Arrival information estimation apparatus, method for estimating arrival information, and program for estimating arrival information

Abstract

To estimate the degree of arrival of a predetermined number of pieces of information among a plurality of pieces of information.SOLUTION: An arrival information estimation apparatus includes an estimation unit. The estimation unit obtains second arrival information relating to the number of contact persons who are estimated to contact a predetermined number of pieces of information of a plurality of pieces of information on the basis of first arrival information relating to the number of estimated contact persons that are estimated to contact at least one piece of information among the plurality of pieces of information and correspondence information. The correspondence information represents the correspondence between an index relating to the plurality of pieces of information and a ratio of the second arrival information to the first arrival information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technology for estimating arrival information. [Background technology]

[0002] In television broadcasts, advertisers' advertisements are broadcast as commercials (Commercial Messages). Commercials are broadly divided into time commercials and spot commercials. Time commercials are commercials that are broadcast in time slots that are bought and sold together with programs. Time commercials are also called program commercials, program commercials, or sponsored commercials.

[0003] Spot commercials are commercials that are broadcast in time slots set by broadcasting stations regardless of the program. Spot commercials are classified into station breaks (SB), which are broadcast between programs, and participation commercials (PT), which are inserted within programs but are not sponsored by the program.

[0004] Reach is sometimes used as an indicator of the advertising effectiveness of a commercial. Reach represents the percentage of people who were exposed to a particular commercial at least once within a certain period of time among a group of survey subjects. Reach is also sometimes called the reach rate.

[0005] Regarding the reach of commercials, an advertising contact situation analysis system capable of performing highly accurate reach estimation is known (see, for example, Patent Document 1). A data processing device is also known that appropriately calculates the scale of the number of people who come into contact with at least one of the first information and the second information published in a plurality of information publishing media (see, for example, Patent Document 2).

[0006] Even when it is not possible to directly measure the distribution of users according to the degree of contact with both the first content and the second content, there is also known an information processing device that can acquire the distribution (see, for example, Patent Document 3). There is also known an analysis system that appropriately identifies incremental reach as the number of people who have not been exposed to the first information but have been exposed to the second information for various types of information published in cross-media (see, for example, Patent Document 4). [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 2018-028859 [Patent Document 2] Japanese Patent Publication No. 2020-161037 [Patent Document 3] Patent Publication No. 2021-157567 [Patent Document 4] Japanese Patent Publication No. 2022-156971 Summary of the Invention [Problem to be solved by the invention]

[0008] The technologies in Patent Documents 1 to 4 make it possible to estimate the reach of commercials that have been aired in the past. However, it is difficult to predict the reach per exposure for a commercial that is scheduled to be aired. The reach per exposure represents the proportion of people among a group of survey subjects who have been exposed to a particular commercial a specified number of times within a certain period of time.

[0009] This problem is not limited to reach, but arises when predicting various types of reach information that indicate the degree to which a commercial has reached survey subjects (reach).Furthermore, this problem is not limited to reach information for television commercials, but arises when predicting reach information that indicates the reach of various information provided through various media.

[0010] In one aspect, the present invention aims to estimate the reach of a predetermined number of pieces of information among a plurality of pieces of information. [Means for solving the problem]

[0011] In one proposal, the arrival information estimation device includes an estimation unit. The estimation unit calculates second arrival information regarding the number of estimated contacts estimated to come into contact with a predetermined number of pieces of information among the plurality of pieces of information based on first arrival information regarding the number of estimated contacts estimated to come into contact with one or more pieces of information among the plurality of pieces of information and correspondence information. The correspondence information represents a correspondence relationship between an index regarding the plurality of pieces of information and a ratio of the second arrival information to the first arrival information. [Effects of the Invention]

[0012] According to one aspect, it is possible to estimate the reach of a predetermined number of pieces of information among a plurality of pieces of information. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a functional configuration diagram of a reachability information estimation device according to an embodiment. [Figure 2] FIG. 1 is a configuration diagram of a prediction system. [Figure 3] FIG. 2 is a functional configuration diagram of a reachability information estimation device included in the prediction system. [Figure 4] FIG. 10 is a diagram showing the composition ratio C(n). [Figure 5] 10 is a flowchart of an estimation process. [Figure 6] FIG. 1 is a diagram illustrating a function F(n). [Figure 7] FIG. 2 is a hardware configuration diagram of an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments will be described in detail with reference to the drawings.

[0015] FIG. 1 illustrates an example of the functional configuration of a reach information estimation device according to an embodiment. The reach information estimation device 101 in FIG. 1 includes an estimation unit 111. The estimation unit 111 calculates second reach information regarding the number of estimated contacts estimated to come into contact with a predetermined number of pieces of information among the plurality of pieces of information, based on first reach information regarding the number of estimated contacts estimated to come into contact with one or more pieces of information among the plurality of pieces of information and correspondence information. The correspondence information represents the correspondence between an index regarding the plurality of pieces of information and the ratio of the second reach information to the first reach information.

[0016] The reach information estimation device 101 in FIG. 1 can estimate the reach of a predetermined number of pieces of information among a plurality of pieces of information.

[0017] A contact is a subject who has come into contact with the subject for whom information is provided. Subjects and contacts can be, for example, households or individuals. Subjects and contacts can also be groups other than households that contain one or more individuals.

[0018] The methods of providing information that are the subject of contact include television broadcasting, radio broadcasting, internet broadcasting, distribution on the web, display on digital signage devices, etc., display on outdoor billboards, publication in newspapers, magazines, etc. The information provided includes advertisements, broadcast programs, news, articles, etc. related to products or services.

[0019] Those who come into contact with the information include viewers of television broadcasts, radio broadcasts, or internet broadcasts, viewers of information distributed on the web, viewers of digital signage devices or billboards, readers of newspapers, magazines, etc.

[0020] The advertisement for the product or service may be a commercial, which may be part of an advertising campaign in which spot commercials are broadcast for a specific purpose over a certain period of time.

[0021] The multiple pieces of information to be exposed may be advertisements for the same product or service, or may be advertisements for different products or services. The multiple pieces of information may be provided by the same method or by different methods. For example, in the case of an advertising campaign on television, the multiple pieces of information may correspond to commercials for a specific product or service that are repeatedly broadcast multiple times over a certain period of time.

[0022] The reach information is information indicating the reach of the provided information. The reach indicates the degree to which the provided information has reached the survey subjects. The reach information may be reach.

[0023] Fig. 2 shows an example configuration of a prediction system including the reach information estimation device 101 of Fig. 1. The prediction system of Fig. 2 includes a reach prediction device 201, a reach information estimation device 202, and a terminal device 203. The reach information estimation device 202 corresponds to the reach information estimation device 101 of Fig. 1.

[0024] The reach information estimation device 202 can communicate with the reach prediction device 201 and the terminal device 203 via a communication network 204. The communication network 204 is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network).

[0025] The reach prediction device 201 predicts the reach R of an advertisement that is scheduled to be provided in a certain period of time, and transmits the predicted reach R to the reach information estimation device 202. The advertisement may be provided repeatedly by the same provision method in a certain period of time, or may be provided by a plurality of different provision methods in a certain period of time.

[0026] Reach R represents the proportion of contacts among a set of survey subjects who are estimated to be exposed to an advertisement at least once within a certain period of time. A set of survey subjects is an example of a target set. Contacts who are estimated to be exposed to the advertisement that will be provided are an example of estimated contacts. Reach R is an example of first reach information regarding the number of estimated contacts who are estimated to be exposed to one or more pieces of information among multiple pieces of information.

[0027] The terminal device 203 transmits the number of contacts M (M is an integer equal to or greater than 1) specified by the user to the reach information estimation device 202. The user of the terminal device 203 is, for example, an advertiser who requests an advertising agency to broadcast a commercial, or an employee of the advertising agency.

[0028] The reach information estimation device 202 predicts the reach R(M) using the reach R received from the reach prediction device 201 and the number of contacts M received from the terminal device 203, and transmits the predicted reach R(M) to the terminal device 203.

[0029] Reach R(M) represents the proportion of a set of survey respondents who are estimated to be exposed to an advertisement M or more times within a certain period of time. Therefore, R(1) = R.

[0030] The terminal device 203 outputs the reach R(M) received from the reach information estimation device 202. This allows the user of the terminal device 203 to consider the advertisement presentation method, presentation schedule, etc., by referring to the reach R(M) of the advertisement to be presented.

[0031] The reach prediction device 201 can calculate the reach R, for example, by the following formula using the number N of advertisements provided in a certain period (N is an integer equal to or greater than 2).

[0032] R=Q(N) (1) Q(1)=P(1) (2) Q(i) = Q(i-1) + I(i) (i = 2 to N) (3) I(i)=P(i)-ΣD(j,i) (4) D(j,i)=P(j)×P(i)×K(j,i) (5)

[0033] P(i) represents the proportion of people estimated to be exposed to the i-th (i = 1 to N) advertisement among the set of survey subjects. K(i,j) represents the coefficient set for the combination of P(i) and P(j). Σ in equation (4) represents the summation for j = 1 to i-1.

[0034] For example, in the case of a television advertising campaign, N advertisements correspond to commercials that are repeatedly broadcast N times. In this case, the predicted audience rating of the i-th commercial can be used as P(i).

[0035] The reach prediction device 201 may obtain the reach R by a calculation method other than the calculation method using the formulas (1) to (5).

[0036] Fig. 3 shows an example of the functional configuration of the arrival information estimation device 202 in Fig. 2. The arrival information estimation device 202 in Fig. 3 includes a communication unit 311, an estimation unit 312, and a storage unit 313. The estimation unit 312 corresponds to the estimation unit 111 in Fig. 1.

[0037] The storage unit 313 stores correspondence relationship information 321. The correspondence relationship information 321 includes functions F(1) to F(L). L is an integer equal to or greater than 1 and equal to or less than N. The functions F(n) (n=1 to L) are functions that convert reach R into composition ratio C(n) and represent the correspondence relationship between reach R and composition ratio C(n). Reach R is an example of an index related to multiple pieces of information.

[0038] The composition ratio C(n) represents the ratio of partial reach PR(n) to reach R. Partial reach PR(n) (n = 1 to L-1) represents the proportion of contacts among a group of survey subjects who are estimated to be exposed to an advertisement n times within a certain period of time. Partial reach PR(L) represents the proportion of contacts among a group of survey subjects who are estimated to be exposed to an advertisement L times or more within a certain period of time. The sum of partial reach PR(1) to partial reach PR(L) is equal to reach R.

[0039] The partial reach PR(n) is an example of second reach information related to the number of estimated contacts estimated to be exposed to a predetermined number of pieces of information among a plurality of pieces of information. The composition ratio C(n) is an example of the ratio of the second reach information to the first reach information.

[0040] Figure 4 shows an example of the composition ratio C(n). The horizontal axis represents the number of contacts n, and the vertical axis represents the composition ratio C(n) (%). For example, C(1) is 19.4 (%), C(2) is 13.4 (%), and C(3) is 8.4 (%). C(15) to C(63), which correspond to n=15 to 63, are all 0 (%).

[0041] The communication unit 311 communicates with the reach prediction device 201 and the terminal device 203 via the communication network 204 .

[0042] The estimation unit 312 receives the reach R from the reach prediction device 201 and the number of contacts M from the terminal device 203 via the communication unit 311 .

[0043] The estimation unit 312 uses each function F(n) included in the correspondence information 321 to find each component ratio C(n) corresponding to the reach R from the reach R. Next, the estimation unit 312 finds the partial reach PR(n) by multiplying the reach R by the component ratio C(n). The estimation unit 312 predicts the reach R(M) by using the partial reach PR(n) to find the reach R(M) using the following equation.

[0044] R(M)=Σpartial reach PR(n) (6)

[0045] In equation (6), Σ represents the summation for n=M to L. When M=L, R(M)=PR(L).

[0046] The estimation unit 312 transmits the predicted reach R(M) to the terminal device 203 via the communication unit 311. The terminal device 203 outputs the reach R(M) received from the reach information estimation device 202. The estimation unit 312 may transmit a partial reach PR(n) to the terminal device 203 instead of the reach R(M), and the terminal device 203 may output the partial reach PR(n) instead of the reach R(M).

[0047] 2, the partial reach PR(n) can be calculated by modeling the correspondence between the reach R and the composition ratio C(n) using the correspondence information 321. This makes it possible to estimate the reach of n advertisements out of N advertisements, and to predict the reach R(M) using the partial reach PR(n).

[0048] Fig. 5 is a flowchart showing an example of estimation processing performed by the reach information estimation device 202 of Fig. 3. First, the estimation unit 312 receives the reach R from the reach prediction device 201 via the communication unit 311 (step 501), and receives the number of contacts M from the terminal device 203 (step 502).

[0049] Next, the estimation unit 312 uses each function F(n) included in the correspondence information 321 to determine each component ratio C(n) from the reach R (step 503), and multiplies the reach R by the component ratio C(n) to determine the partial reach PR(n) (step 504).

[0050] Next, the estimation unit 312 uses the partial reach PR(n) to calculate the reach R(M) according to equation (6) (step 505), and transmits the reach R(M) to the terminal device 203 via the communication unit 311 (step 506).

[0051] The estimation unit 312 can generate the function F(n) included in the correspondence information 321, for example, by using the actual reach AR of an advertisement provided during a certain period in the past and the actual partial reach APR(n) for each n number of contacts. The actual reach AR represents the proportion of contacts who have been exposed to an advertisement at least once during a certain period in the past among a set of survey subjects. The actual partial reach APR(n) represents the proportion of contacts who have been exposed to an advertisement n times during a certain period in the past among a set of survey subjects.

[0052] For example, in the case of a commercial on television broadcast, the estimation unit 312 analyzes audience rating data for the commercial collected in advance for a certain period of time to tally up the proportion of viewers who viewed the commercial at least once as the actual reach AR. Furthermore, the estimation unit 312 analyzes the audience rating data to tally up the proportion of viewers who viewed the commercial n times as the actual partial reach APR(n). The actual partial reach APR(n) is tallied for each number of views n.

[0053] In addition, in the case of advertisements distributed on the Web, the estimation unit 312 analyzes advertising effectiveness measurement data of the advertisement collected in advance for a certain period of time to tally up the proportion of viewers who viewed the advertisement at least once as the actual reach AR. Furthermore, the estimation unit 312 analyzes the advertising effectiveness measurement data to tally up the proportion of viewers who viewed the advertisement n times as the actual partial reach APR(n). The actual partial reach APR(n) is tallied for each number of views n.

[0054] Advertising effectiveness measurement data is collected, for example, through advertising effectiveness measurement surveys conducted by research companies that investigate exposure to web advertisements. Advertising effectiveness measurement surveys are conducted using tag managers used for conversion measurement, etc. Tracking tags are embedded in the surveyed advertisements in advance, and when a viewer views an advertisement, information about the viewer, the date and time of viewing, etc. is identified. The identified information is then collected as advertising effectiveness measurement data.

[0055] The estimation unit 312 aggregates the achieved reach AR and achieved partial reach APR(n) for various advertisements, and determines the ratio of the achieved partial reach APR(n) to the achieved reach AR as the achieved composition ratio AC(n).

[0056] Next, the estimation unit 312 performs fitting to fit the parameters of the function to the actual reach AR and the actual composition ratio AC(n), thereby generating an approximate function that converts the actual reach AR into the actual composition ratio AC(n).The estimation unit 312 then uses the generated approximate function as the function F(n).

[0057] The estimation unit 312 may perform fitting using the least squares method, etc. As the approximation function, a linear function, an exponential function, a logarithmic function, a polynomial function, a gamma function, etc. may be used.

[0058] FIG. 6 shows an example of a function F(n) that converts reach R to composition ratio C(n). FIG. 6(a) shows an example of function F(1). The horizontal axis represents reach R and actual reach AR (%), and the vertical axis represents composition ratio C(n) and actual composition ratio AC(n). Dashed line 601 represents function F(1), and the points around dashed line 601 represent actual reach AR and actual composition ratio AC(1) of various advertisements. In this example, function F(1) is described by the following equation:

[0059] C(1)=2E-06R 3 -0.0002R 2 -0.001R+0.786 (7)

[0060] 6(b) shows an example of function F(2). Dashed line 602 represents function F(2), and the dots around dashed line 602 represent the achieved reach AR and achieved composition ratio AC(2) of various ads. In this example, function F(2) is described by the following equation:

[0061] C(2)=1E-06R 3 -0.0003R 2 +0.0177R+0.0719 (8)

[0062] 6(c) shows an example of the function F(3). Dashed line 603 represents the function F(3), and the dots around dashed line 603 represent the actual reach AR and actual composition ratio AC(3) of various advertisements. In this example, the function F(3) is described by the following equation:

[0063] C(3)=2E-07R 3 -0.0001R 2 +0.0097R-0.0963 (9)

[0064] 6(d) shows an example of the function F(4). The dashed line 604 represents the function F(4), and the dots around the dashed line 604 represent the achieved reach AR and achieved composition ratio AC(4) of various advertisements.

[0065] 6(e) shows an example of the function F(5). The dashed line 605 represents the function F(5), and the dots around the dashed line 605 represent the achieved reach AR and achieved composition ratio AC(5) of various advertisements.

[0066] 6(f) shows an example of the function F(6). The dashed line 606 represents the function F(6), and the dots around the dashed line 606 represent the achieved reach AR and achieved composition ratio AC(6) of various advertisements.

[0067] The function F(n) may be a function that converts an index other than reach R into the composition ratio C(n). Other indexes that can be used include the GRP (Gross Rating Point) or TRP (Target Rating Point) of commercials over a certain period, the number of commercials aired over a certain period, etc. GRP, TRP, and the number of commercials are examples of indexes relating to multiple pieces of information.

[0068] GRP is the total viewership rating for households, and TRP is the total viewership rating for individuals. GRP and TRP are examples of information relating to the total number of people estimated to be exposed to each of multiple pieces of information. The number of commercials is an example of the number of multiple pieces of information.

[0069] When GRP is used, the estimation unit 312 aggregates the actual GRP, actual reach AR, and actual partial reach APR(n) for various CMs, and calculates the ratio of the actual partial reach APR(n) to the actual reach AR as the actual composition ratio AC(n).

[0070] Next, the estimation unit 312 performs fitting to fit the parameters of the function to the actual GRP and the actual composition ratio AC(n), thereby generating an approximate function that converts the actual GRP to the actual composition ratio AC(n).The estimation unit 312 then uses the generated approximate function as the function F(n).

[0071] The reach prediction device 201 predicts the GRP of a commercial that is scheduled to be broadcast in a certain period of time, and transmits the predicted GRP together with the reach R to the reach information estimation device 202.

[0072] The estimation unit 312 obtains each component ratio C(n) corresponding to the GRP from the received GRP using each function F(n) included in the correspondence information 321. The estimation unit 312 then obtains the partial reach PR(n) by multiplying the reach R by the component ratio C(n), and obtains the reach R(M) using the partial reach PR(n) according to equation (6).

[0073] When TRP is used, the estimation unit 312 aggregates the actual TRP, actual reach AR, and actual partial reach APR(n) for various commercials, and calculates the ratio of the actual partial reach APR(n) to the actual reach AR as the actual composition ratio AC(n).

[0074] Next, the estimation unit 312 performs fitting to fit the parameters of the function to the actual TRP and the actual composition ratio AC(n), thereby generating an approximate function that converts the actual TRP to the actual composition ratio AC(n).The estimation unit 312 then uses the generated approximate function as the function F(n).

[0075] The reach prediction device 201 predicts the TRP of a commercial that is scheduled to be broadcast in a certain period of time, and transmits the predicted TRP together with the reach R to the reach information estimation device 202.

[0076] The estimation unit 312 calculates each component ratio C(n) corresponding to the TRP from the received TRP using each function F(n) included in the correspondence information 321. The estimation unit 312 then calculates the partial reach PR(n) by multiplying the reach R by the component ratio C(n), and calculates the reach R(M) using the partial reach PR(n) according to equation (6).

[0077] When the number of commercials is used, the estimation unit 312 aggregates the actual number of commercials, actual reach AR, and actual partial reach APR(n) for various commercials, and calculates the ratio of the actual partial reach APR(n) to the actual reach AR as the actual composition ratio AC(n).

[0078] Next, the estimation unit 312 performs fitting to fit the parameters of the function to the actual number of CMs and the actual composition ratio AC(n), thereby generating an approximate function that converts the actual number of CMs into the actual composition ratio AC(n).The estimation unit 312 then uses the generated approximate function as the function F(n).

[0079] The user of the terminal device 203 specifies the number of commercials to be broadcast. The terminal device 203 transmits the number of commercials specified by the user to the reach information estimation device 202.

[0080] The estimation unit 312 calculates each component ratio C(n) corresponding to the number of received commercials from the number of commercials using each function F(n) included in the correspondence information 321. The estimation unit 312 then calculates the partial reach PR(n) by multiplying the reach R by the component ratio C(n), and calculates the reach R(M) using the partial reach PR(n) according to equation (6).

[0081] If it is difficult to perform fitting using only one type of function, fitting can be performed by combining multiple types of functions. Piecewise defined functions can be used as approximation functions that combine multiple types of functions. Piecewise defined functions include different types of functions for each interval of variables that represent indicators such as reach R, GRP, TRP, and number of commercials.

[0082] For example, when GRP is used as an index, a piecewise-defined function that combines two types of functions may be used as an approximate function. In this case, the domain of GRP is divided into interval A where GRP=0 to S and interval B where GRP>S, and different types of functions are used for interval A and interval B. The value S corresponding to the boundary between the two intervals may be specified by the user. The value of GRP when the function for interval A is maximum in the domain may be used as S.

[0083] Even when fitting using only one type of function is difficult, the fitting accuracy of the approximation function can be improved by using a piecewise-defined function. This improves the prediction accuracy of the composition ratio C(n), partial reach PR(n), and reach R(M) predicted using the function F(n).

[0084] The configurations of the arrival information estimation device 101 in Fig. 1 and the arrival information estimation device 202 in Fig. 3 are merely examples, and some of the components may be omitted or changed depending on the use or conditions of the arrival information estimation device. The configuration of the prediction system in Fig. 2 is merely an example, and some of the components may be omitted or changed depending on the use or conditions of the prediction system.

[0085] The flowchart in FIG. 5 is merely an example, and some of the processing may be omitted or changed depending on the configuration or conditions of the prediction system.

[0086] The component ratio C(n) shown in FIG. 4 and the function F(n) shown in FIG. 6 are merely examples, and the component ratio C(n) and the function F(n) change depending on the information provided and the method of providing it.

[0087] Equations (1) to (9) are merely examples, and other calculation formulas may be used depending on the configuration or conditions of the prediction system.

[0088] Fig. 7 shows an example of the hardware configuration of an information processing device (computer) used as the arrival information estimation device 101 in Fig. 1 and the arrival information estimation device 202 in Fig. 3. The information processing device in Fig. 7 includes a CPU (Central Processing Unit) 701, a memory 702, an input device 703, an output device 704, an auxiliary storage device 705, a medium drive device 706, and a network connection device 707. These components are hardware and are connected to each other by a bus 708.

[0089] The memory 702 is, for example, a semiconductor memory such as a read-only memory (ROM), a random access memory (RAM), or a flash memory, and stores programs and data used in processing. The memory 702 may operate as the storage unit 313 in FIG.

[0090] The CPU 701 (processor) operates as the estimation unit 111 in Fig. 1 by executing a program using the memory 702, for example. The CPU 701 also operates as the estimation unit 312 in Fig. 3 by executing a program using the memory 702.

[0091] The input device 703 is, for example, a keyboard, a pointing device, etc., and is used to input instructions or information from an operator or user. The output device 704 is, for example, a display device, a printer, a speaker, etc., and is used to output inquiries or instructions to an operator or user and processing results. The processing results may be reach R(M) or partial reach PR(n).

[0092] The auxiliary storage device 705 is, for example, a magnetic disk device, an optical disk device, a magneto-optical disk device, a tape device, etc. The auxiliary storage device 705 may be a hard disk drive or a solid state drive (SSD). The information processing device stores programs and data in the auxiliary storage device 705 and can use them by loading them into the memory 702. The auxiliary storage device 705 may operate as the storage unit 313 in FIG. 3.

[0093] The medium drive device 706 drives a portable recording medium 709 and accesses the recorded contents thereof. The portable recording medium 709 is a memory device, a flexible disk, an optical disk, a magneto-optical disk, etc. The portable recording medium 709 may be a CD-ROM (Compact Disk Read Only Memory), a DVD (Digital Versatile Disk), a USB (Universal Serial Bus) memory, etc. An operator or user can store programs and data in the portable recording medium 709 and load them into the memory 702 for use.

[0094] In this way, the computer-readable recording medium that stores the program and data used in the processing is a physical (non-transitory) recording medium such as the memory 702, the auxiliary storage device 705, or the portable recording medium 709.

[0095] The network connection device 707 is a communication device that is connected to the communication network 204 and performs data conversion associated with communication. The information processing device receives programs and data from an external device via the network connection device 707 and can load them into the memory 702 for use. The network connection device 707 may operate as the communication unit 311 in FIG. 3.

[0096] 7, some components may be omitted depending on the application or conditions. For example, if an interface with an operator or user is not required, the input device 703 and the output device 704 may be omitted. If the portable recording medium 709 or the communication network 204 is not used, the medium drive device 706 or the network connection device 707 may be omitted.

[0097] The reach prediction device 201 and the terminal device 203 in FIG. 2 can be information processing devices similar to those in FIG.

[0098] Although the disclosed embodiments and their advantages have been described in detail, those skilled in the art may make various modifications, additions, and omissions without departing from the scope of the invention as clearly set forth in the claims. [Explanation of symbols]

[0099] 101, 201 Arrival information estimation device 111, 312 Estimation part 201 Reach Prediction Device 203 Terminal Equipment 204 Communication Network 311 Communications Department 313 Storage section 321 Correspondence Information 601~606 dashed line 701 CPU 702 memory 703 Input Device 704 Output Device 705 Auxiliary storage 706 Media drive unit 707 Network Connection Device 708 Bus 709 Portable Recording Media

Claims

1. an estimation unit that calculates second arrival information regarding the number of estimated contacts who are estimated to come into contact with a predetermined number of pieces of information among the plurality of pieces of information, based on first arrival information regarding the number of estimated contacts who are estimated to come into contact with one or more pieces of information among the plurality of pieces of information and correspondence information; The reaching information estimation device, characterized in that the correspondence information represents a correspondence between an index related to the plurality of pieces of information and a ratio of the second reaching information to the first reaching information.

2. the indicator is the first arrival information, The arrival information estimation device described in claim 1, characterized in that the estimation unit uses the correspondence information to determine the ratio corresponding to the first arrival information, and determines the second arrival information using the determined ratio and the first arrival information.

3. the index is information about the total number of estimated contacts estimated to come into contact with each of the plurality of pieces of information, The arrival information estimation device described in claim 1, characterized in that the estimation unit uses the correspondence information to calculate the ratio corresponding to information regarding the total number of estimated contacts estimated to come into contact with each of the multiple pieces of information, and calculates the second arrival information using the calculated ratio and the first arrival information.

4. the index is the number of the plurality of pieces of information; The arrival information estimation device described in claim 1, characterized in that the estimation unit uses the correspondence information to determine the ratio corresponding to the number of the multiple pieces of information, and determines the second arrival information using the determined ratio and the first arrival information.

5. The first arrival information represents a proportion of the number of estimated contacts in a target population who are estimated to come into contact with the one or more pieces of information; The arrival information estimation device according to any one of claims 1 to 4, characterized in that the second arrival information represents the proportion of the number of estimated contacts in the target group who are estimated to come into contact with the specified number of pieces of information.

6. a computer executes a process of obtaining second reaching information regarding the number of estimated contacts estimated to come into contact with a predetermined number of pieces of information among the plurality of pieces of information, based on first reaching information regarding the number of estimated contacts estimated to come into contact with one or more pieces of information among the plurality of pieces of information and the correspondence relationship information; A method for estimating arrival information, characterized in that the correspondence information represents a correspondence between an index related to the plurality of pieces of information and a ratio of the second arrival information to the first arrival information.

7. a computer is caused to execute a process of obtaining second reaching information regarding the number of estimated contacts who are estimated to come into contact with a predetermined number of pieces of information among the plurality of pieces of information, based on first reaching information regarding the number of estimated contacts who are estimated to come into contact with one or more pieces of information among the plurality of pieces of information and the correspondence relationship information; A reach information estimation program, characterized in that the correspondence information represents a correspondence between an index related to the plurality of pieces of information and a ratio of the second reach information to the first reach information.

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