JP7475300B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

A technology is disclosed that selects an advertisement to be presented to a user from among multiple advertisements based on words in which the user has shown interest over a period going back to the present.

Japanese Patent No. 6025155

However, while the above-mentioned conventional technology can present advertisements to users based on the user's interests, it is not possible to know whether the user is interested in the presented advertisement.

This application has been made in consideration of the above, and aims to estimate whether a user who has come into contact with an advertisement is interested in the advertisement.

The information processing device of the present application is characterized by comprising: an advertisement exposure estimation unit that estimates that a user has been exposed to an advertisement based on historical information of the user indicating the user's daily behavioral history, such as the user's location history and product purchase history, when the user takes an action that may lead to the user seeing or hearing an advertisement, such as visiting or passing by a place where an advertisement is posted or broadcast, or purchasing a magazine in which the advertisement is published; a measurement unit that measures the number of people who have been exposed to the advertisement among a group of a predetermined number of people who can identify individual users , and the number of people who have been exposed to the advertisement, who have taken a predetermined action that is estimated to be related to the advertisement, such as an intermediate action but not a final action corresponding to the conversion targeted by the advertisement, after being exposed to the advertisement in the historical information; a calculation unit that calculates a first ratio indicating the proportion of people who have taken the predetermined action from exposure to the advertisement, based on the measured number of people who have been exposed to the advertisement and the measured number of people who have taken the predetermined action; and a number estimation unit that estimates the number of people among an arbitrary group of users who have been exposed to the advertisement, based on the number of people who have taken the predetermined action in the historical information and the first ratio.

According to one aspect of the embodiment, it is possible to estimate whether a user who has come into contact with an advertisement is interested in the advertisement.

FIG. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. FIG. 3 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. FIG. 4 is a diagram illustrating an example of the configuration of an information providing device according to an embodiment. FIG. 5 is a diagram illustrating an example of the user information database. FIG. 6 is a diagram illustrating an example of the history information database. FIG. 7 is a diagram illustrating an example of the behavior information database. FIG. 8 is a diagram illustrating an example of the number-of-persons information database. FIG. 9 is a flowchart showing a processing procedure according to the embodiment. FIG. 10 is a diagram illustrating an example of a hardware configuration.

Below, the information processing device, information processing method, and information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing device, information processing method, and information processing program according to the present application are not limited to these embodiments. In addition, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[1. Overview of information processing method]
First, an overview of an information processing method performed by an information processing device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. Note that Fig. 1 will be described by taking as an example a case in which it is estimated whether a user who has come into contact with an advertisement has an interest in the advertisement.

As shown in FIG. 1, the information processing system 1 includes a terminal device 10, an information providing device 100, and an external search device 200. The terminal device 10, the information providing device 100, and the external search device 200 are each connected to each other via a network N (see FIG. 2) in a wired or wireless manner so as to be able to communicate with each other. In this embodiment, the terminal device 10 and the external search device 200 cooperate with the information providing device 100.

The terminal device 10 is a smart device such as a smartphone or tablet used by a user U, and is a mobile terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). The terminal device 10 has a screen such as a liquid crystal display with touch panel functions, and accepts various operations on displayed data such as content, such as tapping, sliding, scrolling, etc., performed by the user U with a finger or a stylus. An operation performed on an area of the screen where content is displayed may be considered to be an operation on the content. The terminal device 10 may be not only a smart device, but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

The information providing device 100 is an information processing device that cooperates with the terminal device 10 of each user U and the external search device 200 to provide API (Application Programming Interface) services for various applications (hereinafter, apps) and the like, as well as various data, to the terminal device 10 of each user U and the external search device 200, and is realized by a server device, a cloud system, or the like.

The information providing device 100 may also be an information processing device that provides some kind of web service online to the terminal device 10 of each user U. For example, the information providing device 100 may provide services such as Internet connection, search services, SNS (Social Networking Service), electronic commerce, electronic payment, online games, online banking, online trading, accommodation and ticket reservations, and video and music distribution as web services. In practice, the information providing device 100 may cooperate with various servers that provide the above-mentioned web services, and may act as an intermediary for the web services or be responsible for processing the web services.

The information providing device 100 can acquire user information about the user U. For example, the information providing device 100 acquires information about the attributes of the user U, such as the gender, age, and residential area of the user U. The information providing device 100 then stores and manages the information about the attributes of the user U together with identification information (such as a user ID) indicating the user U.

The information providing device 100 also obtains various types of history information (log data) indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID, etc. For example, the information providing device 100 obtains a location history, which is a history of the location and date and time of the user U, from the terminal device 10. The information providing device 100 also obtains a search history, which is a history of search queries entered by the user U, from a search server (search engine). The information providing device 100 also obtains a browsing history, which is a history of content viewed by the user U, from a content server. The information providing device 100 also obtains a purchase history, which is a history of product purchases and payment processing by the user U, from an electronic commerce server or a payment processing server. The information providing device 100 may also obtain a listing history, which is a history of listings on the marketplace by the user U, and a sales history, from an electronic commerce server or a payment server. The information providing device 100 also obtains a posting history, which is a history of posts by the user U, from a posting server that provides a word-of-mouth posting service or an SNS server.

The external search device 200 is an information processing device that provides an app or website having a search window (search box) to the terminal device 10 of the user U, and is realized by a server device, a cloud system, or the like. For example, the external search device 200 accepts a search query input by the user U into the search window using the terminal device 10, and provides search results corresponding to the input search query to the terminal device 10 of the user U. The app having a search window may be a browser, an SNS app, or the like, or may be a search bar on a home screen. It may also be a map app or a car navigation app (navigation app), or the like. The website having a search window is not limited to a search site or a portal site, and may also be an EC (Electronic Commerce) site, an online mall, a price comparison site, an SNS site, a video site (video sharing site, video distribution site), or the like.

The information providing device 100 and the external search device 200 may be the same device. In other words, the information providing device 100 and the external search device 200 may be integrated.

In this embodiment, the information providing device 100 collects various information about the user U, and when the user U comes into contact with an advertisement, it estimates whether the user U has become interested in the advertisement. Specifically, the information providing device 100 estimates contact with the advertisement from the user U's daily behavior history. In addition, when the user U takes a predetermined behavior, it acquires environmental information related to the situation of the user U. Then, when contact with the advertisement is estimated, it estimates from the environmental information that the predetermined behavior is behavior associated with contact with the advertisement (interest behavior). At this time, the information providing device 100 estimates that the user U has become interested in the advertisement by estimating that the predetermined behavior is behavior associated with contact with the advertisement. In other words, the information providing device 100 estimates that the user U has become interested in the advertisement and has taken a behavior associated with contact with the advertisement.

Note that the predetermined action is different from the final action (purchase, visit, etc.) that is the purpose of the advertisement. For example, the predetermined action is an intermediate action (intermediate action) that is performed between the advertisement contact and the action. Here, as an example of the predetermined action, a search is performed in a search engine or the like using a keyword related to the content of the advertisement as a search query. The search engine may be an in-site search engine for electronic commerce used in an EC site, an online mall, etc. Furthermore, the predetermined action is not limited to one action, but may be multiple actions. For example, the information providing device 100 may determine that the user U has performed the predetermined action when the user U has performed any one of multiple predetermined actions. Furthermore, the predetermined action is not limited to a single action, but may be a series of consecutive actions. For example, the information providing device 100 may determine that the user U has performed the predetermined action when the user U has performed a series of actions in a predetermined order.

For example, the information providing device 100 estimates the date and time of contact with the advertisement from the date and time included in the user U's daily behavior history and the date and time when the advertisement was presented. The date and time when the advertisement was presented may be the period during which the advertisement was presented. In addition, as environmental information, information regarding the date and time when the user U took a specified action is acquired. Then, if the date and time when the advertisement was contacted and the date and time when the specified action was taken satisfy a specified condition, it is estimated that the specified action is action associated with contact with the advertisement.

Alternatively, the information providing device 100 estimates the location where the user U came into contact with the advertisement from location information included in the user U's daily behavior history and location information of the advertisement. In addition, as environmental information, location information on the location where the user U took a specified behavior is acquired. Then, if the location where the user U came into contact with the advertisement and the location where the specified behavior was taken satisfy a specified condition, it is estimated that the specified behavior is a behavior associated with contact with the advertisement.

The information providing device 100 may also acquire, as environmental information, detection information indicating the results of detection of the surroundings by the terminal device 10 of the user U when the user U performs a predetermined behavior, and may infer, based on the detection information, that the predetermined behavior is a behavior associated with contact with an advertisement. For example, the information providing device 100 acquires, as detection information, sensor information indicating the detection results of various sensors mounted on the terminal device 10 of the user U.

In addition, the information providing device 100 may acquire, as environmental information, device signal detection information indicating that the terminal device 10 of the user U has detected a signal emitted by a device presenting an advertisement, and may infer, based on the device signal detection information, that a specific behavior is a behavior associated with contact with the advertisement.

Furthermore, when contact with an advertisement is estimated, the information providing device 100 estimates the user's degree of interest in the advertisement from the timing of the predetermined behavior. For example, the information providing device 100 estimates the user's degree of interest in the advertisement from the number of times a predetermined behavior is performed after contact with the advertisement. Alternatively, the information providing device 100 estimates the user's degree of interest in the advertisement from the interval from the time of contact with the advertisement to the time the predetermined behavior is performed. Furthermore, when contact with multiple advertisements related to the same target is estimated, the information providing device 100 may estimate the user's degree of interest in each advertisement from the timing of the predetermined behavior.

[1-1. basic action〕
For example, as shown in FIG. 1, the information providing device 100 routinely collects various information related to each user U from the terminal device 10 of each user U (step S1). When each user U is registered, the system obtains user information regarding the attributes, etc. of each user U, and stores user identification information for identifying the user U in association with the user information. In addition, the information providing device 100 constantly or periodically collects user identification information and behavior history from the terminal device 10 of each user U. At this time, the information providing device 100 collects user identification information and behavior history of the user U from the terminal device 10 of each user U. As the behavior history of the user U, various types of history information showing the behavior of the user U are collected. Among the history information, the location history, which is the history of the location and date and time of the user U, includes the movement in the city or in a building. The history information includes the user U's history, the history of visits to facilities, the history of entering stores, etc. In other words, the history information includes the location information. The location information includes information on the location of the user U and date and time.

Next, the user U comes into contact with the advertisement (step S2). For example, the user U is presented with online advertisements such as listing advertisements and display advertisements (banner advertisements) when browsing various websites or using apps. The advertisements may be pushed to the terminal device 10. Alternatively, the user U is presented with offline advertisements such as television advertisements and ODM (Outdoor Media) advertisements when watching television or traveling. Note that television advertisements are merely an example. In reality, the advertisements may be audio advertisements such as radio advertisements, newspaper advertisements, magazine advertisements, etc. In addition, ODM advertisements are advertisements through media that are encountered in places other than the home, such as transportation advertisements in train stations, trains, and buses, and outdoor advertisements in towns and commercial facilities. Advertisements through digital signage that display images and text, in-store announcements, vehicle advertisements, etc. are also included. The user U also receives flyers and the like that are distributed at specified locations.

Next, the user U takes an interest behavior (step S3). In this embodiment, the user U takes a search behavior as the interest behavior. For example, the user U uses the terminal device 10 to input a search query into a search window of an app or website having a search window (search box), and searches for a store that handles the transaction object (goods, services, etc.) listed in the advertisement or a facility listed in the advertisement. The search behavior may be a map search. For example, the user U may use the terminal device 10 to search for a store that handles the transaction object (goods, services, etc.) listed in the advertisement or the location on a map of the facility listed in the advertisement.

Then, the external search device 200 provides interest information related to the interest behavior of the user U to the information providing device 100 (step S4). For example, the external search device 200 provides the information providing device 100 with the user identification information and location information of the user U together with the search query input by the user U. This location information includes information related to the location and date and time when the user U performed the search behavior.

Next, the information providing device 100 estimates that the user U has come into contact with the advertisement from various information related to the user U, particularly from the user U's location and behavior history (step S5). For example, the information providing device 100 estimates that the user U has come into contact with the online advertisement delivered when viewing the content, from the viewing history, which is the history of the content viewed by the user U.

Alternatively, the information providing device 100 infers from the location history, which is a history of the user U's location and date and time, that the user U was in (or near) the location where the offline advertisement was presented, or that the user U was in that location during the period or time when the offline advertisement was presented, and infers that the user U was exposed to the offline advertisement. Note that the location where the offline advertisement is presented is not limited to downtown or inside a building, but also includes the inside of a vehicle such as a train or bus.

The information providing device 100 may also infer from the purchase history, which is the history of the user U's product purchases and payment processing, that the user U purchased a newspaper or magazine in which an advertisement was published, and that the user U came into contact with the advertisement published in that newspaper or magazine. The information providing device 100 may also infer from the user U's SNS, schedule information, etc., that the user U watches a specific television or radio program, or that the user U reads a newspaper every morning as a daily habit, and infer that the user U came into contact with an advertisement through television, radio, newspaper, etc. The advertisement may be a television or radio commercial, or an advertisement within a program.

Next, the information providing device 100 estimates, from the interest information from the external search device 200, that the search behavior of user U is search behavior associated with exposure to an advertisement (step S6). For example, the information providing device 100 receives, as interest information from the external search device 200, the search query input by user U, user identification information and location information of user U, and estimates, from the search query and the location and date and time at which user U performed the search behavior, that user U's search behavior is search behavior associated with exposure to an advertisement.

For example, if the location and date/time at which user U performed a search behavior are within a predetermined range (e.g., within a 200 m radius from the location where the advertisement is presented or within 10 minutes from the time the advertisement is presented) with respect to the location and date/time at which user U is estimated to have come into contact with the advertisement, the information providing device 100 estimates that user U's search behavior is search behavior associated with contact with the advertisement. In practice, the information providing device 100 may not only compare the location and date/time, but also determine whether keywords such as the names/categories/features of the transaction objects (products, services, etc.) or facilities listed in the advertisement are related to the search query entered by user U, and if it is determined that they are related, it may estimate that user U's search behavior is search behavior associated with contact with the advertisement. The relationship between the keywords and the search query may be determined by natural language processing such as word distributed representations (word vectors).

Next, when the information providing device 100 estimates that the search behavior of the user U is a search behavior associated with contact with the advertisement, it estimates that the user U is interested in the advertisement (step S7). At this time, the information providing device 100 may estimate the degree of interest of the user U in the advertisement. For example, the information providing device 100 may quantify (score) the degree of interest of the user U in the advertisement according to the number of times the user U performed a search behavior (search frequency, etc.) and the interval (time, distance, etc.) from contact with the advertisement to the search behavior. In addition, the information providing device 100 may determine the degree of relevance between the search query entered by the user U and keywords such as the name/category/features of the transaction object (product, service, etc.) or facility, etc., listed in the advertisement, as the degree of interest of the user U in the advertisement. The degree of relevance between the keyword and the search query may be estimated by natural language processing such as word distributed representation (word vector).

Next, the information providing device 100 estimates the number and percentage of users who were exposed to the advertisement and became interested in it from the search behavior of an unspecified number of users (step S8). For example, the information providing device 100 estimates how many (how many people, what percentage) of users who are presumed to have been exposed to the advertisement took search behavior associated with exposure to the advertisement. In other words, the information providing device 100 estimates the number and percentage of users who took search behavior associated with exposure to the advertisement among users who are presumed to have been exposed to the advertisement.

The information providing device 100 then provides the external search device 200 with estimated information based on the results of the above estimation (step S9). For example, the information providing device 100 provides the external search device 200 with information that can identify user U's search behavior as search behavior associated with exposure to an advertisement, as estimated information. In other words, the information providing device 100 notifies the external search device 200 that user U's search behavior is search behavior associated with exposure to an advertisement.

Then, the external search device 200 provides information to the terminal device 10 of the user U who input the search query based on the search results corresponding to the search query and the estimated information from the information providing device 100 (step S10). For example, the external search device 200 identifies the search behavior of the user U as search behavior associated with exposure to an advertisement based on the estimated information from the information providing device 100, and provides search results related to the transaction objects or facilities listed in the advertisement to the terminal device 10 of the user U with priority.

At this time, the external search device 200 may rearrange the order of search results corresponding to the search query based on the estimated information from the information providing device 100, and may preferentially display search results related to the transaction objects or facilities advertised in the advertisements that the user U is estimated to have come into contact with on the terminal device 10. In other words, the external search device 200 may rearrange (sort) the search results corresponding to the search query based on the estimated information and provide them to the terminal device 10 of the user U.

Alternatively, based on the estimated information from the information providing device 100, the external search device 200 may provide, together with the search results corresponding to the search query, an online advertisement equivalent to the advertisement that the user U is estimated to have come into contact with (if it was originally an online advertisement, it may be presented again), various information related to the transaction object or facility listed in the advertisement (detailed information, word-of-mouth information, coupon/benefit information, etc.), online advertisements for other products related to the advertised product (products in the same category, products of the same brand, etc.), etc.

In this way, in this embodiment, behaviors such as searches that accompany exposure to an advertisement are used to measure "interest" between "ad exposure" and "actions such as purchases/visits" (behavior) that result from the exposure to the advertisement.

Until now, when measuring the effectiveness of non-digital advertising, the only way to confirm "ad exposure" was through unreliable memory-based surveys such as "Did you see the ad?". Additionally, the only ways to measure "actions" were through surveys of people who purchased a product or service or visited a store or facility, or by correlating advertising exposure with increases or decreases in purchases or visits, which are influenced by various other measures.

Therefore, in this embodiment, we created an indicator called "interest" between "contact" and "action" that can capture relatively objective and nearly complete information, and use this indicator to measure effectiveness.

For example, in cases where the time of transmission of specific information can be determined down to the second, such as TV or radio commercials, or where the time when the ad was visible can be determined using location information, such as outdoor or train ODM ads, if a related search is conducted during or within a few minutes of exposure to the ad, this is deemed to indicate "interest" as a result of exposure to the ad, and this is measured. In this way, "exposure" and behavior resulting from "interest" are linked based on the overlap or time difference between the time of transmission and the time of the action.

It is also possible to measure the degree of "interest" based on the frequency and duration of "interest behavior" (number of searches, whether it led to a purchase, whether a related search was conducted after some time had passed, whether the person started watching related news, etc.). In other words, it is possible to measure the strength of interest rather than simply using an "interest" index.

[1-2. Methods for estimating "advertising exposure" and "actions" based on "interests"]
Next, we will explain how to accurately infer "ad exposure" and "action" using the "interest" index. Here, we will explain a method for estimating the number of people who "exposed to an ad" and the number of people who took an "action" based on the number of people who took an "interest behavior."

Note that "interest behavior" and "action" are different behaviors. For example, "interest behavior" refers to behavior resulting from exposure to an ad (such as a search) that does not achieve the advertising objective. "Action" refers to the final behavior aimed at achieving the advertising objective (such as purchasing a product or visiting a facility).

First, the information providing device 100 identifies users in advance and creates a panel of 100 or 1,000 people whose "ad exposure" and "actions" can be completely tracked. It then estimates the overall ratio of "ad exposure" and "actions" from the ratio of "ad exposure" to "interest behavior" within the panel, the ratio of "interest behavior" to "action," and the overall index of "interest behavior."

For example, the information providing device 100 prepares a panel of 1,000 people. The "advertising exposure" of these 1,000 users and their "actions" such as purchases or visits resulting from the advertising exposure are accurately tracked in some way. Advertisement exposure may be tracked by questionnaires given to users or by taking pictures using a camera installed on a television or other device. Purchases may be tracked by payment on a company's own EC site in the case of online purchases, or by electronic payment or credit card payment in the case of offline purchases.

In addition, the information providing device 100 accurately grasps the "interest behaviors" (mainly searches) caused by the ad exposure of these 1,000 people, and measures the ratio of "ad exposure" to "interest behaviors" (e.g., 2%) and the ratio of "interest behaviors" to "actions" (e.g., 1%). For example, the information providing device 100 calculates the ratio of "ad exposure" to "interest behaviors" based on the total number of "ad exposures" and the total number of "interest behaviors". In addition, the information providing device 100 calculates the ratio of "interest behaviors" to "actions" based on the total number of "interest behaviors" and the total number of "actions". In this way, the information providing device 100 can grasp the respective ratios of "ad exposure" and "actions" to the total number of "interest behaviors".

In actual measurements, the information providing device 100 can grasp the total number of "interest behaviors" that is not limited to the number of 1,000 people in the panel, so it estimates the total number of "advertising exposures" and the total number of "actions" caused by advertising exposures, using the total number of "interest behaviors" and the respective ratios of "advertising exposures" and "actions." For example, if the total number of "interest behaviors" is "100,000 people," the ratio of "advertising exposures" is "2%," and the ratio of "advertising-induced purchases" is "1%, the information providing device 100 estimates the total number of "advertising exposures" to be "100,000 people/2%" = "5 million people," and the total number of "advertising-induced purchases" to be "100,000 people x 1%" = "1,000 people." This makes it possible to estimate the number of users who have been exposed to advertisements and the number of users who will take actions, for example, from the number of users who have entered a specific keyword into the external search device 200 and performed a search.

It should be noted that utilization of the "interest" index, or estimation of "advertising contact" and "action" based on the above-mentioned "interest" index, is possible in almost real time. As a result, unlike conventional questionnaire surveys, immediate feedback can be obtained, making it possible to change or optimize the content of transmission in almost real time. The information providing device 100 has a mechanism for changing the content of distribution based on this feedback, and a mechanism for preparing multiple transmission patterns in advance and optimizing based on feedback, making it possible to achieve something similar to AB testing and optimization in digital advertising in non-digital advertising as well. In this way, in this embodiment, AB testing in non-digital advertising and optimization in (almost) real time can be achieved.

[1-3. Causation between "ad exposure" and "action"]
Next, a method for more accurately linking the causality between "ad exposure" and "action" will be described.

In the past, there was no way to link the causality between "ad exposure" and "action," and it was necessary to judge based on correlation, making it difficult to compare with a control group. For example, with online ads such as listing ads and display ads (banner ads), it is possible to know whether an ad was clicked or whether a user who viewed the ad made a conversion (CV) such as a purchase, but it was not possible to know whether a user who did not click or convert was interested in the ad. In reality, even if a user does not directly click or convert on the ad, they may be interested in keywords such as the name/category/features of the transaction object (product, service, etc.) or facility listed in the ad. In addition, a user may become interested in an ad after deleting it and request the same ad to be displayed again. Such a user may purchase a product in the future. For example, if an ad is presented to such a user again at a later date, there is a possibility that this time the user will click or convert. Furthermore, with offline ads such as television ads and ODM (Outdoor Media) ads, it was not possible to know whether a user who came into contact with an ad was interested in the ad. Therefore, the following method is proposed.

First, when the information providing device 100 uses the above estimation method to infer the total number of "advertising exposure" and "purchases" (actions), it classifies them into four groups: "exposed to an ad and made a purchase," "exposed to an ad and did not make a purchase," "purchased without being exposed to an ad," and "not exposed to an ad and not making a purchase," and estimates the total number for each group.

For example, the information providing device 100 obtains in advance from a panel of 1,000 people the "proportion of people who purchased after being exposed to the advertisement" and the "proportion of people who did not purchase after being exposed to the advertisement" in the group based on the number of people who were "exposed to the advertisement," the number of people who took "interest behavior" after being exposed to the advertisement, and the number of people who "purchased" after taking the interest behavior.The information providing device 100 then uses the total number of "people who purchased after being exposed to the advertisement" and the total number of "people who did not purchase after being exposed to the advertisement" using the total number of "interest behaviors" actually measured and the respective ratios.

Furthermore, the information providing device 100 calculates in advance from a panel of 1,000 people the "proportion of people who purchased without being exposed to an advertisement" and the "proportion of people who were not exposed to an advertisement and did not purchase" in the group based on the number of people who took the "interest behavior" without being exposed to an advertisement and the number of people who did not "purchase" after taking the interest behavior. Then, using the total number of "interest behaviors" actually measured and the above respective ratios, the information providing device 100 infers the total numbers of the above "people who purchased without being exposed to an advertisement" and "people who were not exposed to an advertisement and did not purchase."

This makes it possible to more accurately infer the effect of exposure to advertising on purchasing. Furthermore, if we also include an estimate of "existing user or not" (existing customer or new customer) in addition to the estimates for the four groups above, it becomes possible to more accurately grasp the retention effect, contribution to brand switching, etc.

That is, in this embodiment, the information providing device 100 measures in advance the number of people who came into contact with the advertisement and the number of people who took a predetermined action (interest action) after coming into contact with the advertisement among a group of a predetermined number of people (such as a panel of 1000 people) in which individual users can be identified. Next, based on the measured number of people who came into contact with the advertisement and the number of people who took the predetermined action, a first ratio is calculated, which indicates the proportion of people who took the predetermined action after coming into contact with the advertisement. After that, based on the number of people who took the predetermined action among the unspecified large group and the first ratio, the number of people who came into contact with the advertisement among the unspecified large group is estimated. Note that the number of people who took the predetermined action among the unspecified large group is, for example, the number of users who performed a search using a specific keyword as a search query in a search engine such as the external search device 200. That is, the number of people who took the predetermined action among the unspecified large group is measurable.

Furthermore, the information providing device 100 measures in advance, among a group of a specified number of people who can identify individual users, the number of people who took a specified action after coming into contact with an advertisement, and the number of people who took the final action intended by the advertisement after taking the specified action. Next, based on the measured number of people who took the specified action and the number of people who took the action, a second ratio is calculated, which indicates the proportion of people who took the action from the specified action. After that, the number of people who took the action among the unspecified large group is estimated based on the number of people who took the specified action among the unspecified large group and the second ratio.

In addition, the information providing device 100 measures in advance, among a group of a specified number of people who can identify individual users, the number of people who took a specified action after coming into contact with the advertisement and the number of people who did not take any action after taking the specified action. Next, based on the measured number of people who took the specified action and the number of people who did not take any action, a third ratio is calculated, which indicates the proportion of people who did not take any action after the specified action. After that, based on the number of people who took the specified action among the unspecified large group, the second ratio, and the third ratio, the number of people among the unspecified large group who took an action after coming into contact with the advertisement and the number of people who did not take any action after coming into contact with the advertisement are estimated.

Furthermore, the information providing device 100 measures in advance the number of people who took a predetermined behavior without coming into contact with an advertisement, and the number of people who took an action after taking the predetermined behavior, among a group of a specified number of people who can identify individual users. Next, based on the measured number of people who took the predetermined behavior and the number of people who took an action, a fourth ratio is calculated, which indicates the proportion of people who took an action from the predetermined behavior. After that, based on the number of people who took the predetermined behavior among an unspecified number of people in the group and the fourth ratio, the number of people in the unspecified number of people in the group who took an action without coming into contact with an advertisement is estimated.

The information providing device 100 also measures in advance the number of people who took a predetermined action without contacting the advertisement and the number of people who did not take any action after taking the predetermined action, among a group of a predetermined number of people who can identify individual users. Next, based on the measured number of people who took the predetermined action and the number of people who did not take any action, a fifth ratio is calculated, which indicates the ratio of people who did not take any action from the predetermined action. Then, based on the number of people who took the predetermined action among the unspecified large number of people and the fifth ratio, the number of people who did not contact the advertisement and did not take any action among the unspecified large number of people is estimated. Note that even if a person did not contact the advertisement and did not take any action, if they take the same predetermined action (interest action) as a person who contacted the advertisement or took an action, they can be said to be a potential consumer. Therefore, as described above, by estimating the number of people who took the predetermined action but did not contact the advertisement and did not take any action, the number of potential consumers can be estimated.

In this way, the information providing device 100 estimates the number of people in an unspecified large group who took an action after coming into contact with an advertisement, the number of people who did not take any action after coming into contact with an advertisement, the number of people who took an action without coming into contact with an advertisement, and the number of people who did not come into contact with an advertisement and did not take any action.

In addition, the information providing device 100 measures in advance the number of new users and the number of existing users among a group of a predetermined number of people from which individual users can be identified.

In addition, the information providing device 100 measures in advance the number of new users and existing users in a group of a specified number of people who were exposed to the advertisement and the number of people who took a specified action after being exposed to the advertisement. Next, based on the measured number of people who were exposed to the advertisement and the number of people who took the specified action for each of the new users and existing users, a first ratio indicating the proportion of people who took the specified action after being exposed to the advertisement is calculated. After that, based on the number of people who took the specified action and the first ratio for each of the new users and existing users in an unspecified large group, the number of people who were exposed to the advertisement is estimated.

In addition, the information providing device 100 measures in advance, for each of new users and existing users in a group of a specified number of people, the number of people who took a specified action after coming into contact with an advertisement, and the number of people who took the final action intended by the advertisement after taking the specified action. Next, for each of new users and existing users, a second ratio indicating the proportion of people who took an action from a specified action is calculated based on the measured number of people who took the specified action and the number of people who took the action. After that, for each of new users and existing users in an unspecified large group, the number of people who took an action is estimated based on the number of people who took the specified action and the second ratio.

[1-4. The effect and contribution of combining multiple advertising methods]
Next, a method for measuring the effect of combining a plurality of advertising techniques and the degree of contribution thereof will be described.

Conventionally, when multiple advertising methods such as TV commercials, digital advertising, and ODM advertising are used, the only way to determine how effective each advertising method is in achieving the final goal (raising awareness, purchasing, etc.) (the effect of each advertising method) was by inferring the correlation between the amount of each method and its changes, and the changes in the final awareness rate and purchase amount, making it difficult to accurately understand the contribution rate. Therefore, we propose the following method.

First, the information providing device 100 prepares a panel in the same manner as in the estimation method described above. In addition, the information providing device 100 combines the identification of the same person by face recognition using cameras installed around the television or ODM advertisement, and infers the whole from the data of people who can be linked, filling in the rest with analogy. For example, if a person searches immediately after touching an ODM advertisement using face recognition, and there is another person who searches at roughly the same time and in the same place, it is considered that they have been in contact. This method makes it possible to estimate the overall contact, interest, and action. Also, by looking at the correlation between the media (advertising medium) that was contacted and the number of contacts and purchases and interests, it is possible to accurately measure effectiveness.

2. Example of information processing system configuration
Next, a configuration of an information processing system 1 including an information providing device 100 according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of a configuration of the information processing system 1 according to an embodiment. As shown in Fig. 2, the information processing system 1 according to an embodiment includes a terminal device 10, an information providing device 100, and an external search device 200. These various devices are connected to each other via a network N so as to be able to communicate with each other by wire or wirelessly. The network N is, for example, a local area network (LAN) or a wide area network (WAN) such as the Internet.

Furthermore, the number of devices included in the information processing system 1 shown in FIG. 2 is not limited to that shown. For example, in FIG. 2, only one terminal device 10 is shown to simplify the illustration, but this is merely an example and is not limiting, and there may be two or more devices.

The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 is a smart device such as a smartphone or a tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game console with a communication function, a car navigation system, a wearable device such as a smart watch or a head-mounted display, smart glasses, etc.

In addition, the terminal device 10 can connect to the network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network), and communicate with the information providing device 100.

The information providing device 100 is, for example, a PC, a server device, a mainframe, a workstation, etc. The information providing device 100 may be realized by cloud computing.

3. Example of terminal device configuration
Next, the configuration of the terminal device 10 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the terminal device 10. As shown in Fig. 3, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40.

(Communication unit 11)
The communication unit 11 is connected to a network N (see FIG. 2 ) by wire or wirelessly, and transmits and receives information to and from the information providing device 100 via the network N. For example, the communication unit 11 is realized by a network interface card (NIC), an antenna, or the like.

(Display unit 12)
The display unit 12 is a display device that displays various information such as position information. For example, the display unit 12 is a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). The display unit 12 is also a touch panel display, but is not limited to this.

(Input unit 13)
The input unit 13 is an input device that accepts various operations from the user U. The input unit 13 also has, for example, buttons for inputting characters, numbers, and the like. If the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. The input unit 13 may be a microphone that accepts voice input from the user U. The microphone may be wireless.

(Positioning unit 14)
The positioning unit 14 receives signals (radio waves) transmitted from satellites of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10 based on the received signals. That is, the positioning unit 14 measures the position of the terminal device 10. Note that the GPS is merely an example of a GNSS (Global Navigation Satellite System).

The positioning unit 14 can also measure the position using various methods other than GPS. For example, the positioning unit 14 may measure the position using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction, etc., as described below.

(Wi-Fi positioning)
For example, the positioning unit 14 uses the Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point to measure the position of the terminal device 10.

(Beacon positioning)
Furthermore, the positioning unit 14 may measure the position by using a Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

(geomagnetic positioning)
In addition, the positioning unit 14 locates the position of the terminal device 10 based on a geomagnetic pattern of a structure that has been measured in advance and a geomagnetic sensor provided in the terminal device 10 .

(RFID positioning)
Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, stores, etc., or has a function of reading an RFID tag, the location of use is recorded together with information on a payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to measure the location of the terminal device 10. Furthermore, the location may be measured by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

If necessary, the positioning unit 14 may use one or a combination of the positioning means described above to determine the position of the terminal device 10.

(Sensor unit 20)
The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices and wireless devices. In the example shown in FIG. 3, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, an air pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

Note that the above-mentioned sensors 21 to 28 are merely examples and are not limiting. In other words, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects the physical movement of the terminal device 10, such as the direction of movement, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects the physical movement of the terminal device 10, such as the tilt in three axial directions, based on the angular velocity of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

Since the terminal device 10 is equipped with the above-mentioned acceleration sensor 21, gyro sensor 22, air pressure sensor 23, etc., it is possible to determine the position of the terminal device 10 using technology such as Pedestrian Dead-Reckoning (PDR) that utilizes these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using positioning systems such as GPS.

For example, the number of steps, walking speed, and distance walked can be calculated using a pedometer that uses the acceleration sensor 21. In addition, the gyro sensor 22 can be used to know the user U's direction of travel, line of sight, and body inclination. In addition, the air pressure detected by the air pressure sensor 23 can be used to know the altitude and floor on which the user U's terminal device 10 is located.

The air temperature sensor 24 detects, for example, the air temperature around the terminal device 10. The sound sensor 25 detects, for example, sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image around the terminal device 10.

The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the environment and situation around the terminal device 10 by detecting air pressure, temperature, sound, and illuminance, and capturing images of the surroundings. In addition, it becomes possible to improve the accuracy of the location information of the terminal device 10 based on the environment and situation around the terminal device 10.

(Control unit 30)
The control unit 30 includes, for example, a microcomputer having a central processing unit (CPU), a read only memory (ROM), a RAM, an input/output port, and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 30 includes a transmission unit 31, a reception unit 32, and a processing unit 33.

(Transmitter 31)
The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21 to 28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the information providing device 100 via the communication unit 11.

(Receiving unit 32)
The receiving unit 32 can receive various information provided by the information providing device 100 and requests for various information from the information providing device 100 via the communication unit 11 .

(Processing Unit 33)
The processing unit 33 controls the entire terminal device 10, including the display unit 12, etc. For example, the processing unit 33 can output various pieces of information transmitted by the transmission unit 31 and various pieces of information received by the reception unit 32 from the information providing device 100 to the display unit 12 for display.

(Memory unit 40)
The storage unit 40 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. Various programs and various data are stored in the storage unit 40.

4. Example of the configuration of the information providing device
Next, a configuration of the information providing device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of the information providing device 100 according to the embodiment. As shown in Fig. 4, the information providing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(Communication unit 110)
The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 2) in a wired or wireless manner.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk, an optical disk, etc. As shown in Fig. 4, the storage unit 120 has a user information database 121, a history information database 122, a behavior information database 123, and a number of people information database 124.

(User Information Database 121)
The user information database 121 stores user information related to the user U. For example, the user information database 121 stores various information such as attributes of the user U. Fig. 5 is a diagram showing an example of the user information database 121. In the example shown in Fig. 5, the user information database 121 has items such as "User ID (Identifier)", "Age", "Gender", "Home", "Workplace", and "Interests".

"User ID" indicates identification information for identifying user U. Furthermore, "age" indicates the age of user U identified by the user ID. Note that "age" may be the specific age of user U identified by the user ID, such as 35 years old. Furthermore, "gender" indicates the gender of user U identified by the user ID.

"Home" indicates the location information of the home of user U, which is identified by the user ID. In the example shown in FIG. 5, "Home" is illustrated as an abstract code such as "LC11", but it may also be latitude and longitude information, etc. For example, "Home" may also be the name of a region or an address.

"Workplace" indicates the location information of the workplace (school in the case of a student) of user U identified by the user ID. In the example shown in FIG. 5, "workplace" is illustrated as an abstract code such as "LC12", but it may also be latitude and longitude information, etc. For example, "workplace" may also be the name of a region or an address.

Furthermore, "interests" indicate the interests of user U identified by the user ID. In other words, "interests" indicate subjects in which user U identified by the user ID is highly interested. For example, "interests" may be search queries (keywords) entered by user U into a search engine. Note that, although one "interest" is illustrated for each user U in the example shown in FIG. 5, there may be multiple "interests."

For example, in the example shown in FIG. 5, the age of user U identified by user ID "U1" is "20s" and the gender is "male." Also, for example, user U identified by user ID "U1" indicates that his home address is "LC11." Also, for example, user U identified by user ID "U1" indicates that his workplace is "LC12." Also, for example, user U identified by user ID "U1" indicates that he is interested in "sports."

In the example shown in FIG. 5, abstract values such as "U1", "LC11", and "LC12" are used to illustrate the data, but it is assumed that concrete information such as character strings and numerical values is stored in "U1", "LC11", and "LC12". In the following figures relating to other information, abstract values may also be illustrated.

The user information database 121 may store various information according to the purpose, not limited to the above. For example, the user information database 121 may store various information related to the terminal device 10 of the user U. The user information database 121 may also store information related to the attributes of the user U, such as demographic attributes, psychographic attributes, geographic attributes, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, place of origin (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not the user has a car, commuting time, commuting route, commuter pass section (station, line, etc.), frequently used station (other than the nearest station to home or workplace), extracurricular activities (location, time period, etc.), hobbies, interests, and lifestyle.

(History information database 122)
The history information database 122 stores various information related to history information (log data) indicating the behavior of the user U. Fig. 6 is a diagram showing an example of the history information database 122. In the example shown in Fig. 6, the history information database 122 has items such as "user ID", "location history", "search history", "browsing history", "purchase history", and "posting history".

"User ID" refers to identification information for identifying user U. "Location history" refers to location history, which is a history of user U's location and movements. "Search history" refers to search history, which is a history of search queries entered by user U. "Browse history" refers to browse history, which is a history of content viewed by user U. "Purchase history" refers to purchase history, which is a history of purchases made by user U. "Post history" refers to posting history, which is a history of posts made by user U. "Post history" may include questions about user U's possessions.

For example, in the example shown in FIG. 6, user U identified by user ID "U1" moves as shown in "location history #1," searches as shown in "search history #1," views content as shown in "browsing history #1," purchases specific products at specific stores as shown in "purchase history #1," and posts as shown in "posting history."

In the example shown in FIG. 6, abstract values such as "U1", "Location History #1", "Search History #1", "Browse History #1", "Purchase History #1", and "Post History #1" are used for illustration, but it is assumed that specific information such as character strings and numerical values is stored in "U1", "Location History #1", "Search History #1", "Browse History #1", "Purchase History #1", and "Post History #1".

The history information database 122 may store various information depending on the purpose, not limited to the above. For example, the history information database 122 may store the usage history of a specific service by the user U. The history information database 122 may also store the history of the user U's visit to a physical store or the history of the user U's visit to a facility. The history information database 122 may also store the payment history of the user U's electronic payment.

(Behavioral Information Database 123)
The behavioral information database 123 stores various information related to the advertisement exposure and interest behavior of the user U. Fig. 7 is a diagram showing an example of the behavioral information database 123. In the example shown in Fig. 7, the behavioral information database 123 has items such as "user ID", "advertisement exposure", "interest behavior", and "action".

"User ID" indicates identification information for identifying user U. "Advertisement exposure" indicates whether or not user U has come into contact with an advertisement. Here, "advertisement exposure" indicates "O" if the user has come into contact with an advertisement, and "X" if the user has not come into contact with an advertisement. The "advertisement exposure" item may be provided for each advertisement (or each advertisement category). "Interest behavior" indicates whether or not user U has taken an interest behavior. Here, "interest behavior" indicates "O" if the user has taken an interest behavior, and "X" if the user has not taken an interest behavior. "Action" indicates whether or not user U has taken an action. Here, "action" indicates "O" if the user has taken an action, and "X" if the user has not taken an action.

For example, in the example shown in FIG. 7, user U, identified by user ID "U1," has "Ad exposure," "Interest behavior," and "Action" all marked as "O," indicating that the user went from exposure to an ad to taking an interest behavior and ultimately taking an action.

The behavioral information database 123 may store various information according to the purpose, not limited to the above. For example, the behavioral information database 123 may store identification information for identifying individual advertisements, information on category names indicating categories of advertisements, etc. The behavioral information database 123 may also store information on behaviors that are determined to be interest behaviors, etc. The behavioral information database 123 may also store information on the degree of interest of user U in advertisements, etc.

(Number of people information database 124)
The number of users information database 124 stores various information related to the number of users U who have come into contact with an advertisement or taken an action, which is estimated from the number of users U who have taken an interest behavior. Fig. 8 is a diagram showing an example of the number of users information database 124. In the example shown in Fig. 8, the number of users information database 124 has items such as "advertisement ID", "number of users who have taken an interest behavior", "first ratio", "number of users who have come into contact with an advertisement", "second ratio", and "number of users who have taken an action".

"Advertising ID" indicates identification information for identifying an advertisement. In reality, the advertising ID may be the category of the advertisement, the corporate brand of the company that provided the advertisement, the product brand of the product featured in the advertisement, etc. Furthermore, if there is no need to identify the advertisement, the "Advertising ID" item may be omitted.

The "number of people who engaged in an interest behavior" indicates the number of people out of an unspecified large group who engaged in an interest behavior. The "first ratio" indicates the proportion of people who engaged in an interest behavior as a result of exposure to an advertisement. The first ratio is calculated based on the number of people who were exposed to an advertisement and the number of people who engaged in an interest behavior out of a group of a specified number of people from which individual users can be identified (such as a panel of 1,000 people). The "number of people exposed to an advertisement" is a number estimated based on the number of people who engaged in an interest behavior and the first ratio, and indicates the number of people out of an unspecified large group who were exposed to an advertisement.

The "second ratio" indicates the percentage of people who took a final action based on their interest behavior. The second ratio is calculated based on the number of people who took an interest behavior and the number of people who took an action among a group of a certain number of people who can identify individual users (such as a panel of 1,000 people). The "number of people who took an action" is a number estimated based on the number of people who took the interest behavior and the second ratio, and indicates the number of people who took an action among an unspecified large group.

For example, in the example shown in FIG. 8, the number of people who took interest behavior with respect to an advertisement identified by the advertisement ID "Advertisement #A" is the interest behavior number "interest behavior #A", the number of people estimated to have come into contact with the advertisement is the advertisement contact number "advertisement contact #A" based on "interest behavior #A" and "first ratio #A", and the number of people estimated to have taken a final action is the action number "action #A" based on "interest behavior #A" and "second ratio #A".

In the example shown in FIG. 8, abstract values such as "advertisement #A", "number of people who engaged in an interest behavior #A", "first ratio #A", "number of people exposed to the ad #A", "second ratio #A" and "number of people who took an action #A" are used for illustration, but specific information such as character strings or numerical values is stored in "advertisement #A", "number of people who engaged in an interest behavior #A", "first ratio #A", "number of people exposed to the ad #A", "second ratio #A" and "number of people who took an action #A".

The number of people information database 124 may store various information according to the purpose, not limited to the above. For example, the number of people information database 124 may store information on behavior determined to be interest behavior. The number of people information database 124 may also store information on a third ratio indicating the proportion of people who did not take action from the interest behavior, the number of people who took action from exposure to an advertisement, and the number of people who did not take action from exposure to an advertisement. The number of people information database 124 may also store information on a fourth ratio indicating the proportion of people who took action from interest behavior without exposure to an advertisement, and the number of people who took action without exposure to an advertisement. The number of people information database 124 may also store information on a fifth ratio indicating the proportion of people who took interest behavior without exposure to an advertisement but did not take action from the interest behavior, and the number of people who did not exposure to an advertisement and did not take action.

(Control unit 130)
Returning to Fig. 4, the description will be continued. The control unit 130 is a controller, and is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or the like, executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information providing device 100 using a storage area such as a RAM as a working area. In the example shown in Fig. 4, the control unit 130 has an acquisition unit 131, a measurement unit 132, a calculation unit 133, an estimation unit 134, and a provision unit 135.

(Acquisition unit 131)
The acquisition unit 131 acquires a search query input by the user U. For example, when the user U inputs a search query into a search engine or the like to perform a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110.

The acquisition unit 131 also acquires user information about user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as a user ID) indicating user U, location information of user U, attribute information of user U, etc. from the terminal device 10 of user U. The acquisition unit 131 may also acquire identification information indicating user U, attribute information of user U, etc. when registering user U. Then, the acquisition unit 131 registers the user information in the user information database 121 of the storage unit 120.

The acquisition unit 131 also acquires various types of history information (log data) indicating the behavior of the user U via the communication unit 110. For example, the acquisition unit 131 acquires various types of history information indicating the behavior of the user U from the terminal device 10 of the user U, or from various servers, etc. based on the user ID, etc. Then, the acquisition unit 131 registers the various types of history information in the history information database 122 of the storage unit 120.

That is, the acquisition unit 131 acquires information about the daily behavioral history of the user U. The acquisition unit 131 also acquires information about advertisements. For example, the acquisition unit 131 acquires, as information about advertisements, information about the date and time and period when the advertisements were presented, and location information about the place where the advertisements were presented.

Furthermore, when user U takes a predetermined action, the acquisition unit 131 acquires environmental information related to the situation of user U. For example, when user U takes a predetermined action, the acquisition unit 131 acquires information related to the date and time when user U takes the predetermined action as environmental information related to the situation of user U. In addition, when user U takes a predetermined action, the acquisition unit 131 acquires location information related to the location where user U takes the predetermined action as environmental information related to the situation of user U.

The acquisition unit 131 may also acquire, as the environmental information, detection information indicating the results of the surroundings detected by the terminal device 10 of the user U when the user U takes a predetermined action. For example, the acquisition unit 131 may acquire, as the detection information, sensor information indicating the detection results of various sensors mounted on the terminal device 10 of the user U. Alternatively, the acquisition unit 131 may acquire, as the environmental information, device signal detection information indicating that the terminal device 10 of the user U has detected a signal emitted by a device that presents an advertisement.

(Measurement unit 132)
The measurement unit 132 measures the number of people who came into contact with the advertisement and the number of people who took a predetermined action after coming into contact with the advertisement among a group of a predetermined number of people (such as a panel of 1000 people) in which individual users can be identified. Furthermore, the measurement unit 132 measures the number of people who took a predetermined action after coming into contact with the advertisement among the group of the predetermined number of people, and the number of people who took the final action aimed at by the advertisement after taking the predetermined action.

Furthermore, the measurement unit 132 measures the number of people in the group of the above-mentioned predetermined number of people who took a predetermined action after coming into contact with the advertisement, and the number of people who did not take any action after taking the predetermined action. Also, the measurement unit 132 measures the number of people in the group of the predetermined number of people who took a predetermined action without coming into contact with the advertisement, and the number of people who took an action after taking the predetermined action. Also, the measurement unit 132 measures the number of people in the group of the predetermined number of people who took a predetermined action without coming into contact with the advertisement, and the number of people who did not take any action after taking the predetermined action.

The measurement unit 132 may measure the number of new users and the number of existing users in the group of the specified number of people. In this case, the measurement unit 132 measures the number of new users and existing users in the group of the specified number of people who came into contact with the advertisement and the number of people who took a specified action after coming into contact with the advertisement. The measurement unit 132 also measures the number of new users and existing users in the group of the specified number of people who took a specified action after coming into contact with the advertisement and the number of people who took the final action aimed at by the advertisement after taking the specified action.

(Calculation unit 133)
The calculation unit 133 calculates a first ratio indicating the ratio of people who have taken a predetermined action after contacting the advertisement, based on the number of people who have been exposed to the advertisement and the number of people who have taken a predetermined action after being exposed to the advertisement, among a group of a predetermined number of people who can identify individual users (such as a panel of 1000 people). Note that, when new users and existing users are distinguished, the calculation unit 133 calculates the first ratio for each of the new users and the existing users.

The calculation unit 133 also calculates a second ratio indicating the proportion of people who have taken an action from a predetermined behavior, based on the number of people who took a predetermined behavior after coming into contact with the advertisement, and the number of people who took the final action targeted by the advertisement after taking the predetermined behavior, out of the group of the predetermined number of people. Note that when a distinction is made between new users and existing users, the calculation unit 133 calculates the second ratio for each of the new users and existing users.

The calculation unit 133 also calculates a third ratio indicating the proportion of people who did not take any action after the predetermined action based on the number of people who took the predetermined action after coming into contact with the advertisement and the number of people who did not take any action after taking the predetermined action, among the group of the predetermined number of people. Note that when a distinction is made between new users and existing users, the calculation unit 133 calculates the third ratio for each of the new users and existing users.

The calculation unit 133 also calculates a fourth ratio indicating the proportion of people who took an action from a predetermined behavior, based on the number of people who took a predetermined behavior without being exposed to the advertisement and the number of people who took an action after taking the predetermined behavior, among the group of the predetermined number of people. Note that when a distinction is made between new users and existing users, the calculation unit 133 calculates the fourth ratio for each of the new users and existing users.

The calculation unit 133 also calculates a fifth ratio indicating the proportion of people who did not take any action after the predetermined action, based on the number of people who took the predetermined action without being exposed to the advertisement and the number of people who did not take any action after taking the predetermined action, among the group of the predetermined number of people. Note that, when a distinction is made between new users and existing users, the calculation unit 133 calculates the fifth ratio for each of the new users and existing users.

(Estimation unit 134)
The estimation unit 134 estimates the contact between the user U and the advertisement, the user's interest in the advertisement and its degree, the number of people who have been exposed to the advertisement and taken actions associated therewith, etc. In this embodiment, the estimation unit includes an advertisement contact estimation unit 134a, an interest behavior estimation unit 134b, a degree estimation unit 134c, and a number-of-people estimation unit 134d.

(Advertisement Contact Estimation Unit 134a)
The advertisement contact estimation unit 134a estimates contact with an advertisement from the daily behavior history of the user U. For example, the advertisement contact estimation unit 134a estimates the date and time of contact with the advertisement from the date and time included in the daily behavior history of the user U and the date and time when the advertisement was presented. The date and time when the advertisement was presented may be the period during which the advertisement was presented. Alternatively, the advertisement contact estimation unit 134a estimates the location of contact with the advertisement from the location information included in the daily behavior history of the user U and the location information of the advertisement.

(Interest behavior estimation unit 134b)
When contact with the advertisement is estimated, the interest behavior estimation unit 134b estimates that the predetermined behavior is behavior (interest behavior) associated with contact with the advertisement from environmental information related to the situation of the user U. At this time, the interest behavior estimation unit 134b related to the situation of the user U estimates that the predetermined behavior is behavior associated with contact with the advertisement, and estimates that the user U has become interested in the advertisement. In other words, the interest behavior estimation unit 134b estimates that the user U has become interested in the advertisement and has taken a behavior associated with contact with the advertisement.

The specified action is different from the final action (purchase, visit, etc.) aimed at by the advertisement. For example, the specified action is an intermediate action (intermediate action) that takes place between exposure to the advertisement and the action. Here, an example of the specified action will be described in which a search is performed in a search engine or the like using keywords related to the content of the advertisement as a search query. The search engine may be an in-site search engine for electronic commerce used in EC sites, online malls, etc. Furthermore, the specified action is not limited to one action, but may be multiple actions. Furthermore, the specified action is not limited to a single action, but may be a series of consecutive actions.

For example, if the date and time when the advertisement was encountered and the date and time when the specified behavior was performed satisfy a predetermined condition, the interest behavior estimation unit 134b estimates that the specified behavior is behavior associated with the encounter with the advertisement. In addition, if the location where the advertisement was encountered and the location where the specified behavior was performed satisfy a predetermined condition, the interest behavior estimation unit 134b estimates that the specified behavior is behavior associated with the encounter with the advertisement.

In addition, when detection information indicating the result of the terminal device 10 of the user U detecting the surroundings when the user U performs a predetermined behavior is acquired as environmental information, the interest behavior estimation unit 134b may estimate that the predetermined behavior is a behavior associated with contact with an advertisement based on the detection information.

In addition, when device signal detection information indicating that the terminal device 10 of the user U has detected a signal emitted by a device presenting an advertisement is acquired as environmental information, the interest behavior estimation unit 134b may estimate that a specific behavior is a behavior associated with contact with the advertisement based on the device signal detection information.

(Degree Estimation Unit 134c)
The degree estimation unit 134c estimates the degree of the user's interest in the advertisement from the timing of the predetermined behavior when contact with the advertisement is estimated. For example, the degree estimation unit 134c estimates the degree of the user's interest in the advertisement from the number of predetermined behaviors after contact with the advertisement. Alternatively, the degree estimation unit 134c estimates the degree of the user's interest in the advertisement from the interval from the time of contact with the advertisement to the time of taking the predetermined behavior. Furthermore, when contact with multiple advertisements related to the same target is estimated, the degree estimation unit 134c may estimate the degree of the user's interest in the advertisement for each advertisement from the timing of the predetermined behavior.

(Number of people estimation unit 134d)
The number estimation unit 134d estimates the number of people from an unspecified number of people who have taken a specified action from the group, based on a first ratio indicating the proportion of people who have taken the specified action after coming into contact with the advertisement.

In addition, the number-of-people estimation unit 134d estimates the number of people who took an action from the unspecified large group based on the number of people who took a predetermined behavior from the unspecified large group and a second ratio indicating the proportion of people who took an action from the predetermined behavior.

In addition, the number estimation unit 134d estimates the number of people in an unspecified large group who took an action as a result of contact with the advertisement and the number of people who did not take any action as a result of contact with the advertisement based on the number of people in the unspecified large group who took a specified action, a second ratio indicating the proportion of people who took an action as a result of the specified action, and a third ratio indicating the proportion of people who did not take any action as a result of the specified action.

In addition, the number estimation unit 134d estimates the number of people in the unspecified large group who took an action without being exposed to the advertisement, based on the number of people in the unspecified large group who took a predetermined behavior and a fourth ratio that indicates the proportion of people who took an action from the predetermined behavior.

In addition, the number estimation unit 134d estimates the number of people in the unspecified large group who were not exposed to the advertisement and did not take any action based on the number of people in the unspecified large group who took a specified action and a fifth ratio that indicates the proportion of people who did not take any action from the specified action.

In this way, the number of people estimation unit 134d estimates the number of people in an unspecified large group who took an action after coming into contact with the advertisement, the number of people who did not take any action after coming into contact with the advertisement, the number of people who took an action without coming into contact with the advertisement, and the number of people who did not come into contact with the advertisement and did not take any action.

In addition, the number of people estimation unit 134d estimates the number of people who have come into contact with the advertisement based on the number of new users and existing users from an unspecified large group who have taken a predetermined action and a first ratio indicating the proportion of people who have taken the predetermined action after coming into contact with the advertisement.

In addition, the number-of-people estimation unit 134d estimates the number of people who took an action from the unspecified large group, based on the number of new users and existing users who took a specified action from the unspecified large group and a second ratio indicating the proportion of people who took an action from the specified action.

(Providing Department)
The providing unit 135 provides estimated information, which is a result of estimation by the estimation unit 134 (advertisement contact estimation unit 134a, interest behavior estimation unit 134b, degree estimation unit 134c, number of people estimation unit 134d), to the terminal device 10 of each user U and the external search device 200 via the communication unit 110. For example, the information providing device 100 provides information that can identify the search behavior of the user U as search behavior associated with contact with an advertisement as estimated information to the terminal device 10 of each user U and the external search device 200 via the communication unit 110. That is, the information providing device 100 notifies the terminal device 10 of each user U and the external search device 200 via the communication unit 110 that the search behavior of the user U is search behavior associated with contact with an advertisement.

The providing unit 135 also provides API (Application Programming Interface) services for various applications (hereinafter, apps) and the like, and various data, to the terminal device 10 of each user U and the external search device 200 via the communication unit 110. The providing unit 135 may also provide some kind of online web service to the terminal device 10 of each user U via the communication unit 110.

5. Processing Procedure
Next, a processing procedure performed by the information providing device 100 according to the embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the information providing device 100.

The acquisition unit 131 of the information providing device 100 acquires information about the daily behavior history of a user U who belongs to a group of a predetermined number of people (e.g., a panel of 1,000 people) from which individual users can be identified, via the communication unit 110 (step S101). For example, the acquisition unit 131 acquires identification information (e.g., a user ID) indicating the user U, as well as the daily location information and various types of history information of the user U.

Then, the advertisement contact estimation unit 134a of the estimation unit 134 of the information providing device 100 estimates contact with the advertisement from the daily behavior history of the user U (step S102). For example, the estimation unit 134 estimates that the user U has come into contact with the advertisement if the user U was near the place where the advertisement was presented on the date and time (or within the period) when the advertisement was presented.

Then, the acquisition unit 131 of the information providing device 100 acquires information related to the interest behavior of the user U and environmental information related to the situation of the user U when the interest behavior was performed. For example, when the user U inputs a search query to a search engine or the like to perform a keyword search, the acquisition unit 131 acquires the search query input by the user U and the location information at that time from the terminal device 10 of the user U or the external search device 200 via the communication unit 110 (step S103).

Then, the interest behavior estimation unit 134b of the estimation unit 134 of the information providing device 100 estimates that the interest behavior taken by user U is an interest behavior associated with contact with the advertisement, based on environmental information related to the situation of user U when the interest behavior was taken (step S104). For example, if user U inputs a search query and performs a search near the place where the advertisement was presented on the date and time (or within the period) when the advertisement was presented, the interest behavior estimation unit 134b estimates that the search is an interest behavior associated with contact with the advertisement.

Next, when contact with the advertisement is estimated, the degree estimation unit 134c of the estimation unit 134 of the information providing device 100 estimates the degree of the user's interest in the advertisement from the timing of the interest behavior (step S105). For example, the degree estimation unit 134c estimates the degree of the user's interest in the advertisement from the number of interest behaviors after contact with the advertisement and the interval from the time of contact with the advertisement to the time the interest behavior is performed.

Next, the measurement unit 132 of the information providing device 100 measures the number of people who came into contact with the advertisement and the number of people who took an interest behavior after coming into contact with the advertisement (step S106). Here, an example of an interest behavior will be described in which a user U inputs a search query into a search engine or the like to perform a keyword search.

Then, the calculation unit 133 of the information providing device 100 calculates a ratio indicating the proportion of each number of people in a group of a predetermined number of people based on the measured number of people (step S107). Note that, when new users and existing users are distinguished, the calculation unit 133 calculates the ratio for each of the new users and existing users. For example, the calculation unit 133 calculates a first ratio indicating the proportion of people who have taken an interest behavior from contact with the advertisement based on the number of people who have come into contact with the advertisement and the number of people who have taken an interest behavior after coming into contact with the advertisement in the group of the predetermined number of people. In addition, the calculation unit 133 calculates a second ratio indicating the proportion of people who have taken an action from an interest behavior based on the number of people who have taken an interest behavior after coming into contact with the advertisement and the number of people who have taken a final action aimed at by the advertisement in the group of the predetermined number of people.

Next, the number of people estimation unit 134d of the estimation unit 134 of the information providing device 100 estimates the number of people in the unspecified large group who came into contact with the advertisement and the number of people who took an action based on the calculated ratio and the number of people in the unspecified large group who took the interest behavior (step S108). For example, the number of people estimation unit 134d estimates the number of people in the unspecified large group who came into contact with the advertisement based on the number of people in the unspecified large group who took the interest behavior and a first ratio indicating the proportion of people who took the interest behavior from contact with the advertisement. In addition, the number of people estimation unit 134d estimates the number of people in the unspecified large group who took an action based on the number of people in the unspecified large group who took the interest behavior and a second ratio indicating the proportion of people who took an action from the interest behavior.

Then, the providing unit 135 of the information providing device 100 provides estimated information based on the estimation results by the estimation unit 134 (advertising contact estimation unit 134a, interest behavior estimation unit 134b, degree estimation unit 134c, number of people estimation unit 134d) to the terminal device 10 of each user U or the external search device 200 via the communication unit 110 (step S109). Note that the estimated information may be the estimation result itself, or various information generated based on the estimation result.

6. Modifications
The terminal device 10 and the information providing device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, modifications of the embodiment will be described below.

6-1. Standalone operation of terminal device
In the above embodiment, some or all of the processes executed by the information providing device 100 and the external search device 200 may actually be executed by the terminal device 10. For example, the processes may be completed in a stand-alone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the information providing device 100 and the external search device 200 in the above embodiment. From the perspective of the user U, it appears that the processes of the information providing device 100 and the external search device 200 are also executed by the terminal device 10. In other words, from another perspective, it can be said that the terminal device 10 is equipped with the information providing device 100 and the external search device 200.

[6-2. Actions other than search actions]
In the above embodiment, the case where the user U who has come into contact with the advertisement performs a search action is described, but the search action is merely one example of a user's action associated with the advertisement. In other words, the action is not limited to a search action, and may be any action that the user may perform in response to the advertisement.

For example, if a user U who is presumed to have come into contact with an advertisement takes a picture of the advertisement and posts it on social media or a video site along with a comment, or contacts other users to ask for their opinions on the product advertised, these can be considered to be actions associated with exposure to the advertisement. Postings on social media or video sites, and contact with other users, can be presumed from various historical information.

In addition, if a user U who is presumed to have come into contact with an advertisement outdoors enters a brick-and-mortar store that sells the advertised product in order to actually examine that product, or approaches the vicinity of the facility featured in the advertisement (goes close by to check it out), these actions can also be considered to be actions associated with exposure to the advertisement. Entry into a brick-and-mortar store or approach to the vicinity of a facility can be presumed from location information.

In addition to movement from outdoors to a physical store, when a user U is presumed to have come into contact with an advertisement such as an in-store announcement or a flyer in a store, moving to the shelf or floor (floor) where the advertised product is located in the store to actually examine that product, or actually purchasing that product, can also be considered to be behavior associated with exposure to an advertisement. Movement within a store can be estimated using technologies such as beacon positioning and pedestrian dead reckoning (PDR).

[6-3. Estimating advertising exposure using information other than location information]
In addition, in the above embodiment, the information providing device 100 estimates that user U has come into contact with an advertisement based on location information, but in reality, it may also estimate that user U has come into contact with an advertisement based on other information without using location information.

For example, the information providing device 100 may acquire audio information that records surrounding environmental sounds from the terminal device 10 of the user U, and if the audio information includes the audio of an audio advertisement or a video advertisement, it may infer that the user U has come into contact with the advertisement.

In addition, the information providing device 100 may acquire surrounding image information from the terminal device 10 of the user U using a photographing function, and if the image information includes an advertisement, it may be inferred that the user U has come into contact with the advertisement.

The information providing device 100 may also acquire sensor information (detection information) indicating the detection results of a sensor from the terminal device 10 of the user U, and if the sensor information indicates that an advertisement has been detected, or if the sensor information indicates a numerical value corresponding to the detection result in the surrounding environment of the advertisement, may infer that the user U has come into contact with the advertisement. For example, if the advertisement repeats a special lighting pattern (flashing, color change, etc.), the information providing device 100 may infer that the user U has come into contact with the advertisement if the sensor information from the terminal device 10 indicates this special lighting pattern. The information providing device 100 may also infer that the user U has come into contact with the advertisement if it acquires device signal detection information from the terminal device 10 of the user U indicating that a signal from a device presenting the advertisement has been detected.

7. Effects
As described above, the information providing device 100 according to the present application includes a measurement unit 132 that measures the number of people who were exposed to an advertisement and the number of people who took a predetermined action after being exposed to the advertisement, among a group of a specified number of people who can identify individual users; a calculation unit 133 that calculates a first ratio indicating the proportion of people who took the predetermined action after being exposed to the advertisement, based on the measured number of people who were exposed to the advertisement and the number of people who took the predetermined action; and a number estimation unit 134d that estimates the number of people from an unspecified number of people who were exposed to the advertisement, based on the first ratio and the number of people who took the predetermined action among the unspecified number of people.

In addition, the measurement unit 132 measures the number of people in a group of a specified number of people who took a specified action after coming into contact with the advertisement, and the number of people who took the final action intended by the advertisement after taking the specified action. Next, the calculation unit 133 calculates a second ratio indicating the proportion of people who took the action from the specified action, based on the measured number of people who took the specified action and the number of people who took the action. Then, the number estimation unit 134d estimates the number of people in the unspecified large group who took the action, based on the number of people in the unspecified large group who took the specified action and the second ratio.

In addition, the measurement unit 132 measures the number of people in a group of a specified number of people who took a specified action after coming into contact with the advertisement, and the number of people who did not take any action after taking the specified action. Next, the calculation unit 133 calculates a third ratio indicating the proportion of people who did not take any action after the specified action, based on the measured number of people who took the specified action and the number of people who did not take any action. Then, the number estimation unit 134d estimates the number of people in the unspecified large group who took an action after coming into contact with the advertisement, and the number of people who did not take any action after coming into contact with the advertisement, based on the number of people in the unspecified large group who took the specified action, the second ratio, and the third ratio.

In addition, the measurement unit 132 measures the number of people in a group of a specified number of people who took a specified behavior without being exposed to an advertisement, and the number of people who took an action after taking the specified behavior. Next, the calculation unit 133 calculates a fourth ratio indicating the proportion of people who took an action from a specified behavior, based on the measured number of people who took the specified behavior and the number of people who took an action. Then, the number estimation unit 134d estimates the number of people in the unspecified large group who took an action without being exposed to an advertisement, based on the number of people in the unspecified large group who took the specified behavior and the fourth ratio.

In addition, the measurement unit 132 measures the number of people in a group of a specified number of people who took a specified action without being exposed to the advertisement, and the number of people who took the specified action but did not take any action. Next, the calculation unit 133 calculates a fifth ratio indicating the proportion of people who did not take any action from the specified action, based on the measured number of people who took the specified action and the number of people who did not take any action. Then, the number estimation unit 134d estimates the number of people in the unspecified large group who were not exposed to the advertisement and did not take any action, based on the number of people in the unspecified large group who took the specified action and the fifth ratio.

In addition, the number of people estimation unit 134d estimates the number of people in an unspecified large group who took an action after coming into contact with the advertisement, the number of people who did not take any action after coming into contact with the advertisement, the number of people who took an action without coming into contact with the advertisement, and the number of people who did not come into contact with the advertisement and did not take any action.

The measurement unit 132 also measures the number of new users and the number of existing users in a group of a specified number of people.

In addition, the measurement unit 132 measures the number of new users and existing users in a group of a specified number of people who were exposed to the advertisement and the number of people who took a specified action after being exposed to the advertisement. Next, the calculation unit 133 calculates a first ratio indicating the proportion of people who took a specified action after being exposed to the advertisement, based on the measured number of people who were exposed to the advertisement and the number of people who took a specified action, for each of the new users and existing users. Then, the number estimation unit 134d estimates the number of people who were exposed to the advertisement, based on the number of people who took the specified action and the first ratio, for each of the new users and existing users in an unspecified large group.

In addition, the measurement unit 132 measures the number of new users and existing users in the group of a specified number of people who took a specified action after coming into contact with the advertisement, and the number of people who took the final action targeted by the advertisement after taking the specified action. Next, the calculation unit 133 calculates a second ratio indicating the proportion of people who took an action from a specified action, based on the measured number of people who took the specified action and the number of people who took the action, for each of the new users and existing users. Then, the number estimation unit 134d estimates the number of people who took an action in the unspecified large group, based on the number of people who took the specified action and the second ratio, for each of the new users and existing users in the unspecified large group.

By using any one or a combination of the above-mentioned processes, the information providing device according to the present application can estimate whether a user who came into contact with an advertisement was interested in the advertisement. In addition, the effectiveness of the advertisement can be measured without relying on the click-through rate (CTR) or conversion rate (CVR) of the advertisement.

8. Hardware Configuration
Moreover, the terminal device 10 and the information providing device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration as shown in Fig. 10, for example. The information providing device 100 will be described below as an example. Fig. 10 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I/F (Interface) 1060, an input I/F 1070, and a network I/F 1080 are connected by a bus 1090.

The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized, for example, by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that primarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), a HDD (Hard Disk Drive), a SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may be a removable storage medium such as a USB memory or a SD (Secure Digital) memory card. The secondary storage device 1050 may be a cloud storage device (online storage device), a NAS (Network Attached Storage), a file server, or the like.

The output I/F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, projector, printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). The input I/F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, keypad, button, scanner, etc., and is realized by a USB, etc.

In addition, the output I/F 1060 and the input I/F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

The output device 1010 and the input device 1020 may be integrated together, such as a touch panel. In this case, the output I/F 1060 and the input I/F 1070 may also be integrated together as an input/output I/F.

The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

The network I/F 1080 receives data from other devices via the network N and sends it to the computing device 1030, and also transmits data generated by the computing device 1030 to other devices via the network N.

The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I/F 1060 and the input I/F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

For example, when the computer 1000 functions as the information providing device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I/F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with other devices via the network I/F 1080 and use the functions and data of a program by calling them from other programs of the other devices.

[9. Other]
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. The above-described components include those that a person skilled in the art can easily imagine, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-described components can be appropriately combined. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

For example, the information providing device 100 described above may be realized by multiple server computers, and depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

The above-described embodiments and variations can be combined as appropriate to the extent that they do not cause inconsistencies in the processing content.

In addition, the above-mentioned "section, module, unit" can be read as "means" or "circuit." For example, an acquisition unit can be read as an acquisition means or an acquisition circuit.

REFERENCE SIGNS LIST 1 Information processing system 10 Terminal device 100 Information providing device 110 Communication unit 120 Storage unit 121 User information database 122 History information database 123 Behavior information database 124 Number of people information database 130 Control unit 131 Acquisition unit 132 Measurement unit 133 Calculation unit 134 Estimation unit 134a Advertisement contact estimation unit 134b Interest behavior estimation unit 134c Degree estimation unit 134d Number of people estimation unit 135 Provision unit

Claims (11)

an advertisement exposure estimation unit that estimates that a user has been exposed to an advertisement when the user has taken an action that may cause the user to see or hear an advertisement, such as visiting or passing by a place where an advertisement is posted or broadcast, or purchasing a magazine in which the advertisement is published, based on history information of the user that indicates the user's daily behavior history, such as the user's location history and product purchase history;
a measurement unit that measures the number of people who have come into contact with an advertisement among a group of a predetermined number of identifiable individual users, and the number of people who have come into contact with the advertisement and have taken a predetermined action that is estimated to be related to the advertisement, such as an intermediate action but not a final action corresponding to a conversion targeted by the advertisement, after coming into contact with the advertisement in the history information;
a calculation unit that calculates a first ratio indicating a ratio of people who have taken the predetermined behavior as a result of contact with the advertisement, based on the measured number of people who have been in contact with the advertisement and the measured number of people who have taken the predetermined behavior;
a number-of-users estimation unit that estimates the number of people who have come into contact with the advertisement from among a group of arbitrary users, based on the number of people who have taken the predetermined behavior in the history information from among the group of arbitrary users and the first ratio;
An information processing device comprising: the measurement unit measures, among the group of the predetermined number of people, the number of people who took the predetermined action after contacting the advertisement, and the number of people who took a final action corresponding to a conversion intended by the advertisement after taking the predetermined action;
the calculation unit calculates a second ratio indicating a ratio of people who have taken the action to the predetermined behavior based on the measured number of people who have taken the predetermined behavior and the measured number of people who have taken the action;
The information processing device according to claim 1 , wherein the number of people estimation unit estimates the number of people who have taken the action in a group of any given users based on the number of people who have taken the predetermined behavior in the group of any given users and the second ratio. the measurement unit measures, among the group of the predetermined number of people, the number of people who took the predetermined action after contacting the advertisement and the number of people who did not take the action after taking the predetermined action;
the calculation unit calculates a third ratio indicating a ratio of people who did not take the action from the predetermined action based on the measured number of people who took the predetermined action and the measured number of people who did not take the action;
The information processing device according to claim 2, characterized in that the number of people estimation unit estimates the number of people in a group of arbitrary users who took the action as a result of contact with the advertisement and the number of people who did not take the action as a result of contact with the advertisement based on the number of people in a group of arbitrary users who took the specified behavior, the second ratio, and the third ratio. the measurement unit measures, among the group of the predetermined number of people, the number of people who took the predetermined behavior without contacting the advertisement and the number of people who took the action after taking the predetermined behavior;
the calculation unit calculates a fourth ratio indicating a ratio of people who have taken an action based on the measured number of people who have taken the predetermined behavior and the measured number of people who have taken the action;
The information processing device according to claim 2 or 3, characterized in that the number of people estimation unit estimates the number of people in a group of arbitrary users who took the action without being exposed to the advertisement based on the number of people in the group of arbitrary users who took the specified behavior and the fourth ratio. the measurement unit measures, among the group of the predetermined number of people, the number of people who took the predetermined action without contacting the advertisement and the number of people who took the predetermined action but did not take the action after;
the calculation unit calculates a fifth ratio indicating a ratio of people who did not take the action from the predetermined action based on the measured number of people who took the predetermined action and the measured number of people who did not take the action;
The information processing device described in any one of claims 2 to 4, characterized in that the number of people estimation unit estimates the number of people in a group of arbitrary users who did not come into contact with the advertisement or take the action based on the number of people in the group of arbitrary users who took the specified action and the fifth ratio. The information processing device described in any one of claims 2 to 5, characterized in that the number of people estimation unit estimates, among an arbitrary group of users, the number of people who took the action after coming into contact with the advertisement, the number of people who did not take the action after coming into contact with the advertisement, the number of people who took the action without coming into contact with the advertisement, and the number of people who did not come into contact with the advertisement and did not take the action. 7. The information processing apparatus according to claim 2, wherein the measurement unit measures the number of new users and the number of existing users in the group of the predetermined number of people. the measurement unit measures, for each of new users and existing users in the group of the predetermined number of people, the number of people who came into contact with the advertisement and the number of people who took a predetermined action after coming into contact with the advertisement;
the calculation unit calculates, for each of new users and existing users, a first ratio indicating a ratio of people who have taken the predetermined behavior as a result of contact with the advertisement, based on the measured number of people who have been in contact with the advertisement and the measured number of people who have taken the predetermined behavior;
The information processing device according to claim 7, characterized in that the number of people estimation unit estimates the number of people who have come into contact with the advertisement for each of new users and existing users in a group of any user, based on the number of people who have taken the specified behavior and the first ratio. the measurement unit measures, for each of new users and existing users among the group of the predetermined number of people, the number of people who took the predetermined action after contacting the advertisement, and the number of people who took a final action equivalent to a conversion aimed at by the advertisement after taking the predetermined action;
the calculation unit calculates, for each of new users and existing users, a second ratio indicating a ratio of people who have taken the action from the predetermined behavior based on the measured number of people who have taken the predetermined behavior and the measured number of people who have taken the action;
The information processing device according to claim 7 or 8, characterized in that the number of people estimation unit estimates the number of people who have taken the action in a group of any given users based on the number of people who have taken the specified behavior and the second ratio for each of new users and existing users in the group of any given users. An information processing method executed by an information processing device,
an advertisement exposure estimation step of estimating that a user has been exposed to an advertisement when the user has taken an action that may lead to the user seeing or hearing an advertisement, such as visiting or passing by a place where an advertisement is posted or broadcast, or purchasing a magazine in which the advertisement is published, based on the user's history information indicating the user's daily behavior history, such as the user's location history and product purchase history;
a measuring step of measuring the number of people who have been exposed to an advertisement among a group of a predetermined number of identifiable individual users, and the number of people who have been exposed to the advertisement and have taken a predetermined action that is estimated to be related to the advertisement, such as an intermediate action but not a final action corresponding to the conversion targeted by the advertisement, after being exposed to the advertisement in the history information;
a calculation step of calculating a first ratio indicating a ratio of people who have taken the predetermined action as a result of contact with the advertisement, based on the measured number of people who have been in contact with the advertisement and the measured number of people who have taken the predetermined action;
a number-of-users estimation step of estimating the number of people who have come into contact with the advertisement from among a group of arbitrary users, based on the number of people who have taken the predetermined behavior in the history information from among the group of arbitrary users and the first ratio;
13. An information processing method comprising: an advertisement exposure estimation step of estimating that a user has been exposed to an advertisement when the user has taken an action that may lead to the user seeing or hearing an advertisement, such as visiting or passing by a place where an advertisement is posted or broadcast, or purchasing a magazine in which the advertisement is published, based on the user's history information indicating the user's daily behavior history, such as the user's location history or product purchase history;
a measurement procedure for measuring the number of people who have been exposed to an advertisement among a group of a predetermined number of identifiable individual users, and the number of people who have been exposed to the advertisement and have taken a predetermined action that is estimated to be related to the advertisement, such as an intermediate action but not a final action corresponding to the conversion targeted by the advertisement, after being exposed to the advertisement in the history information;
a calculation step of calculating a first ratio indicating a ratio of people who have taken the predetermined action as a result of contact with the advertisement, based on the measured number of people who have been in contact with the advertisement and the measured number of people who have taken the predetermined action;
a number-of-users estimation step of estimating the number of people who have come into contact with the advertisement from among a group of arbitrary users, based on the number of people who have taken the predetermined behavior in the history information from among the group of arbitrary users and the first ratio;
An information processing program for causing a computer to execute the above.

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