WO2022219916A1 - Dispositif de calcul d'effet de transfert de client - Google Patents

Dispositif de calcul d'effet de transfert de client Download PDF

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Publication number
WO2022219916A1
WO2022219916A1 PCT/JP2022/006220 JP2022006220W WO2022219916A1 WO 2022219916 A1 WO2022219916 A1 WO 2022219916A1 JP 2022006220 W JP2022006220 W JP 2022006220W WO 2022219916 A1 WO2022219916 A1 WO 2022219916A1
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WO
WIPO (PCT)
Prior art keywords
user
attribute
attributes
store
calculation
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PCT/JP2022/006220
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English (en)
Japanese (ja)
Inventor
周 石川
知洋 三村
慎 石黒
仁嗣 川崎
喬 鈴木
曉 山田
Original Assignee
株式会社Ｎｔｔドコモ
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Application filed by 株式会社Ｎｔｔドコモ filed Critical 株式会社Ｎｔｔドコモ
Priority to JP2023532522A priority Critical patent/JP7430847B2/ja
Publication of WO2022219916A1 publication Critical patent/WO2022219916A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present disclosure relates to a customer referral effect calculation device that calculates the increase in the number of visitors to a target store as the customer referral effect.
  • a probability calculation unit an attribute distribution change amount calculated by the attribute distribution change amount calculation unit for each combination of the plurality of attributes; and a visit probability calculation unit for each combination of the plurality of attributes.
  • an expected value of the number of visitors for each store category for each combination of the plurality of attributes is calculated, and the store for each of the calculated combinations of the plurality of attributes is calculated.
  • the expected number of visitors for each category is totaled for each store category, and the obtained total value for each store category is divided by the number of stores for each store category in the calculation target area, and the value of the store category is calculated.
  • a customer referral effect calculation unit that calculates the customer referral effect of the store.
  • the customer referral effect calculation unit calculates, based on (1) attribute distribution change amount for each combination of multiple attributes and (2) visit probability for each store category for each combination of multiple attributes, Calculate the expected number of visitors for each store category for each combination, sum up the expected number of visitors for each store category for each combination of multiple attributes calculated, and obtain the store category The value obtained by dividing the total value for each by the number of stores for each store category in the calculation target area for the time being is calculated as the "customer referral effect for stores in the store category.”
  • new equipment for cashless payment or point card use can be obtained based on the location information, the first user attribute information and the store visitor attribute log while suppressing the introduction cost without installing new equipment at the retail store etc. From the "attribute distribution change" and "visit probability for each store category" for each combination of multiple attributes in the calculation target area, it is possible to objectively and appropriately calculate the customer referral effect from a third party's point of view.
  • FIG. 1 is a configuration diagram of a customer referral effect calculation device and peripheral devices according to an embodiment of the invention.
  • FIG. It is a figure which shows the data example recorded on store visitor attribute log DB. It is a figure which shows the example of the data recorded on location information DB. It is a figure which shows the data example recorded on user attribute information DB. It is a flowchart which shows the process performed in a customer referral effect calculation apparatus. It is a figure which shows the hardware structural example of a customer referral effect calculation apparatus.
  • a customer referral effect calculation device according to an embodiment of the invention will be described below with reference to the drawings.
  • the customer referral effect calculation device 10 includes a store visitor attribute log management server 20, a location information management server 30, and a user attribute information management server 40 as peripheral devices.
  • the store visitor attribute log management server 20 stores the store visitor attribute log DB 22 that stores the store visitor attribute log, and stores the store visitor attribute log from an external device (not shown, such as a server on the store side, a POS terminal, etc.). and a store visitor attribute log recording unit 21 that acquires and records in the store visitor attribute log DB 22 .
  • the customer referral effect calculation device 10 includes an acquisition unit 11 , an attribute distribution change amount calculation unit 12 , a visit probability calculation unit 13 , a customer referral effect calculation unit 14 , and an output unit 15 . Each functional unit will be described below.
  • the acquisition unit 11 obtains the position information indicating the user's position from the position information DB 32, the first user attribute information indicating the user's attribute from the user attribute information DB 42, and obtains the store visit information of the user who visited the store. It is a functional unit that acquires a store visitor attribute log indicating a person's attribute and visit history from the store visitor attribute log DB 22 .
  • the attribute distribution change amount calculation unit 12 acquires a second user attribute information based on the position information and the first user attribute information and indicates the attribute of the user located in the calculation target area at the calculation target time to be the calculation target.
  • the visit probability calculation unit 13 calculates the "number of unique users" located in the calculation target area at the calculation target time for each combination of a plurality of attributes, which is acquired based on the position information and the first user attribute information.
  • the ratio of the "total number of visitors" obtained from the store visitor attribute log as the number of visit history corresponding to the store category to be calculated, the time to be calculated, and the area to be calculated is calculated for each store category for each combination of multiple attributes is a functional unit that calculates the probability of visiting
  • the customer referral effect calculation unit 14 calculates the attribute distribution change amount calculated by the attribute distribution change amount calculation unit 12 for each combination of a plurality of attributes, and the visit probability calculation unit 13 for each combination of a plurality of attributes. Based on the calculated visit probability for each store category, calculate the expected number of visitors for each store category for each combination of multiple attributes, and calculate the expected number of visitors for each store category for each combination of multiple attributes Sum the expected number of visitors for each store category and calculate the total value for each store category. Calculate the value divided by the number of stores for each store category in the target area as the customer referral effect for the stores in the store category. It is a functional part that
  • the output unit 15 is a functional unit that outputs the customer referral effect for the store of the store category calculated by the customer referral effect calculation unit 14 .
  • output corresponds to various aspects such as display output on the user's display of the customer referral effect calculation device 10, print output to a printer, etc., transfer of data related to the customer referral effect to an external device, etc. do.
  • a store visitor attribute log is recorded for each store visit by a store visitor.
  • Store location latitude/longitude
  • store visitor attribute information (sex, age, place of residence, etc.), and information such as visit time are included.
  • the location information indicating the location of the user is recorded in the location information DB 32 at the timing when the user registers the location or at a predetermined cycle.
  • the user ID of the user is recorded in the location information DB 32 at the timing when the user registers the location or at a predetermined cycle.
  • the user ID of the user is recorded in the location information DB 32 at the timing when the user registers the location or at a predetermined cycle.
  • the user ID of the user location registration time
  • location information latitude and longitude
  • the user's location information (latitude and longitude) can be obtained using the direction of radio wave output at the access point, the amount of transmission delay of the user terminal, triangular positioning, and the like.
  • the first user attribute information indicating the user attribute is newly registered at the timing when the user starts to use it.
  • the first user attribute information includes information such as the user ID of the user and attribute information (sex, age, place of residence, etc.).
  • the processing executed by the customer referral effect calculation device 10 according to the present embodiment will be described below with reference to FIG.
  • the processing in FIG. 5 is triggered by, for example, an instruction to start execution from a user, an instruction to start execution based on a predetermined processing schedule, or the like.
  • step S1 of FIG. 5 the acquisition unit 11 acquires the position information indicating the user's position from the position information DB 32, the first user attribute information indicating the user's attribute from the user attribute information DB 42, and visits the store.
  • Store visitor attribute logs indicating the attributes and visit history of the store visitor who is the user who visited the store are acquired from the store visitor attribute log DB 22 .
  • the attribute distribution change amount calculation unit 12 calculates the attribute distribution change amount for each combination of a plurality of attributes according to the following procedure. That is, the attribute distribution change amount calculation unit 12 extracts the user located in the calculation target area at the calculation target time from the position information, and converts the second user attribute information indicating the attribute of the extracted user into the first user attribute information. Acquired from the user attribute information, and aggregates the corresponding number of persons for each combination of a plurality of attributes in the acquired second user attribute information. Further, the attribute distribution change amount calculation unit 12 extracts from the location information users who are located in the calculation target area at the comparison target time, and converts third user attribute information indicating attributes of the extracted users to the first user attribute information.
  • the attribute distribution change amount calculation unit 12 calculates, as an attribute distribution change amount, the difference between the corresponding number of people at the calculation target time and the corresponding number of people at the comparison target time for each combination of a plurality of attributes.
  • the total population estimation process may be performed in consideration of the user share rate of the mobile communication service provider (carrier).
  • the attribute distribution at the overall population level is obtained by dividing the number of people obtained by aggregation by a specific mobile communication service provider by the user share rate of that communication service provider in the mobile communication service industry. The amount of change can be calculated.
  • the visit probability calculation unit 13 calculates the visit probability for each attribute combination and store category as follows.
  • the visit probability calculation unit 13 extracts users located in the calculation target area at the calculation target time from the position information, and sends second user attribute information indicating the extracted user attributes to the first user attribute information.
  • the attribute information is acquired, and the corresponding number of persons for each combination of a plurality of attributes in the acquired second user attribute information is aggregated. As a result, the number of unique users SN located in the calculation target area at the calculation target time for each combination of a plurality of attributes is acquired.
  • the visit probability calculation unit 13 extracts records corresponding to the store category to be calculated, the time to be calculated, and the area to be calculated from the store visitor attribute log, and calculates the number of obtained records as the total number of visitors SM . and Then, the visit probability calculation unit 13 calculates the ratio of the total number of visitors SM to the number of unique users SN as the visit probability for each store category for each combination of a plurality of attributes.
  • step S4 the customer referral effect calculation unit 14 calculates the attribute distribution change amount calculated by the attribute distribution change amount calculation unit 12 and the visit probability calculation unit 13 for each combination of a plurality of attributes. Calculate the expected number of visitors for each store category for each combination of multiple attributes by multiplying the visit probability for each store category by the attribute distribution change amount based on the visit probability for each store category obtained, The expected number of visitors for each store category for each combination of multiple attributes calculated is totaled for each store category, and the total value obtained for each store category is calculated as the number of stores for each store category in the calculation target area. , is calculated as the customer referral effect for the stores in the store category.
  • step S5 the output unit 15 displays the customer referral effect of the store of the store category calculated by the customer referral effect calculation unit 14 on the user's display of the customer referral effect calculation device 10, for example.
  • output can take various forms such as display output, print output, and data transfer to the outside.
  • location information, first user attribute information and Appropriate referral of customers from an objective, third-party perspective based on the "attribute distribution variation" and "visit probability for each store category" for each combination of multiple attributes in the calculation target area obtained based on store visitor attribute logs The effect can be calculated.
  • the attribute distribution change amount calculation unit 12 calculates the difference between the corresponding number of people at the calculation target time and the corresponding number of people at the comparison target time for each combination of a plurality of attributes for the "attribute distribution change amount". By calculating as , the attribute distribution change amount can be calculated appropriately.
  • the visit probability calculation unit 13 extracts users located in the calculation target area at the calculation target time from the position information, and calculates the attribute of the extracted users.
  • the multiple attributes The number of unique users located in the calculation target area at the calculation target time for each combination of . In this way, the appropriate number of unique users located in the calculation target area at the calculation target time can be obtained.
  • the customer referral effect calculation unit 14 calculates the visit probability and the attribute distribution change amount for each store category for each combination of a plurality of attributes. By multiplying by , the expected value of the number of visitors for each store category for each combination of a plurality of attributes is calculated. In this way, it is possible to calculate an appropriate expected number of visitors for each store category with a simple method while avoiding identification of individual users.
  • FIG. 1 shows a configuration example in which the store visitor attribute log management server 20, the location information management server 30, and the user attribute information management server 40 exist as peripheral devices outside the customer referral effect calculation device 10.
  • the customer referral effect calculation device 10 may employ a configuration in which at least one of the above three servers has the function.
  • the two functional units may adopt a configuration in which they are distributed and arranged in a plurality of physically separated devices. device 10”.
  • each functional block may be implemented using one device that is physically or logically coupled, or directly or indirectly using two or more devices that are physically or logically separated (e.g. , wired, wireless, etc.) and may be implemented using these multiple devices.
  • a functional block may be implemented by combining software in the one device or the plurality of devices.
  • Functions include judging, determining, determining, calculating, calculating, processing, deriving, investigating, searching, checking, receiving, transmitting, outputting, accessing, resolving, selecting, choosing, establishing, comparing, assuming, expecting, assuming, Broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc. can't
  • a functional block (component) that makes transmission work is called a transmitting unit or transmitter.
  • the implementation method is not particularly limited.
  • the customer referral effect calculation device in one embodiment of the present disclosure may function as a computer that performs the processing in this embodiment.
  • FIG. 6 is a diagram showing a hardware configuration example of the customer referral effect calculation device 10 according to an embodiment of the present disclosure.
  • the customer referral effect calculation device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.
  • the term "apparatus” can be read as a circuit, device, unit, or the like.
  • the hardware configuration of the customer referral effect calculation device 10 may be configured to include one or more of each device shown in the figure, or may be configured without including some of the devices.
  • Each function in the customer referral effect calculation device 10 is performed by the processor 1001 performing calculations by loading predetermined software (programs) onto hardware such as the processor 1001 and the memory 1002, and controlling communication by the communication device 1004. , and controlling at least one of reading and writing of data in the memory 1002 and the storage 1003 .
  • the processor 1001 for example, operates an operating system and controls the entire computer.
  • the processor 1001 may be configured by a central processing unit (CPU) including interfaces with peripheral devices, terminals, arithmetic units, registers, and the like.
  • CPU central processing unit
  • the processor 1001 reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and executes various processes according to them.
  • programs program codes
  • software modules software modules
  • data etc.
  • the program a program that causes a computer to execute at least part of the operations described in the above embodiments is used.
  • FIG. Processor 1001 may be implemented by one or more chips. Note that the program may be transmitted from a network via an electric communication line.
  • the memory 1002 is a computer-readable recording medium, and is composed of at least one of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. may be
  • ROM Read Only Memory
  • EPROM Erasable Programmable ROM
  • EEPROM Electrical Erasable Programmable ROM
  • RAM Random Access Memory
  • the memory 1002 may also be called a register, cache, main memory (main storage device), or the like.
  • the memory 1002 can store executable programs (program code), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.
  • the storage 1003 is a computer-readable recording medium, for example, an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disc, a magneto-optical disc (for example, a compact disc, a digital versatile disc, a Blu-ray disk), smart card, flash memory (eg, card, stick, key drive), floppy disk, magnetic strip, and/or the like.
  • Storage 1003 may also be called an auxiliary storage device.
  • the storage medium described above may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003 .
  • the communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called a network device, a network controller, a network card, a communication module, or the like.
  • the input device 1005 is an input device (for example, keyboard, mouse, microphone, switch, button, sensor, etc.) that receives input from the outside.
  • the output device 1006 is an output device (eg, display, speaker, LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated (for example, a touch panel).
  • Each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be configured using a single bus, or may be configured using different buses between devices.
  • notification of predetermined information is not limited to being performed explicitly, but may be performed implicitly (for example, not notifying the predetermined information). good too.
  • Input/output information may be stored in a specific location (for example, memory) or managed using a management table. Input/output information and the like can be overwritten, updated, or appended. The output information and the like may be deleted. The entered information and the like may be transmitted to another device.
  • a and B are different may mean “A and B are different from each other.”
  • the term may also mean that "A and B are different from C”.
  • Terms such as “separate,” “coupled,” etc. may also be interpreted in the same manner as “different.”

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Abstract

L'invention concerne un dispositif de calcul d'effet de transfert de client 10 comprenant : une unité de calcul de variation de distribution d'attribut 12 qui calcule une variation de distribution d'attribut pour chaque combinaison d'attributs à partir de différences dans des informations d'attribut d'utilisateur dans une zone cible de calcul entre un temps cible de calcul et un temps cible de comparaison; une unité de calcul de probabilité de visite 13 qui calcule, en tant que probabilité de visite pour chaque catégorie de magasin, le rapport du nombre total de visiteurs obtenu en tant que nombre d'historiques de visite correspondant à une catégorie de magasin cible, le temps cible de calcul, et la zone cible de calcul par rapport au nombre d'utilisateurs uniques situés dans la zone cible de calcul au moment cible de calcul pour chaque combinaison d'attributs; et une unité de calcul d'effet de transfert de client 14 qui calcule un nombre attendu de visiteurs pour chaque catégorie de magasin pour chaque combinaison d'attributs à partir de la variation de distribution d'attribut et de la probabilité de visite pour chaque catégorie de magasin, et calcule un effet de transfert de client en divisant de manière égale la valeur totale des nombres attendus de visiteurs pour chaque catégorie de magasin par le nombre de magasins.
PCT/JP2022/006220 2021-04-12 2022-02-16 Dispositif de calcul d'effet de transfert de client WO2022219916A1 (fr)

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JP2021-067188 2021-04-12
JP2021067188A JP2023054378A (ja) 2021-04-12 2021-04-12 送客効果算出装置

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010117954A (ja) * 2008-11-14 2010-05-27 Hitachi Ltd ポイントの管理方法、およびポイントの管理システム
JP2013246747A (ja) * 2012-05-29 2013-12-09 Fuji Xerox Co Ltd プログラム及びキャンペーン管理装置
JP2020071605A (ja) * 2018-10-30 2020-05-07 クロスロケーションズ株式会社 データ分析装置、データ分析システム、データ分析方法およびプログラム

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010117954A (ja) * 2008-11-14 2010-05-27 Hitachi Ltd ポイントの管理方法、およびポイントの管理システム
JP2013246747A (ja) * 2012-05-29 2013-12-09 Fuji Xerox Co Ltd プログラム及びキャンペーン管理装置
JP2020071605A (ja) * 2018-10-30 2020-05-07 クロスロケーションズ株式会社 データ分析装置、データ分析システム、データ分析方法およびプログラム

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JPWO2022219916A1 (fr) 2022-10-20
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