JP2008165564A - New customer classification supporting method, new customer classification supporting program, and new customer classification supporting device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To efficiently approach a customer, and to newly create business negotiation, and to increase a customer layer. <P>SOLUTION: This new customer classification supporting device includes: a credit judging part 201 for judging whether or not the credit of a customer is a predetermined value or more; a relevant product introduction judgement part 202 for reading predetermined information from a customer information database 251, and for judging whether or not the relevant product has been already introduced based on the read information; an object product introduction judging part 203 for reading the predetermined information from an order reception information database 252, and for judging whether or not the object product has been already introduced by a customer based on the read information; and a customer list preparation part 204 for distributing customers to at least four categories based on the judgement result of the judging part, and for creating a customer list for every category to which the customers have been distributed. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a new customer classification support method, a new customer classification support program, and a new customer classification support apparatus, which execute a support process for classification of customers who sell a predetermined product (specifically, for example, a storage system) using a computer. About.

  Conventionally, the most common method for identifying a customer in sales activities is to extract only by the intuition and experience of the sales staff based on the collected customer information.

  In addition, as a conventional technology, it is easy to narrow down the partners for developing sales activities, and it is possible to make some guesses about the products that the other party needs. In an environment where bookstores that sell products of different industries (books and computer-related products) and IT / SI vendors can use the joint procurement and joint delivery services of logistics companies to efficiently discover and expand The IT / SI vendor obtains sales information for a product in a different industry from the company's product (computer-related product) from a logistics company, and the customer information in the different industry from the acquired information on the sales of the different industry. There is a technology related to a sales activity support processing method that analyzes customer behavior and develops a sales activity suitable for the customer behavior for each customer based on the analysis result. In the following Patent Document 1 see.).

JP 2003-85386 A

  However, with the conventional method, it has been difficult to extract only the approaching customers from a large list of customers. In addition, extracting only based on the insight and experience of the sales representatives not only causes variations in the extraction method depending on the skills of the sales representatives, but also overlooks good customers and overlooks opportunities for approaching those customers. There was a problem that there was a high possibility of being lost.

  In addition, sales activities generally have different responsibilities for each product, and it is difficult to grasp the status of other responsibilities, so the extracted customers are already existing customers of the company by other responsibilities, etc. There is a problem that a lot of useless labor is caused by performing duplicate sales activities for the same customer.

  Furthermore, depending on the intuition and experience of the sales staff, there is a tendency for many approaches to customers who are easy to sell, and there is a problem in that the approaches to customers vary. As described above, the conventional method has a problem in that efficient sales activities cannot be performed and further sales results cannot be obtained.

  In order to solve the above-described problems caused by the prior art, the present invention is a new customer classification support method and a new customer classification support method for approaching customers more efficiently, creating new business negotiations, and expanding the customer base. It is an object to provide a program and a new customer classification support apparatus.

  In order to solve the above-described problems and achieve the object, a new customer classification support method, a new customer classification support program, and a new customer classification support device according to the present invention sell a predetermined product (hereinafter referred to as “target product”). When executing support processing of customer classification using a computer, predetermined information is read from a customer database, and based on the read information, the customer is a product other than the target product, and the target product is A first determination step for determining whether or not a related product (hereinafter referred to as “related product”) has already been introduced, and predetermined information is read from the order receiving database, and the customer is based on the read information. A second determination step of determining whether or not the target product has already been introduced, and causing the computer to execute the determination by the first to third determination steps. Based on the results, characterized in that distributing the customer to at least four categories.

  The new customer classification support method, the new customer classification support program, and the new customer classification support device according to the present invention further include a customer list creation step of creating a customer list for each of the sorted classifications regarding the customer. It is made to perform.

  The new customer classification support method, the new customer classification support program, and the new customer classification support device according to the present invention may be configured so that the customer determines that the customer has the credit when the credit of the customer is a predetermined value or more. It may be determined whether a related product has already been introduced.

  Further, in the new customer classification support method, the new customer classification support program, and the new customer classification support device according to the present invention, as a result of the second determination step being determined by the first determination step, the customer has already When the related product is introduced, it may be determined whether or not the customer has already introduced the target product.

  According to the present invention, it is possible to efficiently sort (filter) a large number of customer lists, thereby supporting efficient sales activities, a new customer classification support method, a new customer classification support program, and a new There is an effect that a customer classification support device can be obtained.

  Embodiments of a new customer classification support method, a new customer classification support program, and a new customer classification support apparatus according to the present invention will be described below in detail with reference to the accompanying drawings.

(System configuration)
FIG. 1 is an explanatory diagram showing an example of the appearance of a new customer classification support apparatus according to an embodiment of the present invention. In FIG. 1, the new customer classification support device is composed of a computer main body 110, an input device 120, and an output device 130. A network such as a LAN, WAN, or the Internet via a router or a modem (not shown). 100 can be connected.

  The computer main body 110 has a CPU, a memory, and an interface. The CPU controls the entire new customer classification support apparatus. The memory is composed of ROM, RAM, HD, optical disk 111, and flash memory. The memory is used as a work area for the CPU.

  Various programs are stored in the memory, and loaded according to instructions from the CPU. Data read / write of the HD and the optical disk 111 is controlled by a disk drive. The optical disk 111 and the flash memory are detachable from the computer main body 110. The interface controls input from the input device 120, output to the output device 130, and transmission / reception with respect to the network 100.

  The input device 120 includes a keyboard 121, a mouse 122, a scanner 123, and the like. The keyboard 121 includes keys for inputting characters, numbers, various instructions, and the like, and inputs data. Further, it may be a touch panel type. The mouse 122 performs cursor movement, range selection, window movement, size change, and the like. The scanner 123 optically reads an image. The read image is captured as image data and stored in a memory in the computer main body 110. Note that the scanner 123 may have an OCR function.

  The output device 130 includes a display 131, a printer 132, a speaker 133, and the like. The display 131 displays data such as a document, an image, and function information as well as a cursor, an icon, or a tool box. The printer 132 prints image data and document data. The speaker 133 outputs sound such as sound effects and reading sounds.

  FIG. 2 is a block diagram showing an example of a functional configuration of the new customer classification support apparatus (particularly, the customer filtering processing unit) according to the embodiment of the present invention. In FIG. 2, the new customer classification support device (customer filtering processing unit) includes a credit judgment unit 201, a related product introduction judgment unit 202, a target product introduction judgment unit 203, a customer list creation unit 204, a customer information database ( DB) 251 and an order information database (DB) 252.

  The credit judgment unit 201 judges whether or not the customer credit is equal to or greater than a predetermined value. For credit, data calculated by a financial institution or a credit research company may be used, or may be calculated independently. Specifically, the credit determining unit 201 can realize its function by, for example, the CPU executing a program stored in the memory.

  The related product introduction determination unit 202 reads predetermined information from the customer information DB 251 and, based on the read information, selects a related product that is a product other than the target product and related to the target product. Determine whether it has already been installed. Specifically, the related products are, for example, personal computers, servers, package software, and the like. The related product introduction determination unit 202 determines whether or not the customer has already introduced the related product only when the credit of the customer is equal to or greater than a predetermined value as a result of the determination by the credit determination unit 201. May be. Specifically, the related product introduction determining unit 202 can realize its function by, for example, the CPU executing a program stored in the memory.

  The target product introduction determination unit 203 reads predetermined information from the order information DB 252 and determines whether the customer has already introduced the target product based on the read information. Specifically, the target product is, for example, a storage system. The target product introduction determining unit 203 determines whether or not the customer has already introduced the target product only when the customer has already introduced the related product as a result of the determination by the related product introduction determining unit 202. You may do it. Specifically, the target product introduction determination unit 203 can realize its function by, for example, the CPU executing a program stored in the memory.

  The customer list creation unit 204 distributes the customers into at least four categories based on the results determined by the credit determination unit 201, the related product introduction determination unit 202, and the target product introduction determination unit 203. Then, a customer list is created for each of the classified categories for those customers. The method for creating the customer list will be described in detail with reference to FIG. Specifically, the customer list creation unit 204 can realize its function by, for example, the CPU executing a program stored in the memory.

(BD cycle)
FIG. 3 is an explanatory diagram showing an outline of the new customer classification support method according to the embodiment of the present invention. In FIG. 3, the business development cycle (BD cycle) includes a “target MIX” step (S301), a “customer filtering” step (S302), an “appoint / visit” step (S303), and an “information provision” step ( S304) consists of four steps (actions). Starting from the “target MIX” step (S301), the process proceeds in the order of “customer filtering” step (S302) → “appoint / visit” step (S303) → “information provision” step (S304). It is a BD cycle to move to the “target MIX” step (S301) and repeat this cycle. The above four steps (actions) will be described in detail below.

(Target MIX)
In the action in the target MIX (S301), customer information is collected by combining various methods. FIG. 4 is an explanatory diagram illustrating an example of a target list in the target MIX process. In FIG. 4, examples of the targeting action include “event exhibition”, “seminar holding”, “selection of top 200 companies”, “extraction of companies with 1000 or more employees”, and the like. Also, for each target action, collect information on its purpose, method, merit, demerit. Thereafter, the process proceeds to customer filtering (S302).

(Customer filtering)
In the action in the customer filtering (S301), the customer information collected in the target MIX (S301) is filtered from the list of event visitors and seminar visitors, and the visiting company and the person in charge are screened.

  Specifically, for example, a filter is applied to exclude existing customers who have account sales or companies with storage systems installed. In addition, a filter is applied to select new customers who have no past record. In addition, a filter is applied to select companies that do not have a storage system installed. By applying customer filtering in this way, the approach list is completed.

  As another consideration, when listing only large companies that can implement a storage system, small and medium-sized companies should create a list in that segment and seek whether they can be used by dealers or partners. Also good.

  FIG. 5 is an explanatory diagram showing an example of sorting in the customer filtering step. In FIG. 5, the filtering target candidate list (customer master) 253 that is the customer information collected by the target MIX (S301) is used as input data, and the input data is subjected to a filtering process, whereby (1) a new customer list and (2) Four lists, a semi-existing customer list, (3) an existing customer list, and (4) a filtering list after six months, are created, and the four lists are used as output data. These four lists can be created by the customer list creation unit 204 shown in FIG.

  Here, (1) a new customer list (approach list) is a user list that has not introduced its own server or uses products of other companies. If the PC user does not use the server, it is assumed to be new. In addition, (2) the semi-existing customer list (approach list) is a user list who has installed a server of his own product and has not installed a storage system. (3) The existing customer list (list with sales representative) is a user list that is a user of (2) semi-existing customer list and has installed a storage system. (4) The re-filtering list after 6 months is a user list with a score of 50 or less and the company cannot trade. And the list of (4) turns the flow again half a year later and sees the change of the score.

  FIG. 6 is an explanatory diagram illustrating an example of a distribution condition. In FIG. 6, “◯” is applicable and “×” is not applicable. In the case of a new customer, a rating of 51 points or more is “◯”, so it is applicable, and a server introduction is “x”, so it is not applicable. Also, storage (system) introduction is “x”, so it is not applicable. A person who satisfies such a distribution condition can be added to the “(1) new customer list” as a new customer. Similarly, in the case of a semi-existing customer, a rating of 51 points or more is “◯”, and the server introduction is also “○”, which is applicable. Also, storage (system) introduction is “x”, so it is not applicable. A person who satisfies such a distribution condition can be included in the “(2) semi-existing customer list” as a semi-existing customer.

  Similarly, in the case of an existing customer, a rating of 51 or higher is “○”, so it is applicable, a server introduction is “O”, “X”, so it may be not applicable, and a storage (system) introduction is “ Yes, so A person who satisfies such a distribution condition can be included in the “(3) existing customer list” as an existing customer. Further, in the case of a filter customer after 6 months, since a score of 51 points or more is “x”, it is targeted for those who are not applicable, and server installation and storage (system) introduction are not judged. A person who satisfies such a distribution condition can be placed in the “(4) 6 months later re-filtering list” as an existing customer. Server installation and storage (system) installation are judged again after 6 months.

  FIG. 7 is an explanatory diagram showing an example of the contents of the customer information DB. In FIG. 7, the customer information DB 251 stores information related to customer credit (credit). In this customer information DB 251, for example, “18 credit research company score” can be extracted and used as a credit value. In addition, a value using an original calculation formula for this score may be used as the credit value.

  FIG. 8 is an explanatory diagram showing an example of the contents of the order information DB. In FIG. 8, the order information DB 252 stores information related to the company's orders. In this order information DB 252, for example, data such as “10 model series”, “11 units”, “12 main models” is extracted, and based on the extracted data, related products (personal computers, servers) Etc.) can be determined.

  FIG. 9 is an explanatory diagram showing an example of the contents of the customer master. In the customer master 253 of FIG. 9, “7 (rating 51 years or more)” = “◯”, “8 (server introduction)” = “O”, “9 (storage introduction)” = “O” “10 (sales death) If “strip information” ”=“ × ”, the information is distributed to the“ existing list ”. Similarly, if “7” = “◯”, “8” = “◯”, “9” = “×”, and “10” = “×”, they are distributed to the “semi-existing list”. If “7” = “◯”, “8” = “×”, “9” = “×”, and “10” = “×”, they are sorted into a “new list”. If “7” = “×”, “8” = “− (whichever is acceptable)”, “9” = “−”, and “10” = “O”, the “dead muscle list” If “7” = “×”, “8” = “×”, “9” = “×”, and “10” = “×”, they are assigned to the “filtering list after 6 months”.

  In this manner, the customer master with the respective attributes can be set to “existing list”, “semi-existing list”, “new list”, “dead list”, “filtering list after 6 months”. it can. Also, the customer lists (1) to (4) shown in FIG. 5 may be reconstructed by reconstructing the customer information DB 251, the order information DB, and the customer list master.

  FIG. 10 is a flowchart illustrating an example of the processing procedure of the customer filtering process. In the flowchart of FIG. 10, a filtering target candidate list is input, and it is determined whether or not the list is a so-called “dead muscle list” (step S1001). Specifically, the determination may be made based on whether or not the sales representative has a deadline flag. In addition, in sales activities, it may be determined whether a customer (unexpected) to be removed is flagged in advance and whether or not the customer has been put on the deadline list.

  In step S1001, when it is a dead-spot list (step S1001: Yes), nothing is done and the process proceeds to step S1010. On the other hand, when the list is not a dead line list (step S1001: No), it is next determined whether or not the credit rating is 51 or more (step S1002). Here, it is good to judge the case where the credit score is changed (change of 50 or less or 51 or more) and extract data for dealing with 51 or more customers.

  In step S1002, when the credit score is 50 points or less (step S1002: No), the process proceeds to step S1010 without doing anything. On the other hand, when the credit score is 51 or more (step S1002: Yes), the matching between the filtering target candidate list and the customer information DB is executed using the customer information DB 251 (step S1003).

  Then, it is determined whether or not there is an operating asset, that is, whether or not a related product has been introduced (step S1004). Whether there is a server (such as a personal computer) can be determined from the operational asset information in the customer information DB 251 and determined by the presence or absence of the server. Here, when there is no active asset (step S1004: No), it distributes to "(1) New customer list (approach list)" (step S1005), and a series of processing is complete | finished. That is, when there is no working asset, the server is not installed, so it is treated as a new customer and distributed to the list.

  On the other hand, if there is an active asset in step S1004 (step S1004: Yes), the matching between the filtering target candidate list and the order information DB 252 is executed using the order information DB 252 (step S1006). That is, when there is an active asset, it may be an existing customer or a semi-existing customer, so that it is passed to the next search. Then, it is determined whether or not the storage system that is the target product has been introduced (step S1007). That is, the presence or absence of a storage system is matched to a user with a server, and if there is a storage system, it is distributed to the existing customer list, and if there is no storage system, it is distributed to the semi-existing customer list. Here, when the storage system is not introduced (step S1007: No), it is distributed to “(2) semi-existing customer list (approach list)” (step S1008), and a series of processing is terminated.

  On the other hand, if the storage system has already been introduced in step S1007 (step S1007: Yes), it is distributed to “(3) existing customer list” (step S1009), and the series of processes is terminated. In step S1010, “(4) Filtering list after 6 months” is assigned (step S1010), and the series of processing ends.

  In this way, customer filtering can be performed. Thereafter, the process proceeds to appointment / visit (S303).

(Appoint / Visit)
Next, the appointment / visit (S303) will be described. When making an appointment call, create a response scenario to maintain uniformity of information to customers. For example, the scenario corresponds to the flow of thanks → interest in storage → troubled problems → appointments. For customers or departments who do not have the authority to introduce, it is recommended to introduce authorized personnel and departments and contact them again by telephone to connect them. In addition, customers who have been introduced should continue to be positioned as important customers as information providers and be included in the list.

  Also, when visiting, adjust if there is a sales person in charge, and accompany or get approval for the visit. For companies that have potential at the time of the visit, it is better to enter the sales phase and take over the sales.

  FIG. 11 is an explanatory diagram showing an outline of the appointment / visit process. In FIG. 11, first, the customer list 1102 is selected by screening the approach list 1100. Then, the company (the person in charge) 1101 contacts the customer 1102. At that time, if the person who made the call is not the person in charge, have another person in charge be introduced and call again. Also, those who are introduced will be important customers. Then, the approach list 1100 is updated. In addition, in the case of refusal, the reason 1106 of refusal is managed and the possibility of re-approaching is maintained.

  If the appointment is OK to the customer, the sales representative 1103 will agree to accompany or visit the customer. Then, a customer 1104 whose appointment is OK is visited and a pipeline for negotiation is made.

  FIG. 12 is a flowchart showing an example of the procedure of the post-process of the appointment / visit process. In the flowchart of FIG. 12, first, the meeting status is input, and information (budget, introduction time, etc.) is added to the customer information DB 251 and the order information DB 252 (step S1201).

  Next, business evaluation is input in three stages (step S1202). If the evaluation is “A. good” (step S1203), a revisit flag is added to “A. approach list” (step S1204), and the series of processing ends.

  In step S1202, if the evaluation is “C. bad” (step S1205), it is added to “B. dead muscle list” (step S1206), and the series of processing ends.

  In step S1202, when the evaluation is “B. normal” (step S1207), as shown in FIG. 13 described later, a five-step evaluation is performed by questionnaire input (step S1208). Here, “5: score 80 or higher” (step S1209) or “4: score 60 or higher” (step S1210) adds a revisit flag to “A. approach list” (step S1204), and a series of processes Exit.

  On the other hand, in step S1208, “3: score 59 or lower” (step S1211) or “2: score 39 or lower” (step S1212) or “1: score 19 or lower” (step S1213) is set to “B. ] (Step S1206), and a series of processing is terminated.

  In this way, the flow after an appointment / visit takes the new customer list (approach list) and semi-existing customer list (approach list) as input data, and approaches list (continue list) and deadline list (future list) Search possibility) is the output data. The deadline list is not chased continuously, but is managed so that it can be approached again in the future.

  After the visit, judge the salesman to be good or bad, and input the data. If the judgment cannot be made, select normal, and then, specifically, for example, as shown in FIG. 13, the questionnaire is divided into five stages, and the score is divided into an approach list and a dead line list. FIG. 13 is an explanatory diagram illustrating an example of a questionnaire after a visit.

  After the visit, evaluate good, normal, and bad, conduct a questionnaire in the normal case, prepare a question with a formula to determine whether to make an approach list or a dead line, this system is WEB Can be calculated automatically.

  As a calculation example, for example, Visit A is 1: 1 2: 3 3: 3 4: 3 5: 3 6: 2 7: 1 8: 2 = (20 + 60 + 50 + 30 + 50 + 20 + 10 + 20) ÷ 8 = 32.5, and the dead muscle list Can be added to. In addition, Visit B is 1: 1 2: 3 3: 3 4: 3 5: 5 6: 3 7: 3 8: 3 = (20 + 60 + 50 + 100 + 100 + 50 + 50 + 50) ÷ 8 = 60, and a revisit flag is added to the approach list. Can do.

  In this way, appointments and visits (actions) can be executed. Thereafter, the process proceeds to information provision (S304).

(Information provided)
The information providing (S304) action includes, for example, regular customer visits. For customers who have been able to make appointments but who have not been able to negotiate, they visit once every two months to provide information on the latest technology and industry trends. In addition to providing information, we will be able to follow the customer's situation and watch over 1000 customer trends that can be immediately proposed according to the time of customer system introduction.

  There are also DM and mail transmission. Along with the events and seminars to be held, the customers who want to attract customers are screened and a guide letter is sent. In addition, the updated customer list performed by the above filtering is used to always save the latest customer list. By these, the mission level of the project can be raised by rotating the BD cycle, and the level of the pre-sales phase can be raised.

  FIG. 14 and FIG. 15 are flowcharts illustrating an example of the processing procedure of the information providing process. In the flowchart of FIG. 14, it is first determined whether it is a visit time (step S1401). When the visit time comes (step S1401: Yes), an “A. visit” message is transmitted (step S1402). Specifically, when a corresponding time comes from the corresponding customer master 253, a visit flag is automatically set so that a visit message can be transmitted. In other words, the timer is operated at the budget time, the visit flag, the appointment date and the like.

  In the flowchart of FIG. 15, if the credit score is 51 or more (step S1501: Yes), it continues without sending any message, but if the credit score is 50 or less (step S1501: No). ) Transmits a message “B: Grade not possible” (step S1502). In this way, as the output data, the notification of the visit data of the approach list and the score result of the approach list are used as the output data. You can notify the hits with a message.

  In this way, information provision (action) can be executed. Thereafter, the process proceeds to the target MIX (S301) again, and this BD cycle can be repeated.

  As described above, according to the present embodiment, when the support process for classification of customers who sell target products is executed using a computer, it is determined whether or not the customer's credit is equal to or greater than a predetermined value, and the customer database The predetermined information is read out, based on the read information, it is determined whether the customer has already introduced the related product, the predetermined information is read out from the order receiving database, and based on the read information Determining whether or not the customer has already introduced the target product, and categorizing the customer into at least four classifications based on the determined result. Therefore, it is possible to efficiently sort (filter) a large number of customer lists, thereby supporting efficient sales activities.

  The new customer classification support method described in the present embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. This program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. The program may be a transmission medium that can be distributed via a network such as the Internet.

(Appendix 1) A new customer classification support method in which a computer executes support processing for classification of a customer who sells a predetermined product (hereinafter referred to as “target product”),
Read predetermined information from the customer database, and based on the read information, the customer has already introduced a product other than the target product and related to the target product (hereinafter “related product”) A first determination step of determining whether or not,
A second determination step of reading predetermined information from the order database and determining whether the customer has already introduced the target product based on the read information;
A distribution step of distributing the customers into at least four classifications based on the results determined by the first and second determination steps;
The new customer classification support method characterized in that the computer is executed.

(Additional remark 2) Furthermore, the said customer is made to perform the customer list creation process which produces a customer list for every said classified classification with respect to the said customer, The new customer classification assistance method of Additional remark 1 characterized by the above-mentioned.

(Additional remark 3) Whether the said customer has already introduced the said related product when the said customer's credit is more than a predetermined value as a result of the said 1st judgment process being judged by the said 1st judgment process The new customer classification support method according to appendix 1 or 2, characterized by:

(Appendix 4) In the second determination step, when the customer has already introduced the related product as a result of the determination by the first determination step, the customer has already introduced the target product. The new customer classification support method according to any one of appendices 1 to 3, wherein it is determined whether or not there is any.

(Supplementary Note 5) A new customer classification support program for causing a computer to execute classification support processing for a customer who sells a predetermined product (hereinafter referred to as “target product”),
Predetermined information is read from the customer database, and based on the read information, the customer has already introduced a product other than the target product and related to the target product (hereinafter referred to as “related product”). A second determination step for determining whether or not
A third determination step of reading predetermined information from the order database and determining whether the customer has already introduced the target product based on the read information;
To the computer,
A new customer classification support program characterized by allocating the customers to at least four classifications based on the results determined in the first to third determination steps.

(Appendix 6) A new customer classification support apparatus that executes a support process for classification of a customer who sells a predetermined product (hereinafter referred to as “target product”),
Read predetermined information from the customer database, and based on the read information, the customer has already introduced a product other than the target product and related to the target product (hereinafter “related product”) First judging means for judging whether or not,
Two determination means for reading predetermined information from the order database and determining whether or not the customer has already introduced the target product based on the read information;
Based on the results determined by the first to third determination means, a distribution means for distributing the customers into at least four classifications;
A new customer classification support apparatus characterized by comprising:

  As described above, the new customer classification support method, the new customer classification support program, and the new customer classification support device according to the present invention are useful for executing the support processing of the classification of customers who sell a predetermined product using a computer. In particular, it is suitable for customer classification for selling systems such as storage systems.

It is explanatory drawing which shows an example of the external appearance of the new customer classification assistance apparatus concerning embodiment of this invention. It is a block diagram which shows an example of a functional structure of the new customer classification | category assistance apparatus concerning embodiment of this invention. It is explanatory drawing which shows the outline | summary of the new customer classification | category support method concerning embodiment of this invention. It is explanatory drawing which shows an example of the target list | wrist in a target MIX process. It is explanatory drawing which shows an example of distribution in a customer filtering process. It is explanatory drawing which shows an example of distribution conditions. It is explanatory drawing which shows an example of the content of customer information DB. It is explanatory drawing which shows an example of the content of order information DB. It is explanatory drawing which shows an example of the content of each list information (customer master). It is a flowchart which shows an example of the procedure of a process of a customer filtering process. It is explanatory drawing which shows the outline | summary of an appointment and a visit process. It is a flowchart which shows an example of the process of the post process of an appointment and a visit process. It is explanatory drawing which shows an example of the questionnaire after a visit. It is a flowchart (the 1) which shows an example of the procedure of a process of an information provision process. It is a flowchart (the 2) which shows an example of the procedure of a process of an information provision process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 201 Credit judgment part 202 Related product introduction judgment part 203 Target product introduction judgment part 204 Customer list preparation part 251 Customer information database (DB)
252 Order Information Database (DB)
253 Customer List

Claims (5)

A new customer classification support method in which a computer executes support processing for classification of a customer who sells a predetermined product (hereinafter referred to as “target product”),
Read predetermined information from the customer database, and based on the read information, the customer has already introduced a product other than the target product and related to the target product (hereinafter “related product”) A first determination step of determining whether or not,
A second determination step of reading predetermined information from the order database and determining whether the customer has already introduced the target product based on the read information;
A distribution step of distributing the customers into at least four classifications based on the results determined by the first and second determination steps;
The new customer classification support method characterized in that the computer is executed.   The new customer classification support method according to claim 1, further comprising: causing the computer to execute a customer list creation step of creating a customer list for each of the sorted classifications regarding the customer.   The said 1st determination process determines whether the said customer has already introduced the said related product, when the credit of the said customer is more than predetermined value, The said product is characterized by the above-mentioned. New customer classification support method. A new customer classification support program for causing a computer to execute classification support processing for a customer who sells a predetermined product (hereinafter referred to as “target product”).
Predetermined information is read from the customer database, and based on the read information, the customer has already introduced a product other than the target product and related to the target product (hereinafter referred to as “related product”). A second determination step for determining whether or not
A third determination step of reading predetermined information from the order database and determining whether the customer has already introduced the target product based on the read information;
To the computer,
A new customer classification support program characterized by allocating the customers to at least four classifications based on the results determined in the first to third determination steps. A new customer classification support device that executes support processing for classification of customers who sell a predetermined product (hereinafter referred to as “target product”),
Read predetermined information from the customer database, and based on the read information, the customer has already introduced a product other than the target product and related to the target product (hereinafter “related product”) First judging means for judging whether or not,
Two determination means for reading predetermined information from the order database and determining whether the customer has already introduced the target product based on the read information;
Based on the results determined by the first to third determination means, a distribution means for distributing the customers into at least four classifications;
A new customer classification support apparatus characterized by comprising:

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