JP7687657B2 - Influencer extraction device, influencer extraction method, program, and recording medium - Google Patents

Description

The present invention relates to an influencer extraction device, an influencer extraction method, a program, and a recording medium.

"Influencer marketing," which utilizes influencers to promote targets, has been attracting attention. An influencer is someone who actively communicates with others and has a great influence on society and others. Patent Document 1 reports a technology for extracting influencers from among users on social media.

JP 2012-78933 A

However, the technology disclosed in Patent Document 1 has the problem that it cannot find influencers in "places where people interact with each other" other than social media.

The present invention aims to provide an influencer extraction device, an influencer extraction method, a program, and a recording medium that can extract influencers in places other than social media.

In order to achieve the above object, the influencer extraction device of the present invention comprises:
The system includes an acquisition unit, an aggregate data generation unit, a setting unit, a predicted value calculation unit, an error calculation unit, and an influencer extraction unit,
The acquisition unit acquires visit information and encounter information,
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
the tally data generation unit generates tally data by tallying up the number of visits and the number of encounters for each visitor based on the visit information and the encounter information;
The setting unit analyzes the aggregated data to set a prediction formula and a standard error for calculating a predicted value of the number of encounters,
The prediction value calculation unit calculates a prediction value of the number of encounters for each of the visitors using the prediction formula,
the error calculation unit calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction unit is a device that extracts, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers.

The influencer extraction method of the present invention includes:
The method includes an acquisition step, an aggregate data generation step, a setting step, a predicted value calculation step, an error calculation step, and an influencer extraction step,
The acquiring step acquires visit information and encounter information,
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
The tabulated data generating step generates tabulated data by tabulating the number of visits and the number of encounters for each visitor based on the visit information and the encounter information,
The setting step analyzes the collected data to set a prediction formula and a standard error for calculating a predicted value of the number of encounters,
The prediction value calculation step calculates a prediction value of the number of encounters for each visitor using the prediction formula,
the error calculation step calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction step is a method for extracting, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers.

The present invention makes it possible to extract influencers in venues other than social media.

FIG. 1 is a block diagram illustrating an example of a configuration of an influencer extraction device according to the first embodiment. FIG. 2 is a block diagram illustrating an example of a hardware configuration of the influencer extraction device according to the first embodiment. FIG. 3 is a flowchart illustrating an example of processing in the influencer extraction device of the first embodiment. FIG. 4 is a schematic diagram showing an example of a process related to generation of the aggregated data. FIG. 5 is a schematic diagram showing an example of a process for setting a prediction formula and a standard error. FIG. 6 is a schematic diagram showing an example of a process for calculating a predicted value. FIG. 7 is a schematic diagram showing an example of a process for calculating an error between the number of encounters in the aggregated data and a predicted value. FIG. 8 is a graph showing the results of an analysis of the correlation between the number of visits and the number of encounters.

In the influencer extraction device of the present invention, for example,
The influencer extraction unit may be configured to exclude from the extraction at least one of the visitors whose error is equal to or greater than a preset upper limit and the visitors whose number of visits is equal to or greater than a preset lower limit.

The influencer extraction device of the present invention includes, for example,
Further, a filtering unit is included,
The acquisition unit acquires at least one of available visit time period information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
The filtering unit filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information,
The aggregated data generation unit may generate aggregated data by aggregating the number of visits and the number of encounters for each visitor based on the filtered attendance information and encounter information.

The influencer extraction device of the present invention includes, for example,
Further, the recording unit is included,
The recording unit may record at least one of the attendance information and the encounter information.

The influencer extraction device of the present invention includes, for example,
Further, a congestion information disclosure unit is included,
The congestion information disclosure unit may generate congestion information indicating the number of visitors in each time period at the arbitrary location based on the attendance information, and disclose the congestion information.

The influencer extraction device of the present invention includes, for example,
Further, a notification unit is included,
The notification unit may notify the attendee who has been extracted as an influencer that he or she has been extracted as an influencer.

In the influencer extraction method of the present invention, for example,
The influencer extraction process may be configured to exclude from the extraction at least one of the visitors whose error is equal to or greater than a predetermined upper limit and the visitors whose number of visits is equal to or greater than a predetermined lower limit.

The influencer extraction method of the present invention includes, for example,
Further, a filtering step is included,
The acquiring step acquires at least one of available visit time zone information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
The filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information,
The aggregated data generating step may generate aggregated data by aggregating the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information.

The influencer extraction method of the present invention includes, for example,
Further, a recording step is included,
The recording step may be performed in a mode in which at least one of the attendance information and the encounter information is recorded.

The influencer extraction method of the present invention includes, for example,
Further, a congestion information disclosure step is included,
The congestion information disclosure step may include generating congestion information indicating the number of visitors in each time period at the arbitrary location based on the attendance information, and disclosing the congestion information.

The influencer extraction method of the present invention includes, for example,
Further, the method includes a notification step,
The notification step may be a step of notifying the attendee who has been extracted as an influencer that he or she has been extracted as an influencer.

The program of the present invention is a program for causing a computer to execute each step of the method of the present invention as a procedure.

The recording medium of the present invention is a computer-readable recording medium on which the program of the present invention is recorded.

In the present invention, an "encounter" is defined as two or more visitors being present at the same time and in the same place, and should be interpreted in the broadest sense, including, for example, passing each other, overtaking each other, crossing each other, and is not limited in any way. Furthermore, an "encounter" is not limited to visitors acknowledging each other, such as by greeting each other or engaging in conversation.

Next, an embodiment of the present invention will be described with reference to the drawings. The present invention is not limited to the following embodiment. In each of the drawings, the same parts are given the same reference numerals. Furthermore, the explanations of each embodiment can be mutually incorporated unless otherwise specified, and the configurations of each embodiment can be combined unless otherwise specified.

[Embodiment 1]
1 is a block diagram showing an example of a configuration of an influencer extraction device 10 according to the present embodiment. As shown in FIG. 1, the device 10 includes an acquisition unit 11, an aggregate data generation unit 12, a setting unit 13, a predicted value calculation unit 14, an error calculation unit 15, and an influencer extraction unit 16. The device 10 may further include a filtering unit 17, a recording unit 18, a congestion information disclosure unit 19, and a notification unit 20 as optional configurations.

The device 10 may be, for example, a single device including each of the above-mentioned parts, or a device to which each of the above-mentioned parts can be connected via a communication line network. The device 10 may also be connected to an external device described later via the communication line network. The communication line network is not particularly limited and may be a publicly known network, for example, wired or wireless. Examples of the communication line network include the Internet line, WWW (World Wide Web), telephone line, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), local 5G, and LPWA. The wireless communication may be a form in which each device communicates directly (Ad Hoc communication), infrastructure communication, indirect communication via an access point, and the like. The device 10 may be incorporated into a server as a system. The device 10 may be, for example, a personal computer (PC, for example, desktop type or notebook type) in which the program of the present invention is installed, a smartphone, a tablet terminal, or the like. Furthermore, the device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the parts is on a server and the other parts are on a terminal.

Figure 2 shows an example block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, a display device 106, a communication device 107, etc. Each part of the device 10 is connected to each other via the bus 103 by its respective interface (I/F).

The central processing unit 101 is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program of the present invention and other programs, and also reads and writes various information. Specifically, for example, the central processing unit 101 functions as an acquisition unit 11, an aggregated data generation unit 12, a setting unit 13, a predicted value calculation unit 14, an error calculation unit 15, and an influencer extraction unit 16. The central processing unit 101 also functions as, for example, a filtering unit 17, a recording unit 18, a congestion information disclosure unit 19, and a notification unit 20.

The bus 103 can also be connected to, for example, an external device. Examples of the external device include an external storage device (external database, etc.), a printer, an external input device, an external display device, an external imaging device, etc. The present device 10 can be connected to an external network (the above-mentioned communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

The memory 102 may be, for example, a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs, such as the program of the present invention, stored in the storage device 104 described below, and the central processing unit 101 receives data from the memory 102 and executes the program. The main memory may be, for example, a RAM (random access memory). The memory 102 may also be, for example, a ROM (read only memory).

The storage device 104 is also referred to as an auxiliary storage device, for example, in contrast to the main memory. As described above, the storage device 104 stores operating programs including the program of the present invention. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads and writes from the recording medium. The recording medium is not particularly limited, and may be, for example, an internal or external type, such as a hard disk drive (HDD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

In the present device 10, the memory 102 and the storage device 104 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used by the present device 10 when executing processing. At least a portion of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner on multiple terminals using blockchain technology or the like. The memory 102 and the storage device 104 may also store, for example, various information recorded by the recording unit 18.

The device 10 may further include, for example, an input device 105 and a display device 106. The input device 105 is, for example, a touch panel, a keyboard, a mouse, etc. The display device 106 is, for example, an LED display, a liquid crystal display, etc.

Next, an example of the influencer extraction method of this embodiment will be described based on the flowchart in FIG. 3. The influencer extraction method of this embodiment is carried out, for example, as follows using the influencer extraction device 10 of FIG. 1. Note that the influencer extraction method of this embodiment is not limited to the use of the influencer extraction device 10 of FIG. 1. Also, the steps in parentheses in FIG. 3 are optional steps.

First, the acquisition unit 11 acquires the visitor information and the encounter information (S11a, acquisition step). The visitor information is information linking identification information indicating each visitor with time information indicating the time when the visitor arrived at an arbitrary location. The encounter information is information linking the identification information of each visitor encountered at the arbitrary location with time information indicating the time of the encounter. The acquisition unit 11 may acquire the visitor information and the encounter information from an external server or an external database via the communication line network, or may acquire the visitor information and the encounter information stored in the memory 102 and the storage device 104 by reading them out. The identification information may be, for example, the identification information of a portable item carried by the visitor when he or she visited the arbitrary location. Examples of the portable item include an admission ticket, an IC (integrated circuit) card, a mobile phone, a smartphone, a wearable device, and the like. The identification information is, for example, an ID (identification), an email address, biometric information (face, fingerprint, voice, etc.), and the like. The arbitrary location is not particularly limited and may be indoors, outdoors, or inside a moving vehicle such as a train. Specific examples of the arbitrary location include a garbage station, a park, a supermarket, a convenience store, etc.

Next, the aggregate data generating unit 12 generates aggregate data that aggregates the number of visits and the number of encounters for each visitor based on the visit information and the encounter information (S12, aggregate data generating step). Since it is considered that the number of encounters increases as the number of visits increases, it can be said that there is a positive correlation between the number of visits and the number of encounters, as described below.

Next, the setting unit 13 analyzes the aggregated data and sets a prediction formula and standard error for calculating the predicted value of the number of encounters (S13, setting step). The details will be described later.

Next, the prediction value calculation unit 14 uses the prediction formula to calculate the predicted number of encounters for each visitor (S14, prediction value calculation process).

Next, the error calculation unit 15 calculates the error between the number of encounters in the aggregated data and the predicted value for each visitor based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error (S15, error calculation process).

Then, the influencer extraction unit 16 extracts the visitors whose error is equal to or greater than a preset threshold value from the attendance information as influencers (S16, influencer extraction process), and ends (END). The influencer extraction unit 16 may also exclude, for example, at least one of the visitors whose error is equal to or greater than a preset upper limit value and the visitors whose number of visits is equal to or greater than a preset lower limit value from the extraction target. The threshold value, the upper limit value, and the lower limit value are not particularly limited and can be set arbitrarily. The error that is equal to or greater than the preset upper limit value may be, for example, an abnormal value. Therefore, by excluding the visitors having such an error from the extraction target, an appropriate influencer can be extracted. Furthermore, the visitors whose number of visits is equal to or greater than a preset lower limit value may not be familiar with the arbitrary place or other visitors, even if the error is equal to or greater than the threshold value. Therefore, by excluding such visitors from the extraction target, an appropriate influencer can be extracted. In other words, an influencer can be described as someone who is encountered more frequently than the number of times they visit.

The influencer extraction unit 16 may, for example, extract a preset number of visitors from the attendance information in order of increasing error, whose error is equal to or exceeds a preset threshold value, as influencers. The preset number of visitors is not particularly limited and can be set arbitrarily. Specifically, when the preset number of visitors is one, for example, the visitor with the highest error is extracted as the influencer.

As described above, the device 10 may further include, for example, a filtering unit 17. In this case, the acquisition unit 11 acquires at least one of the available visit time zone information and the attribute information (S11b, acquisition step). The available visit time zone information is information indicating the time zone during which the visitor can visit the arbitrary location. The time zone may be, for example, a 30-minute time zone or an hourly time zone. The time zone may also be, for example, a time zone by day of the week. The attribute information is the attribute of the visitor, and specifically, for example, attributes such as gender, age, hobbies, and occupation. The available visit time zone information and the attribute information are linked, for example, to the identification information of the visitor. The acquisition unit 11 may acquire the available visit time zone information and the attribute information from an external server or an external database via the communication line network, or may acquire the available visit time zone information and the attribute information by reading out the available visit time zone information and the attribute information stored in the memory 102 and the storage device 104. As shown in FIG. 3, the step (S11b) may be performed in parallel with the step (S11a), or may be performed in sequence. The sequence is not particularly limited.

After executing the steps (S11a) and (S11b), the filtering unit 17 filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and attribute information (S17, filtering step). In other words, if the time period indicated by the available attendance time period information for visitor X is "Monday to Friday, 2pm to 5pm", the filtering unit 17 extracts (filters) attendance information and encounter information having time information within the time period "Monday to Friday, 2pm to 5pm" from the attendance information and encounter information. Also, if the attribute indicated by the attribute information for visitor Y is "40s, female", the filtering unit 17 extracts (filters) attendance information and encounter information of visitors having attribute information of "40s, female" from the attendance information and encounter information.

After executing step (S17), in step (S12), the aggregated data generation unit 12 generates aggregated data that aggregates the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information.

In this way, by filtering the attendance information and the encounter information using the available attendance time period information, it is possible to prevent the error from becoming large due to, for example, schedule conflicts of visitor X. Furthermore, by filtering the attendance information and the encounter information using the attribute information, it is possible to extract influencers based on visitor attributes, for example, and diversity is created in the extracted influencers. As influencers become more diverse, the people who are influenced by the influencers also become more diverse, which ultimately leads to a more diverse audience.

As described above, the device 10 may further include, for example, a recording unit 18. The recording unit 18 records information such as the visit information, the encounter information, the available visit time period information, and the attribute information (S18, recording step). The step (S18) may be executed, for example, before the step (S11a) and the step (S11b) as shown in FIG. 3. The method of recording each piece of information is not particularly limited.

When recording the attendance information, the recording unit 18 may, for example, record the attendance information by reading the identification information of the carried item when the visitor carrying the carried item enters the arbitrary location. Also, when recording the attendance information, the recording unit 18 may, for example, record the attendance information by detecting that the visitor carrying the carried item has entered the arbitrary location using Wi-Fi (registered trademark) or Bluetooth (registered trademark), etc. Furthermore, when recording the attendance information, the recording unit 18 may record the attendance information by detecting that the visitor has entered the arbitrary location using a sensor, a camera, etc.

When recording the encounter information, the recording unit 18 may record the encounter information by detecting that multiple visitors carrying the portable items have entered the arbitrary location using, for example, Wi-Fi (registered trademark) or Bluetooth (registered trademark). Furthermore, when recording the encounter information, the recording unit 18 may record the encounter information by estimating that one visitor and the other visitor have encountered each other when the other visitor arrives at the arbitrary location within a certain time (for example, within 5 minutes) before or after the arrival of the other visitor.

When recording the available visiting time zone information and the attribute information, the recording unit 18 may record the available visiting time zone information and the attribute information, for example, by accepting input of the available visiting time zone information and the attribute information from the visitor's terminal (e.g., a mobile phone, smartphone, PC, wearable device, etc.).

As described above, the device 10 may further include, for example, a congestion information disclosure unit 19. The congestion information disclosure unit 19 generates congestion information indicating the number of visitors for each time period at the arbitrary location based on the visitation information, and discloses the congestion information (S19, congestion information disclosure step). Specifically, the congestion information disclosure unit 19 may use, for example, a numerical value obtained by calculating the average number of visitors for each time period as the congestion information. The time period may be, for example, a time period every 30 minutes, or a time period every hour. In addition, the time period may be, for example, a time period by day of the week. The method of disclosing the congestion information is not particularly limited, and may be, for example, by transmitting (notifying) the congestion information to the visitor's terminal via the communication line network, or by displaying the congestion information on the display device 106. The step (S19) may be executed, for example, before the step (S11a) as shown in FIG. 3. Furthermore, the step (S19) may be executed after the step (S18) or before the step (S18), as shown in FIG. 3, for example. A factor that may increase the error between the number of encounters in the aggregated data and the predicted value is that, for example, the visitor visits the arbitrary place during a crowded time period, which results in the number of encounters being greater than the number of visits per visit. Therefore, by disclosing the crowded situation to the visitor before the visitor arrives, it is possible to extract visitors who, with an awareness of communication with others, deliberately visit the arbitrary place during a crowded time period.

In addition, for example, the congestion information may be disclosed to visitors in advance without using the device 10.

As described above, the device 10 may further include, for example, a notification unit 20. The notification unit 20 notifies the visitor who has been extracted as an influencer that he/she has been extracted as an influencer (S20, notification process). "Extracted" can also be referred to as "selected", for example. The method of notification is not particularly limited, and for example, the visitor's terminal may be notified that he/she has been selected as an influencer, or the display device 106 may display the visitor who has been selected as an influencer. In addition to being selected as an influencer, the notification unit 20 may also notify the visitor of request information. The request information is information requesting the promotion of a promotion target. The promotion target is not particularly limited, and may be a product, a service, a place (a specific area or facility, etc.), etc., and may be tangible or intangible. The request information may include, for example, information about the promotion target, and information about the place, date, and time when the promotion is performed. The arbitrary location from which the influencer is extracted may be different from the location that is the target of the promotion and the location where the promotion is carried out. For example, an influencer at a supermarket may be extracted from among visitors to the supermarket, and the person may be requested (notified) to act as an influencer for the garbage station as well. In this way, by asking for the cooperation of the extracted influencer, the target of the promotion can be promoted efficiently and effectively. The step (S20) is executed, for example, following the step (S16), as shown in FIG. 3.

According to this embodiment, for example, by calculating the error between the number of encounters in the aggregated data and the predicted value, influencers in places other than social media can be extracted.

[Embodiment 2]
An example of the processes related to generating aggregated data, setting a prediction formula and a standard error, calculating a predicted value, calculating an error between the number of encounters in the aggregated data and the predicted value, and extracting influencers in the present invention will be described using the influencer extraction device 10 in Fig. 1. Note that these processes are not limited to the use of the influencer extraction device 10 in Fig. 1.

Figure 4 is a diagram showing an example of a process for generating aggregated data. An example of the visit information and encounter information is shown in the upper part of Figure 4. In the figure, "user" means the identification information of the visitor, and "date" means each time information. The aggregated data generation unit 12 generates aggregated data by aggregating the number of visits and the number of encounters for each visitor from the visit information and encounter information, as shown in the middle part of Figure 4. As mentioned above, there is a positive correlation between the number of visits and the number of encounters, so a scatter plot such as that shown in the lower part of Figure 4 is generated from the aggregated data.

5 is a diagram showing an example of a process for setting a prediction formula and a standard error. The top part of FIG. 5 shows the aggregated data shown in FIG. 4. The setting unit 13 can set a prediction formula and a standard error for the prediction formula by performing a linear regression analysis using, for example, the number of encounters in the aggregated data as a target variable and the number of visits as an explanatory variable. The middle part of FIG. 5 shows an example of the prediction formula and the standard error set based on the analysis result by performing a linear regression analysis using the aggregated data shown in the top part of FIG. 5. In other words, the setting unit 13 can be said to perform a process for obtaining a linear approximation shown by a dotted line in the scatter diagram shown in the bottom part of FIG. 5. In this example, the prediction formula is set by an approach for obtaining a linear approximation as described above, but this is an example, and the setting of the prediction formula by the setting unit 13 is not limited to this. The setting unit 13 may set the prediction formula by another method, such as an approach for obtaining a curve approximation. Hereinafter, the setting unit 13 will be described assuming that the prediction formula is set by an approach for obtaining a linear approximation.

Figure 6 is a diagram showing an example of a process for calculating a predicted value. The aggregated data shown in Figure 6 is data obtained by adding the predicted value of the number of encounters to the aggregated data shown in Figure 4. The predicted value of the number of encounters is calculated by the predicted value calculation unit 14 using the prediction formula shown in the lower part of Figure 5.

Fig. 7 is a diagram showing an example of a process for calculating an error between the number of encounters in the aggregated data and the predicted value. The aggregated data shown in Fig. 7 is data obtained by adding an error between the number of encounters and the predicted value to the aggregated data shown in Fig. 6. The error is calculated by the error calculation unit 15 using the standard error shown in the lower part of Fig. 5. Specifically, the error is calculated by the following formula (1), as also shown in Fig. 7. In other words, the error calculation unit 15 can be said to perform a process for calculating a difference between each plot and the linear approximation in the scatter diagram shown in the lower part of Fig. 7, for example.

Error = (number of encounters – predicted number of encounters) / standard error (1)

For example, if the preset threshold is 1.5, the influencer extraction unit 16 extracts visitor C, whose error is greater than 1.5, as an influencer from among the multiple visitors in the aggregated data shown in FIG. 7.

[Embodiment 3]
Based on the results of the verification carried out by the present inventors, the correlation between the number of visits and the number of encounters will be described.

In this verification, a recording device that records the visitor information was installed at the entrance to the experimental site. The recording information is a device that records the identification information of a visitor's terminal (smartphone) when the visitor holds the terminal over the device. Furthermore, in this verification, visitors whose visit intervals were less than 5 minutes were determined to have "encountered" each other, and the number of visits and the number of encounters were calculated for each visitor. The correlation coefficient between the number of visits and the number of encounters was then calculated, and the correlation was analyzed. Figure 8 shows the results of the analysis of the correlation between the number of visits and the number of encounters. As shown in Figure 8, it was found that there is a strong positive correlation between the number of visits and the number of encounters.

[Embodiment 4]
The program of the present embodiment is a program for causing a computer to execute each step of the method of the present invention as a procedure. In the present invention, the "procedure" may be read as "processing." The program of the present embodiment may be recorded, for example, in a computer-readable recording medium. The recording medium is not particularly limited, and examples thereof include a read-only memory (ROM), a hard disk (HD), and an optical disk.

The present invention has been described above with reference to the embodiments, but the present invention is not limited to the above embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

<Additional Notes>
Some or all of the above embodiments may be described as follows, but are not limited to the following supplementary notes.
(Appendix 1)
The system includes an acquisition unit, an aggregate data generation unit, a setting unit, a predicted value calculation unit, an error calculation unit, and an influencer extraction unit,
The acquisition unit acquires visit information and encounter information,
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
the tally data generation unit generates tally data by tallying up the number of visits and the number of encounters for each visitor based on the visit information and the encounter information;
The setting unit analyzes the aggregated data to set a prediction formula and a standard error for calculating a predicted value of the number of encounters,
The prediction value calculation unit calculates a prediction value of the number of encounters for each of the visitors using the prediction formula,
the error calculation unit calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction unit extracts, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers.
(Appendix 2)
The influencer extraction device described in Appendix 1, wherein the influencer extraction unit excludes from the extraction targets at least one of the visitors whose error is equal to or greater than a predetermined upper limit value and the visitors whose number of visits is equal to or greater than a predetermined lower limit value.
(Appendix 3)
Further, a filtering unit is included,
The acquisition unit acquires at least one of available visit time period information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
The filtering unit filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information,
The influencer extraction device according to claim 1 or 2, wherein the aggregated data generation unit generates aggregated data that aggregates the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information.
(Appendix 4)
Further, the recording unit is included,
The influencer extraction device according to any one of appendices 1 to 3, wherein the recording unit records at least one of the attendance information and the encounter information.
(Appendix 5)
Further, a congestion information disclosure unit is included,
The influencer extraction device described in any one of Appendix 1 to 4, wherein the congestion information disclosure unit generates congestion information indicating the number of visitors at each time period at the arbitrary location based on the attendance information, and discloses the congestion information.
(Appendix 6)
Further, a notification unit is included,
The influencer extraction device according to any one of appendices 1 to 5, wherein the notification unit notifies the attendee who has been extracted as an influencer that he or she has been extracted as an influencer.
(Appendix 7)
The method includes an acquisition step, an aggregate data generation step, a setting step, a predicted value calculation step, an error calculation step, and an influencer extraction step,
The acquiring step acquires visit information and encounter information,
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
The tabulated data generating step generates tabulated data by tabulating the number of visits and the number of encounters for each visitor based on the visit information and the encounter information,
The setting step analyzes the collected data to set a prediction formula and a standard error for calculating a predicted value of the number of encounters,
The prediction value calculation step calculates a prediction value of the number of encounters for each visitor using the prediction formula,
the error calculation step calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction method includes extracting, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers.
(Appendix 8)
The influencer extraction method described in Appendix 7, wherein the influencer extraction process excludes from the extraction targets at least one of the visitors whose error is equal to or greater than a preset upper limit and the visitors whose number of visits is equal to or greater than a preset lower limit.
(Appendix 9)
Further, a filtering step is included,
The acquiring step acquires at least one of available visit time zone information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
The filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information,
The influencer extraction method according to claim 7 or 8, wherein the aggregated data generation step generates aggregated data that aggregates the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information.
(Appendix 10)
Further, a recording step is included,
The influencer extraction method according to any one of appendices 7 to 9, wherein the recording step records at least one of the attendance information and the encounter information.
(Appendix 11)
Further, a congestion information disclosure step is included,
The influencer extraction method according to any one of appendices 7 to 10, wherein the congestion information disclosure step generates congestion information indicating the number of visitors at each time period at the arbitrary location based on the attendance information, and discloses the congestion information.
(Appendix 12)
Further, the method includes a notification step,
12. The influencer extraction method according to any one of appendices 7 to 11, wherein the notification step notifies the attendee extracted as an influencer that he or she has been extracted as an influencer.
(Appendix 13)
A program for causing a computer to execute procedures including an acquisition procedure, an aggregate data generation procedure, a setting procedure, a predicted value calculation procedure, an error calculation procedure, and an influencer extraction procedure:
The acquisition step acquires visit information and encounter information,
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
the step of generating aggregated data includes generating aggregated data by aggregating the number of visits and the number of encounters for each visitor based on the visit information and the encounter information;
The setting step includes setting a prediction formula and a standard error for calculating a predicted value of the number of encounters by analyzing the collected data,
the prediction value calculation step calculates a prediction value of the number of encounters for each of the visitors using the prediction formula;
the error calculation step calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction step extracts, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers.
(Appendix 14)
The program described in Appendix 13, wherein the influencer extraction procedure excludes from the extraction at least one of the visitors whose error is equal to or greater than a predetermined upper limit and the visitors whose number of visits is equal to or greater than a predetermined lower limit.
(Appendix 15)
Further comprising a filtering step,
The acquisition step acquires at least one of available visit time zone information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
the filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information;
The program according to claim 13 or 14, wherein the aggregated data generation step generates aggregated data that aggregates the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information.
(Appendix 16)
Further, the method includes a recording procedure.
The program described in any one of appendices 13 to 15, wherein the recording step records at least one of the attendance information and the encounter information.
(Appendix 17)
In addition, it includes congestion information disclosure procedures,
The program described in any one of Appendices 13 to 16, wherein the congestion information disclosure procedure generates congestion information indicating the number of visitors at the arbitrary location for each time period based on the attendance information, and discloses the congestion information.
(Appendix 18)
It also includes notification procedures,
18. The program described in any one of appendices 13 to 17, wherein the notification step notifies the attendee who has been extracted as an influencer that he or she has been extracted as an influencer.
(Appendix 19)
A computer-readable recording medium having a program according to any one of appendices 13 to 18 recorded thereon.

According to the present invention, it is possible to extract influencers in places other than social media. Therefore, the present invention is useful, for example, when conducting influencer marketing.

REFERENCE SIGNS LIST 10 influencer extraction device 11 acquisition unit 12 aggregate data generation unit 13 setting unit 14 predicted value calculation unit 15 error calculation unit 16 influencer extraction unit 17 filtering unit 18 recording unit 19 congestion information disclosure unit 20 notification unit 101 central processing unit 102 memory 103 bus 104 storage device 105 input device 106 display device 107 communication device

Claims (10)

The system includes an acquisition unit, an aggregate data generation unit, a setting unit, a predicted value calculation unit, an error calculation unit, and an influencer extraction unit,
The acquisition unit is
Obtaining visitor information,
Based on the attendance information , if there is another visitor within a certain time period before or after the arrival time of the one visitor, a pair of the one visitor and the other visitor and the arrival time of the one visitor are acquired as encounter information;
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
the tally data generation unit generates tally data by tallying up the number of visits and the number of encounters for each visitor based on the visit information and the encounter information;
The setting unit analyzes the aggregated data to set a prediction formula and a standard error for calculating a predicted value of the number of encounters,
The prediction value calculation unit calculates a prediction value of the number of encounters for each of the visitors using the prediction formula,
the error calculation unit calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction unit extracts, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers. The influencer extraction device according to claim 1, wherein the influencer extraction unit excludes from the extraction at least one of the visitors whose error is equal to or exceeds a preset upper limit and the visitors whose number of visits is equal to or exceeds a preset lower limit. Further, a filtering unit is included,
The acquisition unit acquires at least one of available visit time period information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
The filtering unit filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information,
The influencer extraction device according to claim 1 or 2, wherein the aggregated data generation unit generates aggregated data by aggregating the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information. Further, the recording unit is included,
The influencer extraction device according to claim 1 , wherein the recording unit records at least one of the attendance information and the encounter information. Further, a congestion information disclosure unit is included,
The influencer extraction device according to claim 1 , wherein the congestion information disclosure unit generates congestion information indicating the number of visitors at the arbitrary location for each time period based on the attendance information, and discloses the congestion information. Further, a notification unit is included,
The influencer extraction device according to claim 1 , wherein the notification unit notifies the attendee who has been extracted as an influencer that he or she has been extracted as an influencer. The method includes an acquisition step, an aggregate data generation step, a setting step, a predicted value calculation step, an error calculation step, and an influencer extraction step,
The obtaining step includes:
Obtaining visitor information,
Based on the attendance information , if there is another visitor within a certain time period before or after the arrival time of the one visitor, a pair of the one visitor and the other visitor and the arrival time of the one visitor are acquired as encounter information;
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
The tabulated data generating step generates tabulated data by tabulating the number of visits and the number of encounters for each visitor based on the visit information and the encounter information,
The setting step analyzes the collected data to set a prediction formula and a standard error for calculating a predicted value of the number of encounters,
The prediction value calculation step calculates a prediction value of the number of encounters for each visitor using the prediction formula,
the error calculation step calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction method includes extracting, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers, and each step is executed by a computer. The influencer extraction method according to claim 7, wherein the influencer extraction step excludes from the extraction at least one of the visitors whose error is equal to or exceeds a preset upper limit and the visitors whose number of visits is equal to or exceeds a preset lower limit. Further, a filtering step is included,
The acquiring step acquires at least one of available visit time zone information and attribute information,
The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location,
The attribute information is information indicating attributes of the visitor,
The filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information,
9. The influencer extraction method according to claim 7, wherein the aggregated data generating step generates aggregated data by aggregating the number of visits and the number of encounters for each visitor based on the filtered visit information and encounter information. A program for causing a computer to execute procedures including an acquisition procedure, an aggregate data generation procedure, a setting procedure, a predicted value calculation procedure, an error calculation procedure, and an influencer extraction procedure:
The acquisition step includes:
Obtaining visitor information,
Based on the attendance information , if there is another visitor within a certain time period before or after the arrival time of the one visitor, a pair of the one visitor and the other visitor and the arrival time of the one visitor are acquired as encounter information;
The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location,
The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter,
the tabulated data generating step generates tabulated data by tabulating the number of visits and the number of encounters for each visitor based on the visit information and the encounter information;
The setting step includes setting a prediction formula and a standard error for calculating a predicted value of the number of encounters by analyzing the collected data,
the prediction value calculation step calculates a prediction value of the number of encounters for each of the visitors using the prediction formula;
the error calculation step calculates, for each visitor, an error between the number of encounters in the aggregated data and the predicted value based on the number of encounters in the aggregated data, the predicted value of the number of encounters, and the standard error;
The influencer extraction step extracts, from among a plurality of attendees, attendees whose error is equal to or greater than a preset threshold value as influencers.

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