JP2020077068A - Evaluation device, evaluation method and program - Google Patents

Abstract

To provide an evaluation device which evaluates an influence of an event to consumption behavior, and an evaluation method and a program thereof.SOLUTION: An evaluation device 20 (server device) evaluates an event, and includes: a consumption activity information acquisition unit which acquires consumption activity information indicating consumption activity of a user before and after the event; a pattern determination unit which determines a pattern of the consumption activity on the basis of the consumption activity information; and an evaluation unit which evaluates the event according to the pattern of the consumption activity. The consumption activity information includes billing information in an application program associated with an application program executed in a user terminal operated by the user.SELECTED DRAWING: Figure 3

Description

  The present invention relates to an evaluation device, an evaluation method and a program.

  The use of applications is promoted using measures such as advertisement. It is rational that the money is distributed according to the effect of the measure. For example, in Patent Document 1, the provider of the guide destination application used in the service and the provider of the guide destination application used in the service are based on the charge amount to the user according to the service provision. A system for setting a distribution amount for a provider of a guidance source application that has guided a user to a guidance destination application is disclosed.

Patent No. 5475180

  However, with the system described in Patent Document 1, it is not possible to determine whether the billing amount has temporarily increased in response to a policy such as an advertisement, or whether the consumption activity has become active due to a policy such as an advertisement.

  The present invention has been made in view of such a background, and an object thereof is to provide a technique capable of evaluating the influence of an event on consumption behavior.

  A main invention of the present invention for solving the above-mentioned problem is an apparatus for evaluating an event, which includes a consumption activity information acquisition unit for acquiring consumption activity information indicating consumption activity of a user before and after the event, and the consumption A pattern determination unit that determines a pattern of the consumption activity based on activity information, and an evaluation unit that evaluates the event according to the pattern of the consumption activity.

  Other problems and solutions to the problems disclosed by the present application will be made clear by the section of the embodiments of the invention and the drawings.

  According to the present invention, it is possible to evaluate the influence of an event on consumption behavior.

It is a figure showing the whole action analysis system composition concerning this embodiment. It is a figure which shows the hardware structural example of the server apparatus 20 which concerns on this embodiment. It is a figure which shows the software structural example of the server apparatus 20 which concerns on this embodiment. It is a figure which shows the structural example of the delivery log memorize | stored in the delivery log memory | storage part 231. FIG. 6 is a diagram showing a configuration example of an authentication log stored in an authentication log storage unit 232. It is a figure which shows the structural example of the payment log memorize | stored in the payment log storage part 233. It is a figure which shows the structural example of the measure log memorize | stored in the measure log memory | storage part 234. 6 is a diagram illustrating standard patterns stored in a pattern storage unit 235. FIG. It is a figure which shows an example which classified the consumption pattern of a user. It is a figure explaining the flow of the evaluation process which the server apparatus 20 performs. It is a figure explaining the flow of the proposal process of the measure by the proposal part 218. It is a figure showing an example of a good pattern ratio of a measure and a proposal for a measure.

  The contents of the embodiments of the present invention will be listed and described. The evaluation device according to the embodiment of the present invention has the following configuration.

[Item 1]
A device for evaluating events,
A consumption activity information acquisition unit that acquires consumption activity information indicating the consumption activity of the user before and after the event,
A pattern determination unit that determines the pattern of the consumption activity based on the consumption activity information,
An evaluation unit that evaluates the event according to the pattern of the consumption activity,
An evaluation device comprising:
[Item 2]
The evaluation device according to item 1,
The consumption activity information includes in-app billing information related to an application program executed in a user terminal operated by the user,
Evaluation device characterized by.
[Item 3]
The evaluation device according to item 1 or 2,
A pattern storage unit for storing a plurality of standard patterns, which are the patterns of the consumption activities to be standard,
The pattern determining unit compares the consumption activity information with the standard pattern, and clusters the consumption activity information with any of the standard patterns.
Evaluation device characterized by.
[Item 4]
The evaluation device according to item 3,
The standard pattern includes at least a first pattern in which the amount of money consumed by the user increases with time and a second pattern in which the amount of money consumed decreases with time.
Evaluation device characterized by.
[Item 5]
The evaluation device according to item 3 or 4,
The standard pattern includes a highly evaluated high evaluation pattern,
The pattern determination unit acquires the pattern of the consumption activity for each user,
The evaluation unit evaluates the event according to the number of users clustered into the high evaluation pattern,
Evaluation device characterized by.
[Item 6]
The evaluation device according to Item 5,
The consumption activity information acquisition unit acquires the consumption activity information for each of the plurality of events,
The pattern determination unit acquires a pattern of the consumption activity for each of the event and the user,
The evaluation unit, for each event, evaluates the event according to a high evaluation number that is the number of the users clustered into the high evaluation pattern,
Further comprising a suggestion unit that makes a suggestion about the content of the event to be held in the future according to the high evaluation number,
Evaluation device characterized by.
[Item 7]
An evaluation device according to item 6,
According to the number of high evaluations, the suggestion unit at least one of performing the same event again, performing the event performed in another, and performing the improved event. To output
Evaluation device characterized by.
[Item 8]
An evaluation method for evaluating an event,
The evaluation device
A consumption activity information acquisition step of acquiring consumption activity information indicating the consumption activity of the user before and after the event,
A pattern determining step of determining a pattern of the consumption activity based on the consumption activity information,
An evaluation step of evaluating the event according to the pattern of the consumption activity,
An evaluation method characterized by executing.
[Item 9]
A program for evaluating events,
Evaluation device
A consumption activity information acquisition unit that acquires consumption activity information indicating the consumption activity of the user before and after the event,
A pattern determination unit that determines a pattern of the consumption activity based on the consumption activity information,
An evaluation unit that evaluates the event according to the pattern of the consumption activity,
A program characterized by making it function as.

  FIG. 1 is a diagram showing an overall configuration of a policy evaluation system according to this embodiment. The measure evaluation system of this embodiment includes a server device 20. The server device 20 is communicably connected to the user terminal 10 via the communication line 30. The communication line 30 is, for example, the Internet, and is constructed by a public telephone line network, a mobile telephone line network, a wireless communication path, Ethernet (registered trademark), or the like.

  The user terminal 10 is a computer operated by a user, and a smartphone or a tablet computer is assumed in this embodiment. The user terminal 10 realizes various functions by downloading and installing an application program (hereinafter abbreviated as an app) and executing the app. The application downloaded and executed by the user terminal 10 includes, for example, a game.

  The server device 20 is a computer that distributes applications, and is realized by, for example, one or more personal computers or workstations. The server 20 delivers the application to the user terminal 10 and provides various services to the application executed in the user terminal 10. For example, the server device 20 provides a service for authenticating a user based on authentication information such as a user name and password, a fingerprint, an iris, and a face image, and functions such as message transmission / reception and data storage.

  The measure evaluation system of the present embodiment attempts to evaluate an event performed by an application provider. The event in the present invention is provided through the Internet such as games, SNS, entertainment, business, utility, lifestyle, work efficiency, healthcare, finance, fashion, meal, books, music, travel, education, sports, etc., or It is a measure for services provided by applications. For example, an event in a game service is carried out regardless of whether it is a regular event or an irregular event, and one example is a policy in which the users alone or the users compete or cooperate with each other to obtain points or items in the game. Other events may include measures to promote the use of the application, such as advertising, stamp rallying, and inviting two applications to send customers to each other. In the present embodiment, the server device 20 evaluates the measure according to the pattern of the consumption activity using the application by the user. In the present embodiment, the consumption activity is assumed to be that the user has paid some kind of money (charged some kind to the user). For example, the user has made an in-app purchase to pay the price of various items or services in the app, or has purchased a coin provided by the server device 20 that can be used for item purchase or service charge. Things are included in consumption activities. The evaluation according to the pattern of consumption activity will be described later in detail, but for example, if the amount paid after the measure is higher than the amount paid before the measure, a high evaluation is given, and conversely the measure This is a method of giving a low rating when the amount paid after the measure is less than the amount paid before.

  FIG. 2 is a diagram showing a hardware configuration example of the server device 20 according to the present embodiment. The server device 20 includes a CPU 201, a memory 202, a storage device 203, a communication interface 204, an input device 205, and an output device 206. The storage device 203 is, for example, a hard disk drive, a solid state drive, a flash memory, or the like that stores various data and programs. The communication interface 204 is an interface for connecting to the communication network 30. For example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone line network, and a wireless communication device for performing wireless communication. , A USB (Universal Serial Bus) connector for serial communication, an RS232C connector, and the like. The input device 205 is, for example, a keyboard, a mouse, a touch panel, a button, a microphone or the like for inputting data. The output device 206 is, for example, a display, a printer, a speaker, or the like that outputs data.

  FIG. 3 is a diagram showing a software configuration example of the server device 20 according to the present embodiment. As shown in the figure, the server device 20 includes an application distribution unit 211, an authentication processing unit 212, a payment processing unit 213, a policy log registration unit 214, a payment log acquisition unit 215, a pattern determination unit 216, an evaluation unit 217, and a proposal unit. 218, a distribution log storage unit 231, an authentication log storage unit 232, a settlement log storage unit 233, a measure log storage unit 234, and a pattern storage unit 235.

  The application distribution unit 211, the authentication processing unit 212, the payment processing unit 213, the measure log registration unit 214, the payment log acquisition unit 215, the pattern determination unit 216, the evaluation unit 217, and the suggestion unit 218 are the CPU 201 included in the server device 20. Is realized by reading the program stored in the storage device 203 into the memory 202 and executing the program. The distribution log storage unit 231, the authentication log storage unit 232, the settlement log storage unit 233, the measure log storage unit 234, and the pattern storage. The unit 235 is realized as a part of a storage area provided by at least one of the memory 202 and the storage device 203 included in the server device 20.

  The distribution log storage unit 231 stores information indicating the distribution history of the application (hereinafter, referred to as distribution log). FIG. 4 is a diagram showing a configuration example of the delivery log stored in the delivery log storage unit 231. As shown in the figure, in the delivery log, information (user ID) identifying the user of the user terminal 10 and information (application ID) identifying the application distributed (downloaded) to the user terminal 10 are stored. , And the date and time when the application was delivered (downloaded) to the user terminal 10 (download date and time). It should be noted that the distribution log may include various types of information related to distribution of the application, such as the data size of the application and the download path of the application.

  The authentication log storage unit 232 stores information indicating a user authentication history (hereinafter referred to as an authentication log). FIG. 5 is a diagram showing a configuration example of an authentication log stored in the authentication log storage unit 232. As shown in the figure, the authentication log includes a user ID that identifies the authenticated user, an application ID that indicates an application that uses the authentication service, and the date and time (login date and time) when the login process was successful. The date and time when login failed may be included.

  The payment log storage unit 233 stores information indicating a payment history (hereinafter referred to as a payment log). FIG. 6 is a diagram showing a configuration example of a payment log stored in the payment log storage unit 233. As shown in the figure, in the payment log, information (payment ID) identifying the payment process, a user ID indicating a user who has made a payment, an application ID identifying an application related to the payment process, and a payment process. The payment date and time when the payment was made, the payment content indicating the content of the payment process, and the payment amount are included. Note that the payment log includes information related to general payment processing. In the present embodiment, the payment log serves as information indicating the consumption activity of the user.

  The measure log storage unit 234 stores information indicating a history of measures (hereinafter referred to as a measure log). FIG. 7 is a diagram showing a configuration example of a measure log stored in the measure log storage unit 234. As shown in the figure, in the measure log, information (vendor ID) indicating the subject who executed the measure, an application ID indicating an application targeted for the measure, information identifying the measure (measure ID), and the measure The month in which the measure was taken (measure implementation month), information indicating the measure content, and the excellent pattern ratio are included. In addition, you may make it set the date and time for the policy implementation month. Further, various kinds of information related to the implementation of the policy such as the content of the policy may be included in the policy log. The excellent pattern ratio is the appearance rate of patterns of consumption activities evaluated as excellent, and will be described in detail later.

  The pattern storage unit 235 stores a standard consumption activity pattern (hereinafter referred to as a standard pattern). FIG. 8 is a diagram illustrating standard patterns stored in the pattern storage unit 235. As shown in the figure, the standard pattern includes a good pattern, a bad pattern, and other patterns. In the present embodiment, the pattern of the consumption activity is a monthly settlement in a predetermined period before and after the policy implementation month (in the present embodiment, it is two months, but can be any period such as one month or three months). It is assumed to be a transition of the total amount of money.

  A good pattern is a pattern in which consumption (payment amount) increases after the measure. In the example of FIG. 8, as an excellent pattern, the amount of settlement increases in the month following the implementation of the measure, and decreases after two months, but is the same as or increases from the level one month before and two months before the implementation of the measure. (Pattern 1), the pattern that the payment amount increases 2 months after the measure even if there is no significant change in the payment amount in the month when the policy is implemented and the month after that (Pattern 2), and the payment amount in the month when the policy is implemented The pattern (Pattern 3) shows that the payment amount has increased and the payment amount has increased for two months thereafter.

  The bad pattern is a pattern in which consumption (payment amount) decreases after the measure. In the example of FIG. 8, as the defective pattern, there is shown a pattern (pattern 4) in which the payment amount does not significantly change after two months, although the payment amount does not significantly change after the implementation month and one month.

  The other patterns are patterns other than the excellent pattern and the defective pattern. In the example of FIG. 8, the payment amount increases only in the implementation month of the measure, and after one month, it returns to the level of one month before and two months before the implementation month (pattern 5). There is a pattern (Pattern 6) that maintains the level of payment amount one month before the measure, and an increase in the amount of payment one month before the measure, but the level two months before the measure The pattern (pattern 7) which is maintained from the implementation month to 2 months later is shown.

  These standard patterns can be extracted by, for example, statistical processing clustering. For example, the payment amount for each user is summed up based on the payment log, and based on the measure log, payment for each month from two months before the month when the measure is taken (measure implementation month) to two months later. Clustering is performed on a pattern in which the total amount of money is arranged over time. The seven standard patterns shown in FIG. 8 are patterns extracted by the inventors by clustering analysis. For each of the patterns obtained by clustering, it is assumed that the user determines whether it is a good pattern, a bad pattern, or another pattern, and the determination result is given. The patterns obtained by the clustering are classified into excellent patterns, defective patterns or other patterns and registered in the pattern storage unit 235.

  The application delivery unit 211 delivers the application to the user terminal 10. The application delivery unit 211 can register the application in the application delivery platform provided by the manufacturer of the operating system of the smartphone, for example. Further, the application distribution unit 211 can also directly transmit the application to the user terminal 10. The application delivery unit 211 creates a delivery log for the delivered application and registers it in the delivery log storage unit 231.

  The authentication processing unit 212 authenticates the user. The authentication processing unit 212 stores, for example, a set of a user name and a password (or a value obtained by giving a password to a hash function) in the storage device 203, and a user name and password (or a password for the hash function) from the user terminal 10. Can be authenticated by comparing with the value obtained by giving. The authentication processing unit 212 can employ various known authentication processing methods. The authentication processing unit 212 registers the authentication result as an authentication log in the authentication log storage unit 233.

  The payment processing unit 213 performs processing related to payment. The payment process is related to various purchase behaviors, for example, when the user purchases an item that can be used in the application, when purchasing coins used in the application, and when paying for viewing information in the application. It is a payment process. Since the payment processing unit 213 performs general payment processing, detailed description thereof is omitted here. The settlement processing unit 213 registers the settlement result including the settlement amount and date and time in the settlement log storage unit 234 as a settlement log.

  The measure log registration unit 214 registers the measure log in the measure log storage unit 234. The measure log registration unit 214 can receive, for example, the input of various kinds of information regarding the measure from the user who took the measure, create a measure log including the received information, and register it in the measure log storage unit 234.

  The payment log acquisition unit 215 acquires the payment log related to the measure to be evaluated. The settlement log acquisition unit 215 receives, for example, the designation of a measure, reads the measure log corresponding to the received measure from the measure log storage unit 214, and sets the measure implementation month of the read measure log as a reference month, and a predetermined period before and after that. (As described above, in the present embodiment, the predetermined period is 2 months, which is 5 months including the reference month.) The settlement log can be read from the settlement log storage unit 233. Further, the payment log acquisition unit 215 can also receive the specification of the reference month without receiving the specification of the measure and read the payment logs for a predetermined period before and after the reference month.

  The pattern determination unit 216 determines a pattern of consumption activities of the user. The pattern determination unit 216 determines, for each user, a pattern of changes in the payment amount in a predetermined period before and after the reference month. If the predetermined period is too short, an analyzable pattern does not appear, and if it is too long, the analysis takes time. The pattern determining unit 216 can determine the pattern of the transition of the payment amount that is most similar to the standard pattern stored in the pattern storage unit 235 as the pattern relating to the transition of the payment amount. The degree of similarity is, for example, the difference between the amount of change in the settlement amount within one month (for example, the difference between the settlement amount two months ago and the settlement amount one month ago) and the variation amount in each month in the standard pattern. Can be asked as Further, the pattern determination unit 216 can specify the one that is closest to the transition of the payment amount obtained from the payment log from the standard patterns by using a general pattern recognition method.

  The evaluation unit 217 evaluates the measures according to the pattern of the consumption activity of the user. In the present embodiment, the evaluation unit 217 determines the ratio of excellent patterns (excellent pattern ratio) in the pattern of the transition of the payment amount obtained for each user (one of the seven patterns, pattern 1 to pattern 7 in the example of FIG. 8). ). Specifically, the percentage of the pattern of the transition of the payment amount calculated for each user is calculated as a good pattern, and the higher the good pattern ratio, the higher the evaluation of the measure. For example, the example of FIG. 9 illustrates a state in which 1000 payment amount transition patterns for a user having a payment log are classified into patterns 1 to 7. In the example of FIG. 8, the excellent patterns are the pattern 1, the pattern 2, and the pattern 3, and in this case, 158 + 238 + 145 = 541 out of 1000 are the excellent patterns. That is, about 54% is the excellent pattern ratio. The evaluation unit 217 can also use this excellent pattern ratio of 54% as an evaluation.

  The proposing unit 218 makes a proposal according to the excellent pattern ratio. The suggestion unit 218 can output that the same measure should be implemented again, for example, for measures for which the good pattern ratio is equal to or higher than a predetermined threshold. In addition, when the good pattern ratio is less than the predetermined threshold value, the proposing unit 218 performs at least one of the fact that the measures implemented in other areas should be implemented and the measures implemented in the past should be improved and implemented. Can be output.

  The processing executed in the policy evaluation system of this embodiment will be described below. FIG. 10 is a diagram illustrating the flow of the evaluation process executed by the server device 20.

  The payment log acquisition unit 215 receives the input of the reference month and the application ID (S301). Note that the settlement log acquisition unit 215 receives the designation of a measure, reads the measure log corresponding to the specified measure from the measure log storage unit 234, and sets the measure execution month and application ID of the read measure log as the reference month. Alternatively, the application ID may be received as an input. The settlement log acquisition unit 215 reads the settlement log corresponding to the application ID, which is the settlement date and time from two months before the reference month to two months after the reference month (S302).

  The pattern determination unit 216 performs the following process for each user related to the read payment log. That is, the pattern determination unit 216 totals the payment amounts for each month for the payment log corresponding to the user (S303), and uses the average of the payment amounts for each month as the reference amount (S304) to determine the payment amount for each month. The transition is compared with the standard pattern stored in the pattern storage unit 235, and the most similar standard pattern is determined as the pattern relating to the transition of the payment amount (S305).

  After the above processing is performed for each user, the evaluation unit 217 counts the number of each pattern (S306) and divides the number of good patterns by the number of all patterns to calculate a good pattern ratio (S307). The proposing unit 218 outputs the excellent pattern ratio (S308).

  As described above, the excellent pattern ratio can be output as the evaluation for the designated measure. The good pattern is a pattern in which the payment amount of the user has increased after the measure from the past log, and if there are many consumers who perform the consumption behavior of the good pattern, the payment amount also increases as a whole. Further, when the excellent pattern ratio is large, it is expected that the customer's LTV (Life Time Value) after the measure will increase. Therefore, the cash flow using the application can be increased.

  Next, the proposing process of the measure to be implemented next by the proposing unit 218 will be described. FIG. 11 is a diagram illustrating a flow of a measure proposal process performed by the proposal unit 218.

  The proposing unit 218 receives the designation of the measure (S321), and obtains the excellent pattern ratio for each designated measure (S322). The excellent pattern ratio is obtained by the processing of FIG. 10 described above. The proposing unit 218 performs the following processing for each designated secret policy.

  When the excellent pattern ratio is equal to or higher than the predetermined threshold value (S323: YES), the proposing unit 218 outputs that the same measure should be implemented again (S324).

  On the other hand, if the good pattern ratio is less than the predetermined threshold value (S323: NO), the suggestion unit 218 has the same vendor ID as the vendor ID of the measure log corresponding to the measure, and has a good pattern ratio equal to or higher than the threshold value. The policy log storage unit 234 is searched for a log (S325).

  If there is no measure log with an excellent pattern ratio equal to or higher than the threshold (S326: NO), the proposing unit 218 outputs that the excellent measure should be laterally deployed (S327), and the application ID of the measure log corresponding to the measure is output. Among the policy logs having different application IDs and the same vendor ID as the vendor ID of the policy log corresponding to the relevant policy, the one having the highest excellent pattern ratio is searched for from the policy log storage unit 234 and output (S328). ).

  On the other hand, when the measure log having the excellent pattern ratio equal to or higher than the threshold value is found (S326: YES), the suggestion unit 218 has the same vendor ID as the vendor ID of the measure log corresponding to the measure and supports the measure. Search for a policy log that has an application ID different from the application ID of the policy log and that has a good pattern ratio less than the threshold value (S329), and outputs that the policy should be improved together with the policy content of the searched policy log. Yes (S330).

  By repeating the above process for each measure, it is possible to make a proposal about the measure to be performed next.

  For example, FIG. 12 shows a state in which the excellent pattern ratios from the measures A to F are calculated so as to be illustrated when the measure implemented in July is designated. Assuming that the threshold of the good pattern ratio is 40%, in the example of FIG. 12, it is output that the same measure should be implemented again for the measures A and B having the good pattern ratio of 40% or more. As for other measures, among the measures in which the excellent pattern ratio is equal to or higher than the threshold value, those that have not been implemented for the application are proposed. In the example of FIG. 12, it is proposed that the measures A or B should be executed for the measures C or E. If all measures with a good pattern ratio above the threshold have been implemented for the app, it is suggested that those measures with a good pattern ratio below the threshold that have not yet been implemented should be improved and implemented. To be done. In the example of FIG. 12, it is suggested that the measure F should be improved and implemented.

  By referring to such a proposal, the user can consider measures to be implemented next by referring to the proposal.

  As described above, according to the measure evaluation system of the present embodiment, it is possible to highly evaluate the measure of increasing consumption based on the pattern of consumption activities before and after the measure. Further, according to the measure evaluation system of the present embodiment, it is possible to propose what kind of measure should be taken next, so that the user can easily consider the next measure.

  Although the present embodiment has been described above, the above embodiment is for facilitating the understanding of the present invention, and is not for limiting and interpreting the present invention. The present invention can be modified and improved without departing from the spirit thereof, and the present invention also includes equivalents thereof.

  For example, in the present embodiment, the server device 20 that distributes the application evaluates and proposes the measures, but an evaluation device different from the server device 20 is provided, and the evaluation device performs the process related to the evaluation and suggestion of the measures. You may do it.

  Further, in the present embodiment, the number of standard patterns is seven, but the number is not limited to this, and any number of patterns that can be classified by clustering analysis or the like may be registered as standard patterns.

  Further, in the present embodiment, the pattern relating to the change in the payment amount is specified from the degree of similarity between the monthly payment amount and the standard pattern, but it is determined by the clustering analysis which standard pattern it belongs to. You can

  Further, in the present embodiment, the total amount of payment amount for each month is obtained and the pattern of the change of the amount for each month is used, but the present invention is not limited to this, and for example, one day, one week, two weeks, etc. You may make it use the pattern of the total amount for every period of arbitrary length.

  Further, in the present embodiment, the excellent pattern is specified by the user, but the present invention is not limited to this, and the payment in the predetermined period after the measure with respect to the total amount of payment in the predetermined period before the measure (two months in the present embodiment) is set. It is also possible to automatically determine that the ratio of the total amount of money exceeds 100% as a good pattern.

  Further, in the present embodiment, when proposing a measure, the past measure of the same vendor ID is searched, but the present invention is not limited to this, and another vendor ID having a high excellent pattern ratio is read out and proposed. You may do it.

  Further, in the present embodiment, the measure for the specific application is evaluated, but the case where the measure is applied to a plurality of applications is also applicable. In this case, a plurality of application IDs may be set in the measure log, and the input of a plurality of application IDs may be accepted in step S301 of FIG.

10 user terminal 20 server device 30 communication line 211 application distribution unit 212 authentication processing unit 213 payment processing unit 213 payment processing unit 214 policy log registration unit 215 payment log acquisition unit 216 pattern determination unit 217 evaluation unit 218 proposal unit 231 distribution log storage unit 232 authentication log storage Part 233 Payment log storage part 234 Measure log storage part 235 Pattern storage part

Claims (9)

A device for evaluating events,
A consumption activity information acquisition unit that acquires consumption activity information indicating the consumption activity of the user before and after the event,
A pattern determination unit that determines the pattern of the consumption activity based on the consumption activity information,
An evaluation unit that evaluates the event according to the pattern of the consumption activity,
An evaluation device comprising: The evaluation device according to claim 1, wherein
The consumption activity information includes in-app billing information related to an application program executed in a user terminal operated by the user,
Evaluation device characterized by. The evaluation device according to claim 1 or 2, wherein
A pattern storage unit for storing a plurality of standard patterns, which are the patterns of the consumption activities to be standard,
The pattern determining unit compares the consumption activity information with the standard pattern, and clusters the consumption activity information with any of the standard patterns.
Evaluation device characterized by. The evaluation device according to claim 3,
The standard pattern includes at least a first pattern in which the amount of money consumed by the user increases with time, and a second pattern in which the amount of money consumed decreases with time.
Evaluation device characterized by. The evaluation device according to claim 3 or 4, wherein
The standard pattern includes a highly evaluated high evaluation pattern,
The pattern determination unit acquires the pattern of the consumption activity for each user,
The evaluation unit evaluates the event according to the number of users clustered into the high evaluation pattern,
Evaluation device characterized by. The evaluation device according to claim 5, wherein
The consumption activity information acquisition unit acquires the consumption activity information for each of the plurality of events,
The pattern determination unit acquires a pattern of the consumption activity for each of the event and the user,
The evaluation unit, for each event, evaluates the event according to a high evaluation number that is the number of the users clustered into the high evaluation pattern,
Further comprising a suggestion unit that makes a suggestion about the content of the event to be held in the future according to the high evaluation number,
Evaluation device characterized by. The evaluation device according to claim 6,
According to the number of high evaluations, the suggestion unit at least one of performing the same event again, performing the event performed in another, and performing the improved event. To output
Evaluation device characterized by. An evaluation method for evaluating an event,
The evaluation device
A consumption activity information acquisition step of acquiring consumption activity information indicating the consumption activity of the user before and after the event,
A pattern determining step of determining a pattern of the consumption activity based on the consumption activity information,
An evaluation step of evaluating the event according to the pattern of the consumption activity,
An evaluation method characterized by executing. A program for evaluating events,
Evaluation device
A consumption activity information acquisition unit that acquires consumption activity information indicating the consumption activity of the user before and after the event,
A pattern determination unit that determines a pattern of the consumption activity based on the consumption activity information,
An evaluation unit that evaluates the event according to the pattern of the consumption activity,
A program characterized by making it function as.