Reach information estimating device, reach information estimating method, and reach information estimating program

Abstract

This reach information estimating device includes an estimating unit. The estimating unit obtains second reach information relating to an estimated number of estimated contacting persons estimated to have come into contact with a predetermined number of pieces of information, among a plurality of pieces of information, on the basis of correspondence relationship information and first reach information relating to the number of estimated contacting persons estimated to have come into contact with one or more pieces of information among the plurality of pieces of information. The correspondence relationship information represents a correspondence relationship between an index relating to the plurality of pieces of information and the ratio of the second reach information to the first reach information.

Description

Title of the Invention: REACH INFORMATION ESTIMATION DEVICE, REACH INFORMATION ESTIMATION METHOD, AND REACH INFORMATION ESTIMATION PROGRAM Technical Field

[0001] The present invention relates to a reach information estimation technique. Background Art

[0002] In television broadcasting, advertisements of advertising sponsors are broadcast as commercial messages (CMs). CMs are roughly classified into time CMs and spot CMs. Time CMs are CMs that are broadcast in time slots, which are sold and purchased together with a program. Time CMs may be referred to as program commercials, program CMs, or sponsor CMs.

[0003] Spot CMs are CMs that are broadcast in time slots determined by a broadcast station, regardless of the programs.  Spot CMs are classified into station breaks (SB) that are broadcast between programs, participations (PT) that are inserted into a program but do not have a sponsor display, and the like.

[0004] ‘Reach’ may be used as an indicator of the advertising effectiveness of a CM. Reach represents the percentage of contact persons who have come into contact with a specific CM once or more during a certain period of time among a set of survey target persons.  Reach may also be referred to as a reach rate.

[0005] There is a known advertising contact analysis system that can perform accurate reach estimation regarding the reach of a CM (see Patent Literature 1, for example). There is also a known data processing device that appropriately calculates the scale of the number of contact persons who come into contact with at least one of first information and second information which is posted on a plurality of information posting media (see Patent Literature 2, for example).

[0006] There is also a known information processing device that can acquire a distribution of users for each degree of contact regarding both first content and second content even in a case where the distribution cannot be directly measured (see Patent Literature 3, for example). There is also a known analysis system that appropriately identifies incremental reach as the scale of the number of contact persons who do not come into contact with first information, but who come into contact with second information regarding various types of information posted on cross media (see Patent Literature 4, for example). Citation List Patent Literature

[0007] Patent Literature 1: JP 2018-028859 A Patent Literature 2: JP 2020-161037 A Patent Literature 3: JP 2021-157567 A Patent Literature 4: JP 2022-156971 A Summary of Invention Technical Problem

[0008] According to the techniques disclosed in Patent Literatures 1 to 4, it is possible to estimate the reach of CMs broadcast in the past. However, it is difficult to predict the reach of each number of contacts for a CM scheduled to be broadcast.  The reach for each number of contacts represents the percentage of contact persons who have come into contact with a specific CM by a designated number of contacts during a certain period of time among a set of survey target persons.

[0009] Note that such a problem is not limited to reach, and occurs in a case of predicting various types of reach information indicating a degree (reach degree) to which a CM reaches a survey target person. In addition, such a problem is not limited to reach information for a television CM, and occurs in a case of predicting reach information indicating a reach degree of various pieces of information provided through various media.

[0010] In one aspect, an object of the present invention is to estimate a reach degree of a predetermined number of pieces of information among a plurality of pieces of information. Solution to Problem

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

[0012] According to one aspect, it is possible to estimate a reach degree of a predetermined number of pieces of information among a plurality of pieces of information. Brief Description of Drawings

[0013] Fig. 1 is a functional configuration diagram of a reach information estimation device according to an embodiment. Fig. 2 is a configuration diagram of a prediction system. Fig. 3 is a functional configuration diagram of a reach information estimation device included in the prediction system. Fig. 4 is a diagram illustrating a composition ratio C(n). Fig. 5 is a flowchart of estimation processing. Fig. 6 is a diagram illustrating a function F(n). Fig. 7 is a hardware configuration diagram of an information processing device. Description of Embodiments

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

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

[0016] According to the reach information estimation device 101 in Fig. 1, it is possible to estimate a reach degree of the predetermined number of pieces of information among the plurality of pieces of information.

[0017] The contact persons are survey target persons who have come into contact with provided information among survey target persons. The survey target persons and the contact persons are, for example, households or individuals. The survey target persons and the contact persons may be groups other than households, including one or more individuals.

[0018] Methods of providing information with which the survey target persons will come into contact include television broadcasting, radio broadcasting, Internet broadcasting, distribution on the Web, display on digital signage devices or the like, display on outdoor signboards or the like, and publication in newspapers, magazines, or the like. The information to be provided is advertisements regarding products or services, broadcast programs, news, articles, and the like

[0019] The contact persons are viewers of television broadcasting, radio broadcasting, or Internet broadcasting, readers of information distributed on the Web, viewers of digital signage devices or signboards, readers of newspapers, magazines, and the like.

[0020] The advertisements regarding products or services may be CMs. The CMs may be CMs in an advertisement campaign in which spot CMs are broadcast for a specific purpose during a certain period of time.

[0021] The plurality of pieces of information with which the survey target persons will come into contact may be advertisements regarding the same product or service or advertisements regarding different products or services. The plurality of pieces of information may be provided by the same providing method or by different providing methods. For example, in a case of an advertisement campaign in television broadcasting, the plurality of pieces of information corresponds to a CM for a specific product or service that is repeatedly broadcast a plurality of times during a certain period of time.

[0022] The reach information is information indicating a reach degree of provided information. The reach degree represents the degree to which the provided information reaches the survey target persons. The reach information may be reach.

[0023] Fig. 2 illustrates a configuration example of a prediction system including the reach information estimation device 101 in Fig. 1. The prediction system in 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 in 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 local area network (LAN) or a wide area network (WAN).

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

[0026] The reach R represents the percentage of contact persons who are estimated to come into contact with the advertisement once or more within the certain period of time among a set of survey target persons. The set of survey target persons is an example of a set of target persons. The contact persons estimated to come into contact with the advertisement scheduled to be provided are examples of estimated contact persons. The reach R is an example of first reach information regarding the number of estimated contact persons who are estimated to come into contact with one or more pieces of information among the plurality of pieces of information.

[0027] The terminal device 203 transmits the number of contacts M (M is an integer of 1 or more) designated by a user to the reach information estimation device 202. The user of the terminal device 203 is, for example, an advertising sponsor who requests an advertisement company to broadcast a CM or an employee of the advertisement company.

[0028] The reach information estimation device 202 predicts 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] The reach R(M) represents the percentage of contact persons who are estimated to come into contact with the advertisement M times or more within the certain period of time among the set of survey target persons. Therefore, R(1) = R.

[0030] The terminal device 203 outputs the reach R(M) received from the reach information estimation device 202. As a result, the user of the terminal device 203 can refer to the reach R(M) of the advertisement scheduled to be provided to consider the method, schedule, and the like for providing the advertisement.

[0031] The reach prediction device 201 can calculate the reach R with, for example, the following equations using the number of advertisements N (N is an integer of 2 or more) to be provided in the certain period of time.

[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) - SD(j, i)    (4) D(j, i) = P(j) x P(i) x K(j, i)    (5)

[0033] P(i) represents a percentage of contact persons who are estimated to come into contact with the i-th (i = 1 to N) advertisement among the set of survey target persons. K(i, j) represents a coefficient set for a combination of P(i) and P(j). S in Equation (4) represents a sum of j = 1 to i - 1.

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

[0035] The reach prediction device 201 may calculate the reach R by another calculation method other than the calculation method using Equations (1) to (5).

[0036] Fig. 3 illustrates a functional configuration example of the reach information estimation device 202 in Fig. 2. The reach 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 information 321. The correspondence information 321 includes functions F(1) to F(L). L is an integer of 1 or more and N or less. The function F(n) (n = 1 to L) is a function that converts the reach R into a composition ratio C(n), and represents the correspondence between the reach R and the composition ratio C(n). The reach R is an example of an indicator for the plurality of pieces of information.

[0038] The composition ratio C(n) represents a percentage of partial reach PR(n) to the reach R. The partial reach PR(n) (n = 1 to L - 1) represents a percentage of contact persons who are estimated to come into contact with the advertisement n times during the certain period of time among the set of survey target persons.  The partial reach PR(L) represents a percentage of contact persons who are estimated to come into contact with the advertisement L times or more during the certain period of time among the set of survey target persons.  The sum of the partial reach PR(1) to the partial reach PR(L) is equal to the reach R.

[0039] The partial reach PR(n) is an example of second reach information regarding the number of estimated contact persons who are estimated to come into contact with the predetermined number of pieces of information among the plurality of pieces of information. The composition ratio C(n) is an example of a ratio of the second reach information to the first reach information.

[0040] Fig. 4 illustrates 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) corresponding 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 via the communication unit 311, and receives the number of contacts M from the terminal device 203.

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

[0044] R(M) = S partial reach PR(n)    (6)

[0045] S in Equation (6) represents a sum of n = M to L. If M = L, then 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 the partial reach PR(n) instead of the reach R(M) to the terminal device 203, and the terminal device 203 may output the partial reach PR(n) instead of the reach R(M).

[0047] According to the prediction system in Fig. 2, by modeling the correspondence between the reach R and the composition ratio C(n) using the correspondence information 321, it is possible to calculate the partial reach PR(n). As a result, it is possible to estimate the reach degree regarding n advertisements among the N advertisements, and to predict the reach R(M) using the partial reach PR(n).

[0048] Fig. 5 is a flowchart illustrating an example of estimation processing performed by the reach information estimation device 202 in 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 calculates each composition ratio C(n) from the reach R using each function F(n) included in the correspondence information 321 (step 503), and calculates the partial reach PR(n) by multiplying the reach R by the composition ratio C(n) (step 504).

[0050] Next, the estimation unit 312 calculates the reach R(M) with Equation (6) using the partial reach PR(n) (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 using, for example, actual result reach AR of an advertisement provided in a past certain period of time and actual result partial reach APR(n) for each number of contacts n.  The actual result reach AR represents a percentage of contact persons who have come into contact with the advertisement once or more during the past certain period of time among a set of survey target persons. The actual result partial reach APR(n) represents the percentage of contact persons who have come into contact with the advertisement n times during the past certain period of time among the set of survey target persons.

[0052] For example, in a case of a CM in television broadcasting, the estimation unit 312 analyzes audience rating data of the CM for a certain period of time collected in advance to aggregate the percentage of viewers who have viewed the CM once or more as the actual result reach AR. The estimation unit 312 further analyzes the audience rating data to aggregate the percentage of viewers who have viewed the CM n times as the actual result partial reach APR(n). The actual result partial reach APR(n) is aggregated for each number of viewing times n.

[0053] Alternatively, in a case of an advertisement distributed on the Web, the estimation unit 312 analyzes advertising effectiveness measurement data of the advertisement for a certain period of time collected in advance to aggregate the percentage of readers who have read the advertisement once or more as the actual result reach AR. The estimation unit 312 further analyzes the advertising effectiveness measurement data to aggregate the percentage of readers who have read the advertisement n times as the actual result partial reach APR(n). The actual result partial reach APR(n) is aggregated for each number of reading times n.

[0054] The advertising effectiveness measurement data is collected in, for example, an advertising effectiveness measurement survey conducted by a survey company that surveys how often readers come into contact with Web advertisements. The advertising effectiveness measurement survey is conducted using a tag manager that is used for conversion measurement and the like. The advertisement to be surveyed is embedded with a tracking tag in advance, and when a reader reads the advertisement, information regarding the reader, a reading date and time, and the like are identified. Then, the identified information is collected as the advertising effectiveness measurement data.

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

[0056] Next, the estimation unit 312 generates an approximation function that converts the actual result reach AR into the actual result composition ratio AC(n) by performing fitting to adapt the function parameters to the actual result reach AR and the actual result composition ratio AC(n). Then, the estimation unit 312 uses the generated approximation function as the function F(n).

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

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

[0059] C(1) = 2E - 06R3 - 0.0002R2 - 0.001R + 0.786    (7)

[0060] Fig. 6(b) illustrates an example of the function F(2).  A broken line 602 represents the function F(2), and points around the broken line 602 represent the actual result reach AR and the actual result composition ratio AC(2) of various advertisements. In this example, the function F(2) is described by the following equation.

[0061] C(2) = 1E - 06R3 - 0.0003R2 + 0.0177R + 0.0719   (8)

[0062] Fig. 6(c) illustrates an example of the function F(3).  A broken line 603 represents the function F(3), and points around the broken line 603 represent the actual result reach AR and the actual result composition ratio AC(3) of various advertisements. In this example, the function F(3) is described by the following equation.

[0063] C(3) = 2E - 07R3 - 0.0001R2 + 0.0097R - 0.0963   (9)

[0064] Fig. 6(d) illustrates an example of the function F(4).  A broken line 604 represents the function F(4), and points around the broken line 604 represent the actual result reach AR and the actual result composition ratio AC(4) of various advertisements.

[0065] Fig. 6(e) illustrates an example of the function F(5).  A broken line 605 represents the function F(5), and points around the broken line 605 represent the actual result reach AR and the actual result composition ratio AC(5) of various advertisements.

[0066] Fig. 6(f) illustrates an example of the function F(6).  A broken line 606 represents the function F(6), and points around the broken line 606 represent the actual result reach AR and the actual result composition ratio AC(6) of various advertisements.

[0067] As the function F(n), a function that converts an indicator other than the reach R into the composition ratio C(n) may be used. As another indicator, a gross rating point (GRP) or a target rating point (TRP) of a CM in a certain period of time, the number of CMs broadcast in a certain period of time, or the like can be used. The GRP, TRP, and the number of CMs are examples of the indicator for the plurality of pieces of information.

[0068] The GRP is a total audience rating for households, and the TRP is a total audience rating for individuals. The GRP and the TRP are examples of information regarding a sum of the number of estimated contact persons who are estimated to come into contact with each of the plurality of pieces of information. The number of CMs is an example of the number of the plurality of pieces of information.

[0069] In a case of using the GRP, the estimation unit 312 aggregates the actual result GRP, the actual result reach AR, and the actual result partial reach APR(n) for various CMs, and calculates the percentage of the actual result partial reach APR(n) to the actual result reach AR as the actual result composition ratio AC(n).

[0070] Next, the estimation unit 312 generates an approximation function that converts the actual result GRP into the actual result composition ratio AC(n) by performing fitting to adapt the function parameters to the actual result GRP and the actual result composition ratio AC(n). Then, the estimation unit 312 uses the generated approximation function as the function F(n).

[0071] The reach prediction device 201 predicts the GRP of a CM scheduled to be provided during 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 calculates each composition ratio C(n) corresponding to the GRP from the received GRP using each function F(n) included in the correspondence information 321.  Then, the estimation unit 312 calculates the partial reach PR(n) by multiplying the reach R by the composition ratio C(n), and calculates the reach R(M) with Equation (6) using the partial reach PR(n).

[0073] In a case of using the TRP, the estimation unit 312 aggregates the actual result TRP, the actual result reach AR, and the actual result partial reach APR(n) for various CMs, and calculates the percentage of the actual result partial reach APR(n) to the actual result reach AR as the actual result composition ratio AC(n).

[0074] Next, the estimation unit 312 generates an approximation function that converts the actual result TRP into the actual result composition ratio AC(n) by performing fitting to adapt the function parameters to the actual result TRP and the actual result composition ratio AC(n). Then, the estimation unit 312 uses the generated approximation function as the function F(n).

[0075] The reach prediction device 201 predicts the TRP of a CM scheduled to be provided during 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 composition ratio C(n) corresponding to the TRP from the received TRP using each function F(n) included in the correspondence information 321.  Then, the estimation unit 312 calculates the partial reach PR(n) by multiplying the reach R by the composition ratio C(n), and calculates the reach R(M) with Equation (6) using the partial reach PR(n).

[0077] In a case of using the number of CMs, the estimation unit 312 aggregates the actual result number of CMs, the actual result reach AR, and the actual result partial reach APR(n) for various CMs, and calculates the percentage of the actual result partial reach APR(n) to the actual result reach AR as the actual result composition ratio AC(n).

[0078] Next, the estimation unit 312 generates an approximation function that converts the actual result number of CMs into the actual result composition ratio AC(n) by performing fitting to adapt the function parameters to the actual result number of CMs and the actual result composition ratio AC(n). Then, the estimation unit 312 uses the generated approximation function as the function F(n).

[0079] The user of the terminal device 203 designates the number of CMs to be broadcast. The terminal device 203 transmits the number of CMs designated by the user to the reach information estimation device 202.

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

[0081] If it is difficult to perform fitting with only one type of function, fitting may be performed by combining a plurality of types of functions. As an approximation function obtained by combining a plurality of types of functions, a piecewise definition function can be used. The piecewise definition function includes different types of functions for each interval of variables representing an indicator, such as reach R, GRP, TRP, or the number of CMs.

[0082] For example, when GRP is used as the indicator, a piecewise definition function obtained by combining two types of functions may be used as the approximation function. In this case, the domain of definition of GRP is divided into an interval A of GRP = 0 to S and an interval B of GRP > S, and different types of functions are used in the interval A and the 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 the interval A is maximum in the domain of definition may be used as S.

[0083] Even if it is difficult to perform fitting with only one type of function, the fitting accuracy of an approximation function can be improved by using the piecewise definition function. As a result, the prediction accuracy of the composition ratio C(n), the partial reach PR(n), and the reach R(M) predicted using the function F(n) is improved.

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

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

[0086] The composition ratio C(n) illustrated in Fig. 4 and the function F(n) illustrated in Fig. 6 are merely examples, and the composition ratio C(n) and the function F(n) change depending on the provided information and the providing method.

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

[0088] Fig. 7 illustrates a hardware configuration example of an information processing device (computer) to be used as the reach information estimation device 101 in Fig. 1 and the reach information estimation device 202 in Fig. 3. The information processing device in Fig. 7 includes a central processing unit (CPU) 701, a memory 702, an input device 703, an output device 704, an auxiliary storage device 705, a medium driving device 706, and a network connection device 707.  These constituent elements 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 to be used for processing.  The memory 702 may operate as the storage unit 313 in Fig. 3.

[0090] The CPU 701 (processor) operates as the estimation unit 111 in Fig. 1 by, for example, executing a program using the memory 702.  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, or the like, and is used for inputting an instruction or information from an operator or a user. The output device 704 is, for example, a display device, a printer, a speaker, or the like, and is used for an inquiry or an instruction to an operator or a user, and outputting a processing result. The processing result may be the reach R(M) or the partial reach PR(n).

[0092] The auxiliary storage device 705 is, for example, a magnetic disk device, an optical disk device, a magnetooptical disk device, a tape device, or the like. The auxiliary storage device 705 may be a hard disk drive or a solid state drive (SSD). The information processing device can store programs and data in the auxiliary storage device 705 and load them into the memory 702 for use.  The auxiliary storage device 705 may operate as the storage unit 313 in Fig. 3.

[0093] The medium driving device 706 drives a portable recording medium 709 to access the recorded contents. The portable recording medium 709 is a memory device, a flexible disk, an optical disk, a magneto-optical disk, or the like. The portable recording medium 709 may be a compact disk read only memory (CD-ROM), a digital versatile disk (DVD), a universal serial bus (USB) memory, or the like.  The operator or the user can store programs and data in the portable recording medium 709, and load them into the memory 702 for use.

[0094] As described above, the computer-readable recording medium that stores programs and data to be used for 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 can receive programs and data from an external device via the network connection device 707, and load them into the memory 702 for use. The network connection device 707 may operate as the communication unit 311 in Fig. 3.

[0096] Note that the information processing device does not need to include all the constituent elements in Fig. 7, and some constituent elements can be omitted depending on the application or conditions. For example, if an interface with the operator or the user is unnecessary, 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 driving device 706 or the network connection device 707 may be omitted.

[0097] As the reach prediction device 201 and the terminal device 203 in Fig. 2, an information processing device similar to that in Fig. 7 can be used.

[0098] Although the embodiments of the disclosure and the advantages have been described in detail, those skilled in the art will be able to make various changes, additions, and omissions without departing from the scope of the invention as clearly set forth in the claims.

[0099] The present application is based on Japanese Patent Application No. 2024-037012 filed on March 11, 2024. All contents are included herein.

Claims

1. A reach information estimation devicecomprising:an estimation unit configured to calculate, based on first reach information regarding the number of estimated contact persons who are estimated to come into contact withone or more pieces of information among a plurality ofpieces of information and correspondence information, second reach information regarding the number of estimated contact persons who are estimated to come into contact with a predetermined number of pieces of information among theplurality of pieces of information, whereinthe correspondence information represents correspondence between an indicator for the plurality ofpieces of information and a ratio of the second reach information to the first reach information.

2. The reach information estimation deviceaccording to claim 1, whereinthe indicator is the first reach information, andthe estimation unit calculates the ratio corresponding to the first reach information using the correspondence information, and calculates the second reachinformation using the calculated ratio and the first reachinformation.

3. The reach information estimation deviceaccording to claim 1, whereinthe indicator is information regarding a sum of the number of estimated contact persons who are estimated to come into contact with each of the plurality of pieces ofinformation, andthe estimation unit calculates the ratio corresponding to the information regarding the sum of the number of estimated contact persons who are estimated to come into contact with each of the plurality of pieces ofinformation using the correspondence information, and calculates the second reach information using the calculated ratio and the first reach information.

4. The reach information estimation deviceaccording to claim 1, whereinthe indicator is the number of the plurality ofpieces of information, andthe estimation unit calculates the ratiocorresponding to the number of the plurality of pieces of information using the correspondence information, and calculates the second reach information using the calculated ratio and the first reach information.

5. The reach information estimation deviceaccording to any one of claims 1 to 4, whereinthe first reach information represents a percentage of the number of estimated contact persons who areestimated to come into contact with the one or more piecesof information among a set of target persons, andthe second reach information represents a percentage of the number of estimated contact persons who areestimated to come into contact with the predeterminednumber of pieces of information among the set of targetpersons.

6. A reach information estimation methodcomprising:performing, by a computer, processing of calculating, based on first reach information regarding the number of estimated contact persons who are estimated to come into contact with one or more pieces of informationamong a plurality of pieces of information and correspondence information, second reach information regarding the number of estimated contact persons who are estimated to come into contact with a predetermined numberof pieces of information among the plurality of pieces ofinformation, whereinthe correspondence information represents correspondence between an indicator for the plurality ofpieces of information and a ratio of the second reachinformation to the first reach information.

7. A reach information estimation programcausing a computer to execute:processing of calculating, based on first reach information regarding the number of estimated contactpersons who are estimated to come into contact with one ormore pieces of information among a plurality of pieces ofinformation and correspondence information, second reachinformation regarding the number of estimated contactpersons who are estimated to come into contact with apredetermined number of pieces of information among theplurality of pieces of information, whereinthe correspondence information represents correspondence between an indicator for the plurality ofpieces of information and a ratio of the second reach information to the first reach information.

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