JP2020126549A - Information processing device and guidance information dispatch program - Google Patents

Abstract

To make it possible to attract customers to efficiently fill vacant seats in a store.SOLUTION: Users U1 to U3 are a plurality of candidates who are transmission destination candidates of guidance information to urge them to visit a target store A. The actual guidance number of times when guidance information has been sent to the user U1 in the past is "25," and the actual store visit number of times when the user U1 has visited the store in response to the guidance information is "10." Therefore, the store visit rate of the user U1 is "40.0%." The actual guidance number of times of the user U2 is "16," and the actual store visit number of times is "6." Therefore, the store visit rate of the user U2 is "37.5%." The actual guidance number of times of the user U3 is "10," and the actual store visit number of times is "3." Therefore, the store visit rate of the user U3 is "30.0%." For example, based on the number of vacant seats "2," the information processing device determines that top two of the store visit rate, the users U1 and U2 from among the users U1 to U3 are transmission destinations of guidance information to urge them to visit the target store A.SELECTED DRAWING: Figure 2

Description

The present invention relates to an information processing device and a guidance information sending program.

Conventionally, when using stores such as restaurants, customers themselves search for stores that meet their needs based on their current location, and consider the availability and distance to the stores from the searched stores. The store to be used is decided. The store side can solicit customers by issuing discount coupons, for example.

As a prior art, for example, when a vacant seat is registered in a model for which a vacant seat notification is registered by a player, an animation screen indicating that a vacant seat has been registered in the model registered in the vacant seat notification is POP-displayed. In addition, a technique for obtaining a proposal of an accepting source that accepts multiple users, selecting the accepting source as a place for collecting multiple users, and notifying a communication terminal of a user who is not in the selected place of guidance information to the selected place There is. Further, there is a technique of distributing electronic coupons in response to a request for issuing an electronic coupon of a store from a mobile terminal.

JP, 2002-263349, A International Publication No. 2014-038264 JP, 2010-231260, A

However, in the conventional technology, it is difficult to determine to which user an e-mail or the like urging the store to be sent can efficiently fill the vacant seats generated in the store.

In one aspect, the present invention aims to enable the attraction of customers to fill empty seats in a store efficiently.

In one embodiment, a reception unit that receives an invitation request from a target store, and a plurality of candidates that are destination candidates of guidance information that prompts visit to the target store in response to the reception of the invitation request. A storage unit that stores, for each candidate, the number of times the guidance record has sent the guidance information that prompts each candidate to visit the store and the number of times the candidate has visited the store according to the guidance information. And a determination unit that determines, based on the number of vacant seats designated by the invitation request, a destination of the guide information that prompts the user to visit the target store from among the plurality of candidates. A device is provided.

Advantageous Effects of Invention According to one aspect of the present invention, there is an effect that it is possible to attract customers who efficiently fill empty seats in a store.

FIG. 1 is an explanatory diagram illustrating an example of the information processing apparatus 101 according to the first embodiment. FIG. 2 is an explanatory diagram showing an example of determining the destination of the guidance information. FIG. 3 is an explanatory diagram illustrating a system configuration example of the information processing system 300 according to the second embodiment. FIG. 4 is a block diagram showing a hardware configuration example of the information processing apparatus 101. FIG. 5 is a block diagram showing a hardware configuration example of the store terminal 301 and the like. FIG. 6 is an explanatory diagram showing an example of the stored contents of the customer information DB 320. FIG. 7 is an explanatory diagram showing an example of the stored contents of the store information DB 330. FIG. 8 is an explanatory diagram showing an example of the stored contents of the store base point information DB 340. FIG. 9 is an explanatory diagram showing an example of the stored contents of the store visit information DB 350. FIG. 10A is an explanatory diagram (part 1) showing an example of stored contents of the attraction management information DB 360. FIG. 10B is an explanatory diagram (part 2) showing an example of the stored contents of the attraction management information DB 360. FIG. 11 is an explanatory diagram showing a specific example of an invitation request from a store. FIG. 12 is a block diagram showing a functional configuration example of the information processing apparatus 101. FIG. 13 is an explanatory diagram showing an example of the stored contents of the distance correction coefficient table 1300. FIG. 14 is an explanatory diagram showing an example of the stored contents of the day correction coefficient table 1400. FIG. 15 is an explanatory diagram showing an example of the stored contents of the extracted customer table 1500. FIG. 16 is an explanatory diagram showing a specific example of the group table. FIG. 17 is an explanatory diagram (No. 1) showing a transition example of the storage content of the extracted customer table 1500. FIG. 18 is an explanatory diagram (part 2) showing an example of changes in the storage contents of the extracted customer table 1500. FIG. 19 is an explanatory diagram (third example) showing a transition example of the storage contents of the extracted customer table 1500. FIG. 20 is an explanatory diagram (Part 4) of the transition example of the storage contents of the extracted customer table 1500. FIG. 21A is an explanatory diagram (part 1) showing an example of registration of new attraction management information. FIG. 21B is an explanatory diagram (part 2) showing an example of registration of new invitation management information. FIG. 22 is a flowchart showing an example of a store visit processing procedure of the information processing apparatus 101. FIG. 23 is a flowchart showing an example of the guidance information dispatch processing procedure of the information processing apparatus 101. FIG. 24 is a flowchart showing an example of a specific processing procedure of the customer extraction processing. FIG. 25 is a flowchart (No. 1) showing an example of a specific processing procedure of the store visit rate calculation processing. FIG. 26 is a flowchart (part 2) illustrating an example of a specific processing procedure of the store visit rate calculation processing. FIG. 27 is a flowchart (No. 3) showing an example of a specific processing procedure of the store visit rate calculation processing. FIG. 28 is a flowchart (part 4) illustrating an example of a specific processing procedure of the store visit rate calculation processing. FIG. 29 is a flowchart showing an example of a specific processing procedure of the priority ranking processing. FIG. 30 is a flowchart showing an example of a specific processing procedure of the target customer determination processing. FIG. 31 is a flowchart showing an example of a specific processing procedure of the sending processing. FIG. 32 is a flowchart showing an example of the attraction completion processing procedure of the information processing apparatus 101.

Embodiments of an information processing apparatus and a guidance information sending program according to the present invention will be described in detail below with reference to the drawings.

(Embodiment 1)
FIG. 1 is an explanatory diagram illustrating an example of the information processing apparatus 101 according to the first embodiment. In FIG. 1, the information processing apparatus 101 is a computer that enables attracting customers who efficiently fill empty seats in a store. The store is, for example, a restaurant, a movie theater, a theater, or the like.

Here, when a vacant seat (vacant seat, vacant room, etc.) occurs in the store, the store wants to promptly attract customers and reduce the number of vacant seats. For example, when a vacant seat occurs in a store, by issuing a discount coupon or the like to a customer existing within a predetermined distance from the store, the customer existing in a short distance from the store is attracted, It is possible to increase the filling rate of.

However, it is difficult for a store clerk or the like to determine how many users can effectively fill the vacant seats by sending an e-mail or the like urging the user to come to the store for the number of vacant seats in the store. For example, when sending an e-mail or the like to the same number of customers as the number of vacant seats, there is a high possibility that no vacant seats will be filled unless one person visits the store.

On the other hand, if you send e-mails to more customers than the number of available seats, it is more likely that the seats will be filled up, but there may be customers who are waiting for seats despite being invited by the store. There is a nature. If such a situation occurs, it may lead to dissatisfaction with the store, which is not preferable for the store.

Therefore, in the present embodiment, the information processing apparatus 101 visits the candidate according to the guide information and the number of times the guide information has been sent to each candidate of the plurality of candidates in the past. The destination of the guide information for prompting the visit to the target store is determined by referring to the actual number of times. As a result, it is possible to attract customers who efficiently fill the vacant seats in the store. Hereinafter, a processing example of the information processing apparatus 101 will be described.

(1) The information processing device 101 receives an invitation request from the target store. Here, the target store is a store of the attraction destination. The invitation request is a request for attracting a customer to a store, and specifically, is a request for sending guide information for prompting a visit to the store.

In the example of FIG. 1, it is assumed that the invitation request 120 from the target store A is received. The invitation request 120 is a request for sending guidance information for urging the target store A to come to the store, and includes the number of vacant seats “2”. The number of vacant seats “2” is the number of customers who want to be attracted to the target store A, and is specified on the store side based on the number of vacant seats generated at the target store A, for example.

(2) The information processing apparatus 101 refers to the storage unit 110 and, based on the number of vacant seats specified in the received invitation request, sends the guide information to the target store from among the plurality of candidates. To decide. Here, the candidate is a user who is a destination candidate of the guide information for prompting the visit to the target store. The memory|storage part 110 memorize|stores the guidance record frequency|count and the store visit frequency|count for each candidate of a some candidate. The guidance record count is the number of times the guide information that prompts each candidate to visit the store is sent. The number of store visits is the number of times each candidate has visited the store according to the guidance information.

Specifically, for example, the information processing apparatus 101 selects a plurality of users based on the position information of each user of the plurality of users and the position information of the base point corresponding to the target store from the plurality of users registered in advance. You may decide to extract a candidate. The base point corresponding to the store is a base point that specifies a range (attraction range) when attracting a user (customer) to the store. As the base point, for example, the location of the store or the nearest station of the store is set.

Then, the information processing apparatus 101 determines the destination of the guide information for urging the user to visit the target store from the extracted candidates. To describe in more detail, for example, the information processing apparatus 101 refers to the storage unit 110, and based on the number of guidance achievements and the number of store visits of each candidate, each candidate visits the store according to the guidance information. Calculate the rate. Then, the information processing apparatus 101 determines, based on the calculated store visit rate and the number of vacant seats of each candidate, the destination of the guide information that prompts the store to visit the target store from among the plurality of candidates.

In the example of FIG. 1, it is assumed that the users U1 to U3 existing in the attraction range 102 of the target store A are extracted as a plurality of candidates from the users U1 to U4. The attraction range 102 is a range of a predetermined distance from the base point of the target store A (location of the target store A). In this case, the information processing apparatus 101 determines the destination of the guide information that prompts the user to visit the target store A from the users U1 to U3.

Here, an example of determining the destination of the guidance information will be described with reference to FIG.

FIG. 2 is an explanatory diagram showing an example of determining the destination of the guidance information. In FIG. 2, the store visit rate list 200 shows the number of guide achievements and the number of store visits of each user U1 to U3. The users U1 to U3 are a plurality of candidates that are destination candidates of the guide information that prompts the user to visit the target store A. Here, the number of vacant seats in the target store A is “2” specified in the invitation request 120.

Specifically, regarding the user U1, the number of times the guidance record has been sent to the user U1 to prompt the user U1 to visit the store A is 10, and the user U1 visits the store A according to the guidance information. The number of achievements is “5”. In addition, the number of times the guidance record has been sent to the user U1 for guiding the user to visit the store B is “10”, and the number of times the user U1 has visited the store B according to the guidance information is “2”. is there. In addition, the number of times the guidance record has been sent to the user U1 for guiding the user to visit the store C is “5”, and the number of times the user U1 has visited the store C according to the guidance information is “3”. is there.

For this reason, when the total number of times of achievements of all stores for the user U1 is summed up, the number of times of guidance results in which the guidance information prompting the user U1 to visit the store is sent is “25”, and the user U1 responds to the guidance information. The number of visits to the store is “10”. Therefore, the store visit rate of the user U1 according to the guide information is “40.0% (=10÷25×100)”.

Similarly, regarding the user U2, when the total number of achievements of all the stores is summed up, the number of the guidance achievements to which the guidance information prompting the user U2 to visit the store is sent is “16”, and the user U2 responds to the guidance information. The actual number of visits to the store is “6”. Therefore, the store visit rate of the user U2 according to the guide information is “37.5% (=6/16×100)”.

Similarly, regarding the user U3, when the total number of achievements of all the stores is summed up, the number of the guidance achievements to which the guidance information prompting the user U3 to visit the store is sent is “10”, and the user U3 responds to the guidance information. The actual number of visits to the store is “3”. Therefore, the store visit rate of the user U3 according to the guide information is “30.0% (=3/10×100)”.

In this case, the information processing apparatus 101, for example, based on the number of vacant seats “2”, the top two users (the number of vacant seats: 2) with the highest calculated store visit rate among the users U1 to U3 are transferred to the target store A. Is determined as the destination of the guide information that prompts the visit of the store. Here, the users U1 and U2 are determined as the destinations.

(3) The information processing apparatus 101 transmits, to the determined destination, guidance information that prompts the user to visit the target store. The guidance information includes, for example, a message prompting the customer to visit the target store and a discount coupon that can be used at the target store. The guide information is sent by, for example, an email using push notification (push type information delivery).

In the example of FIG. 1, the guide information 130 for urging the user to visit the target store A is transmitted to the users U1 and U2. Specifically, for example, the guidance information 130 includes a message urging the user to visit the target store A and a discount coupon that can be used at the target store A, and is sent to the users U1 and U2 by e-mail using push notification. To be done.

As described above, according to the information processing apparatus 101 according to the first embodiment, it is possible to select a user who has a high possibility of coming to the store according to the guidance information as a destination of the guidance information that prompts the store to come to the store. .. As a result, it is possible to attract an appropriate number of users who fill the vacant seats of the store.

In the example of FIG. 1, as a destination of the guidance information 130, among the users U1 to U3 existing in the attraction range 102 of the store A, a user who has a high ratio of actually visiting the store according to the guidance information transmitted in the past. U1 and U2 can be selected. As a result, it is possible to attract users who are in a short distance from the store A and who are likely to come to the store according to the guide information in a timely manner, and to fill the vacant seats in the store A quickly. Become.

(Embodiment 2)
Next, a system configuration example of the information processing system 300 according to the second embodiment will be described. The information processing system 300 is a computer system including the information processing apparatus 101 illustrated in FIG. 1, and is applied to, for example, a customer guidance service that guides a customer to a store. The description of the same parts as those described in the first embodiment will be omitted.

FIG. 3 is an explanatory diagram illustrating a system configuration example of the information processing system 300 according to the second embodiment. In FIG. 3, the information processing system 300 includes an information processing device 101, a plurality of store terminals 301, and a plurality of customer terminals 302. In the information processing system 300, the information processing apparatus 101, the plurality of store terminals 301, and the plurality of customer terminals 302 are connected via a wired or wireless network 310. The network 310 is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

Here, the information processing apparatus 101 has a customer information DB (Database) 320, a store information DB 330, a store base point information DB 340, a store visit information DB 350, and an attraction management information DB 360. For example, the information processing device 101 is a server. The storage contents of the various databases 320, 330, 340, 350, 360 will be described later with reference to FIGS. 6 to 10.

The store terminal 301 is a computer used by the store. The store is a store that receives customer guidance services such as restaurants, movie theaters, and theaters. The store terminal 301 is, for example, a PC (Personal Computer), a tablet PC, a smartphone, or the like. The customer terminal 302 is a computer used by a customer. The customer is a user who is registered in advance to receive the guidance from the store. The customer terminal 302 is, for example, a tablet PC, a smartphone or the like.

(Example of hardware configuration of information processing apparatus 101)
Next, a hardware configuration example of the information processing apparatus 101 will be described with reference to FIG.

FIG. 4 is a block diagram showing a hardware configuration example of the information processing apparatus 101. In FIG. 4, the information processing apparatus 101 includes a CPU (Central Processing Unit) 401, a memory 402, a disk drive 403, a disk 404, a communication I/F (Interface) 405, and a portable recording medium I/F 406. , And a portable recording medium 407. Further, each component is connected by a bus 400.

Here, the CPU 401 controls the entire information processing apparatus 101. The CPU 401 may have a plurality of cores. The memory 402 includes, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), and a flash ROM. Specifically, for example, the flash ROM stores an OS (Operating System) program, the ROM stores an application program, and the RAM is used as a work area of the CPU 401. The program stored in the memory 402 is loaded into the CPU 401 to cause the CPU 401 to execute the coded process.

The disk drive 403 controls the reading/writing of the data with respect to the disk 404 according to control of CPU401. The disk 404 stores the data written under the control of the disk drive 403. Examples of the disk 404 include a magnetic disk and an optical disk.

The communication I/F 405 is connected to the network 310 via a communication line and is connected to external computers (for example, the store terminal 301 and the customer terminal 302 shown in FIG. 3) via the network 310. The communication I/F 405 controls the interface between the network 310 and the inside of the apparatus, and controls the input/output of data from/to an external computer. For the communication I/F 405, for example, a modem or a LAN adapter can be adopted.

The portable recording medium I/F 406 controls reading/writing of data with respect to the portable recording medium 407 under the control of the CPU 401. The portable recording medium 407 stores the data written under the control of the portable recording medium I/F 406. Examples of the portable recording medium 407 include a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disk), and a USB (Universal Serial Bus) memory.

Note that the information processing apparatus 101 may include, for example, an SSD (Solid State Drive), an input device, a display, and the like, in addition to the above-described components. In addition, the information processing apparatus 101 may not include, for example, the disk drive 403, the disk 404, the portable recording medium I/F 406, and the portable recording medium 407 among the above-described components.

(Example of hardware configuration of store terminal 301 and customer terminal 302)
Next, a hardware configuration example of the store terminal 301 and the customer terminal 302 will be described with reference to FIG. However, in FIG. 5, the store terminal 301 and the customer terminal 302 are described as “store terminal 301 and the like”.

FIG. 5 is a block diagram showing a hardware configuration example of the store terminal 301 and the like. In FIG. 5, the store terminal 301 or the like includes a CPU 501, a memory 502, a GPS (Global Positioning System) unit 503, a communication I/F (Interface) 504, a display 505, an input device 506, and a portable recording medium. It has an I/F 507 and a portable recording medium 508. Further, each component is connected by a bus 500.

Here, the CPU 501 controls the entire store terminal 301 and the like. The CPU 501 may have a plurality of cores. The memory 502 is a storage unit including, for example, a ROM, a RAM, a flash ROM, and the like. Specifically, for example, a flash ROM or a ROM stores various programs, and a RAM is used as a work area of the CPU 501. The program stored in the memory 502 is loaded into the CPU 501 to cause the CPU 501 to execute the coded processing.

The GPS unit 503 receives radio waves from GPS satellites and outputs position information of the store terminal 301 and the like. The position information of the store terminal 301 or the like is, for example, information specifying one point on the earth such as latitude and longitude. Further, the information processing apparatus 101 may correct the position information output from the GPS unit 503 by using DGPS (Differential GPS). Further, as the satellite, for example, a satellite of the quasi-zenith satellite system may be used.

The communication I/F 504 is connected to the network 310 via a communication line, and is connected to an external computer (for example, the information processing apparatus 101) via the network 310. The communication I/F 504 administers an interface between the network 310 and the inside of the own device, and controls the input/output of data from an external device.

The display 505 is a display device that displays data such as a document, an image, and functional information as well as a cursor, an icon, or a tool box. As the display 505, for example, a liquid crystal display, an organic EL (Electroluminescence) display, or the like can be adopted.

The input device 506 has keys for inputting characters, numbers, various instructions, etc., and inputs data. The input device 506 may be a keyboard, a mouse, etc., or may be a touch panel type input pad, a numeric keypad, etc.

The portable recording medium I/F 507 controls data read/write with respect to the portable recording medium 508 according to the control of the CPU 501. The portable recording medium 508 stores the data written under the control of the portable recording medium I/F 507.

The store terminal 301 and the like may include, for example, an HDD (Hard Disk Drive), an SSD, a scanner, a printer, and the like, in addition to the above-described components.

(Memory contents of various DBs 320, 330, 340, 350, 360)
Next, the storage contents of the various databases 320, 330, 340, 350, 360 of the information processing apparatus 101 will be described with reference to FIGS. The various DBs 320, 330, 340, 350, 360 are realized by a storage device such as the memory 402 and the disk 404 shown in FIG. 4, for example.

FIG. 6 is an explanatory diagram showing an example of the stored contents of the customer information DB 320. 6, the customer information DB 320 has fields for customer name, gender, age, mail address, guidance permission, weekday daytime guidance, weekday night information, holiday daytime information, holiday night information, and current position. By setting information in each field, customer information (for example, customer information 600-1 to 600-4) is stored as a record.

Here, the customer name is the name of the customer who receives the guidance from the store by the customer guidance service. Here, it is assumed that the customer can be uniquely identified by the customer name. Gender is the gender of the customer. Age is the age of the customer. The telephone number is the telephone number of the customer terminal 302 used by the customer. The email address is the email address of the customer. The customer information may include the customer's telephone number.

The guidance permission indicates whether to permit guidance to the customer. For the guidance permission, for example, one of “individual setting”, “permit”, and “not permit” is set. The guidance permission “individual setting” indicates that whether or not to permit the guidance is individually set (weekday daytime guidance, weekday night information, holiday daytime information, holiday night information). Guidance permission “Allow” indicates that guidance is permitted. Guide permission "Do not permit" indicates that the guide is not permitted.

Weekday daytime guidance indicates whether or not to permit weekday daytime guidance. The daytime is, for example, 11:00 to 15:00. The weekday night guidance indicates whether or not to permit guidance during the evening hours on weekdays. The evening time zone is, for example, the time zone from 15:00 to 19:00. The holiday daytime guidance indicates whether or not guidance during the daytime on a holiday is permitted. The holiday night guidance indicates whether or not to permit guidance during the evening hours of the holiday.

The current position is position information indicating the current position of the customer. The position information indicating the current position of the customer is, for example, position information (for example, latitude and longitude) of the customer terminal 302 output from the GPS unit 503 (see FIG. 5) of the customer terminal 302 used by the customer. The current position is updated, for example, based on the position information of the customer terminal 302 that is periodically transmitted from the customer terminal 302 to the information processing apparatus 101.

The customer information may include customer preference information, for example. The preference information is information indicating a customer's preference, and may include, for example, information indicating a shop or a system permitted to guide the customer (guidance permission condition).

FIG. 7 is an explanatory diagram showing an example of the stored contents of the store information DB 330. In FIG. 7, the store information DB 330 has fields of a store name, a telephone number, an email address, an address, a system, store position information, and an attracting frequency threshold value. By setting information in each field, store information (for example, store information 700-1 to 700-4) is stored as a record.

Here, the store name is the name of the store. The telephone number is the telephone number of the store. The email address is the email address of the store. The address is the address of the store. The system is the system to which the store belongs. In the case of a restaurant, examples of the system of the store include pork cutlet, beef bowl, buckwheat, and grilled meat.

The store position information is position information indicating the location of the store (for example, latitude and longitude). The attracting frequency threshold value is a threshold value used when determining the group to which the customer belongs. A detailed description of the attracting frequency threshold value will be given later.

FIG. 8 is an explanatory diagram showing an example of the stored contents of the store base point information DB 340. In FIG. 8, the store base point information DB 340 has fields of a store name, a base point name, a base point position information, a base point address, and an attraction distance, and by setting information in each field, the store base point information (for example, the store base point information 800-1 to 800-6) are stored as records.

Here, the store name is the name of the store. The base point name is the name of the base point corresponding to the store. The base point corresponding to the store is a base point for specifying the attraction range when attracting customers to the store. Multiple base points can be set for one store. For example, it is possible to attract a wide range of customers by setting a plurality of base points. The base point position information is position information (for example, latitude and longitude) of the base point. The base address is the base address. The attraction distance is a distance (radius) that specifies a circular attraction range centered on the base point corresponding to the store. The unit of the attraction distance is m (meter), for example.

FIG. 9 is an explanatory diagram showing an example of the stored contents of the store visit information DB 350. In FIG. 9, the store visit information DB 350 has fields for customer name, store date, store time, day of the week, store name, and system. By setting information in each field, store information (for example, store information 900-1 ~900-13) is stored as a record.

Here, the customer name is the name of the customer. The visit date is the date when the customer visited the store. The visit time is the time when the customer came to the store. The day of the week is the day on which the customer came to the store. The store name is the name of the store visited by the customer. The system is the system of the store that the customer visited.

10A and 10B are explanatory diagrams showing an example of the stored contents of the attraction management information DB 360. In FIG. 10A and FIG. 10B, the attraction management information DB 360 includes an attraction store, a system, a base point name, a distance, a day of the week, a privilege, a customer name, an attraction start date, an attraction start time, a customer attraction date, a customer attraction time, and a customer visit date. It has a field of customer visit time. By setting information in each field, invitation management information (for example, invitation management information 1000-1 to 1000-14) is stored as a record.

Here, the attraction store indicates the name of the attraction destination store. The system indicates the system of the attracting store. The base point name indicates the name of the base point corresponding to the attracting store. The distance indicates the attraction distance of the attraction store. The day of the week indicates the day of the week when the customer is attracted to the attracting store. The privilege indicates a privilege when attracting a customer to the attracting store. The customer name indicates the name of the customer who has attracted to the attracting store.

The attraction start date indicates the date when the attraction to the attraction store is started. The attraction start time indicates the time when attraction to the attraction store is started. The customer attraction date indicates the date when the customer is attracted to the attraction store. The customer attraction time indicates the time when the customer is attracted to the attraction store. The customer visit date indicates the date when the customer visited the attraction store. The customer visit time indicates the time when the customer visited the attracting shop. The customer visit date "***" and the customer visit time "***" indicate that the customer did not visit the attracting shop.

Although illustration is omitted, the attraction management information includes, for example, information for identifying the attraction end date and time (attraction end date, attraction end time) at which the attraction to the attraction store is terminated.

(Specific example of invitation request from store)
Next, a specific example of the invitation request from the store will be described.

FIG. 11 is an explanatory diagram showing a specific example of an invitation request from a store. In FIG. 11, the attraction request 1100 includes information about the attraction store, system, attraction number, base point, distance, day, privilege, attraction start date, attraction start time, attraction end date, and attraction end time.

Here, the attraction store indicates the name of the attraction store. The system indicates the system of the attracting store. The attracting number is the number of customers attracting to the attracting store, and corresponds to the number of vacant seats generated at the attracting store. The base point indicates the name of the base point corresponding to the attracting store. The distance indicates the attraction distance of the attraction store. The day of the week indicates the day of the week when the customer is attracted to the attracting store.

The privilege indicates a privilege when attracting a customer to the attracting store. The attraction start date indicates the date when attraction to the attraction store is started. The attraction start time indicates the time when attraction to the attraction store is started. The attraction end date indicates the date when attraction to the attraction store is finished. The attraction end time indicates the time at which the attraction to the attraction store ends. The period from the attraction start date and time to the attraction end date and time corresponds to, for example, the usable period of the privilege (discount coupon).

The invitation request may include the attraction priority customer type. The attraction priority customer type is a type for narrowing down the customers who preferentially attract to the attraction store, and corresponds to a “group” described later. For example, the attraction priority customer type is designated when the store side wants to attract only customers who have a history of visiting the store, or when it wants to attract only new customers.

(Example of functional configuration of information processing apparatus 101)
FIG. 12 is a block diagram showing a functional configuration example of the information processing apparatus 101. In FIG. 12, the information processing apparatus 101 includes a reception unit 1201, an extraction unit 1202, a calculation unit 1203, a determination unit 1204, a communication unit 1205, and a storage unit 1210. Specifically, for example, the reception unit 1201 to the communication unit 1205 cause the CPU 401 to execute a program stored in a storage device such as the memory 402, the disk 404, and the portable recording medium 407 illustrated in FIG. 4, or The communication I/F 405 realizes that function. The processing result of each functional unit is stored in a storage device such as the memory 402 or the disk 404, for example. The storage unit 1210 is realized by a storage device such as the memory 402 and the disk 404, for example. Specifically, for example, the storage unit 1210 stores various DBs 320, 330, 340, 350, 360.

The reception unit 1201 receives customer information. Here, the customer information is information about the customer who receives the guidance from the store by the customer guidance service. For example, the customer information is the customer information 600-1 to 600-4 shown in FIG. Specifically, for example, the receiving unit 1201 receives the customer information from the customer terminal 302, and receives the received customer information.

The received customer information is registered in the customer information DB 320 shown in FIG. 6, for example. However, the current position of the customer information is sequentially updated based on the position information of the customer terminal 302 that is periodically transmitted from the customer terminal 302 to the information processing apparatus 101.

The reception unit 1201 receives store information. Here, the store information is information about stores that receive the customer guidance service. For example, the store information is the store information 700-1 to 700-4 illustrated in FIG. 7. Specifically, for example, the reception unit 1201 receives the store information from the store terminal 301, and receives the received store information.

The received store information is registered in the store information DB 330 shown in FIG. 7, for example.

The reception unit 1201 receives store base point information. Here, the store base point information is information on the base point corresponding to the store. The base point corresponding to the store is a base point for specifying the attraction range when attracting customers to the store. As the base point corresponding to the store, for example, the store or the nearest station of the store is set.

For example, the store base point information is the store base point information 800-1 to 800-6 illustrated in FIG. 8. Specifically, for example, the reception unit 1201 receives the store base point information from the store terminal 301 to receive the received store base point information. Note that a plurality of base points can be designated for one store.

The received store base point information is registered in the store base point information DB 340 shown in FIG. 8, for example.

The reception unit 1201 receives the store visit information. Here, the store visit information is information for notifying that the customer has visited the store. For example, the store visit information is the store visit information 900-1 to 900-13 illustrated in FIG. 9. Specifically, for example, the reception unit 1201 receives the store visit information from the store terminal 301 to receive the received store visit information.

The received store visit information is registered in the store visit information DB 350 shown in FIG. 9, for example. Note that the store visit information may be input to the store terminal 301 in the store by, for example, an operation input by a store clerk, or may be automatically input when a customer pays using electronic money or the like.

The reception unit 1201 receives an invitation request from the target store. Here, the invitation request is a request for attracting a customer to the target store, and specifically, is a request for sending guide information for urging the customer to visit the target store. For example, the invitation request is the invitation request 1100 shown in FIG.

Specifically, for example, the reception unit 1201 receives the invitation request 1100 from the store terminal 301, and thus receives the received invitation request 1100. The attraction request is input to the store terminal 301 in the store by, for example, an operation input by a clerk when a vacant seat occurs in the store.

The receiving unit 1201 may also receive the distance correction coefficient information. Here, the distance correction coefficient information indicates a distance correction coefficient for correcting the store visit rate. Specifically, for example, the receiving unit 1201 receives the distance correction coefficient information by receiving the distance correction coefficient information from the store terminal 301.

The received distance correction coefficient information is registered in the distance correction coefficient table 1300 as shown in FIG. 13, for example. Note that the distance correction coefficient information is input to the store terminal 301 in the store, for example, by an operation input of a store clerk. The distance correction coefficient table 1300 is stored in the storage unit 1210, for example. Here, the stored contents of the distance correction coefficient table 1300 will be described.

FIG. 13 is an explanatory diagram showing an example of the stored contents of the distance correction coefficient table 1300. In FIG. 13, the distance correction coefficient table 1300 stores distance correction coefficient information (for example, distance correction coefficient information 1300-1 to 1300-4) as a record.

The distance correction coefficient information indicates a coefficient (distance correction coefficient) corresponding to the distance comparison result in association with the store name. The store name is the name of the attracting store (target store). The distance comparison result is a comparison result between the attraction distance of the attraction store and the attraction distance of the store (with a record). The store (with a track record) is a store with a track record that the customer has visited according to the guidance information.

Here, the distance comparison result “=actual” indicates that the attraction distance of the target store and the attraction distance of the store (with a record) match. The distance comparison result “≦achievement” indicates that the attraction distance of the target store is equal to or less than the attraction distance of the store (with a record). The distance comparison result “>actual” indicates that the attraction distance of the target store is larger than the attraction distance of the store (with a record). The distance comparison result “no track record” indicates that there is no store (with track record).

Returning to the description of FIG. 12, the receiving unit 1201 may receive the day-of-week correction coefficient information. Here, the day-of-the-week correction coefficient information indicates a day-of-week correction coefficient for correcting the store visit rate. Specifically, for example, the receiving unit 1201 receives the day-of-week correction coefficient information from the store terminal 301, and receives the received day-of-week correction coefficient information.

The received day-of-week correction coefficient information is registered in the day-of-week correction coefficient table 1400 as shown in FIG. 14, for example. The day-of-the-week correction coefficient information is input to the store terminal 301 in the store, for example, by a clerk's operation input. The day correction coefficient table 1400 is stored in, for example, the storage unit 1210. Here, the stored contents of the day correction coefficient table 1400 will be described.

FIG. 14 is an explanatory diagram showing an example of the stored contents of the day correction coefficient table 1400. In FIG. 14, the day correction coefficient table 1400 stores day correction coefficient information (for example, day correction coefficient information 1400-1 to 1400-6) as a record.

The day-of-the-week correction coefficient information indicates a coefficient (day-of-week correction coefficient) according to the comparison result of the days of the week in association with the store name. The store name is the name of the target store. The comparison result of the day of the week is a comparison result of the day of the week when attracting to the target store (the attracting day) and the day of the week when visiting the store (there is a record) (the day when the store comes).

Here, the comparison result “match” of the days of the week indicates that the attracting day of the week and the day of the week at the time of coming to the store match. The comparison result “mismatch” of the days of the week indicates that the attracting day of the week and the day of the week when the customer came to the store did not match. However, for the comparison result “mismatch” on the day of the week, four patterns of coefficients (day of week correction coefficient) are provided according to the combination of the attracting day of the week and the day of the week when the store is visited. The comparison result “no track record” on the day of the week indicates that there is no store (with track record).

Returning to the description of FIG. 12, the extraction unit 1202 selects the target store based on the position information of each user of the plurality of users and the position information of the base point corresponding to the target store from the plurality of users registered in advance. Candidates that are candidates for the destination of the guide information that prompts the visit of the store are extracted. Here, the pre-registered user is, for example, a customer whose customer information is registered in the customer information DB 320.

In addition, the position information of the user is specified from the current position included in the customer information in the customer information DB 320, for example. Further, the position information of the base point corresponding to the target store is specified, for example, from the base position information included in the store base point information of the target store in the store base point information DB 340. When there are a plurality of base points corresponding to the target store, the position information of each base point of the plurality of base points is specified.

Specifically, for example, the extraction unit 1202 extracts, as candidates, customers existing within a predetermined distance from the base point corresponding to the target store in response to the reception unit 1201 receiving the invitation request. The predetermined distance can be set arbitrarily and is, for example, the attraction distance included in the store base point information of the target store. The attraction distance corresponds to the maximum distance from the base point corresponding to the target store that specifies the attraction range of the target store. The base point and the attraction distance of the target store may be specified by the attraction request from the target store.

As a result, a customer (user) existing in a short distance from the target store or the nearest station to the target store can be extracted as a candidate for a destination of the guide information for prompting the visit to the target store.

Further, the extraction unit 1202 may exclude, from among a plurality of pre-registered users, a user whose invitation time (guidance time) specified in the invitation request does not satisfy the guide permission condition, from the extraction target. .. That is, the extraction unit 1202 may extract candidates from among a plurality of users registered in advance, among users whose invitation time (guidance time) specified in the invitation request satisfies the guide permission condition. ..

Here, the guidance permission condition indicates a condition for permitting guidance to the user. The guidance permission condition is specified, for example, from weekday daytime guidance, weekday nighttime guidance, holiday daytime guidance, and holiday nighttime guidance included in the customer information in the customer information DB 320. The attraction time is specified by, for example, the day of the week or the time zone (the time zone from the attraction start time to the attraction end time).

Taking the invitation request 1100 shown in FIG. 11 as an example, the invitation time (guidance time) is Wednesday (weekday) from 11:30 to 12:00. In this case, for example, a customer whose weekday daytime guidance in the customer information is “not permitted” does not satisfy the guidance permission condition and is therefore excluded from the extraction target.

As a result, customers who do not meet the pre-registered guidance permission conditions during the day of the week or the time of day when they are invited to the target store can be excluded from the recipients of the guidance information. It is possible to prevent the guidance information from being sent during the time period.

Further, the extraction unit 1202 may exclude, from the plurality of pre-registered users, a user whose target store or system of the target store does not satisfy the guidance permission condition, from the extraction target. That is, the extraction unit 1202 may extract candidates from among a plurality of users registered in advance, among users whose target store or system of the target store satisfies the guidance permission condition. Note that the guide conditions regarding the store and the system of the store are specified, for example, from preference information (not shown) included in the customer information of each user.

The extracted candidate information is registered in the extracted customer table 1500 as shown in FIG. 15, for example. The extracted customer table 1500 is stored in the storage unit 1210, for example. Here, the stored contents of the extracted customer table 1500 will be described.

FIG. 15 is an explanatory diagram showing an example of the stored contents of the extracted customer table 1500. In FIG. 15, the extracted customer table 1500 has a customer name, store name, visit record, attraction record (own store), attraction record (other store), other store (same system), group, number of attraction, number of successful attraction, primary It has fields for store visit rate, distance correction, day correction, and store visit rate. By setting information in each field, extracted customer information (for example, extracted customer information 1500-1 to 1500-4) is stored as a record.

Here, the customer name is a customer name of a candidate who is a destination candidate of the guide information for prompting the visit to the target store. The store name is the store name of the target store. The store visit record indicates whether or not the candidate has visited the target store. The attraction record (own store) indicates whether the candidate has visited the target store according to the guidance information from the target store. The attraction record (other store) indicates whether the candidate has visited the other store according to the guidance information from the other store.

The other store (same system) indicates whether or not the candidate has visited the other store according to the guidance information from the other store in the same system as the target store. Group indicates the group to which the candidate belongs. The group will be described later with reference to FIG. The number of invitations indicates the number of times guidance has been sent to guide the candidate to the store. The number of successful invitations indicates the number of times the candidate has visited the store according to the guidance information.

The primary store visit rate indicates a temporary store visit rate. The distance correction indicates a distance correction coefficient for correcting the store visit rate (primary store visit rate). The day of the week correction indicates a day of the week correction coefficient for correcting the store visit rate (primary store visit rate). The store visit rate indicates the ratio of candidates visiting the store according to the guide information.

In the example of FIG. 15, the extracted customer information 1500-1 to 1500 is the result of setting the extracted customer name of each candidate and the extracted store name of the target store in the customer name and store name fields in the extracted customer table 1500. -4 is stored as a new record. However, at this point, the visit record of each extracted customer information 1500-1 to 1500-4, the attraction record (own store), the attraction record (other store), another store (same system), group, the number of times of attraction, the number of times of successful attraction The fields of primary store visit rate, distance correction, day of week correction, and store visit rate are blank.

Returning to the description of FIG. 12, the calculation unit 1203 refers to the storage unit 1210, and each candidate visits the store according to the guidance information based on the number of times the candidate has achieved the guidance and the number of times each candidate has visited the store. Calculate the store visit rate. Here, the storage unit 1210 stores the guidance record count and the visit record count for each of the plurality of candidates that are candidates for the destination of the guide information that prompts the store to visit the target store.

The guidance record count is the number of times the guide information that prompts each candidate to visit the store is sent. The number of store visits is the number of times each candidate has visited the store according to the guidance information.

Specifically, for example, the calculation unit 1203 searches the attraction management information DB 360 shown in FIG. 10 for attraction management information in which the customer name of the extracted candidate is set in the customer name field. Then, the calculation unit 1203 sets the number of retrieved attracting management information items as the attracting number of the extracted customer information of the candidate in the extracted customer table 1500. The number of invitations corresponds to the number of guidance results.

Next, the calculation unit 1203 counts the number of pieces of invitation management information in which the customer visit date and time (customer arrival date, customer arrival time) is set among the retrieved invitation management information. The attraction management information in which the customer visit date and time is set is the attraction management information that has been successfully attracted. Then, the calculation unit 1203 sets the counted number of attracting management information items as the number of times of successful attracting of the extracted customer information of the candidate in the extracted customer table 1500. The number of successful invitations corresponds to the number of store visits.

Next, the calculation unit 1203 refers to the extracted customer table 1500 and divides the number of successful attracting times of each candidate by the number of attracting times to calculate the store visit rate of each candidate. More specifically, for example, the calculation unit 1203 calculates the visit ratio of each candidate by obtaining “the number of successful attracting times/the number of attracting times×100” for each candidate.

The calculated store visit rate of each candidate is set to the primary store visit rate of the extracted customer information of each candidate in the extracted customer table 1500, for example.

Further, the calculation unit 1203 may compare the attraction distance of the target store with the attraction distance of the store (with a track record), and correct the calculated visit rate of the candidate based on the comparison result. Here, the attraction distance is the maximum distance from the base point corresponding to the store that identifies the attraction range of the store. The store (with a track record) is a store with a track record that the candidate has visited according to the guidance information.

Specifically, for example, the calculation unit 1203 refers to the store base point information DB 340 to identify the attraction distance of the target store. Next, the calculation unit 1203 searches the attraction management information DB 360 for attraction management information corresponding to the combination of the store name of the target store and the customer name of the candidate. At this time, the calculation unit 1203 may search for attraction management information corresponding to the customer name of the candidate. That is, the calculation unit 1203 may search for the attraction management information sent to the candidate, including a store different from the target store.

Then, the calculation unit 1203 determines whether or not there is attraction management information that has been successfully attracted from the retrieved attraction management information. The attraction management information that has been successfully attracted is attraction management information in which a customer visit date and time (customer visit date, customer visit time) is set. Here, when there is attraction management information that has been successfully attracted, the calculation unit 1203 identifies the distance included in the attraction management information as the attraction distance of the store (with a record).

When there are a plurality of attraction management information items that have been successfully attracted, the calculation unit 1203 determines, for example, the maximum distance of the distances included in the plurality of attraction management information items that have been successfully attracted by the store (with a track record). May be specified as the attraction distance.

Then, the calculation unit 1203 compares the attracting distance of the specified target store with the attracting distance of the specified store (with a track record). Next, the calculation unit 1203 refers to the distance correction coefficient table 1300 shown in FIG. 13 to identify the distance correction coefficient corresponding to the store name of the target store and the comparison result.

For example, if the store name of the target store is “Food-Shop-1,” and the attraction distance of the target store is larger than the attraction distance of the store (with a track record), the distance correction coefficient “0.7” is specified. The specified distance correction coefficient is set to the distance correction of the extracted customer information of each candidate in the extracted customer table 1500, for example.

Then, the calculation unit 1203 corrects the store visit rate of the candidate by multiplying the calculated store visit rate of the candidate by the specified distance correction coefficient. If the attraction management information is not retrieved, or if there is no attraction management information that has been successfully attracted, the distance correction coefficient corresponding to the result of comparison of the store name of the target store and the distance "no track record" is specified. To be done.

An example of correcting the store visit rate using the distance correction coefficient will be described later with reference to FIG.

Further, the calculation unit 1203 compares the day of the week specified by the invitation request with the day of the week when the candidate visited the store in accordance with the guidance information, and based on the comparison result, corrects the calculated store visit rate of the candidate. You may decide to do it. Here, the day of the week designated by the invitation request is a day of the week on which the customer is attracted to the target store (attraction day).

Specifically, for example, the calculation unit 1203 searches the attraction management information DB 360 for attraction management information corresponding to the combination of the store name of the target store and the customer name of the candidate. Then, the calculation unit 1203 determines whether or not there is attraction management information that has been successfully attracted from the retrieved attraction management information. Here, when there is attraction management information that has been successfully attracted, the calculation unit 1203 identifies the day of the week included in the attraction management information as the day of the week when visiting the store. The day of the week when the candidate comes to the store is the day when the candidate came to the store according to the guidance information.

When there are a plurality of invitation management information items that have been successfully attracted, the calculation unit 1203 determines, for example, the day of the week in which the latest start date of the attraction management information items that have been successfully attracted is included in the attraction management information item. It may be specified as the day of the week when coming to the store. Further, the calculation unit 1203 may specify the day of the week included in each of the plurality of attraction management information items that have been successfully attracted, as the day of the week when visiting the store.

Then, the calculation unit 1203 compares the day of the week (inviting day) designated by the invitation request with the specified day of the week when the store visits. Next, the calculation unit 1203 refers to the day-of-week correction coefficient table 1400 shown in FIG. 14 to specify the day-of-week correction coefficient corresponding to the store name of the target store and the comparison result.

For example, the store name of the target store is “Food-Shop-1”, the invitation day of the week is “Monday”, and the day of the week when the store is coming is “Tuesday”. In this case, since each day of the week is a weekday and does not match, the day of the week correction coefficient “0.9” is specified. The specified day correction coefficient is set to the day correction of the extracted customer information of each candidate in the extracted customer table 1500, for example.

Then, the calculation unit 1203 corrects the store visit rate of the candidate by multiplying the calculated store visit rate of the candidate by the specified day correction coefficient. If the attraction management information is not retrieved, or if there is no attraction management information that has been successfully attracted, the day-of-week correction coefficient corresponding to the store name of the target store and the day of the week comparison result “no achievement” is specified. To be done.

An example of correcting the store visit rate using the day of the week correction coefficient will be described later with reference to FIG.

The corrected store visit rate of each candidate is set to the store visit rate of the extracted customer information of each candidate in the extracted customer table 1500, for example.

The deciding unit 1204 decides the destination of the guide information for prompting the visit to the target store. Specifically, for example, the determination unit 1204 determines a plurality of candidates based on the calculated store visit rate of each candidate from the plurality of extracted candidates and the number of vacant seats designated by the invitation request. From among the persons, the destination of the guide information for urging the user to visit the target store is determined.

In the following description, a customer who is the destination of the guide information for prompting the visit to the target store may be referred to as a “target customer”. In addition, the number of vacant seats in the target store designated by the invitation request may be referred to as “vacant seat number N”. In the example of FIG. 11, the number N of vacant seats designated in the invitation request 1100 is “3” of the invitation number.

More specifically, for example, the deciding unit 1204 selects the top N candidates having a high calculated store visit rate (or corrected store visit rate) from the plurality of extracted candidates as target customers. You may decide to decide. The store visit rate of each candidate (or the store visit rate after correction) is specified from the extracted customer table 1500, for example.

Further, for example, the determination unit 1204 may calculate the target value T based on the number N of vacant seats designated by the invitation request. For example, the target value T is represented by “the number of vacant seats N×100”. That is, if the number N of vacant seats is "N=3", the target value T is "T=300". In this case, the determination unit 1204 sets the priority of each candidate in the descending order of the calculated store visit rate (or the corrected store visit rate). Then, the determination unit 1204 sequentially calculates the total store visit rate K by adding the store visit rates of the candidates (or the corrected store visit rates) in the order of the set priority, and calculates the total store visit rate K as the calculated total store visit rate K. The target customer may be determined as a candidate when the difference from the target value T becomes the minimum.

An example of determining the target customer using the target value T will be described later with reference to FIG.

Further, the determination unit 1204 may determine the group to which each candidate belongs from the plurality of groups based on the visit record of each extracted candidate according to the guidance information. In this case, the determination unit 1204 may preferentially determine the target customer from among the plurality of extracted candidates who belong to the group designated by the invitation request.

Here, the group is for classifying customers having common characteristics. The group specified in the invitation request is, for example, the attraction priority customer type included in the invitation request. The attraction priority customer type (group) is designated when it is desired to narrow down the customers who preferentially attract customers to the store.

Specifically, for example, the determination unit 1204 refers to a group table 1600 as shown in FIG. 16, and determines a plurality of groups {HA, HB based on the record of visits by each candidate according to the guidance information. , HD, HC, AA, AB, NA, NB, NC}, the group to which each candidate belongs is determined.

FIG. 16 is an explanatory diagram showing a specific example of the group table. In FIG. 16, a group table 1600 is information for classifying customers (candidates) into any of the groups HA, HB, HD, HC, AA, AB, NA, NB, and NC. “N” in the group table 1600 is a threshold that can be arbitrarily set for each store, and is, for example, a threshold of the number of invitations included in the store information.

“#” of “#$” for identifying the group is one of H, A, and N. H indicates that there is a record of visiting the target store. A indicates that there is no record of coming to the target store, but there is a record of coming to another store (same system). N indicates that there is no record of coming to the target store and no record of visiting to another store (same system). The other store (same system) is another store of the same system as the target store.

For example, the group H$ is designated when wanting to attract customers who have a history of visiting the store. Further, the group A$ is designated when it is desired to attract a customer who has a history of visiting other stores of the same system as a new customer. Further, the group A$ is designated when it is desired to attract new customers.

“$” of “#$” for identifying the group is any of A, B, C and D. A indicates that the number of invitations is n or more. B indicates that the number of times of attraction is 0 or more and less than n. C indicates that the number of invitations is 0. D indicates that there is no record of visits to the attraction.

For example, the group #A has a higher number of invitations than the group #B, and thus can be said to have a high creditworthiness in the store visit rate. The groups #C and #D are targets when the response to the attraction is low or unknown customers belong to the group #A or #B.

When only the number of candidates that does not satisfy the number of vacant seats is present in the group designated by the invitation request, the determination unit 1204 may target the candidates belonging to other groups, or belong to the designated group. Only candidates may be targeted.

Note that the specific processing content when determining the group to which each candidate belongs, or preferentially determining the target customer from the candidates belonging to the group specified in the invitation request will be described later. This will be described later with reference to the flowcharts shown in FIGS.

Returning to the description of FIG. 12, the communication unit 1205 transmits guide information for urging the determined target customer to visit the target store. The guidance information includes, for example, message information that prompts a visit to the target store and coupon information that can be used at the target store. The coupon information is a privilege such as a free ticket or a discount ticket.

More specifically, the guidance information includes, for example, information indicating the store name, system, privilege, coupon usable period of the target store. Also, in the guidance information, for example, "30% off for those who visit by 12:00!", "First come, first served! We are offering 30% off to the first 4 people." May include a message indicating the presence of the. Specifically, for example, the communication unit 1205 sends the guide information to the target customer using an electronic mail service, SMS (Short Message Service), or the like. The email address and telephone number of the target customer are specified, for example, from the customer information of the target customer.

Further, when the guide information is transmitted to the target customer, new attraction management information is registered in the attraction management information DB 360. In the fields of attracting store, system, base point name and distance of the new attracting management information, the store name, system, base point name and attracting distance of the target store are set. In the fields of the day of the week, privilege, start date, and start time of the new attraction management information, the day of the week, privilege, attraction start date, and attraction start time included in the attraction request are set.

The customer name of the target customer is set in the customer name field of the new invitation management information. In the fields of customer attraction date and customer attraction time of the new attraction management information, the date and time when the guide information is transmitted to the target customer are set. However, in the initial state, the fields of customer visit date and customer visit time of the new attraction management information are blank.

When the target customer visits the target store according to the guidance information, the visit date and the visit time are set in the fields of the customer visit date and the customer visit time of the corresponding attraction management information in the attraction management information DB 360. .. In addition, if the target customer does not come to the target store even after the attraction end date and time (attraction end date, attraction end time) of the invitation request, each of the customer visit date and the customer visit time of the attraction management information is displayed. "*** (invalidation)" is set in the field.

(Example of transition of stored contents of extracted customer table 1500)
Next, a transition example of the storage content of the extracted customer table 1500 will be described with reference to FIGS. Here, a transition example of the storage content of the extracted customer table 1500 will be described by taking the extracted customer information 1500-1 of the extracted customer information 1500-1 to 1500-4 illustrated in FIG. 15 as an example. The extracted customer information 1500-1 to 1500-4 is information of each candidate extracted in response to the invitation request 1100 shown in FIG.

17 to 20 are explanatory diagrams showing transition examples of the storage contents of the extracted customer table 1500. First, the calculation unit 1203 searches the store visit information DB 350 for store store visit information corresponding to the combination of the store name “Food-Shop-1” of the target store and the candidate customer name “Hoge1”. In the example shown in FIG. 9, the store visit information 900-2 is searched, and it shows that there is a store visit record to the target store. In this case, the calculation unit 1203 sets “present” in the store visit record of the extracted customer information 1500-1.

Next, the calculation unit 1203 searches the attraction management information DB 360 for attraction management information corresponding to the candidate's customer name “Hoge1”. In the example of FIGS. 10A and 10B, invitation management information 1000-1, 1000-6 is searched. Here, the attraction management information 1000-1 is information on the target store. The attraction management information 1000-6 is information on another store whose system is different from that of the target store.

In this case, the calculation unit 1203 sets “present” in the attraction record (own store) of the extracted customer information 1500-1, sets “present” in the attraction record (other store), and sets it in another store (same system). Set "-(Null)". Further, in the invitation management information 1000-1 and 1000-6, customer visit date and time (customer visit date, customer visit time) are set. In this case, the calculation unit 1203 sets “2” as the number of attracting times and “2” as the number of successful attracting times of the extracted customer information 1500-1.

Next, the determination unit 1204 refers to the group table 1600 and determines the group to which the candidate with the customer name “Hoge1” belongs. Here, the attracting frequency threshold value of the target store with the store name “Food-Shop-1” is “5” from the store information 700-1. The attracting frequency “2” of the candidate with the customer name “Hoge1” is less than the attracting frequency threshold value.

Therefore, the group to which the candidate with the customer name “Hoge1” belongs is “group HB”. In this case, the calculation unit 1203 sets “HB” to the group of the extracted customer information 1500-1. Further, the calculation unit 1203 calculates the store visit rate “100%” of the candidate with the customer name “Hoge1” by dividing the number of successful invitations “2” by the number of invitations “2”. In this case, the calculation unit 1203 sets “100” to the primary store visit rate of the extracted customer information 1500-1.

Next, the calculation unit 1203 refers to the attraction request 1100 (or the store base point information 800-1 in the store base point information DB 340) and specifies the attracting distance “180 m” of the target store. In addition, the calculation unit 1203 refers to the attraction management information 1000-1 to identify the attraction distance “180 m” of the store (with experience).

Then, the calculation unit 1203 compares the attracting distance “180 m” of the specified target store with the attracting distance “180 m” of the specified store (with experience). Here, the attraction distances match. Therefore, the calculation unit 1203 refers to the distance correction coefficient table 1300, and the distance correction coefficient “1” corresponding to the store name “Food-Shop-1” of the target store and the distance comparison result “=actual result”. Specify. In this case, the calculation unit 1203 sets “1.0” for the distance correction of the extracted customer information 1500-1.

Next, the calculation unit 1203 compares the day of the week “Wed” specified in the invitation request 1100 with the day of the week “Wed” in the invitation management information 1000-1. Here, the days of the week match. Therefore, the calculation unit 1203 refers to the day-of-week correction coefficient table 1400 and sets the day-of-week correction coefficient “1” corresponding to the shop name “Food-Shop-1” of the target shop and the day-of-week comparison result “match”. Identify. In this case, the calculation unit 1203 sets “1.0” for the day correction of the extracted customer information 1500-1.

Next, the calculation unit 1203 refers to the extracted customer information 1500-1, and multiplies the primary store visit rate “100” by the specified distance correction coefficient “1.0” and the day correction coefficient “1.0”. , The store visit rate “100” of the candidate with the customer name “Hoge1” is calculated. In this case, the calculation unit 1203 sets “100” as the store visit rate of the extracted customer information 1500-1.

Similarly, when the extracted customer information 1500-2 to 1500-4 are processed, the stored contents of the extracted customer table 1500 become as shown in FIG.

(Example of target customer decision)
Next, an example of determining the target customer using the target value T will be described. Here, a case where the target customer is determined based on the extracted customer table 1500 shown in FIG. 20 will be described as an example. However, the group (attraction priority customer type) is not specified.

First, the determination unit 1204 calculates the target value T (=N×100) based on the attracting number of persons (N=3) designated by the attracting request 1100. Here, the target value T is “T=300”. Next, the determination unit 1204 refers to the extracted customer table 1500 and sets the priority order of each candidate in descending order of store visit rate.

Here, the priority of each candidate is “(1) Hoge1, (2) Hoge2, (3) Hoge3, (4) Hoge4”. The numbers in parentheses indicate the priority. Next, the determination unit 1204 sequentially calculates the total store visit rate K by adding the store visit rates of the candidates in the descending order of priority, and calculates the difference between the total store visit rate K and the target value T.

Here, the total store visit rate K when the store visit rates up to the priority order 3 are added is “263”, and the total store visit rate K when the store visit rates up to the priority order 4 is added is “326”. Therefore, the difference from the target value T "300" is smaller when the store visit rates up to the priority order 4 are added.

Therefore, the determination unit 1204 determines the candidates having the priority orders 1 to 4 as the target customers. That is, the candidate with the customer name “Hoge1”, the candidate with the customer name “Hoge2”, the candidate with the customer name “Hoge3”, and the candidate with the customer name “Hoge4” are determined as the target customers. In this case, the communication unit 1205 transmits guidance information for urging the target customers (Hoge1, Hoge2, Hoge3, Hoge4) to visit the target store (Food-Shop-1).

As a result, new attraction management information as shown in FIGS. 21A and 21B is registered in the attraction management information DB 360.

21A and 21B are explanatory diagrams showing an example of registration of new invitation management information. 21A and 21B, attraction management information 2100-1 to 2100-4 is newly registered in the attraction management information DB 360. At this point, the fields of customer visit date and customer visit time of invitation management information 2100-1 to 2100-4 are blank.

In addition, even if the attraction end date and time (at 12:00 on November 28th, 2018/11/28) of the attraction request 1100 has not passed, if the customer does not visit the target store, the customer visit date and the customer visit time of the attraction management information are displayed. “***” is set in each field. Further, in the case of a privilege such as the first-come-first-served number of people, the number of customers may be secured before the end date and time of the attraction. In such a case, for example, even if the invitation end date and time has not passed, “***” is set in each field of the customer visit date and the customer visit time of the attraction management information corresponding to the target customer who has not visited the target store. Is set.

(Various processing procedures of information processing apparatus 101)
Next, various processing procedures of the information processing apparatus 101 will be described. First, the store visit processing procedure of the information processing apparatus 101 will be described with reference to FIG.

FIG. 22 is a flowchart showing an example of a store visit processing procedure of the information processing apparatus 101. In the flowchart of FIG. 22, first, the information processing apparatus 101 determines whether store visit information has been received from the store terminal 301 (step S2201). Here, the information processing apparatus 101 waits for receiving store visit information from the store terminal 301 (step S2201: No).

Then, when the information processing apparatus 101 receives the store visit information from the store terminal 301 (step S2201: Yes), the received information about the store visit is registered in the store visit information DB 350 (step S2202). Next, the information processing apparatus 101 searches the attraction management information DB 360 for attraction management information corresponding to the visit information by referring to the customer name, date, time, store name, and system included in the visit information. (Step S2203).

Here, the attraction management information corresponding to the visit information is, for example, attraction management information in which the customer name, attraction start date, attraction store and system match the customer name, store date, store name and system included in the visit information. is there. However, the visit date and time (visit date, visit time) is included in the time zone from the attraction start date and time (attraction start date and attraction start time) of the target store (attraction store) to the attraction end date and time (attraction end date and attraction end time) It may be a condition that it is done.

Then, the information processing apparatus 101 determines whether or not the attraction management information has been searched (step S2204). Here, when the invitation management information is not retrieved (step S2204: No), the information processing apparatus 101 ends the series of processes according to this flowchart.

On the other hand, when the invitation management information is searched (step S2204: Yes), the information processing apparatus 101 sets the visit date and the visit date included in the visit information in the fields of the customer arrival date and the customer arrival time of the retrieved invitation management information. The time is set (step S2205), and the series of processes according to this flowchart is ended.

Thereby, the visit information can be registered in response to the visit of the customer to the store. Further, the customer visit date and customer visit time of the invitation management information can be updated according to the visit information from the target store (inviting store).

Next, the guide information dispatch processing procedure of the information processing apparatus 101 will be described with reference to FIG.

FIG. 23 is a flowchart showing an example of the guidance information dispatch processing procedure of the information processing apparatus 101. In the flowchart of FIG. 23, the information processing apparatus 101 first determines whether or not an invitation request has been received from the store terminal 301 (step S2301). Here, the information processing apparatus 101 waits for receiving an invitation request from the store terminal 301 (step S2301: No).

Then, when the information processing apparatus 101 receives the invitation request from the store terminal 301 (step S2301: Yes), the information processing apparatus 101 executes a customer extraction process for extracting candidates that are candidates for the destination of the guidance information that prompts the user to visit the target store. Yes (step S2302). A specific processing procedure of the customer extraction processing will be described later with reference to FIG.

The information processing apparatus 101 then selects an unselected candidate that has not been selected from the candidates extracted in step S2302 (step S2303). Then, the information processing apparatus 101 executes a store visit rate calculation process for calculating the store visit rate of the selected candidate (step S2304). A specific processing procedure of the store visit rate calculation processing will be described later with reference to FIGS. 25 to 28.

Next, the information processing apparatus 101 determines whether or not there is an unselected candidate that has not been selected among the candidates extracted in step S2302 (step S2305). Here, when there is an unselected candidate (step S2305: Yes), the information processing apparatus 101 returns to step S2303.

On the other hand, when there is no unselected candidate (step S2305: No), the information processing apparatus 101 executes a priority ranking process for prioritizing the candidates extracted in step S2302 (step S2306). A specific processing procedure of the priority ranking processing will be described later with reference to FIG.

Next, the information processing apparatus 101 executes a target customer determination process of determining a target customer as a destination of the guide information for prompting the visit to the target store from the candidates extracted in step S2302 (step S2307). ). A specific processing procedure of the target customer determination processing will be described later with reference to FIG.

Then, the information processing apparatus 101 executes the sending process of sending the guide information for prompting the visit to the target store to the determined target customer (step S2308), and ends the series of processes according to this flowchart. The specific processing procedure of the sending processing will be described later with reference to FIG.

Next, a specific processing procedure of the customer extraction processing in step S2302 shown in FIG. 23 will be described with reference to FIG.

FIG. 24 is a flowchart showing an example of a specific processing procedure of the customer extraction processing. In the flowchart of FIG. 24, first, the information processing apparatus 101 acquires, from the store information DB 330, store information corresponding to the attracting store (store name) included in the attracting request received in step S2301 illustrated in FIG. 23 (step). S2401).

Next, the information processing apparatus 101 identifies the day of the week included in the received invitation request (step S2402). When the invitation request does not include the day of the week, the information processing apparatus 101 identifies the day of the week from the invitation start date included in the invitation request, for example.

Next, the information processing apparatus 101 determines whether the received invitation request includes the base point and the attraction distance (step S2403). Here, when the attraction request includes the base point and the attraction distance (step S2403: Yes), the information processing apparatus 101 proceeds to step S2405.

On the other hand, when the invitation request does not include the base point and the attraction distance (step S2403: No), the information processing apparatus 101 refers to the store base point information DB 340 to identify the base point and the attraction distance of the target store (step S2404). .. Then, the information processing apparatus 101 adds various information to the invitation request (step S2405).

The information added to the invitation request includes, for example, the day of the week, the base point, the attraction distance, and the like. However, the invitation request from the target store may include all the information in advance.

Next, the information processing apparatus 101 selects unselected customer information that has not been selected from the customer information DB 320 (step S2406). Then, the information processing apparatus 101 refers to the selected customer information and determines whether or not the guidance timing designated by the invitation request satisfies the guidance permission condition (step S2407).

If the guidance permission condition is not satisfied (step S2407: NO), the information processing apparatus 101 moves to step S2411. On the other hand, when the guidance permission condition is satisfied (step S2407: Yes), the information processing apparatus 101 calculates the distance from the base point of the target store included in the invitation request to the current position of the customer (step S2408).

The position information of the base point of the target store is specified, for example, from the base position information of the store base point information corresponding to the target store in the store base point information DB 340. Further, the current position of the customer is specified from the current position of the customer information selected in step S2406.

Then, the information processing apparatus 101 determines whether or not the calculated distance is equal to or less than the attraction distance of the target store included in the attraction request (step S2409). Here, when it is larger than the attraction distance (step S2409: No), the information processing apparatus 101 moves to step S2411.

On the other hand, when the distance is less than the attracting distance (step S2409: Yes), the information processing apparatus 101 registers the information (customer customer name, store name of the target store) regarding the customer identified from the selected customer information in the extracted customer table 1500. Yes (step S2410). Then, the information processing apparatus 101 determines whether or not there is unselected customer information that has not been selected from the customer information DB 320 (step S2411).

If there is unselected customer information (step S2411: YES), the information processing apparatus 101 returns to step S2406. On the other hand, when there is no unselected customer information (step S2411: No), the information processing apparatus 101 returns to the step that called the customer extraction process.

As a result, it is possible to extract the target store and the customers who are in a short distance from the nearest station of the target store as candidates of the destinations of the delivery destination of the guide information that prompts the user to visit the target store.

Next, a specific processing procedure of the store visit rate calculation processing of step S2304 shown in FIG. 23 will be described with reference to FIGS. 25 to 28.

25 to 28 are flowcharts showing an example of a specific processing procedure of the store visit rate calculation processing. In the flowchart of FIG. 25, first, the information processing apparatus 101 obtains, from the store visit information DB 350, store visit information corresponding to a combination of the customer name of the candidate selected in step S2303 illustrated in FIG. 23 and the store name of the target store. A search is performed (step S2501).

Then, the information processing apparatus 101 determines whether or not the store visit information has been searched (step S2502). Here, when the store visit information is retrieved (step S2502: Yes), the information processing apparatus 101 sets "present" in the store visit record of the extracted customer information of the selected candidate in the extracted customer table 1500 (step S2502). S2503) and the process moves to step S2505.

On the other hand, when the store visit information is not retrieved (step S2502: No), the information processing apparatus 101 sets “none” as the store visit record of the extracted customer information of the selected candidate (step S2504). Next, the information processing apparatus 101 searches the attraction management information DB 360 for attraction management information corresponding to the customer name of the selected candidate (step S2505).

Then, the information processing apparatus 101 determines whether or not the attraction management information is retrieved (step S2506). Here, when the invitation management information is retrieved (step S2506: Yes), the information processing apparatus 101 sets the number of retrieved invitation management information to the number of invitations of the extracted customer information of the selected candidate (step). S2507).

Next, the information processing apparatus 101 attracts the extracted customer information of the selected candidate by selecting the number of invitation management information items in which the customer visit date and time (customer visit date, customer visit time) is set from the retrieved invitation management information. The number of successes is set (step S2508), and the process proceeds to step S2601 shown in FIG.

In addition, when the attraction management information is not searched in step S2506 (step S2506: No), the information processing apparatus 101 determines each attraction record of the extracted customer information of the selected candidate (attraction record (own store), attraction record). “Other” and “other store (same system)” are set to “none” (step S2509), and the process proceeds to step S2611 shown in FIG.

In the flowchart of FIG. 26, first, the information processing apparatus 101 determines whether or not the attraction management information of the target store is included in the data (attraction management information) in which the customer visit date and time is set among the retrieved attraction management information. It is determined (step S2601). Here, when there is the attraction management information of the target store (step S2601: Yes), the information processing apparatus 101 sets “present” in the attraction record (own store) of the extracted customer information of the selected candidate (step). (S2602), and proceeds to step S2604.

On the other hand, when there is no attraction management information of the target store (step S2601: No), the information processing apparatus 101 sets “none” to the attraction record (own store) of the extracted customer information of the selected candidate (step S2603). .. Next, the information processing apparatus 101 determines whether or not the attraction management information of other stores is included in the data in which the customer visit date and time is set among the retrieved attraction management information (step S2604).

Here, when there is no attraction management information of another store (step S2604: No), the information processing apparatus 101 sets “none” to the attraction record (other store) of the extracted customer information of the selected candidate (step). (S2605), and proceeds to step S2611.

On the other hand, when there is attraction management information of another store (step S2604: Yes), the information processing apparatus 101 sets “present” in the attraction record (other store) of the extracted customer information of the selected candidate (step S2606). .. Next, the information processing apparatus 101 acquires the store information of another store having the attraction management information from the store information DB 330 (step S2607).

Then, the information processing apparatus 101 refers to the acquired store information of the other store and determines whether there is another store of the same system as the target store (step S2608). Here, when there is another store of the same system (step S2608: Yes), the information processing apparatus 101 sets “exist” in the other store (same system) of the extracted customer information of the selected candidate (step S2609). ), and proceeds to step S2611.

On the other hand, when there is no other store of the same system (step S2608: No), the information processing apparatus 101 sets “none” to the other store (same system) of the extracted customer information of the selected candidate (step S2610). Then, the information processing apparatus 101 refers to the extracted customer information of the selected candidate, sets the group to which the candidate belongs (step S2611), and proceeds to step S2701 shown in FIG.

In the flowchart of FIG. 27, first, the information processing apparatus 101 refers to the extracted customer information of the selected candidate and calculates the primary store visit rate of the candidate (step S2701). Next, the information processing apparatus 101 searches the attraction management information DB 360 for attraction management information corresponding to the combination of the store name of the target store and the customer name of the selected candidate (step S2702).

Then, the information processing apparatus 101 determines whether or not there is invitation management information that has been successfully attracted among the retrieved invitation management information (step S2703). Here, when there is attraction management information that has been successfully attracted (step S2703: YES), the information processing apparatus 101 determines that the attraction distance of the target store is equal to or less than the attraction distance of the store (there is a record) specified from the attraction management information. It is determined whether or not (step S2704).

Here, when the attraction distance of the target store is equal to or less than the attraction distance of the store (there is a record) (step S2704: YES), the information processing apparatus 101 refers to the distance correction coefficient table 1300 and stores the store name of the target store, A coefficient corresponding to the distance comparison result “≦actual result” is set as the distance correction coefficient (step S2705), and the process proceeds to step S2801 shown in FIG.

On the other hand, when the attracting distance of the target store is larger than the attracting distance of the store (there is a record) (step S2704: NO), the information processing apparatus 101 refers to the distance correction coefficient table 1300 and the store name of the target store and A coefficient corresponding to the comparison result “>actual result” is set as the distance correction coefficient (step S2706), and the process proceeds to step S2801 shown in FIG.

In addition, in step S2703, when there is no attraction management information that has been successfully attracted (step S2703: No), the information processing apparatus 101 refers to the distance correction coefficient table 1300 and compares the store name of the target store with the distance. A coefficient corresponding to "no track record" is set as the distance correction coefficient (step S2707), and the process proceeds to step S2801 shown in FIG.

In the flowchart of FIG. 28, the information processing apparatus 101 first searches the attraction management information DB 360 for attraction management information corresponding to the combination of the store name of the target store and the customer name of the selected candidate (step S2801). .. However, the information processing apparatus 101 may use the search result of step S2702 shown in FIG.

Then, the information processing apparatus 101 determines whether or not there is invitation management information that has been successfully attracted among the retrieved invitation management information (step S2802). Here, if there is invitation management information that has been successfully attracted (step S2802: Yes), the information processing apparatus 101 is identified by the day of the week when the store is visited identified from the attraction management information and in step S2402 shown in FIG. It is determined whether or not the day of the week (day of invitation) matches (step S2803).

Here, when the days of the week match (step S2803: Yes), the information processing apparatus 101 refers to the day of the week correction coefficient table 1400, and a coefficient corresponding to the store name of the target shop and the comparison result “match” of the days of the week. Is set to the day correction coefficient (step S2804), and the process proceeds to step S2807.

On the other hand, when the days of the week do not match (step S2803: No), the information processing apparatus 101 refers to the day of the week correction coefficient table 1400, the store name of the target store, the comparison result of the days of the week “mismatch”, and each day of the week (attraction) A coefficient corresponding to the type of day of the week (day of the week when visiting the store) (weekday, weekend) is set as the day correction coefficient (step S2805), and the process proceeds to step S2807.

In step S2802, when there is no attraction management information that has been successfully attracted (step S2802: No), the information processing apparatus 101 refers to the day correction coefficient table 1400 and compares the store name of the target store with the day of the week. The coefficient corresponding to "no track record" is set as the day correction coefficient (step S2806).

Then, the information processing apparatus 101 calculates the store visit rate of the selected candidate by multiplying the primary store visit rate calculated in step S2701 shown in FIG. 27 by the set distance correction coefficient and the day correction coefficient ( Step S2807), and returns to the step in which the store visit rate calculation process is called.

Accordingly, it is possible to calculate the store visit rate indicating the likelihood of each candidate coming to the target store according to the guidance information.

Next, with reference to FIG. 29, a specific processing procedure of the priority ranking processing of step S2306 shown in FIG. 23 will be described.

FIG. 29 is a flowchart showing an example of a specific processing procedure of the priority ranking processing. In the flowchart of FIG. 29, the information processing apparatus 101 first determines whether or not there is a group designation (attraction priority customer type) in the invitation request (step S2901). If there is a group designation (step S2901: YES), the information processing apparatus 101 refers to the extracted customer table 1500 and selects an unselected candidate that has not been selected (step S2902).

Next, the information processing apparatus 101 refers to the extracted customer table 1500 and determines whether or not the selected candidate group matches the group specified in the invitation request (step S2903). If the groups do not match (step S2903: NO), the information processing apparatus 101 moves to step S2905.

On the other hand, if the groups match (step S2903: YES), the information processing apparatus 101 registers the extracted customer information of the selected candidate in the extracted customer table 1500 in the priority order table (not shown) (step S2904). Then, the information processing apparatus 101 determines whether or not there is an unselected candidate that has not been selected (step S2905).

Here, when there is an unselected candidate (step S2905: YES), the information processing apparatus 101 returns to step S2902. On the other hand, when there is no unselected candidate (step S2905: No), the information processing apparatus 101 rearranges the candidates registered in the priority order table so that the store visit rate is in descending order (step S2906).

Next, the information processing apparatus 101 rearranges the unregistered candidates in the priority order table so that the store visit rate is in descending order (step S2907). Then, the information processing apparatus 101 registers the unregistered candidates after sorting in descending order in the priority order table (step S2908), and returns to the step in which the priority ordering process is called.

If no group is designated in step S2901 (step S2901: NO), the information processing apparatus 101 rearranges the candidates registered in the extracted customer table 1500 so that the store visit rate is in descending order (step S2909). Then, the information processing apparatus 101 registers the candidates after being sorted in the descending order in the priority order table (step S2910), and returns to the step in which the priority ordering process is called.

Thereby, the priority can be set for each candidate.

Next, a specific processing procedure of the target customer determination processing of step S2307 shown in FIG. 23 will be described with reference to FIG.

FIG. 30 is a flowchart showing an example of a specific processing procedure of the target customer determination processing. In the flowchart of FIG. 30, first, the information processing apparatus 101 calculates a target value T based on the number N of vacant seats (the number of attracted persons) designated by the attracting request (step S3001). Next, the information processing apparatus 101 sets the total store visit rate K to “K=0” (step S3002).

Then, the information processing apparatus 101 selects an unselected candidate from the head of the priority order table (step S3003), and specifies the store visit rate x of the selected candidate (step S3004). Next, the information processing apparatus 101 adds the store visit rate x of the identified candidates to the total store visit rate K (step S3005).

Then, the information processing apparatus 101 determines whether the target value T is equal to or more than the total store visit rate K (step S3006). Here, when the target value T is equal to or more than the total store visit rate K (step S3006: Yes), the information processing apparatus 101 registers the customer information of the selected candidate in the target customer table (not shown) (step S3007).

Then, the information processing apparatus 101 records the absolute value a of the target value T and the total store visit rate K (step S3008). Next, the information processing apparatus 101 determines whether or not there is an unselected candidate that has not been selected from the priority order table (step S3009). Here, when there is an unselected candidate (step S3009: Yes), the information processing apparatus 101 returns to step S3003.

On the other hand, when there is no unselected candidate (step S3009: No), the information processing apparatus 101 returns to the step in which the target customer determination process is called.

If the target value T is less than the total store visit rate K in step S3006 (step S3006: No), the information processing apparatus 101 calculates the absolute value b between the target value T and the total store visit rate K (step S3010). .. Then, the information processing apparatus 101 determines whether the absolute value a is larger than the absolute value b (step S3011).

Here, when the absolute value a is larger than the absolute value b (step S3011: Yes), the information processing apparatus 101 registers the customer information of the selected candidate in the target customer table (step S3012), and the target customer determination process. Return to the step that called. On the other hand, when the absolute value a is less than or equal to the absolute value b (step S3011: No), the information processing apparatus 101 returns to the step of calling the target customer determination process.

Accordingly, it is possible to determine the target customer to whom the guide information for prompting the visit to the target store is sent.

Next, a specific processing procedure of the sending processing of step S2308 shown in FIG. 23 will be described with reference to FIG.

FIG. 31 is a flowchart showing an example of a specific processing procedure of the sending processing. In the flowchart of FIG. 31, first, the information processing apparatus 101 selects an unselected target customer from the target customer table (step S3101). Next, the information processing apparatus 101 creates a guide information mail addressed to the target customer who prompts the customer to visit the target store based on the customer information of the selected target customer (step S3102).

Then, the information processing apparatus 101 transmits the created guide information mail (step S3103). Next, the information processing apparatus 101 generates attraction management information corresponding to the selected target customer (step S3104). Then, the information processing apparatus 101 registers the generated attraction management information in the attraction management information DB 360 (step S3105).

Next, the information processing apparatus 101 determines whether or not there is an unselected target customer that has not been selected from the target customer table (step S3106). Here, when there is an unselected target customer (step S3106: Yes), the information processing apparatus 101 returns to step S3101.

On the other hand, when there is no unselected target customer (step S3106: No), the information processing apparatus 101 returns to the step in which the sending process is called. As a result, it is possible to send a guide information mail urging each target customer to visit the target store.

Next, the attraction completion processing procedure of the information processing apparatus 101 will be described with reference to FIG. The attraction completion process is executed for each attraction request received in step S2301 shown in FIG.

FIG. 32 is a flowchart showing an example of the attraction completion processing procedure of the information processing apparatus 101. In the flowchart of FIG. 32, first, the information processing apparatus 101 determines whether or not the attraction end date and time (attraction end date, attraction end time) of the attraction request received in step S2301 illustrated in FIG. S3201).

Here, the information processing apparatus 101 waits for the attraction end date and time to elapse (step S3201: No). Then, when the invitation end date and time has passed (step S3201: Yes), the information processing apparatus 101 selects the invitation management information corresponding to the invitation request (step S3202).

The attraction management information corresponding to the invitation request is the attraction management information registered in the attraction management information DB 360 when the guide information mail is sent in response to the invitation request.

Next, the information processing apparatus 101 determines whether or not the customer visit date and time (customer visit date, customer visit time) of the selected invitation management information is not set (step S3203). If the customer visit date and time has already been set (step S3203: No), the information processing apparatus 101 proceeds to step S3205.

On the other hand, when the customer visit date and time is not set (step S3203: Yes), the information processing apparatus 101 invalidates the customer visit date and time of the selected invitation management information (step S3204). Then, the information processing apparatus 101 determines whether or not there is unselected attraction management information corresponding to the attraction request (step S3205).

If there is unselected attraction management information (step S3205: YES), the information processing apparatus 101 returns to step S3202. On the other hand, when there is no unselected attraction management information (step S3205: No), the information processing apparatus 101 ends the series of processes according to this flowchart.

As a result, it is possible to invalidate the data (attraction management information) that is being attracted in response to the attraction end date and time having passed.

As described above, according to the information processing apparatus 101 according to the second embodiment, in response to receiving the invitation request from the target store, a plurality of destination candidates of the guide information that prompts the target store to come to the store are selected. The target customer can be determined from among the plurality of candidates based on the number of vacant seats designated by the invitation request by referring to the number of guidance results and the number of store visits of each candidate. Then, according to the information processing apparatus 101, it is possible to transmit the guidance information that prompts the determined target customer to visit the target store.

This makes it possible to select customers who are likely to come to the store according to the guidance information as destinations of the guidance information that prompts the store to visit, and to attract an appropriate number of customers who fill the vacant seats of the store. You can enable it.

Further, according to the information processing apparatus 101, guidance information that prompts the user to visit the target store based on the position information of each user and the position information of the base point corresponding to the target store from among a plurality of registered users. It is possible to extract a plurality of candidates as the destination candidates of and to determine the target customer from the extracted candidates.

As a result, it is possible to attract customers in a short distance from the store or the nearest station in a timely manner.

In addition, according to the information processing apparatus 101, the visit ratio of each candidate visiting the store according to the guidance information is calculated based on the number of times the candidate has been guided and the number of times the store has been visited, and the calculated visit of each candidate. A target customer can be determined from a plurality of candidates based on the rate and the number of vacant seats.

As a result, it is possible to select, as the destination of the guide information for prompting the visit to the store, a customer having a high ratio of actually visiting the store according to the guide information sent in the past.

Further, according to the information processing apparatus 101, it is possible to correct the calculated visit rate of each candidate based on the result of comparing the attraction distance of the target store and the attraction distance of the store (with a record). The attraction distance of the target store is the maximum distance from the base point corresponding to the target store that specifies the attraction range of the target store. The attraction distance of a store (with a track record) is the maximum distance from the base point corresponding to the store that identifies the attraction range of the store that each candidate has visited according to the guide information.

This makes it possible to determine the likelihood of each candidate coming to the store in accordance with the guide information in consideration of the attraction distance when actually visiting the store in accordance with the guide information sent in the past. For example, when the attraction distance of the target store is larger than the attraction distance of the store (with a track record), it is determined that the possibility of coming to the store is low, and the visit ratio can be adjusted to be small.

Further, according to the information processing apparatus 101, the visit rate of each candidate calculated based on the result of comparing the day of the week designated by the invitation request with the day when each candidate visited the store according to the guidance information. Can be corrected.

This makes it possible to determine the likelihood of each candidate coming to the store in accordance with the guide information in consideration of the days of the week when the store actually arrived in accordance with the guide information sent in the past. For example, if the current invitation day is different from the day of the week when the store is visited, it can be determined that the possibility of coming to the store is low, and the store visit rate can be adjusted to be small. At this time, if the difference between the days of the week is weekdays and weekends (Saturdays and Sundays), the visit rate can be adjusted to be smaller than that when the weekdays differ.

Further, according to the information processing apparatus 101, the group to which each candidate belongs is determined from the plurality of groups based on the visit record of each candidate according to the guidance information, and among the plurality of candidates, The target customer can be preferentially determined from the candidates belonging to the group designated by the invitation request.

As a result, in response to the request from the store side of what type of customer (for example, a customer who has a history of visiting the store, a new customer, etc.), the destination of the guide information that prompts the store to be visited. Can be narrowed down.

Further, according to the information processing apparatus 101, it is possible to transmit the guidance information including the coupon information usable at the target store to the determined target customer. As a result, it is possible to give a benefit to the customer who visits the store in accordance with the guide information and increase the store visit rate.

From these things, according to the information processing system 300 and the information processing apparatus 101 according to the second embodiment, when a vacant seat occurs in a store, it is possible to attract customers who are in a short distance from the store or the like in a timely manner. It becomes possible to fill empty seats in the store quickly and increase the filling rate. In addition, by attracting an appropriate number of customers to fill the vacant seats in the store, it is possible to prevent the occurrence of customers who are not filled up or who wait for vacant seats despite being invited from the store. ..

The guide information sending method described in the present embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. The guide information sending program is recorded in a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, a DVD, or a USB memory, and is executed by being read from the recording medium by the computer. The guide information sending program may be distributed via a network such as the Internet.

The information processing apparatus 101 described in the present embodiment can also be realized by a special-purpose IC such as a standard cell or a structured ASIC (Application Specific Integrated Circuit) or a PLD (Programmable Logic Device) such as an FPGA.

Regarding the above-described embodiment, the following additional notes are further disclosed.

(Supplementary Note 1) A reception unit that receives an invitation request from a target store,
In response to accepting the invitation request, for each candidate of a plurality of candidates for the destination of the guide information for prompting the visit to the target store, encourage each candidate to visit the store Based on the number of vacant seats designated by the invitation request, with reference to a storage unit that stores the number of times the guidance information has been sent and the number of times the candidates have visited the store according to the guidance information, From a plurality of candidates, a determination unit that determines the destination of the guide information for prompting the visit to the target store,
An information processing device comprising:

(Supplementary Note 2) Guidance for prompting visit to the target store based on the position information of each user of the plurality of users and the position information of the base point corresponding to the target store from the plurality of users registered in advance An extraction unit for extracting a plurality of candidates as information destination candidates,
The determining unit is
The information processing apparatus according to appendix 1, wherein the destination of the guide information for urging the user to visit the target store is determined from the plurality of candidates extracted by the extraction unit.

(Supplementary Note 3) The determining unit
With reference to the storage unit, based on the number of times of guidance of each candidate and the number of times of visit of each candidate, calculate the visit ratio of each candidate according to the guidance information,
3. The information processing device according to appendix 1 or 2, wherein the destination is determined from the plurality of candidates based on the calculated store visit rate of each candidate and the number of vacant seats.

(Supplementary Note 4) The determining unit
The maximum distance from the base point corresponding to the target store that identifies the attraction range of the target store, and the maximum distance from the base point that corresponds to the store that identifies the attraction range of the store that each candidate has visited according to the guidance information Compare with distance,
Based on the result of comparison, correct the calculated visit rate of each candidate,
4. The information processing device according to appendix 3, wherein the destination is determined from the plurality of candidates based on the corrected store visit rate of each candidate and the number of vacant seats.

(Supplementary Note 5) The determining unit
The day of the week designated by the invitation request is compared with the day of the week when each of the candidates comes to the store according to the guide information,
Based on the result of comparison, correct the calculated visit rate of each candidate,
5. The information processing apparatus according to appendix 3 or 4, wherein the destination is determined from the plurality of candidates based on the corrected store visit rate and the number of vacant seats of each candidate.

(Supplementary Note 6) Based on the store visit record of each of the candidates according to the guide information, the group to which the candidate belongs is determined from a plurality of groups
The destination is preferentially determined from among the candidates belonging to the group specified by the invitation request among the plurality of candidates, according to any one of appendices 1 to 5. Information processing equipment.

(Supplementary note 7) The information according to any one of supplementary notes 1 to 6, further comprising: a communication unit that transmits guidance information that prompts a visit to the target store to the determined destination. Processing equipment.

(Supplementary note 8) The information processing device according to any one of supplementary notes 1 to 7, wherein the guidance information includes coupon information that can be used at the target store.

(Appendix 9) Accepting invitation requests from target stores,
In response to accepting the invitation request, for each candidate of a plurality of candidates for the destination of the guide information for prompting the visit to the target store, encourage each candidate to visit the store Based on the number of vacant seats designated by the invitation request, referring to a storage unit that stores the number of times the guidance information has been sent and the number of times the candidates have visited the store according to the guidance information, From a plurality of candidates, determine the destination of the guide information for prompting the visit to the target store,
A guide information sending program characterized by causing a computer to execute processing.

101 Information Processing Device 102 Attraction Range 110, 1210 Storage Unit 120, 1100 Attraction Request 130 Guidance Information 200 List of Visiting Rates 301 Store Terminal 302 Customer Terminal 300 Information Processing System 310 Network 320 Customer Information DB
330 Store information DB
340 store base information DB
350 Visit information DB
360 invitation management information DB
400,500 Bus 401,501 CPU
402, 502 Memory 403 Disk drive 404 Disk 405, 504 Communication I/F
406, 507 Portable recording medium I/F
407, 508 Portable recording medium 503 GPS unit 505 Display 506 Input device 1201 Receiving section 1202 Extracting section 1203 Calculating section 1204 Determining section 1205 Communication section 1300 Distance correction coefficient table 1400 Day correction coefficient table 1500 Extraction customer table 1600 Group table

Claims (8)

A reception section that accepts invitation requests from target stores,
In response to accepting the invitation request, for each candidate of a plurality of candidates for the destination of the guide information for prompting the visit to the target store, encourage each candidate to visit the store Based on the number of vacant seats designated by the invitation request, referring to a storage unit that stores the number of times the guidance information has been sent and the number of times the candidates have visited the store according to the guidance information, From a plurality of candidates, a determination unit that determines the destination of the guide information for prompting the visit to the target store,
An information processing device comprising: A destination of guide information that prompts a visit to the target store based on position information of each user of the plurality of users and position information of a base point corresponding to the target store from among a plurality of users registered in advance. It further has an extraction unit for extracting a plurality of candidates as candidates,
The determining unit is
The information processing apparatus according to claim 1, wherein a destination of guide information for urging the user to visit the target store is determined from the plurality of candidates extracted by the extraction unit. The determining unit is
With reference to the storage unit, based on the number of times of guidance of each candidate and the number of times of visit of each candidate, calculate the visit ratio of each candidate according to the guidance information,
The information processing apparatus according to claim 1, wherein the destination is determined from the plurality of candidates based on the calculated store visit rate of each candidate and the number of vacant seats. The determining unit is
The maximum distance from the base point corresponding to the target store that identifies the attraction range of the target store, and the maximum distance from the base point that corresponds to the store that identifies the attraction range of the store that each candidate has visited according to the guidance information Compare with distance,
Based on the result of comparison, correct the calculated visit rate of each candidate,
The information processing apparatus according to claim 3, wherein the destination is determined from the plurality of candidates based on the corrected store visit rate of each candidate and the number of vacant seats. The determining unit is
The day of the week designated by the invitation request is compared with the day of the week when each of the candidates comes to the store according to the guide information,
Based on the result of comparison, correct the calculated visit rate of each candidate,
The information processing apparatus according to claim 3 or 4, wherein the destination is determined from the plurality of candidates based on the corrected store visit rate of each candidate and the number of vacant seats. .. Based on the store visit record of each of the candidates according to the guide information, determine the group to which each candidate belongs from a plurality of groups,
The destination is preferentially determined from among the candidates belonging to the group designated by the invitation request among the plurality of candidates, according to any one of claims 1 to 5. The information processing device described. The information processing apparatus according to claim 1, further comprising a communication unit that transmits guide information that prompts a visit to the target store to the determined destination. Accepting invitation requests from target stores,
In response to accepting the invitation request, for each candidate of a plurality of candidates for the destination of the guide information for prompting the visit to the target store, encourage each candidate to visit the store Based on the number of vacant seats designated by the invitation request, referring to a storage unit that stores the number of times the guidance information has been sent and the number of times the candidates have visited the store according to the guidance information, From a plurality of candidates, determine the destination of the guide information for prompting the visit to the target store,
A guide information sending program characterized by causing a computer to execute processing.

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