WO2022219916A1 - Customer transfer effect calculation device - Google Patents

Customer transfer effect calculation device 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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user
attribute
attributes
store
calculation
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PCT/JP2022/006220
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French (fr)
Japanese (ja)
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周 石川
知洋 三村
慎 石黒
仁嗣 川崎
喬 鈴木
曉 山田
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株式会社Ｎｔｔドコモ
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Priority to JP2023532522A priority Critical patent/JP7430847B2/en
Publication of WO2022219916A1 publication Critical patent/WO2022219916A1/en

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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

A customer transfer effect calculation device 10 comprises: an attribute distribution variation calculation unit 12 that calculates an attribute distribution variation for each combination of attributes from differences in user attribute information in a calculation target area between a calculation target time and a comparison target time; a visit probability calculation unit 13 that calculates, as a visit probability for each shop category, the ratio of the total number of visitors obtained as the number of visit histories corresponding to a target shop category, the calculation target time, and the calculation target area with respect to the number of unique users located in the calculation target area at the calculation target time for each combination of attributes; and a customer transfer effect calculation unit 14 that calculates an expected number of visitors for each shop category for each combination of attributes from the attribute distribution variation and the visit probability for each shop category, and calculates a customer transfer effect by equally dividing the total value of the expected numbers of visitors for each shop category by the number of shops.

Description

送客効果算出装置Customer Referral Effect Calculator
　本開示は、対象の店舗における訪問者数の増加量を送客効果として算出する送客効果算出装置に関する。 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.
　観光促進などを目的として、あるエリアへの人の流れを促進するような交通費補助等の施策が行われることがある。その際に、多くの場合、施策を行った者（施策実行のためのコストを負担した者）は、送客対象である小売店舗等から送客料を受け取る。その際の送客料は、(1)固定料金であるケース、(2)小売店舗等からの自己申告によって変動させた料金であるケース、(3)キャッシュレス支払い又はポイントカード利用による利用者識別および購買内容の把握・分析を行って購買額に応じて変動させた料金であるケースなどが考えられる。特に、キャッシュレス支払い又はポイントカード利用による利用者識別および購買内容の把握・分析については、さまざまな技術が提案されている（特許文献１参照）。 For the purpose of promoting tourism, measures such as transportation subsidies may be implemented to promote the flow of people to a certain area. At that time, in many cases, the person who implemented the policy (the person who bears the cost of implementing the policy) receives the customer referral fee from the retail store or the like that is the customer referral target. At that time, the customer referral fee is (1) fixed fee, (2) variable fee based on self-declaration from retail store, (3) user identification by cashless payment or point card use. Also, it is possible to think of a case where the price is changed according to the purchase amount by grasping and analyzing the purchase details. In particular, various techniques have been proposed for user identification and understanding/analysis of purchase details through cashless payment or point card use (see Patent Literature 1).
特開２０１５－１０３２２１号公報JP 2015-103221 A
　ところが、従来の送客料の設定については、以下のような課題があった。送客料を固定料金とする場合は、送客効果が予期せずに大きいとき又は小さいときに、施策を行った者と小売店舗等のうち何れかの経済的負担が大きくなり適正さを欠く事態になりうるという懸念があった。また、小売店舗等からの自己申告によって送客料を変動させる場合は、第三者視点からの適正な送客効果の把握ができないおそれがあった。さらに、キャッシュレス支払い又はポイントカード利用による利用者識別および購買内容の把握・分析結果に基づき送客料を変動させる場合は、小売店舗等での導入コストが大きくなるおそれがあった。 However, there were the following issues regarding the setting of conventional customer referral fees. If the customer referral fee is set as a fixed fee, when the customer referral effect is unexpectedly large or small, the economic burden on either the person who implemented the measure or the retail store, etc. will increase and it will not be appropriate. I was worried that something might happen. In addition, when the customer referral fee is changed by self-declaration from the retail store, etc., there is a possibility that the appropriate customer referral effect cannot be grasped from a third party's point of view. Furthermore, if the customer referral fee is changed based on the identification and analysis results of the user identification and purchase details by cashless payment or point card use, there is a risk that introduction costs at retail stores and the like will increase.
　本開示は、上記課題を解消するために成されたものであり、小売店舗等での導入コストを抑えつつ、第三者視点から適正に送客効果を算出することを目的とする。 This disclosure was made to solve the above problems, and aims to appropriately calculate the customer referral effect from a third party's perspective while reducing the introduction cost at retail stores and the like.
　本開示に係る送客効果算出装置は、利用者の位置を示す位置情報、利用者の属性を示す第１の利用者属性情報、並びに、店舗を訪問した利用者である店舗訪問者の属性および訪問履歴を示す店舗訪問者属性ログを取得する取得部と、前記位置情報および前記第１の利用者属性情報に基づいて取得される、算出対象とされる算出対象時間に算出対象エリアに位置した利用者の属性を示す第２の利用者属性情報と、前記位置情報および前記第１の利用者属性情報に基づいて取得される、比較対象とされる比較対象時間に前記算出対象エリアに位置した利用者の属性を示す第３の利用者属性情報と、の差分から、複数の属性の組合せそれぞれについての属性分布変化量を算出する属性分布変化量算出部と、前記位置情報および前記第１の利用者属性情報に基づいて取得される、前記複数の属性の組合せそれぞれについての前記算出対象時間に前記算出対象エリアに位置したユニーク利用者人数、に対する、算出対象の店舗カテゴリ、前記算出対象時間および前記算出対象エリアに該当する訪問履歴の数として前記店舗訪問者属性ログから得られる延べ訪問者数、の比率を、前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問確率として算出する訪問確率算出部と、前記複数の属性の組合せそれぞれについての、前記属性分布変化量算出部により算出された属性分布変化量、および、前記複数の属性の組合せそれぞれについての、前記訪問確率算出部により算出された前記店舗カテゴリごとの訪問確率に基づいて、前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問者数期待値を算出し、算出された前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問者数期待値を前記店舗カテゴリごとに合計し、得られた前記店舗カテゴリごとの合計値を前記算出対象エリアにおける前記店舗カテゴリごとの店舗数で当分した値を、前記店舗カテゴリの店舗についての送客効果として算出する送客効果算出部と、を備える。 The customer referral effect calculation device according to the present disclosure includes position information indicating the position of the user, first user attribute information indicating the attribute of the user, and attributes of the store visitor who is the user who visited the store and an acquisition unit that acquires a store visitor attribute log indicating a visit history; Positioned in the calculation target area at a comparison target time to be compared, which is acquired based on the second user attribute information indicating the user attribute, the position information, and the first user attribute information third user attribute information indicating attributes of a user; and an attribute distribution change amount calculation unit for calculating an attribute distribution change amount for each combination of a plurality of attributes from the difference between the position information and the first user attribute information; store category to be calculated, the time to be calculated, and A visit that calculates the ratio of the total number of visitors obtained from the store visitor attribute log as the number of visit histories corresponding to the calculation target area as the visit probability for each of the store categories for each combination of the plurality of attributes. 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. Based on the calculated visit probability for each store category, 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. and a customer referral effect calculation unit that calculates the customer referral effect of the store.
　上記の送客効果算出装置では、取得部が、上記の利用者の位置情報、第１の利用者属性情報、および店舗訪問者属性ログを取得すると、属性分布変化量算出部が、上記の位置情報および第１の利用者属性情報に基づいて取得される、算出対象時間に算出対象エリアに位置した利用者の属性を示す第２の利用者属性情報と、上記の位置情報および第１の利用者属性情報に基づいて取得される、比較対象時間に算出対象エリアに位置した利用者の属性を示す第３の利用者属性情報と、の差分から、複数の属性の組合せそれぞれについての属性分布変化量を算出するとともに、訪問確率算出部が、上記の位置情報および第１の利用者属性情報に基づいて取得される、複数の属性の組合せそれぞれについての算出対象時間に算出対象エリアに位置したユニーク利用者人数、に対する、算出対象の店舗カテゴリ、算出対象時間および算出対象エリアに該当する訪問履歴の数として店舗訪問者属性ログから得られる延べ訪問者数、の比率を、複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問確率として算出する。さらに、送客効果算出部が、 (1)複数の属性の組合せそれぞれについての属性分布変化量および(2)複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問確率に基づいて、複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問者数期待値を算出し、算出された複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問者数期待値を店舗カテゴリごとに合計し、得られた店舗カテゴリごとの合計値を算出対象エリアにおける店舗カテゴリごとの店舗数で当分した値を「当該店舗カテゴリの店舗についての送客効果」として算出する。これにより、キャッシュレス支払い又はポイントカード利用のための新たな設備を小売店舗等に設置することなく導入コストを抑えつつ、位置情報、第１の利用者属性情報および店舗訪問者属性ログに基づき得られる算出対象エリアにおける複数の属性の組合せそれぞれについての「属性分布変化量」と「店舗カテゴリごとの訪問確率」から、客観的に第三者視点から適正に送客効果を算出することができる。 In the customer referral effect calculation device, when the acquisition unit acquires the location information of the user, the first user attribute information, and the store visitor attribute log, the attribute distribution change amount calculation unit calculates the position second user attribute information indicating the attribute of a user located in the calculation target area at the calculation target time, which is acquired based on the information and the first user attribute information; Change in attribute distribution for each combination of a plurality of attributes from the difference between third user attribute information indicating the attributes of users located in the calculation target area at the time to be compared, which is acquired based on the user attribute information In addition to calculating the amount, the visit probability calculation unit obtains based on the position information and the first user attribute information, unique visitor located in the calculation target area at the calculation target time for each combination of a plurality of attributes The ratio of the total number of visitors obtained from the store visitor attribute log as the number of visit histories corresponding to the calculation target store category, calculation target time and calculation target area to the number of users, each combination of multiple attributes is calculated as the visit probability for each store category. In addition, 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." As a result, 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.
　本開示によれば、小売店舗等での導入コストを抑えつつ、第三者視点から適正に送客効果を算出することができる。 According to this disclosure, it is possible to appropriately calculate the customer referral effect from a third party's perspective while reducing the introduction cost at retail stores.
発明の実施形態に係る送客効果算出装置および周辺装置の構成図である。1 is a configuration diagram of a customer referral effect calculation device and peripheral devices according to an embodiment of the invention; FIG. 店舗訪問者属性ログＤＢに記録されたデータ例を示す図である。It is a figure which shows the data example recorded on store visitor attribute log DB. 位置情報ＤＢに記録されたデータ例を示す図である。It is a figure which shows the example of the data recorded on location information DB. 利用者属性情報ＤＢに記録されたデータ例を示す図である。It is a figure which shows the data example recorded on user attribute information DB. 送客効果算出装置において実行される処理を示すフロー図である。It is a flowchart which shows the process performed in a customer referral effect calculation apparatus. 送客効果算出装置のハードウェア構成例を示す図である。It is a figure which shows the hardware structural example of a customer referral effect calculation apparatus.
　以下、図面を参照しながら、発明の実施形態に係る送客効果算出装置について説明する。 A customer referral effect calculation device according to an embodiment of the invention will be described below with reference to the drawings.
　図１に示すように、本実施形態に係る送客効果算出装置１０には、周辺装置として、店舗訪問者属性ログ管理サーバ２０、位置情報管理サーバ３０、および、利用者属性情報管理サーバ４０が存在する。このうち、店舗訪問者属性ログ管理サーバ２０は、店舗訪問者属性ログを格納する店舗訪問者属性ログＤＢ２２と、図示しない外部装置（例えば店舗側のサーバ、ＰＯＳ端末など）から店舗訪問者属性ログを取得して店舗訪問者属性ログＤＢ２２に記録する店舗訪問者属性ログ記録部２１とを備える。位置情報管理サーバ３０は、さまざまな利用者の位置情報を格納する位置情報ＤＢ３２と、図示しない外部装置（例えば利用者端末、無線アクセスポイントなど）から上記位置情報を取得して位置情報ＤＢ３２に記録する位置情報記録部３１とを備える。利用者属性情報管理サーバ４０は、さまざまな利用者の属性情報である第１の利用者属性情報を格納する利用者属性情報ＤＢ４２と、図示しない外部装置から上記第１の利用者属性情報を取得して利用者属性情報ＤＢ４２に記録する利用者属性情報記録部４１とを備える。 As shown in FIG. 1, the customer referral effect calculation device 10 according to the present embodiment 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. exist. Of these, 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 position information management server 30 acquires the position information from a position information DB 32 that stores position information of various users and an external device (for example, a user terminal, a wireless access point, etc.) (not shown) and records it in the position information DB 32. and a position information recording unit 31 for recording. The user attribute information management server 40 acquires the first user attribute information from a user attribute information DB 42 that stores first user attribute information, which is attribute information of various users, and from an external device (not shown). and a user attribute information recording unit 41 for recording in the user attribute information DB 42.
　送客効果算出装置１０は、取得部１１、属性分布変化量算出部１２、訪問確率算出部１３、送客効果算出部１４、および出力部１５を備える。以下、各機能部について説明する。 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.
　取得部１１は、利用者の位置を示す位置情報を位置情報ＤＢ３２から、利用者の属性を示す第１の利用者属性情報を利用者属性情報ＤＢ４２から、店舗を訪問した利用者である店舗訪問者の属性および訪問履歴を示す店舗訪問者属性ログを店舗訪問者属性ログＤＢ２２から、それぞれ取得する機能部である。 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 .
　属性分布変化量算出部１２は、位置情報および第１の利用者属性情報に基づいて取得される、算出対象とされる算出対象時間に算出対象エリアに位置した利用者の属性を示す第２の利用者属性情報と、位置情報および第１の利用者属性情報に基づいて取得される、比較対象とされる比較対象時間に算出対象エリアに位置した利用者の属性を示す第３の利用者属性情報と、の差分から、複数の属性の組合せそれぞれについての属性分布変化量を算出する機能部である。 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. Third user attribute indicating the attribute of the user located in the calculation target area at the comparison target time to be compared, which is acquired based on the user attribute information and the position information and the first user attribute information It is a functional unit that calculates an attribute distribution change amount for each combination of a plurality of attributes from the difference between information and.
　訪問確率算出部１３は、位置情報および第１の利用者属性情報に基づいて取得される、複数の属性の組合せそれぞれについての算出対象時間に算出対象エリアに位置した「ユニーク利用者人数」に対する、算出対象の店舗カテゴリ、算出対象時間および算出対象エリアに該当する訪問履歴の数として店舗訪問者属性ログから得られる「延べ訪問者数」の比率を、複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問確率として算出する機能部である。 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
　送客効果算出部１４は、複数の属性の組合せそれぞれについての、属性分布変化量算出部１２により算出された属性分布変化量、および、複数の属性の組合せそれぞれについての、訪問確率算出部１３により算出された店舗カテゴリごとの訪問確率に基づいて、複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問者数期待値を算出し、算出された複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問者数期待値を店舗カテゴリごとに合計し、得られた店舗カテゴリごとの合計値を算出対象エリアにおける店舗カテゴリごとの店舗数で当分した値を、店舗カテゴリの店舗についての送客効果として算出する機能部である。 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
　出力部１５は、送客効果算出部１４により算出された店舗カテゴリの店舗についての送客効果を出力する機能部である。ここでの「出力」は、送客効果算出装置１０のユーザのディスプレイ等への表示出力、プリンタ等への印刷出力、外部装置への送客効果に係るデータの転送など、さまざまな態様に該当する。 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 . Here, "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.
　図２に示すように、店舗訪問者属性ログＤＢ２２には、店舗訪問者による店舗訪問ごとに店舗訪問者属性ログが記録されていき、店舗訪問者属性ログには、訪問した店舗の店舗カテゴリ、店舗所在地（緯度・経度）、店舗訪問者の属性情報（性別、年代、居住地など）、訪問時刻などの情報が含まれる。 As shown in FIG. 2, in the store visitor attribute log DB 22, 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.
　図３に示すように、位置情報ＤＢ３２には、利用者の位置を示す位置情報が、当該利用者が位置登録したタイミング又は予め定められた周期で記録されていき、位置情報には、レコードＩＤ、利用者の利用者ＩＤ、位置登録時刻、利用者の位置情報（緯度・経度）などの情報が含まれる。このうち、利用者の位置情報（緯度・経度）は、アクセスポイントにおける電波を出力している向き、利用者端末の伝送遅延量、３点測位等を用いて求められる。 As shown in FIG. 3, 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, location registration time, and location information (latitude and longitude) of the user. Of these, 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.
　図４に示すように、利用者属性情報ＤＢ４２には、利用者の属性を示す第１の利用者属性情報が、利用者による利用開始タイミングで新規登録され、その後、利用者からの修正依頼があったタイミング等で更新されていき、第１の利用者属性情報には、利用者の利用者ＩＤ、属性情報（性別、年代、居住地など）などの情報が含まれる。 As shown in FIG. 4, in the user attribute information DB 42, 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.).
　以下、図５を用いて、本実施形態に係る送客効果算出装置１０において実行される処理について説明する。図５の処理は、例えば、ユーザからの実行開始指示、予め定められた処理スケジュールに基づく実行開始指示などをトリガーにして実行開始される。 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.
　図５のステップＳ１において、取得部１１は、利用者の位置を示す位置情報を位置情報ＤＢ３２から、利用者の属性を示す第１の利用者属性情報を利用者属性情報ＤＢ４２から、店舗を訪問した利用者である店舗訪問者の属性および訪問履歴を示す店舗訪問者属性ログを店舗訪問者属性ログＤＢ２２から、それぞれ取得する。 In 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 .
　次に、ステップＳ２において、属性分布変化量算出部１２は、以下の手順で、複数の属性の組合せそれぞれについての属性分布変化量を算出する。即ち、属性分布変化量算出部１２は、算出対象時間に算出対象エリアに位置した利用者を位置情報から抽出し、抽出された利用者の属性を示す第２の利用者属性情報を第１の利用者属性情報から取得し、取得された第２の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計する。また、属性分布変化量算出部１２は、比較対象時間に算出対象エリアに位置した利用者を位置情報から抽出し、抽出された利用者の属性を示す第３の利用者属性情報を第１の利用者属性情報から取得し、取得された第３の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計する。そして、属性分布変化量算出部１２は、複数の属性の組合せそれぞれについて、算出対象時間における該当人数と比較対象時間における該当人数との差分を、属性分布変化量として算出する。なお、上記の該当人数の集計においては、必要に応じ、移動体通信サービス提供会社（キャリア）の利用者シェア率を考慮した全人口推定処理を実施してもよい。この場合、特定の移動体通信サービス提供会社によって集計で得られた該当人数を、移動体通信サービス業界における当該通信サービス提供会社の利用者シェア率で割り算することで、全人口レベルでの属性分布変化量を算出できる。 Next, in step S2, 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. Acquired from the user attribute information, and counts the corresponding number of persons for each combination of a plurality of attributes in the acquired third user attribute information. Then, 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. In addition, in the totalization of the number of applicable persons, if necessary, the total population estimation process may be performed in consideration of the user share rate of the mobile communication service provider (carrier). In this case, 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.
　次に、ステップＳ３において、訪問確率算出部１３は、以下のようにして、属性組合せと店舗カテゴリごとの訪問確率を算出する。まず、訪問確率算出部１３は、算出対象時間に算出対象エリアに位置した利用者を位置情報から抽出し、抽出された利用者の属性を示す第２の利用者属性情報を第１の利用者属性情報から取得し、取得された第２の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計する。これにより、複数の属性の組合せそれぞれについての算出対象時間に算出対象エリアに位置したユニーク利用者人数ＳＮが取得される。次に、訪問確率算出部１３は、店舗訪問者属性ログにおいて、算出対象の店舗カテゴリ、算出対象時間および算出対象エリアに該当するレコードを抽出し、得られたレコード数を延べ訪問者数ＳＭとする。そして、訪問確率算出部１３は、ユニーク利用者人数ＳＮに対する延べ訪問者数ＳＭの比率を、複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問確率として算出する。 Next, in step S3, the visit probability calculation unit 13 calculates the visit probability for each attribute combination and store category as follows. First, 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. Next, 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.
　例えば、店舗カテゴリ「カフェ」に関し、「性別が男性」と「年代が４０代以下」という属性の組合せについてのユニーク利用者人数ＳＮが1000人で、同じ属性の組合せ（男性且つ４０代以下）についての延べ訪問者数ＳＭが120人である場合、上記属性の組合せ（男性且つ４０代以下）についての店舗カテゴリ「カフェ」の訪問確率として、0.12（＝120／1000）が算出される。 For example, regarding the store category “Cafe”, the number of unique users SN for the combination of attributes “male gender” and “age under 40” is 1000, and the combination of the same attributes (male and under 40) is 120, 0.12 (=120/1000) is calculated as the probability of visiting the store category "café" for the above attribute combination (male and under 40's).
　次に、ステップＳ４において、送客効果算出部１４は、複数の属性の組合せそれぞれについての、属性分布変化量算出部１２により算出された属性分布変化量、および、訪問確率算出部１３により算出された店舗カテゴリごとの訪問確率に基づいて、店舗カテゴリごとの訪問確率と属性分布変化量とを乗算することで、複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問者数期待値を算出し、算出された複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問者数期待値を店舗カテゴリごとに合計し、さらに、得られた店舗カテゴリごとの合計値を算出対象エリアにおける店舗カテゴリごとの店舗数で当分した値を、店舗カテゴリの店舗についての送客効果として算出する。 Next, in 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.
　例えば、「性別が男性」と「年代が４０代以下」という属性の組合せについての対象エリアの属性分布変化量（人数増加量）が100人であり、上記属性の組合せ（男性且つ４０代以下）についての店舗カテゴリ「カフェ」の訪問確率が0.12である場合、対象エリアに位置する店舗カテゴリ「カフェ」への訪問者数期待値は12人（＝100×0.12）となる。そして、この値を、対象エリア内の「カフェ」の既知の店舗数で当分した値が、店舗カテゴリ「カフェ」の送客効果として算出される。 For example, the attribute distribution change amount (increase in the number of people) in the target area for the attribute combination of "gender is male" and "age is under 40" is 100 people, and the combination of the above attributes (male and under 40) is 0.12, the expected number of visitors to the store category "cafe" located in the target area is 12 (=100×0.12). Then, a value obtained by dividing this value by the known number of "cafe" shops in the target area for the time being is calculated as the customer referral effect of the shop category "cafe".
　さらに、ステップＳ５において、出力部１５は、送客効果算出部１４により算出された店舗カテゴリの店舗についての送客効果を、例えば送客効果算出装置１０のユーザのディスプレイ等へ表示出力する。なお、前述したように、「出力」は、表示出力、印刷出力、外部へのデータ転送など、さまざまな態様を採りうる。 Further, in 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. As described above, "output" can take various forms such as display output, print output, and data transfer to the outside.
　以上説明した発明の実施形態によれば、キャッシュレス支払い又はポイントカード利用のための新たな設備を小売店舗等に設置することなく導入コストを抑えつつ、位置情報、第１の利用者属性情報および店舗訪問者属性ログに基づき得られる算出対象エリアにおける複数の属性の組合せそれぞれについての「属性分布変化量」と「店舗カテゴリごとの訪問確率」から、客観的に第三者視点から適正に送客効果を算出することができる。 According to the embodiments of the invention described above, 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.
　また、属性分布変化量算出部１２は、「属性分布変化量」については、複数の属性の組合せそれぞれについて、算出対象時間における該当人数と比較対象時間における該当人数との差分を、属性分布変化量として算出することで、属性分布変化量を適切に算出できる。 In addition, 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.
　また、訪問確率算出のための「ユニーク利用者人数」について、訪問確率算出部１３は、算出対象時間に算出対象エリアに位置した利用者を位置情報から抽出し、抽出された利用者の属性を示す第２の利用者属性情報を第１の利用者属性情報から取得し、取得された第２の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計することで、複数の属性の組合せそれぞれについての算出対象時間に算出対象エリアに位置したユニーク利用者人数を取得する。このようにして、算出対象時間に算出対象エリアに位置した適切なユニーク利用者人数を取得できる。 In addition, regarding the “number of unique users” for calculating the visit probability, 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. By acquiring the second user attribute information shown from the first user attribute information and aggregating the number of people who correspond to each combination of multiple attributes in the acquired second user attribute information, 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.
　また、送客効果算出のための「店舗カテゴリごとの訪問者数期待値」について、送客効果算出部１４は、複数の属性の組合せそれぞれについての、店舗カテゴリごとの訪問確率と属性分布変化量とを乗算することで、複数の属性の組合せそれぞれについての店舗カテゴリごとの訪問者数期待値を算出する。このようにして、利用者個人が特定されることを回避しつつ、簡易な手法で店舗カテゴリごとの適切な訪問者数期待値を算出できる。 In addition, regarding the "expected number of visitors for each store category" for calculating the customer referral effect, 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.
　（変形例）
　なお、図１には、店舗訪問者属性ログ管理サーバ２０、位置情報管理サーバ３０、および、利用者属性情報管理サーバ４０が、送客効果算出装置１０の外部に、周辺装置として存在する構成例を示したが、送客効果算出装置１０が、上記３つのサーバのうち少なくとも１つの機能を兼ね備えた構成を採用してもよい。 (Modification)
Note that 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.
　また、図１の構成例で、送客効果算出装置１０が備える、取得部１１、属性分布変化量算出部１２、訪問確率算出部１３、送客効果算出部１４、および出力部１５の計５つの機能部は、物理的に分離された複数の装置に分散して配置された構成を採用してもよく、その場合、当該物理的に分離された複数の装置の全体が「送客効果算出装置１０」に相当するものと把握される。 In addition, in the configuration example of FIG. The two functional units may adopt a configuration in which they are distributed and arranged in a plurality of physically separated devices. device 10”.
　また、上記実施形態の説明に用いたブロック図は、機能単位のブロックを示している。これらの機能ブロック（構成部）は、ハードウェア及びソフトウェアの少なくとも一方の任意の組み合わせによって実現される。また、各機能ブロックの実現方法は特に限定されない。すなわち、各機能ブロックは、物理的又は論理的に結合した１つの装置を用いて実現されてもよいし、物理的又は論理的に分離した２つ以上の装置を直接的又は間接的に（例えば、有線、無線などを用いて）接続し、これら複数の装置を用いて実現されてもよい。機能ブロックは、上記１つの装置又は上記複数の装置にソフトウェアを組み合わせて実現されてもよい。 Also, the block diagrams used in the description of the above embodiments show blocks in units of functions. These functional blocks (components) are realized by any combination of at least one of hardware and software. Also, the method of implementing each functional block is not particularly limited. That is, 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.
　機能には、判断、決定、判定、計算、算出、処理、導出、調査、探索、確認、受信、送信、出力、アクセス、解決、選択、選定、確立、比較、想定、期待、見做し、報知（broadcasting）、通知（notifying）、通信（communicating）、転送（forwarding）、構成（configuring）、再構成（reconfiguring）、割り当て（allocating、mapping）、割り振り（assigning）などがあるが、これらに限られない。たとえば、送信を機能させる機能ブロック（構成部）は、送信部（transmitting　unit）、送信機（transmitter）と呼称される。いずれも、上述したとおり、実現方法は特に限定されない。 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 For example, a functional block (component) that makes transmission work is called a transmitting unit or transmitter. In either case, as described above, the implementation method is not particularly limited.
　例えば、本開示の一実施の形態における送客効果算出装置は、本実施形態における処理を行うコンピュータとして機能してもよい。図６は、本開示の一実施の形態に係る送客効果算出装置１０のハードウェア構成例を示す図である。上述の送客効果算出装置１０は、物理的には、プロセッサ１００１、メモリ１００２、ストレージ１００３、通信装置１００４、入力装置１００５、出力装置１００６、バス１００７などを含むコンピュータ装置として構成されてもよい。 For example, 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.
　なお、以下の説明では、「装置」という文言は、回路、デバイス、ユニットなどに読み替えることができる。送客効果算出装置１０のハードウェア構成は、図に示した各装置を１つ又は複数含むように構成されてもよいし、一部の装置を含まずに構成されてもよい。 In the following explanation, 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.
　送客効果算出装置１０における各機能は、プロセッサ１００１、メモリ１００２などのハードウェア上に所定のソフトウェア（プログラム）を読み込ませることによって、プロセッサ１００１が演算を行い、通信装置１００４による通信を制御したり、メモリ１００２及びストレージ１００３におけるデータの読み出し及び書き込みの少なくとも一方を制御したりすることによって実現される。 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 .
　プロセッサ１００１は、例えば、オペレーティングシステムを動作させてコンピュータ全体を制御する。プロセッサ１００１は、周辺装置とのインターフェース、端末、演算装置、レジスタなどを含む中央処理装置（ＣＰＵ：Central　Processing　Unit）によって構成されてもよい。 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.
　また、プロセッサ１００１は、プログラム（プログラムコード）、ソフトウェアモジュール、データなどを、ストレージ１００３及び通信装置１００４の少なくとも一方からメモリ１００２に読み出し、これらに従って各種の処理を実行する。プログラムとしては、上述の実施の形態において説明した動作の少なくとも一部をコンピュータに実行させるプログラムが用いられる。上述の各種処理は、１つのプロセッサ１００１によって実行される旨を説明してきたが、２以上のプロセッサ１００１により同時又は逐次に実行されてもよい。プロセッサ１００１は、１以上のチップによって実装されてもよい。なお、プログラムは、電気通信回線を介してネットワークから送信されても良い。 Also, 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. As the program, a program that causes a computer to execute at least part of the operations described in the above embodiments is used. Although it has been explained that the above-described various processes are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. 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.
　メモリ１００２は、コンピュータ読み取り可能な記録媒体であり、例えば、ＲＯＭ（Read　Only　Memory）、ＥＰＲＯＭ（Erasable　Programmable　ＲＯＭ）、ＥＥＰＲＯＭ（Electrically　Erasable　Programmable　ＲＯＭ）、ＲＡＭ（Random　Access　Memory）などの少なくとも１つによって構成されてもよい。メモリ１００２は、レジスタ、キャッシュ、メインメモリ（主記憶装置）などと呼ばれてもよい。メモリ１００２は、本開示の一実施の形態に係る無線通信方法を実施するために実行可能なプログラム（プログラムコード）、ソフトウェアモジュールなどを保存することができる。 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 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.
　ストレージ１００３は、コンピュータ読み取り可能な記録媒体であり、例えば、ＣＤ－ＲＯＭ（Compact　Disc　ＲＯＭ）などの光ディスク、ハードディスクドライブ、フレキシブルディスク、光磁気ディスク(例えば、コンパクトディスク、デジタル多用途ディスク、Ｂｌｕ－ｒａｙ（登録商標）ディスク)、スマートカード、フラッシュメモリ(例えば、カード、スティック、キードライブ)、フロッピー（登録商標）ディスク、磁気ストリップなどの少なくとも１つによって構成されてもよい。ストレージ１００３は、補助記憶装置と呼ばれてもよい。上述の記憶媒体は、例えば、メモリ１００２及びストレージ１００３の少なくとも一方を含むデータベース、サーバその他の適切な媒体であってもよい。 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 .
　通信装置１００４は、有線ネットワーク及び無線ネットワークの少なくとも一方を介してコンピュータ間の通信を行うためのハードウェア（送受信デバイス）であり、例えばネットワークデバイス、ネットワークコントローラ、ネットワークカード、通信モジュールなどともいう。 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.
　入力装置１００５は、外部からの入力を受け付ける入力デバイス（例えば、キーボード、マウス、マイクロフォン、スイッチ、ボタン、センサなど）である。出力装置１００６は、外部への出力を実施する出力デバイス（例えば、ディスプレイ、スピーカー、LEDランプなど）である。なお、入力装置１００５及び出力装置１００６は、一体となった構成（例えば、タッチパネル）であってもよい。　また、プロセッサ１００１、メモリ１００２などの各装置は、情報を通信するためのバス１００７によって接続される。バス１００７は、単一のバスを用いて構成されてもよいし、装置間ごとに異なるバスを用いて構成されてもよい。 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.
　本開示において説明した各態様／実施形態は単独で用いてもよいし、組み合わせて用いてもよいし、実行に伴って切り替えて用いてもよい。また、所定の情報の通知（例えば、「Ｘであること」の通知）は、明示的に行うものに限られず、暗黙的（例えば、当該所定の情報の通知を行わない）ことによって行われてもよい。 Each aspect/embodiment described in the present disclosure may be used alone, may be used in combination, or may be used by switching along with execution. In addition, the notification of predetermined information (for example, notification of “being X”) is not limited to being performed explicitly, but may be performed implicitly (for example, not notifying the predetermined information). good too.
　以上、本開示について詳細に説明したが、当業者にとっては、本開示が本開示中に説明した実施形態に限定されるものではないということは明らかである。本開示は、請求の範囲の記載により定まる本開示の趣旨及び範囲を逸脱することなく修正及び変更態様として実施することができる。したがって、本開示の記載は、例示説明を目的とするものであり、本開示に対して何ら制限的な意味を有するものではない。 Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be practiced with modifications and variations without departing from the spirit and scope of the present disclosure as defined by the claims. Accordingly, the description of the present disclosure is for illustrative purposes and is not meant to be limiting in any way.
　本開示において説明した各態様／実施形態の処理手順、シーケンス、フローチャートなどは、矛盾の無い限り、順序を入れ替えてもよい。例えば、本開示において説明した方法については、例示的な順序を用いて様々なステップの要素を提示しており、提示した特定の順序に限定されない。 The order of the processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in the present disclosure may be changed as long as there is no contradiction. For example, the methods described in this disclosure present elements of the various steps using a sample order, and are not limited to the specific order presented.
　入出力された情報等は特定の場所（例えば、メモリ）に保存されてもよいし、管理テーブルを用いて管理してもよい。入出力される情報等は、上書き、更新、又は追記され得る。出力された情報等は削除されてもよい。入力された情報等は他の装置へ送信されてもよい。 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.
　本開示において使用する「に基づいて」という記載は、別段に明記されていない限り、「のみに基づいて」を意味しない。言い換えれば、「に基づいて」という記載は、「のみに基づいて」と「に少なくとも基づいて」の両方を意味する。 The term "based on" as used in this disclosure does not mean "based only on" unless otherwise specified. In other words, the phrase "based on" means both "based only on" and "based at least on."
　本開示において、「含む（include）」、「含んでいる（including）」及びそれらの変形が使用されている場合、これらの用語は、用語「備える（comprising）」と同様に、包括的であることが意図される。さらに、本開示において使用されている用語「又は（or）」は、排他的論理和ではないことが意図される。 Where "include," "including," and variations thereof are used in this disclosure, these terms are inclusive, as is the term "comprising." is intended. Furthermore, the term "or" as used in this disclosure is not intended to be an exclusive OR.
　本開示において、例えば、英語でのa,　an及びtheのように、翻訳により冠詞が追加された場合、本開示は、これらの冠詞の後に続く名詞が複数形であることを含んでもよい。 In this disclosure, if articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are plural.
　本開示において、「ＡとＢが異なる」という用語は、「ＡとＢが互いに異なる」ことを意味してもよい。なお、当該用語は、「ＡとＢがそれぞれＣと異なる」ことを意味してもよい。「離れる」、「結合される」などの用語も、「異なる」と同様に解釈されてもよい。 In the present disclosure, the term "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."
　１０…送客効果算出装置、１１…取得部、１２…属性分布変化量算出部、１３…訪問確率算出部、１４…送客効果算出部、１５…出力部、２０…店舗訪問者属性ログ管理サーバ、２１…店舗訪問者属性ログ記録部、２２…店舗訪問者属性ログＤＢ、３０…位置情報管理サーバ、３１…位置情報記録部、３２…位置情報ＤＢ、４０…利用者属性情報管理サーバ、４１…利用者属性情報記録部、４２…利用者属性情報ＤＢ、１００１…プロセッサ、１００２…メモリ、１００３…ストレージ、１００４…通信装置、１００５…入力装置、１００６…出力装置、１００７…バス。 DESCRIPTION OF SYMBOLS 10... Customer referral effect calculation apparatus 11... Acquisition part 12... Attribute distribution change amount calculation part 13... Visit probability calculation part 14... Customer referral effect calculation part 15... Output part 20... Store visitor attribute log management Server 21... Store visitor attribute log recording unit 22... Store visitor attribute log DB 30... Location information management server 31... Location information recording unit 32... Location information DB 40... User attribute information management server 41 User attribute information recording unit 42 User attribute information DB 1001 Processor 1002 Memory 1003 Storage 1004 Communication device 1005 Input device 1006 Output device 1007 Bus.

Claims (4)

  1. 　利用者の位置を示す位置情報、利用者の属性を示す第１の利用者属性情報、並びに、店舗を訪問した利用者である店舗訪問者の属性および訪問履歴を示す店舗訪問者属性ログを取得する取得部と、
    　前記位置情報および前記第１の利用者属性情報に基づいて取得される、算出対象とされる算出対象時間に算出対象エリアに位置した利用者の属性を示す第２の利用者属性情報と、前記位置情報および前記第１の利用者属性情報に基づいて取得される、比較対象とされる比較対象時間に前記算出対象エリアに位置した利用者の属性を示す第３の利用者属性情報と、の差分から、複数の属性の組合せそれぞれについての属性分布変化量を算出する属性分布変化量算出部と、
    　前記位置情報および前記第１の利用者属性情報に基づいて取得される、前記複数の属性の組合せそれぞれについての前記算出対象時間に前記算出対象エリアに位置したユニーク利用者人数、に対する、算出対象の店舗カテゴリ、前記算出対象時間および前記算出対象エリアに該当する訪問履歴の数として前記店舗訪問者属性ログから得られる延べ訪問者数、の比率を、前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問確率として算出する訪問確率算出部と、
    　前記複数の属性の組合せそれぞれについての、前記属性分布変化量算出部により算出された属性分布変化量、および、前記複数の属性の組合せそれぞれについての、前記訪問確率算出部により算出された前記店舗カテゴリごとの訪問確率に基づいて、前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問者数期待値を算出し、算出された前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問者数期待値を前記店舗カテゴリごとに合計し、得られた前記店舗カテゴリごとの合計値を前記算出対象エリアにおける前記店舗カテゴリごとの店舗数で当分した値を、前記店舗カテゴリの店舗についての送客効果として算出する送客効果算出部と、
    　を備える送客効果算出装置。     Acquisition of location information indicating the location of the user, first user attribute information indicating the attributes of the user, and a store visitor attribute log indicating the attributes and visit history of the store visitor who is the user who visited the store an acquisition unit that
    second user attribute information indicating an attribute of a user located in the calculation target area at the calculation target time, which is acquired based on the position information and the first user attribute information; third user attribute information indicating an attribute of a user located in the calculation target area at a comparison target time to be compared, which is acquired based on the position information and the first user attribute information; an attribute distribution change amount calculation unit that calculates an attribute distribution change amount for each combination of a plurality of attributes from the difference;
    Number of unique users located in the calculation target area at the calculation target time for each combination of the plurality of attributes, acquired based on the location 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 histories corresponding to the store category, the calculation target time, and the calculation target area is calculated as the store category for each combination of the plurality of attributes. A visit probability calculation unit that calculates the visit probability for each
    The attribute distribution change amount calculated by the attribute distribution change amount calculation unit for each combination of the plurality of attributes, and the store category calculated by the visit probability calculation unit for each combination of the plurality of attributes Calculate the expected number of visitors for each of the store categories for each of the plurality of attribute combinations based on the visit probability for each of the plurality of attribute combinations, and calculate the number of visitors for each of the store categories for each of the plurality of attribute combinations The value obtained by summing the number expected value for each store category, and dividing the obtained total value for each store category by the number of stores for each store category in the calculation target area, is the customer referral for the stores of the store category. a customer referral effect calculation unit that calculates the effect;
    A customer referral effect calculation device comprising:
  2. 　前記属性分布変化量算出部は、
    　前記算出対象時間に前記算出対象エリアに位置した利用者を前記位置情報から抽出し、抽出された利用者の属性を示す前記第２の利用者属性情報を前記第１の利用者属性情報から取得し、取得された前記第２の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計するとともに、前記比較対象時間に前記算出対象エリアに位置した利用者を前記位置情報から抽出し、抽出された利用者の属性を示す前記第３の利用者属性情報を前記第１の利用者属性情報から取得し、取得された前記第３の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計し、
    　前記複数の属性の組合せそれぞれについて、前記算出対象時間における該当人数と前記比較対象時間における該当人数との差分を、前記属性分布変化量として算出する、
    　請求項１に記載の送客効果算出装置。     The attribute distribution change amount calculation unit
    A user located in the calculation target area at the calculation target time is extracted from the position information, and the second user attribute information indicating the extracted user attribute is acquired from the first user attribute information. and counting the number of users for each combination of a plurality of attributes in the obtained second user attribute information, and extracting users located in the calculation target area at the comparison target time from the position information. , acquiring the third user attribute information indicating the extracted user attribute from the first user attribute information, and for each combination of a plurality of attributes in the acquired third user attribute information Aggregate the corresponding number of people in
    For each combination of the plurality of attributes, calculating a difference between the corresponding number of people at the calculation target time and the corresponding number of people at the comparison target time as the attribute distribution change amount.
    The customer referral effect calculation device according to claim 1 .
  3. 　前記訪問確率算出部は、
    　前記算出対象時間に前記算出対象エリアに位置した利用者を前記位置情報から抽出し、抽出された利用者の属性を示す前記第２の利用者属性情報を前記第１の利用者属性情報から取得し、取得された前記第２の利用者属性情報における複数の属性の組合せそれぞれについての該当人数を集計することで、前記複数の属性の組合せそれぞれについての前記算出対象時間に前記算出対象エリアに位置したユニーク利用者人数を取得する、
    　請求項１又は２に記載の送客効果算出装置。     The visit probability calculation unit
    A user located in the calculation target area at the calculation target time is extracted from the position information, and the second user attribute information indicating the extracted user attribute is acquired from the first user attribute information. Then, by aggregating the number of people corresponding to each combination of a plurality of attributes in the acquired second user attribute information, the number of persons located in the calculation target area at the calculation target time for each of the plurality of attribute combinations Get the number of unique users who have
    The customer referral effect calculation device according to claim 1 or 2.
  4. 　前記送客効果算出部は、
    　前記複数の属性の組合せそれぞれについての、前記店舗カテゴリごとの訪問確率と前記属性分布変化量とを乗算することで、前記複数の属性の組合せそれぞれについての前記店舗カテゴリごとの訪問者数期待値を算出する、
    　請求項１～３の何れか一項に記載の送客効果算出装置。     The customer referral effect calculation unit
    By multiplying the visit probability for each store category and the attribute distribution change amount for each combination of the plurality of attributes, the expected number of visitors for each store category for each combination of the plurality of attributes is calculated. calculate,
    The customer referral effect calculation device according to any one of claims 1 to 3.
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JP2010117954A (en) * 2008-11-14 2010-05-27 Hitachi Ltd Point management method and point management system
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Publication number Priority date Publication date Assignee Title
JP2010117954A (en) * 2008-11-14 2010-05-27 Hitachi Ltd Point management method and point management system
JP2013246747A (en) * 2012-05-29 2013-12-09 Fuji Xerox Co Ltd Program and campaign management device
JP2020071605A (en) * 2018-10-30 2020-05-07 クロスロケーションズ株式会社 Data analysis device, data analysis system, data analysis method, and program

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