Information processing device, method and program

Abstract

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

Description

[Technical Field]

[0001] The present invention relates to an information processing device, method, and program for executing a future prediction including future sales of a product or service by utilizing events of the product or service. [Background technology]

[0002] BACKGROUND ART Conventionally, when selling a specific product, a technique is known in which past sales history data of the product is statistically analyzed to predict future sales of the product (see, for example, Patent Document 1).

[0003] Furthermore, when various events such as exhibitions and competitions are held, a technique for measuring the effectiveness of the event is known (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-358402 [Patent Document 2] Japanese Patent Application Laid-Open No. 2003-167976 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the past sales history data of a product used in the technology described in Patent Document 1 reflects the economic environment at the time the product was sold in the past. For this reason, conventional analysis is not only unable to evaluate the merits and demerits of a product's value and future prospects, but also unable to make rational and objective evaluations of future responses, such as how to improve the product to increase its appeal, which products should be discontinued and which should be retained (improved), and whether newly developed products have value (competitiveness).

[0006] As a method for predicting sales, there are no known methods other than the method described in Patent Document 1, which relies solely on statistical analysis of data that reflects past economic environments.

[0007] Furthermore, while the technology described in Patent Document 2 can measure the effectiveness of various recently held events such as exhibitions and competitions, there was no known method for predicting sales based on the measured effectiveness of the events.

[0008] This invention has been made in light of the above circumstances, and its purpose is to provide an information processing device, method, and program that utilizes events related to a product or service to perform future predictions, including future sales of the product or service. [Means for solving the problem]

[0009] In order to solve the above problems, a first aspect of the present invention is an information processing device comprising an event planning unit that sets information necessary for implementing an event for a product or service, an event announcement unit that notifies consumers of the event and acquires event participation acceptance information including information on consumers who wish to participate in the event, an incentive distribution unit that distributes incentives for the event to consumers who wish to participate in the event, an event implementation unit that acquires incentive use information including information on consumers who have used the incentive in connection with the implementation of the event, a VfM primary evaluation unit that generates a primary evaluation including an objective evaluation of the product or service based on reaction behavior information including the acquired event participation acceptance information and incentive use information, a market value evaluation unit that conducts research on the product or service among consumers based on the generated primary evaluation, generates a market value evaluation of the product or service based on the results of the research including subjective evaluations of consumers, and performs a future prediction including future sales of the product or service based on the generated market value evaluation, and a VfM evaluation reporting unit that outputs information related to the results of the future prediction.

[0010] In a second aspect of the present invention, the event planning department sets VfM indices, which are indices for any measurement items that should be focused on regarding the product or service, and the primary evaluation includes a VfM index evaluation based on the VfM indices and actual values ​​for the set arbitrary measurement items that should be focused on.

[0011] In a third aspect of the present invention, the VfM primary evaluation unit includes a hypothesis generation unit that generates hypotheses regarding the market value evaluation of the product or service based at least on the VfM index evaluation, and the market value evaluation unit conducts research regarding the product or service based on the generated hypotheses.

[0012] In a fourth aspect of the present invention, the market value assessment unit is provided with a hypothesis correction unit that determines whether the generated hypothesis was valid based on the results of the research, and corrects the generated hypothesis if it is determined that the generated hypothesis was invalid, and the market value assessment unit conducts second or subsequent research on the product or service to consumers based on the corrected hypothesis after the correction, and generates a market value assessment of the product or service based on the results of the second or subsequent research.

[0013] In a fifth aspect of the present invention, the market value assessment unit performs a future forecast including the future sales by performing 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 responses from the consumer.

[0014] In a sixth aspect of the present invention, the VfM index includes at least one of an index for a measurement item related to consumers who have used the incentive multiple times, an index for a measurement item related to consumers who have not used the incentive, and an index for a measurement item related to consumers who have selected a specific incentive. [Effects of the Invention]

[0015] According to a first aspect of the present invention, in conjunction with the implementation of an event for a product or service, incentive use information including information on consumers who used the incentives for the event is acquired, and a primary evaluation including an objective evaluation of the product or service is generated based on reaction behavior information including the incentive use information. Consumer research is then conducted based on the generated primary evaluation, and a market value evaluation of the product or service is generated based on the results of the research including subjective evaluations from the consumers. Future forecasts, including future sales of the product or service, are then performed based on the generated market value evaluation.

[0016] Such event- and research-based market valuation and forecasting processes can be based on data that reflects the current economic environment rather than historical economic conditions, making such forecasts more reliable across a broad range of forecasting fields than approaches that rely solely on statistical analysis of historical data.

[0017] Furthermore, as described above, the future predictions can be made using consumer research to reflect, for example, the subjective opinions of buyers. 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 make an evaluation from a different perspective than the information obtained in the primary evaluation. This makes it possible to make such future predictions more purposeful and accurate.

[0018] Based on the results of such future predictions, it becomes possible to determine, for example, which products should be continued to be sold, which services should be continued to be provided, which products should be discontinued, which services should be discontinued, or which products and services should be improved. Furthermore, since the above future predictions not only predict future sales but also reveal the value of commercial materials, it becomes possible to determine production plans for how many products should be produced and plans for the scale of service provision.

[0019] According to a second aspect of the present invention, VfM indices are set as indices for any measurement items of interest for the product or service, and the primary evaluation includes a VfM index evaluation based on the VfM indices and actual values ​​for the set arbitrary measurement items of interest.

[0020] In this way, the primary evaluation can be generated based on numerically estimable indicators called VfM indicators. Since the research is conducted based on the primary evaluation generated in this way, the market value evaluation and future forecast can be made more accurate. Furthermore, since VfM indicators can be set for any measurement item, it is possible to perform market value evaluation and future forecast focusing on required items.

[0021] Furthermore, information on consumer responses or behaviors resulting from the event can be sequentially accumulated, and actual values ​​for the measurement items related to the VfM index can be calculated over time from the accumulated information. This information may be continuously accumulated, for example, over multiple events. Using the actual values ​​calculated over time, it is possible to evaluate and analyze trends and developments in the market value assessment over time. This evaluation and analysis of trends and developments in the market value assessment enables accurate predictions of marketing activities for a product or service, sales, and product or service development and discontinuation. Furthermore, since actual values ​​for the measurement items related to the VfM index are calculated for the VfM index, which is an index of any measurement item of interest for the product or service, it is possible to apply the same VfM index to consumer responses or behaviors toward a product, for example, to cross-sectional evaluations of products from other companies, industries, and sectors.

[0022] According to a third aspect of the present invention, a hypothesis is generated based at least on the VfM index evaluation, and research related to the product or service is conducted based on the generated hypothesis.

[0023] The hypotheses generated in this manner are based on numerically estimable indicators known as VfM indicators. For example, in research conducted based on these hypotheses as described above, the research items included in the research may be generated based on these hypotheses, and the consumers targeted by the research may be selected based on these hypotheses. By performing the market value evaluation generation process and future forecast process based on such research, it is possible to achieve more accurate future forecasts.

[0024] According to a fourth aspect of the present invention, whether the generated hypothesis was valid or not is determined based on the results of the research, and if it is determined that the generated hypothesis was invalid, the generated hypothesis is corrected. Thereafter, second or subsequent researches regarding the product or service are conducted on consumers based on the corrected hypothesis, and a market value assessment of the product or service is generated based on the results of the second or subsequent researches.

[0025] In this way, it is possible to recursively correct the hypothesis until it becomes valid, and therefore the future prediction results derived from the corrected hypothesis after the correction can be made more reliable.

[0026] According to a fifth aspect of the present invention, in the future prediction process for the product or service, the future prediction is performed by performing at least one of an extended estimation using a simple estimation method and an extended estimation using a complex estimation method (e.g., a Bayesian network method) based on the attribute information of each consumer who responded to the research and the research responses from the consumer. For example, in the extended estimation using the simple estimation method, a deductive extended estimation is performed, and in the extended estimation using the complex estimation method, a deductive or recursive extended estimation is performed.

[0027] By performing such an expanded estimation, it is possible to predict the sales contribution of not only the limited number of consumers who are the subject of the research, but also the potential consumers of the product or service. Even if the distribution of attribute information of the consumers who are the subject of the research is biased compared to the distribution of attribute information of all consumers who are the potential buyers of the product or service, the future forecast based on the expanded estimation can be made more accurate by, for example, appropriately weighting each attribute to correct the distribution bias. Furthermore, by logging actual results, converting them into indicators (information), accumulating them, and analyzing them, rather than simply making "sales forecasts," it is possible to make more accurate forecasts of marketing activities, sales related to products or services, and the direction of development and revision / elimination.

[0028] According to a sixth aspect of the present invention, the VfM index includes at least one of an index for a measurement item related to consumers who have used the incentive multiple times, an index for a measurement item related to consumers who have not used the incentive, and an index for a measurement item related to consumers who have selected a particular incentive.

[0029] Here, consumers who have used incentives multiple times include, for example, consumers who rate the value of the product or service as high. Therefore, by setting a VfM index for measurement items related to consumers who have used incentives multiple times, it is possible to evaluate the positive aspects of the value of the product or service.

[0030] Furthermore, consumers who do not use incentives include, for example, consumers who wanted to participate in the event but did not use the incentive, and consumers who received an incentive but did not use it. Therefore, by setting VfM indices for measurement items related to consumers who do not use the incentive, it is possible to verify, for example, whether there is room to improve the evaluation of the product or service, or what methods should be used to improve the evaluation of the product or service.

[0031] Furthermore, for example, when consumers select an incentive from multiple types (discounts, redemptions, points, etc.), it may be necessary to obtain statistics on consumers who select a specific incentive. For example, the type of consumer evaluation of the value of the product or service can be determined for each incentive selected. Therefore, by setting VfM indicators for measurement items related to consumers who selected the specific incentive, it is possible to more accurately evaluate the value of the product or service. In other words, by preparing multiple incentives desired by consumers and setting and analyzing a base value for measuring market value for each incentive, the accuracy of the future forecast can be improved.

[0032] By using the VfM indexes described above in, for example, the primary valuation generation process, the hypothesis generation process, or the research implementation process, the market value assessment based on the research can be made more accurate, and therefore the future forecast based on the market value assessment can also be made more accurate.

[0033] According to each aspect of the present invention, it is possible to provide an information processing device, method, and program for executing future predictions including future sales of a product or service by utilizing events of the product or service. [Brief explanation of the drawings]

[0034] [Figure 1] 1 is a schematic configuration diagram of a future prediction system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of a VfM marketing system in the future prediction system shown in FIG. 1. [Figure 3A] 3 is a flowchart showing an example of a primary evaluation generation process executed by the control unit shown in FIG. 2. [Figure 3B] 3 is a flowchart showing an example of a future prediction process executed by the control unit shown in FIG. 2. [Figure 4A]FIG. 2 is a diagram showing an example of a setting status based on calculation presence / absence setting information and VfM index setting information according to the first embodiment of the present invention. [Figure 4B] FIG. 3 is a diagram showing an example of a setting state based on calculation presence / absence setting information according to the first embodiment of the present invention. [Figure 5] FIG. 3 is a diagram showing an example of notification response log information according to the first embodiment of the present invention. [Figure 6] FIG. 3 is a diagram showing an example of coupon issuance log information according to the first embodiment of the present invention. [Figure 7] FIG. 2 is a diagram showing an example of coupon usage log information according to the first embodiment of the present invention. [Figure 8A] FIG. 1 is a diagram showing an example of a VfM index evaluation based on VfM indexes and actual values ​​for any measurement item of interest, generated as part of a primary evaluation, according to the first embodiment of the present invention. [Figure 8B] FIG. 1 is a diagram showing an example of a VfM index evaluation based on VfM indexes and actual values ​​for any measurement item of interest, generated as part of a primary evaluation, according to the first embodiment of the present invention. [Figure 8C] FIG. 1 is a diagram showing an example of a VfM index evaluation based on VfM indexes and actual values ​​for any measurement item of interest, generated as part of a primary evaluation, according to the first embodiment of the present invention. [Figure 8D] FIG. 1 is a diagram showing an example of a VfM index evaluation based on VfM indexes and actual values ​​for any measurement item of interest, generated as part of a primary evaluation, according to the first embodiment of the present invention. [Figure 9] FIG. 2 is a diagram illustrating an example of a pie chart showing coupon usage by consumer gender and age, generated as part of a primary evaluation, according to a first embodiment of the present invention. [Figure 10] A figure showing an example of a line graph generated as part of the primary evaluation in the first embodiment of the present invention, which summarizes the reaction status of female consumers at each stage of their reactions or behavior toward products, etc., by age. [Figure 11] FIG. 2 is a diagram showing an example of a pie chart showing coupon usage by occupation of consumers, generated as part of a primary evaluation, according to the first embodiment of the present invention. [Figure 12A] FIG. 3 is a diagram showing an example of a hypothesis generation table showing a correspondence relationship between detection conditions based on VfM index evaluation and hypotheses to be generated according to the first embodiment of the present invention. [Figure 12B] FIG. 3 is a diagram showing an example of a hypothesis generation table showing a correspondence relationship between detection conditions based on VfM index evaluation and hypotheses to be generated according to the first embodiment of the present invention. [Figure 13A] FIG. 2 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 13B] FIG. 3 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 13C] FIG. 2 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 13D] FIG. 2 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 13E] FIG. 3 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 13F] FIG. 3 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 13G] FIG. 3 is a diagram showing an example of a display screen of a questionnaire item according to the first embodiment of the present invention. [Figure 14A] FIG. 3 is a diagram showing an example of a display screen of a questionnaire summary result according to the first embodiment of the present invention. [Figure 14B] FIG. 3 is a diagram showing an example of a display screen of a questionnaire summary result according to the first embodiment of the present invention. [Figure 14C] FIG. 3 is a diagram showing an example of a display screen of a questionnaire summary result according to the first embodiment of the present invention. [Figure 14D] FIG. 3 is a diagram showing an example of a display screen of a questionnaire summary result according to the first embodiment of the present invention. [Figure 14E] FIG. 3 is a diagram showing an example of a display screen of a questionnaire summary result according to the first embodiment of the present invention. [Figure 15A] FIG. 3 is a diagram showing an example of a result of sales forecasting according to the first embodiment of the present invention. [Figure 15B] FIG. 3 is a diagram showing an example of a result of sales forecasting according to the first embodiment of the present invention. [Figure 15C] FIG. 3 is a diagram showing an example of a result of sales forecasting according to the first embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0035] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. [First embodiment] (composition) FIG. 1 is a schematic diagram of a future prediction system according to a first embodiment of the present invention.

[0036] The future prediction system of this embodiment includes, for example, a Value for Money (VfM) marketing system 1, an operator terminal 2 which is a personal computer (PC) terminal including, for example, a smartphone or tablet type, and is operated by an operator, a consumer information database 3, an event announcement system 4, an event participation acceptance system 5, an incentive management system 6, and a research system 7, all of which are connected via a network (NW), for example, the Internet.

[0037] The VfM marketing system 1 can generate a market value assessment as a value relative to price for any product or service (hereinafter referred to as "product, etc."), such as a product that will soon be released for sale or a service that will soon be launched for sale, or a product or service that has already been released for sale or launched for sale, and perform future forecasts including future sales.

[0038] More specifically, the VfM marketing system 1 uses an event announcement system 4, an event participation acceptance system 5, an incentive management system 6, and a research system 7 in response to operation signals transmitted from an operator terminal 2 to implement events such as campaigns, advertisements, or promotions for the above-mentioned products, etc., and further conducts research on the products, etc. among consumers, and based on the results of this research, generates a market value assessment as a value relative to the price of the above-mentioned products, etc. Based on the generated market value assessment, the VfM marketing system 1 can make future predictions, including future sales of the above-mentioned products, etc.

[0039] The following description will be given taking as an example a case where the VfM marketing system 1 implements the above-mentioned event for each product, etc., and executes the above-mentioned market value assessment generation process and future prediction process for each product, etc., as described above; however, the market value assessment generation process and future prediction process for products, etc., according to the present disclosure are not limited to this. For example, the above-mentioned event such as a campaign, advertisement, or promotion for the product, etc. may also be, for example, a campaign, advertisement, or promotion event related to a store, brand, etc. In this case, the VfM marketing system 1 implements an event such as a campaign, advertisement, or promotion related to the store, brand, etc., and further conducts consumer research related to the store, brand, etc., thereby enabling the above-mentioned market value assessment generation process and future prediction process to be executed for each store, brand, etc.

[0040] The consumer information database 3 stores consumer identification information, such as a consumer number, used by consumers to react or act on the products, etc., that are the subject of the event, in association with the consumer's attribute information. The consumer identification information may be, for example, a membership service ID that can identify individuals and covers the majority of the population. The attribute information may also include consumer attribute information, such as the consumer's gender, age group, and residential area, as well as other personal information specific to the consumer.

[0041] The event announcement system 4, the event participation acceptance system 5, and the incentive management system 6 are each a system used to implement the above-mentioned event.

[0042] The event announcement system 4 announces the event to consumers and collects response information using various media, such as email newsletters, smartphone (mobile phone) and tablet applications, television commercial messages (TVCM), and social networking services (SNS) such as LINE (registered trademark). The event participation acceptance system 5 accepts applications to participate in the event using, for example, a website such as a special site for the event. The incentive management system 6 distributes incentives for the event to consumers who wish to participate in the event using, for example, the website, and collects incentive usage information related to the incentives as the event is held. The incentives may be, for example, coupons such as free vouchers or discount coupons for products, bonus points that consumers earn when purchasing the products, or coupons such as free vouchers or discount coupons for other products that consumers earn when purchasing the products.

[0043] The research system 7 conducts research on consumers regarding the above-mentioned products, etc., and collects research responses from consumers. The research is, for example, a questionnaire regarding the above-mentioned products, etc.

[0044] In this embodiment, a VfM marketing system 1 will be described as a non-limiting example of an information processing device. Figure 2 is a block diagram showing the functional configuration of the VfM marketing system 1 in the future prediction system shown in Figure 1. Note that the configuration of the VfM marketing system 1 shown in Figure 2 is merely an example, and the components of the VfM marketing system 1 may exist as physically separate devices in any combination.

[0045] The VfM marketing system 1 includes, as hardware components, a control unit 11, a storage unit 12, and a communication interface unit 13.

[0046] The communication interface unit 13 includes, for example, one or more wired or wireless communication interface units. The communication interface unit 13 inputs operation signals input by the operator terminal 2 to the control unit 11. The communication interface unit 13 also outputs information output from the control unit 11 to the event announcement system 4, the event participation acceptance system 5, the incentive management system 6, and the research system 7, and inputs information transmitted from the event announcement system 4, the event participation acceptance system 5, the incentive management system 6, and the research system 7 to the control unit 11. Furthermore, the communication interface unit 13 outputs information related to the results of future predictions, including future sales of the above-mentioned products, output from the control unit 11, to, for example, the operator terminal 2 or an external output device such as a network printer (not shown).

[0047] The storage unit 12 is configured with a storage medium such as a nonvolatile memory that can be written to and read from as needed, such as a hard disk drive (HDD) or a solid state drive (SSD), and to realize this embodiment, it includes a VfM index master 121, a VfM index calculation status storage unit 122, a reaction behavior log information storage unit 123, a VfM index history information storage unit 124, a primary evaluation storage unit 125, a research response log information storage unit 126, and a market value evaluation storage unit 127. The VfM indexes used in this embodiment are indexes for any measurement items that should be noted regarding the above-mentioned products, etc. Performance values ​​for the measurement items can be numerically evaluated by comparing them with the VfM indexes.

[0048] The VfM indicator master 121 stores measurement items related to, for example, consumer reactions or behavior toward the above-mentioned products. These measurement items are stored in advance, for example, by an operator. These measurement items include, for example, criterion items representing the number of consumers who responded or behaved in response to the above-mentioned products, which can be directly calculated from each piece of information representing the results of the consumer's reactions or behavior toward the above-mentioned products, and items calculated by any combination of the criterion items. These measurement items are not limited to items measuring numerical values ​​generated in conventional campaign research, such as the number of website page views or survey results. In particular, these measurement items may also be items measuring numerical values ​​calculated or analyzed from performance figures generated in various activities.

[0049] The VfM index calculation storage unit 122 stores calculation / non-calculation setting information and VfM index setting information. The calculation / non-calculation setting information indicates an operator's setting of whether each of the measurement items stored in the VfM index master 121 is to be a measurement item for which actual values ​​are calculated in conjunction with the implementation of the event. The VfM index setting information indicates the setting of VfM indexes for any measurement item of interest regarding the product, etc., among the measurement items for which calculation is to be performed. The VfM index for each measurement item set by the VfM index setting information is a target value for the measurement item, such as a numerical value such as the number of consumers, number of units, or sales amount, or a numerical value obtained by expressing these as a percentage of a reference value. The reference value may be set in advance by the operator or automatically based on actual values ​​for other measurement items different from the measurement item. The VfM index may be set for some of the measurement items for which calculation is to be performed, or for all of the measurement items for which calculation is to be performed.

[0050] The reaction behavior log information storage unit 123 stores reaction behavior information, which is information resulting from consumers' reactions or actions toward the products, etc. The reaction behavior information includes, for example, event announcement information including information on consumers who were notified of the event; event announcement opening information including information on consumers who opened or read the event announcement; event announcement viewing information including information on consumers who opened or read the event announcement and viewed, for example, the event announcement page on a website; event participation acceptance information including information on consumers who wish to participate in the event accepted on the event announcement page; incentive distribution information including information on consumers who received the incentive among the participating consumers; and incentive use information including information on consumers who used the distributed incentive in connection with the event. The consumer information included in the reaction behavior information is, for example, the consumer identification information, such as the consumer number. The consumer information included in the event announcement information, event announcement opening information, event announcement viewing information, event participation acceptance information, incentive distribution information, and incentive use information for the same consumer is linked to one another. Furthermore, the consumer information included in each of these pieces of reaction behavior information is linked to the consumer's identification information stored in the above-mentioned consumer information database 3. For example, the consumer information included in the incentive distribution information and the incentive use information may include a coupon ID of the coupon serving as the incentive, which is issued so as to uniquely identify the consumer's identification information stored in the consumer information database 3. Furthermore, each piece of reaction behavior information may include, for example, information on the date and time when the consumer's reaction or behavior was performed, and information on the medium used by the consumer.

[0051] The VfM index history information storage unit 124 stores the performance values ​​of the measurement items to be calculated.

[0052] The primary evaluation storage unit 125 stores the primary evaluations including the objective evaluations of the products etc. generated under the control of the VfM primary evaluation unit 115.

[0053] The research response log information storage unit 126 stores research response information, which is information on responses to the above-mentioned research from consumers.

[0054] The market value assessment storage unit 127 stores the market value assessment of the above-mentioned product etc. generated under the control of the market value assessment unit 116 .

[0055] In order to execute the processing functions of this embodiment, the control unit 11 includes an event planning unit 111, an event notification unit 112, an incentive distribution unit 113, an event implementation unit 114, a VfM primary evaluation unit 115, a market value evaluation unit 116, and a VfM evaluation reporting unit 117. The control unit 11 includes a processor such as a CPU (Central Processing Unit) and a program memory, and all of the processing functions of the above-mentioned units are realized by causing the processor to execute a program stored in the program memory. Note that these processing functions are not limited to those realized using a program stored in the program memory, and may also be realized using a program provided over a network.

[0056] The event planning unit 111 executes a process of setting information necessary for implementing an event for a product or the like in response to an operation signal transmitted from the operator terminal 2 and received by the control unit 11. For example, the event planning unit 111 sets the purpose, target, outline, and goal of the event to be implemented. For example, the event planning unit 111 accesses the VfM index calculation / non-calculation storage unit 122 and executes a process of writing calculation / non-calculation setting information and VfM index setting information. The process of writing the calculation / non-calculation setting information sets the measurement items to be calculated. The process of writing the VfM index setting information sets VfM indexes for any measurement items to be focused on regarding the product or the like that is the subject of the event, out of the measurement items to be calculated.

[0057] The event notification unit 112 executes a process of sending an event notification instruction to the event notification system 4 to notify consumers of the event. In response to the event notification instruction, the event notification system 4 notifies consumers of the event via various advertising media. The event notification unit 112 executes a process of acquiring, from the event notification system 4, event notification information including information on consumers who have been notified of the event, and event notification opening information including information on consumers who have opened or read the event notification, and stores the acquired event notification information and event notification opening information in the reaction behavior log information storage unit 123.

[0058] Next, the event announcement unit 112 executes a process of transmitting an event participation acceptance instruction to the event participation acceptance system 5 to accept a request to participate in the event from the consumer in response to the announcement. In response to the event participation acceptance instruction, the event participation acceptance system 5 accepts the request to participate in the event.

[0059] Thereafter, the event announcement unit 112 executes a process of acquiring event announcement viewing information including information of consumers who viewed the announcement page of the event on a website, for example, from the event participation acceptance system 5, and storing the acquired event announcement viewing information in the reaction behavior log information storage unit 123. Furthermore, the event announcement unit 112 executes a process of acquiring event participation acceptance information including information of consumers who wish to participate in the event, which was accepted on the announcement page of the event, from the event participation acceptance system 5, and storing the acquired event participation acceptance information in the reaction behavior log information storage unit 123.

[0060] The incentive distribution unit 113 first executes a process of reading out event participation acceptance information stored in the reaction behavior log information storage unit 123. Thereafter, the incentive distribution unit 113 executes a process of sending an incentive distribution instruction to 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. In response to the incentive distribution instruction, the incentive management system 6 distributes the incentives for the event to the consumers who wish to participate in the event. The incentive distribution unit 113 executes a process of acquiring incentive distribution information including information on consumers to whom the incentives have been distributed from the incentive management system 6, and storing the acquired incentive distribution information in the reaction behavior log information storage unit 123.

[0061] 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 in conjunction with the implementation of the event. In response to the incentive collection instruction, the incentive management system 6 collects the incentive use information. Thereafter, the event implementation unit 114 executes a process of acquiring incentive use information including information on consumers who have used the distributed incentive from the incentive management system 6, and storing the acquired incentive use information in the reaction behavior log information storage unit 123.

[0062] Here, actual values ​​for the measurement items to be calculated, which are set by the calculation setting information, are calculated from reaction behavior information, such as event announcement information, event announcement opening information, event announcement viewing information, event participation acceptance information, incentive distribution information, and incentive use information, stored in the reaction behavior log information storage unit 123. Specifically, under the control of an actual value calculation unit (not shown) included in the control unit 11, the calculation setting information stored in the VfM index calculation setting unit 122 is first referenced. Then, under the control of the actual value calculation unit, reaction behavior information related to the measurement items to be calculated, which are set by the referenced calculation setting information, is read from the reaction behavior information stored in the reaction behavior log information storage unit 123, 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.

[0063] The VfM primary evaluation unit 115 generates a primary evaluation including an objective evaluation of the product, etc., and executes a process of storing information about the generated primary evaluation in the primary evaluation storage unit 125. The primary evaluation includes, for example, a VfM index evaluation, a routine analysis processing result, a detailed analysis processing result, and a hypothesis.

[0064] Specifically, the VfM primary evaluation unit 115 first executes a process of reading out VfM indices for the arbitrary measurement items of interest, which are indicated by the VfM index setting information stored in the VfM index calculation presence / absence storage unit 122, and the actual values ​​for the measurement items stored in the VfM index history information storage unit 124. The VfM primary evaluation unit 115 executes a process of generating VfM index evaluations based on the read-out VfM indices and actual values.

[0065] The VfM primary evaluation unit 115 can execute the following processes depending on the results of the generated VfM index evaluation. Specifically, 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, executes a routine analysis process that analyzes the results of the aggregation based on pre-set criteria, such as by process (event notification, event notification opening, event notification viewing, event participation acceptance, incentive distribution, and incentive usage), by advertising medium, by time period, and by consumer attributes such as age and gender. Furthermore, the VfM primary evaluation unit 115 may execute a detailed analysis process based on the read reaction behavior information, analyzing the results using criteria different from those used in the routine analysis process. In the routine analysis process and the detailed analysis process, for example, attribute information of the consumer associated with the consumer's identification information, such as a consumer number, is obtained from the consumer information database 3 based on the consumer's identification information included in the reaction behavior information.

[0066] The hypothesis generation unit 1151 included in the VfM primary evaluation unit 115 executes a process of generating a hypothesis related to the market value evaluation of the product, etc., based at least on the generated VfM index evaluation. The generated hypothesis may be based on the results of the routine analysis process and the detailed analysis process, for example.

[0067] The market value evaluation unit 116 executes the process of generating market value evaluation as a value relative to the price of the above-mentioned product, etc., and includes a research implementation management unit 1161 , a hypothesis correction unit 1162 , and a future prediction unit 1163 .

[0068] The research implementation management unit 1161 reads out the primary evaluations stored in the primary evaluation storage unit 125, and executes a process of conducting research on the above-mentioned products, etc. based on the read out primary evaluations. The research is, for example, a questionnaire on the above-mentioned products, etc.

[0069] For example, the research implementation management unit 1161 reads out hypotheses stored in the primary evaluation storage unit 125 and generates research items for the above-mentioned products, etc., based on the read out hypotheses. The research implementation management unit 1161 then executes a process of sending a research implementation instruction to the research system 7 to have the research system 7 conduct a research on consumers, including the generated research items. In response to the research implementation instruction, the research system 7 conducts the above-mentioned research. The research is conducted, for example, targeting consumers who participated in the above-mentioned event and who have been set as research targets in advance. The research implementation management unit 1161 then acquires research response information indicating the responses to the above-mentioned research, including the consumers' subjective evaluations, from the research system 7 and executes a process of storing the acquired research response information in the research response log information storage unit 126. Furthermore, consumers who are targets of the above-mentioned research can be set as model members by expanding their profiles from the profiles of research members.

[0070] The hypothesis correction unit 1162 executes a process of reading out research response information stored in the research response log information storage unit 126. Next, the hypothesis correction unit 1162 executes a process of determining whether the hypothesis was valid based on the read research response information. The determination threshold used in this determination process is, for example, set in advance when the research item is generated. If it is determined that the hypothesis was invalid, the hypothesis correction unit 1162 executes a process of correcting the hypothesis. The research implementation process may be executed again based on the corrected hypothesis after the correction.

[0071] The market value evaluation unit 116 executes a process of reading out research response information stored in the research response log information storage unit 126. Thereafter, the market value evaluation unit 116 executes a process of generating a market value evaluation of the product or the like based on the research response indicated by the read research response information, and storing the generated market value evaluation in the market value evaluation storage unit 127.

[0072] The future prediction unit 1163 performs a future prediction including future sales of the above-mentioned products, etc. based on the generated market value assessment, and performs a process of storing information related to the results of the future prediction in the market value assessment memory unit 127.

[0073] The VfM evaluation reporting unit 117 reads out information relating to the results of the future prediction stored in the market value evaluation memory unit 127, and executes a process of outputting the information relating to the results of the future prediction, including the future sales of the read-out products, etc., to, for example, the operator terminal 2 or an external output device not shown.

[0074] (operation) Next, the operation of the VfM marketing system 1 configured as above will be described. Fig. 3A is a flow diagram showing an example of a primary evaluation generation process 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. 2. Fig. 3B is a flow diagram showing an example of a future prediction process executed by the market value evaluation unit 116 of the control unit 11 shown in Fig. 2.

[0075] These flow charts will be explained in detail below with reference to Figures 4A to 15C, using the example of a case where beverage manufacturer A is executing a future forecast including future sales of "Healthy Cafe Sweet," a special health food coffee drink targeted at young women that will be released on April 1, 2018.

[0076] (1) Primary evaluation generation process First, the primary evaluation generation process 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 thereto.

[0077] In step S11, under the control of the event planning unit 111, the control unit 11 accesses the VfM index calculation / non-calculation storage unit 122 and writes the calculation / non-calculation setting information and the VfM index setting information as a setting process for information necessary for implementing an event for a product, etc. The calculation / non-calculation setting information write process sets measurement items to be calculated. The VfM index setting information write process sets VfM indexes for any measurement items to be focused on regarding the product, etc., that are the subject of the event, among the measurement items to be calculated. Note that, in the writing process of the calculation / non-calculation setting information and the VfM index setting information, the calculation / non-calculation setting information and VfM index setting information for measurement items are stored differently for each project, for example. Under the control of the event planning unit 111, the event planning process may, for example, include planning the event itself, defining target consumers for the event, and setting consumers to whom the event will be announced. Note that the target consumers may be defined using consumer attribute information stored in the consumer information database 3, for example.

[0078] In the example of the product "Healthy Cafe Sweets," we set up a campaign event that doubles as a promotion for the product. As an incentive for the event, we set up a campaign where everyone who enters the campaign will receive a coupon that can be used at a designated convenience store for a free copy of the product "Healthy Cafe Sweets." Furthermore, we define women in their twenties as the target consumers, and all members of a designated membership service stored in the consumer information database 3 as consumers to whom the event will be announced.

[0079] 4A and 4B show tables illustrating an example of the setting status based on the calculation setting information and the VfM index setting information used in the campaign for the product "Healthy Cafe Sweet."

[0080] The calculation status column in the tables of Figures 4A and 4B shows the setting status based on the calculation status setting information. In the calculation status setting information, the measurement items marked with a circle in the calculation status column are set as the measurement items to be calculated. Specifically, for "11. Notification," "a. Number" and "b. Notification rate" are set as the measurement items to be calculated. For "12. Opened / Read," "13. Viewed," "14. Coupon Application," "21. Coupon Issue," and "31. Coupon Redemption," "a. Number," "b. Notification rate," "c. Cost per result," and "d. Unit price increase rate" are set as the measurement items to be calculated. Furthermore, for "41. Duplicate Applications," "a. Number," "51. Redemption Index," and "61. Cost Effectiveness Index" are also set as the measurement items to be calculated. Meanwhile, the target value column in the table of Figure 4A shows the setting status based on the VfM indicator setting information. The VfM index setting information sets the VfM index shown in the target value column for each of the measurement items of interest for the "Healthy Cafe Sweet" product among the measurement items to be calculated. Specifically, for "11. Notification," a target value of 76,250,000 is set as the VfM index for "a. Number." For "12. Opened / Read," a target value of 22,593,750 is set as the VfM index for "a. Number," and a target value of 29.63% is set as the VfM index for "b. Notification rate." For "13. Views," a target value of 19,826,563 is set as the VfM index for "a. Number," and a target value of 26.00% is set as the VfM index for "b. Notification rate." For "14. Coupon Applications," the target VfM indicator for "a. Number" is 87,500, and the target VfM indicator for "b. Notification Rate" is 0.11%. For "21. Coupon Issuance," the target VfM indicator for "a. Number" is 70,000, and the target VfM indicator for "b. Notification Rate" is 0.09%. For "31. Coupon Redemption," the target VfM indicator for "a. Number" is 46,667, and the target VfM indicator for "b. Notification Rate" is 0.06%.

[0081] In the table in Figure 4A, "a. Number" for "13. Views" is the total number of times the campaign application page on Company A's website was viewed, and "b. Notification rate" for "14. Coupon application," "21. Coupon issuance," and "31. Coupon redemption" is the percentage of consumers who responded or acted in accordance with each item out of the total number of consumers who were notified. In the table of Figure 4B, "51. Redemption Index" is calculated from "31. Coupon Redemption" / "13. Viewing", and "61. Cost Effectiveness Index" is calculated from "31. Coupon Redemption" / "Total Advertising Fee".

[0082] In step S12, the control unit 11, under the control of the event notification unit 112, uses the event notification system 4 to notify consumers of an event such as a product. Thereafter, under the control of the event notification unit 112, the control unit 11 acquires, from the event notification system 4, event notification information including information on consumers who have been notified of the event, and event notification opening information including information on consumers who have opened or read the event notification. The acquired event notification information and event notification opening information are stored in the reaction behavior log information storage unit 123. Note that, for example, in cases where the event notification system 4 uses conventional advertising media whose announcement performance is difficult to grasp, the event notification information or event notification opening information may be acquired by inputting the advertising media used to become aware of the event when a consumer expresses a desire to participate in the event on a website.

[0083] In step S13, the control unit 11, under the control of the event announcement unit 112, acquires, from the event participation acceptance system 5, event announcement viewing information including information on consumers who have viewed the event announcement page on a website, for example, and event participation acceptance information including information on consumers who wish to participate in the event that has been accepted on the event announcement page. The acquired event announcement viewing information and event participation acceptance information are stored in the reaction behavior log information storage unit 123. Note that, for example, in the case where the event participation acceptance system 5 uses a conventional announcement page whose viewing history is difficult to grasp, the event announcement viewing information may be acquired by inputting the medium through which the consumer viewed the event announcement when expressing their desire to participate in the event.

[0084] Figure 5 is a diagram showing an example of announcement response log information for a campaign for the product "Healthy Cafe Sweet." In Figure 5, records with "Advertisement Sent" as the "Response Type" correspond to event announcement information. Records with "View" as the "Response Type" correspond to event announcement viewing information. Records with "Application" as the "Response Type" correspond to event participation acceptance information.

[0085] In step S14, under the control of the incentive distribution unit 113, the control unit 11 reads the event participation acceptance information stored in the reaction behavior log information storage unit 123, and distributes incentives for the event to consumers who wish to participate in the event, using the incentive management system 6, based on the read event participation acceptance information. The incentive may be, for example, a coupon such as a free exchange ticket for the product or the like. Thereafter, under the control of the incentive distribution unit 113, the control unit 11 obtains incentive distribution information from the incentive management system 6, including information on consumers to whom the incentive has been distributed. The obtained incentive distribution information is stored in the reaction behavior log information storage unit 123.

[0086] Figure 6 shows an example of coupon issuance log information for a campaign for the product "Healthy Cafe Sweets." In Figure 6, records with "Coupon Issued" as the "Response Type" correspond to incentive distribution information.

[0087] Next, in step S15, the control unit 11, under the control of the event implementation unit 114, acquires incentive use information including information on consumers who have used the incentive in connection with the implementation of the event from the incentive management system 6. The acquired incentive use information is stored in the reaction behavior log information storage unit 123.

[0088] Figure 7 shows an example of coupon usage log information for a campaign for the product "Healthy Cafe Sweets." In Figure 7, records showing "Coupon Usage" as the "Response Type" correspond to incentive usage information.

[0089] Actual values ​​are calculated for the measurement items to be calculated that are set by the calculation on / off setting information from reaction behavior information such as the event announcement information and event announcement opening information acquired in step S12, the event announcement viewing information and event participation acceptance information acquired in step S13, the incentive distribution information acquired in step S14, and the incentive use information acquired in step S15, and the calculated actual values ​​are sequentially stored in the VfM index history information storage unit 124.

[0090] Next, in steps S16, S17, and S18, the control unit 11 generates a primary evaluation including an objective evaluation of the above-mentioned products, etc. under the control of the VfM primary evaluation unit 115. The primary evaluation may also include, for example, the media effect (viewed by advertising medium, applied for, exchanged) when announcing the event to consumers.

[0091] In step S16, under the control of the VfM primary evaluation unit 115, the control unit 11 reads out the VfM index for the arbitrary measurement item of interest stored in the VfM index calculation presence / absence storage unit 122 and the actual value for the measurement item stored in the VfM index history information storage unit 124. Thereafter, under the control of the VfM primary evaluation unit 115, the control unit 11 generates a VfM index evaluation based on the read-out VfM index and actual value.

[0092] In step S17, the control unit 11, under the control of the VfM primary evaluation unit 115, reads out the above-mentioned reaction behavior information stored in the reaction behavior log information storage unit 123, and executes the above-mentioned standard analysis processing and detailed analysis processing based on the read-out reaction behavior information.

[0093] In step S18, the control unit 11 generates a hypothesis for the market value evaluation of the product, etc., based at least on the generated VfM index evaluation, under the control of the hypothesis generation unit 1151 included in the VfM primary evaluation unit 115. The generated hypothesis is an accumulation of the degree of fulfillment and achievement of the plan for the product or service. Note that the generated hypothesis may be based on the results of the routine analysis process and the detailed analysis process, for example.

[0094] Figures 8A, 8B, 8C, and 8D show tables illustrating an example of VfM index evaluation based on VfM indexes and actual values ​​for any measurement item of interest in a campaign for the product "Healthy Cafe Sweet." In the examples of Figures 8A, 8B, 8C, and 8D, actual values ​​are calculated for each measurement item by advertising medium. Furthermore, for the measurement items "14. Coupon Application," "21. Coupon Issuance," and "31. Coupon Redemption," actual values ​​are calculated by target coverage. In the tables of Figures 8A, 8B, and 8C, the VfM index for each measurement item is, for example, the number of consumers shown in the "Goal" column, or a target value, such as 100%, calculated as a percentage of the number of consumers shown in the "Goal" column, with the number of consumers calculated for each measurement item being the base value. The tables of Figures 8A, 8B, and 8C also show goal achievement status as the VfM index evaluation. The coupon redemption goal achievement status shown in the table in Figure 8C indicates that the target coverage goal achievement rate was below 90% for all advertising media. This means that the target group, female office workers in their 20s, redeemed fewer coupons than expected. The "61. Cost Effectiveness Index" shown in the table in Figure 8D is calculated based on a total advertising fee of ¥5,000,000 for advertising media 1, ¥10,000,000 for advertising media 2, and ¥30,000,000 for advertising media 3.

[0095] Figure 9 shows an example of a pie chart showing the coupon usage status by consumer gender and age in the campaign for the product "Healthy Cafe Sweets," generated by the routine analysis process, which is part of the primary evaluation described above. This routine analysis process makes it possible to evaluate whether the target was covered. The total value of the product / service target demographic for each gender / year composition ratio (%) shown in Figure 9 becomes an indicator called the "target achievement rate."

[0096] The graph in Figure 9 shows that fewer women in their 20s are redeeming coupons than expected. The graph in Figure 9 also shows that the "Healthy Cafe Sweets" product is generally popular among a wide range of women in their 20s to 40s, with the highest proportion of coupon usage among women in their 30s. It also shows that there is a certain demand for the "Healthy Cafe Sweets" product among men in their 20s to 40s, although not as much as women.

[0097] Figure 10 shows an example of a line graph generated by the detailed analysis process, which is part of the primary evaluation described above, in a campaign for the product "Healthy Cafe Sweets," summarizing the reaction or behavioral status of female consumers at each stage of the product "Healthy Cafe Sweets" by age.

[0098] The graph in Figure 10 shows that the most views of the campaign application page on Company A's website for the product "Healthy Cafe Sweets" were from women in their 20s, but the application rate for women in their 20s was lower than that for women in their 30s and 40s.

[0099] Figure 11 shows an example of a pie chart showing coupon usage by consumer occupation type in a campaign for the product "Healthy Cafe Sweets," generated by the detailed analysis process that is part of the primary evaluation described above.

[0100] The graph in Figure 11 shows that the highest percentage of coupon usage for the "Healthy Cafe Sweets" product is among office and assistant occupations, but the percentage of coupon usage is also high among professional and technical occupations.

[0101] From the tables and graphs shown in Figures 8A to 11, hypotheses regarding the market value assessment of the product "Healthy Cafe Sweets" are generated.

[0102] For example, since the target women in their twenties have a high number of views but a low number of applications, it is conceivable that they stop applying after checking the product introduction on the website. In this case, under the control of the hypothesis generation unit 1151, a hypothesis is generated that the content of the campaign announcement does not match the preferences of women in their twenties. The generated hypothesis may be output from the VfM marketing system 1 to an external display unit (not shown), for example, and may be confirmed after an operator reviews the display on the display unit.

[0103] It can also be seen that the number of coupon usages is high among women in their 20s to 40s who do desk work, but also among men in their 20s to 40s. In this case, under the control of the hypothesis generation unit 1151, a hypothesis is generated that people who do not usually exercise much are purchasing the product for health reasons. The generated hypothesis may be output from the VfM marketing system 1 to an external display unit (not shown), for example, and the display on the display unit may be reviewed by an operator before being confirmed.

[0104] The hypothesis may be generated as follows based on the above VfM index evaluation.

[0105] 12A and 12B are diagrams showing an example of a hypothesis generation table indicating the correspondence between detection conditions based on VfM index evaluation and hypotheses to be generated. The hypothesis generation table may be set by an operator, for example, when registering a project in step S11. In the example of the hypothesis generation table shown in FIGS. 12A and 12B, under the control of the hypothesis generator 1151, if the VfM index evaluation satisfies a certain detection condition in the hypothesis generation table, a hypothesis associated with that detection condition is generated. In the examples shown in FIGS. 8A, 8B, and 8C, the goal achievement status as a VfM index evaluation corresponding to each measurement item satisfies the detection conditions No. 13 and No. 15, and a hypothesis associated with that detection condition is generated. Note that the hypothesis generation tables shown in FIGS. 12A and 12B are merely examples. For example, the detection conditions in the hypothesis generation table may be any combination of VfM index evaluations based on any VfM index.

[0106] Alternatively, for a VfM index for each measurement item, a hypothesis may be set in advance to be generated when the actual value for that measurement item does not satisfy the numerical value associated with that VfM index. The hypothesis may be set, for example, by an operator when registering the project in step S11. When there is a VfM index for which the actual value for the measurement item does not satisfy the numerical value associated with that VfM index, a hypothesis corresponding to that VfM index is generated under the control of the hypothesis generator 1151.

[0107] (2) Future prediction processing Next, the future prediction process shown in the flow of Fig. 3B will be described in detail. Note that the future prediction process shown in the flow of Fig. 3B is a non-limiting example, and the future prediction process of the present disclosure is not limited thereto.

[0108] In step S21, the control unit 11 conducts research on the above-mentioned products, etc. to consumers based on the hypothesis generated in step S18 under the control of the research implementation management unit 1161 included in the market value evaluation unit 116. The research is, for example, a questionnaire on the above-mentioned products, etc. to consumers.

[0109] In the research implementation process, for example, under the control of the research implementation management unit 1161, research items for the above-mentioned products, etc. are generated based on the above-mentioned hypotheses, and information related to the generated research items is output to a research system 7 external to the VfM marketing system 1. The research system 7 conducts a research including the generated research items, targeting consumers who have participated in the campaign and have been previously set as research targets. Then, under the control of the research implementation management unit 1161, the control unit 11 acquires research response information indicating the above-mentioned research responses, including the consumers' subjective evaluations, from the research system 7. The generated research items may be output from the VfM marketing system 1 to an external display unit (not shown), for example, and the display on the display unit may be reviewed and confirmed by an operator. Furthermore, the target consumers of the above-mentioned research may be set by expanding the profile of a model member from the research member. In this way, the above-mentioned research may be conducted after the research items and research targets are extracted.

[0110] In addition, in the process of conducting research based on the above hypothesis, the research may be conducted recursively. For example, the research may be conducted recursively on consumers who are not within the scope of the current research but are previously set as research targets, or on consumers selected based on consumer information included in the response behavior information related to the above hypothesis. Alternatively, the research may be conducted recursively, including new research items that are regenerated, or added, modified, or deleted based on the hypothesis. In this way, the accuracy of future predictions can be improved by expanding or reducing the consumers to be researched and conducting the research recursively.

[0111] The above describes an example of one embodiment of the process for conducting hypothesis-based research, in which research items are generated based on a hypothesis and research including the generated research items is conducted targeting consumers who have been set as research targets in advance. However, the process for conducting hypothesis-based research is not limited to this. For example, research with pre-set research items may be conducted targeting consumers selected based on consumer information included in the reaction behavior information related to the hypothesis. Alternatively, research including research items generated based on the hypothesis may be conducted targeting consumers selected based on consumer information included in the reaction behavior information related to the hypothesis.

[0112] In the above description, a hypothesis is generated based on the VfM index evaluation in step S18, and research is conducted based on the generated hypothesis. However, the hypothesis generation process in step S18 is not essential. For example, research may be conducted using any combination of the VfM index evaluation generated in step S16 and the results of the routine analysis process and the detailed analysis process executed in step S17.

[0113] 13A to 13G are diagrams showing an example of a display screen of questionnaire items generated based on hypotheses for the example product "Healthy Cafe Sweets." The questionnaire items shown in FIGS. 13A and 13B are questionnaire items that can be used for sales forecasting. The questionnaire items shown in FIG. 13C are questionnaire items generated based on the hypothesis that the campaign announcement content does not match the preferences of women in their twenties. The questionnaire items shown in FIGS. 13D and 13E are questionnaire items generated based on the hypothesis that people who do not usually exercise much purchase the product for health reasons. The questionnaire items shown in FIGS. 13F and 13G are questionnaire items generated based on the hypothesis associated with detection condition No. 15 in the example of FIGS. 12A and 12B. 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. Furthermore, the targets of the research may be selected from consumers who have been set as research targets in advance, for example, women in their 20s who applied for a campaign for the product "Healthy Cafe Sweets" but did not use a coupon, and consumers in their 20s to 40s who used a coupon.

[0114] In step S22, the control unit 11 determines whether the hypothesis is valid or not based on the research response information under the control of the hypothesis correction unit 1162 included in the market value evaluation unit 116. The determination threshold used in the determination process is registered, for example, during the process of step S11.

[0115] 14A to 14E show examples of screens displaying the results of a survey of women in their twenties who viewed the product "Healthy Cafe Sweets" but did not use a coupon, and consumers in their twenties to forties who did use a coupon. The survey results shown in Figs. 14A to 14E correspond to the survey items shown in Figs. 13C to 13G, respectively.

[0116] As shown in Figures 14A to 14E, each answer option for a questionnaire item reflects a consumer's subjective opinion regarding the hypothesis associated with that questionnaire item. Therefore, for example, by comparing the percentage of responses for each option to the questionnaire item "Q14. Please tell us why you did not apply for the Healthy Cafe Sweets campaign" shown in Figure 14A with a preset threshold, it is possible to determine whether the hypothesis that the campaign announcement content does not match the preferences of women in their twenties is valid. Using a similar process, it is possible to determine whether the percentage of responses for each option to the questionnaire items shown in Figures 14B and 14C is valid, and it is possible to determine whether the hypothesis that people who do not exercise much purchase the product for health reasons is valid. Furthermore, it is possible to determine whether the hypothesis generated when the detection condition No. 15 described above in connection with the example of Figures 12A and 12B is satisfied is valid, and ...

[0117] The process of determining whether a hypothesis is valid may be performed using, for example, Structural Equation Modeling (SEM). In such a process, for example, for a model as a hypothesis related to consumer purchases, the model fitness is calculated from the survey results of research items corresponding to observed variables, and the model fitness is compared with a determination threshold to determine whether the hypothesis is valid.

[0118] If it is determined in step S22 that the hypothesis is invalid, the process branches from step S23 to step S24.

[0119] In step S24, the control unit 11 corrects the hypothesis under the control of the hypothesis correction unit 1162, and then the process from step S21 is executed again. In the hypothesis correction process, the hypothesis is corrected based on the difference between the evaluation (generated by the system) from the event log (expected value) and its result (actual value), for example.

[0120] In the hypothesis correction process, when multiple hypotheses are generated as in the above example, for example, a hypothesis determined to be invalid from the multiple hypotheses may be removed. Furthermore, when hypotheses are generated based on detection conditions as in the examples of Figures 12A and 12B, the hypothesis correction process may, for example, modify the detection conditions and the hypotheses associated with the detection conditions, and then regenerate hypotheses associated with detection conditions that satisfy the VfM index evaluation. Furthermore, when structural equation modeling is used to determine whether a hypothesis is valid as described above, the hypothesis correction process may customize the hypothesis using mathematical techniques such as factor analysis.

[0121] On the other hand, if it is determined in step S22 that the hypothesis is valid, the process branches from step S23 to step S25.

[0122] In step S25, the control unit 11, under the control of the market value assessment unit 116, generates a market value assessment of the product, etc., based on the research responses indicated by the research response information. The market value assessment can be considered a market satisfaction level (a benchmarkable numerical value), and is a final index group consisting of the degree of satisfaction with product / service planning items (targets), their sum of additions and subtractions, and affirmative (purchase) probability by consumer attribute. Taking the product as an example, the market value assessment can be expressed, for example, as follows: Σ(rating × weight) = taste 5 × 10% + quantity 3 × 5% + …

[0123] In step S26, under the control of the future prediction unit 1163 included in the market value evaluation unit 116, the control unit 11 performs a future prediction, including future sales of the product, etc., based on the market value evaluation generated in step S25. In this future prediction process, the future prediction is performed, for example, by performing at least one of a simple estimation-based extended estimation and a complex estimation-based extended estimation, such as a Bayesian network-based extended estimation, based on the attribute information of each consumer included in the research response information and the research responses from the consumer indicated by the research response information. Note that the Bayesian network-based extended estimation may be performed using a purchase probability indicated by the research response information, by setting a purchase probability between response options for each research item. The purchase probability may be set by an operator, for example, when generating or confirming a research item.

[0124] Figures 15A, 15B, and 15C show examples of sales forecast results for the product "Healthy Cafe Sweets." The sales forecasts in Figures 15A, 15B, and 15C can be performed, for example, based on the aggregated results of consumer responses to the questionnaire items shown in Figures 13A and 13B. Figure 15A shows the results of a sales forecast based on simple estimation from market sales volume. Figure 15B shows the results of a sales forecast based on simple estimation by consumer attributes. Figure 15C shows the results of a sales forecast based on expanded estimation using a Bayesian network method. The campaign response coefficients in the examples of Figures 15A, 15B, and 15C may be preset coefficients or may be derived based on consumer responses to the corresponding questionnaire items.

[0125] In step S27, under the control of the VfM evaluation reporting unit 117, the control unit 11 outputs information regarding the results of the future prediction, including future sales of the above-mentioned products, etc., performed in step S26, to an external output device not shown.

[0126] (effect) (1) First, under the control of the event planning unit 111, calculation / non-calculation setting information and VfM index setting information are written to the VfM index calculation / non-calculation memory unit 122. The calculation / non-calculation setting information write process sets the measurement items to be calculated. The VfM index setting information write process sets VfM indexes for any measurement items to be focused on regarding the products, etc. that are the subject of the event, among the measurement items to be calculated. Under the control of the event announcement unit 112, incentive distribution unit 113, and event implementation unit 114, an event for products, etc. is implemented using the event announcement system 4, event participation acceptance system 5, and incentive management system 6, and incentive use information is acquired in conjunction with the implementation of the event, including information on consumers who have used the incentives for the event. Under the control of the VfM primary evaluation unit 115, a primary evaluation, including an objective evaluation of the products, etc., is generated based on the reaction behavior information, including the acquired incentive use information. In the primary evaluation generation process, under the control of the VfM primary evaluation unit 115, a VfM index evaluation is generated based on the VfM index for the arbitrary measurement item of interest indicated by the VfM index setting information and the actual value for the measurement item, and under the control of the hypothesis generation unit 1151, a hypothesis for a market value evaluation of the product, etc. is generated based at least on the generated VfM index evaluation. Thereafter, under the control of the research implementation management unit 1161, research on the product, etc. is conducted on consumers based on the generated hypothesis. Under the control of the market value evaluation unit 116, a market value evaluation of the product, etc. is generated based on responses to the conducted research. Under the control of the future prediction unit 1163, a future prediction including future sales of the product, etc. is performed based on the generated market value evaluation.

[0127] Such event- and research-based market valuation and forecasting processes can be based on data that reflects the current economic environment rather than historical economic conditions, making such forecasts more reliable across a broad range of forecasting areas than sales forecasting methods that rely solely on statistical analysis of past data.

[0128] Furthermore, as described above, the future predictions can be made using consumer research to reflect, for example, the subjective opinions of buyers. 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 make an evaluation from a different perspective than the information obtained in the primary evaluation. This makes it possible to make such future predictions more purposeful and accurate.

[0129] As described above, the primary evaluation can be generated based on numerically estimable indicators known as VfM indicators. Since the research is conducted based on the primary evaluations generated in this way, the market value evaluation and future forecast can be made more accurate. Furthermore, since VfM indicators can be set for any measurement item, market value evaluation and future forecast can be performed focusing on required items.

[0130] Furthermore, information on consumer responses or behaviors resulting from the event can be sequentially accumulated, and actual values ​​for the measurement items related to the VfM index can be calculated over time from the accumulated information. This information may be continuously accumulated, for example, over multiple events. Using the actual values ​​calculated over time, it is possible to evaluate and analyze trends and developments in the market value assessment over time. This evaluation and analysis of trends and developments in the market value assessment enables accurate predictions of marketing activities for a product or service, sales, and product or service development and discontinuation. Furthermore, since actual values ​​for the measurement items related to the VfM index are calculated for the VfM index, which is an index of any measurement item of interest for the product or service, it is possible to apply the same VfM index to consumer responses or behaviors toward a product, for example, to cross-sectional evaluations of products from other companies, industries, and sectors.

[0131] Furthermore, the generated hypotheses are based on numerically estimable indicators known as VfM indicators. For example, in research conducted based on the hypotheses described above, the research items included in the research may be generated based on such hypotheses, and the consumers targeted by the research may be selected based on such hypotheses. By performing the market value evaluation generation process and future forecast process based on such research, it is possible to achieve more accurate future forecasts.

[0132] Based on the results of such future predictions, it becomes possible to determine, for example, which products should be continued to be sold, which services should be continued to be provided, which products should be discontinued, which services should be discontinued, or which products and services should be improved. Furthermore, since the above future predictions not only predict future sales but also reveal the value of commercial materials, it becomes possible to determine production plans for how many products should be produced and plans for the scale of service provision.

[0133] (2) Under the control of the hypothesis correction unit 1162, it is determined whether the hypothesis was valid or not based on the research response information indicating the responses to the conducted research. If it is determined that the hypothesis was invalid, the hypothesis is corrected under the control of the hypothesis correction unit 1162. Thereafter, the processing described above is executed again under the control of the research implementation management unit 1161.

[0134] In this way, it is possible to recursively correct the hypothesis until it becomes valid, and therefore the future prediction results derived from the corrected hypothesis after the correction can be made more reliable.

[0135] (3) In the future prediction process for the products, etc., the future prediction is performed by, for example, performing at least one of an extended estimation using a simple estimation method and an extended estimation using a complex estimation method (e.g., a Bayesian network method) based on the attribute information of each consumer included in the research response information and the research responses from the consumer indicated in the research response information. For example, in the extended estimation using the simple estimation method, a deductive extended estimation is performed, and in the extended estimation using the complex estimation method, a deductive or recursive extended estimation is performed.

[0136] By performing such an expanded estimation, it is possible to predict the sales contribution of not only the limited number of consumers who are the subject of the research, but also the potential consumers of the product or service. Even if the distribution of attribute information of the consumers who are the subject of the research is biased compared to the distribution of attribute information of all consumers who are the potential buyers of the product or service, the future forecast based on the expanded estimation can be made more accurate by, for example, appropriately weighting each attribute to correct the distribution bias. Furthermore, by logging actual results, converting them into indicators (information), accumulating them, and analyzing them, rather than simply making "sales forecasts," it is possible to make more accurate forecasts of marketing activities, sales related to products or services, and the direction of development and revision / elimination.

[0137] [Other embodiments] The present invention is not limited to the above-described embodiments. For example, in the first embodiment, an example in which one type of incentive, such as a coupon, is used as the incentive has been described. However, multiple types of incentives may be used simultaneously, such as multiple types of coupons, such as free exchange vouchers and discount vouchers, bonus points acquired by consumers when purchasing a product, and multiple types of coupons, such as free exchange vouchers and discount vouchers for other products, acquired by consumers when purchasing a product. In this case, the incentive distribution information and incentive use information may include, for example, information indicating the type of incentive acquired and used by the consumer. Furthermore, in addition to the above-described incentive management system, an incentive management system for managing each incentive may be used.

[0138] The VfM index may also include at least one of an index for a measurement item related to consumers who have used an incentive multiple times, an index for a measurement item related to consumers who have not used an incentive, and an index for a measurement item related to consumers who have selected a particular incentive.

[0139] Here, consumers who have used incentives multiple times include, for example, consumers who rate the value of the product or service as high. Therefore, by setting a VfM index for measurement items related to consumers who have used incentives multiple times, it is possible to evaluate the positive aspects of the value of the product or service.

[0140] Furthermore, consumers who do not use incentives include, for example, consumers who wanted to participate in the event but did not use the incentive, and consumers who received an incentive but did not use it. Therefore, by setting VfM indices for measurement items related to consumers who do not use the incentive, it is possible to verify, for example, whether there is room to improve the evaluation of the product or service, or what methods should be used to improve the evaluation of the product or service.

[0141] Furthermore, for example, when consumers select an incentive from multiple types (discounts, redemptions, points, etc.), it may be necessary to obtain statistics on consumers who select a specific incentive. For example, the type of consumer evaluation of the value of the product or service can be determined for each incentive selected. Therefore, by setting VfM indicators for measurement items related to consumers who selected the specific incentive, it is possible to more accurately evaluate the value of the product or service. In other words, by preparing multiple incentives desired by consumers and setting and analyzing a base value for measuring market value for each incentive, the accuracy of the future forecast can be improved.

[0142] In this way, by using the VfM indexes described above in, for example, the primary valuation generation process, the hypothesis generation process, or the research implementation process, the market value assessment based on the research can be made more accurate, and therefore the future forecast based on the market value assessment can also be made more accurate.

[0143] In the first embodiment, an example was described in which a VfM index evaluation is generated based on the VfM index and actual value for an arbitrary measurement item of interest. Here, for example, for each measurement item to be calculated, which is set by the calculation on / off setting information, a period during which reaction behavior information related to the measurement item can be obtained is set, and if the actual value for the measurement item during that period does not satisfy the numerical value related to the VfM index, a determination is made as to whether or not a reminder needs to be sent to the consumer based on the remaining period of the period and the VfM index and actual value for the measurement item, and a reminder is sent based on the determination.

[0144] In the first embodiment, an example was described in which the reaction behavior information includes consumer identification information such as a consumer number. In such an example, as described above, the attribute information of the consumer related to the reaction behavior information is acquired by utilizing the correspondence between the consumer identification information and the attribute information of the consumer stored in the consumer information database. However, the reaction behavior information may also include the consumer attribute information itself in addition to the consumer identification information such as the consumer number as described above.

[0145] In the first embodiment, the hypothesis is corrected when it is determined that the hypothesis is invalid. However, if the hypothesis is invalid, a new hypothesis may be acquired.

[0146] In addition, the configuration of the VfM marketing system and the structure of the data stored in the VfM index master, VfM index calculation / non-calculation memory unit, reaction behavior log information memory unit, VfM index history information memory unit, primary evaluation memory unit, research response log information memory unit, and market value evaluation memory unit can be modified and implemented in various ways without departing from the spirit of this invention.

[0147] In short, this invention is not limited to the above-described embodiments, and in the implementation stage, the components can be modified and embodied without departing from the spirit of the invention. Furthermore, various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiments. For example, some components may be omitted from all the components shown in the embodiments. Furthermore, components from different embodiments may be appropriately combined. [Explanation of symbols]

[0148] 1...VfM marketing system, 11...control unit, 111...event planning section, 112...event announcement section, 113...incentive distribution section, 114...event implementation section, 115...VfM primary evaluation section, 1151...hypothesis generation section, 116...market value evaluation section, 1161...research implementation management section, 1162...hypothesis correction section, 1163...future prediction section, 117...VfM evaluation report section, 12...storage unit, 121...VfM index master, 122...VfM index Target calculation presence / absence memory unit, 123...reaction behavior log information memory unit, 124...VfM index history information memory unit, 125...primary evaluation memory unit, 126...research response log information memory unit, 127...market value evaluation memory unit, 13...communication interface unit, 2...operator terminal, 3...consumer information database, 4...event announcement system, 5...event participation acceptance system, 6...incentive management system, 7...research system, NW...communication network

Claims

1. Acquire actual values ​​for any measurement items of interest regarding the product or service from information on the results of consumer reactions or actions in connection with the implementation of the event, Generate a hypothesis regarding the market value evaluation of the goods or services based at least on an evaluation based on indicators and actual values ​​for any measurement items of interest regarding the goods or services; Conducting consumer research on the goods or services based on a hypothesis regarding the market value of the goods or services; making a computer execute a future prediction including future sales of the product or service based on the results of the research including the subjective evaluations of the consumers; performing the future forecast including future sales comprises performing at least one of an extended estimation using a simple estimation method and an extended estimation using a complex estimation method based on attribute information of each consumer who responded to the research and the research responses from the consumer; Computer program.

2. The indicator for any measurement item of interest regarding the goods or services is a VfM indicator, The computer program according to claim 1 , wherein the evaluation based on an index and a performance value for any measurement item of interest for the product or service is a VfM index evaluation based on the VfM index and a performance value.

3. The computer program of claim 1 , wherein the market value assessment represents a value relative to a price for the goods or services.

4. The computer program further comprises: Determine whether the hypothesis was valid based on the results of the research, and correct the hypothesis if it is determined that the hypothesis was invalid; Conducting second or subsequent research related to the goods or services to the consumers based on the revised hypothesis; 4. The computer program of claim 1, which causes a computer to generate a market value assessment of the product or service based on the results of the second or subsequent research.

5. The computer program further comprises: The computer program according to claim 1 , which causes a computer to execute the following: outputting information relating to the results of the future prediction.

6. An information processing method executed by an information processing device, Acquire actual values ​​for any measurement items of interest regarding the product or service from information on the results of consumer reactions or actions in connection with the implementation of the event, Generate a hypothesis regarding the market value evaluation of the goods or services based at least on an evaluation based on indicators and actual values ​​for any measurement items of interest regarding the goods or services; Conducting consumer research on the goods or services based on hypotheses regarding the market value of the goods or services; and making a future forecast, including future sales of the product or service, based on the results of the research, including the subjective evaluations of the consumers; performing the future forecast including future sales comprises performing at least one of an extended estimation using a simple estimation method and an extended estimation using a complex estimation method based on attribute information of each consumer who responded to the research and the research responses from the consumer; Information processing methods.

7. Memory and a processor coupled to the memory, The information processing device, wherein the processor is configured to execute the computer program according to claim 1 , stored in the memory.

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