Sales support system, server equipment, sales support method, and sales support program

Abstract

To provide a sales support system, a server device, a sales support method, and a sales support program that enable grasping a customer's reaction to a real estate appraisal report.SOLUTION: An operation display unit 240 of an information terminal device 200 displays a real estate appraisal document and acquires an operation log corresponding to an operation for the displayed real estate appraisal document, and a communication unit 210 transmits the acquired operation log to a server 100. A communication unit 110 of the server 100 receives the operation log from the information terminal device 200, and an operation information generation unit 131 generates operation information from the received operation log and stores the generated operation information in an operation information storage unit 123.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a sales support system, a server device, a sales support method, and a sales support program.

Background Art

[0002] The real estate market is booming, and many real estates such as condominiums are being sold and bought. When the owner of a real estate considers selling and buying the real estate, the owner often requests an appraisal of the real estate from a real estate company. However, since each real estate has different conditions, it has been difficult to calculate an appropriate appraisal price for the target real estate. In order to solve such problems, a real estate appraisal support system that calculates an objective and highly valid real estate price by calculating a market index of location elements related to the location of a property has been disclosed (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] After a real estate company that has received a request for an appraisal from the owner of a real estate creates and sends a real estate appraisal report according to each real estate, it waits for an inquiry from the requester or a request for sales mediation. Real estate appraisal reports are often created on paper, and real estate companies have been unable to grasp how the real estate appraisal reports they have handed over to the requesters have been handled thereafter and whether the requesters are interested in the appraisal content.

[0005] The present invention has been made in view of the above, and an object thereof is to provide a sales support system, a server device, a sales support method, and a sales support program that can grasp the customer's reaction to a real estate appraisal report. [Means for solving the problem]

[0006] To solve the above-mentioned problems, the present invention is characterized in that the information terminal device displays a real estate appraisal report, acquires an operation log corresponding to the operation on the displayed real estate appraisal report, transmits the acquired operation log to a server, the server generates operation information from the operation log transmitted from the information terminal device, and stores the generated operation information in a storage unit. [Effects of the Invention]

[0007] As described above, the present invention has the effect of being able to grasp the customer's reaction to the real estate appraisal report. [Brief explanation of the drawing]

[0008] [Figure 1] This is an explanatory diagram showing an example of the sales support system 10 according to this embodiment. [Figure 2] This is a block diagram showing the configuration of the server device 100 according to this embodiment. [Figure 3] This is an explanatory diagram showing an example of the data structure of the client information storage unit 121. [Figure 4] This is a flowchart showing the operation information acquisition process performed by the server device 100 and the information terminal device 200. [Figure 5] This flowchart shows the expected value calculation process performed by the server device 100. [Figure 6] This is an explanatory diagram showing an example of a screen displaying the management status of real estate appraisal reports. [Modes for carrying out the invention]

[0009] The following describes embodiments of the sales support system, server equipment, sales support method, and sales support program according to the present application, with reference to the attached drawings. The following description is illustrative of embodiments of the present application, and the sales support system, server equipment, sales support method, and sales support program according to the present application are not limited to these embodiments.

[0010] Figure 1 is an explanatory diagram showing an example of a sales support system 10 according to this embodiment. The sales support system 10 comprises a server device 100 and information terminal devices 200-1 to n (hereinafter referred to as information terminal device 200).

[0011] The server device 100 is a computer that acquires operational information regarding the real estate appraisal report sent to the client and calculates the expected probability of contract acquisition based on the operational information regarding the real estate appraisal report, and is a so-called web server. The server device 100 sends and receives data with the information terminal device 200 via network N. Network N is any communication network or combination thereof, such as the Internet, intranet, LAN (Local Area Network), VPN (Virtual Private Network), mobile communication network, etc., and may be partially or entirely wired or wireless.

[0012] Here, we will explain the real estate appraisal report. A real estate appraisal report is a document that contains the appraised price of a property, etc., prepared by a real estate company or other entity seeking to obtain a real estate brokerage contract in response to an appraisal request from a client who owns real estate such as an apartment. Traditionally, real estate appraisal reports are often prepared on paper, but in this embodiment, they are prepared as a web page. This allows the client to freely display the real estate appraisal report on the information terminal device 200 at any time by specifying the URL (Uniform Resource Locator) of the client's dedicated web page. In this specification, the real estate appraisal report is also referred to as the "appraisal report."

[0013] The information terminal device 200 is a computer operated by the client, and can be a smartphone, tablet, personal computer, etc. The information terminal device 200 can be any device that can launch a browser and display and operate the real estate appraisal report.

[0014] Figure 2 is a block diagram showing the configuration of the server device 100 according to this embodiment. The server device 100 includes a communication unit 110, a storage unit 120, a control unit 130, and an operation display unit 140.

[0015] The communication unit 110 responds to a request for a real estate appraisal report sent from the information terminal device 200 by sending the web page of the real estate appraisal report to the information terminal device 200. The communication unit 110 receives the operation log for the real estate appraisal report sent from the information terminal device 200 and stores it in the operation log storage unit 122.

[0016] The memory unit 120 stores the client information memory unit 121, the operation log memory unit 122, the operation information memory unit 123, the learning model 124, and the feature quantity memory unit 125.

[0017] Figure 3 is an explanatory diagram showing an example of the data structure of the client information storage unit 121. The client information storage unit 121 stores information about the client of the real estate appraisal report. The client information storage unit 121 stores the real estate appraisal report ID, property information, client information, the real estate appraisal report URL indicating the storage location of the real estate appraisal report created in response to the request, the appraisal report creation date, and other information.

[0018] The real estate appraisal report ID is information that uniquely identifies a real estate appraisal report. The property information is information about the property for which the real estate appraisal report is created, such as property name, location, transportation, land rights, usage area, etc. The requester information is information about the requester, such as name, family composition, age, gender, and annual income. The real estate appraisal report URL is a URL indicating the storage location of the real estate appraisal report created in response to a request, and the appraisal report creation date is the date when the real estate appraisal report was created. As other information, a requester ID that uniquely identifies the requester, an update date if the real estate appraisal report is updated, and the content described in the real estate appraisal report (such as the appraisal price and the cases used as the basis for the appraisal) may be stored.

[0019] The operation log storage unit 122 stores, for each real estate appraisal report, the operation log for the web page of the real estate appraisal report transmitted from the information terminal device 200. The operation log includes at least the operation date and time and the operation content.

[0020] The operation information storage unit 123 stores, for each real estate appraisal report, the operation information generated from the operation log stored in the operation log storage unit 122. As an example, the operation information stores the cumulative viewing time of the real estate appraisal report, the cumulative viewing time for each item of the real estate appraisal report, the viewing time zone of the real estate appraisal report, the number of instructions for each item of the real estate appraisal report, the number of contacts to the person in charge of the real estate appraisal report, the frequency and timing, the number of characters of the contact message to the person in charge of the real estate appraisal report, the number of received contact messages, the presence or absence of a predetermined keyword included in the contact message, etc. Note that the instructions for each item of the real estate appraisal report include clicking on an item button or a link to a page displaying the content of the item, selecting an item by gazing at the item for a predetermined time, and instructing an item by voice.

[0021] The learning model 124 is a learning model that is trained to input operation information for an appraisal report of real estate and output features for estimating the degree of expectation of obtaining a contract (hereinafter referred to as "contract acquisition expectation degree"), and a neural network or the like can be adopted. The learning model 124 may include information such as parameters of the learning model. Further, the learning model 124 may be trained to input any one or more of property information, client information, and market condition information in addition to the operation information for the appraisal report of real estate and output features.

[0022] The feature storage unit 125 inputs operation information for an appraisal report of real estate for which a contract has been obtained into the learning model and stores the features output from the learning model. The feature storage unit 125 may input operation information, property information, client information, and market condition information corresponding to the operation information and the learning model into the learning model trained with any one or more of property information, client information, and market condition information in addition to the operation information for the appraisal report of real estate and store the features output from the learning model.

[0023] The control unit 130 will be described. The control unit 130 includes an operation information generation unit 131, a client information acquisition unit 132, a market condition information acquisition unit 133, a feature output unit 134, a similarity calculation unit , an expectation degree calculation unit 136, an appraisal report update unit 137, and a learning model generation unit 138.

[0024] The operation information generation unit 131 generates operation information from the operation log for each appraisal report of real estate stored in the operation log storage unit 122, and stores the generated operation information in the operation information storage unit 123 in association with the appraisal report of real estate.

[0025] The client information acquisition unit 132 acquires property information and client information associated with an appraisal report of real estate (appraisal report ID) from the client information storage unit 121. The client information acquisition unit 132 acquires operation information associated with the appraisal report of real estate from the operation information storage unit 121.

[0026] The market information acquisition unit 133 acquires market information. More specifically, the market information acquisition unit 133 acquires market information by receiving input of market information via the operation display unit 140, or by crawling websites that publish statistics and information related to real estate, such as those of the government, think tanks, newspapers, and real estate companies. Examples of market information include price information of the property being appraised and properties in the vicinity of its location, price trends and supply and demand balance of real estate in Japan and in specific regions, the Nikkei average, and real estate-related events in Japan and overseas.

[0027] The feature output unit 134 inputs operational information regarding the real estate appraisal report into a learning model and outputs features from the learning model. The feature output unit 134 inputs operational information regarding the real estate appraisal report, along with one or more of the following: property information, client information, and market information, into a learning model that has been trained on these two types of information, and outputs features from the learning model.

[0028] The similarity calculation unit 135 calculates the similarity between the features output from the learning model by the feature output unit 134 and the features stored in the feature storage unit 125. As an example, the similarity is calculated as cosine similarity, which is the cosine value of the angle between two vectors, where each feature is treated as a vector.

[0029] The expectation calculation unit 136 calculates the expected value of contract acquisition based on the calculated similarity. For example, if the similarity calculation unit 135 calculates the similarity as cosine similarity, the value is between -1 and 1, so it is converted to a value that is easier to understand as an expectation (for example, between 0 and 100).

[0030] The appraisal report update unit 137 updates the real estate appraisal report when it receives a request for a real estate appraisal report from the information terminal device 200 and a predetermined period of time has elapsed since the previous real estate appraisal report web page was sent.

[0031] The learning model generation unit 138 generates a learning model trained on the operation information (property information, client information, market information) of the real estate appraisal report for which a contract was obtained. The learning model generation unit 138 stores the generated learning model in the learning model 124.

[0032] The operation display unit 140 includes an input unit and a display unit. The input unit receives instructions for a real estate appraisal report, and the display unit displays information related to the received real estate appraisal report, such as the expected probability of securing a contract, in association with the real estate appraisal report.

[0033] Next, the information terminal device 200 will be described. As mentioned above, the information terminal device 200 is a computer operated by the client requesting the creation of a real estate appraisal report, and has general functions. The information terminal device 200 includes a communication unit 210, a storage unit 220, a control unit 230, and an operation display unit 240 (not shown). The communication unit 210 receives the web page of the real estate appraisal report from the server device 100. The operation display unit 240 displays the web page of the real estate appraisal report. The operation display unit 240 accepts input for operations on the real estate appraisal report and displays the operation results.

[0034] The following describes the operation information acquisition process performed by the sales support system 10 configured as described above. Figure 4 is a flowchart showing the operation information acquisition process procedure performed by the server device 100 and the information terminal device 200.

[0035] The operation display unit 240 of the information terminal device 200 receives instructions for a real estate appraisal report (step S401). For example, it receives instructions for a real estate appraisal report by providing a URL included in an email sent to the client. The communication unit 210 sends the request for the real estate appraisal report to the server device 100 (step S402).

[0036] The communication unit 110 of the server device 100 receives a request for a real estate appraisal report and stores the request as an operation log in the operation log storage unit 122 (step S403). The appraisal report update unit 137 determines whether a predetermined period has elapsed since the last real estate appraisal report was created (step S404). More specifically, the appraisal report update unit 137 determines whether a predetermined period has elapsed since the appraisal report creation date (or last update date) stored in the client information storage unit 121. If it is determined that a predetermined period has elapsed since the last real estate appraisal report was created (step S404: Yes), the market information acquisition unit 133 acquires current market information, and the appraisal report update unit 137 updates the real estate appraisal report based on the acquired market information (step S405). As a result, the client can obtain an appraisal result that takes into account the current market information. If it is determined that the prescribed period has not elapsed since the last real estate appraisal report was created (Step S404: No), the real estate appraisal report will not be updated, and the process will proceed to Step S406.

[0037] The communications unit 140 obtains a real estate appraisal report (step S406) and transmits the web page of the obtained real estate appraisal report to the information terminal device 200 (step S407).

[0038] The communication unit 210 of the information terminal device 200 receives the web page of the real estate appraisal report, and the operation display unit 240 displays the real estate appraisal report (step S408). The operation display unit 240 accepts instructions regarding the real estate appraisal report (step S409). Instructions regarding the real estate appraisal report include, for example, instructions to scroll the web page, instructions to click on links for each item in the real estate appraisal report, instructions to click on links to other web pages, instructions to download files, instructions to contact the person in charge, etc. The communication unit 210 sends an operation log including the date and time of the operation and the details of the operation to the server device 100 (step S410). The communication unit 110 of the server device 100 receives the operation log and stores the received operation log in the operation log storage unit 122 (step S411).

[0039] The operation display unit 240 of the information terminal device 200 displays a screen corresponding to the operation (step S412). The operation display unit 240 determines whether the display of the real estate appraisal report has finished (step S413). Whether the display of the real estate appraisal report has finished is determined by whether the web page has been closed. If it is determined that the display of the real estate appraisal report has not finished (step S413: No), the unit accepts instructions for the real estate appraisal report in step S409. If it is determined that the operation for the real estate appraisal report has finished (step S413: Yes), the communication unit 210 transmits a message to the server device 100 indicating that the display of the real estate appraisal report has finished (step S414).

[0040] The communication unit 110 of the server device 100 receives a notification that the display of the real estate appraisal report has finished, and the operation information generation unit 131 generates operation information based on the operation log stored in the operation log storage unit 122 (step S415). The operation information generation unit 131 stores the operation information in the operation information storage unit 123 (step S416).

[0041] In this way, by generating a real estate appraisal report as a web page and displaying it on the information terminal device 200 according to the client's instructions, it is possible to obtain information about the client's interaction with the web page. This allows us to understand the client's reaction to and level of interest in the real estate appraisal report in the form of interaction information, unlike when a real estate appraisal report is provided on paper.

[0042] Furthermore, if the server device 100 has executed the process of storing the operation information in step S416 in the operation information storage unit 123, and the operation display unit 140 has received an instruction to display operation information for the real estate appraisal report, the server device 100 may retrieve the operation information associated with the real estate appraisal report from the operation information storage unit 123 and display the retrieved operation information.

[0043] Next, we will explain the process of calculating the expected probability of securing a contract from operational information (operational information, property information, client information, market information). Figure 5 is a flowchart of the expected probability calculation process performed by the server device 100.

[0044] The operation display unit 140 of the server device 100 receives instructions for real estate appraisal reports (step S501). For example, it may request all real estate appraisal reports that have the potential to be contracted, or it may narrow down the selection by sales representative or the period during which the real estate appraisal reports were created. Alternatively, instead of receiving instructions for real estate appraisal reports from the operation display unit 140, instructions for real estate appraisal reports may be received from an information terminal device operated by a representative of a real estate company connected via the network N. The client information acquisition unit 132 acquires operation information (property information, client information) for each real estate appraisal report (step S502). More specifically, the client information acquisition unit 132 acquires operation information associated with the real estate appraisal report from the operation information storage unit 123. The client information acquisition unit 132 acquires property information and client information associated with the real estate appraisal report from the client information storage unit 121.

[0045] The market information acquisition unit 133 acquires market information (step S503). The feature output unit 134 inputs operation information (property information, client information, market information) for each real estate appraisal report into the learning model (step S504). The feature output unit 134 outputs features from the learning model (step S505).

[0046] The similarity calculation unit 135 calculates the similarity based on the features output from the learning model and the features stored in the feature memory unit 125 (step S506). The expectation calculation unit 136 calculates the expected value of contract acquisition from the similarity (step S507).

[0047] The operation display unit 140 displays the real estate appraisal report and the expected probability of securing a contract in association (step S508). Figure 6 is an explanatory diagram showing an example of a screen that shows the management status of the real estate appraisal report. The display screen 61 shows the client name, appraisal type, appraisal target, real estate appraisal report creation date and last update date, expected probability of securing a contract, and a link to the appraisal report.

[0048] In this way, the likelihood of securing a contract can be quantified and confirmed by comparing the features of real estate appraisal reports that have resulted in past contracts with the features of real estate appraisal reports currently being sought for contract acquisition. This allows for a more proactive or appropriate approach to cases with a high probability of securing a contract, thereby increasing the chances of success.

[0049] Next, the process of generating the learning model described above will be explained. The learning model generation unit 138 obtains operation information for real estate appraisal reports that have resulted in contracts from the operation information storage unit 123 as training data. The learning model generation unit 138 inputs multiple training data into the learning model and trains the learning model to output features related to contract acquisition. The learning model generation unit 138 may also train the learning model to correctly determine whether or not a contract was acquired by using verification data that includes operation information for real estate appraisal reports that have resulted in contracts and operation information for real estate appraisal reports that have not resulted in contracts. This can improve the accuracy of the expected value of contract acquisition. In addition, in accordance with the expected value determination process shown in Figure 5 above, one or more of the following may be used as training data in addition to operation information for real estate appraisal reports: property information, client information, and market information.

[0050] In this way, by generating a learning model that has been trained on operational information related to real estate appraisal reports that have resulted in contracts, it is possible to calculate the expected probability of contract acquisition based on the client's response to the real estate appraisal report. Furthermore, by generating a learning model that includes real estate property information, client information, and market information, it is possible to calculate the expected probability of contract acquisition that also takes other factors into consideration.

[0051] The hardware configuration of the server device 100 and information terminal device 200 according to the above-described embodiment is that of a normal computer, including one or more processors such as a CPU (Central Processing Unit), MPU (Micro-Processing Unit), or GPU (Graphics Processing Unit), and external storage devices such as ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), flash memory, SSD (Solid State Drive), and a communication control device. The above-described configuration and functions are realized by the CPU and other components reading and operating programs stored in the ROM, RAM, HDD, etc. Note that each function may be realized by electronic circuits such as ASIC (Application Specific Integrated Circuit) or PLD (Programmable Logic Device).

[0052] The programs that run on the server unit 100 and the information terminal device 200 may be stored on a computer connected to a network such as the Internet and provided by downloading them via the network, or they may be provided as installable or executable files recorded on a computer-readable storage medium such as a CD-ROM, DVD, USB memory, or SD card. Furthermore, the programs that implement the above-mentioned functions and processes may be provided in the form of APIs (Application Programming Interfaces), SaaS (Software as a Service), or cloud computing.

[0053] The present invention is not limited to the embodiments described above, and does not necessarily have to be physically configured as shown. Furthermore, the present invention can be configured by functionally or physically dividing, integrating, replacing, modifying, or deleting all or part of the components described in the embodiments, depending on various loads and usage conditions. [Explanation of Symbols]

[0054] 100…Server device, 110…Communication unit, 120…Storage unit, 121…Client information storage unit, 122…Operation log storage unit, 123…Operation information storage unit, 124…Learning model, 125…Feature storage unit, 130…Control unit, 131…Operation information generation unit, 132…Client information acquisition unit, 133…Market information acquisition unit, 134…Feature output unit, 135…Similarity calculation unit, 136…Expected value calculation unit, 137…Appraisal report update unit, 138…Learning model generation unit, 200…Information terminal device

Claims

1. A sales support system comprising an information terminal device operated by a client requesting a real estate appraisal, and a server connected to the information terminal device via a network, The aforementioned information terminal device is A means for displaying a real estate appraisal report prepared in response to the request of the aforementioned client, An operation log acquisition means for acquiring an operation log corresponding to the operation performed by the client on the real estate appraisal report displayed by the appraisal report display means, A transmission means for transmitting the operation log acquired by the operation log acquisition means to the server, The aforementioned server, A feature storage means that uses a learning model trained to take operational information for a real estate appraisal report as input and output feature quantities, inputs operational information for a real estate appraisal report that has been acquired into the learning model, and stores the feature quantities output from the learning model, Operation information generation means for generating operation information from the operation log transmitted from the information terminal device, A feature output means inputs the operation information generated by the operation information generation means into the learning model and outputs the feature quantities, Similarity calculation means for calculating the similarity between the feature quantities output by the feature quantity output means and the feature quantities stored in the feature quantity storage means, An expectation calculation means for calculating the expected contract acquisition rate, which is the expected rate of acquiring a contract from the client, based on the aforementioned similarity, A display means that displays the expected contract acquisition rate of the client, corresponding to the real estate appraisal report, A sales support system equipped with these features.

2. A server device connected via a network to an information terminal device operated by a client requesting a real estate appraisal, A feature storage means that uses a learning model trained to take operational information for a real estate appraisal report as input and output feature quantities, inputs operational information for a real estate appraisal report that has been acquired into the learning model, and stores the feature quantities output from the learning model, A receiving means for receiving operation logs corresponding to operations performed by the client on a real estate appraisal report prepared at the request of the client, Operation information generation means for generating operation information from the operation log received by the receiving means, A feature output means inputs the operation information generated by the operation information generation means into the learning model and outputs the feature quantities, Similarity calculation means for calculating the similarity between the feature quantities output by the feature quantity output means and the feature quantities stored in the feature quantity storage means, An expectation calculation means for calculating the expected contract acquisition rate, which is the expected rate of acquiring a contract from the client, based on the aforementioned similarity, A display means that displays the expected contract acquisition rate of the client, corresponding to the real estate appraisal report, A server device equipped with the following features.

3. The aforementioned operation information is, Cumulative viewing time of the aforementioned real estate appraisal report, The cumulative viewing time for each item in the aforementioned real estate appraisal report, Viewing hours for the aforementioned real estate appraisal report, The number of times instructions are given for each item in the aforementioned real estate appraisal report, The number, frequency, and timing of contact with the person in charge of the aforementioned real estate appraisal report, The number of characters in the message sent to the person in charge of the aforementioned real estate appraisal report, The number of times the aforementioned contact message was received, and the presence or absence of a predetermined keyword included in the aforementioned contact message. The server device according to claim 2, which is at least one of the following.

4. The aforementioned learning model is further trained to take real estate property information as input and output the aforementioned features, The feature memory means uses a learning model that has been trained to take the operation information and the property information as input and output the feature quantities, inputs the operation information and the property information for which a contract was obtained into the learning model, and stores the feature quantities output from the learning model. The server device according to claim 2 or 3, wherein the feature output means inputs the operation information and the property information of the real estate described in the real estate appraisal report into the learning model and outputs the feature.

5. The aforementioned learning model is further trained to take market information as input and output the aforementioned features, The feature memory means uses a learning model that has been trained to take the operation information, property information, and market information as input and output the feature, inputs the operation information, property information, and market information for which a contract was obtained into the learning model, and stores the feature output from the learning model. The server device according to claim 4, wherein the feature output means inputs the operation information, the property information of the real estate described in the real estate appraisal report, and the market information into the learning model and outputs the feature.

6. A method performed by a computer connected via a network to an information terminal device operated by a client requesting a real estate appraisal, The computer includes a feature memory unit that inputs operational information for a real estate appraisal report into a learning model that has been trained to take operational information for a real estate appraisal report as input and output feature quantities, and stores the feature quantities output from the learning model. A receiving step that receives an operation log corresponding to the operations performed by the client on the real estate appraisal report prepared at the request of the client, An operation information generation step that generates operation information from the operation log received in the reception step, A feature output step in which the operation information generated by the operation information generation step is input to the learning model and the feature quantities are output, A similarity calculation step which calculates the similarity between the feature quantities output by the feature quantity output step and the feature quantities stored in the feature quantity storage unit, An expectation calculation step to calculate the expected contract acquisition rate, which is the expected rate of acquiring a contract from the client, based on the aforementioned similarity, A display step that shows the expected contract acquisition rate of the client for whom the real estate appraisal report is displayed, in correspondence with the real estate appraisal report, Sales support methods, including those mentioned above.

7. A sales support program characterized by causing a computer to execute the sales support method described in claim 6.

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