Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device, an information processing method, and an information processing program that can analyze attributes of customers who visit a physical store in a deeper manner.SOLUTION: An information processing device includes: an acquisition unit for acquiring terminal information of customers who visit a store; an extraction unit for extracting attribute information from the acquired terminal information; a determination unit for referring to an identifier list of people with specific attributes stored in a storage unit, and determining whether or not an identifier included in the extracted attribute information is included in the identifier list; and a classification unit for classifying the customer into the specific attribute when the identifier included in the attribute information is determined to be included in the identifier list.SELECTED DRAWING: Figure 3

Description

[Technical field]

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

[0002] Conventionally, there are known technologies for measuring the effectiveness of advertisements. For example, there is a technology for measuring the effectiveness of advertisements based on the number of actions estimated to have been induced by the advertisement within a predetermined time period after the advertisement is broadcast (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-140015 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, with the above-mentioned conventional technology, it is difficult to measure the advertising effect on visits to a brick-and-mortar store. Therefore, it is difficult to obtain information such as what kind of advertisements customers who visit a brick-and-mortar store have been exposed to on the web and what kind of attributes the customers have.

[0005] The present application aims to provide an information processing device, an information processing method, and an information processing program capable of more deeply analyzing the attributes of customers who visit a physical store. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, the information processing device of the present application has an acquisition unit that acquires terminal information of a customer who visits a store, an extraction unit that extracts attribute information from the acquired terminal information, a determination unit that refers to an identifier list of people with a specific attribute stored in a memory unit and determines whether an identifier included in the extracted attribute information is included in the identifier list, and a classification unit that classifies the customer into the specific attribute when it is determined that the identifier included in the attribute information is included in the identifier list. Effect of the Invention

[0007] According to one aspect of the embodiment, the attributes of customers who visit a physical store can be analyzed in more depth. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram for explaining an example of the configuration of a measurement system according to an embodiment. [Diagram 2] FIG. 2 is a diagram for explaining an example of store visit detection logic according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of the configuration of the measurement apparatus according to the embodiment. [Figure 4] FIG. 4 is a diagram for explaining an example of advertisement display and visit to a store according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of classification according to the embodiment. [Figure 6] FIG. 6 is a diagram for explaining an example of a web page visit and a store visit according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of an identifier list according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating another example of classification according to the embodiment. [Figure 9] FIG. 9 is a flowchart showing an information processing procedure according to the embodiment. [Figure 10] FIG. 10 is a hardware configuration diagram showing an example of a computer that realizes the functions of the measuring device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the information processing device, the information processing method, and the information processing program according to the present invention will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present invention are not limited to the present embodiment. In addition, the same parts in the following embodiments are given the same reference numerals, and duplicated explanations will be omitted.

[0010] (Embodiment) 1. Introduction In recent years, an increasing number of companies are creating management strategies tailored to their user demographics and needs by analyzing the attributes and purchasing history of their users to gain a deeper understanding of them. When companies conduct user analysis, it is important to understand the user's behavioral history; for example, advertising effectiveness can be analyzed from the user's advertisement viewing history. Information on user attributes is also important in user analysis, and some companies accumulate attributes of a population that includes users in a database and provide data according to attributes.

[0011] However, by analyzing the advertising effect from the user's advertisement viewing history, it is difficult to measure the advertising effect on the visit to the company's physical store. In addition, to obtain information on the attributes of visitors to the physical store, for example, if the visitor is a user of the company, it is possible to obtain the information by reading a point card or the like at the time of purchase, but it is difficult to obtain information on the attributes of visitors who have not made a purchase.

[0012] Therefore, the measurement device according to the present embodiment aims to perform a deeper analysis of the attributes of customers who visit a physical store. Specifically, the measurement device extracts an identifier included in the attribute information from the terminal information of the customer who visits the store, determines whether the extracted identifier is included in an identifier list of people with a specific attribute, and performs a process of classifying the customer into a specific attribute if the identifier is included. This allows the measurement device to perform a deeper analysis of the attributes of customers who visit a physical store.

[0013] [2.Overview] First, the configuration of a measurement system 1 according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining an example of the configuration of a measurement system 1 according to an embodiment.

[0014] 1, the measurement system 1 may be configured to include a user device 10, an advertiser device 20, a detection device 30, and a measurement device 100. In addition, the user device 10, the advertiser device 20, the detection device 30, and the measurement device 100 may be connected to each other via a network N in a wired or wireless manner so as to be able to communicate with each other.

[0015] The user device 10 is an information processing terminal used by a user, and may be, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), etc. The user here refers to a general consumer (end user) who goes to a real store (brick and mortar store) to shop or receive services.

[0016] For example, the user device 10 displays a screen (advertising medium) including the delivered advertisement. In such a case, the user is a user who has come into contact with the advertisement. In this embodiment, contact with an advertisement may include not only the advertisement being displayed, but also a predetermined user operation on the advertisement (e.g., clicking). A user whose interest has been heightened by coming into contact with an advertisement in this manner may visit the store that is the subject of the advertisement. In addition, in this embodiment, a visit (contact) to a predetermined web page that is not an advertisement may be referred to as a web page visit.

[0017] The advertiser device 20 is an information processing terminal used by an advertiser, and may be, for example, a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, etc. The advertiser here refers to a source that submits advertisements, and may be, for example, a company that is a client of a distribution company that mediates advertisement distribution, and may operate a store.

[0018] For example, the advertiser device 20 can perform various settings related to advertisement delivery and store visit measurement in response to the operation of the advertiser, for the measurement device 100. In addition, the advertiser device 20 can also submit the advertisement itself to the measurement device 100 in response to the operation of the advertiser.

[0019] The detection device 30 is a device installed in a physical store operated by an advertiser (e.g., a client company of a distribution service provider) for detecting visitors. For example, the detection device 30 may be a communication device such as Wi-Fi (registered trademark) or Bluetooth (registered trademark) installed in the store of the client company. When an interaction is obtained with a specific tag that is previously assigned to an advertisement or a Web page, the detection device 30 detects the user having the user device 10 as a visitor to the store. Note that, when no interaction is obtained with a specific tag that is previously assigned to an advertisement or a Web page, the detection device 30 may detect the user as an unidentifiable user. In addition, the detection device 30 may be provided by a distribution service provider or a partner company of the distribution service provider.

[0020] The measurement device 100 is an example of an information processing device according to an embodiment of the present disclosure, and performs a series of information processing related to store visit measurement and classification of store visitors. Note that the measurement device 100 in this embodiment is a measurement device that classifies the attributes of users who come into contact with an advertisement or a Web page.

[0021] The measurement device 100 performs the following process as information processing according to the embodiment. The measurement device 100 acquires terminal information of customers who visit the store. The measurement device 100 also extracts attribute information from the acquired terminal information. The measurement device 100 also refers to an identifier list of people with specific attributes stored in a storage unit, and determines whether an identifier included in the extracted attribute information is included in the identifier list. If the measurement device 100 determines that the identifier included in the attribute information is included in the identifier list, it classifies the customer into a specific attribute.

[0022] In addition, the user device 10, the advertiser device 20, and the detection device 30 are edge computers that perform edge processing near the customer, while the measurement device 100 may be a cloud computer that performs processing on the cloud side. In other words, the measurement device 100 may be a server device.

[0023] (About store visit logic) Next, the logic from store visit detection to classification will be described with reference to FIG. 2. FIG. 2 is a diagram showing an example of the store visit detection logic according to the embodiment. First, according to the example of FIG. 2, in the advertisement distribution service provided by the distribution company, medium M1, medium M2, and medium M3 are defined as the advertising media on which the submitted advertisement will be published. In addition, a tag TG for store visit detection is inserted into the submitted advertisement. Note that the insertion of the tag TG may be performed by the measurement device 100.

[0024] In this state, when the user device 10 displays medium M1, which is a screen including an advertisement with a tag TG inserted therein, it transmits advertisement contact information indicating that the user of the user device 10 has come into contact with the advertisement to the measurement device 100. The advertisement contact information may include terminal information for identifying the user device 10, the advertisement posting date and time, medium information indicating the medium M1 on which the advertisement was posted, and the like. The advertisement contact information may be stored in the contact / visit information database 121 as one record in the delivery history of advertisement delivery. The advertisement contact information may also include attribute information of the user who came into contact with the advertisement and information regarding the terminal on which the advertisement was contacted.

[0025] Furthermore, when the user device 10 displays a Web page into which the tag TG is inserted, the user device 10 transmits visit information indicating that the user of the user device 10 has visited the specified Web page to the measurement device 100. The visit information may include terminal information for identifying the user device 10, attribute information related to the specified Web page, and the like. Furthermore, the attribute information may include information indicating the user's preferences, such as being a smoker. Furthermore, the visit information may be accumulated in the contact / visit information database 121 as one record serving as a delivery history in the advertisement delivery.

[0026] Assume that a user who has become an advertisement-exposed user by coming into contact with an advertisement visits a physical store equipped with a detection device 30, as shown in FIG. 2. In this case, the user device 10 and the detection device 30 are connected by short-range wireless communication, and as a result, the detection device 30 detects the user having the user device 10 as a visitor to the store via short-range wireless communication. Then, the detection device 30 transmits store visit information indicating the detection of the visit to the measurement device 100. The store visit information may include terminal information for identifying the user device 10, store information for identifying the store, and the like. In addition, the store visit information may include attribute information of the user having the user device 10. The user attribute information may include an identifier corresponding to a specific attribute. Furthermore, the store visit information may be accumulated in the store visit information database 122. Note that the store visit information may be accumulated in the contact / visit information database 121 as one record as a delivery history in the advertisement delivery. Here, examples of short-range wireless communication include Bluetooth, Wi-Fi, and the like. In addition, when acquiring information on store visits and contact with outdoor advertisements, a GNSS (Global Navigation Satellite System) positioning technology including a GPS (Global Positioning System) may be utilized. For example, the user device 10 may acquire information on store visits and contact with outdoor advertisements by using location information acquired based on the GNSS positioning technology or the like by an application installed on the user device 10.

[0027] The measurement device 100 extracts attribute information from the terminal information included in the store visit information stored in the store visit information database 122. The measurement device 100 refers to the contact / visit information database 121 and an attribute database 123 including a list of identifiers of people with specific attributes (described later) to determine whether or not an identifier included in the extracted attribute information is included in the identifier list. If the measurement device 100 determines that the identifier included in the attribute information is included in the identifier list, it classifies the user who is a store visitor into a specific attribute and stores the attribute in the classification database 124.

[0028] 3. Configuration of the Measuring Device 100 3 is a diagram showing an example of the configuration of a measurement device according to an embodiment. The measurement device 100 includes a communication unit 110, a storage unit 120, and a control unit .

[0029] (Communication unit 110) The communication unit 110 is realized by a NIC (Network Interface Card), an antenna, etc. The communication unit 110 is connected to various networks via wired or wireless connection, and transmits and receives information to and from external devices.

[0030] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 has a contact / visit information database 121, a store visit information database 122, an attribute database 123, and a classification database 124. In the following description, the database may be referred to as DB (DataBase).

[0031] (Contact / Visit Information Database 121) The contact / visit information database 121 stores the advertisement contact information and visit information of the user. For example, as shown in FIG. 2, the contact / visit information database 121 can store contact information transmitted when the user device 10 displays a medium M1, which is a screen including an advertisement with an inserted tag TG. Specifically, the contact / visit information database 121 may store, as the advertisement contact information, advertisement information, the contacted advertisement, the date and time of contact with the advertisement, the terminal number, the model, information on the user who contacted the advertisement, attribute information, and other information necessary for measuring the effectiveness of the advertisement. In addition, for example, the contact / visit information database 121 can store visit information transmitted when the user device 10 displays a Web page with an inserted tag TG. Specifically, the contact / visit information database 121 may store, as the visit information, terminal information identifying the user device 10 of the user, the date and time of contact with the Web page, information on the inserted tag TG, and other information on the Web page.

[0032] (Store Visit Information Database 122) The store visit information database 122 stores user store visit information. For example, as shown in Fig. 2, when the user device 10 visits a brick-and-mortar store equipped with a detection device 30 and is detected based on short-range wireless communication technology, the store visit information database 122 can store store visit information indicating the store visit detection transmitted by the detection device 30. Specifically, the store visit information database 122 may store terminal information for identifying the user device 10, store information for identifying the store, attribute information of the user who owns the user device 10, and the like. Note that the user attribute information may include an identifier corresponding to a specific attribute.

[0033] (Attribute database 123) The attribute database 123 stores a list of identifiers of people having specific attributes. For example, the attribute database 123 stores a person's identifier and information on the person's attributes in association with each other. The person's identifier is an identifier that identifies a person. Examples of the information on a person's attributes include information on whether or not the person has come into contact with an advertisement (previous advertisement) previously distributed by an advertiser (for example, a client company of a distribution company), and information on whether or not the person has come into contact with an advertisement (current advertisement) currently distributed by an advertiser. Examples of the information on a person's attributes include information that indicates a user's preferences, such as being a smoker. The attribute database 123 is a database acquired in advance from a partner company of the distribution company. The attribute database 123 may also be a list of multiple types of identifiers with different specific attributes.

[0034] (Taxonomy Database 124) The classification database 124 stores the results of categorizing users who are store visitors according to specific attributes. For example, the classification database 124 stores the results of categorizing the total number of people exposed to the current advertisement, the number of people exposed to the current advertisement (number of unique users), and the number of visitors to a physical store for a certain advertiser according to whether or not they have been exposed to a previous advertisement. Here, whether or not they have been exposed to a previous advertisement includes cases where they have not previously seen another advertisement from the advertiser, cases where they have previously seen another advertisement from the advertiser, and cases where it is impossible to distinguish.

[0035] Also, for example, the classification database 124 stores the result of classifying the identifiers of people who visited a certain Web page into a specific attribute by inserting a tag TG indicating that the Web page has a specific attribute in the Web page. That is, the classification database 124 stores the number of people in the identifier list having the specific attribute, the number of customers, and the number of customers who have an identifier having the specific attribute as the classification result. Here, the identifier list is, for example, a list that associates a specific attribute with a person's identifier.

[0036] (Control unit 130) The control unit 130 is realized by a CPU (Central Processing Unit), MPU (Micro Processing Unit), etc., executing various programs stored in a storage device inside the measuring device 100 using a RAM as a working area. The control unit 130 is also a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0037] The control unit 130 has an acquisition unit 131, an extraction unit 132, a determination unit 133, and a classification unit 134 as functional units, and may realize or execute the actions of the processes described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Fig. 3, and may be other configurations as long as they perform the information processing described below. Also, each functional unit indicates a function of the control unit 130, and does not necessarily have to be physically distinct.

[0038] The acquisition unit 131 acquires advertisement contact information of a user who has come into contact with an advertisement from the user device 10. For example, the acquisition unit 131 can acquire, as advertisement contact information, terminal information for identifying the user device 10, the advertisement that has been contacted, the date and time of contact with the advertisement, information on the inserted tag TG, and the like from the user device 10. Note that, if the advertisement contact information is stored in an external device other than the user device 10, the acquisition unit 131 can acquire the contact information from the external device. The acquisition unit 131 stores the acquired advertisement contact information in the contact / visit information DB 121.

[0039] The acquisition unit 131 acquires visit information of a user who displayed a Web page into which a tag TG is inserted from the user device 10. For example, the acquisition unit 131 can acquire, as the visit information, terminal information for identifying the user device 10, attribute information related to the Web page into which the tag TG is inserted, and the like from the user device 10. Note that, if the visit information is stored in an external device other than the user device 10, the acquisition unit 131 can acquire contact information from the external device. The acquisition unit 131 stores the acquired visit information in the contact / visit information DB 121.

[0040] The acquisition unit 131 acquires store visit information of a user (store visitor) who visited a physical store from the detection device 30. For example, the acquisition unit 131 can acquire, as store visit information, terminal information for identifying the user device 10, store information for identifying the store, attribute information of the user who owns the user device 10, and the like from the detection device 30. Note that, when the store visit information is stored in an external device other than the detection device 30, the acquisition unit 131 can acquire contact information from the external device. The acquisition unit 131 stores the acquired store visit information in the store visit information DB 122.

[0041] When a classification instruction for generating classification data of visitors to a physical store is input from the advertiser device 20, the acquisition unit 131 acquires store visit information from the store visit information DB 122. Here, the store visit information includes terminal information for identifying the user device 10, store information for identifying the store, attribute information of the user who owns the user device 10, etc. The acquisition unit 131 outputs the acquired store visit information to the extraction unit 132.

[0042] The extraction unit 132 extracts attribute information from the store visit information input from the acquisition unit 131. For example, the extraction unit 132 extracts an identifier corresponding to a specific attribute included in the attribute information. Specifically, for example, the extraction unit 132 extracts identifiers such as an identifier corresponding to information on whether or not the user has come into contact with an advertisement (previous advertisement) distributed by an advertiser in the past, and an identifier corresponding to information on whether or not the user has come into contact with an advertisement (current advertisement) distributed by an advertiser. In addition, for example, the extraction unit 132 extracts an identifier corresponding to information indicating the user's preference, such as being a smoker. The extraction unit 132 outputs the extracted identifier (attribute information) to the determination unit 133.

[0043] The determination unit 133 receives an identifier from the extraction unit 132. The determination unit 133 refers to the advertisement contact information and visit information in the contact / visit information DB 121 and the identifier list of a person having a specific attribute in the attribute DB 123. The determination unit 133 determines, based on the referenced data, whether or not an identifier included in the input attribute information is included in the contact / visit information DB 121 and the identifier list indicating the specific attribute in the attribute DB 123. The determination unit 133 outputs a determination result to the classification unit 134.

[0044] The classification unit 134 classifies the customer into a specific attribute according to the determination result input from the determination unit 133. When the determination result is a positive result, the classification unit 134 associates the customer's identifier with the specific attribute and stores it in the classification DB 124. That is, when the determination result is that an identifier included in the attribute information is included in the contact / visit information DB 121 and the identifier list indicating a specific attribute in the attribute DB 123, the classification unit 134 associates the identifier with the specific attribute and stores it in the classification DB 124. After storing the customer's identifier in the classification DB 124 in association with the specific attribute, the classification unit 134 determines whether or not classification of all customers has been completed.

[0045] On the other hand, when the determination result indicates a negative result, the classification unit 134 determines whether or not classification of all customers has been completed without storing the identifier of the visitor in the classification DB 124. In other words, when the determination result indicates that the identifier included in the attribute information is not included in the contact / visit information DB 121 or the identifier list indicating a specific attribute in the attribute DB 123, the classification unit 134 determines whether or not classification of all customers has been completed without storing the identifier of the visitor in the classification DB 124.

[0046] If the classification unit 134 determines that classification of all store visitors has not been completed, it instructs the acquisition unit 131 to acquire the store visit information of the next store visitor from the store visit information DB 122. If the classification unit 134 determines that classification of all store visitors has been completed, it outputs the classification result and transmits it to the advertiser device 20.

[0047] Here, an example of classification will be described with reference to FIG. 4 and FIG. 5. FIG. 4 is a diagram for explaining an example of advertisement display and store visit according to the embodiment. FIG. 5 is a diagram showing an example of classification according to the embodiment. FIG. 4 shows the distribution period of the advertisement that a user has come into contact with when visiting a physical store on June 17th. At this time, if the user has come into contact with an advertisement delivered in the current advertisement delivery period 50 and has not come into contact with an advertisement delivered in the previous advertisement delivery period 51, the user is classified as "not having seen another advertisement before" in the classification database 124a of FIG. 5. Here, the advertisement delivered in the current advertisement delivery period 50 is an example of a first advertisement. Also, in this case, coming into contact with an advertisement delivered in the current advertisement delivery period 50 is an example of a first attribute. Note that the previous advertisement delivery period 51 may include not only the advertisement delivery period immediately before the current advertisement delivery period 50, but also the advertisement delivery period of the advertiser in the past.

[0048] In addition, since the user is a store visitor, the count values ​​for each of the items "Total number of people exposed to this ad," "Number of people exposed to this ad," and "Number of store visitors" in the "Have not seen another ad before" column are increased by "1." Note that for users who have not visited the store but have been exposed to an ad delivered in the current ad delivery period 50, the count values ​​for each of the items "Total number of people exposed to this ad," and "Number of people exposed to this ad" in the "Have not seen another ad before" column are increased by "1." In addition, for users who have been exposed to an ad delivered multiple times in the current ad delivery period 50, the count values ​​for each of the items "Total number of people exposed to this ad" in the "Have not seen another ad before" column are increased by "1" from the second exposure onwards.

[0049] Next, if the user has been exposed to an advertisement delivered in the current advertisement delivery period 50 and also exposed to an advertisement delivered in the previous advertisement delivery period 51, the user is classified as "has previously seen another advertisement" in the classification database 124a. Here, the advertisement delivered in the previous advertisement delivery period 51 is an example of a second advertisement. In addition, exposure to an advertisement delivered in the previous advertisement delivery period 51 in this case is an example of a second attribute. Note that the previous advertisement delivery period 51 may include not only the advertisement delivery period immediately before the current advertisement delivery period 50, but also the advertisement delivery period of the advertiser in the past.

[0050] In addition, since the user is a store visitor, the count values ​​for each of the items "Total number of people exposed to this ad," "Number of people exposed to this ad," and "Number of store visitors" in the "Saw a different ad before" column are increased by "1." Note that for users who have not visited the store but have been exposed to an ad delivered in the current ad delivery period 50, the count values ​​for each of the items "Total number of people exposed to this ad," and "Number of people exposed to this ad" in the "Saw a different ad before" column are increased by "1." In addition, for users who have been exposed to an ad delivered in the current ad delivery period 50 multiple times, the count values ​​for each of the items "Total number of people exposed to this ad" in the "Saw a different ad before" column are increased by "1" from the second exposure onwards.

[0051] Next, if the attribute information of the store visit information does not include an identifier corresponding to a specific attribute, the user is classified as "undeterminable" in the classification database 124a. In addition, since the user is a store visitor, the count values ​​of each of the items "Total number of people exposed to this advertisement", "Number of people exposed to this advertisement", and "Number of store visitors" in the "undeterminable" column are increased by "1". Note that, for users who have not visited the store but have been exposed to the advertisement delivered in the current advertisement delivery period 50, the count values ​​of each of the items "Total number of people exposed to this advertisement" and "Number of people exposed to this advertisement" in the "undeterminable" column are increased by "1". In addition, for users who have been exposed to the advertisement delivered multiple times in the current advertisement delivery period 50, the count values ​​of each of the items "Total number of people exposed to this advertisement" in the "undeterminable" column are increased by "1" from the second exposure onwards.

[0052] Although not shown, a case where the user has not been exposed to an advertisement delivered in the current advertisement delivery period 50 and has been exposed to an advertisement delivered in the previous advertisement delivery period 51 can be classified in a similar manner. For example, similar to the classification database 124a, the horizontal axis items may be set to "total number of people exposed to previous advertisement", "number of people exposed to previous advertisement", and "number of store visitors", and the vertical axis items may be set to "not have seen another advertisement before" and "saw another advertisement before". In other words, the classification database 124 may be able to classify store visitors who have been exposed to past advertisements but have not been exposed to the current advertisement.

[0053] Next, another example of classification will be described with reference to FIG. 6 to FIG. 8. FIG. 6 is a diagram for explaining an example of a Web page visit and a store visit according to the embodiment. FIG. 7 is a diagram showing an example of an identifier list according to the embodiment. FIG. 8 is a diagram showing another example of classification according to the embodiment. FIG. 6 shows a case where a user browses (visits) page A1 on April 1, browses page A2 on April 2, and then visits a physical store on April 5. Here, pages A1 and A2 are predetermined Web pages related to a physical store, and a tag TG is inserted. Furthermore, the tag TG includes information of a specific attribute, and visit information indicating that the user has the specific attribute is transmitted from the user device 10 to the measurement device 100. For example, if the specific attribute is "smoker", the user is considered to be a person who has "smoker" as the specific attribute, and the identifier of the person is registered in the attribute database 123a. That is, the attribute database 123a is an example of an identifier list of a person who has "smoker" as the specific attribute. Furthermore, the specific attribute included in the tag TG is an example of a third attribute indicating a person who has visited a predetermined Web page related to a physical store. Furthermore, the specific attribute included in the tag TG is an example of a fourth attribute indicating the customer's preference.

[0054] Next, a case will be described in which the measurement device 100 executes a classification process after the user visits a brick-and-mortar store on April 5. In the classification database 124b shown in FIG. 8, since the user is a visitor, the count values ​​of the items "number of visitors" and "number of visitors having an identifier with a specific attribute" are incremented by "1." Here, the attribute information included in the visit information of the user may include a third attribute indicating a visitor to a specific web page related to the brick-and-mortar store. In addition, the attribute information included in the visit information of the user may include a fourth attribute indicating the visitor's preferences.

[0055] For customers who do not have an identifier with a specific attribute, the count value in the "number of customers" field is incremented by "1." The value in the "number of people in the identifier list with a specific attribute" field in the classification database 124b is, for example, the number of people registered in the attribute database 123a as people with the specific attribute "smoker."

[0056] Furthermore, the measurement device 100 may use visitor information transmitted from the detection device 30 as a trigger to determine whether an identifier included in the attribute information of the visitor information is included in the attribute database 123a, and transmit the determination result to the detection device 30, etc. In this case, a store clerk at the physical store can recognize that the visitor is a person with the specific attribute of "smoker" by obtaining the determination result from the detection device 30, etc.

[0057] 4. Information Processing Procedures Next, an information processing procedure of the measurement device according to the embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the information processing procedure according to the embodiment.

[0058] When a classification instruction is input from the advertiser device 20, the acquisition unit 131 of the measurement device 100 acquires the store visit information from the store visit information DB 122 (step S101). The acquisition unit 131 outputs the acquired store visit information to the extraction unit 132.

[0059] The extraction unit 132 extracts attribute information from the store visit information input from the acquisition unit 131 (Step S102). The extraction unit 132 outputs the extracted attribute information to the determination unit 133.

[0060] When the attribute information is input, the determination unit 133 refers to the advertisement contact information and visit information in the contact / visit information DB 121 and the identifier list of a person having a specific attribute in the attribute DB 123 (step S103). The determination unit 133 determines whether or not an identifier included in the input attribute information is included in the contact / visit information DB 121 and the identifier list indicating the specific attribute in the attribute DB 123 (step S104). The determination unit 133 outputs the determination result to the classification unit 134.

[0061] If the determination result is positive (step S104: Yes), the classification unit 134 classifies the identifier of the visitor in association with a specific attribute, stores the classified identifier in the classification DB 124 (step S105), and proceeds to step S106.

[0062] On the other hand, if the determination result is a negative result (step S104: No), the classification unit 134 does not store the identifier of the visitor in the classification DB 124, and proceeds to step S106.

[0063] The classification unit 134 determines whether classification of all store visitors is complete (step S106). If the classification unit 134 determines that classification of all store visitors is not complete (step S106: No), the process returns to step S101, and instructs the acquisition unit 131 to acquire the store visit information of the next store visitor from the store visit information DB 122. If the classification unit 134 determines that classification of all store visitors is complete (step S106: Yes), the classification unit 134 outputs the classification result and transmits it to the advertiser device 20 (step S107), and ends the process.

[0064] 5. Effects As described above, the measuring device 100 according to the embodiment includes an acquisition unit 131 that acquires terminal information of a customer who visits a store, an extraction unit 132 that extracts attribute information from the acquired terminal information, a determination unit 133 that refers to an identifier list (attribute database 123) of people with a specific attribute stored in the memory unit 120 and determines whether an identifier included in the extracted attribute information is included in the identifier list, and a classification unit 134 that classifies the customer into a specific attribute when it is determined that the identifier included in the attribute information is included in the identifier list.

[0065] In this way, the measurement device 100 classifies the customer into a specific attribute by determining whether the identifier included in the attribute information of the customer who visited the store is included in the identifier list of people with a specific attribute, and therefore can perform a more in-depth analysis of the attributes of the customer who visited the physical store. Also, the measurement device 100 compares the identifier included in the attribute information of the customer with the identifier list, and therefore can perform a more in-depth analysis of the attributes of the customer who visited the physical store while maintaining the anonymity of people with a specific attribute.

[0066] Furthermore, in the measurement device 100, the attribute information includes a first attribute indicating a person to whom the first advertisement for the store was delivered, and the specific attribute includes the first attribute.

[0067] In this way, the measurement device 100 can determine that the store visitor has come into contact with the first advertisement, since the specific attribute indicates the person to whom the first advertisement for the store was delivered.

[0068] In addition, in the measurement device 100, the attribute information includes a second attribute indicating a person to whom a second advertisement for the store, which was distributed before the first advertisement for the store, was distributed, and the specific attribute includes the second attribute.

[0069] In this way, the measurement device 100 can determine that the store visitor has come into contact with the second advertisement because the specific attribute indicates a person to whom the second advertisement for the store was delivered.

[0070] Furthermore, in the measurement device 100, the attribute information includes a third attribute indicating a person who visited the store's web page, and the specific attribute includes the third attribute.

[0071] In this way, the measurement device 100 can determine that a customer has visited the store's web page because a specific attribute indicates a person who has visited the store's web page.

[0072] Furthermore, in the measurement device 100, the attribute information includes a fourth attribute indicating a preference of the customer, and the specific attribute includes the fourth attribute.

[0073] In this way, the measurement device 100 can determine the preferences of customers since specific attributes indicate the preferences of customers.

[0074] Furthermore, in the measuring device 100, the identifier list is a list of multiple types of identifiers having different specific attributes.

[0075] In this way, the measurement device 100 compares the identifiers of customers with multiple types of identifier lists, allowing for a more in-depth analysis of the attributes of customers who visit a physical store based on various attributes.

[0076] [6. Hardware Configuration] The measuring device 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in Fig. 10. Fig. 10 is a hardware configuration diagram showing an example of a computer that realizes the functions of the measuring device. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, a HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0077] The CPU 1100 operates and controls each unit based on a program stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, programs that depend on the hardware of the computer 1000, and the like.

[0078] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, etc. The communication interface 1500 receives data from other devices via a predetermined communication network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0079] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0080] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0081] For example, when the computer 1000 functions as the measuring device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from other devices via a predetermined communication network.

[0082] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be embodied in other forms that incorporate various modifications and improvements based on the knowledge of those skilled in the art, including the forms described in the Disclosure of the Invention section.

[0083] [7. Other] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0084] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads and usage conditions. In addition, the above-mentioned embodiments can be appropriately combined within a range that does not cause contradictions in the processing contents. [Explanation of symbols]

[0085] 1. Measurement system 100 Measuring Equipment 110 Communications Department 120 Storage section 121 Contact / Visit Information Database 122 Visit information database 123 Attribute Database 124 Taxonomic Database 130 Control section 131 Acquisition Department 132 Extraction part 133 Judgment section 134 Classification Department

Claims

1. An acquisition unit that acquires terminal information of a customer who visits a store; An extraction unit that extracts attribute information from the acquired terminal information; a determination unit that refers to an identifier list of a person having a specific attribute stored in a storage unit and determines whether an identifier included in the extracted attribute information is included in the identifier list; a classification unit that classifies the customer into the specific attribute when it is determined that the identifier included in the attribute information is included in the identifier list; 13. An information processing device comprising:

2. the attribute information includes a first attribute indicating a person to whom a first advertisement for the store has been distributed; The specific attribute includes the first attribute.

2. The information processing apparatus according to claim 1,

3. the attribute information includes a second attribute indicating a person to whom a second advertisement for the store was delivered prior to the first advertisement for the store; the specific attribute includes the second attribute; 2. The information processing apparatus according to claim 1,

4. the attribute information includes a third attribute indicating a person who has visited the web page of the store; the specific attribute includes the third attribute, 2. The information processing apparatus according to claim 1,

5. the attribute information includes a fourth attribute indicating a preference of the store customer; the specific attribute includes the fourth attribute, 2. The information processing apparatus according to claim 1,

6. the identifier list is a list of a plurality of types of identifiers each having a different specific attribute; 2. The information processing apparatus according to claim 1,

7. An acquisition step of acquiring terminal information of a customer who visits the store; An extraction step of extracting attribute information from the acquired terminal information; a determination step of referring to an identifier list of a person having a specific attribute stored in a storage unit and determining whether or not an identifier included in the extracted attribute information is included in the identifier list; a classification step of classifying the customer into the specific attribute when it is determined that the identifier included in the attribute information is included in the identifier list; An information processing method characterized by being executed by a computer.

8. An acquisition step of acquiring terminal information of a customer who visits a store; An extraction step of extracting attribute information from the acquired terminal information; a determination step of referring to an identifier list of a person having a specific attribute stored in a storage unit and determining whether or not an identifier included in the extracted attribute information is included in the identifier list; a classification step of classifying the customer into the specific attribute when it is determined that the identifier included in the attribute information is included in the identifier list; An information processing program characterized by causing a computer to execute the above.

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