JP2022123116A - 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)2022,JPO&INPIT

Description

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

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

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

Japanese Patent Application Laid-Open No. 2002-358402 JP 2003-167976 A

However, the past sales history data of the product used in the technology described in Patent Document 1 reflects the economic environment at the time when the product was sold in the past. For this reason, conventional analysis cannot evaluate the relative merits of products and their future prospects. It is not possible to make a rational and objective evaluation of the future response, such as whether the product should be left (improved) or whether the newly developed product has value (competitiveness).

As a method of predicting sales, there has been no known method of sales prediction other than the method described in Patent Document 1, which depends only on statistical analysis of data reflecting the past economic environment.

In addition, the technique described in Patent Document 2 can measure the effects of events such as various exhibitions and competitions that have recently been held. No method was known to make the prediction.

The present invention has been made in view of the above circumstances, and its object is to provide an information processing apparatus and method for executing future predictions including future sales of goods or services using events of the goods or services. and to provide programs.

In order to solve the above-mentioned problems, a first aspect of the present invention is an information processing apparatus comprising an event planning section for setting information necessary for holding an event for goods or services, and an event planning section for notifying consumers of the event. an event notification unit for acquiring event participation acceptance information including information on consumers who wish to participate in the event; an incentive distribution unit for distributing incentives for the event to consumers who wish to participate in the event; Based on the event implementation unit that acquires incentive use information including information on consumers who used the incentive, and reaction behavior information including the acquired event participation acceptance information and incentive use information, according to the implementation, A VfM primary evaluation unit that generates a primary evaluation including an objective evaluation of goods or services, and conducts research on the goods or services to consumers based on the generated primary evaluation, including consumer subjective evaluation A market valuation unit that generates a market valuation of the goods or services based on the results of the research, and executes future forecasts including future sales of the goods or services based on the generated market valuation. and a VfM evaluation reporting unit that outputs information related to the result of the future prediction.

In a second aspect of the present invention, the event planning unit sets a VfM index that is an index for an arbitrary measurement item to be focused on with respect to the goods or services, and the primary evaluation is based on the set focus. It is intended to include VfM index evaluation based on VfM index and actual values for arbitrary measurement items to be measured.

In a third aspect of the present invention, the VfM primary evaluation unit includes a hypothesis generation unit that generates a hypothesis related to market value evaluation of the goods or services based at least on the VfM index evaluation, and the market value evaluation unit conducts research on the goods or services based on the generated hypothesis.

In a fourth aspect of the present invention, the market valuation unit determines whether or not the generated hypothesis is valid based on the results of the research, and determines that the generated hypothesis was not valid. A hypothesis correcting unit that corrects the generated hypothesis if determined, and the market value evaluation unit conducts second and subsequent research related to the product or service to the consumer based on the corrected hypothesis after the correction. and generate a market valuation of the goods or services based on the results of the second and subsequent researches.

In a fifth aspect of the present invention, the market value evaluation unit performs an expanded estimation of a simple estimation method and a complicated estimation method based on the attribute information of each consumer who responded to the research and the research response from the consumer. By executing at least one of the expanded estimation of the above, the future prediction including the future sales is executed.

A sixth aspect of the present invention is that the VfM index includes an index for measurement items related to consumers who have used the incentive multiple times, an index for measurement items related to consumers who have not used the incentive, and indicators of measurement items related to consumers who have selected specific incentives.

According to the first aspect of the present invention, when an event for goods or services is held, incentive use information including information about consumers who have used the incentive of the event is acquired, and reaction behavior including the incentive use information is obtained. Based on the information, a primary rating is generated that includes an objective rating of the goods or services. Thereafter, consumer research is conducted based on the generated primary evaluation, and a market value evaluation of the goods or services is generated based on the results of the research, including subjective consumer evaluation. A future forecast including future sales of the goods or services is performed based on the generated market valuation.

Such event and research-based market valuation generation and forward-looking processes may be based, for example, on data that reflects the current economic environment rather than historical economic conditions. Therefore, it is possible to make such future predictions more reliable over a wide range of prediction fields compared to methods that make predictions that rely only on statistical analysis of past data. .

Further, in the future prediction, as described above, the consumer's subjective opinion can be reflected, for example, by using consumer research. 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 from the information obtained in the primary evaluation. becomes possible. Therefore, it is possible to make such future prediction more purposeful and more accurate.

Based on the results of such future predictions, for example, which products should be sold and which services should be continued, which products should be discontinued, and which services should be discontinued, or , it is possible to determine which goods and services should be improved. In addition, since the above future forecast not only predicts future sales, but also the value of products, it is possible to plan the production plan of how much product should be produced and the scale of the provision of services. can also be determined.

According to the second aspect of the present invention, a VfM index is set as an index for any measurement item to which attention should be paid regarding the goods or services. The primary evaluation includes VfM index evaluation based on the VfM index and actual values for the set arbitrary measurement items to be focused on.

In this way, it is possible to generate the primary evaluation based on a numerically evaluable index called the VfM index. Since the research is conducted based on the primary evaluation generated in this way, it is possible to make the market value evaluation and future prediction more accurate. In addition, since the VfM index can be set for any measurement item, it is possible to perform market value evaluation and future prediction focusing on necessary items.

In addition, with the implementation of the event, information on the results of consumer reactions or actions is sequentially accumulated, and actual values for the measurement items related to the VfM index are calculated in chronological order from the accumulated information. be able to. The information may be continuously accumulated while the event is executed multiple times, for example. By using the actual values calculated in time series in this way, it is also possible to evaluate and analyze trends and transitions of the market value evaluation in time series. Based on the evaluation and analysis of trends and transitions in the above-mentioned market value evaluation, future forecasts regarding the operation of marketing activities related to products or services, future forecasts of sales related to products or services, and directions for development, revision and abolition of products or services It is possible to make highly accurate future predictions regarding Furthermore, with respect to the VfM index, which is an index for any measurement item to be focused on for the above goods or services, since actual values for measurement items related to the VfM index are calculated, It is also possible to cross-evaluate other companies, other industries, products of other industries, etc. by applying the same VfM index to reactions or behaviors.

According to a third aspect of the invention, a hypothesis is generated based at least on the VfM index evaluation. Research on the above-mentioned goods or services is carried out based on the generated hypotheses.

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

According to the fourth aspect of the present invention, it is determined whether or not the generated hypothesis was valid based on the results of the research, and if it is determined that the generated hypothesis was not valid The hypotheses generated above are corrected. After that, the second and subsequent research on the above-mentioned goods or services for consumers is conducted based on the amended hypotheses after the amendment, and the market value of the above-mentioned goods or services is determined based on the results of the second and subsequent research. A rating is generated.

In this way, it is possible to recursively correct the hypothesis until the hypothesis becomes valid. Therefore, the future prediction result derived from the corrected hypothesis after the correction is more reliable. It becomes possible to do.

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

By carrying out such an expanded estimation, it is possible to predict how much the consumers who are the potential purchasers of the above goods or services, as well as the limited consumers who are the target of the above research, will contribute to sales. It is possible to In addition, even if the distribution of the attribute information of the consumers targeted for the research is biased compared to the distribution of the attribute information of all consumers who are potential purchasers of the above-mentioned goods or services, for example By appropriately weighting each piece of attribute information and correcting the bias in the distribution, it is possible to make the future prediction based on the expanded estimation more accurate. In addition, it is not a simple "sales forecast", but by logging the results, accumulating them as indicators (information), and analyzing them, future forecasts regarding the operation of marketing activities, sales related to products or services, development, revision and abolition directions can be made highly accurate.

According to the sixth aspect of the present invention, the VfM index includes an index for measurement items related to consumers who have used the incentives multiple times and measurement items related to consumers who have not used the incentives. At least one of a metric and a metric about a consumer opting for a particular incentive is included.

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

Consumers who do not use incentives include, for example, consumers who wished to participate in the above event but did not use incentives, and consumers who received incentives but did not use them. Includes consumers who do not Therefore, by setting a VfM index for measurement items related to consumers who do not use the above incentives, for example, it is possible to verify whether there is room for improving the evaluation of the above goods or services, It is possible to verify what kind of method should be taken to improve such evaluation.

Furthermore, for example, when a consumer selects an incentive to use from among multiple types of incentives (discounts, exchanges, points, etc.), there are cases where it is desired to obtain statistics of consumers who have selected a specific incentive. For example, the type of consumer evaluation of the value of the goods or services can be defined for each incentive selected by the consumer. Therefore, by setting the VfM index for measurement items related to consumers who have selected the above-mentioned specific incentives, it is possible to evaluate the value of the above-mentioned goods or services with higher quality. That is, by preparing a plurality of incentives sought by consumers and setting a reference value for measuring the market value for each incentive for analysis, it is possible to improve the accuracy of the 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 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, method, and program for predicting the future including future sales of the product or service using an event 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; FIG. FIG. 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 rating generation process performed by the control unit shown in FIG. 2; FIG. 3 is a flowchart showing an example of future prediction processing executed by the control unit shown in FIG. 2; FIG. 4 is a diagram showing an example of a setting state based on calculation presence/absence setting information and VfM indicator setting information according to the first embodiment of the present invention; The figure which shows an example of the setting condition by the calculation presence/absence setting information which concerns on the 1st Embodiment of this invention. The figure which shows an example of the notification reaction log information which concerns on the 1st Embodiment of this invention. The figure which shows an example of the coupon issue log information which concerns on the 1st Embodiment of this invention. The figure which shows an example of the coupon utilization log information which concerns on the 1st Embodiment of this invention. FIG. 4 is a diagram showing an example of VfM index evaluation based on the VfM index and actual values for arbitrary measurement items of interest, generated as part of the primary evaluation, according to the first embodiment of the present invention; FIG. 4 is a diagram showing an example of VfM index evaluation based on the VfM index and actual values for arbitrary measurement items of interest, generated as part of the primary evaluation, according to the first embodiment of the present invention; FIG. 4 is a diagram showing an example of VfM index evaluation based on the VfM index and actual values for arbitrary measurement items of interest, generated as part of the primary evaluation, according to the first embodiment of the present invention; FIG. 4 is a diagram showing an example of VfM index evaluation based on the VfM index and actual values for arbitrary measurement items of interest, generated as part of the primary evaluation, according to the first embodiment of the present invention; FIG. 4 is a diagram showing an example of a pie chart showing coupon usage status by consumer gender and age generated as part of the primary evaluation according to the first embodiment of the present invention; A diagram showing an example of a line graph in which responses to products, etc., or reaction states for each process of actions, generated as part of primary evaluation, are aggregated by age of female consumers, according to the first embodiment of the present invention. . FIG. 10 is a diagram showing an example of a pie chart showing the coupon utilization status of consumers by occupation, generated as part of the primary evaluation, according to the first embodiment of the present invention; FIG. 4 is a diagram showing an example of a hypothesis generation table showing correspondence relationships between detection conditions based on VfM index evaluation and hypotheses to be generated, according to the first embodiment of the present invention; FIG. 4 is a diagram showing an example of a hypothesis generation table showing correspondence relationships between detection conditions based on VfM index evaluation and hypotheses to be generated, according to the first embodiment of the present invention; The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire item which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire total result which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire total result which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire total result which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire total result which concerns on the 1st Embodiment of this invention. The figure which shows an example of the display screen of the questionnaire total result which concerns on the 1st Embodiment of this invention. The figure which shows an example of the result of the sales prediction which concerns on the 1st Embodiment of this invention. The figure which shows an example of the result of the sales prediction which concerns on the 1st Embodiment of this invention. The figure which shows an example of the result of the sales prediction which concerns on the 1st Embodiment of this invention.

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

The future prediction system of the present embodiment is, for example, a value for money (VfM) marketing system 1, a personal computer (PC) terminal including, for example, a smartphone or tablet type, and an operator terminal 2 operated by an operator, It comprises 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) such as the Internet. It is

VfM marketing system 1, for example, any product or service (hereinafter referred to as "products, etc. ), a market valuation as a value for price can be generated to perform forward-looking projections, including future sales.

More specifically, the VfM marketing system 1 uses the event notification system 4, the event participation reception system 5, the incentive management system 6, and the research system 7 according to the operation signal transmitted from the operator terminal 2. Conducting events such as campaigns, advertisements, or promotions for the above-mentioned products, etc., conducting research on the products, etc. for consumers, and based on the results of these research, determining the value of the above-mentioned products, etc. against the price Generate a market valuation. Based on the generated market valuation, the VfM marketing system 1 can perform future forecasts including future sales of the above-mentioned products and the like.

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

The consumer information database 3 contains identification information of consumers such as consumer numbers that are used by consumers to react or act on the products, etc. that are the targets of the event, and attribute information of the consumers. are associated with each other and stored. The identification information of the consumer may be, for example, an ID of a member service that can identify an individual and covers the majority of the population. In addition, the attribute information may include consumer attribute information such as the consumer's sex, age group, and residential area, and other consumer-specific personal information.

The event notification system 4, the event participation acceptance system 5, and the incentive management system 6 are systems used to implement the 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). to notify consumers of the 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, for example, uses the website to distribute incentives for the event to consumers who wish to participate in the event, and collects incentive usage information related to the incentives as the event is held. do. The incentives include, for example, coupons such as free exchange tickets and discount tickets for products, etc., bonus points that consumers acquire when purchasing the above products, etc., or Coupons such as free exchange tickets or discount tickets for other products may also be used.

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

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. As shown in FIG. Note that the configuration of the VfM marketing system 1 shown in FIG. 2 is merely an example, and each component included in the VfM marketing system 1 may exist as physically separate devices in any combination.

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

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

The storage unit 12 is configured by a storage medium such as a non-volatile memory that can be written and read at any time, such as a HDD (Hard Disc Drive) or an SSD (Solid State Drive). Master 121, VfM index calculation presence/absence storage unit 122, reaction behavior log information storage unit 123, VfM index history information storage unit 124, primary evaluation storage unit 125, research response log information storage unit 126, market value evaluation and a storage unit 127 . The VfM index used in the present embodiment is an index for any measurement item to which attention should be paid regarding the above-mentioned products and the like. A 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 products. The measurement items are stored in advance by the operator, for example. Such measurement items are, for example, standard items representing the number of consumers who reacted or acted on the above-mentioned products, etc., which can be directly aggregated from various information representing the results of consumers' reactions or actions to the above products, etc., or Contains items that can be calculated by any combination. Such measurement items are not limited to items that measure numerical values that occur in conventional campaign research, such as the number of page views on a website, aggregate values of questionnaires, and the like. In particular, such a measurement item may be an item that measures numerical values obtained by calculating or analyzing performance numerical values generated in various activities.

The VfM index calculation presence/absence storage unit 122 stores calculation presence/absence setting information and VfM index setting information. The calculation presence/absence setting information allows the operator to set whether or not each of the above measurement items stored in the VfM index master 121 is to be a measurement item to be calculated for calculating the actual value associated with the implementation of the above 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 which attention should be paid regarding the above-mentioned product etc. among the measurement items to be calculated. The VfM index for each measurement item set by the VfM index setting information is a target value for the measurement item, such as the number of consumers, the number of units, or the sales amount, or a numerical value such as It is a numerical value expressed as a percentage with respect to the reference value. The reference value may be set in advance by the operator, or may be automatically set based on the actual values of other measurement items different from the measurement item. . 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, which is information on the result of a consumer's reaction or action to the product or 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 announcement of the event, Event notice viewing information, including information on consumers who have opened or read the notice of the event and viewed the notice page of the event on the website, for example, the request to participate in the event received on the notice page of the event Event participation acceptance information including consumer information, incentive distribution information including information on consumers to whom the above incentives have been distributed among the consumers who wish to participate, and use of the distributed incentives in conjunction with the implementation of the above events contains incentive use information that includes information about consumers who have The consumer information included in the reaction behavior information is, for example, identification information of the consumer such as the consumer number. The above-mentioned consumer information included in each of the event notification information, event notification opening information, event notification viewing information, event participation acceptance information, incentive distribution information, and incentive use information pertaining to the same consumer is linked to each other. there is Further, the consumer information included in each of these reaction behavior information is associated with the consumer identification information stored in the consumer information database 3 described above. For example, the consumer information included in the incentive distribution information and the incentive use information is a coupon as the incentive issued so as to uniquely identify the consumer identification information stored in the consumer information database 3. may include the coupon ID of Furthermore, each piece of reaction/behavior information may include, for example, information on the date and time when the consumer's reaction or action was performed, and information on the media used by the consumer.

The VfM index history information storage unit 124 stores the actual values of 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 reply log information storage unit 126 stores research reply information, which is the information of the research reply from the consumer.

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

The control unit 11 includes an event planning unit 111, an event notification unit 112, an incentive distribution unit 113, an event execution unit 114, a VfM primary evaluation unit 115, a market value It has an evaluation unit 116 and a VfM evaluation report unit 117 . The control unit 11 includes a processor such as a CPU (Central Processing Unit) and a program memory, and all the processing functions of the above-described units are realized by causing the processor to execute the programs stored in the program memory. . Note that these processing functions are not limited to being implemented using programs stored in the program memory, and may be implemented using programs provided through a network.

The event planning section 111 executes processing for setting information necessary for carrying out an event, such as merchandise, according to an operation signal transmitted from the operator terminal 2 and received by the control unit 11 . For example, set the purpose, audience, outline setting, 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 processing for writing calculation presence/absence setting information and VfM index setting information. A measurement item to be calculated is set by the writing processing of the calculation presence/absence setting information. By the writing process of the VfM index setting information, the VfM index is set for an arbitrary measurement item to which attention should be paid in relation to an event target product or the like among the measurement items to be calculated.

The event notification unit 112 executes processing for transmitting an event notification instruction for notifying consumers of the event to the event notification system 4 . In response to the event notification instruction, the event notification system 4 notifies consumers of the event through various advertising media. The event notification unit 112 receives from the event notification system 4 event notification information including the information of the consumer who announced the event, and the event notification including the information of the consumer whose notification of the event was opened or read. opening information, and 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 to accept 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.

After that, the event notification unit 112 acquires event announcement browsing information including, for example, information of consumers who browsed the event announcement page on the website from the event participation acceptance system 5, and obtains the acquired event announcement browsing information. is stored in the reaction behavior log information storage unit 123 . Further, the event notification unit 112 acquires event participation acceptance information including information of consumers who wish to participate in the event received on the event announcement page from the event participation acceptance system 5, A process of storing the reception information in the reaction behavior log information storage unit 123 is executed.

The incentive distributing unit 113 first executes a process of reading the event participation acceptance information stored in the reaction behavior log information storage unit 123 . After that, 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 instruction to distribute the incentive, the incentive management system 6 distributes the incentive for the event to consumers who wish to participate in the event. The incentive distribution unit 113 acquires incentive distribution information including information on consumers to whom the incentive has been distributed from the incentive management system 6, and stores the acquired incentive distribution information in the reaction behavior log information storage unit 123. to run.

The event implementation unit 114 executes a process of transmitting an incentive collection instruction to the incentive management system 6 to collect the incentive use information related to the distributed incentive along with the implementation of the event. The incentive use information is collected by the incentive management system 6 according to the incentive collection instruction. After that, the event implementation unit 114 acquires incentive use information including information on consumers who used the distributed incentives from the incentive management system 6, and stores the acquired incentive use information in the reaction behavior log information storage unit 123. Execute the process to be stored in the

Here, from reaction behavior information such as event notification information, event notification opening information, event notification viewing information, event participation acceptance information, incentive distribution information, and incentive use information stored in the reaction behavior log information storage unit 123, the above Actual values are calculated for the measurement items to be calculated that are set by the calculation presence/absence setting information. Specifically, under the control of the actual value calculation section (not shown) provided 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. After that, under the control of the actual value calculation unit, among the reaction behavior information stored in the reaction behavior log information storage unit 123, Reaction behavior information is read, and actual values for the measurement items to be calculated are sequentially calculated based on the read reaction behavior information. The calculated actual values are sequentially stored in the VfM index history information storage unit 124 .

The VfM primary evaluation unit 115 generates a primary evaluation including an objective evaluation of the product or the like, and executes a process of storing the generated primary evaluation information in the primary evaluation storage unit 125 . The primary evaluation includes, for example, VfM index evaluation, standard analysis processing results, detailed analysis processing results, and hypotheses.

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

The VfM primary evaluation unit 115 can execute the following processing 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, Analysis based on aggregated results based on pre-set axes such as acceptance of event participation, distribution of incentives, and use of incentives, advertising media, time period, and consumer attributes such as age and gender. Perform analytical processing. Further, the VfM primary evaluation unit 115 may execute a detailed analysis process for analyzing using axes different from the standard analysis process based on the read reaction behavior information. In the standard analysis process and the detailed analysis process, for example, based on the identification information of the consumer such as the consumer number included in the reaction behavior information, from the consumer information database 3, the relevant Consumer attribute information is acquired and used.

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

The market value evaluation unit 116 executes a process of generating a market value evaluation as a value for the price of the product or the like, and includes a research implementation management unit 1161, a hypothesis correction unit 1162, and a future prediction unit 1163. there is

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

For example, the research implementation management unit 1161 reads the hypotheses stored in the primary evaluation storage unit 125, and generates research items such as the above-mentioned products based on the read hypotheses. After that, the research implementation management unit 1161 executes a process of transmitting to the research system 7 a research implementation instruction for conducting research on the consumer including the generated research item. The research is carried out by the research system 7 according to the research implementation instruction. The research is conducted, for example, on consumers set in advance as research targets among the consumers who participated in the event. After that, the research implementation management unit 1161 acquires research response information indicating the above-mentioned research response including 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 memorizing process. In addition, consumers targeted for the above research can expand and set model members from their profile rather than research members.

The hypothesis correction unit 1162 executes a process of reading research reply information stored in the research reply log information storage unit 126 . Next, the hypothesis correction unit 1162 executes processing for determining whether or not the hypothesis is valid based on the read research reply information. The determination threshold used in the determination process is set in advance, for example, when research items are generated. When it is determined that the hypothesis is not valid, the hypothesis correction unit 1162 executes processing for correcting the hypothesis. The research execution process may be executed again based on the corrected hypothesis after the correction.

The market value evaluation unit 116 executes processing for reading research reply information stored in the research reply log information storage unit 126 . After that, the market value evaluation unit 116 generates a market value evaluation of the product or the like 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 stored.

The future prediction unit 1163 executes a future prediction including future sales of the product based on the generated market value evaluation, and causes the market value evaluation storage unit 127 to store information about the result of the future prediction. Execute the process.

The VfM evaluation reporting unit 117 reads out the information related to the future prediction result stored in the market value evaluation storage unit 127, and reads out the information related to the future prediction result including the future sales of the read products. A process of outputting the 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 described. 3A shows an example of primary evaluation generation processing executed by the event planning unit 111, the event notification unit 112, the incentive distribution unit 113, the event implementation unit 114, and the VfM primary evaluation unit 115 of the control unit 11 shown in FIG. FIG. 2 is a flow diagram showing; 3B is a flow diagram showing an example of future prediction processing executed by market value evaluation section 116 of control unit 11 shown in FIG.

Below, for these flow diagrams, future forecasts including future sales of the product "Healthy Cafe Sweet", which is a special health coffee drink for young women to be released on April 1, 2018 by beverage company A, are shown below. An example of execution will be described in detail with reference to FIGS. 4A to 15C.

(1) Primary Evaluation Generation Processing First, the primary evaluation generation processing shown in the flow of FIG. 3A will be described 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, the control unit 11, under the control of the event planning unit 111, accesses the VfM index calculation presence/absence storage unit 122 as a process of setting information necessary for holding an event such as products, and stores the calculation presence/absence setting information. and the VfM index setting information. A measurement item to be calculated is set by the writing processing of the calculation presence/absence setting information. By the writing process of the VfM index setting information, the VfM index is set for an arbitrary measurement item to which attention should be paid in relation to an event target product or the like among the measurement items to be calculated. Note that in the process of writing the calculation presence/absence setting information and the VfM index setting information, for example, the calculation presence/absence and the VfM index for the measurement items are stored differently for each project. As the event planning process, under the control of the event planning unit 111, for example, the planning of the event itself, the definition of target consumers in the event, and the setting of consumers to whom the event will be announced may be executed. Target consumers may be defined by using consumer attribute information stored in the consumer information database 3, for example.

In the example of the product "Healthy Cafe Sweet", setting is made to use a campaign event that also serves as a promotion for the product. In addition, as an incentive for the event, all those who enter the campaign will receive a coupon, which is a free exchange ticket for the product "Healthy Cafe Sweet", which can be used at a predetermined convenience store. Furthermore, women in their twenties are defined as target consumers, and all members of a predetermined member service stored in the consumer information database 3 are set as consumers to whom the event will be announced.

FIGS. 4A and 4B show tables showing an example of setting conditions based on calculation presence/absence setting information and VfM index setting information used in the campaign for the product “Healthy Cafe Sweet”.

In the column of calculation presence/absence in the tables of FIGS. 4A and 4B, the setting status by the calculation presence/absence setting information is shown. In the calculation presence/absence setting information, the measurement items for which ◯ is indicated in the calculation presence/absence column are set as the measurement items to be calculated. Specifically, in "11. notification", "a. number" and "b. notification rate" are set as measurement items to be calculated. In "12. Opened/read", "13. Viewed", "14. Coupon applied", "21. Coupon issued", and "31. Coupon redeemed", "a. number", "b. ”, “c. Unit price per result”, and “d. Unit price increase rate” are set as measurement items to be calculated. Furthermore, "a. Number" of "41. Duplicate application", "51. Exchange index" and "61. Cost effect index" are also set as measurement items to be calculated. On the other hand, the target value column in the table of FIG. 4A shows the setting status according to the VfM index setting information. According to the VfM index setting information, the VfM index shown in the target value column is set for each arbitrary measurement item to be focused on regarding the product "Healthy Cafe Sweet" among the measurement items to be calculated. . Specifically, in "11. Notice", a target value of 76,250,000 is set as the VfM index for "a. Number". In "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. ing. In "13. Browse", 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. 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. In "21. Coupon issuance", 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 Exchange", 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.

In the table of FIG. 4A, "a. Number" of "13. Viewed" is the total number of times the campaign application page on Company A's website was viewed, and "14. Coupon application" and "21. Coupon issue ” and “31. Coupon redemption” are the ratio of the number of consumers who responded or acted corresponding to each item to the number of notified consumers.
In the table of FIG. 4B, "51. Exchange index" is calculated from "31. Coupon exchange"/"13. Browsing", and "61. Cost effect index" is calculated from "31. Coupon exchange"/" It is calculated from the “total advertising fee”.

In step S<b>12 , 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 such as a product. After that, the control unit 11, under the control of the event notification unit 112, receives from the event notification system 4 the event notification information including the information of the consumer who notified the event, and whether the notification of the event has been opened or read. Acquire event announcement opening information including information about consumers who have become. The acquired event notification information and event notification opening information are stored in the reaction behavior log information storage unit 123 . It should be noted that, for example, when the event notification system 4 uses conventional advertising media for which it is difficult to grasp the notification results, the event notification information or the event notification opening information is provided by the consumer who wishes to participate in the event on the website. It may be acquired by inputting the advertisement medium used for recognizing the event at the time.

In step S13, the control unit 11, under the control of the event notification unit 112, receives from the event participation acceptance system 5, for example, event announcement browsing information including information of consumers who browsed the announcement page of the event on the website; and event participation reception information including information of consumers who wish to participate in the event received on the event announcement page. The acquired event announcement browsing information and event participation acceptance information are stored in the reaction behavior log information storage unit 123 . It should be noted that, for example, when the event participation acceptance system 5 uses a conventional announcement page for which it is difficult to grasp the viewing record, the event announcement browsing information is used to notify the event when the consumer wishes to participate in the event. It may be obtained by inputting the medium being browsed.

FIG. 5 is a diagram showing an example of notification reaction log information in a campaign for the product "Healthy Cafe Sweet". In FIG. 5, a record showing "advertisement sending" as "reaction type" corresponds to event notification information. Also, a record in which "browse" is indicated as the "reaction type" corresponds to event announcement browse 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, the 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 voucher for the product or the like. After that, the control unit 11 acquires incentive distribution information including the information of the consumer to whom the incentive is distributed from the incentive management system 6 under the control of the incentive distribution section 113 . The acquired 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, a record showing "coupon issue" as "reaction type" corresponds to incentive distribution information.

Next, in step S15, the control unit 11, under the control of the event implementation section 114, acquires incentive use information including information on consumers who have used the incentive in conjunction with the implementation of the event from the incentive management system 6. do. The obtained incentive use 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 showing "coupon use" as the "reaction type" corresponds to the incentive use 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 use acquired in step S15 Actual values are calculated for the measurement items to be calculated set by the calculation presence/absence setting information from reaction behavior information such as information, and the calculated actual values are sequentially stored in the VfM index history information storage unit 124 .

Next, in steps S16, S17, and S18, the control unit 11, under the control of the VfM primary evaluation section 115, generates primary evaluations including objective evaluations of the above-mentioned products and the like. The primary evaluation may also include, for example, the media effect at the time of event announcement to consumers (viewed by advertising media, applied, exchanged).

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

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 based on the read reaction behavior information, the above-described Executes standard analysis processing and detailed analysis processing.

In step S18, the control unit 11, under the control of the hypothesis generation unit 1151 included in the VfM primary evaluation unit 115, generates a hypothesis related to the market value evaluation of the product or the like based at least on the generated VfM index evaluation. . The generated hypotheses are the accumulation of the degree of satisfaction and the degree of achievement of the plan related to the product or service. Note that the generated hypothesis may be based on the results of the standard analysis process and the detailed analysis process, for example.

8A, 8B, 8C, and 8D are tables showing an example of VfM index evaluation based on the VfM index and the actual value for arbitrary measurement items of interest in the campaign for the product “Healthy Cafe Sweet.” is illustrated. In the examples of FIGS. 8A, 8B, 8C, and 8D, actual values for each measurement item are calculated for each advertising medium. For the measurement item "31. Coupon Exchange", the actual values are calculated for each target cover. In the tables of FIGS. 8A, 8B, and 8C, as VfM indicators for measurement items, for example, the number of consumers shown in the target column and the number of consumers shown in the target column are used as reference values for each measurement For example, 100%, which is the target value of the number of consumers calculated for the item expressed as a percentage with respect to the reference value, is used. Also, the tables of FIGS. 8A, 8B, and 8C show, for example, the target achievement status 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% when any advertising medium is used. This means that female office workers in their twenties, who had been targeted, did not redeem coupons as much as expected. "61. Cost effectiveness index" shown in the table of FIG. The advertisement fee is calculated as ¥30,000,000.

FIG. 9 shows an example of a pie chart showing the usage of coupons by gender and age of consumers generated by the standardized analysis process, which is part of the primary evaluation, in the campaign for the product "Healthy Cafe Sweet". It is a diagram. It is possible to evaluate whether the target has been covered by the standardized analysis process. The total value of the product service target layer for each gender/year composition ratio (%) shown in FIG. 9 is an index of "target achievement rate".

From the graph shown in FIG. 9, it can be seen that women in their twenties do not exchange coupons than expected. In addition, from the graph shown in Figure 9, the product "Healthy Cafe Sweet" is generally popular among women in their 20s to 40s, and among them, women in their 30s account for the largest percentage of coupon usage. I understand. In addition, it can be seen that there is a certain demand for the product "Healthy Cafe Sweet" among men in their 20s to 40s, although not as much as women.

FIG. 10 shows the reaction status for each step of the reaction or action to the product "Healthy Cafe Sweet" generated by the detailed analysis processing which is part of the primary evaluation in the campaign for the product "Healthy Cafe Sweet". It is a figure which shows an example of the line graph which aggregated by female consumer's age.

From the graph shown in Figure 10, women in their 20s had the highest number of views on the application page for the campaign on Company A's website for the product "Healthy Cafe Sweet," but the rate at which women in their 20s applied was in their 30s to 40s. It can be seen that the number of

FIG. 11 is a diagram showing an example of a pie chart showing the coupon utilization status of consumers by job type, generated by the detailed analysis process, which is part of the primary evaluation, in the campaign for the product "Healthy Cafe Sweet". be.

From the graph shown in FIG. 11, it can be seen that the ratio of coupon usage for the product “Healthy Cafe Sweet” is the highest among office workers and assistants, but the ratio of coupon usage by specialists and engineers is also high.

From the tables and graphs shown in FIGS. 8A to 11, hypotheses regarding the market valuation of the product "Healthy Cafe Sweet" are generated.

For example, since the number of applications is low despite the large number of views by women in their 20s, who are the target, it is conceivable that they stopped 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 content of the campaign announcement does not match the preferences of women in their twenties. For example, the generated hypothesis may be output from the VfM marketing system 1 to an external display unit (not shown), and may be confirmed after the operator reviews the display on the display unit.

In addition, it can be seen that the number of coupons used by women in their 20s to 40s with desk work is high, but the number of coupons used by men in their 20s to 40s is also high. In this case, under the control of the hypothesis generation unit 1151, a hypothesis is generated that people who do not exercise often purchase it for their health. For example, the generated hypothesis may be output from the VfM marketing system 1 to an external display unit (not shown), and may be confirmed after the operator reviews the display on the display unit.

The hypothesis may be generated as follows based on the VfM index evaluation.

12A and 12B are diagrams showing an example of a hypothesis generation table showing correspondence relationships between detection conditions based on VfM index evaluation and hypotheses to be generated. The hypothesis generation table may be set by the operator when registering the project in step S11, for example. In the example of the hypothesis generation table of FIGS. 12A and 12B, under the control of the hypothesis generation unit 1151, when the VfM index evaluation satisfies a certain detection condition in the hypothesis generation table, it is associated with the detection condition. A hypothesis is generated. In the examples of FIGS. 8A, 8B, and 8C, the target achievement status as the VfM index evaluation corresponding to each measurement item is No. 13 and no. A hypothesis is generated that satisfies 15 detection conditions and is associated with the detection conditions. Note that the hypothesis generation tables shown in FIGS. 12A and 12B are merely examples, 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, for the VfM index for each measurement item, a hypothesis may be set in advance that is generated when the actual value for the measurement item does not satisfy the numerical value for the VfM index. The setting of the hypothesis may be set by the operator when registering the project in step S11, for example. When there is a VfM index such that the actual value of the 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, future prediction processing shown in the flow of FIG. 3B will be described in detail. Note that the future prediction processing shown in the flow of FIG. 3B is a non-limiting embodiment, and the future prediction processing of the present disclosure is not limited to this.

In step S21, the control unit 11, under the control of the research execution management section 1161 provided in the market value evaluation section 116, conducts research on the above-mentioned products and the like for consumers based on the hypotheses generated in step S18. The research is, for example, a questionnaire to consumers regarding the above-mentioned products and the like.

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

In addition, in the process of conducting research based on the above hypothesis, research may be conducted recursively. For example, consumption selected based on consumer information included in the above reaction behavior information related to the above hypotheses, or consumers in a range other than the current research range among consumers set in advance as research targets The research may be conducted recursively on individuals. Alternatively, research may be recursively performed that includes new research items that are regenerated, added, modified, or deleted based on the hypothesis. In this way, it is possible to increase or decrease the number of consumers to be researched and conduct research recursively, thereby improving the accuracy of future prediction.

In the above, as one embodiment of the research implementation process based on the hypothesis, research items are generated based on the hypothesis, and research including the generated research items is performed for consumers who are set as research targets in advance. I explained an example. However, the hypothetical research implementation process is not limited to this. For example, research in which research items are set in advance may be conducted on consumers selected based on consumer information included in the reaction behavior information related to the hypothesis. Alternatively, research including research items generated based on hypotheses may be conducted on consumers selected based on consumer information included in reaction behavior information related to hypotheses.

A case has been described where 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 processing in step S18 is not an essential configuration. For example, the VfM index evaluation generated in step S16 and the results of the standard analysis process and the detailed analysis process performed in step S17 may be used in any combination to conduct research. .

13A to 13G are diagrams showing examples of display screens of questionnaire items generated based on hypotheses in the example of the product "healthy cafe 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 generated based on the above hypothesis that the contents of campaign announcements do not match the tastes of women in their twenties. Also, the questionnaire items shown in FIGS. 13D and 13E are generated based on the above hypothesis that people who do not usually exercise a lot purchase for their health. Further, the questionnaire items shown in FIGS. 13F and 13G are No. 1 in the examples of FIGS. 12A and 12B. These are questionnaire items generated based on the hypotheses associated with the 15 detection conditions. For example, questionnaire items are associated with hypotheses in advance, and as described above, questionnaire items associated with the hypotheses are generated based on the hypotheses. In addition, among the consumers set in advance as research targets, for example, women in their 20s who applied for the campaign for the product “Healthy Cafe Sweet” but did not use coupons, and 20 women who used coupons. Consumers in their 40s to 40s may be selected.

In step S22, the control unit 11, under the control of the hypothesis correction section 1162 provided in the market value evaluation section 116, determines whether or not the hypothesis is valid based on the research reply information. 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 the results of a questionnaire targeting women in their 20s who viewed the product "Healthy Cafe Sweet" but did not use coupons, and consumers in their 20s to 40s who used coupons. It is a figure which shows an example of the display screen of a total result. The aggregated results of the questionnaires shown in FIGS. 14A to 14E respectively correspond to the questionnaire items shown in FIGS. 13C to 13G.

As shown in FIGS. 14A to 14E, each answer option for a questionnaire item reflects the consumer's subjective opinion regarding the hypothesis related to the questionnaire item. Therefore, for example, "Q14. Please tell me the reason why you did not apply for the 'Healthy Cafe Sweet' campaign" shown in Fig. 14A. By comparing the ratio of responses for each option to the questionnaire item "" with a preset judgment threshold, it is possible to determine whether the hypothesis that the contents of the campaign announcement do not match the tastes of women in their twenties was appropriate. can be determined. By the same processing, it is determined whether or not the hypothesis that people who do not exercise often purchase for their health is valid from the ratio of responses for each option to the questionnaire items shown in FIGS. 14B and 14C. can be done. Also, from the ratio of responses for each option to the questionnaire items shown in FIGS. 14D and 14E, No. It can be determined whether the hypotheses generated when the 15 detection conditions are satisfied were valid.

It should be noted that the process of determining whether or not the hypothesis was valid may be performed using, for example, Structural Equation Modeling (SEM). In such determination processing, for example, for a hypothetical model related to consumer purchases, the model fitness is calculated from the survey results of the research items corresponding to the observed variables, and the model fitness is compared with the judgment threshold. By doing so, it is determined whether or not 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 hypothesis under the control of the hypothesis correction section 1162, and then the process 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 a plurality of hypotheses are generated as in the above example, for example, a hypothesis determined as not valid among the plurality of hypotheses is excluded. good too. Further, when hypotheses are generated based on detection conditions as in the examples of FIGS. 12A and 12B, in the hypothesis correction process, for example, after correcting the detection conditions and the hypotheses associated with the detection conditions, , the hypothesis associated with the detection condition that the VfM index evaluation satisfies may be generated again. Furthermore, in the case where structural equation modeling is used to determine whether the hypothesis was valid as described above, the hypothesis correction process should use mathematical techniques such as factor analysis to customize the hypothesis. may

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, under the control of the market value evaluation section 116, generates a market value evaluation of the product or the like based on the research answer indicated by the research answer information. The market value evaluation is a value that can be said to be market satisfaction (a numerical value that can be benchmarked). It is the final index group that is constructed. Taking the case where the product is food, for example, the market valuation is expressed as follows.
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 section 1163 provided in the market value evaluation section 116, makes future predictions including future sales of the above-mentioned products based on the market value evaluation generated in step S25. Run. In the future prediction process, 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 of a simple estimation method, The future prediction is performed by performing at least one of a complex estimation method, for example, an augmented estimation of a Bayesian network method. In addition, the expansion estimation of the Bayesian network method, for example, is performed by setting the purchase probability among the answer options for each item of the above research and using the purchase probability indicated by the above research answer information. may The purchase probability may be set by the operator, for example, when generating or finalizing research items.

15A, 15B, and 15C are diagrams showing an example of sales prediction results for the product "Healthy Cafe Sweet" in the example of the product "Healthy Cafe Sweet." Sales forecasting in FIGS. 15A, 15B, and 15C can be performed, for example, based on the aggregate results of consumer responses to the questionnaire items shown in FIGS. 13A and 13B. FIG. 15A shows the result of sales prediction based on simple estimation from the number of market sales. FIG. 15B shows the results of sales prediction based on simple estimation for each consumer attribute. FIG. 15C shows the results of sales prediction based on Bayesian network method expansion estimation. 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 consumer responses to corresponding questionnaire items. You can use anything.

In step S27, the control unit 11, under the control of the VfM evaluation reporting unit 117, outputs information on the result of the future prediction including the future sales of the product executed in step S26 to an external output (not shown). Output to device.

(effect)
(1) First, calculation presence/absence setting information and VfM index setting information are written in the VfM index calculation presence/absence storage unit 122 under the control of the event planning unit 111 . A measurement item to be calculated is set by the writing processing of the calculation presence/absence setting information. By the writing process of the VfM index setting information, the VfM index is set for an arbitrary measurement item to which attention should be paid in relation to an event target product or the like among the measurement items to be calculated. Under the control of the event notification unit 112, the incentive distribution unit 113, and the event execution unit 114, the event notification system 4, the event participation acceptance system 5, and the incentive management system 6 are used to carry out an event such as a product. Incentive use information including information on consumers who used the incentive of the event is acquired along with the implementation of the event. Under the control of the VfM primary evaluation unit 115, a primary evaluation including an objective evaluation of the product or the like is generated based on the acquired reaction behavior information including the incentive use information. In the primary evaluation generation process, under the control of the VfM primary evaluation unit 115, based on the VfM index for the arbitrary measurement item to be focused indicated by the VfM index setting information and the actual value for the measurement item , a VfM index evaluation is generated, and under the control of the hypothesis generation unit 1151, a hypothesis regarding the market value evaluation of the product or the like is generated based at least on the generated VfM index evaluation. After that, under the control of the research implementation management unit 1161, research on the above-mentioned products and the like for consumers is carried out based on the generated hypothesis. Under the control of the market valuation unit 116, a market valuation of the product or the like is generated based on the answers to the conducted research. Under the control of the future prediction unit 1163, future prediction including future sales of the above-mentioned products etc. is executed based on the generated market valuation.

Such event and research-based market valuation generation and forward-looking processes may be based, for example, on data that reflects the current economic environment rather than historical economic conditions. Therefore, it is possible to make such future forecasts more reliable over a wide range of forecasting fields compared to sales forecasting methods that rely solely on statistical analysis of past data. Become.

Further, in the future prediction, as described above, the consumer's subjective opinion can be reflected, for example, by using consumer research. 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 from the information obtained in the primary evaluation. becomes possible. Therefore, it is possible to make such future prediction more purposeful and more accurate.

Further, as described above, it is possible to generate the primary evaluation based on a numerically evaluable index called the VfM index. Since the research is conducted based on the primary evaluation generated in this way, it is possible to make the market value evaluation and future prediction more accurate. In addition, since the VfM index can be set for any measurement item, it is possible to perform market value evaluation and future prediction focusing on necessary items.

In addition, with the implementation of the event, information on the results of consumer reactions or actions is sequentially accumulated, and actual values for the measurement items related to the VfM index are calculated in chronological order from the accumulated information. be able to. The information may be continuously accumulated while the event is executed multiple times, for example. By using the actual values calculated in time series in this way, it is also possible to evaluate and analyze trends and transitions of the market value evaluation in time series. Based on the evaluation and analysis of trends and transitions in the above-mentioned market value evaluation, future forecasts regarding the operation of marketing activities related to products or services, future forecasts of sales related to products or services, and directions for development, revision and abolition of products or services It is possible to make highly accurate future predictions regarding Furthermore, with respect to the VfM index, which is an index for any measurement item to be focused on for the above goods or services, since actual values for measurement items related to the VfM index are calculated, It is also possible to cross-evaluate other companies, other industries, products of other industries, etc. by applying the same VfM index to reactions or behaviors.

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

Based on the results of such future predictions, for example, which products should be sold and which services should be continued, which products should be discontinued, and which services should be discontinued, or , it is possible to determine which goods and services should be improved. In addition, since the above future forecast not only predicts future sales, but also the value of products, it is possible to plan the production plan of how much product should be produced and the scale of the provision of services. can also be determined.

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

In this way, it is possible to recursively correct the hypothesis until the hypothesis becomes valid. Therefore, the future prediction result derived from the corrected hypothesis after the correction is more reliable. It becomes possible to do.

(3) In the process of predicting the future of the above-mentioned products, for example, simple estimation is made based on the attribute information of each consumer included in the above-mentioned research response information and the above-mentioned research responses from the above-mentioned consumers indicated by the above-mentioned research response information. The future prediction is performed by performing at least one of extended estimation of the method and extended estimation of the complex estimation method (eg, Bayesian network method). For example, the extended estimation of the simple estimation method performs an a priori extended estimation, and the extended estimation of the complex estimation method performs an a priori or recursive extended estimation.

By performing such an expanded estimation, it is possible to predict how much the consumers who are the potential purchasers of the above goods or services, as well as the limited consumers who are the target of the above research, will contribute to sales. It is possible to In addition, even if the distribution of the attribute information of the consumer targeted for the research is biased compared to the distribution of the attribute information of all consumers who are the potential purchasers of the product or service, for example By appropriately weighting each piece of attribute information and correcting the bias in the distribution, it is possible to make the future prediction based on the expanded estimation more accurate. In addition, instead of a simple "sales forecast", by logging the results, accumulating them as indicators (information), and analyzing them, future forecasts regarding the operation of marketing activities, sales related to products or services, development, and directions for revision and abolition can be made highly accurate.

[Other embodiments]
This invention is not limited to the above embodiments. For example, in the above-described first embodiment, an example in which one type of incentive such as a coupon is used as an incentive has been mainly described. However, as incentives, multiple types of coupons such as free exchange tickets and discount tickets, bonus points that consumers acquire when purchasing products, etc., free exchange tickets for other products that consumers acquire when purchasing products, etc. Multiple types of incentives, such as multiple types of coupons such as discount coupons, may be used at the same time. In this case, the above-described incentive distribution information and incentive use information may include, for example, information indicating the types of incentives that consumers obtain and use. In addition to the incentive management system described above, an incentive management system that manages each incentive may be used for each incentive.

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

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

Consumers who do not use incentives include, for example, consumers who wished to participate in the above event but did not use incentives, and consumers who received incentives but did not use them. Includes consumers who do not Therefore, by setting a VfM index for measurement items related to consumers who do not use the above incentives, for example, it is possible to verify whether there is room for improving the evaluation of the above goods or services, It is possible to verify what kind of method should be taken to improve such evaluation.

Furthermore, for example, when a consumer selects an incentive to use from among multiple types of incentives (discounts, exchanges, points, etc.), there are cases where it is desired to obtain statistics of consumers who have selected a specific incentive. For example, the type of consumer evaluation of the value of the goods or services can be defined for each incentive selected by the consumer. Therefore, by setting the VfM index for measurement items related to consumers who have selected the above-mentioned specific incentives, it is possible to evaluate the value of the above-mentioned goods or services with higher quality. That is, by preparing a plurality of incentives sought by consumers and setting a reference value for measuring the market value for each incentive for analysis, it is possible to improve the accuracy of the 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 with higher accuracy. It becomes possible to do. Therefore, the future prediction based on the market value evaluation can also be made more accurate.

Further, in the above-described first embodiment, an example has been described in which a VfM index evaluation is generated based on the VfM index and the actual value for an arbitrary measurement item of interest. Here, for example, for each measurement item to be calculated set by the calculation presence/absence setting information, a period in which reaction behavior information related to the measurement item can be acquired 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 consumers based on the VfM index and the actual value for the measurement item for the remaining period of the relevant period. It may be determined whether or not there is, and a reminder may be given according to the determination.

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

Further, in the above-described first embodiment, it has been mainly described 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, VfM marketing system configuration, VfM index master, VfM index calculation presence/absence storage unit, reaction behavior log information storage unit, VfM index history information storage unit, primary evaluation storage unit, research response log information storage unit, market value evaluation storage The structure and the like of 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, and can be embodied by modifying constituent elements without departing from the scope of the present invention at the implementation stage. Also, various inventions can be formed by appropriate combinations of the plurality of constituent elements disclosed in the above embodiments. For example, some components may be omitted from all components shown in the embodiments. Furthermore, constituent elements of different embodiments may be combined as appropriate.

Reference Signs List 1 VfM marketing system 11 control unit 111 event planning unit 112 event notification unit 113 incentive distribution unit 114 event execution unit 115 VfM primary evaluation unit 1151 hypothesis generation unit 116 Market value evaluation unit 1161 Research implementation management unit 1162 Hypothesis correction unit 1163 Future prediction unit 117 VfM evaluation report unit 12 Storage unit 121 VfM index master 122 VfM index calculation presence/absence storage unit , 123... Reaction behavior log information storage unit 124... VfM index history information storage unit 125... Primary evaluation storage unit 126... Research reply log information storage unit 127... Market value evaluation storage unit 13... Communication interface unit 2 ... operator terminal, 3 ... consumer information database, 4 ... event notification system, 5 ... event participation acceptance system, 6 ... incentive management system, 7 ... research system, NW ... communication network

In short, the present invention is not limited to the above-described embodiments as they are, and can be embodied by modifying constituent elements without departing from the scope of the present invention at the implementation stage. Also, various inventions can be formed by appropriate combinations of the plurality of constituent elements disclosed in the above embodiments. For example, some components may be omitted from all components shown in the embodiments. Furthermore, constituent elements of different embodiments may be combined as appropriate.
The invention described in the scope of claims at the time of filing the present application will be additionally described below.
[C1]
an event planning department that sets up information necessary to implement an event for goods or services;
an event notification unit that notifies consumers of the event and acquires event participation acceptance information including information about 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 execution unit that acquires incentive use information including information about consumers who have used the incentive when the event is held;
a VfM primary evaluation unit that generates a primary evaluation including an objective evaluation of the goods or services based on the obtained reaction behavior information including the event participation acceptance information and the incentive use information;
conducting research on the goods or services for consumers based on the generated primary evaluation, and generating a market value evaluation of the goods or services based on the results of conducting the research, including consumer subjective evaluations; , a market valuation unit that performs a future forecast including future sales of said goods or services based on said generated market valuation;
a VfM evaluation reporting unit that outputs information related to the future prediction result;
Information processing device.
[C2]
The event planning department sets a VfM index that is an index for an arbitrary measurement item to be focused on with respect to the product or service,
The information processing apparatus according to C1, wherein the primary evaluation includes VfM index evaluation based on the VfM index and actual values for the set arbitrary measurement items to be focused on.
[C3]
The VfM primary evaluation unit comprises a hypothesis generation unit that generates a hypothesis related to the market value evaluation of the goods or services based at least on the VfM index evaluation,
The information processing device according to C2, wherein the market valuation unit conducts research related to the product or service based on the generated hypothesis.
[C4]
The market valuation unit determines whether the generated hypothesis was valid based on the results of the research, and if it is determined that the generated hypothesis was not valid, the generated A hypothesis correction unit that corrects the hypothesis,
The market value evaluation unit conducts second and subsequent research related to the product or service for consumers based on the corrected hypothesis after the correction, and based on the results of the second and subsequent research, the product Or the information processing device according to C3, which generates a market valuation of a service.
[C5]
The market value evaluation unit 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 research response from the consumer. 5. The information processing device according to any one of C1 to 4, wherein the future prediction including the future sales is performed by doing.
[C6]
The VfM index includes an index for measurement items related to consumers who have used the incentive multiple times, an index for measurement items related to consumers who have not used the incentive, and a consumer who has selected a specific incentive. The information processing device according to C2, including at least one of the indicators for such measurement items.
[C7]
An information processing method executed by a device comprising a processor and a memory,
the process of setting the information necessary for the performance of the goods or services event;
a process 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;
Acquiring incentive usage information including information about consumers who have used the incentive in accordance with the implementation of the event;
a step of generating a primary evaluation including an objective evaluation of the goods or services based on the obtained reaction behavior information including the event participation acceptance information and the incentive use information;
conducting research on the goods or services for consumers based on the generated primary evaluation, and generating a market value evaluation of the goods or services based on the results of conducting the research, including consumer subjective evaluations; , performing a future forecast including future sales of said goods or services based on said generated market valuation;
a process of outputting information related to the result of the future prediction;
An information processing method comprising:
[C8]
A program for causing a computer to function as each unit included in the information processing apparatus according to any one of C1 to C6.

Claims (8)

an event planning department that sets up information necessary to implement an event for goods or services;
an event notification unit that notifies consumers of the event and acquires event participation acceptance information including information about 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 execution unit that acquires incentive use information including information about consumers who have used the incentive when the event is held;
a VfM primary evaluation unit that generates a primary evaluation including an objective evaluation of the goods or services based on the obtained reaction behavior information including the event participation acceptance information and the incentive use information;
conducting research on the goods or services for consumers based on the generated primary evaluation, and generating a market value evaluation of the goods or services based on the results of conducting the research, including consumer subjective evaluations; , a market valuation unit that performs a future forecast including future sales of said goods or services based on said generated market valuation;
and a VfM evaluation reporting 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 an arbitrary measurement item to be focused on with respect to the product or service,
2. The information processing apparatus according to claim 1, wherein said primary evaluation includes a VfM index evaluation based on a VfM index and an actual value for said set arbitrary measurement item to which attention should be paid. The VfM primary evaluation unit comprises a hypothesis generation unit that generates a hypothesis related to the market value evaluation of the goods or services based at least on the VfM index evaluation,
3. The information processing apparatus according to claim 2, wherein said market value evaluation unit conducts research related to said product or service based on said generated hypothesis. The market valuation unit determines whether the generated hypothesis was valid based on the results of the research, and if it is determined that the generated hypothesis was not valid, the generated A hypothesis correction unit that corrects the hypothesis,
The market value evaluation unit conducts second and subsequent research related to the product or service for consumers based on the corrected hypothesis after the correction, and based on the results of the second and subsequent research, the product 4. The information processing device according to claim 3, which generates a market valuation of a service. The market value evaluation unit 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 research response from the consumer. 5. The information processing apparatus according to any one of claims 1 to 4, wherein future prediction including said future sales is performed by: The VfM index includes an index for measurement items related to consumers who have used the incentive multiple times, an index for measurement items related to consumers who have not used the incentive, and a consumer who has selected a specific incentive. 3. The information processing apparatus according to claim 2, comprising at least one of an index for such measurement item. An information processing method executed by a device comprising a processor and a memory,
the process of setting the information necessary for the performance of the goods or services event;
a process of notifying consumers of the event and obtaining event participation acceptance information including information of consumers who wish to participate in the event;
a process of distributing incentives for the event to consumers who wish to participate in the event; a process of acquiring incentive usage information including information on consumers who have used the incentives in accordance with the implementation of the event;
a step of generating a primary evaluation including an objective evaluation of the goods or services based on the obtained reaction behavior information including the event participation acceptance information and the incentive use information;
conducting research on the goods or services for consumers based on the generated primary evaluation, and generating a market value evaluation of the goods or services based on the results of conducting the research, including consumer subjective evaluations; , performing a future forecast including future sales of said goods or services based on said generated market valuation;
and a process of outputting information related to the result of the future prediction. A program for causing a computer to function as each unit included in the information processing apparatus according to any one of claims 1 to 6.

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