distribution of printed matter.

[0022] Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0023] [Figure 1] It is an example of a block diagram of a posting support system according to an embodiment. [Figure 2] It is a diagram showing an example of a data structure of area statistical information. [Figure 3] It is a diagram showing an example of a hardware configuration of a posting support device. [Figure 4] It is a diagram showing an example of a hardware configuration of an operator terminal. [Figure 5] It is a diagram showing an example of a flow of an initial distribution area proposal process. [Figure 6] This diagram shows an example of the workflow for processing distribution results. [Figure 7] This diagram shows an example of the workflow for the next distribution area proposal process. [Figure 8] This figure shows an example of the flow of area information acquisition processing. [Figure 9] This figure shows an example of an initial distribution area candidate prompt. [Figure 10] This figure shows an example of an initial distribution area proposal prompt. [Figure 11] This figure shows an example of a flyer language prompt. [Figure 12] This figure shows an example of a reflection prompt. [Figure 13] This figure shows an example of a prompt for a candidate distribution area for the next delivery. [Figure 14] This figure shows an example of a prompt for suggesting the next distribution area. [Figure 15] This figure shows an example of an area information creation prompt. [Modes for carrying out the invention]

[0024] A posting support system 1 (or simply referred to as an analysis system) equipped with a posting support function, to which an embodiment according to one aspect of the present invention is applied, will be described below with reference to the drawings.

[0025] By using the posting support system 1 according to this embodiment, it is possible to make highly accurate effect predictions without having to repeatedly distribute flyers (including all other printed materials) and collect information in a brute-force manner.

[0026] Figure 1 is an example of a block diagram of a posting support system according to this embodiment. The posting support system 1 includes a posting support device 100, a generation AI service 200, and an operator terminal 300.

[0027] The operator terminal 300 is a terminal used by users of the posting support system 1, such as users who plan and execute posting operations, or administrators responsible for the use and management of posting operations. The operator terminal 300 is connected to the posting support device 100 via the network 50 so as to be able to communicate.

[0028] Network 50 is a communication network such as a LAN (Local Area Network), WAN (Wide Area Network), the Internet, or a mobile phone network. Network 50 may also be a VPN (Virtual Private Network) on a wireless communication network such as a mobile phone network.

[0029] The posting support device 100 is a so-called server device. However, it is not limited to this, and the posting support device 100 may be any kind of information processing device such as a personal computer, smartphone, workstation, PDA (Personal Data Assistant), or tablet device.

[0030] The posting support device 100 receives requests for posting tasks to be performed from the operator terminal 300 via the network 50, and appropriately executes support processing for posting tasks according to the requests, or makes suggestions regarding the next posting task based on the posting results.

[0031] In this process, the posting support device 100, via the network 50, appropriately delegates support for or proposals for posting operations to the generating AI service 200 in response to requests for posting operations.

[0032] When the operator terminal 300 receives a predetermined response from the posting support device 100 via the network 50, it performs output processing. The operator terminal 300 also transmits input information received from the input device, etc. (described later), to the posting support device 100 via the network 50.

[0033] The operator terminal 300 comprises a processing unit 310, an input receiving unit 320, a display unit 330, and a communication unit 340. The processing unit 310 includes a browser unit 311.

[0034] The browser unit 311 receives input related to the posting work via the input reception unit 320 and passes this information to the posting support device 100. When the browser unit 311 receives a predetermined response from the posting support device 100, it outputs it via the display unit 330.

[0035] The input receiving unit 320 receives input from the operator of the operator terminal 300, such as a user or administrator, and passes it on to the browser unit 311. The input receiving unit 320 receives text from a hardware keyboard, a software keyboard, etc., but is not limited to this; for example, it may receive voice input via a microphone and identify the text, or it may identify the text using eye-tracking input, etc. The display unit 330 receives screen information in a predetermined format instructed to be displayed by the posting support device 100 from the communication unit 340 and draws it on the display device. The communication unit 340 communicates with the posting support device 100 via the network 50.

[0036] The Generative AI Service 200 is a service that provides the functions of so-called generative AIs such as GPT, Gemini, and Claude via an API (Application Programming Interface), etc. The Generative AI Service 200 provides commands (prompts) in natural language to the Generative AI and causes it to generate the desired result. The Generative AI Service 200 comprises an AI processing unit 210 and a communication unit 220.

[0037] In this embodiment, when the generation AI service 200 receives instructions, for example via an API, it causes the generation AI to generate response information and sends the result to the source of the instructions as a return value of the API. At that time, the generation AI receives information related to the response, reads that information as part of the information to be added and considered, and reflects it in the generation of the response. It is desirable that the generation AI has been previously trained on at least economic statistics or demographic statistics of the distributed area.

[0038] For example, when the generating AI service 200 receives instructions to perform support for posting work, it presents the requested information while taking into account the given input information. For example, if it is instructed to evaluate the degree of match between the distributed flyers and the distribution area, the generating AI service 200 calculates the degree of match by taking into account the characteristics of the area and the characteristics of the flyer.

[0039] The posting support device 100 includes a processing unit 110, a storage unit 120, and a communication unit 130. The processing unit 110 includes a request reception unit 111, an initial distribution area proposal unit 112, a distribution performance reception unit 113, an advertising match determination unit 114, a distribution method proposal unit 115, an advertising content proposal unit 116, a next distribution area proposal unit 117, and an area information acquisition unit 118. The storage unit 120 includes area statistical information 121 and map information 122 that associates topographic and location information with address information (e.g., address notation). Although not shown in the figures, the storage unit 120 also stores information about the flyers entered (flyer content, distribution area, QR code (registered trademark) for each distribution area, URL, number of flyers, etc.) associated with each user or administrator. Note that the data structures and data management methods described below are examples and are not limited to them. For example, it could be a relational database, graph database, vector database, text data, or a structured data management method such as HTML (HyperText Markup Language).

[0040] Figure 2 shows an example of the data structure for area statistics. Area statistics 121 stores information name 121a, source 121b, and inclusion information 121c in association with each other.

[0041] Information name 121a is information that distinguishes information from other information. Here, information includes statistical information concerning the economy or population structure of a given area or multiple areas. For example, information name 121a includes "Census data," "Resident registration data," "Economic census," "Retail sales statistics," and "Commercial facility map."

[0042] Source 121b is information indicating the source of the information identified by information name 121a. For example, "Census data" is from the Statistics Bureau of the Ministry of Internal Affairs and Communications, "Resident Basic Register data" is from each local government, "Economic Census" is from the Statistics Bureau of the Ministry of Internal Affairs and Communications, "Retail Sales Statistics" is from the Ministry of Economy, Trade and Industry, "Commercial Facility Map" is from the Ministry of Land, Infrastructure, Transport and Tourism, etc. In addition to area statistical information 121, information obtained from data services provided by external systems may be retained, or data services provided by external systems may be used at the time of processing.

[0043] The included information 121c is information that indicates the information mainly contained in the information identified by the information name 121a. For example, for "Census data," it includes population, age groups, and number of households by region; for "Basic Resident Register data," it includes the number of households and gender ratio by town and district; for "Economic Census," it includes the number of commercial facilities and establishments by industry; for "Retail Sales Statistics," it includes consumer spending data by region; and for "Commercial Facility Map," it includes commercial clusters by area, etc.

[0044] Returning to the explanation of Figure 1, the request reception unit 111 receives the details of the support for posting work from the user via the browser unit 311 of the operator terminal 300, through the network 50. Specifically, the request reception unit 111 accepts one of the following: an initial distribution area proposal to determine the distribution area before distributing the flyers; utilization of distribution results to show areas for improvement based on the distribution results after the flyers have been distributed; or a next distribution area proposal to determine the distribution area for the next time and beyond based on the distribution results after the flyers have been distributed. Alternatively, the request reception unit 111 accepts input of flyer attribute information (information that forms the profile of the flyer distributor, such as industry, sales base, business hours, services offered, target audience, expected average customer spending, etc.) and a predetermined trade area where the flyers are being considered for distribution (location information to identify the starting area, landmarks (such as stations or buildings), or address information (at least prefecture, city, town, village, or postal code, etc.) or its range (radius of 3km, etc.)).

[0045] When the initial distribution area proposal unit 112 receives a business support instruction for the initial distribution area proposal, it performs the initial distribution area proposal process described later. Specifically, the initial distribution area proposal unit 112 uses attribute information and trade area information to propose the distribution areas to be used when distributing the specified flyers thereafter.

[0046] The distribution record receiving unit 113 accepts input of distribution results (distributed flyers, distribution areas, number of flyers, etc.) after the distribution of flyers. Specifically, the distribution record receiving unit 113 accepts input of the specified flyers that were distributed, the areas where the flyers were distributed, and the areas where the flyers were not distributed. Alternatively, if the information of the distributed flyers, information identifying one or more distributed areas, and the response after distribution (number of accesses for each distribution area) are already stored in the storage unit 120 of the posting support device 100, the distribution record receiving unit 113 may read and acquire the distribution areas and area information of the flyers, as well as the number of distributions, the number of conversions, and the conversion rate as the effect of the flyers, from the storage unit 120 according to the information of the distributed flyers. In this case, the distribution record receiving unit 113 can also be called the distribution record acquisition unit.

[0047] When the advertising match determination unit 114 receives a business support instruction for utilizing distribution results, it takes on part of the distribution results utilization processing described later and evaluates the degree of match between the distributed flyers and the distribution areas after the flyers have been distributed. Specifically, the advertising match determination unit 114 calculates and presents the degree of match between a given flyer and the distributed area for each distributed area, using statistical information on the economy of that area.

[0048] In the advertising match determination process, the advertising match determination unit 114 may take the flyer and the distribution area as input and have the generating AI calculate and present the match degree. Alternatively, in the advertising match determination process, the advertising match determination unit 114 may calculate and present the match degree using statistical information regarding the population composition of the distribution area.

[0049] Furthermore, in the ad match degree determination process, the ad match degree determination unit 114 may calculate and present the match degree using text information obtained by web crawling of the distributed areas. Alternatively, the ad match degree determination unit 114 may instruct the generation AI service 200 to use text information obtained by web crawling of at least the distributed areas as supplementary information, and in the ad match degree determination process, the flyer and the distributed areas may be input, and the generation AI may calculate and present the match degree.

[0050] When the distribution method proposal unit 115 receives instructions for business support regarding the utilization of distribution results, it takes on part of the distribution results utilization processing described later, and after the distribution of the flyers, it proposes effective distribution methods for the areas where the flyers were distributed. Specifically, the distribution method proposal unit 115 proposes improvement plans for the distribution methods of the specified flyers for each distribution area.

[0051] When the Advertising Content Proposal Department 116 receives instructions for business support regarding the utilization of distribution results, it takes on part of the distribution results utilization process described later, and after the distribution of the flyers, it makes effective improvement suggestions for the content of the distributed flyers. Specifically, the Advertising Content Proposal Department 116 proposes improvement plans for the content of the designated flyers for each distribution area.

[0052] The Next Distribution Area Proposal Unit 117, upon receiving instructions for business support regarding the proposal of the next distribution area, performs the next distribution area proposal process described below. Specifically, the Next Distribution Area Proposal Unit 117 uses information on the designated flyer, areas where the flyers have already been distributed, and areas where they have not yet been distributed to propose the distribution areas to be included in subsequent distributions of the designated flyers. In addition, during the next distribution area proposal process, the unit selects and proposes distribution areas from the areas where the flyers have not yet been distributed.

[0053] The area information acquisition unit 118 performs the area information acquisition process described later when it is necessary to acquire information about a predetermined area in any of the processes of initial distribution area proposal, utilization of distribution results, or next distribution area proposal, and acquires information that represents the characteristics of the economy and population structure of the predetermined area using statistical information and information obtained as a result of web search (web crawling).

[0054] The communication unit 130 communicates with other devices, namely the generation AI service 200 and the operator terminal 300, via the network 50.

[0055] Figure 3 shows an example of the hardware configuration of a posting support device. The posting support device 100 comprises a processor 101, memory 102, storage 103, communication device 104, and a bus 105 connecting the devices. In addition, the posting support device 100 may also include an input device.

[0056] The processor 101 is a computing device such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit), and it executes processing according to a program recorded in memory 102 or storage 103. This program is, for example, an application program that can be executed on an OS (Operating System) program. In the posting support device 100, processing is performed by the processor 101, which operates according to a program read from memory 102 or storage 103. The processing unit 110, the request reception unit 111, the initial distribution area proposal unit 112, the distribution performance reception unit 113, the advertisement match determination unit 114, the distribution method proposal unit 115, the advertisement content proposal unit 116, the next distribution area proposal unit 117, and the area information acquisition unit 118 each realize their respective functions by the processor 101 executing a program.

[0057] Memory 102 is a storage device such as RAM (Random Access Memory) or flash memory, and functions as a storage area where programs and data are temporarily read. Storage 103 is a writable and readable storage device. The functions of the storage unit 120, area statistics information 121, and map information 122 are realized by memory 102 or storage 103. Note that the functions of the storage unit 120 may be realized by a storage device connected via the communication device 104.

[0058] The communication device 104 is an interface for connecting the posting support device 100 to an external device. For example, the communication device 104 is a network card that performs wired communication via a wired connection. Alternatively, the communication device 104 performs wireless communication using an antenna that can utilize predetermined radio waves (e.g., 5GHz band, 2.4GHz band, etc.) to establish a connection with the generated AI service 200 and the operator terminal 300 according to the Wi-Fi standard.

[0059] Furthermore, the processing of each component of the posting support device 100 may be performed on one piece of hardware or on multiple pieces of hardware. Also, the processing of each component of the posting support device 100 may be implemented by one program or by multiple programs.

[0060] The communication unit 130 of the above-described posting support device 100 is implemented by the communication device 104. The above is an example of the hardware configuration of the posting support device 100.

[0061] Each component of the posting support device 100 can be further classified into many more components depending on the processing content. Alternatively, each component can be classified to perform even more processing tasks.

[0062] Furthermore, each processing unit (processing unit 110, request reception unit 111, initial distribution area proposal unit 112, distribution performance reception unit 113, ad match determination unit 114, distribution method proposal unit 115, ad content proposal unit 116, next distribution area proposal unit 117, area information acquisition unit 118) may be constructed using dedicated hardware (ASIC, GPU, etc.) to realize its respective function. Also, the processing of each processing unit may be executed on a single piece of hardware or on multiple pieces of hardware.

[0063] Figure 4 shows an example of the hardware configuration of an operator terminal. The operator terminal 300 includes an input device 301, a processor 302, storage 303, memory 304, a display 305, a communication device 306, and a bus 307 connecting the devices.

[0064] The input device 301 is a variety of input devices such as a keyboard, mouse, or touch panel. The input reception unit 320 of the operator terminal 300 is realized by the input device 301 and the processor 302. The processor 302 is a computing device such as a CPU or GPU, and executes processing according to a program recorded in the memory 304 or storage 303. This program is, for example, an application program that can be executed on an OS program. In the operator terminal 300, processing is performed by the processor 302, which operates according to the program read from the memory 304 or storage 303. The processing unit 310 and the browser unit 311 realize their respective functions when the processor 302 executes a program.

[0065] Memory 304 is a storage device such as RAM or flash memory, and functions as a storage area where programs and data are temporarily read. Storage 303 is a writable and readable storage device.

[0066] The display 305 is a display device such as a liquid crystal display or an organic EL display. The display unit 330 is realized by the display 305 and the processor 302.

[0067] The communication device 306 is an interface for connecting the operator terminal 300 to an external device. For example, the communication device 306 is a network card that performs wired communication via a wired connection. Alternatively, the communication device 306 performs wireless communication using an antenna that can utilize predetermined radio waves (e.g., 5GHz band, 2.4GHz band, etc.) to establish a connection with the posting support device 100 using the Wi-Fi standard. The communication unit 340 of the operator terminal 300 is realized by the communication device 306 and the processor 302.

[0068] Furthermore, the processing of each component of the operator terminal 300 may be performed on one piece of hardware or on multiple pieces of hardware. Also, the processing of each component of the operator terminal 300 may be implemented by one program or by multiple programs.

[0069] The input device 301, processor 302, storage 303, memory 304, display 305, and communication device 306 are connected to each other by connecting wires such as a bus 307. The above is an example of the hardware configuration of the operator terminal 300.

[0070] Each configuration of the operator terminal 300 can be further classified into many more components depending on the processing content. Alternatively, each component can be classified to perform even more processing.

[0071] Furthermore, each processing unit (processing unit 310, browser unit 311, input reception unit 320, display unit 330, communication unit 340) may be constructed using dedicated hardware (ASIC, GPU, etc.) to realize its respective function. Also, the processing of each processing unit may be executed on a single piece of hardware or on multiple pieces of hardware.

[0072] The Generative AI Service 200 is a service provided outside of the Posting Support System 1, which uses Generative AI. However, it is not limited to this, and may also use Generative AI maintained by Posting Support System 1. In that case, the Generative AI may include Generative AI that uses Large Language Models (LLMs).

[0073] The generative AI is a pre-trained model created by machine learning or deep learning a large-scale language model using a neural network (NN) with language data. The AI ​​processing unit 210 is implemented by the generative AI. The generative AI is not limited to a neural network (NN); other known methods may be used. Furthermore, it is desirable to use a generative AI that has been tuned to achieve higher accuracy through techniques such as few-shot learning and fine-tuning.

[0074] The communication unit 220 communicates with another device, the posting support device 100, via the network 50.

[0075] Next, the operation of the posting support system 1 in this embodiment will be described. The flyers should include a QR code associated with a URL that prospective customers can access, and it is desirable that this URL includes information that distinguishes the distribution area (e.g., district, block). Although not shown in the figures, it is desirable that the storage unit 120 of the posting support device 100 collects and stores information that identifies the number of accesses for at least each URL.

[0076] First, in this embodiment, a user or administrator of the posting support system 1 acts as an operator and uses the operator terminal 300 to input information regarding a request for business support and the planned distribution of advertisements to the posting support device 100. This becomes the request information, and the operator terminal 300 requests a response from the posting support device 100. The posting support device 100 then appropriately performs the support processing for the posting work in response to the request, or, based on the posting results, responds to the operator terminal 300 with a response or suggestion regarding the next posting work, thereby completing the support.

[0077] For example, an operator uses the operator terminal 300 to input information identifying the starting area and information such as the profile of the flyer distributor, and transmits it to the posting support device 100. The posting support device 100 uses the generation AI service 200 to propose an initial distribution area for the flyer and presents it to the operator terminal 300. This is an overview of the initial distribution area proposal process, which will be described later.

[0078] For example, an operator uses the operator terminal 300 to input information about the flyer, the areas where it has been distributed, and the response after distribution, and transmits this information to the posting support device 100. The posting support device 100 uses the generation AI service 200 to suggest the degree to which the flyer matches the areas where it has been distributed, areas for improvement in the distribution method, and areas for improvement in the content of the flyer, and presents these suggestions to the operator terminal 300. This is an overview of the distribution results utilization process, which will be described later.

[0079] For example, an operator uses the operator terminal 300 to input information about the flyer and the areas where the flyers have already been distributed, and sends this information to the posting support device 100. The posting support device 100 uses the generation AI service 200 to suggest the next distribution area for the flyers to be distributed in the future, and presents this information to the operator terminal 300. This is an overview of the next distribution area suggestion process, which will be described later.

[0080] Figure 5 shows an example of the initial distribution area proposal process flow. The initial distribution area proposal process flow starts when the operator terminal 300 receives a start request from the operator.

[0081] First, the browser unit 311 of the operator terminal 300 receives input of information identifying the starting area and source information such as the profile of the flyer distributor, and transmits this information to the posting support device 100 (step S101). Specifically, after the user logs in and is authenticated, the browser unit 311 of the operator terminal 300 receives location information identifying the starting area, landmarks (e.g., stations or buildings) or address information (at least prefecture, city, town, village, or postal code, etc.) and information that will be part of the flyer distributor's profile, such as industry, business locations, business hours, services offered, target audience, expected average customer spending, etc., and transmits this information to the posting support device 100.

[0082] Then, the request receiving unit 111 of the posting support device 100 acquires area information for the starting area (step S102). Specifically, when the request receiving unit 111 receives information for the starting area entered by the user, it identifies a predetermined area (chome, or ōaza / koaza) that includes the starting area, and requests the area information acquisition unit 118 to acquire area information for the identified area.

[0083] The area information acquisition unit 118 performs the area information acquisition process described later and passes the area information to the initial distribution area proposal unit 112. The area information includes statistical information on the area's economy, statistical information on its population structure, etc.

[0084] Then, the request reception unit 111 acquires area information in the vicinity of the starting area (step S103). Specifically, the request reception unit 111 identifies areas in the vicinity of a predetermined area that includes the starting area (for example, within a 3km radius, or within a radius specified in advance by the user), and requests the area information acquisition unit 118 to acquire area information for each of the identified nearby areas.

[0085] The area information acquisition unit 118 performs the area information acquisition process described later for each neighboring area and passes the area information for each neighboring area to the initial distribution area proposal unit 112. The area information includes statistical information on the area's economy, statistical information on its population structure, etc.

[0086] Then, the initial distribution area proposal unit 112 creates an initial distribution area candidate prompt, as described later, and instructs the generation AI service 200 to create a candidate initial distribution area (step S104). Specifically, the initial distribution area proposal unit 112 creates an initial distribution area candidate prompt to create a candidate initial distribution area and instructs the generation AI service 200 to do so.

[0087] The generation AI service 200 creates initial distribution area candidates according to the initial distribution area candidate prompt (step S105). Specifically, the generation AI service 200 uses the flyer distribution prerequisite information, flyer distribution source information, starting area information, and neighboring area information to calculate the degree of match for each area and create initial distribution area candidates including the area name, the degree of match, and the reason for selecting the area as a candidate.

[0088] Then, the initial distribution area proposal unit 112 creates an initial distribution area proposal prompt, as described later, and instructs the generation AI service 200 to create an initial distribution area proposal (step S106). Specifically, the initial distribution area proposal unit 112 creates an initial distribution area proposal prompt to create an initial distribution area proposal and instructs the generation AI service 200 to do so.

[0089] The generating AI service 200 creates an initial distribution area proposal in accordance with the initial distribution area proposal prompt (step S107). Specifically, the generating AI service 200 uses the prerequisite information for flyer distribution, the information of the flyer distribution source, and the area information of the candidate initial distribution areas created in step S105 to create an initial distribution area proposal that includes the area name, keywords, reasons for recommendation, and suggestions for distribution methods according to the characteristics of the area.

[0090] Then, the initial distribution area proposal unit 112 creates screen information using the initial distribution area proposal (step S108). Specifically, the initial distribution area proposal unit 112 creates screen information including the area name, keywords, reasons for recommendation, and proposed distribution methods according to the characteristics of the area included in the initial distribution area proposal.

[0091] Then, the browser unit 311 of the operator terminal 300 displays the screen information of the proposed initial distribution area on the display unit 330 (step S109).

[0092] The above is an example of the flow for the initial distribution area proposal process. This process allows you to determine the distribution area before distributing the flyers.

[0093] Figure 6 shows an example of the distribution results utilization process flow. The distribution results utilization process flow starts when the operator terminal 300 receives a start request from the operator.

[0094] First, the browser unit 311 of the operator terminal 300 receives input of information about the distributed flyers, information identifying the distribution area, and responses after distribution, and transmits this information to the posting support device 100 (step S201). Specifically, after the user logs in and is authenticated, the browser unit 311 of the operator terminal 300 receives input of information such as specific images of the distributed flyers, information identifying one or more distribution areas such as landmarks (e.g., stations or buildings) or address information (at least prefecture, city, town, village, or postal code), the number of flyers distributed in each distribution area, and information on responses after flyer distribution, such as CV (conversion: number of accesses to the URL posted on the flyer) and CVR (conversion rate: ratio of the number of accesses to the URL posted on the flyer to the number of effective contacts), and transmits this information to the posting support device 100.

[0095] Then, the distribution record reception unit 113 creates a flyer language conversion prompt, as described later, and instructs the AI ​​service 200 to convert the flyers into language (step S202). Specifically, the distribution record reception unit 113 receives image data of the distributed flyers that have been received, creates a flyer language conversion prompt to obtain the flyer's metadata in language, and instructs the AI ​​service 200 to do so.

[0096] The generation AI service 200 creates flyer-related information according to the flyer language prompt (step S203). Specifically, the generation AI service 200 analyzes the image data of the distributed flyer and creates flyer-related information including predetermined flyer metadata (flyer description, target customer base (including reasons), peak demand times (including reasons), coupon information, QR code associated with a URL for prospective customers who see the flyer to access, etc.).

[0097] Then, the distribution record reception unit 113 obtains area information for the areas where the flyers were distributed (step S204). Specifically, the distribution record reception unit 113 requests the area information acquisition unit 118 to obtain area information for the areas where the flyers have already been distributed.

[0098] The area information acquisition unit 118 performs the area information acquisition process described later and hands over the area information to the distribution record reception unit 113. The area information includes statistical information on the area's economy, statistical information on its population structure, etc.

[0099] Then, the distribution performance receiving unit 113 creates a review prompt, described later, and instructs the unit to create an advertisement match score and points for improvement in the distribution method and advertisement content (step S205). Specifically, the distribution performance receiving unit 113 takes the distribution area and area information of one or more flyers as input, and the number of flyers distributed, the number of conversions, and the conversion rate as flyer effectiveness, and creates a review prompt for each distributed area that outputs the match score between the flyer and the area (high, medium, or low), the reason for the judgment, points for improvement in the flyer distribution method, and points for improvement in the flyer content, and instructs the generation AI service 200 to do so.

[0100] The generating AI service 200 creates an ad match score and suggestions for improvements to the distribution method and ad content, following the review prompt (step S206). Specifically, the generating AI service 200 takes the area and area information of the distributed flyers and the effectiveness of the distribution (number of flyers distributed, number of conversions, conversion rate) as input and creates flyer improvement suggestion information that includes the ad match score, suggestions for improvements to the ad distribution method and suggestions for improvements to the ad content. For example, the generating AI service 200 quantifies the match score according to the conversion rate and identifies a predetermined evaluation value (either high, medium, or low) to determine the ad match score.

[0101] In step S205, the distribution performance receiving unit 113 creates a review prompt (described later) and instructs it to create an advertisement match score and points for improvement in the distribution method and advertisement content. However, it is not limited to this, and the advertisement match score determination process may be performed by the advertisement match score determination unit 114, the distribution method proposal process by the distribution method proposal unit 115, and the advertisement content improvement point proposal process by the advertisement content proposal unit 116. Furthermore, the advertisement match score determination unit 114, the distribution method proposal unit 115, and the advertisement content proposal unit 116 may each create a prompt and instruct the generation AI service 200 to create flyer improvement proposal information including the advertisement match score, points for improvement in the advertisement distribution method, and points for improvement in the advertisement content, respectively. By doing so, the amount of tokens per inquiry can be reduced, the occurrence of hallucination and hang-ups can be suppressed, and highly accurate evaluations and improvement points can be obtained.

[0102] Then, the distribution performance reception unit 113 creates screen information using the flyer proposal information (step S207). Specifically, the distribution performance reception unit 113 creates screen information including the degree of matching of the advertisements included in the flyer proposal information, areas for improvement in the advertisement distribution method, and areas for improvement in the advertisement content.

[0103] Then, the browser unit 311 of the operator terminal 300 displays screen information on the display unit 330, including the ad match rate, areas for improvement in the ad distribution method, and areas for improvement in the ad content (step S208).

[0104] The above is an example of the distribution results utilization process flow. If information on distributed flyers, information identifying one or more distribution areas, and post-distribution responses (number of accesses for each distribution area) are already stored in the storage unit 120 of the posting support device 100, the browser unit 311 may, in step S201, receive the information on distributed flyers and transmit it to the posting support device 100. Then, in step S205, the distribution results reception unit 113 may read and obtain the distribution area and area information of the flyers, the number of distributions, the number of conversions, and the conversion rate as flyer effectiveness from the storage unit 120, and create a review prompt, described later, to instruct the system to create an advertising match score and points for improvement in the distribution method and advertising content. According to the distribution results utilization process, points for improvement based on distribution results can be shown after the distribution of flyers.

[0105] Figure 7 shows an example of the flow of the next distribution area proposal process. The next distribution area proposal process starts when the operator terminal 300 receives a start request from the operator.

[0106] First, the browser unit 311 of the operator terminal 300 receives input of information about the distributed flyers, information identifying the distribution area, and information about the flyer distributor such as a profile, and transmits this information to the posting support device 100 (step S301). Specifically, after the user logs in and is authenticated, the browser unit 311 of the operator terminal 300 receives input of information such as specific images of the distributed flyers, information identifying one or more distribution areas such as landmarks (e.g., stations or buildings) or address information (at least prefecture, city, town, village, or postal code), information such as the number of flyers distributed in each distribution area, and information that constitutes the flyer distributor's profile, such as industry, business location, business hours, services offered, and expected average customer spending, and transmits this information to the posting support device 100.

[0107] Then, the distribution record reception unit 113 creates a flyer language conversion prompt, as described later, and instructs the AI ​​service 200 to convert the flyer into language (step S302). Specifically, the distribution record reception unit 113 receives image data of the distributed flyers that have been received, creates a flyer language conversion prompt to obtain the flyer's metadata in language, and instructs the AI ​​service 200 to do so.

[0108] The generation AI service 200 creates flyer-related information according to the flyer language prompt (step S303). Specifically, the generation AI service 200 analyzes the image data of the distributed flyer and creates flyer-related information including the predetermined flyer metadata (flyer description, target customer base (including reasons), peak demand times (including reasons), coupon information).

[0109] Then, the distribution record reception unit 113 obtains area information for the areas where the flyers were distributed (step S304). Specifically, the distribution record reception unit 113 requests the area information acquisition unit 118 to obtain area information for the areas where the flyers have already been distributed.

[0110] The area information acquisition unit 118 performs the area information acquisition process described later and hands over the area information to the distribution record reception unit 113. The area information includes statistical information on the area's economy, statistical information on its population structure, etc.

[0111] Then, the distribution record receiving unit 113 acquires area information for the area near where the flyers were distributed (step S305). Specifically, the distribution record receiving unit 113 identifies the area near the designated area that includes the area where the flyers were distributed (for example, within a 3km radius, or within a radius specified in advance by the user), and requests the area information acquisition unit 118 to acquire area information for each of the identified nearby areas.

[0112] The area information acquisition unit 118 performs the area information acquisition process described later for each neighboring area and passes the area information for each neighboring area to the next distribution area proposal unit 117. The area information includes statistical information on the area's economy, statistical information on its population structure, etc.

[0113] Then, the next distribution area proposal unit 117 creates a next distribution area candidate prompt, as described later, and instructs the next distribution area proposal unit 117 to create a candidate for the next distribution area (step S306). Specifically, the next distribution area proposal unit 117 creates a next distribution area candidate prompt to create a candidate for the next distribution area and instructs the generation AI service 200 to do so.

[0114] The generating AI service 200 creates candidates for the next distribution area in accordance with the next distribution area candidate prompt (step S307). Specifically, the generating AI service 200 uses flyer-related information, flyer distribution source information, distributed area information, and nearby area information as candidate areas to calculate the degree of agreement with the target audience, the response rate of the flyer, and the degree of agreement with the flyer effectiveness factors of the distributed area for each area, and creates candidates for the next distribution area, including the area name, the degree of match, and the reason for selecting it as a candidate.

[0115] Then, the next distribution area proposal unit 117 creates a next distribution area proposal prompt, as described later, and instructs the unit to create a proposal for the next distribution area (step S308). Specifically, the next distribution area proposal unit 117 creates a next distribution area proposal prompt to create a proposal for the next distribution area, deciding which area to distribute to from the candidates for the next distribution area created in step S307, and instructs the generation AI service 200 to do so.

[0116] The generating AI service 200 creates a proposal for the next distribution area in accordance with the next distribution area proposal prompt (step S309). Specifically, the generating AI service 200 uses flyer-related information, flyer distribution source information, and candidate area information to create a proposal for the next distribution area, including area name, keywords, reasons for recommendation, and suggested distribution methods according to the characteristics of the area.

[0117] Then, the next distribution area proposal unit 117 creates screen information using the next distribution area proposal (step S310). Specifically, the next distribution area proposal unit 117 creates screen information including the area name, keywords, reasons for recommending, and suggested distribution methods according to the characteristics of the area. For example, the next distribution area proposal may include, as the area name, "○○ 1-chome", as keywords, "families, education-related, commercial facilities", as reasons for recommending, "There are many families and young people, and educational services are abundant. It is close to the station and has many commercial facilities, so we expect a high response rate to flyers. The area is safe and has a comfortable living environment," and as a distribution method, "Distribute around the station and inside commercial facilities, and especially on weekends and in the evenings to reach more people."

[0118] Then, the browser unit 311 of the operator terminal 300 displays screen information for the next distribution area proposal on the display unit 330 (step S311).

[0119] The above is an example of the flow of the next distribution area proposal process. If the information of the distributed flyers, the information identifying one or more distributed areas, the response after distribution (number of accesses for each distribution area), and the information of the flyer distributor are already stored in the storage unit 120 of the posting support device 100, the browser unit 311 may, in step S301, receive the information of the distributed flyers and transmit it to the posting support device 100. Then, in step S306, the next distribution area proposal unit 117 may read and obtain flyer-related information, flyer distributor information, distributed area information, and area information of nearby areas as candidate areas from the storage unit 120 according to the information of the distributed flyers, and create a next distribution area candidate prompt, as described later, to instruct the unit to create a candidate for the next distribution area. According to the next distribution area proposal process, it is possible to determine the next distribution area based on the distribution results after the distribution of flyers.

[0120] Figure 8 shows an example of the area information acquisition process flow. The area information acquisition process flow is started when it is called as a subroutine in steps S102 and S103 of the initial distribution area proposal process, step S204 of the distribution results utilization process, and steps S304 and S305 of the next distribution area proposal process, etc.

[0121] First, the area information acquisition unit 118 acquires area statistical information for the received area (step S401). Specifically, the area information acquisition unit 118 extracts economic information and population composition information related to the received area for each piece of information identified by the information name 121a of the area statistical information 121.

[0122] Then, the area information acquisition unit 118 acquires web information for the received area (step S402). Specifically, the area information acquisition unit 118 performs web crawling and acquires information on the top (for example, up to 5) web pages.

[0123] Then, the area information acquisition unit 118 creates an area information creation prompt and instructs the AI ​​service 200 to create area information for the received area (step S403). Specifically, the area information acquisition unit 118 uses the statistical information of the area acquired in step S401 and the information obtained as a result of the web search (web crawling) in step S402 as input to create an area information creation prompt that will create area information that will be characteristic of the economy and population structure of the received area, and instructs the generation AI service 200 to do so.

[0124] The generating AI service 200 creates area information in accordance with the area information creation prompt (step S404). Specifically, the generating AI service 200 uses statistical information of the target area and information obtained as a result of a web search (web crawling) to create area information in the form of text (for example, text of 500 characters or less in Japanese) that describes the characteristics of the economy and population structure of the received area.

[0125] Then, the area information acquisition unit 118 acquires area information from the generation AI service 200.

[0126] The above is an example of the area information acquisition process flow. According to the area information acquisition process, area information, including economic and demographic information, can be obtained for each designated area at the town or village level. For example, demographic information includes population, gender ratio, percentage of foreigners, number of households, age group (e.g., in 10-year increments), and daytime / nighttime population composition. Economic information includes average annual income, public safety, level of commercial facilities, and the reproductive capacity of parks and educational facilities.

[0127] Figure 9 shows an example of an initial distribution area candidate prompt. Example 600 of the initial distribution area candidate prompt includes a command 601 that calculates the degree of match for each area using the prerequisite information for flyer distribution 602, the flyer distribution source information 603, the area information of the starting area and the area information of neighboring areas (candidate areas in the figure) 604, and creates a candidate initial distribution area including the area name, the degree of match, and the reason for selecting it as a candidate. Example 600 of the initial distribution area candidate prompt also includes an example output format 607. Furthermore, Example 600 of the initial distribution area candidate prompt may also include evaluation criteria 605 and constraints 606.

[0128] Figure 10 shows an example of an initial distribution area suggestion prompt. Example 650 of the initial distribution area suggestion prompt includes a command 651 that uses the prerequisite information for flyer distribution 652, the flyer distribution source information 653, and the area information 654 of the candidate initial distribution areas to create a suggestion for an initial distribution area, including the area name, keywords, reasons for recommendation, and suggested distribution methods according to the characteristics of the area. Example 650 of the initial distribution area suggestion prompt also includes an example output format 655.

[0129] Figure 11 shows an example of a flyer language prompt. Example flyer language prompt 700 includes a command 701 that analyzes the image data of a given flyer and creates flyer-related information including the flyer's metadata (flyer description, target audience (including reasons), peak demand times (including reasons), coupon information) 702. Example flyer language prompt 700 also includes an example output format 703.

[0130] Figure 12 shows an example of a reflection prompt. Example reflection prompt 750 includes a command 751 that takes the area where the flyers were distributed, area information of the distributed area, distribution effectiveness (number of flyers distributed, number of conversions, conversion rate) 752, and flyer description (flyer meta information) 753 as input to create flyer improvement suggestion information, including the degree of ad matching, areas for improvement in the ad distribution method, and areas for improvement in the ad content. Example reflection prompt 750 also includes an example output format 754.

[0131] Figure 13 shows an example of a prompt for candidate next distribution areas. Example 800 of the prompt for candidate next distribution areas includes an instruction 801 that uses flyer-related information 802, flyer distribution source information 803, distributed area information 804, and area information of nearby areas as candidate areas 805 to calculate the degree of agreement with the target audience, the response rate of the flyer, and the degree of agreement with the flyer effectiveness factors of the distributed areas for each area, and uses this as an evaluation criterion 806 to create a candidate for the next distribution area, including the area name, degree of match, and reason for selection. Example 800 of the prompt for candidate next distribution areas also includes an example output format 807.

[0132] Figure 14 shows an example of a next distribution area suggestion prompt. Example 850 of the next distribution area suggestion prompt includes a command 851 that uses flyer-related information 852, flyer distribution source information 853, and area information of candidate areas 854 to create a suggestion for the next distribution area, including the area name, keywords, reasons for recommendation, and suggested distribution methods according to the characteristics of the area. Example 850 of the next distribution area suggestion prompt also includes an example output format 855.

[0133] Figure 15 shows an example of an area information creation prompt. Example 900 of the area information creation prompt includes a command 901 that uses statistical information of the target area and information 902 obtained as a result of a web search (web crawling) to create area information in the form of text (for example, text of 500 characters or less in Japanese) that describes the characteristics of the economy and population structure of the received area. Example 900 of the area information creation prompt also includes an example 903 of the output format.

[0134] The above describes a posting support system 1 to which an embodiment of the present invention is applied. According to the posting support system 1 of this embodiment, it is possible to provide an environment that supports simple and highly effective analysis regarding the distribution of printed materials.

[0135] The present invention is not limited to the embodiments described above. The embodiments described above can be modified in various ways within the scope of the technical idea of ​​the present invention. For example, in the embodiments described above, the generative AI service 200 includes a generative AI that uses a large-scale language model (LLM), but it is not limited to this and may be created using other modeling methods.

[0136] Furthermore, while the processing in the above embodiment can support the posting operations of companies and other organizations, it is not limited to this. For example, it can also be used to provide appropriate advertising support to political activists, influencers, and others.

[0137] Furthermore, the technical elements of the embodiments described above may be applied individually, or they may be divided into multiple parts, such as program components and hardware components, and applied accordingly.

[0138] The present invention has been described above, focusing on its embodiments. [Explanation of Symbols]

[0139] 1...Posting support system, 50...Network, 100...Posting support device, 110...Processing unit, 111...Request reception unit, 112...Initial distribution area proposal unit, 113...Distribution performance reception unit, 114...Advertisement match determination unit, 115...Distribution method proposal unit, 116...Advertisement content proposal unit, 117...Next distribution area proposal unit, 118...Area information acquisition unit, 120...Storage unit, 121...Area statistical information, 122...Map information, 130...Communication unit, 200...Generating AI service, 210...AI processing unit, 220...Communication unit, 300...Operator terminal, 310...Processing unit, 311...Browser unit, 320...Input reception unit, 330...Display unit, 340...Communication unit.

Claims

1. The processor, A distribution record acquisition process that acquires a specified printed material, the areas where the printed material has been distributed, and the areas where the printed material has not been distributed. A next distribution area proposal process that uses the predetermined printed material, the distributed area, and the undistributed area to evaluate the degree of match between the metadata of the printed material and the predetermined characteristics of the undistributed area using a predetermined generating AI, and outputs and proposes the distribution area to be distributed when the predetermined printed material is distributed in the future, based on the result of the evaluation, A distribution method proposal process that inputs metadata of the printed material and predetermined characteristics of the distributed area into the generating AI and outputs improvement proposals for the distribution method of the predetermined printed material for each distributed area, An analysis system characterized by performing the following actions.

2. The analysis system according to claim 1, In the process of proposing the next distribution area, the distribution area is selected and proposed from the previously undistributed areas. An analysis system characterized by the following features.

3. The analysis system according to claim 1, An advertising content suggestion process that inputs metadata of the printed material and predetermined characteristics of the distributed area into the generating AI, and outputs improvement suggestions for the content of the predetermined printed material for each distributed area. An analysis system characterized by performing the following actions.

4. The analysis system according to claim 1, An advertising match determination process that presents the degree of match between the predetermined printed material and the distributed area for each of the distributed areas. An analysis system characterized by performing the following actions.

5. The analysis system according to claim 4, In the aforementioned ad match determination process, the match degree is calculated and presented using statistical information on the economy of the distributed area. An analysis system characterized by the following features.

6. The analysis system according to claim 4, It comprises a predetermined generating AI that has been pre-trained on statistical information regarding the economy of at least the distributed area, In the aforementioned ad matching degree determination process, the printed material and the distribution area are input, and the generating AI calculates and presents the matching degree. An analysis system characterized by the following features.

7. The analysis system according to claim 4, In the aforementioned ad match determination process, the match degree is calculated and presented using statistical information regarding the population composition of the distributed area. An analysis system characterized by the following features.

8. The analysis system according to claim 4, It comprises a predetermined generating AI that has been pre-trained on statistical information regarding the population composition of at least the distributed area, In the aforementioned ad matching degree determination process, the printed material and the distribution area are input, and the generating AI calculates and presents the matching degree. An analysis system characterized by the following features.

9. The analysis system according to claim 4, In the aforementioned ad match determination process, the match degree is calculated and presented using text information obtained by web crawling of the distributed area. An analysis system characterized by the following features.

10. The analysis system according to claim 4, It includes a predetermined generating AI that utilizes text information obtained by web crawling in at least the aforementioned distributed area as supplementary information, In the aforementioned ad matching degree determination process, the printed material and the distribution area are input, and the generating AI calculates and presents the matching degree. An analysis system characterized by the following features.

11. The processor, A request acceptance process that accepts input of attribute information for a specified printed material and a specified commercial area where the printed material is being considered for distribution. An initial distribution area proposal process that uses the attribute information and the trade area to evaluate the degree of match between the attribute information and predetermined characteristics of the trade area using a predetermined generating AI, and outputs and proposes distribution areas to be distributed when the predetermined printed materials are distributed thereafter. A distribution record acquisition process that acquires a specified printed material and the distribution area where the printed material was distributed, A distribution method proposal process that inputs metadata of the printed material and predetermined characteristics of the distributed area into the generating AI and outputs improvement proposals for the distribution method of the predetermined printed material for each distributed area, An analysis system characterized by performing the following actions.

12. An analysis method using a computer system, wherein the computer system has a processor, A distribution record acquisition process that acquires a specified printed material, the areas where the printed material has been distributed, and the areas where the printed material has not been distributed. A next distribution area proposal process that uses the predetermined printed material, the distributed area, and the undistributed area to evaluate the degree of match between the metadata of the printed material and the predetermined characteristics of the undistributed area using a predetermined generating AI, and outputs and proposes the distribution area to be distributed when the predetermined printed material is distributed in the future, based on the result of the evaluation, A distribution method proposal process that inputs metadata of the printed material and predetermined characteristics of the distributed area into the generating AI and outputs improvement proposals for the distribution method of the predetermined printed material for each distributed area, An analytical method characterized by performing the following.

13. In the processor, A distribution record acquisition process that acquires a specified printed material, the areas where the printed material has been distributed, and the areas where the printed material has not been distributed. A next distribution area proposal process that uses the predetermined printed material, the distributed area, and the undistributed area to evaluate the degree of match between the metadata of the printed material and the predetermined characteristics of the undistributed area using a predetermined generating AI, and outputs and proposes the distribution area to be distributed when the predetermined printed material is distributed in the future, based on the result of the evaluation, A distribution method proposal process that inputs metadata of the printed material and predetermined characteristics of the distributed area into the generating AI and outputs improvement proposals for the distribution method of the predetermined printed material for each distributed area, An analysis program characterized by performing the following actions.

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