WO2014141517A1

WO2014141517A1 - Information delivery system, information delivery method, and computer program

Info

Publication number: WO2014141517A1
Application number: PCT/JP2013/076812
Authority: WO
Inventors: 保文千村
Original assignee: 沖電気工業株式会社
Priority date: 2013-03-15
Filing date: 2013-10-02
Publication date: 2014-09-18
Also published as: JP2014178918A

Abstract

[Problem] To provide an information delivery system, an information delivery method, and a computer program capable of effectively delivering information while following a customer tendency subject to a fluctuating change. [Solution] This information delivery system is characterized in being provided with: a classification unit for classifying acquired information about a plurality of customers into a plurality of customer groups; an analysis unit for analyzing the distribution tendency of the customers classified into the customer groups; and a delivery plan creation unit for creating, on the basis of the result of analysis about the distribution tendency of the customers, a delivery plan for delivering a content.

Description

Information distribution system, information distribution method, and computer program

The present invention relates to an information distribution system, an information distribution method, and a computer program.

In recent years, a digital signage system using a medium for transmitting information (contents) such as images and characters using digital display devices such as a display by using digital technology for display and communication has been popularized. is doing. Information transmission using this digital signage is often used even in places with relatively high publicity, such as outdoors, storefronts, public spaces, and transportation.

Digital signage as described above is often used as an advertising medium. For example, when digital signage is used as an advertising medium, there are many cases where advertising content is displayed on digital signage and the advertiser is charged in accordance with the number of times displayed.

JP 2010-55594 A

On the other hand, the existing digital signage system transmits information according to a predetermined distribution plan. In addition, digital signage installed in a place where a large number of people come and go is transmitting information to an unspecified number. Due to such a configuration, in the existing digital signage system, there are cases where content is displayed on the digital signage even when the target transmission target (hereinafter referred to as “customer”) is not present.

Also, there are many cases where the flow of people coming and going from the digital signage installation location changes in a fluid manner according to changes in the time zone, time, and surrounding environment. For example, the flow of people may change due to the establishment of a new facility in the vicinity of a place where digital signage is installed, and the tendency of customers around the place where digital signage is installed may also change. In such a case, even if advertising content is displayed on digital signage based on a distribution plan created before the facility is created, the same advertising effect is not always obtained.

Therefore, there is a need for a digital signage system that can effectively deliver information while following the changing trend of customers even in situations where the flow of people changes in a fluid manner. .

One approach to this problem is to understand customer behavior patterns. For example, in Patent Document 1, an individual is identified by face recognition from images of surveillance cameras installed in a plurality of shooting areas, and a flow line of the identified individual is calculated and accumulated as a history. Techniques for analyzing customer behavior patterns based on history are disclosed.

However, the technology according to Patent Document 1 grasps customer behavior patterns based on the flow of each individual within a very limited range such as in a specific store. For this reason, it is difficult for the technology according to Patent Document 1 to analyze the tendency of customers who change in a fluid manner for an unspecified number of customers in a relatively wide range such as an installation area of digital signage.

Therefore, the present invention has been made in view of the above problems, and the object of the present invention is to effectively distribute information while following the trend of customers who are changing dynamically. An object is to provide a new and improved information distribution system, an information distribution method, and a computer program.

In order to solve the above problems, according to an aspect of the present invention, a classification unit that classifies acquired information of a plurality of customers into a plurality of customer groups, and the distribution tendency of the customers among the plurality of customer groups An information distribution system comprising: an analysis unit that analyzes a distribution plan; and a distribution plan creation unit that creates a distribution plan for distributing content based on the analysis result of the customer distribution tendency .

The analysis unit may analyze the distribution tendency of the customers every predetermined time.

The analysis unit may analyze the distribution tendency of the customers for each predetermined place.

The distribution plan creation unit may create the distribution plan by weighting the content to be distributed based on the distribution tendency of the customers included in each of the plurality of customer groups.

The distribution plan creation unit may create the distribution plan by setting the customer group in which the customers are most distributed as a distribution target of the content.

The distribution plan creation unit identifies the content to be distributed by comparing the analysis result with a first list in which the customer group and the content are associated in advance, and creates the distribution plan Also good.

The distribution plan creation unit may identify the content to be distributed by comparing the analysis result with a second list in which the content is weighted for each customer group, and may create the distribution plan. Good.

In the second list, the content is weighted according to the type of the content, and the distribution plan creation unit compares the second list with the analysis result to thereby distribute the content. The type may be specified and the distribution plan may be created.

In order to solve the above-mentioned problem, according to another aspect of the present invention, classification of information on a plurality of customers acquired on the basis of an image of a customer captured by an imaging unit into a plurality of customer groups in real time An analysis unit that analyzes the distribution tendency of the customers among the plurality of customer groups for each predetermined time and / or predetermined place, and the distribution of the customers when analyzed in real time by the analysis unit There is provided an information distribution system comprising: a distribution plan creation unit that creates a distribution plan for distributing content in real time based on a trend analysis result.

In order to solve the above-mentioned problem, according to another aspect of the present invention, the step of classifying the acquired information of a plurality of customers into a plurality of customer groups, and There is provided an information distribution method comprising: analyzing a distribution trend; and creating a distribution plan for distributing content based on the analysis result of the distribution trend of the customer.

In order to solve the above problem, according to another aspect of the present invention, in the computer, the step of classifying the acquired information of a plurality of customers into a plurality of customer groups, and between the plurality of customer groups, There is provided a computer program for executing the steps of analyzing the distribution tendency of the customer and creating a distribution plan for distributing contents based on the analysis result of the distribution tendency of the customer. .

As described above, according to the present invention, it is possible to provide an information distribution system, an information distribution method, and a computer program capable of effectively distributing information while following a changing trend of customers. It becomes possible to do.

It is a figure for demonstrating the outline | summary of the information delivery system which concerns on embodiment of this invention. It is the figure which showed the system configuration | structure of the information delivery system which concerns on a comparative example. It is a figure for demonstrating schematic operation | movement of the information delivery system which concerns on embodiment of this invention. It is the figure which showed the system configuration | structure of the information delivery system which concerns on embodiment of this invention. 1 is a block diagram of an information distribution system according to an embodiment of the present invention. It is the figure which showed an example of the customer profile. It is a figure for demonstrating a customer trend. It is the figure which showed an example of the customer trend table. It is the figure which showed an example of the customer request table. It is the figure which showed an example of the content table. It is the figure which showed an example of the delivery plan. It is the flowchart which showed a series of operation | movement of the information delivery system which concerns on embodiment of this invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In addition, in this specification and drawing, about the component which has the substantially same function structure, duplication description is abbreviate | omitted by attaching | subjecting the same code | symbol.

[Overview]
First, an outline of the information distribution system 1 according to the present embodiment will be described with reference to FIG. FIG. 1 is a diagram for explaining an overview of an information distribution system 1 according to the present embodiment. The information distribution system 1 according to the present embodiment transmits information to customers around the digital signage 700 by displaying content on the digital signage 700 arranged in the outdoors, a storefront, a public space, a transportation facility, or the like. It is a system for. Hereinafter, the case where the digital signage 700 is used as an advertising medium will be described as an example for the information distribution system 1 according to the present embodiment.

The information distribution system 1 according to the present embodiment is based on customer information acquired by a camera (for example, a surveillance camera) 501 or an ATM (Automated Teller Machine) 502a (or a POS (Point Of Sale system) terminal 502b). In addition, the trend of what kind of customers (for example, what kind of age and gender customers) exist according to the time zone and place is analyzed. Then, the information distribution system 1 distributes the content to each digital signage 700 in accordance with the tendency obtained as the analysis result.

[Comparative example]
Here, as an information distribution system 1a according to the comparative example, an example in which an individual customer is specified and content is displayed on the digital signage 700 according to the specified customer will be described with reference to FIG. FIG. 2 is a diagram showing a system configuration of the information distribution system 1a according to the comparative example.

The information distribution system 1a includes a customer information acquisition unit 500, a customer information processing apparatus 100, a content distribution server 300, a content DB 600, and a digital signage 700.

In the information distribution system 1a, the attributes (for example, age, sex, visit / purchase history, etc.) of customers to be distributed are specified in detail for each content to be distributed, and each content is stored in the content DB 600.

In addition, the information distribution system 1a acquires customer customer information using the camera 501, ATM 502a, POS terminal 502b, etc. as the customer information acquisition unit 500, for example. The information distribution system 1a identifies the individual customer by analyzing the acquired information with the personal information processing apparatus 100. The information distribution system 1a extracts the corresponding content from the content DB 600 in accordance with the identified customer personal attributes, and displays the extracted content on the digital signage 700 installed in the vicinity of the customer.

With such a configuration, for example, when a customer visits a store, the information distribution system 1a identifies the customer and directs the customer to the customer via the digital signage 700 provided in the ATM 502a or the POS terminal 502b. Information can be transmitted.

However, in the case of the information distribution system 1a shown in FIG. 2, since the content is displayed for the specified individual customer, it is estimated what kind of customer will come to the place where the digital signage 700 is installed. Is difficult. Therefore, it is necessary to store content corresponding to each customer in the content DB 600 regardless of whether or not the content is displayed. Therefore, there is a case where the content DB 600 having a relatively large capacity is required. Further, since it is also possible to store content in which it is unclear whether or not it is displayed in the content DB 600, the capacity of the content DB 600 is compressed, and variations of content targeted for a specific customer may be limited.

In addition, since the information distribution system 1a specifies the content to be distributed only by the information about the individual customer, it is difficult to switch the content to be distributed in response to changes in the season and surrounding environment.

Therefore, the information distribution system 1 according to the present embodiment classifies an unspecified number of customers, not individuals, into a plurality of customer groups (for example, males in their 30s and females in their 20s), and sets the time and place for each customer group. The corresponding customer distribution tendency (hereinafter referred to as “customer distribution tendency”) is analyzed. Then, the information distribution system 1 creates a distribution plan for content to be displayed on the digital signage 700 according to the customer distribution tendency around the digital signage 700 based on the analysis result.

Thereby, for example, the information distribution system 1 according to the present embodiment estimates a customer group having the largest number of customers around the digital signage 700 and causes the digital signage 700 to display advertisement content targeted for distribution. To improve advertising effectiveness. Hereinafter, details of the information distribution system 1 according to the present embodiment will be described.

[Rough operation]
First, a schematic operation of the information distribution system 1 according to the present embodiment will be described with reference to FIG. FIG. 3 is a diagram for explaining a schematic operation of the information distribution system 1 according to the present embodiment.

The information distribution system 1 identifies customer attributes (for example, age, gender, occupation) from customer information acquired by the camera 501, ATM 502a, POS terminal 502b, etc., and sets each customer based on the identified attributes. Classify into customer profile D110. The customer profile is information indicating a group of customers classified based on a predetermined attribute condition. As the customer profile, for example, classification such as “male 30s office worker” and “20 female students” can be cited.

The information distribution system 1 stores customer visit tendency data (that is, the number of customers) for each customer profile D110. Based on the customer visit tendency data for each customer profile D110 accumulated in this way, the information distribution system 1 analyzes the customer distribution trend for each specific condition such as location and time zone, and the customer trend data. D120 is created. This customer trend data D120 indicates what customer profile D110 the customer corresponding to is distributed for each specific condition.

The information distribution system 1 identifies the advertisement type D130 based on the customer request by comparing the created customer trend data D120 with a list of advertisement types requested for each customer corresponding to the customer profile D110. As a list of advertisement types requested for each customer, for example, a list of popular advertisement types may be used according to the age and sex of the customer.

As a specific example, the information distribution system 1 specifies the customer profile D110 in which the most customers are distributed as a distribution target based on the customer trend data D120. Then, the information distribution system 1 specifies the advertisement type D130 that is most frequently requested by the customer corresponding to the specified customer profile D110. The advertisement type D130 specified in this way indicates an effective advertisement type for a customer group having the largest number of customers under a specific condition.

The information distribution system 1 identifies the advertising content D150 to be distributed by comparing the advertising target customer D140 set in advance for each advertising content with the advertisement type D130 based on the customer trend data D120 and the customer request. The identified advertisement content D150 reflects the customer distribution tendency and the customer request, in other words, it can be said that the advertisement content corresponds to the customer trend. Hereinafter, a specific configuration for the information distribution system 1 to operate as described above will be described.

[Constitution]
First, the system configuration of the information distribution system 1 according to the present embodiment will be described with reference to FIG. FIG. 4 is a diagram showing a system configuration of the information distribution system 1 according to the present embodiment. As shown in FIG. 4, the information distribution system 1 according to the present embodiment includes a customer information acquisition unit 500, a customer information processing apparatus 100, an analysis server 200, a content distribution server 300, a content proxy 650, a digital signage 700.

Customer information acquisition unit 500, customer information processing apparatus 100, content proxy 650, and digital signage 700 are connected via network N2. The analysis server 200 and the content distribution server 300 are connected via a network N3. Each of the networks N2 and N3 can use various networks such as a LAN (Local Area Network) and a WAN (Wide Area Network). The networks N2 and N3 are connected via the network N1. Specific examples of the network N1 include the Internet and a dedicated line. In the example shown in FIG. 4, the networks N1, N2, and N3 are described as separate networks. However, if each configuration of the information distribution system 1 can communicate with each other, the network configuration shown in FIG. It is not limited to. For example, the networks N1, N2, and N3 may be configured by a single network.

Next, a detailed configuration of the information distribution system 1 according to the present embodiment will be described with reference to FIG. FIG. 5 is a block diagram of the information distribution system 1 according to the present embodiment, showing an example of each configuration included in each of the customer information processing apparatus 100, the analysis server 200, the content distribution server 300, and the content proxy 650. Yes.

(Customer information acquisition unit 500)
The customer information acquisition unit 500 acquires customer customer information. The customer information acquisition unit 500 is configured by a device that can acquire information for specifying a customer, such as a camera 501, an ATM 502a, a POS terminal 502b, and the like. For example, when the camera 501 is used as the customer information acquisition unit 500, the camera 501 acquires an image of the customer's appearance as customer information. Further, when the ATM 502a or the POS terminal 502b is used as the customer information acquisition unit 500, the information about the customer input to each is acquired as customer information.

In addition, as for the customer information to be acquired, as long as the customer attributes (age, gender, occupation, etc.) can be specified, the type and combination of information are not limited. For example, in the case of the example shown in FIG. 5, it is only necessary to include at least one of a customer image acquired by the camera 501 and customer information acquired by the ATM 502a or the POS terminal 502b.

The customer information acquisition unit 500 outputs the acquired customer information to the customer information processing apparatus 100.

(Customer information processing apparatus 100)
The customer information processing apparatus 100 accumulates the customer information acquired by the customer information acquisition unit 500, and identifies corresponding customer attributes (for example, age, sex, occupation, etc.) for each customer based on the stored customer information. By doing so, it is classified into a plurality of customer groups. Details of the customer information processing apparatus 100 will be described below.

The customer information processing apparatus 100 includes a customer information storage unit 101 and a customer information classification unit 102.

The customer information storage unit 101 stores the customer information acquired by the customer information acquisition unit 500 in association with information indicating the acquisition location of the customer information and information indicating the acquisition timing of the customer information.

In addition, you may specify the information which shows the acquisition place of customer information based on the customer information acquisition part 500 used as the transmission source of customer information. In this case, the customer information processing apparatus 100 may store the installation position of each customer information acquisition unit 500 in advance. Further, by providing the customer information acquisition unit 500 with a configuration for specifying position information such as GPS (Global Positioning System), the customer information acquisition unit 500 of the transmission source may be notified of the acquisition location of the customer information. The information indicating the acquisition timing of the customer information may be provided to the customer information acquisition unit 500 as a transmission source by providing a timing unit in the customer information acquisition unit 500.

The customer information classification unit 102 reads each customer information stored in the customer information storage unit 101. The customer information classifying unit 102 specifies attributes such as the age, sex, and occupation of each customer from each read customer information. For example, when the customer information is information related to the customer input at the ATM 502a or the POS terminal 502b, the customer information classification unit 102 selects the target attribute (for example, age, gender, occupation, What is necessary is just to extract the information corresponding to a family structure etc.).

In the case where the customer information is an image acquired by the camera 501, the customer information classifying unit 102 identifies each customer (one or more people) included in the image by using a face recognition technology, By analyzing the face of each identified customer, the age and gender of each customer can be estimated. Moreover, the customer information classification | category part 102 can also estimate the occupation of the customer by identifying the clothing of each customer in an image. Specifically, when the customer in the image wears a suit, the customer information classification unit 102 estimates the customer's occupation as “company employee”, and when wearing a uniform of a certain bank, The customer's occupation may be estimated as a “bank employee”. In addition, if the information regarding a customer can be specified as an attribute from the image, the type of information specified as the attribute and the information specifying method are not limited to the above-described example.

The customer information classification unit 102 identifies the attributes of each customer based on the acquired customer information, and then classifies each customer into a customer profile based on the identified attributes. Hereinafter, a specific example of the customer profile will be described with reference to FIG. FIG. 6 is a diagram showing an example of a customer profile, and shows an example of a customer profile table D200 for classifying customer information into each customer profile (ie, customer group).

As shown in FIG. 6, the customer profile table D200 includes a customer profile D210 and attribute information D220 as a reference for classifying the customer profile. In the example illustrated in FIG. 6, as the attribute information D220, sex D221, age D222, occupation D223, and family structure D224 are included.

Customer profile D210 shows an identifier for identifying each customer profile. In the example shown in FIG. 6, the first character of the customer profile D210 corresponds to the gender D221. For example, when the first character of the customer profile D210 is “M”, the gender D221 indicates “male”, and when “W” is indicated, it indicates “female”.

The second to third characters of the customer profile D210 correspond to the age D222. For example, when the gender D221 is “male” and the second and third characters of the customer profile D210 are “20”, the age D222 is “18-22”. As described above, in the customer profile table D200 shown in FIG. 6, “male” is classified into the generations “18-22”, “23-30”, “30-50”, and “50 or more”.

The fourth character of customer profile D210 corresponds to occupation D223. For example, when the fourth character of the customer profile D210 is “S”, it indicates that the occupation D223 is “student”. Similarly, when the fourth character of the customer profile D210 is “C”, the occupation D223 is “company employee”, and when it is “N”, the occupation D223 is “unemployed”.

The fifth character of the customer profile D210 corresponds to the family structure D224. For example, when the fifth character of the customer profile D210 is “S”, it indicates that the family structure D224 is “1”, that is, a single person. Further, when the fifth character of the customer profile D210 is “F”, it indicates that the family structure D223 is “2 or more”, that is, a single person.

As a specific example, a customer profile D210 indicated by “W20SS” indicates a group of “single” customers who are “students” of “female” who are “18-25” years old. Further, the customer profile D210 indicated by “M30CF” indicates a group of customers who are “men” of “23-30” and “non-persons”.

In addition, you may change suitably the reference | standard of the value of each attribute contained as the attribute information D220 for classifying into the customer profile D210 according to operation. Further, the reference of the value of another attribute may be changed according to the value of any attribute in the attribute information D220. For example, in the example shown in FIG. 6, when the gender D221 is “female”, unlike the case of “male”, the criterion of the age D222 is changed depending on whether or not the occupation D223 is “student”.

Further, the customer profile D210 shown in FIG. 6 is merely an example, and the form is not limited as long as each customer profile can be identified. The customer profile table D200 may be created in advance and stored in a location where the customer information classification unit 102 can read out.

After classifying each customer into a customer profile, the customer information classifying unit 102 provides, for each customer, a corresponding customer profile D210, information indicating the acquisition location of the customer information in which the customer is specified, and information indicating the acquisition timing. Correlate and output to analysis server 200.

(Analysis server 200)
Based on the customer profile D210 notified from the customer information classification unit 102 for each customer, the information indicating the acquisition location and the information indicating the acquisition timing of the customer information identified by the customer, the analysis server 200 Analyze distribution trends. Details of the analysis server 200 will be described below.

The analysis server 200 includes a customer trend analysis unit 201 and a customer trend data storage unit 202.

The customer trend analysis unit 201 acquires, from the customer information classification unit 102, a customer profile D210, information indicating a customer information acquisition location and information indicating an acquisition timing for each customer.

The customer trend analysis unit 201 counts the number of customers corresponding to each customer profile D210 for each acquisition location and acquisition timing of the corresponding customer information. The customer information acquisition location and acquisition timing unit for counting the number of customers may be appropriately changed. As a specific example, the customer trend analysis unit 201 aggregates the number of customers corresponding to each customer profile D210, for example, every 10 minutes for each customer information acquisition unit 500. Based on the tabulation result, the customer trend analysis unit 201 can recognize the customer distribution tendency at every 10 minutes in the location corresponding to each customer information acquisition unit 500.

The customer trend analyzing unit 201 stores customer trend data storage results (hereinafter simply referred to as “customer count results”) of the number of customers corresponding to each customer profile D210 for each acquisition location and acquisition timing of customer information. Stored in the unit 202.

Also, the customer trend analysis unit 201 reads out the total number of customers for each customer profile D210 from the customer trend data storage unit 202 at a predetermined timing. The customer trend analysis unit 201 analyzes the customer distribution trend of each customer profile for each place and time zone based on the total number of customers read from the customer trend data storage unit 202, and creates a customer trend table D300. . Below, the specific content of the process which concerns on preparation of the customer trend table D300 by the customer trend analysis part 201 is demonstrated, referring FIG.7 and FIG.8.

First, refer to FIG. FIG. 7 is a diagram for explaining a customer trend, and schematically shows a behavior pattern tendency of a customer group corresponding to a certain customer profile. In FIG. 7, positions p1 to p7 indicate installation positions of the digital signage 700, respectively.

In FIG. 7, for example, routes r11 and r12 indicate the behavior pattern of the customer group corresponding to the “30s male office worker” in a certain time zone. In the example indicated by the routes r11 and r12, for example, the monitoring camera (camera 501) of “station”, the ATM 502a of “bank”, and the POS terminal 502b of “restaurant” are corresponded to “male company employees in their 30s”. The tendency of the customer group to move along the time series appears in the total number of customers. That is, by displaying information on “30s male office worker” as a transmission target on the digital signage 700 installed at the positions p1, p7, p6, and p5 along the routes r11 and r12 based on the counting result, Effective information transmission is possible.

As another example, routes r21 to r23 indicate behavior patterns of a customer group corresponding to “20s female office worker” in a certain time zone. In the example indicated by the routes r21 to r23, the “station” monitoring camera (camera 501), the “clothing store” POS terminal 502b, the “life shop” POS terminal 502b, and the “restaurant” POS terminal 502b in this order. The tendency of the customer group corresponding to “20s female office worker” to move along the time series appears in the total number of customers. In other words, based on the totaled results, along the routes r21 to r23, the digital signage 700 installed at the positions p1, p2, p3, p4, and p5 displays information for “20s female employee” as a transmission target. Thus, effective information transmission is possible.

Therefore, the customer trend analysis unit 201 uses the customer count table D300 indicating the customer distribution tendency of each customer profile for each location (particularly, the location where the digital signage 700 is installed) and each time zone based on the total number of customers. Create Hereinafter, the contents of the customer trend table D300 will be described first with reference to FIG. 8, and then the process of creating the customer trend table D300 by the customer trend analysis unit 201 will be described. FIG. 8 is a diagram showing an example of the customer trend table D300.

In the example shown in FIG. 8, the customer trend table D300 includes a location D310, a date and time D320, profile data D330, and other conditions D340.

The place D310 indicates the installation position of each digital signage 700. The date and time D320 indicates a schedule and a time zone. Note that the date and time D320 may be only information indicating a schedule or only information indicating a time zone.

The profile data D330 indicates the distribution of the number of customers for each customer profile in the location indicated by the location D310 and the schedule and time indicated by the date and time D320. In the example illustrated in FIG. 8, the profile data D330 includes a customer profile D331 and a ratio D332. The customer profile D331 corresponds to the customer profile D210 of the customer profile table D200 shown in FIG. The ratio D332 indicates the ratio of the number of customers for each customer profile D331 to the total number of customers that meet the conditions indicated by the location D310 and the date and time D320.

In the example shown in FIG. 8, the profile data D330 records the ratio D332 of each customer profile D331 in the descending order of the total number of customers under the conditions indicated by the location D310 and the date and time D320.

As a specific example, in the profile data D330 shown in FIG. 8, in the condition that the location D310 is “p3” and the date and time D320 is “mm / dd hh: mm”, the ratio D332 of the customer group whose customer profile D331 is “W20SF” Indicates “50%”. That is, in the place indicated by “p3”, “50%” of the total customers in the schedule and time zone of “mm / dd hh: mm” are the customer group indicated by “W20SF”, that is, “20 generations”. It is shown that it is a “non-single person” of “female student”. Further, the customer group ratio D332 corresponding to the other customer profile D331 indicates that the customer group corresponding to “M30CS” is “30%” and the customer group corresponding to “W30CS” is “20%”. Yes.

Note that the profile table D330 does not necessarily include information for the customer profile D331 in which the corresponding customer is not detected under the conditions indicated by the location D310 and the date and time D320. Further, in the example shown in FIG. 8, the profile data D330 is created based on the ratio for each customer profile D331, but is not limited to the ratio D332 if the distribution tendency of customers in each customer profile D331 is known. For example, instead of the ratio D332, the total number of customers for each customer profile D331 may be used as it is.

Also, the profile data D330 may be created separately according to conditions other than the location D310 and the date and time D320. For example, even in the same place and at the same time zone, the flow of people may change depending on other conditions other than the place and date and time, such as weather, day of the week, and the presence or absence of nearby events. Therefore, the customer trend table D300 may specify a condition other than the place D310 and the date and time D320 as another condition D340 so that the profile table D330 can be created for each combination of conditions. As a specific example, when “weather” is included as another condition D340, profile data D300 having the same conditions for the location, schedule, and time zone is used for “rainy” and “rainy”. It can be created separately for each case.

Next, processing related to creation of the profile table D330 by the customer trend analysis unit 201 will be described. The customer trend analysis unit 201 calculates the total number of customers read from the customer trend data storage unit 202 based on the acquisition location and acquisition timing of the corresponding customer information, the date and time D320, the location D310, and other The conditions are summarized for each condition D340 (hereinafter referred to as “aggregation condition”).

Note that the customer trend analysis unit 201 may specify whether or not the read result of the number of customers corresponds to another condition D340 based on the acquisition location and acquisition timing of the corresponding customer information. For example, when the other condition D340 indicates the condition of “weather”, the customer trend analysis unit 201 may specify by comparing the acquisition location and acquisition timing of the customer information with the record result regarding the past weather. Good. When the other condition D340 indicates “the presence or absence of an event in the vicinity”, the customer trend analysis unit 201 compares the acquisition location and acquisition timing of the customer information with the past event record. What is necessary is just to specify.

The customer trend analysis unit 201 calculates the ratio D332 of the number of customers for each customer profile D331 under each condition (that is, the customer distribution trend) based on the result of counting the number of customers collected for each condition. The customer trend analysis unit 201 creates profile data D330 based on the ratio D332 of the number of customers corresponding to each calculated customer profile D331. The customer trend analysis unit 201 creates a customer trend table D300 by associating each created profile data D330 with the corresponding aggregation condition (that is, date and time D320, place D310, and other conditions D340).

Note that the conditions of the location D310 (that is, the installation location of the digital signage 700) and the acquisition location of the customer information (that is, the location of the customer information acquisition unit 500) do not necessarily match. When the condition of the location D310 and the acquisition location of the customer information do not match, the customer trend analysis unit 201 displays the total result of the customer information acquired by the customer information acquisition unit 500 closest to the location indicated by the location D310. It may be adopted as the number of customers. As another example, the customer trend analysis unit 201 adds the weighted result of the customer information acquired by each customer information acquisition unit 500 according to the distance to the location indicated by the location D310. Thus, the number of customers at each location indicated by the location D310 may be calculated.

Further, the customer trend table D300 shown in FIG. 8 is an example, and the unit for creating the profile data D330 is not limited to the example shown in FIG. For example, profile data D330 may be created for each location D310, or profile data D330 may be created for each date and time D320.

Further, the frequency with which the customer trend analysis unit 201 creates the customer trend table D300 and the period for analyzing the distribution of the number of customers for each customer profile may be changed as appropriate according to the operation. As a specific example, the customer trend analysis unit 201 may analyze the distribution of the number of customers for each customer profile over the past year every other year. As another example, the customer trend analysis unit 201 may analyze the distribution of the number of customers for each customer profile over the past two weeks every other week.

Here, refer to FIG. 5 again. The customer trend analysis unit 201 stores the created customer trend table D300 in the customer trend data storage unit 202. The customer trend analysis unit 201 may store the created customer trend table D300 in the customer trend data storage unit 202 each time. As another example, when the customer trend table D300 created in the past is already stored in the customer trend data storage unit 202, the customer trend analysis unit 201 uses the newly created customer trend table D300. The content of the customer trend table D300 that has already been created may be updated. The customer trend table D300 corresponds to the customer trend data D120 in FIG.

(Content distribution server 300)
The content distribution server 300 creates a distribution plan for distributing contents to each digital signage 700 based on the customer trend table D300 stored in the customer trend data storage unit 202. Details of the content distribution server 300 will be described below.

The content distribution server 300 includes a distribution plan creation unit 301, a customer request data storage unit 302, and a content storage unit 303.

The customer request data storage unit 302 stores a customer request table D400 created for each predetermined place and time. In the customer request table D400, a list of content types with high requests for each customer group indicated by the customer profile is recorded. Hereinafter, an example of the customer request table D400 will be described with reference to FIG. FIG. 9 is a diagram showing an example of the customer request table D400.

As shown in FIG. 9, the customer request table D400 includes one or more customer request data D410. The customer request data D410 includes a profile D411, a number of people D412 and a popularity type D413.

Profile D411 corresponds to customer profile D210 of customer profile table D200 shown in FIG.

The number of people D412 indicates the number of customers corresponding to the profile D411.

In the popularity type D413, the types of content that are highly requested (popular) by customers corresponding to the profile D411 are stored side by side in descending order of demand.

For example, in the example shown in FIG. 9, the customer request data D410 corresponds to a customer whose profile D410 is “W20SF”, that is, a “20s female student” and “non-single person”. It shows that it is “5 people”. Further, in the customer request data D410 shown in FIG. 9, there is a high demand for contents related to “restaurant” indicated by the type c1 for customers corresponding to “students in their 20s” and “non-single persons”. This indicates that there is a high demand for content related to “clothes” indicated by c2.

The customer request table D400 is created in advance and stored in the customer request data storage unit 302. Note that the customer request table D400 may be created based on, for example, a survey of trends at each time and place. Further, the customer request table D400 indicates the advertisement type D130 based on the customer request in FIG. 3, and corresponds to an example of “second list”.

The content storage unit 303 stores a content management table D500. The content management table D500 is a control table in which distribution conditions such as distribution target, distribution location, distribution schedule and time zone, and the type of the content are indicated for each content. Hereinafter, an example of the content management table D500 will be described with reference to FIG. FIG. 10 shows an example of the content management table D500.

As shown in FIG. 10, the content management table D500 includes a content NoD 510, a profile table D520, a content type D530, and a content D540.

Content NoD510 is an identifier for identifying each content.

The profile table D520 indicates the distribution conditions of the content indicated by the content NoD510. Specifically, the profile table D520 indicates a content distribution target for each place or time. In the example illustrated in FIG. 10, the profile table D520 includes a customer profile D521, a location D522, and a presentation date (time) D523.

Customer profile D521 corresponds to customer profile D210 of customer profile table D200 shown in FIG. That is, the customer profile D521 indicates a customer group that is a distribution target of the content indicated by the content NoD510.

The location D522 indicates a location where the content indicated by the content No. D510 is distributed, and an identifier indicating each predetermined location is set. For example, when “ALL” is designated for the location D522, it means that the content is distributed at all locations without particularly designating the location. When “STA” is designated, it means that the content is distributed by the digital signage 700 installed at the “station”. In addition, when “BNK” is designated, it means that the content is distributed using the digital signage 700 installed in the “bank”. Note that the setting value of the location D522 shown in FIG. 10 is merely an example, and may be added, deleted, and changed as appropriate according to the operation.

Presentation month (time) D523 indicates the schedule and time zone for distributing the content indicated by the content No. D510. Only the schedule may be specified for the presentation date (time) D523, or only the time zone may be specified. In addition, in the presentation date (time) D523, a period for distributing the corresponding content may be designated, for example, “1/11 10:00 to 2/1 20:00”.

Content type D530 indicates the type of content indicated by content NoD510. In the content type D530, a value similar to the type set in the popularity type D413 of the customer request data D410 is set. As a specific example, when the content relates to “restaurant”, the content type D530 is set with the type c1. Similarly, when the content relates to “clothes”, the type c2 is set in the content type D530.

Content D540 indicates data of the content indicated by content NoD510. In the content D540, actual data of the content may be registered, or a link to the actual data of the content may be registered.

Thus, in the content management table D500, for each content, the customer profile to be distributed, the distribution location, the distribution schedule and time zone, and the type of the content are shown. As a specific example, the content whose content NoD510 is indicated by “C000012” is distributed to customers whose customer profile D521 is “W20SF”, “M30CS”, and “W30CS”, and content type D530 is content of type c2. It is shown that. In addition, when the content whose content No. D510 is “C000012” is targeted for distribution to customers whose customer profile D521 is “M30CS” (that is, “30-year-old male company employee” and “single-person” customers) It shows that information is transmitted using only the digital signage 700 installed at the “station”.

The content management table D500 is created in advance and stored in the content storage unit 303. In addition, actual content data may be stored in the content storage unit 303. The content management table D500 indicates the advertising target customer D140 in FIG. 3, and corresponds to an example of “first list”.

The distribution plan creation unit 301 creates a content distribution plan D600 based on a customer trend table D300 indicating customer trends, a customer request table D400 indicating customer requests, and a content management table D500 indicating distribution conditions for each content. . Details of the distribution plan creation unit 301 will be described below.

The delivery plan creation unit 301 reads the customer behavior table D300 from the customer trend data storage unit 202 at a predetermined timing. At this time, when a plurality of customer behavior tables D300 are stored in the customer trend data storage unit 202, the distribution plan creation unit 301 may read the latest customer behavior table D300.

Here, refer to FIG. The distribution plan creation unit 301 extracts profile data D330 for each place D310, date and time D320, and other conditions D340 from the read customer behavior table D300. Based on the extracted profile data D330, the distribution plan creation unit 301 identifies a customer profile D331 that is a content distribution target. At this time, for example, the distribution plan creation unit 301 may set the customer profile D331 having the highest ratio D332 in the profile data D330 (or the customer profile D331 with the highest m (m is an integer)) as the content distribution target. As another example, the distribution plan creation unit 301 may call each customer profile D331 shown in the profile data D330 based on the ratio D332 as a content distribution target (hereinafter simply referred to as “distribution target”). ) May be weighted.

Next, the distribution plan creation unit 301 obtains the customer request table D400 corresponding to the same condition as the profile data D330 specifying the distribution target (that is, created for the same location and time) from the customer request data storage unit 302. read out.

Here, refer to FIG. The distribution plan creation unit 301 compares the identified distribution target with the read customer request table D400, and identifies the type of content to be distributed for each distribution target. Specifically, the distribution plan creation unit 301 extracts customer request data D410 in which the distribution target customer profile is set in the profile D410 from the customer request table D400. The distribution plan creation unit 301 identifies the type of content to be distributed based on the popularity type D413 of the extracted customer request data D410. At this time, the distribution plan creation unit 301 may use the type with the highest rank among the popular types D413 (or the type with the highest k (k is an integer)) as the type of content to be distributed. As another example, the distribution plan creation unit 301 may weight the type of content to be distributed based on the ranking shown in the popularity type D413.

Next, the distribution plan creation unit 301 reads the content management table D500 from the content storage unit 303. Reference is now made to FIG. The distribution plan creation unit 301 searches the content management table D500 using the location D310, the date and time D320, the identified distribution target, and the identified content type as a search key, and identifies the content to be distributed. For example, when the customer profile to be distributed is “M30CS”, the location D310 is “station”, the identified content type is type c2, and the date and time D320 is included in “yy / mm / ddhh: mm” The distribution plan creation unit 301 identifies the content whose content NoD510 is “C000012”.

When weighting is performed on the distribution target, the distribution plan creation unit 301 identifies the content for each distribution target, and determines each identified content based on the weight of the distribution target corresponding to the content. What is necessary is just to weight. Also, in the case where weighting is performed for each type of content, the distribution plan creation unit 301 may identify and weight the content in the same manner as in the case where the distribution target is weighted. .

Based on the processing described above, the distribution plan creation unit 301 identifies the content to be distributed for each aggregation condition (that is, the conditions indicated by the place D310, the date D320, and the other condition D340). It can be said that the content specified as described above is a type of content desired by a customer group in which a large number of customers are distributed under the conditions indicated as the aggregation condition and requested by the customer group.

After specifying the content to be distributed, the distribution plan creation unit 301 creates a distribution plan D600. Here, the delivery plan D600 will be described with reference to FIG. FIG. 11 is a diagram showing an example of the distribution plan D600.

As shown in FIG. 11, the delivery plan D600 includes a location D610, a date and time D620, a content table D630, and other conditions D640.

The location D610, the date and time D620, and other conditions D640 correspond to the location D310, the date and time D320, and other conditions D340 in the customer behavior table D300 shown in FIG.

The content table D630 includes one or more content NoD631 of the content to be distributed. The content NoD 631 corresponds to the content NoD 510 of the content management table D500 illustrated in FIG.

That is, the distribution plan D600 is a control table in which the content No. D631 of the content to be distributed is associated with each of the conditions (hereinafter referred to as “distribution conditions”) indicated by the place D610, the date and time D620, and the other conditions D640. .

Next, processing related to creation of the delivery plan D600 by the delivery plan creation unit 301 will be described. Based on the content management table D500, the distribution plan creation unit 301 identifies the content NoD 631 of the content to be distributed for each distribution condition (that is, the conditions indicated by the place D610, the date D620, and the other conditions D640). The distribution plan creation unit 301 creates a content table D630 based on the identified content No 631.

The delivery plan creation unit 301 associates the created content table D630 with the corresponding delivery condition. In this way, the distribution plan creation unit 301 creates the content table D630 for each distribution condition and creates the distribution plan D600 by associating it with the distribution condition. In the created distribution plan D600, for each specific distribution condition, a customer group in which many customers are distributed under the conditions indicated by the distribution condition, and the type of content desired by the customer group Will be shown as a list.

Here, refer to FIG. 5 again. Based on the content management table D500, the distribution plan creation unit 301 extracts content corresponding to the content No 631 specified in each content table D630 in the distribution plan D600. The distribution plan creation unit 301 associates the content extracted based on the content management table D500 and the content No 631 in the content table D630 with the content table D630. In the content management table D500, when a content link is associated with the content D540, the distribution plan creation unit 301 may acquire actual content data from the link destination.

The distribution plan creation unit 301 transmits the distribution plan D600 and each content associated with each content table D630 in the distribution plan 600 to the content proxy 650.

(Content Proxy 650)
The content proxy 650 includes a distribution data storage unit 651 and a distribution unit 652. The content proxy 650 distributes content to each digital signage 700 based on the distribution plan D600 created by the distribution plan creation unit 301. Details of the content proxy 650 will be described below.

The distribution data storage unit 651 stores a distribution plan D600 and each content distributed to each digital signage 700 based on the distribution plan D600. The content proxy 650 causes the distribution data storage unit 651 to store the distribution plan D600 acquired from the distribution plan creation unit 301 and each content associated with each content table D630 in the distribution plan 600.

The distribution unit 652 distributes the contents to be displayed on the digital signage 700 to each digital signage 700 based on the distribution plan D600 stored in the distribution data storage unit 651. Hereinafter, an example of a specific operation of the distribution unit 652 will be described with reference to FIG.

The distribution unit 652 extracts a content table D630 in which content to be distributed to each digital signage 700 is indicated for each time zone indicated by the date and time D620 of the distribution plan D600. At this time, the distribution unit 652 may extract the content table D630 by combining other conditions D640 (for example, conditions such as weather). As a specific example, the distribution unit 652 extracts which one of the “sunny” content table D630 and the “rainy” content table D630 depending on the weather in the time zone indicated by the date and time D620. May be switched.

The distribution unit 652 distributes each content associated with the content table D630 (indicated by the content NoD631) to the digital signage 700 corresponding to the location indicated by the location D610. The digital signage 700 displays the content acquired from the distribution unit 652.

In the example shown above, the distribution unit 652 is configured to control the content displayed on each digital signage 700 based on the distribution plan D600. However, the content displayed on the digital signage 700 itself may be controlled. . In this case, the distribution unit 652 creates a distribution plan D600 for each digital signage 700 (hereinafter referred to as “sub-distribution plan D600a”) from the distribution plan D600 stored in the distribution data storage unit 651. Specifically, the distribution unit 652 extracts data for each value indicated by the location D610, and creates a sub-distribution plan D600a for each location D610 based on the extracted data.

For each location D610, the distribution unit 652 converts the created sub-distribution plan D600a and each content associated with the content table D630 in the sub-distribution plan D600a to the digital signage 700 corresponding to the location indicated by the location D610. Deliver to.

The digital signage 700 stores the sub distribution plan D600a acquired from the distribution unit 652 and each content. Then, the digital signage 700 may specify the content table D630 for each condition indicated by the date and time D620 and other conditions D640 based on the sub-distribution plan D600a, and display the content based on the specified content table D630. .

In the above description, an example in which the customer request table D400 is created in advance has been described. However, as another example, the information distribution system 1 may automatically generate and update the customer request table D400. In this case, for example, the type of content displayed when the customer information processing apparatus 100 responds (attention) to the digital signage 700 together with the customer information acquired by the customer information acquisition unit 500. To record.

Note that the customer information processing apparatus 100 may determine whether or not the customer has reacted to the digital signage 700, for example, depending on whether or not the customer has stopped for a certain period of time before the digital signage 700. As another example, the customer information processing apparatus 100 may recognize the movement of the customer's line of sight using face recognition technology and determine whether the customer has reacted to the digital signage 700 based on the recognition result. As another example, the customer information processing apparatus 100 may determine whether or not the customer has reacted to the digital signage 700 depending on whether or not the customer has acquired a coupon at the POS terminal 502b.

In the customer information processing apparatus 100 as described above, for example, the analysis server 200 categorizes and aggregates the type of content recorded for each customer for each location, time, and customer profile. Request data D410 can be created. The analysis server 200 can generate the customer request table D400 by arranging the customer request data D410 created for each customer profile in the order of the number of customers corresponding to the customer profile. When the customer request table D400 has already been created, the analysis server 200 stores each customer request data D410 in the customer request table D400 based on the customer profile and the total number of customers for each content type. Update. Then, the analysis server 200 may update the customer request table D400 by rearranging the updated customer request data D410 according to the number of people D412 in each customer request data D410.

[Series of operations]
Next, a series of operations of the information distribution system 1 according to the present embodiment will be described with reference to FIG. FIG. 12 is a flowchart showing a series of operations of the information distribution system 1 according to the present embodiment.

(Step S101)
The customer information acquisition unit 500 acquires customer customer information. The customer information acquisition unit 500 is configured by a device that can acquire information for specifying a customer, such as a camera 501, an ATM 502a, a POS terminal 502b, and the like. For example, when the camera 501 is used as the customer information acquisition unit 500, the camera 501 acquires an image of the customer's appearance as customer information. Further, when the ATM 502a or the POS terminal 502b is used as the customer information acquisition unit 500, the information about the customer input to each is acquired as customer information.

In the case where the customer information is an image acquired by the camera 501, the customer information classifying unit 102 identifies each customer (one or more people) included in the image by using a face recognition technology, By analyzing the face of each identified customer, the age and gender of each customer can be estimated. Moreover, the customer information classification | category part 102 can also estimate the occupation of the customer by identifying the clothing of each customer in an image. In addition, if the information regarding a customer can be specified as an attribute from the image, the type of information specified as the attribute and the information specifying method are not limited to the above-described example.

The customer information classification unit 102 identifies the attributes of each customer based on the acquired customer information, and then classifies each customer into a customer profile based on the identified attributes.

The customer trend analysis unit 201 counts the number of customers corresponding to each customer profile D210 for each acquisition location and acquisition timing of the corresponding customer information. The customer information acquisition location and acquisition timing unit for counting the number of customers may be appropriately changed.

The customer trend analysis unit 201 causes the customer trend data storage unit 202 to store the total result of the number of customers corresponding to each customer profile D210 for each customer information acquisition location and acquisition timing.

(Step S102)
Further, the customer trend analysis unit 201 reads out the total number of customers for each customer profile D210 from the customer trend data storage unit 202 at a predetermined timing. Based on the total number of customers read from the customer trend data storage unit 202, the customer trend analysis unit 201 distributes the customer distribution of each customer profile for each location (particularly the location where the digital signage 700 is installed) and for each time period. The trend is analyzed and a customer trend table D300 is created.

Specifically, the customer trend analysis unit 201 calculates the total number of customers read from the customer trend data storage unit 202 based on the acquisition condition (that is, the acquisition timing and the acquisition timing of the corresponding customer information). , Date D320, place D310, and other conditions D340).

The customer trend analysis unit 201 stores the created customer trend table D300 in the customer trend data storage unit 202.

Here, refer to FIG. The distribution plan creation unit 301 extracts profile data D330 for each place D310, date and time D320, and other conditions D340 from the read customer behavior table D300. Based on the extracted profile data D330, the distribution plan creation unit 301 identifies a customer profile D331 that is a content distribution target. At this time, for example, the distribution plan creation unit 301 may set the customer profile D331 having the highest ratio D332 in the profile data D330 (or the customer profile D331 with the highest m (m is an integer)) as the content distribution target. As another example, the distribution plan creation unit 301 may weight each customer profile D331 indicated in the profile data D330 as a content distribution target based on the ratio D332.

(Step S103)
Next, the distribution plan creation unit 301 obtains the customer request table D400 corresponding to the same condition as the profile data D330 specifying the distribution target (that is, created for the same location and time) from the customer request data storage unit 302. read out.

(Step S104)
Next, the distribution plan creation unit 301 reads the content management table D500 from the content storage unit 303. Reference is now made to FIG. The distribution plan creation unit 301 searches the content management table D500 using the location D310, the date and time D320, the identified distribution target, and the identified content type as a search key, and identifies the content to be distributed. For example, when the customer profile to be distributed is “M30CS”, the location D310 is “station”, the identified content type is type c2, and the date and time D320 is included in “yy / mm / dd hh: mm”. The distribution plan creation unit 301 identifies the content whose content NoD510 is “C000012”.

(Step S105)
Based on the processing described above, the distribution plan creation unit 301 identifies the content to be distributed for each aggregation condition (that is, the conditions indicated by the place D310, the date and time D320, and the other condition D340). It can be said that the content specified as described above is a type of content desired by a customer group in which a large number of customers are distributed under the conditions indicated as the aggregation condition and requested by the customer group.

After specifying the content to be distributed, the distribution plan creation unit 301 creates a distribution plan D600.

Specifically, the distribution plan creation unit 301 specifies the content No. D631 of the content to be distributed for each distribution condition (that is, the conditions indicated by the place D610, the date and time D620, and other conditions D640) based on the content management table D500. To do. The distribution plan creation unit 301 creates a content table D630 based on the identified content No 631.

The content proxy 650 stores the distribution plan D600 acquired from the distribution plan creation unit 301 and each content associated with each content table D630 in the distribution plan 600 in the distribution data storage unit 651.

The distribution unit 652 distributes the contents to be displayed on the digital signage 700 to each digital signage 700 based on the distribution plan D600 stored in the distribution data storage unit 651.

Specifically, the distribution unit 652 extracts a content table D630 in which content to be distributed to each digital signage 700 is indicated for each time period indicated by the date and time D620 of the distribution plan D600. At this time, the distribution unit 652 may extract the content table D630 by combining other conditions D640 (for example, conditions such as weather).

As described above, the information distribution system 1 according to the present embodiment includes a customer trend table D300 indicating customer trends, a customer request table D400 indicating customer requests, and a content management table D500 indicating distribution conditions for each content. Based on the above, a delivery plan D600 is created. This customer trend table D300 is sequentially updated according to the customer distribution tendency of the customer group at that time. Therefore, the information distribution system 1 can specify the distribution target of the content while following the customer distribution tendency that changes fluidly for each place and time zone.

Note that the timing and frequency of updating the customer trend table D300 can be changed as appropriate. Therefore, for example, the information distribution system 1 can grasp the distribution of the number of customers for each customer profile in the medium to long term by analyzing the data over the past year every other year, or the past 2 every other week. It is also possible to grasp in the short term by analyzing the data over the week.

In addition, the information distribution system 1 identifies the attribute from the customer information such as the image of the customer's appearance and classifies it into the customer profile without using the personal information as it is. Thereby, in the information distribution system 1, it becomes possible to prevent the situation where personal information leaks or the situation which conflicts with the Personal Information Protection Law.

Further, the information distribution system 1 specifies the type of content to be distributed based on the customer trend table D300 and the customer request table D400. As a result, the information distribution system 1 can distribute, for example, a type of content frequently requested by the customer group to the customer group having the largest number of customers under a specific condition.

As described above, the information distribution system 1 follows the customer distribution tendency that changes fluidly according to the conditions such as the location, the time zone, and the weather, and the customer group The content requested by the user is displayed on the digital signage 700. For this reason, it is possible to reduce the occurrence of a situation in which content is displayed on the digital signage 700 in a situation where there is no target transmission target in the vicinity, and to effectively distribute information.

In addition, since the information distribution system 1 specifies the content to be distributed according to the customer distribution tendency, it is not necessary to store extra content in the content proxy 650 or the digital signage 700. Therefore, the information distribution system 1 can reduce the required capacity of the storage unit for storing content. In addition, the information distribution system 1 can increase the variation of content for each target as the required capacity is reduced.

The series of operations described above can be configured by a program for causing the CPU of a device that operates each component of the information distribution system 1 according to the present embodiment to function. This program may be configured to be executed via an OS (Operating System) installed in the apparatus. In addition, the position of the program is not limited as long as the apparatus including the configuration for executing the above-described processing can be read. For example, the program may be stored in a recording medium connected from the outside of the apparatus. In this case, it is preferable to connect the recording medium storing the program to the apparatus so that the CPU of the apparatus executes the program.

The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention pertains can come up with various changes or modifications within the scope of the technical idea described in the claims. Of course, it is understood that these also belong to the technical scope of the present invention.

DESCRIPTION OF SYMBOLS 100 Customer information processing apparatus 101 Customer information storage part 102 Customer information classification part 200 Analysis server 201 Customer trend analysis part 202 Customer trend data storage part 300 Content delivery server 301 Delivery plan preparation part 302 Customer request data storage part 303 Content storage part 500 Customer Information acquisition unit 650 Content proxy 700 Digital signage

Claims

A classification unit that classifies the acquired information of multiple customers into multiple customer groups,
An analysis unit for analyzing a distribution tendency of the customers among the plurality of customer groups;
A delivery plan creation unit for creating a delivery plan for delivering content based on the analysis result of the customer distribution trend;
An information distribution system comprising:
The information distribution system according to claim 1, wherein the analysis unit analyzes the distribution tendency of the customers at predetermined time intervals.
The information distribution system according to claim 1, wherein the analysis unit analyzes a distribution tendency of the customer for each predetermined place.
4. The information according to claim 3, wherein the distribution plan creation unit weights the content to be distributed based on the distribution tendency of the customers included in each of the plurality of customer groups, and creates the distribution plan. Distribution system.
5. The information distribution system according to claim 4, wherein the distribution plan creation unit creates the distribution plan with the customer group in which the customers are distributed most being the content distribution target.
The delivery plan creation unit identifies the content to be delivered by comparing the analysis result with a first list in which the customer group and the content are associated in advance, and creates the delivery plan The information distribution system according to claim 1.
The distribution plan creation unit identifies the content to be distributed by comparing the analysis result with a second list weighted with the content for each customer group, and creates the distribution plan. The information distribution system according to claim 1, wherein
In the second list, the content is weighted according to the type of the content,
The distribution plan creation unit identifies a type of the content to be distributed by comparing the second list with the analysis result, and creates the distribution plan. Information distribution system.
A classification unit that classifies information of a plurality of customers acquired based on customer images captured by the imaging unit into a plurality of customer groups in real time;
An analysis unit that analyzes the distribution tendency of the customers among the plurality of customer groups for each predetermined time and / or predetermined location;
When analyzed in real time by the analysis unit, based on the analysis result of the customer distribution trend, a distribution plan creation unit that creates a distribution plan for distributing content in real time;
An information distribution system comprising:
Classifying the acquired information of multiple customers into multiple customer groups,
Analyzing a distribution tendency of the customers among the plurality of customer groups;
Creating a distribution plan for distributing content based on the analysis result of the customer distribution trend;
An information distribution method comprising:
On the computer,
Classifying the acquired information of multiple customers into multiple customer groups,
Analyzing a distribution tendency of the customers among the plurality of customer groups;
Creating a distribution plan for distributing content based on the analysis result of the customer distribution trend;
A computer program for executing