Marketing assisting system and computer program

Abstract

To provide an assisting system and program for effective marketing by utilizing information in a community on a web.SOLUTION: A marketing assisting system comprises: a first storage unit for storing company-side data related to customers of a company; a second storage unit for storing community-side data related to user activities in a community on a web where users can post information; an analysis unit for reading the company-side data from the first storage unit and the community-side data from the second storage unit, and comparing and analyzing the read company-side data and the read community-side data; and a display device. The analysis unit identifies common users common to the customers of the company and the community users, combines the company-side data and the community-side data for each common user, and outputs and displays analysis results in which the common users are classified into multiple segments based on purchase date information and post date information.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a marketing support system and a computer program, and more particularly, to a marketing support system and a computer program that utilize information in a community where users exchange information such as on a website.

Background Art

[0002] Marketing is an important factor for a company to improve profits when selling products or providing services. Conventionally, marketing has been performed in which a company analyzes factors contributing to an increase in sales and analyzes the importance of customers to the company based on information previously provided by members and information related to product sales (for example, Patent Document 1).

[0003] However, in recent years, the consumer society has diversified, and in order to efficiently sell products or provide services, it is required to analyze consumer trends in more detail. In conventional marketing, customer information is collected by methods such as questionnaires and analyzed together with information related to sales. However, with the customer information collected by such methods, in particular, the psychology of customers cannot be sufficiently analyzed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] By the way, in recent years, for certain companies, communities may be formed on websites for exchanges such as information sharing among users. In such communities, users who actively participate become fans of the company and are highly likely to continuously purchase the company's products or use its services. Therefore, by using the information in the community for marketing, effective marketing that also reflects the psychology of customers can be expected.

[0006] In view of such circumstances, the present invention has been made, and its object is to provide a marketing support system and a computer program for effectively conducting marketing by utilizing information in a community on a website.

Means for Solving the Problems

[0007] To achieve the above object, the marketing support system of the present invention includes a first storage unit that stores company-side data related to customers in a company, a second storage unit that stores community-side data related to the activities of users in a community on a website where users can send information, and an analysis unit that reads the company-side data from the first storage unit and reads the community-side data from the second storage unit, and matches and analyzes the read company-side data and the read community-side data, and a display device. The company-side data includes at least a customer identifier that can identify each customer and purchase date information indicating that each customer has purchased a product in the company. The community-side data includes at least a user identifier that can be matched with the customer identifier and can identify each user in the community, and transmission date information indicating that each user has sent information in the community. The analysis unit includes a specifying means for specifying common users common to the company's customers and the community's users from the customer identifier and the user identifier, a combining means for combining the company-side data and the community-side data for each common user specified by the specifying means, and a classification analysis means for classifying and analyzing the common users into a plurality of segments based on the purchase date information of the company-side data combined by the combining means and the transmission date information of the community-side data combined by the combining means. The display device is configured to display the analysis result output from the classification analysis means of the analysis unit.

[0008] By analyzing data using the customer identifiers of enterprise-side data and the user identifiers of community-side data and displaying the analysis results, it is possible to identify common users shared by the enterprise and the community, and it becomes possible to integrally analyze the information of the enterprise and the community. Therefore, not only the data related to the customers of the enterprise but also the data related to the users of the community can be utilized for marketing. The community-side data contains useful information that can analyze the psychology of customers. By classifying and analyzing common users into multiple segments using this information, the psychology of customers can be reflected. Therefore, it becomes easier to hypothesize and verify measures for improving sales and acquiring fans, and it becomes possible to conduct effective marketing.

[0009] The analysis unit further includes a purchase frequency calculation means for calculating the purchase frequency for a specific period for each customer of the enterprise from the purchase date information, and a transmission frequency calculation means for calculating the number of transmissions for a specific period for each user of the community from the transmission date information. The classification and analysis means of the analysis unit is preferably configured to classify and analyze common users into segments based on the purchase frequency calculated by the purchase frequency calculation means and the number of transmissions calculated by the transmission frequency calculation means, and output the analyzed analysis results. The number of transmissions of users in the community is a unique psychological indicator that the user wants to engage with the enterprise, different from the objective attributes and purchase-related data of the customers of the enterprise. By analyzing the number of transmissions together with the purchase frequency, it is possible to classify common users into segments while reflecting the psychological indicator, and it becomes possible to conduct effective marketing.

[0010] The enterprise-side data includes the purchase amount of goods purchased by the customer from the enterprise. The classification and analysis means of the analysis unit is also preferably configured to analyze the trend of the total amount obtained by summing up the purchase amounts of the common users constituting the segment for each segment, and output the analyzed analysis results. By outputting the analysis results in this way, it becomes easier to verify, for example, whether the measures for a certain segment during a specific period were effective in terms of the sales of the enterprise.

[0011] It is also preferable that the classification analysis means of the analysis unit is configured to analyze the number of transitions between segments for common users constituting the segment and output the analyzed analysis result. By outputting the analysis result in this way, it becomes easier to verify, for example, whether the measures for a certain segment during a specific period were effective in terms of acquiring fans.

[0012] The community-side data includes text information transmitted by each user in the community. The classification analysis means of the analysis unit further includes a purchase frequency calculation means for calculating the purchase frequency for a specific period for each customer of the company from the purchase date information, a frequent phrase extraction means for extracting frequent phrases for a specific period for each user of the community from the transmission date information and the text information, and a frequent phrase output means for classifying and analyzing common users into segments based on the purchase frequency calculated by the purchase frequency calculation means and outputting, as the analysis result, the frequent phrases extracted by the frequent phrase extraction means for each segment in a tag cloud format (a format in which the more frequent the phrase, the larger the font size). The text information transmitted by each user in the community shows the user's concerns and is very useful information for marketing, different from the objective attributes of the customers the company has and the data related to purchases. By analyzing the frequent phrases extracted from the text information together with the purchase frequency of the company-side data, the relationship between the purchase frequency and the user's concerns can be visually clarified, and effective marketing can be carried out.

[0013] The computer program of the present invention is a program for causing a computer to combine and analyze enterprise-side data regarding customers in an enterprise and community-side data regarding the activities of users in a community on a website where users can send information. The enterprise-side data includes at least a customer identifier capable of identifying each customer and purchase date information indicating that each customer has purchased goods in the enterprise. The community-side data includes at least a user identifier that can be matched with the customer identifier and can identify each user in the community, and transmission date information indicating that each user has sent information in the community. The computer program causes the computer to execute a procedure for identifying common users common to the enterprise's customers and the community's users from the customer identifier and the user identifier, a procedure for combining the enterprise-side data and the community-side data for each common user, and a procedure for outputting an analysis result obtained by classifying and analyzing the common users into a plurality of segments based on the purchase date information and the transmission date information.

[0014] By configuring in this way, marketing can be automatically and efficiently performed. Further, by analyzing data using the customer identifier of the enterprise-side data and the user identifier of the community-side data in this way, common users common to the enterprise and the community can be identified, and it becomes possible to integrally analyze the information of the enterprise and the community. Therefore, not only the data regarding the customers that the enterprise has but also the data regarding the users that the community has can be utilized for marketing. The community-side data contains useful information that can analyze the psychology of customers. By classifying and analyzing the common users into a plurality of segments using these, the psychology of customers can also be reflected. Therefore, it becomes easier to hypothesize and verify measures for improving sales and acquiring fans, and it becomes possible to perform effective marketing.

[0015] It is preferable to cause a computer to execute a procedure for calculating the purchase frequency for a specific period for each customer of an enterprise from the purchase date information, a procedure for calculating the number of transmissions for a specific period for each user of a community from the transmission date information, and a procedure for outputting an analysis result obtained by classifying and analyzing common users into segments based on the number of transmissions and the purchase frequency. The number of transmissions of a user in a community is a unique psychological indicator that the user wants to engage with the enterprise, different from the objective attributes of customers the enterprise has and the data related to purchases. Thus, by causing the computer to analyze the number of transmissions together with the purchase frequency, it is possible to classify common users into segments reflecting the psychological indicator, and efficiently conduct effective marketing.

[0016] Enterprise-side data includes the purchase amount of goods purchased by customers from the enterprise, and it is also preferable to cause a computer to execute a procedure for outputting an analysis result obtained by analyzing the trend of the total amount obtained by summing up the purchase amounts of common users constituting a segment for each segment. By outputting the analysis result in this way, it becomes easier to verify, for example, whether a measure for a certain segment during a specific period was effective in terms of the enterprise's sales.

[0017] It is also preferable to cause a computer to execute a procedure for outputting an analysis result obtained by analyzing the number of transition people between segments for common users constituting each segment. By outputting the analysis result in this way, it becomes easier to verify, for example, whether a measure for a certain segment during a specific period was effective in terms of acquiring fans.

[0018] Community-side data includes the text information transmitted by each user in the community. From the purchase date information, a procedure for calculating the purchase frequency for a specific period for each customer of the company, a procedure for extracting frequently used phrases for a specific period for each user in the community from the transmission date information and the text information, a procedure for classifying common users into segments according to the purchase frequency, and as an analysis result, a procedure for outputting frequently used phrases in a tag cloud format for each segment are preferably executed by a computer. The text information transmitted by each user in the community, unlike the objective attributes of the customers held by the company and the data related to purchases, indicates the user's concerns and is very useful information for marketing. By having the computer analyze the frequently used phrases extracted from the text information together with the purchase frequency of the company-side data in this way, the relationship between the purchase frequency and the user's concerns can be visually clarified, and effective marketing can be carried out.

Advantages of the Invention

[0019] According to the present invention, regarding users common to a company and a community, they can be classified and analyzed into a plurality of segments, and effective marketing that also reflects the psychology of customers can be efficiently carried out.

Brief Description of the Drawings

[0020]

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Mode for Carrying Out the Invention

[0021] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Although the description will be made with reference to the drawings as necessary, the illustrated contents are only schematically and exemplarily shown for the understanding of the present invention, and the appearance and dimensional ratios may be different from the actual ones. Further, although the embodiments will be specifically described below, the present invention is not limited to these embodiments.

[0022] (First Embodiment) FIG. 1 is a block diagram schematically showing the overall configuration of a marketing support system 100 according to a first embodiment of the present invention. The marketing support system 100 in the present embodiment is a system that combines and analyzes enterprise-side data 10 regarding customers in an enterprise and community-side data 30 regarding user activities in a community on a website where users can send information. In this marketing support system 100, for users (common users) common to the enterprise and the community, analysis results classified and analyzed into a plurality of segments can be output as a report to a display device 140. In the marketing support system 100 of the present embodiment, a case where the enterprise-side data 10 is stored in the enterprise-side management device 110 and the community-side data 30 is stored in the community-side management device 130 will be described as an example. However, the enterprise-side data 10 and the community-side data 30 may be stored in the same device by a cloud service or the like.

[0023] The marketing support system 100 is configured to combine and analyze the enterprise-side data 10 and the community-side data 30. Specifically, this marketing support system 100 has a first storage unit 1 and a second storage unit 2 that can store information. The first storage unit 1 can acquire and store the enterprise-side data 10 from the enterprise-side management device 110. The second storage unit 2 can acquire and store the community-side data 30 from the community-side management device 130.

[0024] Also, the marketing support system 100 has an analysis unit 4 that calculates and analyzes data according to a stored computer program and outputs a report of the analysis result via the display device 140. The analysis unit 4 executes the processing of the procedure of the computer program and realizes the functions of the marketing support system 100. Further, the marketing support system 100 has a third storage unit 3 that stores the result of calculating the data by the analysis unit 4.

[0025] FIG. 2 is a block diagram schematically showing the configuration of the analysis unit 4 in the marketing support system 100 according to the first and second embodiments.

[0026] As shown in the figure, the analysis unit 4 includes data acquisition means 4a that acquires enterprise-side data from the enterprise-side management device and community-side data from the community-side management device, enterprise-side data aggregation means 4b that aggregates information for each customer for a specific period from the enterprise-side data acquired by the data acquisition means 4a and stored in the first storage unit 1, community-side data aggregation means 4c that aggregates information for each user for a specific period from the community-side data acquired by the data acquisition means 4a and stored in the second storage unit 2, specifying means 4d that specifies common users common to the enterprise's customers and the community's users from the customer identifier and the user identifier, combining means 4e that combines the enterprise-side data and the community-side data for each common user specified by the specifying means 4d, classification analysis means 4f that classifies and analyzes the common users into a plurality of segments based on the purchase date information of the enterprise-side data combined by the combining means 4e and the transmission date information of the community-side data combined by the combining means, purchase frequency calculation means 4g that calculates the purchase frequency for each enterprise customer for a specific period from the purchase date information of the goods purchased by the customer at the enterprise, transmission frequency calculation means 4h that calculates the number of transmissions for each community user for a specific period from the transmission date information, frequent phrase extraction means 4i that extracts frequent phrases for each community user for a specific period from the transmission date information and the text information, and frequent phrase output means 4j (second embodiment) that classifies and analyzes the common users into segments according to the purchase frequency calculated by the purchase frequency calculation means 4g, and outputs the frequent phrases extracted by the frequent phrase extraction means 4i (second embodiment) for each segment in a tag cloud format (a format in which the more frequent the phrase, the larger the font size).

[0027] Note that the analysis unit 4 may have any configuration as long as it can process the procedures of a computer program, and may be read as other terms such as a computer, a processor, a controller, a CPU, an integrated circuit, an ASIC, a PLD, or an FPGA. Also, in the analysis unit 4, the first storage unit 1, the second storage unit 2, the third storage unit 3, and the analysis unit 4 may be a single computer or the like as long as they can realize the functions they have. Further, the marketing support system 100 of the present embodiment may be constructed on the same cloud service as the storage devices 110 and 130.

[0028] FIG. 3 is an image diagram showing an example of the company-side data 10 analyzed by the marketing support system 100, created to assist in understanding the present invention. Also, FIG. 4 is an image diagram showing an example of the community-side data 30 analyzed by the marketing support system 100, created to assist in understanding the present invention. Hereinafter, the present embodiment will be described based on FIGS. 3 and 4, but the company-side data 10 and the community-side data 30 are not limited thereto.

[0029] The company-side data 10 includes at least a customer identifier 11 that can identify each customer. The customer identifier 11 is not particularly limited, but as shown in FIG. 3, an example is a personal e-mail address used for creating an account as a customer of the company. Also, the company-side data 10 includes action information 16, which is some action of the customer with respect to the company, and date information 15 thereof. The action information 16 includes, for example, information that the customer has purchased a product from the company, and may also include information such as having only browsed the product. In the present embodiment, the action information 16 and the date information 15 are combined to form purchase date information 14 indicating that each customer has purchased a product from the company.

[0030] Also, as shown in FIG. 3, in this embodiment, the enterprise-side data 10 includes an enterprise-side ID 12 corresponding to the customer identifier 11, product information 17 (product ID 18 for specifying the product and unit price 19 of the product) that has been the target of purchase or viewing, etc., and the number of purchases 20. In addition, the enterprise-side data 10 may include information regarding the attributes of the customers acquired by the enterprise, such as the customer's address, age, gender, occupation, education level, annual income, etc. Further, it may include information regarding the participation status in events held by the enterprise, etc. Also, these pieces of information may be divided into multiple data as long as they can be integrated by the customer identifier 11, product ID 18, etc., even if they are not integrated into one piece of enterprise-side data 10.

[0031] The community-side data 30 includes at least a user identifier 31 that can be matched with the customer identifier 11 and can identify each user of the community. The user identifier 31 is not particularly limited, but as shown in FIG. 4, an example is a personal e-mail address used for creating an account as a user of the community. If the e-mail addresses as identifiers are the same, each identifier can be matched, and it can be specified as a common user common to the enterprise and the community. The customer identifier 11 and the user identifier 31 are not limited to e-mail addresses, and may be information that can specify a common user common to the enterprise and the community by means of OAuth, OpenID Connect, etc.

[0032] Also, as shown in FIG. 4, the community-side data 30 includes action information 36, which is the user's actions in the community, and date information 35. In the present embodiment, the action information 36 and the date information 35 are combined to form the transmission date information 34 on which each user transmitted information in the community. The action information 36 includes, for example, information such as "post", "reply" for transmitting information as text in the community, and "good" for transmitting a favorable emotion without including text. In addition, the action information 36 may also include information such as information on logging in to the community, information on browsing web pages within the community, and information on performing searches. Further, the community-side data 30 may include the display name 32 of the user in the community, the community usage start date information 33 of the user, web page information 37 such as a URL on which the user has taken actions, and the like. Further, the community-side data 30 may include other information obtained in the community, such as the response status to a questionnaire conducted within the community.

[0033] FIG. 5 is a flowchart showing the procedure for combining and analyzing the company-side data 10 and the community-side data 30 by the marketing support system 100 and outputting the analysis result as a report. In the marketing support system 100 according to the present embodiment, the data acquisition means 4a, the company-side data aggregation means 4b, the community-side data aggregation means 4c, the specifying means 4d, the combining means 4e, the classification analysis means 4f, the purchase frequency calculation means 4g, the transmission frequency calculation means 4h, the frequently occurring phrase extraction means 4i, and the frequently occurring phrase output means 4j of the analysis unit 4 are realized by a computer program incorporated in the analysis unit 4. However, the frequently occurring phrase extraction means 4i and the frequently occurring phrase output means 4j are realized in the analysis unit 4 in the second embodiment. Hereinafter, with reference to FIG. 5, the analysis performed by the analysis unit 4 and the procedure for outputting the analysis result will be described.

[0034] First, the data acquisition means 4a of the analysis unit 4 acquires the enterprise-side data 10 from the enterprise-side management device 110 and acquires the community-side data 30 from the community-side management device 130 (step S1). The enterprise-side data 10 is stored in the first storage unit 1, and the community-side data 30 is stored in the second storage unit 2.

[0035] Next, the enterprise-side data aggregation means 4b of the analysis unit 4 aggregates information for each customer for a specific period (for example, one month) from the enterprise-side data 10 stored in the first storage unit 1 based on the purchase date and time information 14 and the customer identifier 11 (step S2). Also, the community data aggregation means 4c of the analysis unit 4 aggregates information for each user for a specific period (for example, one month) from the community-side data 30 stored in the second storage unit 2 based on the transmission date information 34 and the user identifier 31 (step S3). FIG. 6 is an image diagram showing an example of the enterprise-side data 10 aggregated for analysis created to assist in understanding the present invention. Further, FIG. 7 is an image diagram showing an example of the community-side data 30 aggregated for analysis created to assist in understanding the present invention. In the present embodiment, as an example, together with the procedure S2 for aggregating the enterprise-side data 10, the purchase frequency calculation means 4g calculates the purchase frequency and the purchase amount for each customer, and together with the procedure S3 for aggregating the community-side data 30, the transmission count calculation means 4h calculates the transmission count for each user. In the present embodiment, the purchase frequency is set to three levels: once or more in the most recent one month, once or more in the most recent three months (and less than once in the most recent one month), and less than once in the most recent three months. However, this threshold value may be set as appropriate. Furthermore, in the present embodiment, together with the procedure S3 for aggregating the community-side data 30, phrases (including clauses) are extracted from the text information 38, and the appearance frequency of the phrases is calculated for each user. Note that the extraction of phrases from the text information 38 itself may use a known method in a tag cloud.

[0036] Note that the order of the procedures for aggregating and calculating these pieces of information is not particularly limited. For example, the aggregation of information for each user (procedure S3) may be performed before or simultaneously with the aggregation of information for each customer (procedure S2). Also, prior to these aggregations, the purchase frequency and purchase amount may be calculated for each customer, and the number of transmission times may be calculated for each user.

[0037] Next, the specifying means 4d of the analysis unit 4 specifies common users common to the company and the community from the customer identifier 11 and the user identifier 31 (procedure S4). As described above, the customer identifier 11 can identify each customer, and the user identifier 31 can identify each user. By matching these respective identifiers, common users common to the company and the community can be specified. Once the common users are specified, data regarding the common users can be combined. Note that customers or users who cannot be specified as common users can be specified as non-common users.

[0038] Next, the combining means 4e of the analysis unit 4 combines the company-side data 10 and the community-side data 30 for each common user (procedure S5). FIG. 8 is an image diagram showing an example of combined data 50 obtained by combining the company-side data and the community-side data, created to facilitate understanding of the present invention. As shown in FIG. 8, through the combining process of the combining means 4e, regarding the common users, the information existing in the company-side data 10 and the information existing in the community-side data 30 are linked. Note that the combined data 50 may include information regarding non-common users or may include information (not shown) regarding common users.

[0039] Next, the classification analysis means 4f of the analysis unit 4 classifies the common users into segments (procedure S6), analyzes the segments, and outputs a report of the analysis results to the display device 140 (procedure S7). The display device 140 displays the report output from the analysis unit 4. FIG. 9 is an image diagram showing data obtained by classifying common users into segments with respect to the combined data 50.

[0040] In this embodiment, as an example, for common users with a call frequency of 1 or more in the most recent 1 month, the segment of common users with a purchase frequency of 1 or more in the most recent 1 month is defined as "α1", the segment of common users with a purchase frequency of 1 or more in the most recent 3 months (and less than 1 in the most recent 1 month) is defined as "β1", and the segment of common users with a purchase frequency of less than 1 in the most recent 3 months is defined as "γ1". For common users with a call frequency of less than 1 in the most recent 1 month, the segment of common users with a purchase frequency of 1 or more in the most recent 1 month is defined as "α2", the segment of common users with a purchase frequency of 1 or more in the most recent 3 months (and less than 1 in the most recent 1 month) is defined as "β2", and the segment of common users with a purchase frequency of less than 1 in the most recent 3 months is defined as "γ2". However, the threshold for classifying the purchase frequency and the threshold for classifying the call frequency can be appropriately set to change the segment classification method.

[0041] The content of the report on the analysis result of segment analysis (the analysis result output in step S7) is not particularly limited as long as it is comparable from the combined data 50. In this embodiment, for example, it is possible to analyze the trend of the total amount obtained by summing up the purchase amounts of the common users who make up each segment for each segment. FIG. 10 shows, as an example of this embodiment, a report of the analysis result 71 analyzing the trend of the total amount obtained by summing up the purchase amounts of the common users who make up each segment for each segment. As shown in FIG. 10, in the report 70, by displaying the trend of the total amount as a graph 73 for the six segments 72 classified by the purchase frequency calculated by the purchase frequency calculation means 4g and the call frequency calculated by the call frequency calculation means 4h, the relationship between each segment and the sales is visually clarified. Therefore, it becomes easier to establish a hypothesis about what measures such as sales promotion should be taken based on the index related to the purchase behavior of common users, that is, the purchase frequency, and the index related to the psychological aspect, that is, the calls in the community. In addition, when measures for sales promotion are implemented for a specific segment during a certain period, it is possible to verify whether the sales promotion measures are effective.

[0042] In this embodiment, for example, it is possible to compare the segments of common users in a specific month with the segments of those common users at the time one month ago. FIG. 11 is a diagram showing the analysis result 81 obtained by comparing the segments of common users in a specific month with the segments of those common users at the time one month ago, as a report. As shown in FIG. 11, in the report 80, the number of common users constituting six segments 82 is displayed, and by displaying the segment 83 to which the common users constituting the segment belonged one month ago and the number 83a thereof, the number of transferred users from other segments becomes visually clear. For a company, customers with a high purchase frequency and frequent postings in the community can be expected to make continuous purchases more than those without such characteristics. In this embodiment, the common users classified into the upper-right segment (i.e., α1) have a greater expectation of continuous consumption and a higher importance for the company. On the other hand, the common users classified into the lower-left segment (i.e., γ2) have a smaller expectation and a lower importance for the company. Therefore, it is important in marketing to transition the common users so as to approach segment α1 (to make them fans of the company). In FIG. 11, it is visually clear from which segment and how many common users have transferred, making it easy to formulate hypotheses about what fan acquisition measures should be taken and easy to verify whether those measures have been effective in acquiring fans for the company.

[0043] In the marketing support system 100 of this embodiment, by combining and analyzing the company-side data 10 and the community-side data 30, not only the data regarding the customers that the company has but also the data regarding the users that the community has can be automatically utilized for marketing. Therefore, in the marketing support system 100 of this embodiment, effective marketing can be efficiently carried out.

[0044] In this embodiment, the marketing support system 100 has a third storage unit 3 that stores the results of data calculations performed by the analysis unit 4. Therefore, the analysis unit 4 can retrieve the calculated data stored in the third storage unit 3. By using the data stored in the third storage unit 3, it is possible to reflect the past total amount and the segments of common users classified in the past in the analysis results without newly calculating past data.

[0045] In this embodiment, the calculation of the purchase frequency and purchase amount for each customer is performed before the combination of the company-side data 10 and the community-side data 30 (procedure S5), but this order is not particularly limited. That is, as long as the classification of common users into segments (procedure S6) can be performed, the calculation of the purchase frequency and purchase amount for each customer may be performed at any stage, and may also be performed after the combination of the data (procedure S5) and before the classification of common users into segments (procedure S6).

[0046] In this embodiment, the company-side data 10 may include information such as the number of times a website is viewed and the number of times a user participates in an event hosted by the company. In this case, if the purchase amount of common users is replaced with the number of times a website is viewed or the number of times a user participates in an event, it is possible to conduct marketing not only for sales analysis but also for participation in events and website viewing, and it becomes easier to hypothesize and verify various measures such as measures to increase website viewing and measures to promote participation in events.

[0047] (Second Embodiment) The marketing support system 100 of the second embodiment is a modification of the marketing support system according to the above-described first embodiment, and has the same configuration, operations, and effects as those of the first embodiment except for the configuration shown below. In the following, mainly the parts different from the marketing support system according to the first embodiment will be described, and the description of overlapping parts will be omitted. In this embodiment, it is different in that it classifies segments and outputs the analysis result regarding text information as a report, but the description of the first embodiment applies by changing the information to be analyzed in the same procedure as in the first embodiment.

[0048] FIG. 12 is an image diagram showing an example of combined data 60 obtained by combining enterprise-side data 10 and community-side data 30, created to facilitate understanding of this embodiment. In this embodiment, the analysis unit 4 classifies common users into segments (procedure S6) for the combined data 60 by the classification analysis means 4f, and outputs a report of the analysis result of segment analysis (procedure S7). FIG. 13 is an image diagram showing data obtained by classifying common users into segments for the combined data 60 in this embodiment.

[0049] In this embodiment, the frequent phrase extraction means 4i aggregates the phrase information 39 (phrases included in the text information 38 and the number of occurrences in a specific period) extracted from the text information 38 of common users constituting each segment. As an analysis result, the frequent phrase output means 4j outputs a report in the form of a tag cloud for each segment with the frequent phrases. FIG. 14 is a diagram showing a report of the analysis result 91 obtained by analyzing the frequent phrases in the form of a tag cloud 93 for each segment 92. In this embodiment, as an example, a segment of common users with a purchase frequency of once or more in the most recent one month is classified as "α", a segment of common users with a purchase frequency of once or more in the most recent three months (and less than once in the most recent one month) is classified as "β", and a segment of common users with a purchase frequency of less than once in the most recent three months is classified as "γ". Note that the output itself in the tag cloud format (a format in which the font size is larger for more frequent phrases) may use a known method for tag clouds. In this way, by analyzing the frequent phrases extracted from the text information together with the purchase frequency of the company-side data, it is possible to visually grasp the relationship between the high purchase frequency and the user's concerns. Therefore, it becomes easier to hypothesize and verify measures, and it becomes possible to efficiently conduct marketing.

[0050] Also in this embodiment, the analysis unit 4 includes data acquisition means 4a, company-side data aggregation means 4b, community-side data aggregation means 4c, identification means 4d, combination means 4e, and classification analysis means 4f, in the same manner as in the case of the first embodiment. Further, purchase frequency calculation means 4g and transmission frequency calculation means 4h may also be provided.

[0051] The present invention is not limited to the above-described embodiments, and various forms with design changes within the scope not departing from the gist of the invention described in the claims are also included in the technical scope.

[0052] In any of the above embodiments, by classifying and analyzing the users common to the company and the community into a plurality of segments by the marketing support system 100, it is possible to conduct effective marketing that also reflects the psychology of customers.

[0053] In addition to the above exemplary embodiments, the configurations of the respective embodiments can be recombined without inhibiting the effects of the present invention.

Industrial Applicability

[0054] The present invention has great industrial applicability in the field of marketing by analyzing enterprise-side data and community-side data.

Explanation of Signs

[0055] 1 First storage unit 2 Second storage unit 3 Third storage unit 4 Analysis unit 4a Data acquisition means 4b Enterprise-side data aggregation means 4c Community-side data aggregation means 4d Identification means 4e Combining means 4f Classification analysis means 4g Purchase frequency calculation means 4h Transmission frequency calculation means 4i Frequent phrase extraction means 4j Frequent phrase output means 5 Output unit 10 Enterprise-side data 11 Customer identifier 12 Enterprise-side ID 14 Purchase date information 15 Date information 16 Action information 17 Product information 18 Product ID 19 Product price 20 Number of purchases 30 Community-side data 31 User identifier 32 Display name 33 Community start date 34 Transmission date information 35 Date information 36 Action information 37 Web page information 38 Text information 39 Phrase information 50, 60 Combined data 70, 80, 90 Reports 71, 81, 91 Analysis results 72, 82, 92 Segments 73 Graph 83 Segment to which it belonged one month ago 83a Number of people 93 Tag cloud 100 Marketing support system 110 Enterprise-side management device 130 Community-side management device 140 Display device

Claims

1. the device comprises a first storage unit storing company-side data relating to customers of the company, a second storage unit storing community-side data relating to user activities in a community on a website where users can post information, an analysis unit reading out the company-side data from the first storage unit and the community-side data from the second storage unit, and comparing and analyzing the read company-side data and the read community-side data, and a display device; The company data includes at least a customer identifier capable of identifying each customer and purchase date information on when each customer purchased a product from the company; the community-side data includes at least a user identifier that can be matched with the customer identifier and that can identify each user of the community, and transmission date information of information transmitted by each user in the community; the analysis unit includes an identification means for identifying common users common to customers of the company and users of the community from the customer identifier and the user identifier, a combination means for combining the company side data and the community side data for each common user identified by the identification means, and a classification and analysis means for classifying and analyzing the common users into a plurality of segments based on the purchase date information of the company side data combined by the combination means and the transmission date information of the community side data combined by the combination means, The marketing support system according to claim 1, wherein the display device is configured to display an analysis result output from the classification analysis means of the analysis unit.

2. The marketing support system of claim 1, characterized in that the analysis unit further includes a purchase frequency calculation means for calculating a purchase frequency for a specific period for each customer of the company from the purchase date information, and a call number calculation means for calculating the number of calls for a specific period for each user of the community from the call date information, and the classification analysis means of the analysis unit is configured to classify and analyze the common users into the segments based on the purchase frequency calculated by the purchase frequency calculation means and the number of calls calculated by the call number calculation means, and to output the analysis results.

3. The company data includes a purchase amount of the customer's purchase of the product from the company, The marketing support system according to claim 1 or 2, characterized in that the classification analysis means of the analysis unit is configured to analyze, for each segment, the trend of a total amount calculated by adding up the purchase amounts of the common users constituting the segment, and to output the analysis results.

4. The marketing support system described in claim 1 or 2, characterized in that the classification analysis means of the analysis unit is configured to analyze the number of transitions between the segments for the common users that constitute the segments, and output the analysis results.

5. the community-side data includes text information posted by each user in the community; The marketing support system described in claim 1, characterized in that the classification and analysis means of the analysis unit further comprises a purchase frequency calculation means for calculating a purchase frequency for a specific period for each customer of the company from the purchase date information, a frequent phrase extraction means for extracting frequent phrases for a specific period for each user of the community from the issue date information and the text information, and a frequent phrase output means for classifying and analyzing the common users into the segments based on the purchase frequency calculated by the purchase frequency calculation means, and outputting the frequent phrases extracted by the frequent phrase extraction means for each segment in tag cloud format as a result of the analysis.

6. A computer program for causing a computer to combine and analyze company-side data relating to customers of the company and community-side data relating to user activities in a community on a website where users can post information, the computer program comprising: The company data includes at least a customer identifier capable of identifying each customer and purchase date information when each customer purchased a product from the company; The community-side data includes at least a user identifier that can be matched with the customer identifier and can identify each user of the community, and information on a posting date when each user posted information in the community, A computer program for causing a computer to execute the steps of: identifying common users common to customers of the company and users of the community from the customer identifier and the user identifier; combining the company side data and the community side data for each common user; and outputting analysis results obtained by classifying and analyzing the common users into multiple segments based on the purchase date information and the sending date information.

7. The computer program of claim 6, which causes the computer to execute the steps of: calculating a purchasing frequency for a specific period for each customer of the company from the purchase date information; calculating the number of calls for a specific period for each user of the community from the call date information; and outputting the analysis results in which the common users are classified into the segments and analyzed based on the number of calls and the purchase frequency.

8. The company data includes a purchase amount of the customer's purchase of the product from the company, The computer program according to claim 6 or 7, wherein the analysis unit is configured to cause the computer to execute a procedure for outputting the analysis results obtained by analyzing the trend in a total amount obtained by adding up the purchase amounts of the common users constituting each of the segments for each of the segments.

9. The computer program according to claim 6 or 7, for causing the computer to execute a procedure of outputting the analysis result obtained by analyzing the number of transitions between the segments for the common users constituting each of the segments.

10. the community-side data includes text information posted by each user in the community; The computer program of claim 6, which causes the computer to execute the steps of: calculating a purchasing frequency for a specific period for each customer of the company from the purchase date information; extracting frequent words and phrases for a specific period for each user of the community from the publication date information and the text information; classifying the common users into the segments based on the purchasing frequency; and outputting the frequent words and phrases in tag cloud format for each segment as the analysis result.

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