Information processing system, information processing method and program

Abstract

To provide information processing system, method and program which provide, to a user, a visualized analysis result of marketing based on the flow of people within a predetermined range where a store is present.SOLUTION: In a data server, a people-flow data obtaining unit obtains one or multiple pieces of people-flow data that represents people staying or moving in a predetermined range at a predetermined date, a store purchase record obtaining unit obtains store purchase record data that represents a purchase of a predetermined commodity by a person at the predetermined date and at a predetermined store in an actual world, and a data managing unit stores and manages the people-flow data and store purchase record data in association with each other in a predetermined database. In a managing server, a data analyzing unit executes a predetermined analysis using the flow of people within a certain range including the store based on the people-flow data and on the store purchase record data both stored in the predetermined database, and a visualized information generating unit generates visualized information including the analysis result, updates the people-flow data at a predetermined time cycle, and generates the visualized information that displays the flow of people in a manner laid over on a map.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Conventionally, there have been systems that analyze the marketing of stores that exist in the real world (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-048780 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is a demand for a service that visualizes the results of marketing analysis based on the flow of people within a specified area where real-world stores exist and provides them to users, but conventional technologies, including Patent Document 1, are unable to meet this demand.

[0005] The present invention has been made in consideration of this situation, and aims to realize a service that visualizes the results of marketing analysis based on the flow of people within a specified area where real-world stores are located, and provides them to users. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing system according to one aspect of the present invention comprises: A people flow data acquisition means for acquiring, as people flow data, one or more data groups indicating people staying and moving within a predetermined range in the real world at a predetermined date and time; a store purchase history acquisition means for acquiring, as store purchase history data, one or more data groups indicating that a person has purchased a predetermined product at a predetermined date and time at a predetermined store in the real world; a data management means for storing and managing the people flow data and the store purchase history data in a predetermined database in association with each other; an analysis means for executing a predetermined analysis using the flow of people within a certain range including the store, based on the people flow data and the store purchase history data stored in the predetermined database; visualization information generation means for generating visualization information including the analysis result by the analysis means; Equipped with.

[0007] An information processing method and a program according to one aspect of the present invention are respectively an information processing method and a program corresponding to the information processing system according to the above-described one aspect of the present invention. [Effects of the Invention]

[0008] According to the present invention, it is possible to realize a service that visualizes the results of a marketing analysis based on the flow of people within a predetermined area where a store is located in the real world and provides the results to a user. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a conceptual diagram showing an overview of the present service that can be realized by various processes executed by an embodiment of the information processing system of the present invention. [Figure 2] This is an image diagram showing an overview of the service shown in Figure 1. [Figure 3] 1 is a diagram showing an example of the configuration of an information processing system applied to the present service of Fig. 1 and Fig. 2, that is, an embodiment of the information processing system of the present invention. Fig. 2 is a diagram showing the configuration of an information processing system including the server of Fig. 1. [Figure 4] FIG. 4 is a block diagram showing the hardware configuration of a data server in the information processing system of FIG. 3. [Figure 5] FIG. 4 is a functional block diagram showing an outline of the functional configuration of the information processing system of FIG. 3. [Figure 6] FIG. 6 is a functional block diagram showing an example of the functional configuration of the data server and the analysis server of FIGS. 2 and 5. [Figure 7] FIG. 7 is a diagram showing an example of association of various data stored in the marketing DB of FIG. 6. [Figure 8] 7 is a diagram showing an example of the structure of store purchase history data stored in the marketing DB of FIG. 6. FIG. [Figure 9] FIG. 7 is a diagram showing an example of the structure of data when a national census is adopted as the official statistical data stored in the marketing DB of FIG. 6. [Figure 10] FIG. 7 is a diagram showing an example of the structure of research data stored in the marketing DB of FIG. 6. [Figure 11] 7 is a diagram showing an example of the structure of online purchase history data stored in the marketing DB of FIG. 6. FIG. [Figure 12] 7 is a diagram showing an example of the structure of people flow data stored in the marketing DB of FIG. 6. FIG. [Figure 13] 7 is a diagram showing a specific example of visualized information showing the results of analysis using data stored in the marketing DB of FIG. 6. FIG. [Figure 14] 14 is a diagram showing a specific example of visualization information showing the results of analysis using data stored in the marketing DB of FIG. 6, which is different from the example of FIG. 13. FIG. [Figure 15] FIG. 15 is a diagram showing an example of a screen of visualized information displayed on a user terminal in relation to the specific example of FIG. 14. [Figure 16] 16 is a diagram showing a specific example of visualization information showing the results of analysis using data stored in the marketing DB of FIG. 6, which is an example different from those of FIGS. 13 to 15. FIG. [Figure 17] FIG. 17 is a diagram showing an example of a screen of visualized information displayed on a user terminal in relation to the specific example of FIG. 16. [Figure 18] 18 is a diagram showing a specific example of visualization information showing the results of analysis using data stored in the marketing DB of FIG. 6, which is an example different from those of FIGS. 13 to 17. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] FIG. 1 is a conceptual diagram showing an overview of this service that can be realized by various processes executed by an embodiment of an information processing system of the present invention.

[0012] As shown in Figure 1, this service is a marketing service that visualizes the results of marketing analysis based on the flow of people (including customers and potential customers) within a specified area (e.g., a trade area) where a real-world (hereinafter referred to as "real") store is located, and provides this to users (e.g., people related to the store). In particular, this service can visualize the flow of people within a given area where real-world stores are located, from start to finish, in real time and present it to the user.

[0013] In order to provide this service, the provider of this service (hereinafter referred to as the "service provider") manages the marketing DB 71 shown in FIG. 1 on the cloud. The marketing DB 71, details of which will be described later, is API-linked with the user DB 72, and stores people flow data DJ, store purchase history data DR, online purchase history data DN, official statistical data DC, and research data DL in association with each other. Users can access the marketing DB71 from anywhere using their own terminal (for example, user terminal 3 in Figure 3 described below), and can use the analysis results based on the data stored in the marketing DB71 to achieve a variety of things as shown in Figure 1. For example, users can understand store customers, potential customers, and traffic flow in advance. That is, users can specifically understand the size, age, day of the week, time of day, seasonal fluctuations, etc. of customers or potential customers within a trade area. Furthermore, for example, users can carry out one-to-one campaigns to encourage customers to visit the store. For example, users can also link with additional promotional tools in the store to increase sales. Additionally, users can measure the effectiveness of campaigns and ask questions about why they visited, which can be useful for future research. Specifically, users can understand what attracted visitors to a store that ran a campaign, and conversely, the reasons why people did not visit.

[0014] Figure 2 is an image diagram showing an overview of the service shown in Figure 1. The diagram on the left side of FIG. 2 shows the positioning of data stored in the marketing DB 71. The left side of the horizontal axis represents real-world data, and the right side represents data in the digital world. The top of the vertical axis represents macro data, and the bottom represents micro data. People flow data DJ is a set of one or more data representing people staying or moving within a specific area (location) in the real world at a specific date and time. People flow data DJ is real data, and therefore micro data. However, personal information is obtained from the information provider after anonymous data processing, so names and other personal information cannot be used to identify individuals. In other words, people flow data DJ is not personal information, but is matched using identifiers assigned according to specific rules. The server (e.g., Data Server 1 in Figure 3) treats it as a group and interprets the information from a more macro perspective. Store purchase history data DR is a set of one or more data indicating that a person purchased a specific product at a specific date and time at a specific real-world store (mainly the user's store). Although store purchase history data DR is real data and therefore micro data, personal information is obtained from the information provider after anonymous data processing, so names and other personal information cannot be used to identify individuals. In other words, store purchase history data DR is not personal information, but is matched using identifiers assigned according to specific rules. A server (e.g., data server 1 in Figure 3) treats it as a set and interprets the information from a more macro perspective. The online purchase history data DN is a type of access log data, and is a group of one or more data items that indicate when a person purchases a specific product at an online store. The online purchase history data DN is digital data, and is more microscopic data (more macroscopic data than research data DL). Official statistical data DC is data showing the results of statistical analysis on a specified subject provided by a specified national or local institution. In this embodiment, data provided by the Statistics Bureau of the Ministry of Internal Affairs and Communications of Japan is used as the official statistical data DC. Specifically, in this embodiment, data from the population census and various surveys on households (Family Income and Expenditure Survey, Household Consumption Survey, National Household Structure Survey, National Survey of Family Income and Expenditure, National Price Statistics Survey, etc.) are used as examples of official statistical data DC. In addition, survey data on corporate activities, low or high tax rates, etc. may also be used as official statistical data DC as needed. Official statistical data DC is real data, and is macro data. Research data is data showing the results of a survey on a specific subject provided by a specific research organization. An example of real data, which is micro data, is qualitative survey data, specifically, for example, in-depth interviews. On the other hand, an example of digital data, which is micro data, is quantitative survey data, specifically, for example, web panel surveys. If necessary, online behavior data (search trends), which is digital data and macro data, can also be stored in the marketing DB 71.

[0015] Based on the data stored in such marketing DB71, the service provider (analysis server 2 in Figure 3 described below) can perform a predetermined analysis using the flow of people (including the store's customers and potential customers) within a certain area (e.g., a trade area) that includes the user's store.

[0016] As shown in Figure 1, in this analysis, people flow data DJ is used to understand real-time people flow. Store purchase history data DR is used to understand purchase history at real stores. Online purchase history data is used to understand purchase history at internet (digital) stores. While such analysis may use individual data (raw data), in this embodiment, the following clustering data can be used to perform more formal analysis. Specifically, a service provider can use statistical analysis to generate meaningful value data (information) by independently clustering consumer groups based on their consumption habits and lifestyle patterns, such as "luxury goods buyers," "vacation spenders," and "totally frugal consumers," using multiple source data, including people flow data DJ, store purchase history data DR, online purchase history data DN, official statistical data DC, and research data DL. This clustering allows the service provider to independently cluster consumer groups based on their consumption habits and lifestyle patterns, such as "luxury goods buyers," "vacation spenders," and "totally frugal consumers." This "meaningful value data (information)" is clustering data. Clustering data is preferably easily reproducible, so it is obtained by simply extracting necessary indicators from official statistical data DC, such as gender, age, family type, disposable income, and household spending habits, and then classifying the data into groups based on those indicators. Therefore, such clustering data is used, for example, to understand the lifestyles of potential customers within a certain area (e.g., a trade area) that includes the user's store, as shown to the right of the official statistical data DC in Figure 1. Research data DL is used to understand human psychology.

[0017] To summarize, the data stored in the marketing DB 71 is a variety of data belonging to the on (real), off (digital), macro, and micro categories, as shown on the left side of Figure 2. In particular, conventional marketing analysis only used people flow data provided by a single specific company. In contrast, the people flow data DJ of this service is an integrated version of people flow data from multiple companies. Furthermore, this service not only uses people flow data DJ, but also connects it with store purchase history data DR, online purchase history data DN, and other data to create a sense of scale before conducting analysis. That is, this service performs analysis by collecting and collating various types of data belonging to on (real), off (digital), macro, and micro categories stored in the marketing DB 71. As a result, based on the results of such analysis, the user can appropriately execute the PDCA cycle, such as "from prediction to execution, execution to feedback, feedback to prediction," as shown on the right side of Figure 2. Here, in the PDCA "prediction" (near future), users can easily understand who will buy what, when, how, and what will be purchased at their store, as well as what the e-commerce and occasions will be like. In the "execution" stage of PDCA, users can concisely create a scenario of their actions. In the "feedback" stage of PDCA, users can receive feedback on whether their actions worked. In this way, this service provides the user with appropriate analysis results using the marketing DB 71. As a result, it is possible to achieve the effect of realizing high-precision, high-speed PDCA. In other words, this service is a completely unprecedented service that provides analytical results that combine people flow data DJ (which is an integration of people flow data provided by multiple companies, not just one company) with a variety of other information, including store purchase history data DR. It is precisely because of this type of service that it is possible to achieve the effects mentioned above.

[0018] The service shown in Figures 1 and 2 above can be summarized as follows: In other words, in the past, the ability to interpret past data and translate it into action was something that only a very select few could do. To solve this problem, this service aims to simplify data utilization. Furthermore, in the past, the data required to implement measures was scattered around the world, but there was no way to integrate this scattered information. To solve this problem, this service integrates multiple data, including people flow data DJ and store purchase history data DR, in the marketing DB71. In addition, in the past, data was collected, analyzed, and countermeasures (measures to implement measures) were created, but there was an issue that the countermeasures often became meaningless because too much time had passed before they were created. Therefore, this service aims to solve this issue by speeding up analysis and the implementation of countermeasures. In this way, this service allows you to get an overview of the data for a specific store and link it to immediate action.

[0019] In other words, when conducting marketing for one or more stores, the following problems have conventionally arisen. In other words, from the perspective of data integration, the following first to fourth problems exist. The first issue is that no companies are using people flow data to make decisions. In other words, although there are many companies that handle people flow data, the amount of data each company has is insufficient to use for decision-making. This is because the people flow data each company uses is for expanded estimation. The second issue is that the companies providing people flow data are unlikely to collaborate (cooperate horizontally). In other words, because each company will be competing with the other, the possibility of integrating the people flow data held by each company is extremely low. The third challenge is that even if people flow data and retail purchasing data are linked, the scale of data that has been grasped up until now has been extremely limited. The fourth issue concerns the frequency of updating people flow data. In other words, conventional people flow data is typically updated monthly, and at the earliest, daily, meaning that users cannot use the data to address current issues. Furthermore, as a fifth issue from the user's perspective, due to the first to fourth issues mentioned above, there is a problem that no service has been built to date that allows users to utilize people flow data.

[0020] This service is capable of solving the first to fifth problems. In other words, this service establishes a position that is not competitive with companies that have people flow data, and allows data to be updated at least every hour. For this reason, this service integrates store purchase history data DR and other data based on people flow data DJ. In other words, this service integrates data from various companies. The service charges users who use the data and provides a level share to the companies that provide the data. In other words, this service achieves something that previously could not be achieved by a single company holding data alone; by combining various data provided by multiple companies, it achieves economies of scale (volume of data) and makes monetization possible. From the user's perspective, this service is able to provide an interface that makes it easy for users who are not good at using data to visually grasp information and take the next step, as data utilization is often just a string of numbers that they need to interpret themselves.The service is like a fish finder that shows the user a lot of fish (a lot of people around the store), allowing them to take action (implement a measure) by casting their line.

[0021] Next, the configuration of an information processing system that is applied to the present service shown in FIGS. 1 and 2 will be described. FIG. 3 is a diagram showing an example of the configuration of an information processing system applied to the present service of FIGS. 1 and 2, that is, an embodiment of the information processing system of the present invention.

[0022] The information processing system shown in FIG. 3 is configured by interconnecting a data server 1, an analysis server 2, and user terminals 3-1 to 3-n (n is an integer value of 1 or more) via a predetermined network such as the Internet.

[0023] The data server 1 and the analysis server 2 in this embodiment are servers managed by a service provider. Each of the user terminals 3-1 to 3-n is configured by a personal computer, a smartphone, a tablet terminal, or the like, and is managed by each of the n users. In the following description, when there is no need to distinguish between the user terminals 3-1 to 3-n, they will be collectively referred to as "user terminal 3."

[0024] FIG. 4 is a block diagram showing the hardware configuration of the data server in the information processing system of FIG.

[0025] The data server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input unit 16, an output unit 17, a memory unit 18, a communication unit 19, and a drive 20.

[0026] The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 into the RAM 13 . The RAM 13 also stores data and the like necessary for the CPU 11 to execute various processes.

[0027] The CPU 11, ROM 12, and RAM 13 are connected to one another via a bus 14. An input / output interface 15 is also connected to this bus 14. An input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0028] The input unit 16 is composed of various hardware leads and the like, and inputs various information. The output unit 17 is composed of various liquid crystal displays and the like, and outputs various information. The storage unit 18 is configured with a DRAM (Dynamic Random Access Memory) or the like, and stores various data. The communication unit 19 controls communications with other devices (for example, the analysis server 2 and user terminals 3-1 to 3-n in FIG. 3) via a network including the Internet.

[0029] The drive 20 is provided as needed. Removable media 21, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is appropriately attached to the drive 20. Programs read from the removable media 21 by the drive 20 are installed in the storage unit 18 as needed. The removable media 21 can also store various data stored in the storage unit 18 in the same way as the storage unit 18.

[0030] Although not shown, the analysis server 2 and the user terminal 3 also have the hardware configuration shown in FIG.

[0031] FIG. 5 is a block diagram showing an outline of the functional configuration of the information processing system of FIG.

[0032] The data server 1 is managed by the service provider SS and has the function of storing and managing data in a marketing DB 71 . The data server 1 has an API Gateway 31 for acquiring data to be stored in the marketing DB 71. The API Gateway 31 has the function of acquiring online purchase history data DN, store purchase history data DR, and research data DL from the user U side (for example, the user DB 72 of the user U) and storing them in the marketing DB 71. On the other hand, the API Gateway 31 acquires people flow data DJ and official statistical data DC from various other information processing devices not shown in FIG. 5 (collectively depicted as other information processing devices 4 in FIG. 6), and stores them in the marketing DB 71. The data management unit 32 manages the data stored in the marketing DB 71 . The Data I / F API 33 has a function for transmitting and receiving data between the data server 1 and the analysis server 2 .

[0033] The analysis server 2 is managed by the service provider SS, and has the function of executing a predetermined analysis based on the data from the data server 1 (data stored in the marketing DB 71). The analysis server 2 includes a data analysis unit 41, a data extraction unit 42, an API Gateway 43, and an application unit 44.

[0034] The data analysis unit 41 acquires data to be analyzed (data stored in the marketing DB 71) from the data server 1, and performs a predetermined analysis based on the data to be analyzed. Here, the predetermined analysis includes the generation of the clustering data described above. When the clustering data is generated by the data analysis unit 41, it is transmitted to the data server 1 as appropriate and stored in the marketing DB 71. The data extraction unit 42 appropriately extracts data to be analyzed, data indicating the analysis results, and the like from the data analysis unit 41 . The API Gateway 43 has a function of transferring data between the data analysis unit 41 and the data extraction unit 42 and the application unit 44 . The application unit 44 communicates with the user terminal 3 (user terminals 3-1 to 3-n in the example of FIG. 5) via a network such as the Internet, receives requests from the user (for example, a request to perform analysis or a request to present the analysis results), and executes a response to the request. For example, in the case of a request to execute a predetermined analysis, the application unit 44 instructs the data analysis unit 41 via the API Gateway 43 to execute the predetermined analysis. For example, in the case of a request to present the results of the predetermined analysis, the application unit 44 acquires the results of the analysis from the data extraction unit 42 via the API Gateway 43 and presents them to the user terminal 3. Examples of various information presented to the user terminal 3 by the application unit 44 will be described later with reference to FIGS.

[0035] In summary, this service can facilitate linkage with the user's core system data (online purchase history data DN, store purchase history data DR, and research data DL) through the data server 1. As a result, the analysis results (analysis output) of the analysis server 2, that is, the added value for the user, can be enhanced. Furthermore, this service can increase the update frequency of data (not only data in the marketing DB 71 but also data resulting from analysis in the data analysis unit 41, etc.). For example, the update frequency can be increased to an hourly level, which was previously unrealizable. As a result, users can utilize data in near real time.

[0036] FIG. 6 is a functional block diagram showing an example of the functional configuration of the data server and the analysis server shown in FIGS. Since the hardware configuration of both the data server 1 and the analysis server 2 can be the configuration shown in Fig. 4, the same components (blocks) will be distinguished from each other by assigning the reference symbols D to the data server 1 and K to the analysis server 12 in Fig. 4. Specifically, for example, the CPU 11 of the data server 1 will be referred to as CPU11D, while the CPU 11 of the analysis server 2 will be referred to as CPU11K.

[0037] As shown in Figure 6, the CPU 11D of the data server 1 functions as a people flow data acquisition unit 151, a store purchase history acquisition unit 152, an online purchase history acquisition unit 153, a public statistical data acquisition unit 154, a research data acquisition unit 155, and a data management unit 32. Furthermore, the storage unit 18D of the data server 1 is provided with a marketing DB 71. In the CPU 11K of the analysis server 2, a data analysis unit 41, a data extraction unit 42, and an application unit 44 function.

[0038] The people flow data acquisition unit 151 acquires one or more data groups indicating people staying and moving within a predetermined range in the real world at a predetermined date and time as people flow data DJ. A specific example of the people flow data DJ will be described later with reference to FIG. The store purchase history acquisition unit 152 acquires, as store purchase history data DR, one or more data groups indicating that a person purchased a predetermined product at a predetermined date and time in a predetermined store (e.g., a user's store) in the real world. A specific example of the store purchase history data DR will be described later with reference to FIG. 8. The online purchase history acquisition unit 153 acquires one or more data groups indicating that a person has purchased a predetermined product at an internet store as online purchase history data DN. A specific example of the online purchase history data DN will be described later with reference to FIG. The official statistical data acquisition unit 154 acquires data indicating the results of statistical analysis of a predetermined subject provided by a predetermined institution of the national or local government as official statistical data DC. A specific example of the official statistical data DC will be described later with reference to FIG. 9. The research data acquisition unit 155 acquires research data DL, which is data showing the results of a survey on a predetermined subject provided by a predetermined survey organization. A specific example of the research data DL will be described later with reference to FIG.

[0039] Although not shown in Figure 6, the people flow data acquisition unit 151, store purchase history acquisition unit 152, online purchase history acquisition unit 153, official statistical data acquisition unit 154, research data acquisition unit 155, and communication unit 19D perform functions such as sending and receiving data via API Gateway 31 in Figure 5. That is, the API Gateway 31 can be understood to be composed of a people flow data acquisition unit 151, a store purchase history acquisition unit 152, an online purchase history acquisition unit 153, a public statistical data acquisition unit 154, a research data acquisition unit 155, and a communication unit 19D in the CPU 11D.

[0040] As described above with reference to FIG. 5, the data management unit 32 stores and manages the people flow data DJ, store purchase history data DR, online purchase history data DN, official statistical data DC, and research data DL in association with each other in the marketing DB 71. A specific example of the association (linking) of the people flow data DJ, store purchase history data DR, online purchase history data DN, official statistical data DC, and research data DL will be described later with reference to FIG.

[0041] Although not shown in Figure 6, the data managed by the data management unit 32, i.e., the data stored in the marketing DB 71, is exchanged with the analysis server 2 via the Data I / F API 33. That is, from the perspective of another information processing device that uses the API, the Data I / F API 33 can be understood to be composed of one or more function blocks (not shown in FIG. 6) that function in the CPU 11K, and a communication unit 19K.

[0042] Of the functional blocks of the analysis server 2, the description of the functional blocks described above with reference to FIG. 5 will be omitted. Although not shown in FIG. 6 for ease of explanation, the API Gateway 43 in FIG. 5 also performs the function of exchanging data between the data analysis unit 41 and the data extraction unit 42 and the application unit 44 in the CPU 11K.

[0043] The application unit 44 includes a visualization information generation unit 441 . The visualization information generation unit 441 generates visualization information including the results of the analysis by the data analysis unit 41. Specific examples of the visualization information will be described later with reference to FIGS.

[0044] Next, specific examples of data stored in the marketing DB 71 shown in FIG. 6 etc. will be described with reference to FIGS.

[0045] FIG. 7 is a diagram showing an example of the association of various data stored in the marketing DB shown in FIG. 6 and the like.

[0046] Each piece of data stored in the marketing DB 71 has multiple items, and if one piece of data has an item with the same content as another piece of data, it is associated with that other piece of data by the item with the same content. Here, "items with the same content" does not necessarily mean an exact match, but means that the content is the same enough to allow association. For example, if the first item in one piece of data is prefecture A and the second item in the other piece of data is city B, and city B is included in prefecture A, then the first and second items may be considered to have the same content. 7, the people flow data DJ and the store purchase history data DR are associated by an item indicating date and time called "datetime." Note that the store purchase history data DR of all stores is not associated with all people flow data DJ, but data with overlapping geographical ranges are associated based on an item indicating latitude and longitude called "long latitude." For example, in the example of FIG. 7, the store purchase history data DR and the online purchase history data DN are associated with each other by an item indicating a transaction ID called "order id." For example, in the example of FIG. 7, census data is used as the official statistical data DC, and the store purchase history data DR and the official statistical data DC are associated with each other by items indicating address-related information.

[0047] FIG. 8 shows an example of the structure of store purchase history data stored in the marketing DB shown in FIG. 6 and the like. In the example of Figure 8, the store purchase history data DR is composed of various sub-information such as "sales detail: purchase details information," "sales: purchase information," "discount: discount information," "tender: payment information," "customer: customer information," "item: product," "item category: product category," and "store: store information." That is, in the marketing DB 71, these small pieces of information are associated with other pieces of information by items of the same content and linked together to form the store purchase history data DR. For example, "sales detail: detailed purchase information" and "item: product" both have an item "item_cd: product code," and are therefore associated by this item. 7, the people flow data DJ and the store purchase history data DR are associated with each other by the item "datetime" indicating the date and time. More precisely, the people flow data DJ and the store purchase history data DR are associated with each other by the item "datetime" indicating the date and time, whereby the people flow data DJ and the store purchase history data DR are associated with each other. For example, "item: product" and "item category: product category" both have an item "item_category_id: product category ID," and are therefore associated by this item. "Sales detail: detailed purchase information" and "sales: purchasing information" both have the item "tran_id: transaction ID," so they are associated by that item. Similarly, "sales: purchasing information" and "discount: discount information," "tender: payment information," and "customer: customer information" all have the item "tran_id: transaction ID," so they are associated by that item. As mentioned above, the store purchase history data DR and the online purchase history data DN are associated with each other by an item indicating a transaction ID called "order id." More precisely, the store purchase history data DR and the online purchase history data DN are associated with each other by an item indicating a transaction ID called "order id." "Sales: purchase information" and "store: store information" both have an item "store id: store ID," and are therefore associated with each other by this item. 7, it has been mentioned above that census data is used as the official statistical data DC, and the store purchase history data DR and the official statistical data DC are associated with each other by items indicating address-related information. More precisely, the store purchase history data DR and the official statistical data DC are associated with each other by addresses identified by items such as "store place: store location" in "store id: store ID."

[0048] FIG. 9 shows an example of the data structure when the national census is used as the official statistical data stored in the marketing DB shown in FIG. 6 and the like. FIG. 10 shows an example of the structure of research data stored in the marketing DB shown in FIG. 6 and the like. FIG. 11 shows an example of the structure of online purchase history data stored in the marketing DB shown in FIG. 6 and the like. FIG. 12 shows an example of the structure of people flow data stored in the marketing DB shown in FIG. 6 and the like.

[0049] Specific examples of data stored in the marketing DB 71 of FIG. 6 etc. have been described with reference to FIGS. Next, with reference to FIGS. 13 to 18, a specific example of visualization information showing the results of analysis using data stored in the marketing DB 71 of FIG. 6 and the like will be described.

[0050] FIG. 13 shows a specific example of visualized information showing the results of analysis using data stored in the marketing DB shown in FIG. 6 and the like. The example in Figure 13 shows the results of an analysis based on online behavior data (search trends) in addition to people flow data DJ, store purchase history data DR, online purchase history data DN, official statistical data DC, and research data DL, among the data stored in the marketing DB 71 in Figure 6, etc. In the example of Figure 13, an interface has been constructed on the user terminal 3 that makes it easy for users who are not good at using data, as data utilization is often just a string of numbers that they need to interpret themselves, to visually grasp the information and take their next step. That is, the user terminal 3 functions as something like a fish finder. By visually checking the user terminal 3 functioning as a fish finder, the user can take action (for example, a campaign or other measure) to cast a fishing line because there are a lot of fish (i.e., a lot of people in the commercial area around the store). In this way, this service can link the necessary information linked to execution to the digital device, the user terminal 3. As a result, the user terminal 3 can display only the information the user needs on the dashboard.

[0051] FIG. 14 shows a specific example of visualized information showing the results of analysis using data stored in the marketing DB of FIG. 6 and the like, and is an example different from that of FIG. FIG. 15 shows an example of a screen of visualized information displayed on a user terminal for the specific example of FIG.

[0052] In the examples of FIGS. 14 and 15, the user is assumed to be a person who belongs to a specific store (such as a store manager or a store clerk). As shown in FIG. 14, in this example, the user has the following problem. In other words, users want to hold special time sales, but they don't know what to do because the sales are often dependent on external factors that are beyond their (user's) control, such as weather, day of the week, and time. In this service, various data stored in the marketing DB 71 shown in Figure 6 and other figures are automatically linked to address such user issues, a predetermined analysis is performed, and visualized information showing the analysis results is displayed on the user terminal 3. 14, based on the people flow data DJ, visualization information alerting that there is a 150% or more increase in the number of people within a 500-meter radius of the store (predetermined store) is displayed on the user terminal 3. Specifically, for example, as shown in FIG. 15, visualization information in which data indicating the number of people (data with circles displayed in colors indicating the number of people) is superimposed on a map including an area with a 500-meter radius from the store (predetermined store) and visualization information of an alert saying "Caution: There is a 150% increase in the number of people within a 500-meter radius of Store A (the store)" are displayed on the user terminal 3. Furthermore, as shown in Fig. 15, specific countermeasures such as "utilizing digital sales promotion," "utilizing in-store sales," and "response plan for changing shelf displays" are displayed as visualized information as implementation measures (countermeasures). Here, "utilizing digital sales promotion" means, for example, sending sales promotion push notifications via LINE (registered trademark) or a predetermined application. Furthermore, the current sales figures for a specific store (own store) and sales forecasts (including forecasts of increases when countermeasures are implemented) are displayed as bar graphs (as such visualized information) for each time period. By visually checking such visualized information, the user can consider appropriate measures to be taken (countermeasures). That is, because there are situations on the ground that cannot be understood from data alone, users can implement the countermeasure they think is best from among specific countermeasures such as "utilizing digital sales promotion," "utilizing in-store sales," and "response plans for changing shelf displays." Specific countermeasures are displayed as visualized information in this way because having options makes it easier to implement them than having no plans (examples of countermeasures) at all. Here, for example, it is assumed that the user decides to take the countermeasure of "utilizing store sales promotions." Then, as shown in Fig. 14 (although not shown in Fig. 15), as a result of linking and analyzing the store purchase history data DR and the research data DL, a product x that was likely to sell well in the same environment in the past and has high price elasticity is automatically picked up, and visualization information including the optimal price for attracting customers along with the product x is displayed on the user terminal 3. By visualizing such visualization information, users can implement a special time sale (sale at the best price) for the target product (x product) at the storefront of a specified store as an action measure (countermeasure) called "utilizing store sales promotions," thereby promoting store visits. In addition, users can appropriately view the visualization information showing the flow of people within a 500m radius, and end the special time sale when the data shows that the flow of people has decreased.

[0053] FIG. 16 shows a specific example of visualization information showing the results of analysis using data stored in the marketing DB of FIG. 6 and the like, and is an example different from those of FIGS. FIG. 17 shows an example of a screen of visualized information displayed on a user terminal for the specific example of FIG.

[0054] In the examples of FIGS. 16 and 17, the user is assumed to be a person involved in the management level of a head office that oversees a plurality of stores. As shown in FIG. 16, in this example, the user has the following problem. In other words, in the past, it took time for management to understand opportunity losses occurring at the site (for example, multiple stores including the designated store (Store A) in the examples of Figures 14 and 15), and there were cases where accurate information about what was happening at the site was not communicated. The reason for this was that reports to management were filtered by various sources, such as the site and middle managers, and this took time. In other words, users have a problem of wanting to obtain accurate information in a short time that will allow management to understand the opportunity losses occurring at the site (for example, multiple stores including a specific store (Store A) in the examples of Figures 14 and 15). In this service, various data stored in the marketing DB 71 shown in Figure 6 and other figures are automatically linked to address such user issues, a predetermined analysis is performed, and visualized information showing the analysis results is displayed on the user terminal 3. For example, as shown in Fig. 16, as a result of linking and analyzing the previous day's people flow data DJ and store purchase history data DR, visualization information that allows the user to grasp the stores that performed poorly and stores that performed well on the previous day is displayed on the user terminal 3. Furthermore, if there is a large gap between the poor and good performance of similar stores, visualization information indicating an alert is displayed on the user terminal 3. In other words, an alert is generated from the user terminal 3. The reason for generating an alert in this way is that there is a possibility that the correct measures have not been taken. Specifically, for example, as shown in Figure 17, on a map of an area including four similar stores, namely Store A, Store B, Store C, and Store D, visualized information is displayed on the user terminal 3, with data showing the flow of people (data with circles displayed in colors that indicate the number of people) superimposed, and visualized information of an alert stating, "Caution: Loss of opportunity cost of 1 million yen due to a similar store the previous day. Target stores are Store A, Store C, and Store D." Furthermore, as shown in Fig. 17, specific countermeasures such as "measure instructions to the relevant store," "instructions to the area manager," and "instructions to the sales promotion department" are displayed as visualized information as implementation measures (countermeasures). Here, "instructions to the sales promotion department" means, for example, instructions to the target stores (here, Store A, Store C, and Store D) to immediately implement sales promotion through specific media. Furthermore, regarding the amount of opportunity loss, the current performance of similar stores (Store A, Store B, Store C, Store D) and sales forecasts (including the loss amount calculated from the performance of successful stores if countermeasures are not implemented) are displayed as bar graphs (as such visualized information) for each time period. By visually checking such visualized information, the user can consider appropriate measures to be taken (countermeasures). That is, because there are situations in the field that cannot be understood from data alone, the user (management) can implement the countermeasure they deem best from specific countermeasures such as "instructions to the relevant store," "instructions to the area manager," and "instructions to the sales promotion department." That is, the user (management) can select the person they deem most appropriate from the relevant store (store manager or clerk), the area manager, and the sales promotion department. Here, "instructions to the relevant store," "instructions to the area manager," and "instructions to the sales promotion department" are assumed to be software buttons. That is, by pressing the software button that indicates the countermeasure they deem best (the person they deem most appropriate), the user can issue instructions to the terminal of the person selected. In this way, as shown in Figure 16, the user can give instructions to the field (the person considered most appropriate among the relevant store, area manager, or sales promotion department) along with evidence using people flow data.

[0055] FIG. 18 shows a specific example of visualization information showing the results of analysis using data stored in the marketing DB of FIG. 6 and the like, and is an example different from those of FIGS.

[0056] In the example of FIG. 18, the user is assumed to be a person who belongs to a specific store (such as a store manager or a store clerk). The screen in the example of FIG. 18 is displayed on the user terminal 3 as a summary page of the predetermined store. In the rectangular areas in Figure 18, the area labeled "Goal" indicates the predicted value analyzed by the service, and the areas labeled "This Week" or "This Month" indicate the results (predicted values) analyzed by the service. In the example in Figure 18, explanatory text is displayed for the sake of convenience, but the rectangular areas actually display outline numerical values. In addition, this rectangular area also functions as a software button, and when pressed (tapped), it jumps to another page (not shown) where detailed numerical values, graphs, etc. are displayed. The circular areas in Figure 18 are software buttons, and when pressed (tapped), a separate page will be displayed showing details of the information within the circular area. The three circular areas in the upper left, namely the circular areas labeled "New Product Information," "Sales Priorities," and "Campaign Details," are software buttons that take you to a separate page showing information about the entire company (your company) that oversees multiple stores, including the specified store. The circular area labeled "People Movement Around the Store" is a software button that, when pressed (tapped), jumps to a separate page that displays visualized information (see the example in Figure 15) in which data indicating people flow (data with circles displayed in colors that indicate the number of people) is superimposed on a map including the store (prescribed store).By visually checking the visualized information, the user can grasp the detailed movements of people around the prescribed store (the user's store).

[0057] Although not shown in Figure 18, if the user is an area manager, the summary page will have the same layout as Figure 18, but at a level that aggregates the multiple stores that the area manager is in charge of (responsible stores), the figures will be the sum of the stores, and individual summaries for each of the stores will also be available.Similarly, a condensed summary page will be presented to higher-ranking regional general managers and executives. In this case, the analysis is carried out by dividing the stores into excellent stores, stores that are above average, stores that need more effort, unprofitable stores, etc., and the analysis results are also displayed on a summary page, etc. Furthermore, if there is data, it may be combined with Tableau, etc., to include visualized information for later analysis.

[0058] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope of achieving the object of the present invention are included in the present invention.

[0059] For example, in the above example, the data stored in the marketing DB 71 is that shown in Fig. 1, but is not particularly limited to the above embodiment as long as it includes at least the people flow data DJ and the store purchase history data DR. For example, although not shown in Fig. 1, the online behavior data (search trends) of Fig. 2 may also be stored in the marketing DB 71.

[0060] Furthermore, the configuration of the information processing system shown in FIG. 3 is merely an example for achieving the object of the present invention, and is not particularly limited. For example, in the above-described embodiment, two servers, a data server 1 and an analysis server 2, are used, but this is not limited to this and may be consolidated into one server or may be distributed across three or more servers.

[0061] Furthermore, the hardware configurations shown in FIG. 4 are merely examples for achieving the object of the present invention, and are not particularly limited.

[0062] 5 and 6 are merely examples and are not particularly limited. That is, it is sufficient for the information processing system to be provided with a function that can execute the above-described series of processes as a whole, and the type of functional block used to realize this function is not particularly limited to the example of FIG. 6.

[0063] 5 and 6, the locations of the functional blocks may be arbitrary. For example, at least some of the functional blocks on the data server 1 side may be provided in the analysis server 2 or other information processing devices, or vice versa. A single functional block may be configured by a single piece of hardware, or may be configured in combination with a single piece of software.

[0064] When the processing of each functional block is performed by software, the program that constitutes the software is installed into a computer or the like from a network or a recording medium. The computer may be a computer built into dedicated hardware, or may be a computer capable of executing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0065] The recording medium containing such a program may not only be composed of removable media that is distributed separately from the device itself in order to provide the program to each user, but may also be composed of recording media that are provided to each user in a state where they are pre-installed in the device itself.

[0066] In this specification, the steps describing the program to be recorded on the recording medium include not only processes that are performed in chronological order, but also processes that are not necessarily performed in chronological order but are performed in parallel or individually. In addition, in this specification, the term "system" refers to an overall device that is made up of a plurality of devices, a plurality of means, etc.

[0067] In summary, the information processing system to which the present invention is applied is sufficient if it has the following configuration, and can take on a variety of different embodiments. That is, an information processing system to which the present invention is applied (for example, an information processing system including the data server 1 and the analysis server 2 in FIG. 3) is A people flow data acquisition means (e.g., the people flow data acquisition unit 151 in FIG. 6) that acquires one or more data groups indicating people staying and moving in a predetermined range in the real world at a predetermined date and time as people flow data (e.g., the people flow data DJ in FIGS. 1, 2, and 12); a store purchase history acquisition means (for example, the store purchase history acquisition unit 132 in FIG. 6) that acquires one or more data groups indicating that a person purchased a predetermined product at a predetermined date and time in a predetermined store in the real world as store purchase history data (for example, the store purchase history data DR in FIGS. 1, 2, and 8); data management means (for example, the data management unit 32 in FIGS. 5 and 6) that associates the people flow data with the store purchase history data (for example, see FIG. 7) and stores and manages them in a predetermined database (for example, the marketing DB 71 in FIGS. 1 and 6); an analysis means (for example, the analysis unit 41 in FIGS. 5 and 6) for executing a predetermined analysis using the flow of people within a certain range including the store, based on the people flow data and the store purchase history data stored in the predetermined database; visualization information generation means (for example, the visualization information generation unit 441 in FIG. 6) for generating visualization information including the analysis results by the analysis means (for example, the visualization information in FIGS. 13 to 18); Equipped with.

[0068] The system further includes an online purchase history acquisition unit (e.g., the online purchase history acquisition unit 153 in FIG. 6) for acquiring one or more data groups indicating that a person has purchased a predetermined product at an online store as online purchase history data (e.g., the online purchase history data DN in FIGS. 1, 2, and 11), the data management means manages the online purchase history data in association with the people flow data and the store purchase history data, the analysis means performs the predetermined analysis based on the online purchase history data in addition to the people flow data and the store purchase history data. It is possible.

[0069] The system further comprises an official statistical data acquisition means (for example, the official statistical data acquisition unit 154 in FIG. 6) for acquiring data showing statistical results on a predetermined subject provided by a predetermined institution of the state or local government as official statistical data (for example, the official statistical data DC in FIGS. 1, 2, and 9), the data management means manages the official statistical data in association with the people flow data and the store purchase history data, the analysis means performs the predetermined analysis based on the official statistical indicator clustering data in addition to the people flow data and the store purchase history data. It is possible.

[0070] The system further includes a research data acquisition unit (e.g., the research data acquisition unit 155 in FIG. 6) for acquiring data showing the results of a survey on a predetermined subject provided by a predetermined research organization as research data (e.g., the research data DL in FIGS. 1, 2, and 10), the data management means manages the research data in association with the people flow data and the store purchase history data, the analysis means performs the predetermined analysis based on the official statistical indicator clustering data in addition to the people flow data and the store purchase history data. It is possible. [Explanation of symbols]

[0071] 1: Data server, 2: Analysis server, 3, 3-1, 3-n: User terminal, 11, 11D, 11K: CPU, 12: ROM, 13: RAM, 14: Bus, 15: Input / output interface, 16: Input section, 17: Output section, 18, 18D: Storage section, 19, 19D, 19K: Communication section, 20: Drive, 21: Removable media, 31: API Gateway, 32: Data management section, 33: Data I / F API, 41: Data analysis section, 42: Data extraction section, 43: API Gateway, 44: Application section, 71: Marketing DB, 72: User DB, 151: People flow data acquisition section, 152: Store purchase history acquisition section, 153: Online purchase history acquisition section, 154: Official statistical data acquisition section, 155: Research data acquisition section, DJ: People flow data, DR: Store purchase history data, DN: Online purchase history data, DC: Official statistical data, DL: Research data

Claims

1. a people flow data acquisition means for acquiring, as people flow data, one or more data groups indicating people staying and moving within a predetermined range in the real world at a predetermined date and time; a store purchase history acquisition means for acquiring, as store purchase history data, one or more data groups indicating that a person has purchased a predetermined product at a predetermined date and time at a predetermined store in the real world; a data management means for storing and managing the people flow data and the store purchase history data in a predetermined database in association with each other; an analysis means for executing a predetermined analysis using the flow of people within a certain range including the store, based on the people flow data and the store purchase history data stored in the predetermined database; visualization information generation means for generating visualization information including the analysis result by the analysis means; Equipped with The people flow data is updated at predetermined time intervals, The visualization information generating means is an information processing system that generates visualization information that displays the flow of people by superimposing it on a map.

2. The method further comprises: acquiring an online purchase history data set that includes one or more data groups indicating that a person has purchased a predetermined product at an online store; the data management means manages the online purchase history data in association with the people flow data and the store purchase history data, the analysis means performs the predetermined analysis based on the online purchase history data in addition to the people flow data and the store purchase history data. The information processing system according to claim 1 .

3. The system further comprises an official statistical data acquisition means for acquiring data showing statistical results on a predetermined subject provided by a predetermined institution of the national or local government as official statistical data, the data management means manages the official statistical data in association with the people flow data and the store purchase history data, the analysis means performs the predetermined analysis based on official statistical indicator clustering data in addition to the people flow data and the store purchase history data. The information processing system according to claim 1 .

4. The system further comprises a research data acquisition means for acquiring data indicating the results of a survey on a predetermined subject provided by a predetermined research organization as research data, the data management means manages the research data in association with the people flow data and the store purchase history data, the analysis means performs the predetermined analysis based on the official statistical indicator clustering data in addition to the people flow data and the store purchase history data. The information processing system according to claim 1 .

5. An information processing method executed by an information processing system, a people flow data acquisition step of acquiring, as people flow data, one or more data groups indicating people staying and moving within a predetermined range in the real world at a predetermined date and time; a store purchase history acquisition step of acquiring, as store purchase history data, one or more data groups indicating that a person has purchased a predetermined product at a predetermined date and time at a predetermined store in the real world; a data management step of storing and managing the people flow data and the store purchase history data in a predetermined database in association with each other; an analysis step of executing a predetermined analysis using the flow of people within a certain range including the store based on the people flow data and the store purchase history data stored in the predetermined database; a visualization information generating step of generating visualization information including the analysis result obtained by the analyzing step, The people flow data is updated at predetermined time intervals, The visualization information generating step is an information processing method for generating visualization information that displays the flow of people by superimposing it on a map.

6. On the computer, a people flow data acquisition step of acquiring, as people flow data, one or more data groups indicating people staying and moving within a predetermined range in the real world at a predetermined date and time; a store purchase history acquisition step of acquiring, as store purchase history data, one or more data groups indicating that a person has purchased a predetermined product at a predetermined date and time at a predetermined store in the real world; a data management step of storing and managing the people flow data and the store purchase history data in a predetermined database in association with each other; an analysis step of executing a predetermined analysis using the flow of people within a certain range including the store based on the people flow data and the store purchase history data stored in the predetermined database; a visualization information generating step of generating visualization information including the analysis result obtained by the analyzing step; Execute a control process including The people flow data is updated at predetermined time intervals, The visualization information generating step is a program for generating visualization information that displays the flow of people by superimposing it on a map.

Images