Customer attraction support device, customer attraction support system, customer attraction support method, and program

Abstract

To support customer attraction by quantifying the strength of keywords, topics, or the like that have been input, predicting the number of attracted customers, the number of clicks, or the like, and selecting keywords suitable for a website or a guide text, or the like.SOLUTION: A customer attraction support device comprises: an input processing unit 11a that accepts an input of a keyword and processes the keyword; a strength determination unit 11d that determines the strength of the keyword; a trend correction unit 11e that performs trend correction of the result determined by the strength determination unit; a prediction unit 11f that predicts at least the number of attracted customers on the basis of the determination result after the trend correction; and an output unit 11h that outputs the prediction result. The prediction unit 11f performs the prediction using a mathematical model obtained by machine learning that employs at least one of seminar information, mail information, and site information as training data.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a technology for quantifying the strength of input keywords, issues, etc., predicting the number of customers and clicks, etc., and supporting customer acquisition. [Background technology]

[0002] Traditionally, the keywords used to compose the content of a website have a significant impact on the number of visitors and clicks to the website, so the keywords have been selected based on the results of various marketing surveys.

[0003] On the other hand, when planning seminars and events, it is also desirable to predict the number of attendees that can be expected at the planning stage and to provide effective information that will attract more attendees.

[0004] For example, Patent Document 1 discloses an order prediction device that includes an acquisition means for acquiring event data related to an event, a prediction means for predicting the attention level and order probability for each data item included in the event data using an attention mechanism based on the event data, and an output means for outputting the attention level and order probability as prediction results. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. WO2022 / 201512A1 Summary of the Invention [Problem to be solved by the invention]

[0006] However, conventional technologies such as Patent Document 1 do not assist in attracting customers by quantifying the strength of input keywords, issues, etc., predicting the number of customers or clicks, etc., and selecting suitable keywords for websites, informational text, etc.

[0007] The present invention has been made in consideration of these problems, and its purpose is to provide technology that assists in attracting customers by quantifying the strength of input keywords, issues, etc., predicting the number of customers and clicks, etc., and selecting keywords that are suitable for websites, informational text, etc. [Means for solving the problem]

[0008] In order to solve the above problem, the customer attraction support device of the first aspect of the present invention comprises an input processing unit that accepts and processes keyword input, a strength determination unit that determines the strength of the keyword, a trend correction unit that performs trend correction on the determination result of the strength determination unit, a prediction unit that predicts at least the number of customers to be attracted based on the determination result after the trend correction, and an output unit that outputs the prediction result, wherein the prediction unit makes the above prediction using a mathematical model obtained by machine learning using as learning data at least one of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar hosting announcements, email information including the title, body text, open rate, and click rate related to email newsletters, and site information including the title, body text, PV value, and viewing time related to websites.

[0009] A customer attraction support device according to a second aspect of the present invention includes an input processing unit that accepts and processes keyword input, a strength determination unit that determines the strength of the keyword, a trend correction unit that performs trend correction on the determination result of the strength determination unit, a generation unit that generates content based on the determination result after the trend correction, and an output unit that outputs the content.

[0010] A customer attraction support system according to a third aspect of the present invention is a customer attraction support system consisting of a user's terminal device and a customer attraction support device, wherein the user's terminal device comprises a first transmitting unit that transmits keywords to the customer attraction support device, a first receiving unit that receives prediction results from the customer attraction support device, and a display unit that displays the prediction results, and the customer attraction support device comprises a second receiving unit that receives keywords from the user's terminal device, an input processing unit that accepts and processes input of the keyword, a strength determination unit that determines the strength of the keyword, a trend correction unit that performs trend correction on the determination result of the strength determination unit, a prediction unit that predicts at least the number of customers to be attracted based on the determination result after the trend correction, and a second transmitting unit that transmits the prediction result to the user's terminal device, and the prediction unit makes the above prediction using a mathematical model obtained by machine learning using as learning data at least any of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar hosting announcements, email information including the title, body, open rate, and click rate related to email newsletters, and site information including the title, body, PV value, and viewing time related to websites.

[0011] A customer attraction support system according to a fourth aspect of the present invention is a customer attraction support system consisting of a user's terminal device and a customer attraction support device, wherein the user's terminal device comprises a first transmitting unit that transmits keywords to the customer attraction support device, a first receiving unit that receives content from the customer attraction support device, and a display unit that displays the content, and the customer attraction support device comprises a second receiving unit that receives keywords from the user's terminal device, an input processing unit that accepts and processes input of the keyword, a strength determination unit that determines the strength of the keyword, a trend correction unit that performs trend correction on the determination result of the strength determination unit, a generation unit that generates content based on the determination result after the trend correction, and a second transmitting unit that transmits the content to the user's terminal device.

[0012] A customer attraction support method according to a fifth aspect of the present invention includes an input processing unit that receives and processes keyword input, a strength determination unit that determines the strength of the keyword, a trend correction unit that performs trend correction on the determination result of the strength determination unit, a prediction unit that predicts at least the number of customers to be attracted based on the determination result after the trend correction, and an output unit that outputs the prediction result, and the prediction unit makes the above prediction using a mathematical model obtained by machine learning using as learning data at least any of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar hosting announcements, email information including the title, body text, open rate, and click rate related to email newsletters, and site information including the title, body text, PV value, and viewing time related to websites.

[0013] A customer attraction support method according to a sixth aspect of the present invention includes an input processing unit that accepts and processes keyword input, a strength determination unit that determines the strength of the keyword, a trend correction unit that performs trend correction on the determination result of the strength determination unit, a generation unit that generates content based on the determination result after the trend correction, and an output unit that outputs the content.

[0014] A program according to a seventh aspect of the present invention causes a computer to function as an input processing unit that accepts and processes keyword input, a strength judgment unit that judges the strength of the keyword, a trend correction unit that performs trend correction on the judgment result of the strength judgment unit, a prediction unit that predicts at least the number of attendees based on the judgment result after the trend correction, and an output unit that outputs the prediction result, wherein the prediction unit makes the above prediction using a mathematical model obtained by machine learning using as learning data at least any of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar invitations, email information including the title, body text, open rate, and click rate related to email newsletters, and site information including the title, body text, PV value, and viewing time related to websites.

[0015] A program according to an eighth aspect of the present invention causes a computer to function as an input processing unit that accepts and processes keyword input, a strength judgment unit that judges the strength of the keyword, a trend correction unit that performs trend correction on the judgment result of the strength judgment unit, a generation unit that generates content based on the judgment result after the trend correction, and an output unit that outputs the content. Effect of the Invention

[0016] According to the present invention, it is possible to provide a technology that assists in attracting customers by quantifying the strength of input keywords, issues, etc., predicting the number of customers and clicks, etc., and selecting keywords suitable for websites, informational text, etc. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a configuration diagram of a customer attraction support system according to an embodiment of the present invention. [Diagram 2] Figure 2 is a diagram showing the configuration of the customer attraction support device of the system. [Diagram 3] FIG. 3 is a configuration diagram of a terminal device in the system. [Figure 4] FIG. 4 is a flowchart showing the steps of the prediction process performed by the system. [Diagram 5] FIG. 5 is a flowchart showing the content generation procedure by the system. [Figure 6] 6(a) to 6(d) show examples of the configuration of various tables managed by the customer attraction support device of the system. [Figure 7] FIG. 7 is an explanatory diagram of the construction of a numerical model using this system. [Figure 8] FIG. 8 is an explanatory diagram of trend correction by the system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0019] FIG. 1 shows the configuration of a customer attraction support system according to an embodiment of the present invention.

[0020] As shown in the figure, the customer attraction support system according to the embodiment of the present invention is configured by connecting a customer attraction support device 1, which is made up of a server device, etc., and a user's terminal device 2 wirelessly or wired via a communication network such as the Internet. As the user's terminal device 2, various devices such as a smartphone, a tablet terminal, a notebook personal computer, or a desktop personal computer can be used.

[0021] In such a configuration, the customer attraction support device 1 accepts and processes text data such as keywords and assignments sent from the user's terminal device 2, judges the strength of the keywords, etc., performs trend correction on the judgment result, and predicts and outputs at least the number of customers to be attracted based on the judgment result after trend correction. The form of this output includes, for example, output to an external output device such as a monitor or printer, as well as transmission of data to the user's terminal device 2.

[0022] Furthermore, the customer attraction support device 1 accepts and processes text data such as keywords and assignments sent from the user's terminal device 2, judges the strength of the keywords, etc., performs trend correction on the judgment result, and generates and outputs content based on the judgment result after trend correction. Again, the form of this output includes output to an external output device such as a monitor or printer, as well as transmission of data to the user's terminal device 2, etc.

[0023] The customer attraction support device 1 constructs and stores a mathematical model obtained by machine learning using as learning data at least one of the following: seminar information including the title, lead text, number of applicants, and number of attendees related to seminar invitations; email information including the title, body text, open rate, and click rate related to email newsletters; and site information including the title, body text, PV value, and viewing time related to websites; and the mathematical model is used to make customer attraction predictions.

[0024] FIG. 2 shows the configuration of a customer attraction support device in the customer attraction support system, and will be described.

[0025] As shown in the figure, the customer attraction support device 1 has a control unit 11, a communication I / F 12, and a storage unit 13. The control unit 11, the communication I / F 12, and the storage unit 13 are communicatively connected via a system bus. In addition to these configurations, an input unit such as a keyboard or a mouse and a display unit such as a liquid crystal display for performing various displays may be provided. The communication I / F 12 is a communication interface realized by, for example, an NIC, connected to a communication network 4 such as the Internet by wire or wirelessly, and communicating with the terminal device 2, etc. The storage unit 13 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, a HDD, or an optical disk device, and stores a program to be executed by the control unit 11 in advance. The storage unit 13 also has a user information storage unit 14, a seminar information storage unit 15, an email information storage unit 16, a site information storage unit 17, and a learning storage unit 18.

[0026] The user information storage unit 14 stores attribute information such as the name, age, sex, address, telephone number, and email address of the registered user, in association with the user ID. The seminar information storage unit 15 stores information such as the title, lead text, date of holding, number of applicants, number of attendees, and application speed of the seminar information in association with the seminar ID, as shown in FIG. 6(a). The mail information storage unit 16 stores information such as the title, text, delivery date, open rate, and click rate of the mail magazine in association with the mail ID, as shown in FIG. 6(b). The site information storage unit 17 stores information such as the title, text, viewing date, PV value, and viewing time of the website in association with the site ID, as shown in FIG. 6(c). The learning storage unit 17 stores and updates mathematical models used in the strength judgment.

[0027] The control unit 11 is realized by a CPU, an MPU, etc. The control unit 11 is composed of an integrated circuit such as an ASIC, an FPGA, etc. The control unit 11 reads out and executes a program stored in the storage unit 13, thereby functioning as an input processing unit 11a, a learning unit 11b, a management unit 11c, a strength determination unit 11d, a trend correction unit 11e, a prediction unit 11f, a generation unit 11g, an output unit 11h, a transmission / reception control unit 11i, and a main control unit 11j.

[0028] In this configuration, the input processing unit 11a accepts and processes input of text data related to keywords and tasks. The learning unit 11b uses the data stored in the storage unit 13 as learning data to build a mathematical model for predicting customer numbers and updates it as appropriate.

[0029] 7, the learning unit 11b executes machine learning using as learning data the "seminar information" including the title, lead text, date, number of applicants, number of attendees, and application speed of the seminar announcement stored in the seminar information storage unit 15, the "email information" including the title, text, delivery date, open rate, and click rate of the e-mail newsletter stored in the e-mail information storage unit 15, and the "site information" including the title, text, view date, PV value, and view time of the website stored in the site information storage unit 17, to build a mathematical model for customer attraction prediction and store it in the learning storage unit 17. This numerical model is updated as appropriate through periodic learning by the learning unit 11b.

[0030] The management unit 11c manages the user information in the user information storage unit 14, the seminar information in the seminar information storage unit 15, the mail information in the mail information storage unit 16, and the site information in the site information storage unit 17, and updates them as appropriate.

[0031] The strength determination unit 11d determines the strength of text data such as keywords and assignments processed by the input processing unit 11a. More specifically, as shown in Fig. 6(d), a predetermined number of pieces of seminar information, email information, and site information having titles highly similar to the input processed text data are extracted, and a weighting coefficient related to type and a weighting coefficient related to time are set for each piece, and a numerical strength is calculated by calculating an average value using these weighting coefficients.

[0032] The trend correction unit 11e performs trend correction on the quantified strength calculated by the strength determination unit 11d. More specifically, as shown in Fig. 8, for example, the trend of the data is estimated from the process of time-series changes in the seminar information, mail information, and site information, and the value at the time of strength measurement is corrected to the value at the timing when the strength is actually to be calculated.

[0033] The prediction unit 11f predicts customer attraction marketing trends such as the number of customers and the number of clicks based on the strength after trend correction using the mathematical model constructed by the learning unit 11b. The generation unit 11g narrows down the strongest keywords from among the input keywords to the top few words based on the strength after trend correction, and generates content such as a guide message using the keywords using a generative AI. The output unit 11h outputs a strength index and content. The transmission / reception control unit 11i functions as a transmission unit (second transmission unit) that transmits data related to the strength index and content in HTML format to the user's terminal device 2, and a reception unit (second reception unit) that receives text data such as keywords and assignments from the user's terminal device 2. In addition, the main control unit 11j is responsible for other controls of the customer attraction support device 1.

[0034] FIG. 3 shows the configuration of a terminal device in the customer attraction support system, and will be described.

[0035] As shown in the figure, the terminal device 2 has a control unit 21, a communication I / F 22, an operation unit 23, a display unit 24, and a storage unit 25. The units 21 to 25 are connected to each other via a system bus so as to be able to communicate with each other. The communication I / F 22 is a communication interface that is realized by, for example, a NIC, is connected to a communication network 3 such as the Internet by wire or wirelessly, and communicates with the customer attraction support device 1, etc. The operation unit 23 is realized by a mouse, a keyboard, etc., and accepts various operation inputs by a user. The display unit 24 is realized by a liquid crystal display, etc., and performs various displays. The operation unit 23 and the display unit 24 may be integrated into a touch panel. The storage unit 25 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, a HDD, or an optical disk device, and stores a program executed by the control unit 21.

[0036] The control unit 21 is realized by a CPU, an MPU, or the like, and may be configured by an integrated circuit such as an ASIC or an FPGA. The control unit 21 functions as a transmission / reception control unit 21a and a display control unit 21b by reading and executing a program stored in the storage unit 25. The transmission / reception control unit 21a functions as a transmission unit (first transmission unit) that transmits text data such as keywords and tasks input by operating the operation unit 23 to the customer attraction support device 1, and a reception unit (first reception unit) that receives data in HTML format or the like sent from the customer attraction support device 1. The display control unit 21b controls the display unit 24 to perform a predetermined display based on the data in HTML format or the like received by the transmission / reception control unit 21a.

[0037] Hereinafter, the procedure of the prediction process performed by the customer attraction support device will be described in detail with reference to the flowchart in FIG.

[0038] When processing starts, input processing unit 11a accepts and processes input of text data related to keywords and assignments (S1). Next, strength determination unit 11d determines the strength of the text data of keywords, assignments, etc. processed by input processing unit 11a (S2). More specifically, as shown in Fig. 6(d), a predetermined number of seminar information, email information, and site information having titles that are highly similar to the input processed text data are extracted, and a weighting coefficient related to type and a weighting coefficient related to time are set for each, and a numerical strength is calculated by calculating an average value using these weighting coefficients.

[0039] Next, the trend correction unit 11e performs trend correction on the quantified strength calculated by the strength determination unit 11d (S3). More specifically, as shown in Fig. 8, for example, the trend of the data is estimated from the process of time-series changes in the seminar information, mail information, and site information, and the value at the time of strength measurement is corrected to the value at the timing when the strength is actually to be calculated.

[0040] Next, the prediction unit 11f predicts the customer attraction marketing tendency such as the number of customers and the number of clicks based on the strength after trend correction using the mathematical model constructed by the learning unit 11b (S4), and the output unit 11h outputs an index of the strength (S5). In this way, a series of processes related to customer attraction prediction is completed.

[0041] Next, the content generation procedure by the customer attraction system will be described in detail with reference to the flowchart of FIG.

[0042] When the process starts, the input processing unit 11a accepts and processes the input of text data related to keywords and assignments (S11). Next, the strength determination unit 11d determines the strength of the text data such as keywords and assignments processed by the input processing unit 11a (S12). More specifically, as shown in Fig. 6(d), a predetermined number of seminar information, email information, and site information having titles that are highly similar to the input processed text data are extracted, and a weighting coefficient related to type and a weighting coefficient related to time are set for each, and a numerical strength is calculated by calculating an average value using these weighting coefficients.

[0043] Next, the trend correction unit 11e performs trend correction on the quantified strength calculated by the strength determination unit 11d (S13). More specifically, as shown in Fig. 8, for example, the trend of the data is estimated from the process of time-series changes in seminar information, mail information, and site information, and the value at the time of strength measurement is corrected to the value at the timing when the strength is actually to be calculated. Then, the generation unit 11g narrows down the strong keywords from the multiple input keywords to the top few words based on the strength after trend correction, and generates content such as a guide message using the keywords by a generative AI (S15), and the output unit 11h outputs the content (S15).

[0044] As described above, according to the embodiment of the present invention, the strength of an input keyword in attracting customers can be quantified and calculated, and based on the strength, a mathematical model obtained by learning can predict marketing trends related to attracting customers (e.g., the number of customers attracted, the number of clicks, etc.), which can indirectly support attracting customers. Furthermore, based on the calculated strength, keywords effective for attracting customers can be selected, and a generative AI can automatically generate an invitation message for a seminar using the keyword, which can also indirectly support attracting customers.

[0045] Although the embodiment of the present invention has been described above, it goes without saying that the present invention is not limited to this embodiment and various improvements and modifications can be made without departing from the spirit and scope of the present invention.

[0046] For example, the learning data used to build a mathematical model for predicting customer numbers is not limited to the seminar information, email information, and site information mentioned above, but it is possible to add a variety of data as learning data depending on the purpose and build a numerical model. [Explanation of symbols]

[0047] 1...customer attraction support device, 2...terminal device, 3...communication network, 11...control unit, 11a...input processing unit, 11b...learning unit, 11c...management unit, 11d...strength determination unit, 11e...trend correction unit, 11f...prediction unit, 11g...generation unit, 11h...output unit, 11i...transmission and reception control unit, 11j...main control unit, 12...communication I / F, 13...memory unit, 14...user information memory unit, 15...seminar information memory unit, 16...email information memory unit, 17...site information memory unit, 18...learning memory unit, 21...control unit, 21a...transmission and reception control unit, 21b...display control unit, 22...communication I / F, 23...operation unit, 24...display unit, 25...memory unit.

Claims

1. an input processing unit that accepts and processes input of a keyword; a strength determination unit for determining the strength of the keyword; A trend correction unit that performs trend correction on the determination result of the strength determination unit; A prediction unit that predicts at least the number of customers based on the judgment result after the trend correction; An output unit that outputs the prediction result, The prediction unit performs the prediction using a mathematical model obtained by machine learning using as learning data at least one of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar announcements, mail information including the title, text, open rate, and click rate related to mail magazines, and site information including the title, text, PV value, and viewing time related to websites. Customer attraction support device.

2. an input processing unit that accepts and processes input of a keyword; a strength determination unit for determining the strength of the keyword; A trend correction unit that performs trend correction on the determination result of the strength determination unit; a generation unit that selects keywords effective for attracting customers based on the judgment result after the trend correction and generates content; An output unit that outputs the content. Customer attraction support device.

3. In a customer attraction support system including a user terminal device and a customer attraction support device, The user's terminal device A first transmission unit that transmits a keyword to the customer attraction support device; A first receiving unit that receives a prediction result from the customer attraction support device; A display unit that displays the prediction result, The customer attraction support device is A second receiving unit that receives a keyword from the terminal device of the user; an input processing unit that receives and processes an input of the keyword; a strength determination unit for determining the strength of the keyword; A trend correction unit that performs trend correction on the determination result of the strength determination unit; A prediction unit that predicts at least the number of customers based on the judgment result after the trend correction; A second transmission unit that transmits the prediction result to a terminal device of the user, The prediction unit performs the prediction using a mathematical model obtained by machine learning using as learning data at least one of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar announcements, mail information including the title, text, open rate, and click rate related to mail magazines, and site information including the title, text, PV value, and viewing time related to websites. Customer attraction support system.

4. In a customer attraction support system including a user terminal device and a customer attraction support device, The user's terminal device A first transmission unit that transmits a keyword to the customer attraction support device; A first receiving unit that receives content from the customer attraction support device; a display unit for displaying the content, The customer attraction support device is A second receiving unit that receives a keyword from the terminal device of the user; an input processing unit that receives and processes an input of the keyword; a strength determination unit for determining the strength of the keyword; A trend correction unit that performs trend correction on the determination result of the strength determination unit; a generation unit that selects keywords effective for attracting customers based on the judgment result after the trend correction and generates content; A second transmission unit that transmits the content to the user's terminal device. Customer attraction support system.

5. The input processing unit receives and processes the input of the keyword, a strength determination unit that determines the strength of the keyword; a trend correction unit performing trend correction on the determination result of the strength determination unit; A prediction unit predicts at least the number of customers based on the judgment result after the trend correction, An output unit outputs the prediction result, The prediction unit performs the prediction using a mathematical model obtained by machine learning using as learning data at least one of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar announcements, mail information including the title, text, open rate, and click rate related to mail magazines, and site information including the title, text, PV value, and viewing time related to websites. How to help attract customers.

6. The input processing unit receives and processes the input of the keyword, a strength determination unit that determines the strength of the keyword; a trend correction unit performing trend correction on the determination result of the strength determination unit; A generation unit selects keywords effective for attracting customers based on the judgment result after the trend correction and generates content; An output unit outputs the content. How to help attract customers.

7. Computer, an input processing unit that accepts and processes input of keywords; a strength determination unit for determining the strength of the keyword; a trend correction unit that performs trend correction on the determination result of the strength determination unit; A prediction unit that predicts at least the number of customers based on the judgment result after the trend correction; an output unit that outputs the prediction result; The prediction unit performs the prediction using a mathematical model obtained by machine learning using as learning data at least one of seminar information including the title, lead text, number of applicants, and number of attendees related to seminar announcements, mail information including the title, text, open rate, and click rate related to mail magazines, and site information including the title, text, PV value, and viewing time related to websites. program.

8. Computer, an input processing unit that accepts and processes input of keywords; a strength determination unit for determining the strength of the keyword; a trend correction unit that performs trend correction on the determination result of the strength determination unit; a generation unit that selects keywords effective for attracting customers based on the judgment result after the trend correction and generates content; and causing the content to function as an output unit. program.

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