JP2024015402A - Information processing device, method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable future prediction of merchandise or the like using an event for the merchandise or the like.

SOLUTION: A VfM index about any measurement item to be focused is set under the control of an event planning part 111. An event for merchandise or the like is executed and incentive use information related to merchandise or the like is acquired under the control of an event notification part 112, an incentive distribution part 113, and an event execution part 114. A primary evaluation of the merchandise or the like including an evaluation related to the VfM index is generated on the basis of reaction behavior information including the incentive use information under the control of a VfM primary evaluation part 115. Research on the merchandise or the like to consumers is executed on the basis of the primary evaluation under the control of a research execution management part 1161, and a market value evaluation of the merchandise or the like is generated on the basis of answers of the research under the control of a market value evaluation part 116. A feature prediction related to the merchandise or the like is executed on the basis of the market value evaluation under the control of a feature prediction part 1163.

SELECTED DRAWING: Figure 2

COPYRIGHT: (C)2024,JPO&INPIT

Description

The present invention relates to an information processing device, method, and program for performing future predictions including future sales of products or services using events of the products or services.

2. Description of the Related Art Conventionally, when selling a specific product, a technique is known in which past sales history data of the product is statistically analyzed and future sales of the product are predicted (for example, see Patent Document 1).

Furthermore, there is a known technique for measuring the effects of events such as various exhibitions and fairs (for example, see Patent Document 2).

Japanese Patent Application Publication No. 2002-358402 Japanese Patent Application Publication No. 2003-167976

However, the past sales history data of the product used in the technique described in Patent Document 1 reflects the economic environment at the time when the product was sold. For this reason, conventional analysis not only cannot evaluate the superiority or inferiority of a product's value or its future prospects, but also how to improve the product to improve its product appeal, which products should be discontinued, which products should be discontinued, etc. It is not possible to make a rational and objective evaluation of future responses, such as whether the product should be retained (improved) or whether the newly developed product has value (competitiveness).

As a method for predicting sales, no sales prediction method other than the method described in Patent Document 1 that relies solely on statistical analysis of data reflecting the past economic environment has been known.

In addition, with the technology described in Patent Document 2, it is possible to measure the effects of events such as various exhibitions and fairs that have been recently held, but it is possible to measure sales based on the effects of the measured events. There was no known method for making predictions.

This invention has been made in view of the above circumstances, and its purpose is to provide an information processing device and method for predicting the future, including the future sales of a product or service, using events related to the product or service. and programs.

In order to solve the above problems, a first aspect of the present invention includes an event planning section that is located in an information processing device and sets information necessary for carrying out an event for goods or services, and that notifies consumers of the event. an event announcement unit that acquires event participation acceptance information including information on consumers who wish to participate in the event; an incentive distribution unit that distributes incentives for the event to consumers who wish to participate in the event; Along with the implementation, an event implementation unit acquires incentive usage information including information of consumers who used the incentive, and based on the reaction behavior information including the acquired event participation acceptance information and incentive usage information, A VfM primary evaluation unit that generates a primary evaluation that includes an objective evaluation of the product or service, and a VfM primary evaluation unit that conducts research regarding the product or service to consumers based on the generated primary evaluation, including the consumer's subjective evaluation. a market value evaluation unit that generates a market value evaluation of the product or service based on the results of the research, and executes a future prediction including future sales of the product or service based on the generated market value evaluation; and a VfM evaluation report unit that outputs information related to the results of the future prediction.

A second aspect of the invention is that the event planning department sets a VfM index that is an index for an arbitrary measurement item to be focused on regarding the product or service, and the primary evaluation is based on the set focus. This includes VfM index evaluation based on the VfM index and actual values for arbitrary measurement items.

A third aspect of the present invention is that the VfM primary evaluation unit includes a hypothesis generation unit that generates a hypothesis related to market value evaluation of the product or service based at least on the VfM index evaluation, and the market value evaluation unit However, research regarding the product or service is conducted based on the generated hypothesis.

A fourth aspect of the invention is that the market value evaluation unit determines whether or not the generated hypothesis is valid based on the results of the research, and determines whether the generated hypothesis is not valid. a hypothesis correction unit that corrects the generated hypothesis when the hypothesis has been determined; is conducted, and a market value evaluation of the product or service is generated based on the results of the second and subsequent research.

A fifth aspect of the present invention is that the market value evaluation section performs an expanded estimation based on a simple estimation method and a complex estimation method based on the attribute information of each consumer who responded to the research and the responses from the consumer to the research. The future forecast including the future sales is executed by executing at least one of the expanded estimation and the expanded estimation.

A sixth aspect of the invention is that the VfM index includes an index for measurement items related to consumers who have used the incentive multiple times, and an index for measurement items related to consumers who have not used the incentive, The index includes at least one of the following indicators regarding measurement items related to consumers who have selected a specific incentive.

According to the first aspect of the present invention, as an event for goods or services is carried out, incentive usage information including information on consumers who used the incentive of the event is acquired, and reaction behavior including the incentive usage information is obtained. Based on the information, a primary evaluation including an objective evaluation of the product or service is generated. Thereafter, research on consumers is conducted based on the generated primary evaluation, and a market value evaluation of the product or service is generated based on the results of the research including the consumer's subjective evaluation. A future prediction including future sales of the product or service is performed based on the generated market value evaluation.

Such market value evaluation generation processing and future prediction processing based on events and research can be based on data that reflects the economic environment at the time, rather than the past economic environment, for example. Therefore, it is possible to make such future predictions more reliable across a wide range of prediction fields compared to methods that rely solely on statistical analysis of past data. .

Furthermore, the future predictions can reflect the subjective opinions of consumers on the buyer side, for example, by using consumer research as described above. By conducting the research based on the primary evaluation as described above, it is possible, for example, to reconfirm the information obtained in the primary evaluation or to evaluate from a different perspective than the information obtained in the primary evaluation. becomes possible. Therefore, it is possible to make such future predictions even more objective and accurate.

Based on the results of such future predictions, for example, which products should be continued to be sold, which services should be continued to be provided, which products should be discontinued, which services should be discontinued, or , it becomes possible to determine which products and services should be improved. In addition, the above future forecast not only predicts future sales but also the value of commercial materials, so it is possible to make production plans to determine how much product to produce, and plans to determine the scale of services to be provided. can also be determined.

According to the second aspect of the present invention, a VfM index is set as an index for an arbitrary measurement item of interest regarding the above-mentioned goods or services. The primary evaluation includes a VfM index evaluation based on the VfM index and actual value for the set arbitrary measurement item of interest.

In this way, it is possible to generate the above-mentioned primary evaluation based on the VfM index, which is an index that can be evaluated numerically. Since the above-mentioned research is carried out based on the primary evaluation generated in this way, it becomes possible to make the above-mentioned market value evaluation and future prediction more accurate. Furthermore, since VfM indicators can be set for arbitrary measurement items, it is possible to perform market value evaluations and future predictions focusing on necessary items.

In addition, along with the implementation of the above event, information on the results of consumers' reactions or actions is sequentially accumulated, and actual values for measurement items related to the above VfM index are calculated in chronological order from the accumulated information. be able to. The information may be continuously accumulated, for example, while the event is executed multiple times. By using the performance values calculated chronologically in this way, it is also possible to evaluate and analyze trends and changes in the market value evaluation chronologically. By evaluating and analyzing trends and changes in the above market value evaluation, future predictions regarding the operation of marketing activities related to products or services, future predictions of sales related to products or services, and direction of development, revision, or abolishment of products or services can be made. It becomes possible to make future predictions with high accuracy. Furthermore, regarding the above-mentioned VfM index, which is an indicator for arbitrary measurement items that should be noted regarding the above-mentioned products or services, actual values for the measurement items related to the VfM index are calculated. By applying the same VfM index to reactions or actions, it is also possible to cross-sectionally evaluate other companies, other industries, and products of other industries.

According to the third aspect of the invention, a hypothesis is generated based at least on the VfM index evaluation. Research regarding the above product or service is conducted based on the generated hypothesis.

The hypothesis generated in this way is based on a numerically evaluable index called a VfM index. For example, in research conducted based on a hypothesis as described above, the research items included in the research may be generated based on such a hypothesis, or the consumers targeted by the research may be based on such a hypothesis. It is also possible to select based on. By performing the market value evaluation generation process and future prediction process based on such research, it becomes possible to make future predictions more accurate.

According to the fourth aspect of the invention, it is determined whether or not the generated hypothesis is valid based on the results of the research, and when it is determined that the generated hypothesis is not valid, The hypothesis generated above is corrected. Thereafter, second and subsequent research regarding the above-mentioned goods or services for consumers is conducted based on the corrected hypothesis after the correction, and based on the results of the second and subsequent research, the market value of the above-mentioned goods or services is determined. A rating is generated.

In this way, it is possible to recursively correct the above-mentioned hypothesis until it becomes valid, and therefore, the above-mentioned future prediction results derived from the corrected hypothesis after the correction are more reliable. It becomes possible to make it a thing.

According to the fifth aspect of the invention, in the future prediction process of the goods or services, a simple estimation method is expanded based on the attribute information of each consumer who responded to the research and the responses from the consumer to the research. The future prediction is performed by performing at least one of estimation and expanded estimation using a complex estimation method (eg, Bayesian network method). For example, in extended estimation using a simple estimation method, deductive extended estimation is performed, and in extended estimation using a complex estimation method, deductive or recursive extended estimation is performed.

By performing such expanded estimation, we can predict how much the consumers who are the potential buyers of the above products or services will contribute to sales, not just the limited number of consumers who are the subject of the above research. It is possible to do so. Furthermore, even if the distribution of attribute information of the consumers targeted for the above research is biased compared to the distribution of attribute information of all consumers who are the potential purchasers of the above products or services, for example, By appropriately weighting each attribute information and correcting the distribution bias, it is possible to make the future prediction based on the expanded estimation more accurate. In addition to simple "sales forecasts", by logging results, accumulating them as indicators (information), and analyzing them, we can predict the future of marketing activities, sales of products or services, and the direction of development, revision, and abolition. can be made highly accurate.

According to the sixth aspect of the invention, the VfM index includes an index for measurement items related to consumers who have used the incentive multiple times, and an indicator for measurement items related to consumers who have not used the incentive. At least one of an indicator and an indicator for a measurement item related to a consumer who has selected a specific incentive is included.

Here, the consumers who have used the incentive multiple times include, for example, consumers who evaluate the above product or service as having high value. Therefore, by setting a VfM index for measurement items related to consumers who have used the above-mentioned incentives multiple times, it is possible to evaluate positive aspects regarding the value of the above-mentioned products or services.

In addition, consumers who do not use incentives include, for example, consumers who did not use incentives even though they wanted to participate in the above event, and consumers who did not use incentives even though they received incentives. Includes consumers who have not. Therefore, by setting VfM indicators for measurement items related to consumers who do not use the above-mentioned incentives, it is possible to verify, for example, whether there is room to improve the evaluation of the above-mentioned products or services, or to It is possible to verify what methods should be taken to improve such evaluations.

Furthermore, for example, when a consumer selects an incentive to use from among multiple types of incentives (discounts, exchanges, points, etc.), it may be desirable to obtain statistics on consumers who have selected a specific incentive. For example, the type of evaluation of the value of the product or service by the consumer can be determined for each incentive selected by the consumer. Therefore, by setting a VfM index for the measurement item related to the consumer who has selected the specific incentive, it is possible to evaluate the value of the product or service with higher quality. That is, by preparing a plurality of incentives desired by consumers and setting and analyzing a reference value for measuring the market value for each incentive, it is possible to improve the accuracy of the above-mentioned future prediction.

By using the VfM index as described above, for example, in the primary evaluation generation process, the hypothesis generation process, or the research implementation process, the market value evaluation based on the research is made more accurate. becomes possible. Therefore, the above future prediction based on the market value evaluation can also be made more accurate.

According to each aspect of the present invention, it is possible to provide an information processing device, a method, and a program that use events of the product or service to predict the future including future sales of the product or service.

1 is a schematic configuration diagram of a future prediction system according to a first embodiment of the present invention. 2 is a block diagram showing the functional configuration of a VfM marketing system in the future prediction system shown in FIG. 1. FIG. 3 is a flow diagram showing an example of a primary evaluation generation process executed by the control unit shown in FIG. 2. FIG. 3 is a flow diagram showing an example of future prediction processing executed by the control unit shown in FIG. 2. FIG. FIG. 3 is a diagram illustrating an example of a setting situation based on calculation/non-calculation setting information and VfM index setting information according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating an example of a setting situation based on calculation/non-calculation setting information according to the first embodiment of the present invention. The figure showing an example of announcement reaction log information concerning a 1st embodiment of this invention. The figure showing an example of coupon issue log information concerning a 1st embodiment of this invention. The figure showing an example of coupon use log information concerning a 1st embodiment of this invention. FIG. 2 is a diagram illustrating an example of VfM index evaluation based on a VfM index and actual value for an arbitrary measurement item of interest, which is generated as part of the primary evaluation, according to the first embodiment of the present invention. FIG. 2 is a diagram illustrating an example of VfM index evaluation based on a VfM index and actual value for an arbitrary measurement item of interest, which is generated as part of the primary evaluation, according to the first embodiment of the present invention. FIG. 2 is a diagram illustrating an example of VfM index evaluation based on a VfM index and actual value for an arbitrary measurement item of interest, which is generated as part of the primary evaluation, according to the first embodiment of the present invention. FIG. 2 is a diagram illustrating an example of VfM index evaluation based on a VfM index and actual value for an arbitrary measurement item of interest, which is generated as part of the primary evaluation, according to the first embodiment of the present invention. The figure which shows an example of the pie chart which was produced|generated as part of primary evaluation based on the 1st Embodiment of this invention and which shows the coupon usage status by consumer's gender and age. A diagram illustrating an example of a line graph in which reactions to products, etc. or reaction status for each step of behavior are aggregated by age of female consumers, generated as part of the primary evaluation, according to the first embodiment of the present invention. . The figure which shows an example of the pie chart which was produced|generated as part of primary evaluation and which shows the coupon usage status by occupation of a consumer based on the 1st Embodiment of this invention. FIG. 2 is a diagram showing an example of a hypothesis generation table showing the correspondence between detection conditions based on VfM index evaluation and generated hypotheses according to the first embodiment of the present invention. FIG. 2 is a diagram showing an example of a hypothesis generation table showing the correspondence between detection conditions based on VfM index evaluation and generated hypotheses according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen for questionnaire items according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen of questionnaire total results according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen of questionnaire total results according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen of questionnaire total results according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen of questionnaire total results according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a display screen of questionnaire total results according to the first embodiment of the present invention. The figure which shows an example of the result of the sales prediction based on the 1st Embodiment of this invention. The figure which shows an example of the result of the sales prediction based on the 1st Embodiment of this invention. The figure which shows an example of the result of the sales prediction based on the 1st Embodiment of this invention.

Embodiments of the present invention will be described below with reference to the drawings.
[First embodiment]
(composition)
FIG. 1 is a schematic configuration diagram of a future prediction system according to a first embodiment of the present invention.

The future prediction system of this embodiment includes, for example, a value for money (VfM) marketing system 1, an operator terminal 2 that is a personal computer (PC) terminal including a smartphone or a tablet type, and is operated by an operator. It includes a consumer information database 3, an event notification system 4, an event participation reception system 5, an incentive management system 6, and a research system 7, which are connected via a network (NW), for example, the Internet. has been done.

The VfM marketing system 1 can store any product or service (hereinafter referred to as "product, etc." ), it is possible to generate a market value evaluation as value for price and perform future predictions including future sales.

More specifically, the VfM marketing system 1 uses the event notification system 4, the event participation acceptance system 5, the incentive management system 6, and the research system 7 in response to operation signals transmitted from the operator terminal 2. We carry out events such as campaigns, advertisements, and promotions for the above-mentioned products, etc., and we also conduct research on the products, etc. to consumers, and based on the results of that research, we evaluate the value of the above-mentioned products, etc. relative to their prices. Generate a market value valuation. Based on the generated market value evaluation, the VfM marketing system 1 can perform future predictions including future sales of the products and the like.

In the following, an example will be described in which the VfM marketing system 1 carries out the above event in units of the above products, etc., and executes the above market value evaluation generation processing and future prediction processing in the units of the above products, etc., as described above. However, the market value evaluation generation process and future prediction process for products, etc. according to the present disclosure are not limited to this. For example, the event such as a campaign, advertisement, or promotion for the above-mentioned product may be an event such as a campaign, advertisement, or promotion related to a store, brand, or the like. In this case, the VfM marketing system 1 carries out events such as campaigns, advertisements, or promotions related to the store or brand, and further conducts research related to the store or brand to consumers. , the above market value evaluation generation process and future prediction process can be executed in units of stores, brands, etc.

The consumer information database 3 stores consumer identification information such as a consumer number, which is used for consumers to react or take actions regarding the above-mentioned products, etc. that are subject to the above-mentioned event, and the consumer's attribute information. are stored in association with each other. Note that the identification information of the consumer may be, for example, an ID of a membership service that covers the majority of the population and allows identification of individuals. Further, the attribute information may include, for example, information on consumer attributes such as the consumer's gender, age group, and residential area, as well as other personal information specific to the consumer.

The event notification system 4, the event participation acceptance system 5, and the incentive management system 6 are systems used to implement the above event.

The event notification system 4 uses various media such as e-mail magazines, smartphone (mobile phone) and tablet applications, television commercial messages (TVCM), and social networking services (SNS) such as LINE (registered trademark). The event will be used to notify consumers of the above event and collect reaction information. The event participation acceptance system 5 accepts requests for participation in the event using, for example, a website such as a special site for the event. The incentive management system 6 distributes incentives for the event to consumers who wish to participate in the event using, for example, the website, and collects incentive usage information regarding the incentive as the event is implemented. do. In addition, such incentives include, for example, coupons such as free exchange coupons and discount coupons for products, etc., bonus points that consumers obtain when purchasing the above products, etc., or bonus points that consumers obtain when purchasing the above products, etc. It may also be a coupon such as a free exchange ticket or discount ticket for other products.

The research system 7 conducts research on the above-mentioned products to consumers, and collects research responses from consumers. The research is, for example, a questionnaire regarding the above-mentioned products.

In this embodiment, a VfM marketing system 1 will be described as a non-limiting example of an information processing device.
FIG. 2 is a block diagram showing the functional configuration of the VfM marketing system 1 in the future prediction system shown in FIG. 1. Note that the configuration of the VfM marketing system 1 shown in FIG. 2 is only an example, and each component included in the VfM marketing system 1 may exist in any combination as physically separate devices.

The VfM marketing system 1 includes a control unit 11, a storage unit 12, and a communication interface unit 13 as hardware.

The communication interface unit 13 includes, for example, one or more wired or wireless communication interface units. The communication interface unit 13 inputs the operation signal input from the operator terminal 2 to the control unit 11 . Further, the communication interface unit 13 outputs the information output from the control unit 11 to the event notification system 4, the event participation acceptance system 5, the incentive management system 6, and the research system 7. Information transmitted from the participation acceptance system 5, incentive management system 6, and research system 7 is input into the control unit 11. Further, the communication interface unit 13 transmits information related to the results of future predictions including future sales of the above-mentioned products outputted from the control unit 11 to an external device such as the operator terminal 2 or a network printer (not shown). Output to an output device etc.

The storage unit 12 is configured of a storage medium such as a non-volatile memory that can be written and read at any time, such as an HDD (Hard Disc Drive) or an SSD (Solid State Drive). master 121, VfM index calculation presence/absence storage section 122, reaction behavior log information storage section 123, VfM index history information storage section 124, primary evaluation storage section 125, research response log information storage section 126, market value evaluation The storage unit 127 is also provided with a storage unit 127. The VfM index used in this embodiment is an index for any measurement item to be noted regarding the above-mentioned products and the like. Numerical evaluation can be performed by comparing the actual value of the measurement item with the VfM index.

The VfM index master 121 stores, for example, measurement items related to consumer reactions or behaviors to the above-mentioned products. The measurement items are stored in advance by, for example, an operator. The measurement item may be, for example, a standard item that represents the number of consumers who have taken a reaction or action that can be directly aggregated from each piece of information that represents the result of a consumer's reaction or action with respect to the product, etc., or a standard item that represents the number of consumers who have taken the relevant reaction or action. Contains items calculated by arbitrary combinations. Such measurement items are not limited to items that measure numerical values generated in conventional campaign research, such as the number of website page views and survey aggregate values. In particular, such measurement items may be items that measure numerical values obtained by calculating or analyzing actual numerical values that occurred during various activities.

The VfM index calculation presence storage unit 122 stores calculation presence/absence setting information and VfM index setting information. The calculation presence/absence setting information is a setting by the operator as to whether or not each of the above-mentioned measurement items stored in the VfM index master 121 is to be a measurement item to be calculated for calculating the actual value in conjunction with the implementation of the above-mentioned event. This is the information shown. The VfM index setting information is information indicating the setting of the VfM index for an arbitrary measurement item to be focused on regarding the above-mentioned products, among the measurement items to be calculated. Note that the VfM index for each measurement item set by the VfM index setting information is a target value for the measurement item, and is, for example, a numerical value such as the number of consumers, the number of items, or the sales amount, or This is a numerical value expressed as a percentage of the reference value. Note that the reference value may be set in advance by the operator, or may be automatically set based on actual values for other measurement items different from the measurement item. . Note that the VfM index may be set for some of the measurement items to be calculated, or may be set for all the measurement items to be calculated.

The reaction behavior log information storage unit 123 stores reaction behavior information that is information about the results of consumers' reactions or actions toward the above-mentioned products and the like. The reaction behavior information includes, for example, event announcement information including information on the consumer who announced the event, event announcement opening information including information on the consumer who opened or read the event announcement, and the event. Event announcement viewing information, including information of consumers who have opened or read the announcement and viewed the event announcement page on the website, and requests to participate in the event accepted on the event announcement page. Event participation acceptance information including information on consumers, incentive distribution information including information on consumers to whom the above-mentioned incentives were distributed among the consumers who wish to participate, and the use of the distributed incentives in conjunction with the implementation of the above-mentioned event. Contains incentive usage information, including information on consumers who have Note that the consumer information included in the reaction behavior information is, for example, identification information of the consumer such as the consumer number. The above consumer information included in each of the event announcement information, event announcement opening information, event announcement viewing information, event participation acceptance information, incentive distribution information, and incentive usage information regarding the same consumer is linked to each other. There is. Further, the consumer information included in each of these reaction behavior information is linked to the consumer identification information stored in the consumer information database 3 described above. For example, the above-mentioned consumer information included in the incentive distribution information and the incentive usage information is a coupon as the above-mentioned incentive, which is numbered so that the consumer's identification information stored in the consumer information database 3 can be uniquely identified. The coupon ID may be included. Furthermore, each reaction behavior information may include, for example, information on the date and time when the consumer's reaction or behavior was performed, and information on the media used by the consumer.

The VfM index history information storage unit 124 stores actual values for the measurement items to be calculated.

The primary evaluation storage unit 125 stores primary evaluations including objective evaluations of the products, etc., generated under the control of the VfM primary evaluation unit 115.

The research response log information storage unit 126 stores research response information that is information on the above-mentioned research responses from consumers.

The market value evaluation storage unit 127 stores the market value evaluation of the above-mentioned products etc. generated under the control of the market value evaluation unit 116.

In order to execute the processing functions in this embodiment, the control unit 11 includes an event planning section 111, an event notification section 112, an incentive distribution section 113, an event implementation section 114, a VfM primary evaluation section 115, and a market value It includes an evaluation section 116 and a VfM evaluation reporting section 117. The control unit 11 includes a processor such as a CPU (Central Processing Unit) and a program memory, and the processing functions of each section described above are realized by causing the processor to execute a program stored in the program memory. . Note that these processing functions are not limited to those implemented using programs stored in the program memory, but may be implemented using programs provided through a network.

The event planning section 111 executes a process of setting information necessary for implementing an event such as a product, in response to an operation signal transmitted from the operator terminal 2 and received by the control unit 11. For example, set the purpose, target, outline, and goals of the event to be held. For example, the event planning unit 111 accesses the VfM index calculation presence/absence storage unit 122 and executes a process of writing calculation/nonexistence setting information and VfM index setting information. The measurement item to be calculated is set by the writing process of the calculation presence/absence setting information. By the writing process of the VfM index setting information, a VfM index is set for an arbitrary measurement item of interest regarding the event target product, etc., among the measurement items to be calculated.

The event notification unit 112 executes a process of transmitting an event notification instruction to the event notification system 4 to notify consumers of the event. In response to the event notification instruction, the event notification system 4 notifies consumers of the event via various advertising media. The event notification unit 112 receives from the event notification system 4 event notification information including information of the consumer who notified the event, and event notification information including information of the consumer who has opened or read the notification of the event. and the opening information, and executes a process of storing the acquired event notification information and event notification opening information in the reaction behavior log information storage unit 123.

Next, the event notification unit 112 executes a process of transmitting an event participation acceptance instruction to the event participation acceptance system 5 for accepting a desire to participate in the event from the consumer in response to the notification. In response to the event participation acceptance instruction, the event participation acceptance system 5 accepts a request to participate in the event.

Thereafter, the event notification unit 112 acquires event notification viewing information including information of consumers who viewed the event notification page on the website from the event participation reception system 5, and acquires event notification viewing information that includes information on consumers who have viewed the event notification page on the website. The process of storing the reaction behavior log information storage unit 123 in the reaction behavior log information storage unit 123 is executed. Further, the event notification unit 112 acquires event participation acceptance information including information of consumers who wish to participate in the event, which is accepted on the event notification page, from the event participation acceptance system 5, and A process of storing the reception information in the reaction behavior log information storage unit 123 is executed.

The incentive distribution unit 113 first executes a process of reading event participation acceptance information stored in the reaction behavior log information storage unit 123. Thereafter, the incentive distribution unit 113 instructs the incentive management system 6 to distribute incentives for the event to consumers who wish to participate in the event, based on the read event participation acceptance information. Execute the process to send the . In response to the incentive distribution instruction, the incentive management system 6 distributes incentives for the event to consumers who wish to participate in the event. The incentive distribution unit 113 acquires incentive distribution information including information on the consumers to whom the incentives have been distributed from the incentive management system 6, and stores the acquired incentive distribution information in the reaction behavior log information storage unit 123. Execute.

The event implementation unit 114 executes a process of transmitting an incentive collection instruction to the incentive management system 6 to collect the incentive usage information regarding the distributed incentives in conjunction with the implementation of the event. In response to the incentive collection instruction, the incentive usage information is collected by the incentive management system 6. Thereafter, the event implementation unit 114 acquires incentive usage information including information on consumers who have used the distributed incentives from the incentive management system 6, and stores the acquired incentive usage information in the reaction behavior log information storage unit 123. Execute the process to store it in

Here, from the reaction behavior information such as event announcement information, event announcement opening information, event announcement viewing information, event participation acceptance information, incentive distribution information, and incentive usage information stored in the reaction behavior log information storage unit 123, the above-mentioned The actual value is calculated for the measurement item to be calculated that is set by the calculation presence/absence setting information. Specifically, under the control of a performance value calculation section (not shown) included in the control unit 11, first, the calculation presence/absence setting information stored in the VfM index calculation presence/absence storage section 122 is referred to. Thereafter, under the control of the performance value calculation unit, among the reaction behavior information stored in the reaction behavior log information storage unit 123, information related to the measurement item to be calculated set by the referenced calculation presence/absence setting information Reaction behavior information is read out, and performance values for the measurement items to be calculated are sequentially calculated based on the read out reaction behavior information. The calculated performance values are sequentially stored in the VfM index history information storage unit 124.

The VfM primary evaluation unit 115 executes a process of generating a primary evaluation including an objective evaluation of the product, etc., and storing information on the generated primary evaluation in the primary evaluation storage unit 125. The primary evaluation includes, for example, a VfM index evaluation, a routine analysis processing result, a detailed analysis processing result, and a hypothesis.

Specifically, the VfM primary evaluation unit 115 first calculates the VfM index for the arbitrary measurement item of interest, which is indicated by the VfM index setting information stored in the VfM index calculation presence/absence storage unit 122, and the VfM index history information. A process of reading out the actual value for the measurement item stored in the storage unit 124 is executed. The VfM primary evaluation unit 115 executes processing to generate a VfM index evaluation based on the read VfM index and actual value.

The VfM primary evaluation unit 115 can execute the following process according to the generated VfM index evaluation result. That is, the VfM primary evaluation unit 115 reads the reaction behavior information stored in the reaction behavior log information storage unit 123, and based on the read reaction behavior information, performs event notification, event notification opening, event notification viewing, A standard method for analyzing results based on preset axes such as event participation acceptance, incentive distribution, and incentive usage processes, advertising media, time of day, and consumer attributes such as age and gender. Perform analysis processing. Furthermore, the VfM primary evaluation unit 115 may execute detailed analysis processing that analyzes using axes different from those in the standard analysis processing, based on the read reaction behavior information. In addition, in the standard analysis processing and detailed analysis processing, for example, based on the consumer identification information such as the consumer number included in the reaction behavior information, the relevant information corresponding to the identification information is retrieved from the consumer information database 3. Consumer attribute information is acquired and used.

The hypothesis generation unit 1151 included in the VfM primary evaluation unit 115 executes a process of generating a hypothesis related to market value evaluation of the product etc., based at least on the generated VfM index evaluation. Note that the generated hypothesis may be based on, for example, the results of the above-mentioned standard analysis process and the above-mentioned detailed analysis process.

The market value evaluation section 116 executes a process of generating a market value evaluation as a value for the price of the above-mentioned products, etc., and includes a research implementation management section 1161, a hypothesis correction section 1162, and a future prediction section 1163. There is.

The research implementation management unit 1161 reads out the primary evaluation stored in the primary evaluation storage unit 125, and executes a process of conducting research regarding the above-mentioned product etc. based on the read primary evaluation. The research is, for example, a questionnaire regarding the above-mentioned products.

For example, the research implementation management unit 1161 reads the hypothesis stored in the primary evaluation storage unit 125, and generates research items such as the above products based on the read hypothesis. Thereafter, the research implementation management unit 1161 executes a process of transmitting a research implementation instruction to the research system 7 to cause the research system 7 to conduct research on consumers including the generated research items. In response to the research implementation instruction, the research system 7 performs the research. The research is conducted, for example, targeting consumers who have been set as research targets in advance from among the consumers who participated in the event. Thereafter, the research implementation management unit 1161 acquires research response information indicating the above-mentioned research responses including the consumer's subjective evaluation from the research system 7, and stores the acquired research response information in the research response log information storage unit 126. Execute the memorization process. Furthermore, the consumers who are the targets of the above-mentioned research can expand and set model members from their profiles rather than research members.

Hypothesis correction unit 1162 executes a process of reading research response information stored in research response log information storage unit 126. Next, the hypothesis correction unit 1162 executes a process of determining whether or not the above hypothesis is valid based on the read research response information. The determination threshold used in the determination process is set in advance, for example, when generating the research item. If it is determined that the hypothesis is not valid, the hypothesis correction unit 1162 executes a process of correcting the hypothesis. The above-mentioned research implementation process may be executed again based on the corrected hypothesis after the correction.

The market value evaluation unit 116 executes a process of reading research response information stored in the research response log information storage unit 126. Thereafter, the market value evaluation unit 116 generates a market value evaluation of the product, etc. based on the research answer indicated by the read research answer information, and stores the generated market value evaluation in the market value evaluation storage unit. 127 is executed.

The future prediction unit 1163 executes a future prediction including future sales of the product etc. based on the generated market value evaluation, and stores information related to the result of the future prediction in the market value evaluation storage unit 127. Execute processing.

The VfM evaluation reporting unit 117 reads information related to the results of the future predictions stored in the market value evaluation storage unit 127, and reports information related to the results of the future predictions including future sales of the read products, etc. A process of outputting information to, for example, the operator terminal 2 or an external output device (not shown) is executed.

(motion)
Next, the operation of the VfM marketing system 1 configured as above will be explained.
FIG. 3A shows an example of the primary evaluation generation process executed by the event planning section 111, event notification section 112, incentive distribution section 113, event implementation section 114, and VfM primary evaluation section 115 of the control unit 11 shown in FIG. FIG. Further, FIG. 3B is a flow diagram showing an example of future prediction processing executed by the market value evaluation section 116 of the control unit 11 shown in FIG. 2.

Below, we will explain the future forecasts of these flowcharts, including the future sales of "Healthy Café Sweet," a tokuho coffee drink aimed at young women that will be released by beverage manufacturer A on April 1, 2018. An example of execution will be described in detail with reference to FIGS. 4A to 15C.

(1) Primary evaluation generation process First, the primary evaluation generation process shown in the flowchart of FIG. 3A will be explained in detail. Note that the primary evaluation generation process shown in the flow of FIG. 3A is a non-limiting embodiment, and the primary evaluation generation process of the present disclosure is not limited to this.

In step S11, under the control of the event planning section 111, the control unit 11 accesses the VfM index calculation presence/absence storage section 122 as a process for setting information necessary for implementing an event such as a product, and accesses the calculation presence/absence setting information. and the above VfM index setting information are written. The measurement item to be calculated is set by the writing process of the calculation presence/absence setting information. By the writing process of the VfM index setting information, a VfM index is set for an arbitrary measurement item of interest regarding the event target product, etc., among the measurement items to be calculated. In addition, in the writing process of the calculation presence/absence setting information and the VfM index setting information, for example, the calculation presence/absence of the measurement item and the VfM index are stored differently for each project. As the event planning process, under the control of the event planning unit 111, for example, planning of the event itself, definition of target consumers for the event, and setting of consumers to be notified of the event may be executed. Note that the target consumers may be defined using consumer attribute information stored in the consumer information database 3, for example.

In the example of the product "Healthy Cafe Sweet", it is set to use a campaign event that also serves as a promotion for the product. Additionally, as an incentive for the event, everyone who enters the campaign will receive a coupon that can be used at a designated convenience store and is a free exchange ticket for the product ``Healthy Café Suite.'' Furthermore, it is assumed that women in their twenties are defined as the target consumers, and all members of a predetermined membership service stored in the consumer information database 3 are set as the consumers to whom the event will be announced.

FIGS. 4A and 4B illustrate a table showing an example of a setting status based on calculation/non-calculation setting information and VfM index setting information used in a campaign for the product "Healthy Café Sweet."

In the calculation presence/absence column in the tables of FIGS. 4A and 4B, the setting status based on the calculation presence/absence setting information is shown. In the calculation presence/absence setting information, measurement items indicated by a circle in the calculation presence/absence column are set as measurement items to be calculated. Specifically, in "11. Notification", "a. Number" and "b. Announcement rate" are set as measurement items to be calculated. "12. Opened/read", "13. Viewed", "14. Coupon application", "21. Coupon issue", and "31. Coupon exchange", "a. Number", "b. Announcement rate" ”, “c. unit price for results”, and “d. unit price increase rate” are set as measurement items to be calculated. Furthermore, "a. Number" of "41. Duplicate applications", "51. Exchange index" and "61. Cost effectiveness index" are also set as measurement items to be calculated. On the other hand, the setting status based on the VfM index setting information is shown in the target value column in the table of FIG. 4A. According to the VfM index setting information, the VfM index shown in the target value column is set for each arbitrary measurement item that should be noted regarding the product "Healthy Cafe Sweet" among the measurement items to be calculated. . Specifically, in "11. Notification", a target value of 76,250,000 is set as the VfM index for "a. Number". For "12. Opened/Read," a target value of 22,593,750 is set as the VfM index for "a. Number," and a target value of 29.63% is set as the VfM index for "b. Announcement rate." ing. In "13. Viewing", a target value of 19,826,563 is set as the VfM index for "a. number", and a target value of 26.00% is set as the VfM index for "b. notification rate". In "14. Coupon application," a target value of 87,500 is set as the VfM index for "a. Number," and a target value of 0.11% is set as the VfM index for "b. Announcement rate." In "21. Coupon issue," a target value of 70,000 is set as the VfM index for "a. Number," and a target value of 0.09% is set as the VfM index for "b. Notification rate." In "31. Coupon redemption," a target value of 46,667 is set as the VfM index for "a. Number," and a target value of 0.06% is set as the VfM index for "b. Notification rate."

In the table of Figure 4A, "a. Number" of "13. Views" is the total number of times the Company A website campaign application page was viewed, and "14. Coupon application" and "21. Coupon issue. ", and "b. Announcement rate" of "31. Coupon redemption" is the ratio of the number of consumers who reacted or took action corresponding to each item out of the number of consumers who were notified.
In the table of FIG. 4B, "51. Redemption index" is calculated from "31. Coupon redemption"/"13. Viewing", and "61. Cost effectiveness index" is calculated from "31. Coupon redemption"/" It is calculated from the total advertising fee.

In step S12, the control unit 11 uses the event notification system 4 under the control of the event notification section 112 to notify consumers of an event regarding a product or the like. Thereafter, under the control of the event notification unit 112, the control unit 11 receives event notification information from the event notification system 4 including information on the consumer who notified the event, and information indicating that the event notification has been opened or read. event announcement opening information including information on consumers who have become The acquired event announcement information and event announcement opening information are stored in the reaction behavior log information storage unit 123. For example, if the event announcement system 4 uses conventional advertising media whose advertisement results are difficult to grasp, the event announcement information or event announcement opening information may be used when a consumer requests to participate in an event on a website. It may also be acquired by inputting the advertising medium used to recognize the event.

In step S13, the control unit 11, under the control of the event notification unit 112, receives event notification viewing information from the event participation reception system 5, including information of consumers who viewed the event notification page on the website, for example; Event participation acceptance information including information of consumers who wish to participate in the event accepted on the event announcement page is acquired. The acquired event announcement viewing information and event participation acceptance information are stored in the reaction behavior log information storage unit 123. Note that, for example, if the event participation acceptance system 5 uses a conventional announcement page whose viewing history is difficult to grasp, the event announcement viewing information may be used to display the event announcement when the consumer wishes to participate in the event. It may also be obtained by inputting the medium being viewed.

FIG. 5 is a diagram showing an example of advertisement reaction log information in a campaign for the product "Healthy Café Sweet." In FIG. 5, the record in which "advertisement sending" is shown as the "reaction type" corresponds to event announcement information. Furthermore, a record in which "viewing" is indicated as the "reaction type" corresponds to event announcement viewing information. Furthermore, a record in which "application" is indicated as the "reaction type" corresponds to event participation acceptance information.

In step S14, the control unit 11 reads the event participation acceptance information stored in the reaction behavior log information storage unit 123 under the control of the incentive distribution unit 113, and based on the read event participation acceptance information, provides an incentive Using the management system 6, incentives for the event are distributed to consumers who wish to participate in the event. The incentive may be, for example, a coupon such as a free exchange ticket for the above-mentioned products. Thereafter, the control unit 11, under the control of the incentive distribution unit 113, obtains incentive distribution information including information on the consumers to whom the incentives have been distributed from the incentive management system 6. The obtained incentive distribution information is stored in the reaction behavior log information storage unit 123.

FIG. 6 shows an example of coupon issuance log information in a campaign for the product "Healthy Cafe Sweet." In FIG. 6, the record in which "coupon issue" is shown as the "reaction type" corresponds to incentive distribution information.

Next, in step S15, the control unit 11, under the control of the event implementation unit 114, acquires incentive usage information including information of consumers who used the above-mentioned incentives in conjunction with the implementation of the above-mentioned event from the incentive management system 6. do. The acquired incentive usage information is stored in the reaction behavior log information storage unit 123.

FIG. 7 shows an example of coupon usage log information in a campaign for the product "Healthy Cafe Sweet." In FIG. 7, the record in which "coupon usage" is shown as the "reaction type" corresponds to incentive usage information.

Event announcement information and event announcement opening information acquired in step S12, event announcement viewing information and event participation acceptance information acquired in step S13, incentive distribution information acquired in step S14, and incentive usage acquired in step S15. Based on the reaction behavior information such as information, performance values are calculated for the measurement items to be calculated set by the calculation presence/absence setting information, and the calculated performance values are sequentially stored in the VfM index history information storage unit 124.

Next, in step S16, step S17, and step S18, the control unit 11 generates a primary evaluation including an objective evaluation of the above-mentioned products etc. under the control of the VfM primary evaluation section 115. The primary evaluation may also include, for example, media effects (viewed, applied for, and exchanged by advertising media) when the event was announced to consumers.

In step S16, the control unit 11, under the control of the VfM primary evaluation section 115, stores the VfM index for the arbitrary measurement item of interest stored in the VfM index calculation presence/absence storage section 122, and the VfM index history information storage section. 124 for the measurement item in question. Thereafter, the control unit 11 generates a VfM index evaluation based on the read VfM index and actual value under the control of the VfM primary evaluation section 115.

In step S17, the control unit 11 reads the reaction behavior information stored in the reaction behavior log information storage unit 123 under the control of the VfM primary evaluation unit 115, and performs the above-mentioned reaction behavior information based on the read reaction behavior information. Executes routine analysis processing and detailed analysis processing.

In step S18, the control unit 11 generates a hypothesis regarding the market value evaluation of the product, etc., based at least on the generated VfM index evaluation, under the control of the hypothesis generation section 1151 included in the VfM primary evaluation section 115. . The generated hypothesis is an accumulation of the degree of sufficiency and achievement of plans related to products or services. Note that the generated hypothesis may be based on, for example, the results of the above-mentioned standard analysis process and the above-mentioned detailed analysis process.

8A, FIG. 8B, FIG. 8C, and FIG. 8D are tables showing an example of VfM index evaluation based on the VfM index and actual value for arbitrary measurement items of interest in the campaign for the product "Healthy Cafe Sweet" is illustrated. In the examples shown in FIGS. 8A, 8B, 8C, and 8D, actual values are calculated for each advertising medium for each measurement item, and further, "14. Coupon application", "21. Coupon issue", Regarding the measurement item "31. Coupon redemption", actual values are calculated for each target cover. In the tables of FIGS. 8A, 8B, and 8C, for example, the number of consumers shown in the target column or the number of consumers shown in the target column is used as the reference value for each measurement as the VfM index for the measurement item. For example, 100%, which is a numerical target value expressed as a percentage of the number of consumers calculated for the item with respect to the reference value, is used. Further, in the tables of FIGS. 8A, 8B, and 8C, for example, the target achievement status is shown as the VfM index evaluation. Here, in the coupon exchange goal achievement status shown in the table of FIG. 8C, it can be seen that the target coverage goal achievement status is less than 90% no matter which advertising medium is used. This means that the targeted female office workers in their 20s were less likely to redeem their coupons than expected. "61. Cost effectiveness index" shown in the table of FIG. 8D is the total advertising fee for advertising medium 1 of ¥5,000,000, the total advertising fee of advertising medium 2 of ¥10,000,000, and the total of advertising medium 3. It is calculated based on the advertising fee of ¥30,000,000.

Figure 9 shows an example of a pie chart showing the coupon usage status by gender and age of consumers, which was generated by the standard analysis process that is part of the above primary evaluation in the campaign for the product "Healthy Cafe Sweet". It is a diagram. Through this routine analysis process, it is possible to evaluate whether the target has been covered. The total value of the product/service target group for each gender/year composition ratio (%) shown in Figure 9 is an index called "target achievement rate."

From the graph shown in Figure 9, it can be seen that women in their 20s do not redeem coupons as expected. Additionally, the graph shown in Figure 9 shows that the product ``Healthy Café Sweet'' is generally popular among women in their 20s to 40s, with the highest proportion of coupon usage among women in their 30s. I understand. Additionally, it can be seen that there is a certain level of demand for the product ``Healthy Café Sweet'' among men in their 20s to 40s, although not as much as women.

Figure 10 shows the reaction status for each step of the reaction or behavior toward the product "Healthy Cafe Sweet" generated by the detailed analysis process that is part of the above primary evaluation in the campaign for the product "Healthy Cafe Sweet". It is a figure showing an example of a line graph tabulated by age of female consumers.

The graph shown in Figure 10 shows that women in their 20s had the highest number of views on the campaign application page on Company A's website for the product "Healthy Café Sweet," but the percentage of women in their 20s applying was in their 30s to 40s. It can be seen that this is lower than that of women.

Figure 11 is a diagram showing an example of a pie chart showing the status of coupon usage by consumers by occupation, which was generated by detailed analysis processing that is part of the above primary evaluation in the campaign for the product "Healthy Cafe Sweet". be.

From the graph shown in Figure 11, it can be seen that the percentage of coupon usage for the product ``Healthy Café Sweet'' is highest among office workers and assistants, but the percentage of coupon usage by professionals and technical workers is also large.

A hypothesis regarding the market value evaluation of the product "Healthy Café Sweet" is generated from the tables and graphs shown in FIGS. 8A to 11.

For example, the number of applications is low despite the large number of views among the target group of women in their 20s, which may indicate that they stop applying after checking the product introduction on the website. In this case, under the control of the hypothesis generation unit 1151, a hypothesis is generated that the campaign announcement content does not match the tastes of women in their 20s. The generated hypothesis may, for example, be output from the VfM marketing system 1 to an external display unit (not shown), and may be finalized after an operator reviews the display on the display unit.

It is also clear that women in their 20s to 40s who work at desk jobs use coupons more often, but men in their 20s to 40s also use more coupons. In this case, under the control of the hypothesis generation unit 1151, a hypothesis is generated that people who do not usually exercise much will purchase this product for their health. The generated hypothesis may, for example, be output from the VfM marketing system 1 to an external display unit (not shown), and may be finalized after an operator reviews the display on the display unit.

Note that the hypothesis may be generated as follows based on the above-mentioned VfM index evaluation.

FIGS. 12A and 12B are diagrams showing an example of a hypothesis generation table showing the correspondence between detection conditions based on VfM index evaluation and generated hypotheses. The hypothesis generation table may be set by the operator, for example, when registering the project in step S11. In the example of the hypothesis generation table in FIGS. 12A and 12B, if the VfM index evaluation satisfies a certain detection condition in the hypothesis generation table under the control of the hypothesis generation unit 1151, it is associated with the detection condition. A hypothesis is generated. In the examples of FIGS. 8A, 8B, and 8C, the goal achievement status as a VfM index evaluation corresponding to each measurement item is No. 13 and no. A hypothesis that satisfies the 15 detection conditions and is associated with the detection conditions is generated. Note that the hypothesis generation table shown in FIG. 12A and FIG. 12B is only an example, and for example, the detection conditions in the hypothesis generation table may use any combination of VfM index evaluations based on any VfM index.

Alternatively, regarding the VfM index for each measurement item, a hypothesis may be set in advance to be generated when the actual value for the measurement item does not satisfy the numerical value related to the VfM index. The hypothesis may be set, for example, by the operator when registering the project in step S11. When there is a VfM index in which the actual value of a measurement item does not satisfy the numerical value related to the VfM index, a hypothesis corresponding to the VfM index is generated under the control of the hypothesis generation unit 1151. .

(2) Future prediction processing Next, the future prediction processing shown in the flowchart of FIG. 3B will be explained in detail. Note that the future prediction process shown in the flow of FIG. 3B is a non-limiting embodiment, and the future prediction process of the present disclosure is not limited thereto.

In step S21, the control unit 11, under the control of the research implementation management section 1161 included in the market value evaluation section 116, conducts research on the above-mentioned products for consumers based on the hypothesis generated in step S18. The research is, for example, a questionnaire survey of consumers regarding the above-mentioned products.

In the research implementation process, for example, under the control of the research implementation management unit 1161, research items such as the above-mentioned products are generated based on the above-mentioned hypothesis, and information related to the generated research items is transmitted outside the VfM marketing system 1. It is output to the research system 7 of. The research system 7 conducts research including the generated research items, targeting consumers who have been set as research targets in advance from among the consumers who have participated in the campaign. Thereafter, the control unit 11, under the control of the research implementation management section 1161, acquires research response information indicating the above-mentioned research responses including the consumer's subjective evaluation from the research system 7. Note that the generated research items may be output from the VfM marketing system 1 to an external display unit (not shown), and may be finalized after an operator reviews the display on the display unit. Furthermore, the consumers who are the targets of the above-mentioned research can expand and set model members from their profiles rather than research members. In this way, the above-mentioned research may be conducted after the research items and research subjects are extracted.

Note that in the research implementation process based on the above hypothesis, the research may be performed recursively. For example, among consumers who have been set as research targets in advance, consumers in a range other than the current research range, or consumption selected based on consumer information included in the above reaction behavior information related to the above hypothesis. Research may be conducted recursively, targeting individuals. Alternatively, research may be recursively performed based on a hypothesis, or may include new research items that have been added, modified, or deleted. In this way, by expanding or contracting the number of consumers to be researched and conducting recursive research, it is possible to improve the accuracy of future predictions.

In the above, as an embodiment of the hypothesis-based research implementation process, research items are generated based on the hypothesis, and research including the generated research items is conducted targeting consumers who have been set as research targets in advance. An example was explained. However, the process of conducting research based on a hypothesis is not limited to this. For example, research for which research items are set in advance may be conducted on consumers selected based on consumer information included in reaction behavior information related to a hypothesis. Alternatively, research including research items generated based on the hypothesis may be conducted on consumers selected based on consumer information included in reaction behavior information regarding the hypothesis.

Note that a case has been described in which a hypothesis is generated based on the VfM index evaluation in step S18, and research is conducted based on the generated hypothesis. However, the hypothesis generation process in step S18 is not an essential configuration. For example, the VfM index evaluation generated in step S16 and the results of the above-mentioned standard analysis process and the above-mentioned detailed analysis process executed in step S17 may be used in any combination to conduct research. .

FIGS. 13A to 13G are diagrams showing examples of display screens of questionnaire items generated based on hypotheses in the example of the product "Healthy Café Sweet." The questionnaire items shown in FIGS. 13A and 13B are questionnaire items that can be used for sales prediction. The questionnaire items shown in FIG. 13C are questionnaire items generated based on the above hypothesis that the campaign announcement content does not match the preferences of women in their 20s. Furthermore, the questionnaire items shown in FIGS. 13D and 13E are questionnaire items generated based on the above hypothesis that people who do not usually exercise much will purchase the products for their health. Furthermore, the questionnaire items shown in FIGS. 13F and 13G are No. 1 in the example of FIGS. 12A and 12B. These are questionnaire items generated based on the above-mentioned hypotheses that are associated with the 15 detection conditions. For example, questionnaire items are associated with hypotheses in advance, and as described above, questionnaire items that are associated with the hypotheses are generated based on the hypotheses. In addition, among the consumers set as research targets in advance, for example, women in their 20s who applied for a campaign for the product "Healthy Cafe Sweet" but did not use the coupon, and 20-year-old women who did use the coupon. Consumers in their 40s to 40s may be selected.

In step S22, the control unit 11 determines whether the hypothesis is valid based on the research response information under the control of the hypothesis correction unit 1162 included in the market value evaluation unit 116. Registration of the determination threshold used in the determination process is set, for example, during the process of step S11.

Figures 14A to 14E show, in the example of the product "Healthy Café Sweet," a questionnaire survey targeting women in their 20s who viewed the product but did not use the coupon, and consumers in their 20s to 40s who used the coupon. It is a figure which shows an example of the display screen of a total result. The total results of the questionnaire shown in FIGS. 14A to 14E correspond to the questionnaire items shown in FIGS. 13C to 13G, respectively.

As shown in FIGS. 14A to 14E, each response option for a questionnaire item reflects the consumer's subjective opinion regarding the hypothesis related to the questionnaire item. Therefore, for example, as shown in FIG. 14A, "Q14. Please tell me why you did not apply for the "Healthy Cafe Sweet" campaign. By comparing the percentage of responses for each option to the questionnaire item "" with a preset judgment threshold, we determined whether the hypothesis that the campaign announcement content did not match the preferences of women in their 20s was valid. can be determined. Through similar processing, it is determined whether or not the hypothesis that people who do not exercise much usually purchase products for their health is valid based on the proportion of responses for each option for the questionnaire items shown in FIGS. 14B and 14C. I can do it. Also, from the proportion of responses for each option to the questionnaire items shown in FIGS. 14D and 14E, No. It can be determined whether the hypothesis generated when the 15 detection conditions are satisfied is valid.

Note that the process of determining whether or not the hypothesis is valid may be performed using, for example, structural equation modeling (SEM). In such judgment processing, for example, for a model as a hypothesis related to consumer purchasing, the degree of model fit is calculated from the survey results of research items corresponding to observed variables, and the degree of model fit is compared with a judgment threshold. This will determine whether the hypothesis was valid.

If it is determined in step S22 that the hypothesis is not valid, the process branches from step S23 to step S24.

In step S24, the control unit 11 corrects the above hypothesis under the control of the hypothesis correction section 1162, and then the processing from step S21 is executed again. In the hypothesis correction process, for example, the hypothesis is corrected based on the difference between the evaluation (generated by the system) from the event log (expected value) and the result (actual value).

In addition, in the hypothesis correction process, when multiple hypotheses are generated as in the example described above, for example, a hypothesis determined to be invalid from among the multiple hypotheses is removed. Good too. In addition, when a hypothesis is generated based on a detection condition as in the example of FIGS. 12A and 12B, the hypothesis correction process involves, for example, correcting the detection condition and the hypothesis associated with the detection condition. The hypothesis associated with the detection condition satisfied by the VfM index evaluation may be generated again. Furthermore, when using structural equation modeling to check whether a hypothesis is valid as described above, the hypothesis correction process involves customizing the hypothesis using mathematical techniques such as factor analysis. It's okay.

On the other hand, if it is determined in step S22 that the above hypothesis is valid, the process branches from step S23 to step S25.

In step S25, the control unit 11 generates a market value evaluation of the product, etc., based on the research response indicated by the research response information, under the control of the market value evaluation section 116. The market value evaluation is a value that can be called market satisfaction (a numerical value that can be benchmarked), and is calculated based on the degree of satisfaction with the product and service planning items (aims), the total value of additions and subtractions, and the probability of affirmation (purchase) by consumer attribute. This is the final set of indicators. Taking the case where the product is a food product as an example, the market value evaluation is expressed as follows, for example.
Σ (evaluation x weight) = taste 5 x 10% + amount 3 x 5% +...

In step S26, the control unit 11, under the control of the future prediction unit 1163 included in the market value evaluation unit 116, generates a future prediction including future sales of the above products based on the market value evaluation generated in step S25. Execute. In the future prediction processing, for example, based on the attribute information of each consumer included in the research response information and the research response from the consumer indicated by the research response information, an expanded estimation using a simple estimation method; The above future prediction is performed by executing at least one of a complex estimation method, for example, an expanded estimation using a Bayesian network method. In addition, the expanded estimation using the Bayesian network method is performed by, for example, setting the purchase probability between the answer options for each item of the above research, and using the purchase probability indicated by the above research response information. It's okay. The purchase probability may be set, for example, by an operator when generating or finalizing a research item.

FIG. 15A, FIG. 15B, and FIG. 15C are diagrams showing an example of the results of sales prediction for the product "Healthy Cafe Sweet" in the example of the product "Healthy Cafe Sweet." The sales predictions in FIGS. 15A, 15B, and 15C can be performed, for example, based on the aggregated results of consumer responses to the questionnaire items shown in FIGS. 13A and 13B. FIG. 15A shows the results of sales prediction based on simple estimation from market sales numbers. FIG. 15B shows the results of sales prediction based on simple estimation by consumer attribute. FIG. 15C shows the results of sales prediction based on expanded estimation using the Bayesian network method. Note that the campaign response coefficients in the examples of FIGS. 15A, 15B, and 15C may be preset coefficients, or may be derived based on the consumer's responses to the corresponding survey items. You may use the one provided.

In step S27, the control unit 11, under the control of the VfM evaluation reporting unit 117, outputs information related to the results of the future prediction including future sales of the above-mentioned products etc. executed in step S26 to an external output (not shown). Output to device.

(effect)
(1) First, under the control of the event planning section 111, calculation/non-calculation setting information and VfM index setting information are written into the VfM index calculation/presence storage section 122. The measurement item to be calculated is set by the writing process of the calculation presence/absence setting information. By the writing process of the VfM index setting information, a VfM index is set for an arbitrary measurement item of interest regarding the event target product, etc., among the measurement items to be calculated. Under the control of the event announcement section 112, incentive distribution section 113, and event execution section 114, the event announcement system 4, event participation acceptance system 5, and incentive management system 6 are used to conduct an event regarding products, etc. Along with the implementation of the event, incentive usage information including information on consumers who used the incentives of the event is acquired. Under the control of the VfM primary evaluation unit 115, a primary evaluation including an objective evaluation of the product etc. is generated based on the reaction behavior information including the acquired incentive usage information. In the generation process of the primary evaluation, under the control of the VfM primary evaluation unit 115, the VfM index for the arbitrary measurement item of interest indicated by the VfM index setting information and the actual value for the measurement item are used. , a VfM index evaluation is generated, and under the control of the hypothesis generation unit 1151, a hypothesis related to the market value evaluation of the above product etc. is generated based at least on the generated VfM index evaluation. Thereafter, under the control of the research implementation management unit 1161, research on the above-mentioned products and the like to consumers is conducted based on the generated hypothesis. Under the control of the market value evaluation unit 116, a market value evaluation of the above-mentioned products, etc. is generated based on the answers to the conducted research. Under the control of the future prediction unit 1163, future predictions including future sales of the above products etc. are executed based on the generated market value evaluation.

Such market value evaluation generation processing and future prediction processing based on events and research can be based on data that reflects the economic environment at the time, rather than the past economic environment, for example. Therefore, such future forecasts can be made more reliable across a wide range of forecasting fields compared to methods that rely solely on statistical analysis of past data to make sales forecasts. Become.

Furthermore, the future predictions can reflect the subjective opinions of consumers on the buyer side, for example, by using consumer research as described above. By conducting the research based on the primary evaluation as described above, it is possible, for example, to reconfirm the information obtained in the primary evaluation or to evaluate from a different perspective than the information obtained in the primary evaluation. becomes possible. Therefore, it is possible to make such future predictions even more objective and accurate.

Further, as described above, it is possible to generate the above-mentioned primary evaluation based on the VfM index, which is an index that can be numerically evaluated. Since the above-mentioned research is carried out based on the primary evaluation generated in this way, it becomes possible to make the above-mentioned market value evaluation and future prediction more accurate. Furthermore, since VfM indicators can be set for arbitrary measurement items, it is possible to perform market value evaluations and future predictions focusing on necessary items.

In addition, along with the implementation of the above event, information on the results of consumers' reactions or actions is sequentially accumulated, and actual values for measurement items related to the above VfM index are calculated in chronological order from the accumulated information. be able to. The information may be continuously accumulated, for example, while the event is executed multiple times. By using the performance values calculated chronologically in this way, it is also possible to evaluate and analyze trends and changes in the market value evaluation chronologically. By evaluating and analyzing trends and changes in the above market value evaluation, future predictions regarding the operation of marketing activities related to products or services, future predictions of sales related to products or services, and direction of development, revision, or abolishment of products or services can be made. It becomes possible to make future predictions with high accuracy. Furthermore, regarding the above-mentioned VfM index, which is an indicator for arbitrary measurement items that should be noted regarding the above-mentioned products or services, actual values for the measurement items related to the VfM index are calculated. By applying the same VfM index to reactions or actions, it is also possible to cross-sectionally evaluate other companies, other industries, and products of other industries.

Furthermore, the generated hypothesis is based on a numerically evaluable index called a VfM index. For example, in research conducted based on a hypothesis as described above, the research items included in the research may be generated based on such a hypothesis, or the consumers targeted by the research may be based on such a hypothesis. It is also possible to select based on. By performing the market value evaluation generation process and future prediction process based on such research, it becomes possible to make future predictions more accurate.

Based on the results of such future predictions, for example, which products should be continued to be sold, which services should be continued to be provided, which products should be discontinued, which services should be discontinued, or , it becomes possible to determine which products and services should be improved. In addition, the above future forecast not only predicts future sales but also the value of commercial materials, so it is possible to make production plans to determine how much product to produce, and plans to determine the scale of services to be provided. can also be determined.

(2) Under the control of the hypothesis correction unit 1162, it is determined whether the above hypothesis is valid or not based on research response information indicating the answers to the conducted research. If it is determined that the hypothesis is not valid, the hypothesis is corrected under the control of the hypothesis correction unit 1162. Thereafter, the process under the control of the research implementation management unit 1161 described above is executed again.

In this way, it is possible to recursively correct the above-mentioned hypothesis until it becomes valid, and therefore, the above-mentioned future prediction results derived from the corrected hypothesis after the correction are more reliable. It becomes possible to make it a thing.

(3) In the future prediction process for the above-mentioned products, etc., for example, simple estimation is performed based on the attribute information of each consumer included in the above-mentioned research response information and the above-mentioned research answers from the consumer indicated by the above-mentioned research response information. The future prediction is performed by executing at least one of the expanded estimation method and the expanded estimation method using a complex estimation method (eg, Bayesian network method). For example, in extended estimation using a simple estimation method, deductive extended estimation is performed, and in extended estimation using a complex estimation method, deductive or recursive extended estimation is performed.

By performing such expanded estimation, we can predict how much the consumers who are the potential buyers of the above products or services will contribute to sales, not just the limited number of consumers who are the subject of the above research. It is possible to do so. Furthermore, even if the distribution of attribute information of the consumers targeted for the above research is biased compared to the distribution of attribute information of all consumers who are the potential purchasers of the above products or services, for example, By appropriately weighting each attribute information and correcting the distribution bias, it is possible to make the future prediction based on the expanded estimation more accurate. In addition to simple "sales forecasts", by logging results, accumulating them as indicators (information), and analyzing them, we can predict the future of marketing activities, sales of products or services, and the direction of development, revision, and abolition. can be made highly accurate.

[Other embodiments]
This invention is not limited to the above embodiments. For example, in the first embodiment, an example in which one type of incentive such as a coupon is used as the incentive has been mainly described. However, incentives include multiple types of coupons such as free vouchers and discount vouchers, bonus points that consumers receive when purchasing products, etc., free vouchers for other products that consumers receive when purchasing products, etc. Multiple types of incentives, such as multiple types of coupons such as discount tickets, may be used simultaneously. In this case, the above-mentioned incentive distribution information and incentive usage information may include, for example, information indicating the type of incentive that the consumer obtains and uses. Further, in addition to the incentive management system described above, an incentive management system for managing each incentive may be used for each incentive.

In addition, the VfM index includes indicators for measurement items related to consumers who have used incentives multiple times, indicators for measurement items related to consumers who have not used incentives, and indicators related to measurement items related to consumers who have selected specific incentives. At least one of the indicators for measurement items may be included.

Here, the consumers who have used the incentive multiple times include, for example, consumers who evaluate the above product or service as having high value. Therefore, by setting a VfM index for measurement items related to consumers who have used the above-mentioned incentives multiple times, it is possible to evaluate positive aspects regarding the value of the above-mentioned products or services.

In addition, consumers who do not use incentives include, for example, consumers who did not use incentives even though they wanted to participate in the above event, and consumers who did not use incentives even though they received incentives. This includes consumers who have not. Therefore, by setting VfM indicators for measurement items related to consumers who do not use the above-mentioned incentives, it is possible to verify, for example, whether there is room to improve the evaluation of the above-mentioned products or services, or to It is possible to verify what methods should be taken to improve such evaluations.

Furthermore, for example, when a consumer selects an incentive to use from among multiple types of incentives (discounts, exchanges, points, etc.), it may be desirable to obtain statistics on consumers who have selected a specific incentive. For example, the type of evaluation of the value of the product or service by the consumer can be determined for each incentive selected by the consumer. Therefore, by setting a VfM index for the measurement item related to the consumer who has selected the specific incentive, it is possible to evaluate the value of the product or service with higher quality. That is, by preparing a plurality of incentives desired by consumers and setting and analyzing a reference value for measuring the market value for each incentive, it is possible to improve the accuracy of the above-mentioned future prediction.

In this way, by using the VfM index as described above, for example, in the primary evaluation generation process, the hypothesis generation process, or the research implementation process, the market value evaluation based on the research can be made more accurate. It becomes possible to make it a thing. Therefore, the above future prediction based on the market value evaluation can also be made more accurate.

Furthermore, in the first embodiment, an example has been described in which a VfM index evaluation is generated based on a VfM index and a performance value for an arbitrary measurement item of interest. Here, for example, for each measurement item to be calculated that is set by the calculation presence/absence setting information, a period during which reaction behavior information related to the measurement item can be obtained is set, and during the period and for the measurement item. If the actual value does not satisfy the numerical value related to the VfM index, it is necessary to remind the consumer based on the remaining period of the relevant period and the VfM index and the actual value for the measurement item. It is also possible to determine whether or not there is, and to perform a reminder depending on the determination.

Furthermore, in the first embodiment, an example has been described in which the reaction behavior information includes consumer identification information such as a consumer number. In such an example, as described above, the consumer's attribute information related to the reaction behavior information is obtained by using the correspondence between the consumer's identification information stored in the consumer information database and the consumer's attribute information. is obtained. However, the reaction behavior information may include the consumer's attribute information itself in addition to the consumer's identification information such as the consumer number as described above.

Further, in the first embodiment, the explanation has been given mainly on the assumption that the hypothesis is corrected when it is determined that the hypothesis is not valid. However, if the hypothesis is not valid, a new hypothesis may be obtained.

In addition, the configuration of the VfM marketing system, VfM index master, VfM index calculation/non-calculation storage section, reaction behavior log information storage section, VfM index history information storage section, primary evaluation storage section, research response log information storage section, market value evaluation storage section The structure of the data stored in each section can also be modified in various ways without departing from the gist of the present invention.

In short, the present invention is not limited to the above-described embodiments as they are, but can be implemented by modifying the constituent elements within the scope of the invention at the implementation stage. Moreover, various inventions can be formed by appropriately combining the plurality of components disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiments. Furthermore, components from different embodiments may be combined as appropriate.

1...VfM marketing system, 11...control unit, 111...event planning department, 112...event notification department, 113...incentive distribution department, 114...event implementation department, 115...VfM primary evaluation section, 1151...hypothesis generation section, 116... Market value evaluation section, 1161...Research implementation management section, 1162...Hypothesis correction section, 1163...Future prediction section, 117...VfM evaluation reporting section, 12...Storage unit, 121...VfM index master, 122...VfM index calculation presence/absence storage section , 123... Reaction behavior log information storage section, 124... VfM index history information storage section, 125... Primary evaluation storage section, 126... Research response log information storage section, 127... Market value evaluation storage section, 13... Communication interface unit, 2 …Operator terminal, 3…Consumer information database, 4…Event notification system, 5…Event participation reception system, 6…Incentive management system, 7…Research system, NW…Communication network

Claims (8)

An event planning department that sets the information necessary to carry out events for products or services;
an event notification unit that notifies consumers of the event and obtains event participation acceptance information including information of consumers who wish to participate in the event;
an incentive distribution unit that distributes incentives for the event to consumers who wish to participate in the event;
an event implementation unit that acquires incentive usage information including information on consumers who have used the incentive in conjunction with the implementation of the event;
a VfM primary evaluation unit that generates a primary evaluation including an objective evaluation of the product or service based on the reaction behavior information including the acquired event participation acceptance information and incentive usage information;
Conducting research regarding the product or service to consumers based on the generated primary evaluation, and generating a market value evaluation of the product or service based on the results of the research including the consumer's subjective evaluation. , a market value evaluation unit that executes future predictions including future sales of the goods or services based on the generated market value evaluation;
An information processing device comprising: a VfM evaluation report unit that outputs information related to the result of the future prediction. The event planning department sets a VfM index that is an index for any measurement item to be noted regarding the product or service,
The information processing apparatus according to claim 1, wherein the primary evaluation includes a VfM index evaluation based on a VfM index and a performance value for the set arbitrary measurement item of interest. The VfM primary evaluation unit includes a hypothesis generation unit that generates a hypothesis related to market value evaluation of the product or service based at least on the VfM index evaluation,
The information processing device according to claim 2, wherein the market value evaluation unit conducts research regarding the product or service based on the generated hypothesis. The market value evaluation unit determines whether or not the generated hypothesis is valid based on the results of the research, and if it is determined that the generated hypothesis is not valid, the market value evaluation unit determines whether the generated hypothesis is valid or not. Equipped with a hypothesis correction unit that corrects the hypothesis,
The market value evaluation department conducts second and subsequent research regarding the product or service to consumers based on the corrected hypothesis after the correction, and based on the results of the second and subsequent research, The information processing device according to claim 3, wherein the information processing device generates a market value evaluation of the service. The market value evaluation department executes at least one of an expanded estimation using a simple estimation method and an expanded estimation using a complex estimation method based on the attribute information of each consumer who responded to the research and the responses from the consumer to the research. The information processing apparatus according to any one of claims 1 to 4, wherein the information processing apparatus executes future prediction including the future sales by doing so. The VfM index includes indicators for measurement items related to consumers who have used the incentive multiple times, indicators for measurement items related to consumers who have not used the incentive, and indicators for consumers who have selected a specific incentive. The information processing device according to claim 2, comprising at least one of the indicators for the measurement items. An information processing method executed by a device including a processor and a memory, the method comprising:
The process of setting the information necessary for the implementation of the product or service event,
a step of notifying consumers of the event and acquiring event participation acceptance information including information of consumers who wish to participate in the event;
distributing incentives for the event to consumers who wish to participate in the event;
Accompanying the implementation of the event, acquiring incentive usage information including information of consumers who used the incentive;
a step of generating a primary evaluation including an objective evaluation of the product or service based on the reaction behavior information including the acquired event participation acceptance information and incentive usage information;
Conducting research regarding the product or service to consumers based on the generated primary evaluation, and generating a market value evaluation of the product or service based on the results of the research including the consumer's subjective evaluation. , performing a future prediction including future sales of the goods or services based on the generated market value evaluation;
An information processing method comprising: outputting information related to the result of the future prediction. A program for causing a computer to function as each section included in the information processing apparatus according to any one of claims 1 to 6.