JP2002197257A - Business support system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a business support system reflecting customer's positive behavior in collecting commodity information through the Internet. SOLUTION: The business support system is provided with a customer database accumulating customer's basic attribute data including an understanding factor for purchasing a commodity, a Web site exhibiting an enterprise homepage on the Internet, a Web activity history database accumulating a Web activity history of every customer based on the log information in the enterprise homepage, and a customer understanding engine determining the timing of customer approach by the analysis of customer's needs characteristics based on the data included in the customer database. Effective business support is achieved through the determination of approach timing to each customer based on the analysis of needs characteristics by customers and the Web activity history by customers.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system for accumulating customer attributes including a factor for understanding product purchase in a database and supporting sales based on the analysis.

[0002]

2. Description of the Related Art Japanese Unexamined Patent Publication No. Hei 5-101108 discloses a correlation between attribute data items of a customer stored in a customer database in order to conduct a proposal-type sales activity based on the potential needs of each individual customer. It is described that a product corresponding to the customer is selected based on the correlation, a sales activity is performed for the customer, and the attribute data is updated based on the performance.

[0003] Japanese Patent Application Laid-Open No. 10-83427 describes a sales support system which can immediately provide information on a campaign which a customer is likely to request when making an inquiry from the customer. This system is
Each business item is provided with a database that stores a table showing the relationship between the customer condition and the potential value corresponding to the ratio of the customer related to the business item among the customers who satisfy the condition. , And the corresponding item is displayed on the display unit.

[0004]

With respect to expensive and long-life products such as automobiles and electric appliances, it is necessary for a company to grasp the individuality of each customer and conduct business activities according to the individuality. Now that the Internet has become widespread, customers tend to collect product information on the Internet. Such a positive movement of the customer is extremely important from the viewpoint of sales promotion, but the conventional sales support system does not sufficiently incorporate such a positive movement of the customer into the sales support data. An object of the present invention is to provide a sales support system that reflects such positive customer movements.

[0005]

SUMMARY OF THE INVENTION The present invention relates to a sales support system for accumulating and managing information about a customer. A website published on the Internet, a web behavior history database that accumulates a web behavior history for each customer based on log information of the company homepage, and a customer need characteristic is analyzed based on data included in the customer database. , A customer understanding engine that determines the timing of reaching out to the customer from the web behavior history.

According to the present invention, the characteristics of the needs of each customer are analyzed, and the timing of reaching each customer is determined from the web behavior history of each customer.
Effective sales support is achieved.

[0007] In one aspect, the needs characteristic in the present invention includes an in-kind confirmation needs characteristic, and the customer database stores basic attributes and comprehension factors for a large number of customers preliminarily accumulated for each need, as well as high needs. The customer understanding engine determines for a given customer the rank of the customer's physical identification needs based on the customer's basic attributes and the understanding factor.

According to the present invention, the rank of each customer's in-kind confirmation needs is determined based on each customer's basic attributes and comprehension factors. Customers can be selected efficiently.

[0009] In still another aspect, the customer understanding engine of the present invention digitizes the content browsing history of the home page for a given customer, and when the number exceeds a judgment reference value, provides an output for instructing the customer to work. I do.

According to the present invention, the content browsing history of the homepage is digitized for each customer, and when this number exceeds the reference value, an action to the customer is instructed. It is possible to work at the corresponding timing. In this way, from among a large number of customers who cannot be individually controlled by the sales staff, it is possible to systematically grasp customers who are moving toward purchasing behavior and carry out sales activities.

[0011] In one embodiment, the customer understanding engine of the present invention calculates a content influence value of each customer based on the content browsing history, and the content influence value is used for an action trigger and an action message generation. And a working message is generated according to the content influence value for generating the message.

In still another embodiment, the sales support system according to the present invention, when the customer accesses the home page after the customer is executed, sets the correction coefficient of the content influence value on the basis of the access result. Modify according to customer. Further, after the action is performed, the customer's needs characteristic can be input from the sales terminal device based on the result of the response to the customer.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing the overall configuration of an embodiment of the system of the present invention. This example shows a sales support system of a company ABC that manufactures and sells automobiles. As a means of contact between the customer 11 and the corporate ABC, that is, an interface includes a website 13 where the corporate ABC homepage is located, an e-mail 15, a telephone 17 and a face-to-face 19 with the corporate ABC sales staff or service staff. .

The web page of the web behavior history database company ABC has a page that can be viewed by anyone without needing an ID and a password, and an ID and password registered in the company ABC in advance, and the ID and password are registered in advance. And a page that can be browsed through authentication of the user. When registering the ID and password, the user enters the name, age, gender, family structure, house type, annual income, existing car information, car life stage, car use, hobby, driving style, etc. on the enrollment form on the homepage Fill out the items and send.

The CGI program of the server of the website 13 checks the sent enrollment form, approves the registration if required items are filled out, and stores the ID and password in the database. The personal data of the potential customer thus obtained is transferred from the website database to the customer database 30 and stored.

A page that can be browsed through authentication of an ID and a password (called login) includes a company A
Detailed guidance of BC products is included in a hierarchical structure. Each page is called a content, and related content is linked in one direction or two directions. Generally, when you select an item on the menu screen and click it, you will go to the top page of that item, and if you click one of the items included in this page, the page of the next level will be displayed. Move on to Clicking on any item on this page will take you to the next level of the page. In this way, detailed information can be accessed as moving to lower layers.

The action history server 27 detects a login for each customer and records (logs) the content accessed by each customer. This log is stored in the Web activity history database 40. Your activity history includes the number of views,
In addition to browsing information of each content including the frequency, the access time, the number of clicks, and the logout time, content transition information, e-mail transaction information, and an e-mail guidance rate are included.

The contents of the home page are stored in the contents master database 61, retrieved by the contents server 59, and transmitted to the customer. As will be apparent from the description below, the customer-specific content generation engine 57 can generate content unique to each customer based on the calculation by the customer understanding engine 20 and transmit the content to the customer.

The distributed database system shown in FIG. 1 constitutes a distributed database system as a whole. The customer understanding engine 20 is managed by a web behavior history database 40 managed by an activity history server 27 and a customer server 35. Customer database 30, service history 5 managed by service history server 43
0, the service database 60 managed by the service server, and the in-vehicle information database 70 managed by the in-vehicle information server 47 can be accessed,
Relevant data can be retrieved and used from these multiple databases for any customer.

The customer database 30 includes a customer number unique to each customer, a vehicle number of a customer who owns a car of the company ABC, a code of a sales office and a service office serving the customer, Stores the basic attributes related to the customer entered at the time of registration.

The response history server 43 stores a record of the response for each customer in the response history database 50 based on the result of the response to the customer input from the call center 21, the service staff, and the sales staff 23 through the input unit 37. I do. More specifically, the response staff ID, the response date and time, the response purpose, the response content, the response time, the response result status, the next scheduled response date and time, the next response purpose, the daily report editing, and the like are stored. Further, the correspondence history database 50 includes a company ABC
A record of what the customer has been working on is also stored. Specifically, a working staff ID, a working date and time, a working time, a working content, a working result status, and the like are stored.

The response history server 43 stores the service data in the service database 60 via the service history service server 55 in response to the input of the service data from the service staff. The service data includes, for each customer, service staff ID, warehousing date and time, warehousing status, inspection and maintenance information, presence or absence of a substitute,
Includes work time, work content, sales, etc.

The on-board information server 47 stores download data from an on-board electronic control unit (ECU) in an on-board information database 70. This database contains, for each customer, the Vin number (vehicle number), date and time mileage, fuel efficiency information, oil stain information, speed information, accelerator information, brake information, lockup information, side brake information,
Information indicating an operation history such as blinker information, gasoline gauge information, and failure diagnosis information is stored.

Customer understanding engine The customer understanding engine 20 calculates the needs characteristics of each customer in accordance with an algorithm described later, determines the timing of working, and generates a working message. The operation result by the customer understanding engine 20 is stored in the action history server.
The data is sent to the customer-specific content generation engine 57 via 27. In response to this, the customer-specific content generation engine 57 refers to the Web behavior history database and the content master database via the content server 59, and generates content that meets the needs characteristics of the customer to be approached.

An electronic file of the content is transmitted by e-mail to the customer to be contacted. When this electronic file is opened on the customer's personal computer, a browser is activated and can be viewed on the browser.
In another embodiment, instead of sending the electronic file directly to the target customer, a content that is specific to the customer is prepared on the homepage, so a message that the user wants to view is sent by e-mail, and the e-mail is sent to the e-mail. Create a link to the URL of the specialized content. The customer who has received the e-mail can enter the home page of the company ABC by clicking the URL described in the e-mail, and can browse the specialized content.

In another embodiment, the calculation result of the customer understanding engine 20 is used as a response / work screen generation unit 31.
Generated as a display screen by the sales staff department
Sent to 23. The sales staff browses this screen with the terminal device and contacts the target customer according to the information and instructions obtained from the screen.

FIG. 2 shows the overall flow of a program executed by the customer understanding engine 20. A need level for a given customer is calculated by an algorithm described in detail later with reference to FIGS. 3 to 5 (201). 6 to 1
The timing to work on a given customer is detected by an algorithm described in detail later with reference to FIG.
3). In accordance with the timing detected in this way, a response in accordance with the customer's needs characteristics is performed (205), and the resulting transaction is analyzed (207), and included in the calculation algorithm of the needs level and the detection algorithm of the response timing in accordance with the analysis result. Correction and tuning of variables and coefficients to be performed are performed (209), and the results are reflected in the algorithm (211) to evolve the program.

The rank variable calculation process Figure 3 shows a process for calculating a rank variable representing needs levels for each plurality of demand for any customer. As described with reference to FIG. 1, the customer's basic attributes, previous car attributes, current car attributes,
Data such as car life behavior is accumulated (301). These data are used when a customer visits a company ABC sales office, service center, etc.
Opportunities such as at the time of contract closing, vehicle inspection, vehicle inspection, etc. are captured in the system.

As the customer's needs characteristics, those shown in a block 303 are set. Based on data such as a questionnaire and past performance data, a need type and a factor that influences each need type are identified, and the degree of influence of the factor is calculated by a discriminant analysis using a variance matrix (305). Based on the calculation results, the individuals (customers) included in the data set are ordered according to their needs types.

FIG. 14 shows the factors that identify the type of need for "actively responding to sales activities" obtained by analyzing the questionnaire survey. FIG. 15 shows the factors that identify the need type of “important to test drive” obtained by analyzing the questionnaire survey. FIG. 16 shows factors for identifying a need type of “requesting provision of product information” obtained by analyzing a questionnaire survey. The degree of influence of each factor on these needs types is calculated by discriminant analysis, and in the next step 309, the needs type of a given customer is determined and used for an operation for determining the rank. Details of step 309 will be described with reference to FIG.

Now, the needs type is simplified for simplicity in FIG.
It is assumed that there are five information needs, information needs, support needs, in-kind confirmation needs, service needs, and person dependencies, as shown in Mr.'s needs ranking table.
The need type shown in FIG. 14 corresponds to “person dependency”, and the need type shown in FIG.
And the needs type shown in FIG. 16 corresponds to “information needs”.

Looking at Mr. A registered on the website of the company ABC, the factors affecting each of the above needs are extracted from the data accumulated in the plurality of databases for Mr. A, and the factor influence degree obtained in step 303 is obtained. Then, the needs of Mr. A are determined based on the above, and the rank in the data set is determined (309). The needs ranking table of Mr. A shown in the upper part of FIG. 5 shows the ranking determined in this way.

The ranking for each need is converted into a percentage with respect to the parameter (the total number of customers) of the data set.
The need level X 'is calculated by multiplying the correction coefficient for the user (311). This correction coefficient is a coefficient set according to the individuality of each customer based on the feedback of the operation result of this system. Looking at the example of Mr. A, the correction coefficient is set to 0.8 because referring to past transaction results has a strong effect on information purchases. Since the needs level is a percentage of the needs ranking as described above, the smaller the value, the higher the needs level.

The needs levels for each of the five needs calculated in this way are ranked into five levels from 1 to 5 based on the judgment reference value (313). An example of the criterion value for each need is shown in the ranking table of FIG. Rank 1 indicates that the degree of influence is the largest. The larger the value of the rank, the smaller the degree of influence. The rank variable determined for Mr. A is shown in the lowermost table of FIG. 5 (315).

[0035] encourage the timing determination process FIG. 6 shows a flow of a process for determining the timing of the appeal for a given customer from companies ABC. When the customer completes the login to the corporate ABC homepage (40
1), a menu screen for content selection is displayed on the customer's browser. The customer can select one from a plurality of contents from the vehicle type selection 405 to the event information 419 (403). The menu page also includes an item for returning to the top page 421. Model selection 4
When content having substantial content from 05 to event information 419 is selected, a flag indicating the selected content is set (425).

If this customer has logged in to this home page for the first time (427), proceed to block 429 to set the access start time, clear the number of clicks, obtain the purchase stage status constant, and initialize the content depth factor. Become Block if not the first login
Proceeding to 431, the number of times of browsing, the number of clicks, and the number of daily browsing are each incremented to obtain a content depth coefficient.

Next, the content influence degree is calculated with reference to the table shown in FIG. The value Z stored in the influence coefficient master table is a coefficient given for each content classification as shown in the uppermost table in FIG. For example, vehicle type selection is 0.5 and vehicle type recommendation is 0.4. The content depth coefficient Z ′ is a coefficient indicating the depth of the content accessed in the above-described hierarchical structure of the homepage. For example, in the field of vehicle type selection, the model page has a depth of 1 and the type (grade) page. Has a depth of 1.5, the exterior color page has a depth of 1.5, and the estimate page has a depth of 2.5. The correction coefficient B for each customer is a coefficient that reflects the operation result of this system and corrects the influence coefficient according to the personality of the customer. The purchase stage status constant C is
A constant that indicates at what stage a given customer is in purchasing a car. For example, the purchase stage status constant for a customer at the stage of collecting information to buy a car,
0.4. The content influence degree M is defined by the following equation.

[0038]

[Equation 1] Content influence degree M = (Z × Z ′) × B + C Then, the following equation quantifies the influence of given customer A's given content of company ABC on A's car purchasing behavior. The calculated browsing influence value is calculated (435).

[0039]

[Equation 2] Influence value of browsing = (number of times of browsing + number of daily times + number of clicks) x M The browsing effect value obtained for Mr. A is compared with the step (increment) reference value (437), and exceeds the reference value. If so, the corresponding content influence value of this customer is incremented. For example, in the chart of FIG. 7, it is assumed that Mr. A has advanced from the menu for selecting a vehicle type (influence coefficient Z = 0.5) to the estimated content with the content depth Z ′ = 2.5. Mr. A's correction coefficient B for model selection content
Is 0.8 as shown in the table. Assuming that Mr. A's purchase stasis @ status constant C is in the stage of information collection C = 0.5, the content influence degree M of Mr. A becomes 1.5 according to Equation 1.

Assuming that the number of browsing times of Mr. A + the number of daily times + the number of clicks is 18, the browsing influence value of Mr. A becomes 27 according to Equation 2. Since Mr. A's browsing influence value exceeds the step reference value 25 for vehicle type selection, "vehicle type selection", "vehicle type recommendation", "vehicle type comparison", "third party review information", And "Exhibition Test Drive Information"
The content influence value of Mr. A for the content of is incremented (439).

The content influence value of Mr. A updated in this way is compared with the approach determination reference value (441). An example of the working judgment reference value is shown in the table at the bottom of FIG. When the content influence value of the arbitrary content exceeds the action determination reference value of the corresponding content, the processing shifts to a process for executing an approach to the customer A from the company ABC according to the content that exceeds the reference value. In the present example, the content influence value in the “vehicle type selection” content of Mr. A is “13”, which exceeds the working judgment reference value “12” of this item. In addition, the content influence value of the “exhibition test ride information” content is 16, which exceeds the reference value 15 for the action of this item. As a result, the action process of FIG. 9 is started for Mr. A.

[0042] Referring to encourage processing FIGS. 9 and 10, following the process of FIG. 6 illustrating a process for implementing the outreach to customers from the company ABC. First, the content influence value of Mr. A shown at the top of FIG. 10 is referred to. Here, an arbitrary customer, Mr. A, will be described as an example, but a similar process is executed for all customers on the database. As shown in the table of FIG. 10, the content is classified into those that trigger a challenge and those that generate a message for the challenge. In the example of Mr. A, as described above, when the content influence value of the "vehicle type selection" content exceeds the challenge determination reference value, the process of the challenge is started. Then, since the content influence value of the “exhibition test ride information” content exceeds the challenge reference value, the process enters a process of generating a message for providing the exhibit test ride information to Mr. A.

In this embodiment, a working message is assembled using the rank variable of the needs identification result of Mr. A described with reference to FIG. When assembling an approach message based on the influence value of the “exhibition test ride information” content, the strongly relevant needs items are “information needs”, “in-kind confirmation needs”, and “person dependence”. The center table in FIG. 10 shows the need items having strong relevance to this content by displaying 1 in the column of “Exhibition Test Ride Information”.

Attention is now paid to the need to confirm the actual product. In this embodiment, the system is in this case programmed to select number a of the message table. This is shown from the center to the bottom of FIG. In other words, if the physical confirmation needs are 1 or 2, the message table
No. a is selected, if it is 3 or 4, b is selected, and if it is 5, c is selected.

Returning to FIG. 9, the working message number is thus determined (502). Subsequently, it is determined whether or not the influence value has increased for contents other than the “exhibition test ride information” contents (503). If the influence value is increasing, the vehicle type name selected by Mr. A is detected from the Web behavior history database 40 (FIG. 1) for the "vehicle type selection" content (505), and the "vehicle type recommendation"
About contents, Web behavior history database 4
The recommended vehicle type name is detected from 0 (507). Similarly, for the “comparison between vehicle types” content, the vehicle type name of the comparison source is detected from the Web behavior history database 40 (509), and for the “third-party criticism information” content, the vehicle type name of the criticism information is detected (511). In addition, the exhibition trial ride information browsed by Mr. A is searched from the Web behavior history database 40, and the browsed model name is detected (513).

If the same vehicle type name is detected (515), the vehicle type name is inserted into the action message generated in the process of FIG. 10 (517). If the detected car model names are not the same, blank characters are inserted into the car model name of the appeal message (519). In this way, the character data of the message table and the vehicle name are combined (523), and the content for the message is prepared (52).
Five).

Referring to encourage implementation and compensation tuning Figure 11, thus completing the content Figure 1
Is transmitted or notified to Mr. A in the form described above in relation to (701). The approach can take a variety of forms, including using a website and e-mail, contacting sales staff, and sending invitations by mail.

For the customer who is determined to work in the processing flow up to FIG. 9, the customer-specific content editing engine according to the instruction from the customer understanding engine 20 in FIG. 1.
57 edits the content for each customer that matches the content impact value of the target customer. In this way, when the customer to be encouraged logs in to the company ABC homepage thereafter, he / she browses the page edited for the customer. Company ABC will notify the target customer via email that such a special page has been prepared,
You can prompt browsing (701).

When a customer is approached according to the system of the present invention, the customer's reaction to the request is detected,
Modify the data in the database for each customer and adapt the system for better future engagement.

After performing the action, the action history server 27 (FIG. 1) detects whether or not the target customer has logged in to the homepage (703).
For example, if there is no log-in within two weeks, and if the request is not resent, a setting is made to send a guide mail to the customer, for example, one week later (707). When the target customer logs in to the homepage after the action, a message prepared for each customer is displayed on the menu page. In the menu page, the menu item of the specially edited content is blinked or a special mark is put on such a menu item to attract the attention of the customer.

In the example of Mr. A described so far,
The appeal message shown in FIG. 10 is displayed, and among the menu items shown in FIG. 11, the vehicle type selection 711 and the event information 725, the exhibition / test ride information 721 is specially edited for Mr. A. It is set to blink. When Mr. A selects any menu item and clicks it, the selected content discrimination flag is set (731). For the first access after the action (7
33), sets the access start time and clears the number of clicks (735). If it is the second or subsequent access, the number of browsing, the number of clicks, and the number of daily browsing are incremented by one (737).

In the example of Mr. A, when a test ride reservation is made on the homepage (739), first, the reserved vehicle type is stored in the database, and the purchase stage status (on the database) of Mr. A is changed to the "will formation" stage. , Add 0.1 to the correction value for Mr. A's content influence value, and clear Mr. A's content influence value for future calculations (741). The test ride reservation may be made by telephone, e-mail, or other means. In this case, the sales staff or the call center inputs test ride reservation data into the system. If the test ride reservation was not made by this action, subtract 1 from the content influence value (743).

Next, the processing shifts to the database update processing of FIG. 12, in which fact information relating to the browsing of each target customer, in this example, Mr. A's web browsing, is obtained from the database (802), and the log and the analysis result of the customer attribute are obtained from the database. (803) and the response script information (8)
05), edit the display data of the sales staff's mobile terminal (807), and update the database for sales staff's mobile terminal display (811).

Block 813 shows information displayed on the portable terminal of the sales staff or the terminal device of the sales office through these steps. The fact information of browsing the web includes the name of the vehicle model for which the test drive reservation was made, the time of the test drive reservation,
There are cases where the car name and the test drive catalog that he is comparing have already been sent, and the supplies (accessories) catalog has been sent.

As the needs information obtained by analyzing the logs and the customer attributes, the information needs to be high for Mr. A, care must be taken of the opinions of the third parties, the need for support is low, the need to check the actual product and the need for test drive are high. And the degree of dependence on persons (dependence on contact with sales staff, etc.) is low.

As information displayed on the portable terminal of the sales staff as a script for responding to Mr. A, the appealing points are that the engine characteristics, fuel efficiency, and riding comfort
The third-party information that Mr. A cares about is the voice of the customer who has already paid, and after the test ride, he or she may have a negotiation.

After the sales staff responds to Mr. A's test drive based on the information provided by the system, the sales staff inputs the response results for the items shown in block 830 to the system (815). The sales staff inputs the progress of the business negotiation, that is, the stage of physical confirmation, test drive, assessment, estimation, negotiation, credit, contract, and delivery. Next, the sales staff responded to Mr. A with what the system indicated for the customer's identification information, “information needs”, “support needs”, “in-kind confirmation needs”, “service needs”, and “person dependency”. Is corrected based on Further, the sales staff inputs the next scheduled contact date with Mr. A into the system.

The correction variables are stored in the customer database 30 based on the input by the sales staff (817). The customer understanding engine 20 determines whether there is a change in the customer identification value after responding to Mr. A (819), and if there is a change, corrects the customer identification factor influence degree (821), and calculates the influence degree coefficient for each customer. Correction is made (823). In this way, the system is updated based on the result of the action by the system operation, and it is possible to execute the operation with higher accuracy.

FIG. 13 shows a specific example of performing the correction in steps 821 and 823 of FIG. The correction coefficient for Mr. A is corrected for each need based on the result of the response from the sales staff to Customer A shown in block 830 in FIG. For example, looking at the information needs, Mr. A's information needs are located at the position indicated by the white triangle mark in block 830 according to the calculation result of the customer understanding engine. Has been corrected to the position indicated by the black triangle mark. On the basis of this, the customer understanding engine corrects the influence of Mr. A's information needs to be small, that is, to increase Mr. A's% ranking (ranking from the top, the higher the numerical value, the lower the ranking). In the example shown in the figure, the correction coefficient for the information need is modified from 0.8 to 0.9. Similarly, the correction coefficient for Mr. A is corrected based on the results shown in block 830 of FIG. 12 for the support needs, the physical check needs, the service needs, and the person dependence. The degree of influence X 'of each need after the correction is represented by rank% X × correction coefficient α.

[0060]

According to the present invention, the needs of each customer are determined based on the basic attributes and comprehension factors of each customer. It is possible to conduct business activities by systematically grasping customers who are moving toward them.

[Brief description of the drawings]

FIG. 1 is a block diagram showing the overall configuration of a system according to an embodiment of the present invention.

FIG. 2 is a flowchart showing the overall processing flow in one embodiment of the present invention.

FIG. 3 is a flowchart showing a flow of a process for determining an identification result rank variable in one embodiment of the present invention.

FIG. 4 is a diagram showing classification of use identification information in one embodiment of the present invention.

FIG. 5 is a diagram showing an example of calculating a need identification result rank variable in one embodiment of the present invention.

FIG. 6 is a flowchart showing a flow of a process for determining a response in one embodiment of the present invention.

FIG. 7 is a diagram showing a calculation example of a content browsing influence degree in one embodiment of the present invention.

FIG. 8 is a diagram showing an example of a content browsing influence value and an action determination reference value according to an embodiment of the present invention.

FIG. 9 is a block diagram showing a process of generating a content to be accessed in one embodiment of the present invention.

FIG. 10 is a diagram showing a process of generating a working message in one embodiment of the present invention.

FIG. 11 is a diagram showing a flow of a process for performing the action in one embodiment of the present invention.

FIG. 12 is a diagram showing a flow of a database update process based on a result of an action in one embodiment of the present invention.

FIG. 13 is a diagram showing a method for calculating a coefficient for correcting the degree of influence in accordance with the result of a response transaction in one embodiment of the present invention.

FIG. 14 is a diagram showing an example of a factor for identifying a person having a demanding type need.

FIG. 15 is a diagram showing another example of a factor for identifying a person having a work-type need.

FIG. 16 is a diagram showing another example of a factor for identifying a person having a work-type need.

[Explanation of symbols]

 Reference Signs List 20 Customer understanding engine 30 Customer database 40 Web behavior history database 57 Customer-specific content generation engine

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Masato Honjo 2-1-1, Minami-Aoyama, Minato-ku, Tokyo Honda Motor Co., Ltd. (72) Takaro Katagiri 2-1-1, Minami-Aoyama, Minato-ku, Tokyo No. 1 Inside Honda Motor Co., Ltd. (72) Inventor Hideya Abe 1-13-2, Nishi-Nippori, Arakawa-ku, Tokyo Youcastle 601 (72) Inventor Kazunobu Noda 4-4-2, Hokuei, Urayasu-shi, Chiba RISSIー ル ver Sakae 303 (72) Inventor Dai Hamaoka 4-18, Uguisudanicho, Shibuya-ku, Tokyo Court Astoria 205 F-term (reference) 5B049 BB11 CC08 EE00 GG00 GG09

Claims (7)

[Claims] 1. A sales support system for accumulating and managing information on a customer, comprising: a customer database for accumulating basic attribute data of the customer including an understanding factor relating to product purchase; and a website for publishing a company homepage on the Internet. A web behavior history database that accumulates a web behavior history for each customer based on the log information of the company homepage; and analyzing a customer's needs characteristic based on data included in the customer database, and providing a customer to the customer from the web behavior history. A sales support system that includes a customer understanding engine that determines when to work. 2. The needs characteristic includes one or more of information needs, support needs, physical confirmation needs, service needs, and person dependence, and the customer database stores in advance the needs for each. The relationship between the basic attribute and the understanding factor and the height of the need is stored for a large number of customers, and the customer understanding engine stores the customer based on the basic attribute and the understanding factor of the customer for a given customer. The sales support system according to claim 1, wherein the rank of the in-kind confirmation needs is determined. 3. The customer understanding engine quantifies the content browsing history of the home page for a given customer, and when the number exceeds a judgment reference value, provides an output for instructing the customer to work. 2. The sales support system according to 2. 4. The customer understanding engine calculates a content impact value of each customer based on the content browsing history, and the content impact value is classified into a trigger for action and a trigger message generation. 3. The sales support system according to 3. 5. The sales support system according to claim 4, wherein an action message is generated according to the content influence value for generating the message. 6. The method according to claim 4, wherein after the customer is accessed, the correction coefficient of the content influence value is corrected in accordance with the customer in response to the customer accessing the homepage based on the access result. Sales support system. 7. The sales support system according to claim 3, wherein after the action is performed, the customer's needs characteristic can be input from the sales terminal device based on the result of the service with the customer.