JP7512635B2 - Housing sales support device, housing sales support system, housing sales support method, and housing sales support program - Google Patents

Description

The present invention relates to a housing sales support device for supporting sales activities related to housing.

As a system for supporting sales activities, Patent Document 1 discloses a technology that accumulates sales know-how and presents the most effective sales activity plan for each sales situation. Patent Document 2 discloses a technology that predicts customer revenue by taking into account the expected probability of a contract for a customer's product, the expected transaction amount when a contract for the product is concluded, and the impact on the increase or decrease in the transaction amount of other contracted products due to the product contract.

In addition, Patent Document 3 and Patent Document 4 also disclose technologies related to the above.

JP 10-124584 A JP 2004-139196 A JP 2002-24523 A JP 2002-149958 A

In Patent Document 1, sales know-how rules are generated and stored in a database. In Patent Document 2, the customer's future profits for each product are summed with the predicted increase or decrease in profits for other products at the time of the contract for that product, and the net profit for each product is predicted for the customer. However, neither of these methods are specialized for sales related to housing, and the above methods may not be applicable to housing sales, where sales are made to a single customer over several decades. In other words, the methods in Patent Documents 1 and 2 have the problem of not being able to adequately support salespeople engaged in housing sales activities.

This disclosure has been made in consideration of the above problems, and one of its objectives is to provide a housing sales support device, etc., for supporting sales activities related to housing.

In view of the above problems, a housing sales support device according to a first aspect of the present disclosure comprises:
A selection means for presenting products to be extracted from at least one of product information related to new construction, product information related to remodeling, and product information related to aftercare services to a potential customer and allowing the customer to select the product;
an integrated information storage means for storing at least one of information on a customer, information on the merchandise, information on the customer's residence, information on a salesperson who deals with the customer, information on contact between the customer and the salesperson, and informal information on informal activities of the customer;
an analysis means for analyzing customers related to the selected product based on information necessary for extracting the customer candidates linked to the selected product, the information being acquired from the integrated information storage means;
The system further comprises an output means for outputting, as a result of the analysis, the customer candidates that satisfy a predetermined condition in terms of the relevance to the product.

A housing sales support system according to a second aspect of the present disclosure includes:
The housing sales support device described above; a robot communicatively connected to the housing sales support device and capable of interacting with customers via voice or text;
The housing sales support device further includes a camera that is communicatively connected to the housing sales support device and is equipped to capture images of the customer's reaction to the product.

A housing sales support method according to a third aspect of the present disclosure includes:
presenting products to be used to extract potential customers from at least one of product information related to new construction, product information related to remodeling, and product information related to aftercare services, and allowing the customer to select the product;
Analyzing customers related to the selected product based on information necessary for extracting the customer candidates linked to the product, the information being obtained from an integrated information storage means that stores at least one of information on the customer, information on the product, information on the customer's residence, information on the salesperson who deals with the customer, information on contact between the customer and the salesperson, and informal information on the customer's activities;
As a result of the analysis, the customer candidates who satisfy a predetermined condition in terms of the relevance to the product are output.

A housing sales support program according to a fourth aspect of the present disclosure includes:
presenting products to be used to extract potential customers from at least one of product information related to new construction, product information related to remodeling, and product information related to aftercare services, and allowing the customer to select the product;
Analyzing customers related to the selected product based on information necessary for extracting the customer candidates linked to the product, the information being obtained from an integrated information storage means that stores at least one of information on the customer, information on the product, information on the customer's residence, information on the salesperson who deals with the customer, information on contact between the customer and the salesperson, and informal information on the customer's activities;
The computer is caused to output the customer candidates that satisfy a predetermined condition in terms of the relevance to the product as a result of the analysis.

The housing sales support program may be stored on a non-transitory recording medium that is readable and writable by a computer.

The present invention provides a housing sales support device and the like for supporting sales activities related to housing.

1 is a block diagram showing a configuration example of a housing sales support system according to a first embodiment of the present disclosure. FIG. 2 is a schematic diagram showing the concept of data centralization in the housing sales support system. 1 is a block diagram showing a configuration example of a housing sales support device according to a first embodiment of the present disclosure. FIG. 2 is a diagram illustrating an example of a data structure of customer information. FIG. 13 is a diagram illustrating an example of a state transition of a customer phase. FIG. 13 is a diagram illustrating an example of a data structure of a customer type. FIG. 4 is a diagram illustrating an example of a data structure of product information. FIG. 2 is a diagram illustrating an example of a data structure of residence information. FIG. 11 is a diagram illustrating an example of a data structure of contact information. FIG. 4 is a diagram illustrating an example of a data structure of sales information. FIG. 13 is a diagram illustrating an example of a data structure of unofficial information. FIG. 13 is a diagram illustrating an example of a prediction model. FIG. 13 is a diagram illustrating an example of a data structure of a customer list. 13 is a flowchart showing an example of an information input operation of the housing sales support device. 13 is a flowchart showing an example of a customer list output operation by the housing sales support device. FIG. 11 is a block diagram showing a configuration example of a housing sales support device according to a second embodiment of the present disclosure. FIG. 2 is a block diagram showing an example of the configuration of an information processing device to which each embodiment can be applied. FIG. 1 is a block diagram showing an example of the configuration of a typical residential sales system.

A conceptual diagram of sales activities related to housing is shown in Figure 18. Sales activities related to housing include, for example, new construction, remodeling, and aftercare services (hereinafter also referred to as aftercare). Here, new construction mainly refers to the business of newly built detached houses (hereinafter also referred to as new construction), and refers to, for example, custom-built houses, prefabricated houses, and rebuilding houses. Remodeling refers to remodeling after the sale of a new house, such as adding rooms, building a new wooden deck, and replacing water-related products such as kitchen and bath units. Products refer to all products sold to customers, including the newly built house itself and kitchen systems (sink and counter set). Aftercare refers to guarantees for the target products after the sale of a new house, and the purchase of consumables and services. Note that products installed in a new house (for example, garbage disposers and ventilation fans) may also be included in the subject of an aftercare warranty. For after-sales, a warranty contract (determining the type of failure covered by the warranty and the warranty period) is concluded when a new building is sold or commercial materials are installed, so if the repair or part replacement is within the warranty, it will be free of charge or at a discounted price, and if it is not within the warranty, there will be a charge.

In sales activities, salespeople from the new construction business, the renovation business, and the after-sales business often each deal with customers. Each business keeps customer information in its own database. Furthermore, sales quality differs depending on the salesperson, which can lead to differences in sales performance.

The customers targeted for sales in these businesses are often the same person or members of the same household, although the timing of the interactions may differ. For example, a customer may purchase a new house, then purchase aftercare services, and then remodel the house, or the husband may purchase a new house, and the wife may take the lead in remodeling the kitchen (water area) and storage. However, because the data held by each salesperson is not centralized, customers have to disclose the same information and give the same explanation to different salespersons each time they purchase a new house, remodel, or aftercare services, which is cumbersome. This is a characteristic of housing-related businesses, because the period between a customer's purchase of a service and the next purchase of a service is long, and the services purchased at each phase are often different. For example, after purchasing a new house, it may take several decades to rebuild and then purchase a new house again, making it difficult to keep customer information fresh and to use it across phases at the right time. However, even for customers who purchase a newly built house, aftercare services and renovations are often carried out over a span of several years, and even if the sales representative changes, by integrating and centrally managing customer information, all sales representatives can see all of the purchase history (construction history) of the customer. It is also convenient for customers not to have to repeatedly explain the same thing to each sales representative. Also, for sales representatives, if they can view the purchase history (construction history) and customer information of the customers they are responsible for, customer service becomes smoother, and even if the sales representative in charge of a certain customer is replaced due to a transfer, the customer information held by each sales representative can be standardized between sales representatives, which ultimately leads to standardized sales quality.

Whether it is the purchase of a new house, renovation materials, or aftercare services, providing interested customers with information on the right products at the right time will lead to increased sales. In order to enable a salesperson to propose the products desired by a customer at the right time, it is effective to collect and analyze information on the customer's official or unofficial activities. Here, official activity is information that a customer transmits to a salesperson or the company to which the salesperson belongs. For example, visiting a housing exhibition, multiple model rooms in the exhibition, and a product showroom (hereinafter referred to as a housing exhibition, etc.), talking with a salesperson, or answering a personal questionnaire at the request of a salesperson corresponds to official activity. Information obtained from official activity is called official information. Unofficial activity is information that a customer does not transmit to a salesperson, and refers to information transmitted by a customer on SNS (social networking service), the customer's open rate for e-mails sent by a salesperson, the customer's web browsing history, etc. Information obtained from unofficial activity is called unofficial information.

Each embodiment of the present disclosure describes a method for improving sales efficiency by centrally managing information about customers and providing customers with information about appropriate products at the appropriate time.

The following explanation focuses on detached houses, but it can also be applied to apartment complexes and other collective housing.

Each embodiment will be described in detail below with reference to the drawings. In the following description of the drawings, identical or similar parts are given identical or similar reference symbols. However, the drawings show the configuration of the embodiment of the present invention in a simplified manner. Furthermore, the embodiment of the present invention described below is merely an example, and can be modified as appropriate within the scope of the same essence.

First Embodiment
(Housing Sales Support System)
Hereinafter, a housing sales support system 100 according to a first embodiment of the present disclosure will be described with reference to the drawings.

Figure 1 is a diagram showing the general configuration of a housing sales support system 100. As shown in Figure 1, the housing sales support system 100 includes at least an automatic response robot 1, a terminal 4 provided by a sales representative, a camera 6, an online information collection robot 7, and a housing sales support device 10.

The housing sales support device 10 includes an integrated information storage unit 2 and a prediction model storage unit 3. When salespeople for each business acquire official information, such as customer information, home information, contact information with customers, sales information about salespeople, and product information, they store the information in the integrated information storage unit 2 via their terminals 4.

The automatic response robot 1 is an artificial intelligence model that has undergone machine learning training regarding home sales, and it interacts (converses) with customers at home exhibitions, renovation exhibitions, and online home sales websites, and stores official customer information obtained during the interaction in the integrated information storage unit 2. In addition, the automatic response robot 1 may also perform sales activities to customers. For example, sales activities for inexpensive products or products that are sold frequently may be performed by the automatic response robot 1, and sales activities for expensive products or products that are sold infrequently may be performed by sales staff. It is preferable that the content of the automatic response robot 1's interactions with customers, the content and results of sales activities are fed back to allow the artificial intelligence model to learn.

The camera 6 is communicatively connected to the housing sales support device 10 and is positioned so that it can capture the customer's reaction to the merchandise. The camera 6 captures at least one of the customer's facial direction, hand movements, line of sight, and movement line at the location where the merchandise is displayed, such as a housing exhibition center. The captured video is analyzed by the video analysis unit 18, which will be described later, and then stored in the integrated information storage unit 2 as unofficial information.

The online information collection robot 7 is a program, such as a crawler, that periodically acquires documents and images on the web and automatically stores them in a database. The online information collection robot 7 acquires information about the customer's informal online activities, such as the Internet, such as information posted on SNS via the customer's terminal 5, the customer's open rate for emails sent by the sales representative, the customer's web browsing history (date and time when the customer viewed a website, the number of times the customer viewed the website, the duration of the visit, etc.), and the customer's web transition history (information indicating which website the customer transitioned from a website to another). The acquired informal activity information is analyzed by the online information analysis unit 19, which will be described later, and then stored in the integrated information storage unit 2 as informal information.

The prediction model storage unit 3 stores a prediction model for predicting customer purchases. The prediction model used is one that has already been trained with teacher data. Data on the results of the purchase prediction (whether customers on the predicted customer list actually purchased the product, etc.) may be fed back to the prediction model. The prediction model is a model that has been machine-learned using data on home sales, and performs contract prediction for a certain product based on official and unofficial information on customers stored in the integrated information storage unit 2, and outputs the prediction result as a customer list. The customer list contains information on customers who are likely to purchase a certain product at a specified time. Each salesperson obtains a customer list for the product they are selling via their terminal 4, and provides the customer with the appropriate product at the appropriate time by conducting sales activities to the customers presented on the customer list. This improves the sales success rate.

The concept of data centralization in the housing sales support system 100 is shown in Figure 2. In order to provide customers with purchasing intentions with appropriate product information at the appropriate time, the housing sales support system 100 centrally manages official and unofficial customer information at multiple stages in the integrated information storage unit 2. The multiple stages include, for example, the stage of identifying potential customers, the stage up to the purchase of the product (such as a newly built detached house), the stage after the product is handed over to the purchase of aftercare services, and the stage from the purchase of renovation products to the sale of the product (such as a newly built detached house).

For example, in the stage of identifying potential customers, the online information gathering robot 7 acquires information about the customer from unofficial information such as social media and e-mails sent by the customer via the terminal 5, and stores the information in the integrated information storage unit 2.

At the stage leading up to the purchase of a product, salespeople create customer charts for customers (potential customers) visiting the exhibition hall, and have the customers fill out questionnaires and the like as part of self-counseling. This information is stored as official information in the integrated information storage unit 2 via terminal 4 or the like. In addition, video footage of customers' attitudes toward the products in the exhibition hall, taken with a camera installed in the exhibition hall, may be analyzed, and the analysis results may be stored as unofficial information in the integrated information storage unit 2.

During the stage from the delivery of the merchandise to the purchase of the aftercare service, the online information collection robot 7 acquires information about the customer from unofficial information such as SNS and e-mail sent by the customer via the terminal 5, and stores this information in the integrated information storage unit 2. Furthermore, the automatic response robot 1 stores official information obtained by chatting with the customer online in the integrated information storage unit 2. Information on the conversation between the customer and the operator at the call center may also be stored in the integrated information storage unit 2 as official information.

From the stage of purchasing renovation materials to selling the materials (such as a newly built house), the salesperson creates a customer chart based on the customer's requests and has the customer fill out a questionnaire or other form of self-counseling. This information is stored in the integrated information storage unit 2 as official information via the terminal 4, etc.

The prediction model may perform prediction processing not only using information from the integrated information storage unit 2, but also using external public data or existing systems.

(Housing sales support device)
3, the housing sales support device 10 includes an automatic response control unit 11, a data management unit 12, a selection unit 13, an acquisition unit 14, a processing unit 15, an analysis unit 16, an output unit 17, a video analysis unit 18, an online information analysis unit 19, a prediction model storage unit 3, and an integrated information storage unit 2. The integrated information storage unit 2 includes a customer information storage unit 21, a product information storage unit 22, a residence information storage unit 23, a contact information storage unit 24, a sales information storage unit 25, and an unofficial information storage unit 26.

The customer information storage unit 21 stores customer information. As shown in FIG. 4, customer information is data consisting of items such as key information, attribute information, additional information, and rank information. Key information is a number that can be the primary key of the data, and includes, for example, a customer number, a business number, and a residence number.

A customer number is an identifier (ID) that can uniquely identify a customer. A sales number is an ID that can uniquely identify a salesperson. A mansion number is an ID that can uniquely identify a mansion owned by a customer.

Attribute information is information that indicates the attributes of a customer, and is data consisting of items such as name, gender, age, current address, past addresses, place of employment, annual income, family composition, and owned vehicles. Attribute information may also include the customer's email address and telephone number. Past addresses are information that indicates where the customer previously lived, and do not have to be a specific address. For example, they may be the prefecture of origin. Owned vehicles are information that indicates the type of car owned, but if the customer does not own a car, this may be left blank.

Additional information is additional information about a customer that is not included in the attribute information above, and is data consisting of items such as cost plan, land ownership, desired completion date, competitors, introducer, questionnaire responses, and purchase history. Cost plan is a plan for fundraising for purchasing merchandise, and specifically refers to owned assets and the results of bank loan screening. Land ownership indicates whether the customer owns land or not. For example, if the customer owns land, details such as the address and square meters may be entered, and if the customer does not own land, the field may be left blank. Competitor indicates whether a competitor is selling to the customer. For example, if there is a competitor, the company name, sales representative name, etc. may be entered, and if there is no competitor, the field may be left blank. There may be multiple competitors. Introducer indicates whether there is another customer who introduced the customer. For example, if there is an introducer, information (customer information) such as the introducer's name may be entered, and if there is no introducer, the field may be left blank. There may be multiple introducers. Survey responses are items in which customers enter information obtained through interviews on paper or electronic media about their personal wishes and conditions, products they are interested in, etc. Purchasing history is the history of products (such as newly built detached houses) purchased from your company or other companies.

Rank information is information for categorizing customers, and is data consisting of items such as customer rank, customer phase, and customer type. Customer rank refers to the rank of a customer's attributes when purchasing housing materials. For example, when purchasing a new detached house, a customer who has a thorough cost plan or who owns land will be ranked higher, and conversely, a customer who is unable to make a cost plan and does not own land will be ranked lower.

Customer phase information is information that indicates whether a customer is a potential customer (customer phase) for a product among new construction, remodeling, and after-sales. For example, as shown in FIG. 5, a customer who does not own a new home and is considering a new home is in Phase A, a customer who owns a new home and is considering an after-sales service is in Phase B, a customer who is considering remodeling after an after-sales service is in Phase C, a customer who is considering remodeling another part after a remodeling service is in Phase D, and a customer who is considering rebuilding the new home again after a remodeling service is in Phase E. In addition, a customer who is considering rebuilding the new home again after an after-sales service is in Phase F, a customer who is considering an after-sales service after a remodeling service is in Phase G, and a customer who is considering remodeling after a new home (including rebuilding) is in Phase H. Note that multiple phases may be applied to one customer. For example, a customer who owns a new home and is considering an after-sales service or a remodeling service may be applied to both Phase B and Phase H.

Customer types are categorizations of customers based mainly on their attribute information. For example, as shown in Figure 6, customer types are classified according to age, sex, annual income, family structure, hobbies, whether or not they own a car, and (housing) preferences. For example, a man in his 50s with an annual income of around 10 million yen, a family of wife and three children, a hobby of driving, owns a car, and prefers conservative housing and interior design would be classified as type α.

The product information storage unit 22 stores product information. The product information consists of a higher class, a middle class that is a detailed version of the higher class, and a lower class that is a detailed version of the middle class. The number of classes is not limited to this, and may be divided into three or more hierarchical classes. As shown in FIG. 7, the product information is classified into, for example, higher classes, new construction products, remodeling products, and after-sales products. As middle classes, for example, new construction products include custom-built homes and ready-built homes, for example, remodeling products include bath units and wood decks, and for example, after-sales products include roofs and exterior walls. As lower classes, the middle classes include product IDs, product names, product features, and flags for each product. Each product is an individual product that can be sold. For example, if a product is a "remodeling" product in a "door" part, a specific door (for example, product ID: 123, product name: Easy Door DX, product feature: the door can be opened and closed with light force) corresponds to this. The selection unit 13 may select from any of the upper class, the middle class, and the lower class as the product for generating the customer list. For example, in FIG. 7, the upper class "new house" may be selected by the selection unit 13, and the output unit 17 may output a customer list of customers predicted to purchase new houses. The middle class "kitchen set" may be selected by the selection unit 13, and the output unit 17 may output a customer list of customers predicted to purchase kitchen sets. The lower class "easy door" may be selected by the selection unit 13, and the output unit 17 may output a customer list of customers predicted to purchase easy doors. It is also possible to select multiple products, and generate a customer list of customers predicted to purchase multiple products. Remodeling products and after-sales products may have the same items in the middle class. For example, in the case of a bath unit, since it may be remodeled or after-sales service may be received due to a water leak, it may be included in any of the middle classes. The flag is information for distinguishing between products handled by a salesperson and products handled by the automatic response robot 1. For example, expensive products or products that require complex explanations are handled by salespeople, while inexpensive products that do not require explanations are handled by the automatic response robot 1 so that customers can easily purchase them. A flag is attached to the product ID (for example, "0" if the person in charge is a salesperson, "1" if the automatic response robot, and "2" if both a salesperson and the automatic response robot).

The residence information storage unit 23 stores residence information. As shown in FIG. 8, the residence information is data including key information, land information, building information, and equipment information as items. The land information is detailed information about the land for construction, and is data including items such as the building site, building coverage ratio, floor area ratio, zoning, ground information, and area. The building information is information about the residence (building), and is data including items such as the building structure, total floor area, building area, layout, orientation, selling price, cost rate, housing performance information, construction schedule, completion date, renovation history, and age. The equipment information is information about the equipment (commercial materials, etc.) provided in the residence, and is data including items such as equipment commercial materials, commercial material ID, commercial material characteristics, and (equipment) construction history. The commercial material ID is the same as the ID of the commercial material information shown in FIG. 7.

The contact information storage unit 24 stores contact information. The contact information is information on contact between a customer and a salesperson, and is data including items such as key information, sales contact information, robot response information, and access information, as shown in FIG.
Sales contact information is data that includes items such as contact date and time, contact method (face-to-face interview, telephone, email, etc.), contact location (showroom, customer's home, etc.), contact source (referral from an affiliated company, etc.), contact destination (referral to another department of the company, etc.), and activity content (explanation of product, etc.). Robot response information is information when the automatic response robot 1 contacts a customer via voice or chat, and is data that includes items such as response date and time, response method (voice, chat, etc.), and response location (showroom, Internet homepage, etc.). Access information is the access history when a customer browses the housing company's Internet homepage, etc.

The sales information storage unit 25 stores sales information. Sales information is information about the sales staff of the housing company, and as shown in FIG. 10, is data that includes items such as key information, sales staff information, corresponding new construction information, corresponding remodeling information, and corresponding after-sales information.

Salesperson information is data that includes items such as the salesperson's name, (hereafter referred to as the salesperson's) gender, age, affiliation, position, years of service, current address, past addresses, family composition, qualifications held, and sales type. A past address is information that indicates where the salesperson previously lived, and does not have to be a specific address. For example, it could be the prefecture of origin. Note that past addresses are also included in customer information. This makes it possible to know in advance that a customer and a salesperson are from the same place, or that the customer's current address is where the salesperson previously lived and is therefore familiar with the area, which can be useful when deciding on a sales representative or when a salesperson is trying to start a conversation with a customer.

Corresponding new construction information is information on new construction sales that a specific salesperson has handled in the past, and is data with items such as the number of business negotiations, number of contracts, number of designs, content of support, number of completed constructions, sales performance, and number of customers. Corresponding renovation information is information on renovation sales that a specific salesperson has handled in the past, and corresponding after-sales information is also information on after-sales sales that a specific salesperson has handled in the past. Corresponding renovation information and corresponding after-sales information are data with the same items as corresponding new construction information.

The unofficial information storage unit 26 stores unofficial information. Unofficial information is unofficial information about the unofficial activities of customers, and includes camera information and online information, as shown in FIG. 11.

The camera information is data that includes items such as the shooting date and time, the shooting location, and the analysis content. The analysis content is the content of the analysis results by the video analysis unit 18, which will be described later.

Online information is data that includes items such as web access history, web transition history, email open rate, and SNS information. Email open rate is the percentage of emails that were opened by customers out of the total emails sent by sales representatives. Other information such as what information the customer opened the email about may also be added. SNS information is personal information that customers disclose on SNS etc. For example, information on lifetime events such as employment, job change, transfer, marriage, childbirth, and child's school enrollment can be triggers for product purchases and is very useful data. In addition, information that reveals the customer's hobbies and preferences (for example, fishing is a hobby, likes modern architecture, etc.) is also useful in analyzing the products that the customer prefers.

Returning to FIG. 3, the video analysis unit 18 analyzes video of the customer captured by a camera 6 installed at a housing exhibition center or the like, and calculates the customer's level of interest in the product based on the results of the analysis. The video analysis is performed, for example, on the customer's facial direction, hand movements, line of sight, and movement line.

By analyzing the direction of the face, it is analyzed where the customer is interested in the product placed. The customer's interest level is calculated by calculating the duration of the face direction. The interest level is a value that indicates the degree of interest of the customer. If a customer turns his/her face toward a product for a long time, he/she is considered to be interested, and if the time is short, he/she is considered to be uninterested. Therefore, the interest level may be calculated in association with the time the face was turned. For example, if the time the face was turned is 1-5 seconds, the interest level is calculated as 1, if it is 5-10 seconds, the interest level is calculated as 2, and if it is 10 seconds or more, the interest level is calculated as 3. The higher the interest level value, the more interested the customer is. The interest level may be linked to the number of times the face was turned. For example, if the number of times the face was turned is 1-2 times, the interest level is calculated as 1, if it is 3-6 times, the interest level is calculated as 2, and if it is 7 times or more, the interest level is calculated as 3. The same method can be applied to the calculation of the interest level described below.

By analyzing video of hand movements, for example by analyzing what a customer picked up or touched, it is possible to analyze what products the customer is interested in (they are interested in things they pick up or touch). Furthermore, by calculating the duration a customer spends picking up or touching a product, it is also possible to calculate the customer's level of interest (a short period of time indicates low interest, a long period of time indicates high interest, etc.).

By analyzing the gaze of a customer through video, for example by analyzing what the customer was looking at, it is possible to analyze what product the customer is interested in (they are interested in what they were looking at). Furthermore, by calculating the duration of the gaze, it is also possible to calculate the customer's level of interest (a short gaze indicates low interest, a long gaze indicates high interest, etc.).

By analyzing the traffic patterns through video, for example by analyzing the video of how customers move through a housing exhibition (exterior routes within the housing exhibition, interior routes within the model room and showroom), it is possible to analyze what products a customer is interested in (they are interested in products that they stop and look at multiple times). Furthermore, by calculating the duration of time they stopped, it is also possible to calculate the customer's level of interest (a short time indicates low interest, a long time indicates high interest, etc.). In addition, the number of times a housing exhibition is viewed and the people accompanying them while viewing can also be subject to video analysis. For example, customers who come to view several times over a period of time, customers who view with a guide, and customers who were alone the first time but came as a couple or family (with children) the second time can be analyzed as having a high level of interest.

Furthermore, the above video analysis can be carried out by applying existing image analysis technology.

The video analysis unit 18 can further improve the accuracy of the analysis by combining the images acquired from the cameras 6 installed at multiple housing exhibition halls, etc. For example, if a video is acquired of a customer at multiple housing exhibition halls (e.g., exhibition hall A, exhibition hall B, and exhibition hall C) looking mostly at the kitchen at exhibition hall A, the children's room at exhibition hall B, and the entrance at exhibition hall C, the video analysis unit 18 can analyze that the customer has inconsistent behavior and has low purchasing motivation. However, if a video is acquired of a customer looking only at kitchens at all exhibition halls A to C, the video analysis unit 18 can analyze that the customer has a high purchasing motivation for kitchen products. In addition, if a video is acquired of a customer looking around at products (newly built detached houses) from a specific housing manufacturer at multiple housing exhibition halls, the video analysis unit 18 can analyze that the customer wishes to purchase products from that housing manufacturer.

The analysis results (calculated interest level, etc.) from the video analysis unit 18 are stored in the integrated information storage unit 2 via the data management unit 12 .
The online information analysis unit 19 analyzes the customer's online activities, such as website access history and transition history, SNS public content, email open rate, etc. Based on the results of the analysis, the online information analysis unit 19 extracts at least one of the customer's attribute changes (employment, job change, marriage, etc.), the customer's hobbies, and the customer's preferences. The extracted information (customer's attribute changes, customer's hobbies, customer's preferences, etc.) is stored in the integrated information storage unit 2 via the data management unit 12.

The automatic response control unit 11 is connected to the automatic response robot 1 so as to be able to communicate with it, and controls the automatic response robot 1. The communication may be wireless, wired, or a combination of these. Specifically, if there is information necessary for the automatic response robot 1 and the customer to converse through voice, text, images, videos, etc., the automatic response control unit 11 acquires the information necessary for the dialogue from the customer information, product information, residence information, business information, and contact information stored in the integrated information storage unit 2, and passes it on to the automatic response robot 1. The information necessary for the dialogue can be determined based on a learned machine learning model. Furthermore, the automatic response control unit 11 analyzes the information obtained by conversing with the customer. In the analysis, the automatic response control unit 11 identifies whether the information from the dialogue is customer information, product information, residence information, business information, or contact information, and distributes and stores the identified information in each storage unit of the integrated information storage unit 2. The automatic response control unit 11 may control the dialogue of the automatic response robot 1 to ask the customer about the information in the item when there is an item that has not been entered in each item of the customer information and residence information, or when the information in each item is out of date. For example, if there is insufficient information in the family structure category of the customer information, the automatic response control unit 11 may cause the automatic response robot 1 to ask questions such as "By the way, how many people are in your family, Mr./Ms. X?". The automatic response control unit 11 may also cause the automatic response robot 1 to ask questions such as "What are your recent hobbies, Mr./Ms. X?" at predetermined intervals about items that change, such as hobbies, interests, and family structure.

The automatic response robot 1 may sell a product to the customer, in which case the automatic response control unit 11 refers to the flag of the product information stored in the product information storage unit 22, and if the product is the product that the automatic response robot 1 is responsible for (for example, "1" for an automatic response robot), it recommends the product to the customer. If the customer responds that they will purchase the product, the automatic response control unit 11 may request an external electronic payment server (not shown) to make an electronic money or credit card payment, or after payment, request an external product shipping business server (not shown) to ship the purchased product.

The data management unit 12 manages the data in the integrated information storage unit 2. The data management unit 12 receives requests for information search, updating, etc. from the salesperson's terminal 4 and the automatic response control unit 11, searches for information in the integrated information storage unit 2, and passes it on to the terminal 4 and the automatic response control unit 11 or updates it. The data management unit 12 receives information search requests from the acquisition unit 14, and passes the searched information on to the acquisition unit 14.

The selection unit 13 selects products from which customer candidates (customer list) are extracted from at least one of product information related to new construction, product information related to renovation, and product information related to aftercare services. The selection unit 13 presents which product a customer list is required for on the display unit (not shown) of the housing sales support device 10 or the display unit 4a of the terminal 4 carried by the salesperson, and allows the salesperson or operator to select the product for which a customer list is required. When the selection unit 13 acquires the selected product, it passes it to the acquisition unit 14.

The acquisition unit 14 acquires information necessary for generating a customer list linked to the selected product from the integrated information storage unit 2 via the data management unit 12. It is assumed that the information necessary for generating a customer list linked to a certain product has been set in advance by a designer or the like. Note that, when there are different products A and B, the information necessary for generating a customer list for product A and the information necessary for generating a customer list for product B may be different. The acquisition unit 14 passes the information necessary for generating a customer list linked to the acquired product to the processing unit 15.

The processing unit 15 processes (pre-processes) the information acquired from the acquisition unit 14 and required to generate a customer list linked to a product. For example, if the information required to generate a customer list includes a current address, the Kanto region, including Tokyo, Kanagawa, Saitama, and Chiba, is processed to the numerical value "0.8," and the Kansai region, including Osaka, Kyoto, Kobe, and Nara, is processed to the numerical value "-0.1." It is assumed that the information required for the processing has been set in advance by a designer or the like. The processed information is handed over to the analysis unit 16.

The analysis unit 16 analyzes customers related to the product based on information necessary for extracting customer candidates linked to the selected product, which is obtained from the integrated information storage unit 2. Specifically, the analysis unit 16 analyzes customers related to the product based on processed information (information necessary for generating a customer list) obtained from the processing unit 15. The analysis unit 16 obtains a prediction model from the prediction model storage unit 3, inputs the processed information into the prediction model as parameters, and obtains an output value. The analysis unit 16 analyzes the output value and extracts customers related to the product. When extracting a customer, it is preferable for the analysis unit 16 to also extract the reason why the customer was extracted, i.e., the parameters that caused the customer to receive a high score.

An example of a simplified prediction using a prediction model will be described with reference to FIG. 12. For example, when the product is a newly built detached house and a score is calculated from the customer's "age" and "family structure" contained in the customer information, the information obtained from the processing unit 15 is as follows:

The parameter "age" is the variable "1" if the age is 48 or over, the coefficient is "5.7", and the variable is "0" if the age is less than that.
The parameter "family structure" is a variable of "2" and a coefficient of "7.2" if the number of household members is less than three, and a variable of "3" and a coefficient of "8.5" if the number of household members is three or more.
Here, the variables are numerical values given to each parameter, and the coefficients are numerical values given as the degree of contribution to the parameters. Both numerical values are set in advance by the designer. As an example, the score calculation formula is as follows: Score = variable 1 x coefficient 1 + variable 2 x coefficient 2 + variable 3 x coefficient 3 ... (Formula 1)
12 corresponds to the variable n and coefficient n in Equation 1. In this case, in pattern 1, the score of a customer aged 48 or younger is 0, in pattern 2, the score of a customer aged 48 or older with a household size of less than three is 5.7+2×7.2=20.1, and in pattern 3, the score of a customer aged 48 or older with a household size of three or more is 5.7+3×8.5=31.2.

The score calculation method is not limited to the above. For example, the parameters may be divided into two stages to calculate the score. In the first stage, the parameters "age" and "number of people in household" are used to classify customers into one of patterns 1, 2, or 3 (see FIG. 12). In the second stage, the customer's score is calculated in each classified pattern using other parameters of the customer. For example, if the other parameters are annual income, land area, and whether or not the customer has children, the value of each parameter is calculated using a normalization process (processing the values so that the average is 0 and the variance is 1), and the calculated values are added together to determine the customer's score. At this time, the calculated parameter values may be weighted, and the weighted values may be added together to determine the customer's score.

Note that the above calculation method is just one example, and the score may be calculated using more parameters.

The analysis unit 16 determines that customers with higher scores are more likely to purchase a newly built house, and generates a customer list by sorting the customers in descending order of score. The generated customer list is passed to the output unit 17.

The output unit 17 outputs customer candidates who meet predetermined conditions in terms of relevance to the product as a result of analysis by the analysis unit 16. A customer who is relevant to the product here refers to, for example, a customer who is likely to be interested in the product, a customer whose preference is matched by the product, or a customer who is likely to purchase the product. A customer who meets predetermined conditions refers to, for example, a customer whose score calculated by the analysis unit 16 is equal to or greater than a predetermined value. When the output unit 17 receives a customer list of candidate customers generated by the analysis unit 16, it displays the list on the display unit (not shown) of the housing sales support device 10 or on the display unit 4a of the salesperson's terminal 4.

The output unit 17 outputs a customer list as shown in FIG. 13, for example. The customer list in FIG. 13 is a list of information on customers who are likely to purchase the "bathroom unit" or "kitchen set" from among the remodeling products in the product information, or who are likely to purchase both at the same time. The customer list is data with items such as customer rank, customer number, customer name, remodeling expectation of the bath unit, remodeling expectation of the kitchen set, remodeling expectation of simultaneous remodeling of the bath unit and kitchen set, reasons, and product information. The customer rank is the ranking of customers in descending order of score. The remodeling expectation is a ranking of the scores of each product divided into five and expressed as stars. For example, a score of 0-20 is 1 star, a score of 21-40 is 2 stars, a score of 41-60 is 3 stars, a score of 61-80 is 4 stars, and a score of 81-100 is 5 stars. The simultaneous remodeling expectation is a value obtained by performing some kind of calculation process and adding up the remodeling expectation of the bath unit and the kitchen set. Alternatively, the value may be a value obtained by quantifying the grounds and materials and weighting the above-mentioned sum. The value of the simultaneous renovation expectation is also displayed as a star mark, ranked by score, in the same way as the above-mentioned renovation expectation. The grounds are the parameters that had the greatest influence on the calculation of the score. For example, in the score calculation formula for the renovation of a bath unit and a kitchen set, the parameters that had the greatest influence were the age of the house, the area, and the layout, as well as the age and family structure of the customer information, and these are displayed as the grounds (ground 1, ground 2, ground 3) linked to each customer number. As for the materials, the B-ID (material ID of the bath unit) and K-ID (material ID of the kitchen set) are displayed along with the characteristics of these materials (e.g., simple, functional, modern).

(Operation of the housing sales support device)
The following describes the operation of the housing sales support device 10. The operation of the housing sales support device 10 can be broadly divided into an operation for inputting and updating information in the integrated information storage unit 2, and an operation for outputting a customer list that predicts customer purchases based on the information in the integrated information storage unit 2. These operations will be described below with reference to the drawings.

(Information input action)
The operation of the housing sales support device 10 for inputting and updating information in the integrated information storage unit 2 will be described with reference to the flow chart of FIG.

In step S101, the data management unit 12 of the housing sales support device 10 receives a request from the automatic response control unit 11 or the terminal 4 to read or write data to the integrated information storage unit 2.

In step S102, the data management unit 12 determines whether the request from the automatic response robot 1 via the automatic response control unit 11 or the request from the terminal 4 is a request to read or write data to the integrated information storage unit 2. If it is a request to read data, the process proceeds to step S103. If it is a request to write data, the process proceeds to step S104.

In step S103, the data management unit 12 executes the read process requested by the automatic response robot 1 or the read process requested by the terminal 4 via the automatic response control unit 11. Specifically, the data management unit 12 acquires the information requested by the automatic response control unit 11 or the terminal 4 from the integrated information storage unit 2, and the data management unit 12 transmits the acquired information to the terminal 4 and the automatic response control unit 11. The automatic response control unit 11 transmits the acquired information to the automatic response robot 1.

In step S104, the data management unit 12 executes the writing process requested by the automatic response robot 1 or the writing process requested by the terminal 4 via the automatic response control unit 11. Specifically, the data management unit 12 writes (newly inputs or updates) the information requested by the automatic response robot 1 or the terminal 4 to the integrated information storage unit 2. The data management unit 12 may send a notification of writing completion to the terminal 4 and the automatic response robot 1.

With this, the housing sales support device 10 completes the information input operation.

(Customer list output operation)
The operation of the housing sales support device 10 for outputting a customer list in which customer purchases are predicted based on the information in the integrated information storage unit 2 will be described with reference to the flowchart of FIG.

In step S201, the selection unit 13 of the housing sales support device 10 selects products from which customer candidates (customer list) are extracted from at least one of product information related to new construction, product information related to renovation, and product information related to aftercare services. The selection unit 13 prompts the salesperson to select products for which a customer list is required, for example, on the display unit 4a of the salesperson's terminal 4. When the selection unit 13 acquires the selected products, it passes them to the acquisition unit 14.

In step S202, the acquisition unit 14 acquires information necessary for generating a customer list linked to the selected product from the integrated information storage unit 2. The acquisition unit 14 passes the acquired information necessary for generating a customer list linked to the product to the processing unit 15.

In step S203, the processing unit 15 processes (pre-processes) the information acquired from the acquisition unit 14, which is necessary for generating a customer list linked to the product. The processing unit 15 passes the processed information to the analysis unit 16.

In step S204, the analysis unit 16 analyzes customers who are related to the product based on the processed information (information necessary to generate a customer list) obtained from the processing unit 15. Customers who are related to the product are customers who are predicted to purchase the product. The analysis unit 16 passes the candidate customers obtained as a result of the analysis to the output unit 17 as a customer list.

In step S205, the output unit 17 transmits the acquired customer list to the sales representative's terminal 4.

This completes the operation of the housing sales support device 10 to output the customer list.

(Effects of the First Embodiment)
According to the first embodiment of the present disclosure, it is possible to support sales activities related to housing because the selection unit 13 selects a product from which customer candidates (customer list) are extracted from at least one of product information related to new construction, remodeling, and aftercare services, the analysis unit 16 analyzes customers related to the product based on information required for extracting customer candidates, and the output unit 17 outputs the customer candidates based on the analysis results.

Second Embodiment
16 , a housing sales support device 30 according to the second embodiment of the present disclosure includes an integrated information storage unit 31, a selection unit 32, an analysis unit 33, and an output unit 34. The housing sales support device 30 is an example of a minimum configuration of the housing sales support device 10 in the first embodiment.

The integrated information storage unit 31 stores at least one of the following: information about the customer, information about the product, information about the customer's residence, information about the salesperson who deals with the customer, information about contact between the customer and the salesperson, and informal information about the customer's informal activities.

The selection unit 32 presents products to be used to extract potential customers from at least one of product information related to new construction, product information related to renovation, and product information related to aftercare services, and allows the customer to select one of these products.

The analysis unit 33 analyzes customers related to the product based on the information necessary to extract customer candidates linked to the selected product, which is obtained from the integrated information storage unit 31.

The output unit 34 outputs customer candidates that meet the specified conditions in terms of relevance to the product as a result of the analysis.

According to the second embodiment of the present disclosure, it is possible to support sales activities related to housing. This is because the selection unit 32 selects a product from which customer candidates (customer list) are extracted from at least one of product information related to new construction, remodeling, and aftercare services, the analysis unit 33 analyzes customers related to the product based on information necessary for extracting customer candidates acquired from the integrated information storage unit 31, and the output unit 34 outputs customer candidates based on the analysis results.

(Information processing device)
In each of the above-described embodiments of the present invention, some or all of the components of the housing sales support device shown in Fig. 3, 16, etc. can be realized by using any combination of an information processing device 500 and a program as shown in Fig. 17. The information processing device 500 includes, as an example, the following configuration.

CPU (Central Processing Unit) 501
ROM (Read Only Memory) 502
RAM (Random Access Memory) 503
A storage device 505 for storing programs 504 and other data
A drive device 507 for reading and writing data from and to the recording medium 506
A communication interface 508 for connecting to a communication network 509
An input/output interface 510 for inputting and outputting data
A bus 511 that connects each component
Each component of the housing sales support device in each embodiment of the present application is realized by the CPU 501 acquiring and executing a program 504 that realizes these functions. The program 504 that realizes the function of each component of the housing sales support device is stored in advance in, for example, the storage device 505 or the RAM 503, and is read by the CPU 501 as necessary. The program 504 may be supplied to the CPU 501 via the communication network 509, or may be stored in advance in the recording medium 506, and the drive device 507 may read the program and supply it to the CPU 501.

There are various variations in the way each device is realized. For example, the housing sales support device may be realized by any combination of separate information processing devices and programs for each component. In addition, multiple components provided in the housing sales support device may be realized by any combination of a single information processing device 500 and programs.

In addition, some or all of the components of the housing sales support device are realized by other general-purpose or dedicated circuits, processors, etc., or combinations of these. These may be configured on a single chip, or may be configured on multiple chips connected via a bus.

Some or all of the components of the housing sales support device may be realized by a combination of the above-mentioned circuits, etc. and programs.

When some or all of the components of the housing sales support device are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or distributed. For example, the information processing devices, circuits, etc. may be realized as a client-server system, cloud computing system, etc., in a form in which each is connected via a communication network.

The present invention has been described above with reference to the present embodiment, but the present invention is not limited to the above embodiment. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

1 Automatic response robot 2 Integrated information storage unit 3 Prediction model storage unit 4 Terminal 4a Display unit 5 Terminal 6 Camera 7 Online information collection robot 10 Housing sales support device 11 Automatic response control unit 12 Data management unit 13 Selection unit 14 Acquisition unit 15 Processing unit 16 Analysis unit 17 Output unit 18 Video analysis unit 19 Online information analysis unit 21 Customer information storage unit 22 Product information storage unit 23 Residence information storage unit 24 Contact information storage unit 25 Sales information storage unit 26 Unofficial information storage unit 30 Housing sales support device 31 Integrated information storage unit 32 Selection unit 33 Analysis unit 34 Output unit 100 Housing sales support system 500 Information processing device 504 Program 505 Storage device 506 Recording medium 507 Drive device 508 Communication interface 509 Communication network 510 Input/output interface 511 Bus

Claims (10)

A selection means for presenting products to be extracted from at least one of product information related to new construction, product information related to remodeling, and product information related to aftercare services to a potential customer and allowing the customer to select the product;
an integrated information storage means for storing at least one of information on a customer, information on the merchandise, information on the customer's house, information on the salesperson dealing with the customer, information on contact between the customer and the salesperson, and informal information on the informal activities of the customer, at a plurality of stages in a sales activity relating to a house;
an analysis means for analyzing customers related to the selected product based on information necessary for extracting the customer candidates linked to the selected product, the information being acquired from the integrated information storage means;
The housing sales support device further comprises an output means for outputting the customer candidates who satisfy predetermined conditions in terms of the relevance to the product as a result of the analysis. The housing sales support device according to claim 1 further comprises a video analysis means for analyzing a video of the customer and calculating the customer's level of interest in the product based on the results of the analysis. The housing sales support device according to claim 1 or claim 2 further comprises an online information analysis means for analyzing the online activities of the customer and extracting at least one of the changes in the customer's attributes, the customer's hobbies, and the customer's preferences based on the results of the analysis. The housing sales support device according to claim 2 or 3 further comprises a data management means for storing at least one of the customer's interest level, the customer's attribute changes, the customer's hobbies, and the customer's preferences in the integrated information storage means as the unofficial information. The product information is made up of a higher class, a middle class that is a detailed version of the higher class, and a lower class that is a detailed version of the middle class.
2. The housing sales support device according to claim 1, wherein said selection means selects said product from any one of said upper class, said middle class and said lower class. 6. The housing sales support device according to claim 1 ; and a robot communicatively connected to the housing sales support device and capable of conversing with customers via voice or text.
A housing sales support system that is communicatively connected to the housing sales support device and includes a camera that is equipped to capture images of the customer's reaction to the merchandise. The housing sales support device includes an automatic response control means for controlling the robot, and the automatic response control means acquires information from an integrated information storage means for storing at least one of information on the customer, information on the product, information on the customer's residence, information on the salesperson serving the customer, information on contact between the customer and the salesperson, and the unofficial information, and causes the robot to converse with the customer, and stores the information obtained as a result of the conversation in the integrated information storage means. The housing sales support system described in claim 6. The housing sales support system according to claim 6, wherein the cameras are installed in multiple locations where the products are displayed and photograph the customers. The computer
presenting products to be used to extract potential customers from at least one of product information related to new construction, product information related to remodeling, and product information related to aftercare services, and allowing the customer to select the product;
Analyzing customers related to the product based on information necessary for extracting the customer candidates associated with the selected product, which is obtained from an integrated information storage means that stores at least one of information on the customer, information on the product, information on the customer's home, information on the salesperson dealing with the customer, information on contact between the customer and the salesperson, and informal information on the informal activities of the customer, at multiple stages in a sales activity related to a house;
A housing sales support method comprising: outputting the customer candidates that satisfy predetermined conditions in the relevance to the product as a result of the analysis. presenting products to be used to extract potential customers from at least one of product information related to new construction, product information related to remodeling, and product information related to aftercare services, and allowing the customer to select the product;
Analyzing customers related to the product based on information necessary for extracting the customer candidates associated with the selected product, which is obtained from an integrated information storage means that stores at least one of information on the customer, information on the product, information on the customer's home, information on the salesperson dealing with the customer, information on contact between the customer and the salesperson, and informal information on the informal activities of the customer, at multiple stages in a sales activity related to a house;
A housing sales support program for causing a computer to output the customer candidates who satisfy predetermined conditions in terms of the relevance to the product as a result of the analysis.

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