JP2010165239A - Content distribution system and method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To distribute content fit for the characteristic of an area installed with a display device. <P>SOLUTION: This content distribution system includes: a display device management table 53 indicating installation spots of the display devices; a target data storage part 14 storing target data indicating an attribute of a target corresponding to content data; a cluster attribute data (original) storage part 11 and a clustering data storage part 12 storing characteristic data of each area about a plurality of areas inside a marketing target area; a distribution table generation part 18 extracting a fit area fit for the target based on the target data and the characteristic data of each area, and specifying the display device disposed in the fit area based on installation spot data; and a distribution server distributing the content data corresponding to the target to the specified display device. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technology for distributing content to a content display device, and particularly to a technology for selecting a display device in a district where there are many customers targeted for the content and distributing the content.

  Advertising activities for displaying various advertising contents on a display device installed in a station premises, a store, a complex commercial facility, or the like are performed. For example, Patent Document 1 describes that a management server distributes content and reproduces the content to a display device (content reproduction computer).

JP 2003-67288 A

Here, when the range where the display device is installed is not a limited range such as in a store, but over a marketing target area of a certain size (for example, the entire Tokyo 23 wards, the Tokyo metropolitan area, etc.) The problem is whether which content can be displayed on which display device can enhance the advertising effect. In other words, you want your advertising content to be visible to potential customers. For this purpose, it is considered effective to select a display device having an installation point in an area where there are many targeted customers from the marketing target area.

  Therefore, an object of the present invention is to select a display device in a district where the installation point is assumed to be a large number of customers targeted for content from among a plurality of display devices installed in the marketing target area. Is to deliver.

  A content distribution system according to one embodiment of the present invention is a distribution system that distributes content data to a plurality of display devices arranged in a marketing target area, and stores a setting point data storage that indicates a setting point of the display device. Means, storage means for storing the content data, target attribute storage means for storing target attribute data indicating target attributes corresponding to the content data, and characteristics of each district for a plurality of districts in the marketing target area Based on the district characteristic storage means for storing data, the target attribute data and the characteristic data of each district, a suitable district suitable for the target is extracted and arranged in the suitable district based on the installation point data. Display device specifying means for specifying the display device being connected, and To be a display device and a delivery means for delivering the content data corresponding to said target.

  In a preferred embodiment, the characteristic data of each district may include clustering data obtained by classifying each district into a plurality of clusters, and cluster attribute data indicating respective attributes of the plurality of clusters. Further, the display device specifying means specifies a matching cluster that matches the target based on the cluster attribute data and the target attribute data, and is classified into the specified matching cluster based on the clustering data. You may make it extract a suitable district.

  In a preferred embodiment, the cluster attribute data has a plurality of attribute items, and the display device specifying means normalizes each value of the cluster attribute data for each attribute item, and attributes associated with the target attribute data A distance between the target attribute data and each cluster may be calculated based on the normalized value of the item, and the matching cluster may be specified based on the calculated distance.

  In a preferred embodiment, a distance between each cluster may be calculated by weighting a normalized value of an attribute item related to the target attribute data according to the target attribute data.

1 shows a configuration diagram of a content distribution system according to an embodiment of the present invention. FIG. An example of the display device management table 53 is shown. An example of the delivery table 55 is shown. 1 is a configuration diagram of an optimization server 10. FIG. An example of the cluster attribute original data 110 is shown. An example of the clustering data 120 is shown. An example of the target data 140 is shown. An example of the normalized data 150 is shown. An example of the distance data 170 is shown. An example of the map display of the installation point of the delivery destination display apparatus 70 is shown. It is a flowchart which shows the process sequence in a content delivery system. It is a flowchart which shows the detailed process sequence of the distance data generation process of step S14 in FIG.

  Hereinafter, a content distribution system according to an embodiment of the present invention will be described with reference to the drawings. The content distribution system according to the present embodiment is a system that distributes and displays advertising content to display devices installed in various places. Then, which advertising content is to be distributed to each display device is determined according to the characteristics of the location where each display device is arranged and the target attribute of the advertising content.

FIG. 1 shows a configuration diagram of a content distribution system according to the present embodiment. As shown in the figure, the present system includes an optimization server 10, a distribution server 30, a data management server 50, and a plurality of display devices 70 connected to the distribution server 30 via a network.
The optimization server 10, the distribution server 30, and the data management server 50 are all configured by, for example, a general-purpose computer system, and individual components or functions in each of the servers 10, 30, 50 described below are, for example, This is realized by executing a computer program.

  The optimization server 10 identifies the content distribution area based on the target attribute data of the advertising content and the analysis result of the marketing target area. For example, the optimization server 10 extracts a suitable district that matches the target based on the characteristic data (clustering data and cluster attribute data) indicating the characteristics for each of the divided areas and the target attribute data. The optimization server 10 specifies the display device 70 arranged in the conformity area based on the installation location data (display device management table 53) of the display device, and generates and stores a distribution table 55 described later.

  The distribution server 30 distributes the content data to the display device 70 based on the distribution table 55 generated by the optimization server 10.

  The display device 70 is installed in a plurality of districts within the marketing target area where many people gather (for example, a station or various commercial facilities) as an installation point, and various places in or around the installation point are installed. ing. Specifically, it is installed in various places such as in front of a station, in a station, in a hotel lobby, a shopping street, a street such as an intersection, a complex facility such as a shopping center, and a supermarket store. Each display device 70 displays content distributed from the distribution server 30, that is, a still image or a moving image advertisement video. The content may include audio.

  The data management server 50 stores data generated or used by the optimization server 10 or the distribution server 30. For example, the data management server 50 stores content data 51 relating to advertisement content to be distributed and a display device management table 53 that stores installation location data including information such as the installation locations of the plurality of display devices 70.

  An example of the display device management table 53 is shown in FIG. As shown in the figure, the display device management table 53 has an installer ID 531, a device ID 532, a size 533, an installation point (address) 534, an installation location 535, and a delivery destination ID 536 as data items.

  The installer ID 531 indicates the subject who installed each display device 70. The device ID 532 is identification information of the display device 70. The size 533 is the display screen size of the display device 70. An installation point (address) 534 is an address of a point where the display device 70 is installed, and an installation location 535 indicates an installation location within the installation point. The distribution destination ID 536 is an IP address used when distributing content. Data in the display device management table 53 may be registered by an operator from an input device (not shown).

  An example of the distribution table 55 is shown in FIG. The distribution table 55 associates contents with display devices as distribution destinations. For example, as shown in the figure, the distribution table 55 includes a matching order 551, an installer ID 552, a device ID 553, a size 554, an installation point (address) 555, an installation location 556, a cluster 557, and a distribution. It has a content ID 558 and a distribution destination ID 559.

  The suitability rank 551 is a rank of determination results obtained by determining the suitability of cluster attribute data and target data (cluster suitability determination), which will be described later. The device ID 553 is identification information of the display device 70, and the display device 70 of the distribution destination is specified thereby. The cluster 557 is a cluster in which the district where the display device 70 is installed is classified. The cluster will be described in detail below. The distribution content ID 558 is identification information of content to be distributed to the display device 70 specified by the device ID 553.

  FIG. 4 is a configuration diagram of the optimization server 10.

  As shown in the figure, the optimization server 10 includes a cluster attribute data (original) storage unit 11, a clustering data storage unit 12, a normalization processing unit 13, a target data storage unit 14, and cluster attribute data (normal). A storage unit 15, a distance calculation unit 16, a cluster attribute data (distance) storage unit 17, and a distribution table generation unit 18, and holds a distribution table 55.

  The cluster attribute data (original) storage unit 11 and the clustering data storage unit 12 store the cluster attribute original data 110 and the clustering data 120 according to the analysis result of the analysis system 9, respectively. The analysis system 9 divides the marketing target area into a plurality of districts having, for example, a town street as a minimum unit, and classifies them into a plurality of clusters, and generates characteristic data (original data 110 and clustering data 120 of cluster attributes) of each district. To do. For example, the analysis system 9 performs cluster analysis based on data indicating the attributes of each district, and clusters each district. Therefore, the analysis system 9 uses the clustering data indicating the range of each district (the range in which the same chome of the same town is the same as the range of the district when divided into town chome units as the minimum unit) and the cluster to which each district belongs. 120 and original data 110 of cluster attributes indicating attributes of each cluster are generated.

  An example of the cluster attribute original data 110 is shown in FIG. That is, the cluster attribute original data 110 includes, as data items, a cluster ID 112, a relative value basic attribute (age composition, house ratio, detached house ratio, etc.) 114, and consumption / purchase attributes (brand faith, environmental preference, consumption desire). , 116, spending patience, etc.). For each item, the value of each cluster is set.

  As described below, the cluster attribute normalization data 150 (see FIG. 8) stored in the cluster attribute data (normalization) storage unit 15 and the cluster attribute data (distance) storage unit 17 are stored. Each of the cluster attribute distance data 170 (see FIG. 9) is obtained by processing the cluster attribute original data 110, and these are also cluster attribute data. Therefore, in the following description, the original data of the cluster attribute is referred to as “original data”, the normalized data of the cluster attribute is referred to as “normalized data”, and the distance data of the cluster attribute is referred to as “distance data”.

  An example of the clustering data 120 is shown in FIG. That is, in the clustering data 120, a district ID 122, a range of each district (address in units of towns) 124, and a cluster ID 126 of a cluster assigned to each district are associated with each other. Further, the clustering data 120 includes absolute value basic attributes 128 such as the average age and estimated income value of each district. Here, a basic attribute (relative value basic attribute 114) indicated as a relative value (relative value data) expressed as a percentage is included in the original data 110 and is to be normalized later. On the other hand, the absolute value basic attribute 128 shown as an average value based on the actual measurement value among the basic attributes of each district is included in the clustering data 120 and used for narrowing down, which will be described later, without being subjected to normalization described later.

  The target data storage unit 14 stores target attribute data (hereinafter referred to as target data) 140. The target data 140 is determined for each content. That is, each content is created for a specific customer segment (target). The target data specifically shows the attributes of the target. FIG. 7 shows an example of the target data 140. For example, the target data 140 includes a content ID, a basic attribute of the target for the content, and characteristics related to consumption / purchase attributes.

Returning to FIG. 4, the normalization processing unit 13 normalizes the original data 110 stored in the cluster attribute data (original) storage unit 11. For example, the normalization processing unit 13 may normalize only attribute item data related to the target data 140. Normalization may be performed, for example, by calculating a Z value for each attribute item. That is, for each attribute item, after calculating an average value and standard deviation of all cluster values, a Z value is obtained by the following formula. Normalized data 150 generated by the normalization processing unit 13 is stored in the cluster attribute data (normalization) storage unit 15.
Z value by attribute item = {(cluster value) − (average value of all clusters)} / standard deviation of all clusters.

  FIG. 8 shows an example of the normalized data 150. In the normalized data shown in the figure, the value of each attribute item for each cluster is normalized to a Z value for each attribute item.

  Based on the normalized data 150 generated by the normalization processing unit 13, the distance calculation unit 16 converts the distance data 170 indicating the distance between clusters for each attribute item. Here, the maximum value among the values (Z values) in each attribute item of the normalized data 150 is used as a reference, and the difference from the maximum value is defined as the distance. For example, in the normalized data 150 of FIG. 8, in the “owned house ratio” item, the value of the cluster “C01” is “1.60”, which is the maximum value. Therefore, the item “owned house ratio” is converted into the absolute value of the difference from 1.60, and this is used as the distance. The distance data 170 shown in FIG. 9 is obtained by converting each item into the distance in this way. The distance data 170 generated by the distance calculation unit 16 is stored in the cluster attribute data (distance) storage unit 17.

  The distribution table generation unit 18 extracts a suitable district that matches the target based on the target data 140 of the target data storage unit 14 and the characteristic data (clustering data and cluster attribute data) of each district. That is, the distribution table generation unit 18 performs cluster suitability determination based on the distance data 170 and the target data 140, and specifies a fit cluster that matches the target. Further, the distribution table generation unit 18 extracts a suitable district classified into the identified suitable cluster based on the clustering data 120.

  For example, the distribution table generation unit 18 selects an item corresponding to the target data 140 from among a plurality of attribute items of the distance data 170. In the example of FIG. 9, the age composition, home ownership ratio, and brand faith associated with the target data 140 are selected.

  Next, the distribution table generation unit 18 adds all the distance data of the selected attribute item for each cluster, and calculates the total distance for each cluster. At this time, weighting may be performed based on the target data 140. For example, in the target data 140 of FIG. 7, “generation” and “importance at the time of purchase” are ranked. In other words, in the “generation”, the first place is “senior”, the second place is “under 10s”, the “priority when purchasing” is the first place “fee”, and the second place is “brand”. Therefore, the age composition “under 10s” and “brand faith” may be multiplied by a weighting factor “2.0”.

  Then, the distribution table generation unit 18 performs cluster suitability determination based on the total distance. That is, the distribution table generation unit 18 extracts clusters as conforming clusters in order from clusters with the smallest total distance, assuming that the clusters with the smaller total distance are more suitable for the target data 140.

  Next, based on the clustering data 120, the distribution table generation unit 18 specifies the districts (matching districts) classified into the matching clusters extracted by the above processing. Since a plurality of districts may be assigned to the same cluster, at that time, the distribution table generation unit 18 extracts all the districts classified as conforming clusters. In addition, when specifying a suitable district, you may narrow down the suitable district using the values of attribute items included in the absolute basic attribute 128 (for example, “average age” and “income estimated value”). That is, when the target data 140 includes conditions regarding “average age” and “income estimated value”, the target data is calculated using the “average age” and “income estimated value” included in the clustering data 120. Areas that are far from the value of 140 may be excluded.

  For example, when “average age is 45 years” is set in the target data 140, in the clustering data 120, subordinate districts when the average age is sorted in the order close to 45 years may be excluded. When the estimated income value is set to 10 million yen ”, the lower districts when the estimated income value is sorted in the order close to 10 million yen in the clustering data 120 may be excluded.

  Further, the distribution table generating unit 18 refers to the display device management table 53 (see FIG. 2), and specifies the display device 70 arranged in the applicable district (town street unit) extracted by the above processing. At this time, the district range (address in units of town chome) 124 of the suitable district is compared with the installation point (location address, town chome) 534 of the display device 70. Further, the distribution destination ID 536 is specified from the device ID 532.

  The distribution table generation unit 18 generates a distribution table 55 (see FIG. 3) based on the compared results.

  When the installation location of the display device 70 of the distribution destination determined in this way is displayed as a map, for example, it is as shown in FIG. That is, a boundary line for each district is drawn on the map, and each district ID and cluster ID are displayed in each district. And the installation position of the display apparatus 70 used as a delivery destination in a suitable area (D25, D26, D29, D34 in the figure) is shown.

  FIG. 11 is a flowchart showing a processing procedure in the content distribution system having the above-described configuration. Hereinafter, description will be given according to this flowchart.

  First, the installation point data of each display device 70 is registered in the display device management table 53 using an input device (not shown) by an operator or the like (S10). Similarly, an operator or the like registers target data for a predetermined content using an input device (not shown) (S12).

  The optimization server 10 converts the original data 110 to generate cluster attribute distance data 170 (S14). Details of the process of generating the distance data 170 will be described later.

  As described above, the distribution table generation unit 18 calculates the total distance for each cluster based on the distance data 170 generated in step S14, and identifies a matching cluster that matches the target data 140 (S16). For example, the distribution table generation unit 18 weights the attribute items ranked in the target data 140 by applying a predetermined weighting factor, and then calculates the total distance for each cluster. A cluster having a small total distance is set as a matching cluster.

  Based on the clustering data 120, the distribution table generation unit 18 specifies districts in the marketing target area that are classified into the matching clusters specified in step S16 (S18). That is, the distribution table generation unit 18 specifies a district classified as a conforming cluster among the districts in the marketing target area.

  And the delivery table production | generation part 18 produces | generates the delivery table 55 (FIG. 3) with reference to said process result and the display apparatus management table 53 (S20).

  Next, the distribution server 30 refers to the distribution table 55 and identifies the device ID 553 and the distribution destination ID (IP address) 536 of the display device 70 that is the content distribution destination. Then, the distribution server 30 acquires the content data 51 corresponding to the distribution table 55 and distributes it to the display device 70 that is the distribution destination (S22). At this time, the distribution frequency may be changed according to the matching order 551 of the distribution table 55. That is, since it is considered that the advertising effect is higher when the content is displayed on the upper display device 70 having the matching order 551, the number of distributions may be increased with respect to the upper display device 70 than the lower display device 70. . Furthermore, the number of distributions may be controlled in accordance with the conditions for optimizing the advertisement placement fee.

  Accordingly, since the content can be broadcast (displayed) in a district where there are many potential customers targeted by each content, it is possible to increase opportunities for the content to be seen by the potential customers.

  FIG. 12 is a flowchart showing a detailed processing procedure of the distance data generation processing in step S14 in FIG.

  First, the normalization processing unit 13 narrows down attribute items based on the target data 140 and selects a target attribute item (S140). This is to normalize only attribute items related to the target data. Thereafter, the normalization processing unit 13 normalizes each attribute value of the selected attribute item by converting it into a Z value, and generates normalized data 150 (S142). The normalization data 150 generated by the normalization processing unit 13 and the cluster attribute data (normalization) storage unit 15 are stored.

  Next, the distance calculation unit 16 converts the normalized data 150 to generate distance data 170 for each attribute item (S144). The generated distance data 170 is stored in the cluster attribute data (distance) storage unit 17.

  The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.

DESCRIPTION OF SYMBOLS 10 Optimization server 11 Cluster attribute data (original) memory | storage part 12 Clustering data memory | storage part 13 Normalization process part 14 Target data memory | storage part 15 Cluster attribute data (normalization) memory | storage part 16 Distance calculation part 17 Cluster attribute data (distance) memory | storage Unit 18 Distribution table generation unit 30 Distribution server 50 Data management server 51 Content data 53 Display device management table 55 Distribution table 70 Display device

Claims (6)

A content distribution system for distributing content data to a plurality of display devices arranged in a marketing target area,
Installation point data storage means for indicating the installation point of the display device;
Storage means for storing the content data;
Target attribute storage means for storing target attribute data indicating an attribute of a target corresponding to the content data;
District characteristic storage means for storing characteristic data of each district for a plurality of districts in the marketing target area;
A display device that extracts a suitable district suitable for the target based on the target attribute data and the characteristic data of each district, and identifies a display device arranged in the suitable district based on the installation point data Specific means,
A content distribution system comprising: distribution means for distributing content data corresponding to the target to the identified display device. The characteristic data of each district is
Clustering data in which each of the districts is classified into a plurality of clusters, and cluster attribute data indicating respective attributes of the plurality of clusters,
The display device specifying means includes
The matching cluster that matches the target is identified based on the cluster attribute data and the target attribute data, and the matching district classified into the identified matching cluster is extracted based on the clustering data. The content distribution system described. The cluster attribute data has a plurality of attribute items,
The display device specifying means includes
Normalizing each value of the cluster attribute data for each attribute item, calculating a distance between the target attribute data and each cluster based on a normalized value of an attribute item related to the target attribute data, and calculating The content distribution system according to claim 2, wherein the matching cluster is identified based on the determined distance.   The content distribution system according to claim 3, wherein a distance between each cluster is calculated by weighting a normalized value of an attribute item related to the target attribute data according to the target attribute data. A distribution method for distributing content data to a plurality of display devices arranged in a marketing target area,
Storing installation point data indicating an installation point of the display device;
Registering target attribute data indicating target attributes corresponding to the content data;
Based on the target attribute data and a plurality of districts in the marketing target area, a suitable district suitable for the target is extracted based on the characteristic data of each district, and is arranged in the suitable district based on the installation point data. Identifying a displayed display device; and
Delivering content data corresponding to the target to the identified display device. A computer program for distributing content data to a plurality of display devices arranged in a marketing target area,
Storing installation point data indicating an installation point of the display device;
Registering target attribute data indicating target attributes corresponding to the content data;
Based on the target attribute data and a plurality of districts in the marketing target area, a suitable district suitable for the target is extracted based on the characteristic data of each district, and is arranged in the suitable district based on the installation point data. Identifying a displayed display device; and
A computer program for causing a computer to execute the step of distributing content data corresponding to the target to the specified display device.

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