JP2015075849A - Introducer candidate extraction system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To extract a central key person under the consideration of a link of a network about relevancy between an expected customer and an introducer.SOLUTION: This introducer candidate extraction system includes: a customer model construction part 10 for constructing a customer model having high introduction possibility on the basis of relation information between existing customers by a customer relation information registration/management device, a human network analysis device and a data management device; an introducer list construction part 20 for listing up introducer candidates having high introduction possibility from among the existing customers by a base point unit on the basis of the constructed customer model; and a visit result feedback part 30 for registering an instruction result after customer visit. The introducer candidate extraction system extracts an introducer key person candidate in accordance with the significance of the introducer from human network information, and categorizes the introducer key person candidate to generate a customer model, and extracts the introducer candidate pertinent to the customer model from the customer list of persons unintroduced at the point of time.

Description

  The present invention relates to a technique for identifying and extracting a person who meets a predetermined condition. In particular, the present invention relates to a technique for extracting from existing customers who may introduce prospective customers from existing customers such as insurance.

  Conventionally, a person in accordance with a predetermined condition / request is extracted from a certain group (set). For example, in sales activities in the service industry such as insurance, prospective customers who may purchase and their introducers are extracted. In such an example, it is necessary to acquire a new customer. In order to acquire new customers, it is necessary to identify potential customers who may purchase. Further, in order to identify the prospective customer, it is necessary to search for the prospective customer by some method. One approach to this is to use an introducer who introduces prospective customers to sales representatives. Japanese Unexamined Patent Publication No. 2009-252008 (Patent Document 1) is known as a background art regarding the identification of such prospective customers and introducers. This publication describes that “customer tree information indicating the relationship between the prospective customer and the introducer is further attached to the prospective customer information and managed by the customer information database”. That is, a technique for searching for a prospective customer or the like using customer tree information indicating this relationship is disclosed.

JP 2009-252008 A

  Patent Document 1 describes that information of an introducer connected to a prospective customer is visualized as a customer tree and managed by a customer information database. However, in Patent Document 1, the customer tree that visually links the relationship between the prospective customer and the introducer is only displayed on the user terminal, and the key person who is the center is extracted in consideration of the network connection. There is no mechanism to do.

  Therefore, an object of the present invention is to solve this problem.

  In order to solve the above problems, introducer key person candidates are extracted according to the importance of the introducer from the network related to past introducers, and the customer model is generated by categorizing the introducer key person candidates. Then, introducer candidates corresponding to the customer model are extracted and output from the customer list of unintroducers at that time. As a more specific aspect, for example, the following configuration is adopted. The present application includes a customer model construction unit 10 that constructs a customer model having a high possibility of introduction based on the relationship information of an existing customer by the customer relationship information registration / management device 60, the human network analysis device 70, and the data management device 80. Introducer candidates that are highly likely to be introduced at each site based on the customer model, an introducer list constructing unit 20 that lists from existing customers, and a visit result feedback unit that registers introduction results after the customer visit 30, the introducer key person candidates are extracted from the human network information according to the importance of the introducer, the introducer key person candidates are categorized, the customer model is generated, and the unintroduced person at that time Method for extracting candidate introducers corresponding to the customer model from the customer list and outputting them to the display device 63 And features.

  According to the present invention, it is possible to introduce a person who is likely to become a key person in a human network by existing existing customers and then introduce an unintroduced customer who is close to the key person. Can increase the sex. Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments.

It is an example of the whole system configuration in one embodiment of the present invention. It is an example of the hardware constitutions for carrying out one embodiment of the present invention. It is an example of the introducer relationship table used in one embodiment of the present invention. It is an example of the introducer table used by one Embodiment of this invention. It is an example of the interstitial table used by one Embodiment of this invention. It is an example of the single point table used by one Embodiment of this invention. It is an example of the interstitial variation point table used by one Embodiment of this invention. It is an example of the key person candidate ranking table used in one embodiment of the present invention. It is an example of the customer model categorization table used in one embodiment of the present invention. It is an example of the customer model ranking table used in one embodiment of the present invention. It is an example of the customer model attribute table used by one Embodiment of this invention. It is an example of the base customer list table used by one Embodiment of this invention. It is an example of the base customer attribute table used by one Embodiment of this invention. It is an example of the flowchart explaining the main processing functions in one Embodiment of this invention. It is an example of the flowchart explaining the process which produces | generates the human network in one Embodiment of this invention. It is the example (the 1) of the flowchart explaining the process which calculates an introducer possibility point in one Embodiment of this invention. It is the example (the 2) of the flowchart explaining the process which calculates the introducer possibility point in one Embodiment of this invention. It is an example of the flowchart explaining the process which ranks the key person candidate in one Embodiment of this invention. It is an example of the flowchart explaining the process which produces | generates the customer model in one Embodiment of this invention. It is an example of a display of a human network in one embodiment of the present invention. It is the example which displayed the difference in the related relationship between the nodes in one Embodiment of this invention. It is an example of a screen of a site introducer candidate in one embodiment of the present invention.

  An embodiment of the present invention will be described below with reference to the drawings. In the present embodiment, a human network is constructed from customer relationship information, a process for constructing a customer model as a key person of an introducer, a process for listing introducer candidates, and setting a visit order and a sales representative, Processing for registering customer information input after a visit to the DB and extracting introducer-related information will be described.

  FIG. 1 is an example of the overall configuration of a processing system in the present embodiment. As shown in FIG. 1, in this embodiment, the customer model construction unit 10, the introducer list construction unit 20, the visit result feedback unit 30, the human network DB 51, the customer model DB 52, the customer DB 53, the results management DB 54, and the customer relationship information DB 55 It is composed of

  The customer model construction unit 10 includes a human network construction 11 that constructs a human network from introducer relationship information, and a customer model construction 12 that constructs a customer model from human network construction information. The constructed human network is stored in the human network DB 51 and read when the customer model is constructed.

  The introducer list construction unit 20 reads information in the customer model DB 52 and sets an introducer candidate list-up 21 for listing introducer candidates from the customer model information, and an introducer visit order from the listed introducer candidates. An introducer visit order setting 22, and a salesperson setting 23 for reading and setting the salesperson who visits each introducer from the results management DB 54.

  The visit result feedback unit 30 includes customer information registration 31 for registering customer information input by a sales representative after visiting the customer in the customer DB 53 and introducer relationship information for extracting introducer relationship information from the registered customer information. It consists of information extraction 32. The extracted introducer relationship information is stored in the customer relationship information DB 55.

FIG. 2 is a diagram illustrating an example of a hardware configuration of the customer relationship information registration / management apparatus 60, the human network analysis apparatus 70, and the data management apparatus 80 in the present embodiment.
As shown in FIG. 2, the customer relationship information registration / management device 60 includes a CPU 61 that performs a visit result feedback process, an input device 62, a display device 63, and a storage device 64. Note that the functions of each unit constituting the above-described processing system are realized by a computer arithmetic device (CPU 61) executing processing according to a program.

The human network analysis device 70 is also configured by the same hardware as the customer relationship information registration / management device 60 such as a CPU 71 that performs a customer model construction process.
The data management device 80 includes a CPU 81 that constructs an introducer list and a storage device 82.
The customer relationship information registration / management apparatus 60, the human network analysis apparatus 70, and the data management apparatus 80 are connected to each other via a network 90 via a CPU.

Next, FIG. 3 to FIG. 13 show the table configuration of each processing unit and DB shown in FIG.
FIG. 3 shows a table configuration of an introducer relationship table 5500 in which introducer relationship data is stored in the customer relationship information DB 55.
As shown in FIG. 3, the introducer relationship table 5500 includes a management code column 5501 for storing a management code for managing introducer relationship data for each case, and an introduction source for storing a customer code of the introduction source of the introduction source. A code column 5502, an introduction destination code column 5503 for storing a customer code of an introduction destination, a relationship code column 5504 for storing a relationship code between the introduction source and the introduction destination customer, and determination data indicating whether or not the introduction destination customer has contracted. The introduction destination contract presence / absence field 5505 is configured.

FIG. 4 shows a table configuration of an introducer table 5300 in which introducer data is stored in the customer DB 53.
As shown in FIG. 4, an introducer table 5300 includes a customer ID column 5301 for storing a customer ID as an introducer, an existing / new column 5302 for storing classification data of existing customers or new customers, and name data of introducers. Name column 5303 to store, gender column 5304 to store introducer's gender data, age column 5305 to store the introducer's age, address column 5306 to store the introducer's address data, and introducer family composition data It consists of a family structure column 5307 and the like.

FIG. 5 shows the table structure of the interstitial point table 5510 in which the interstitial content corresponding to the interstitial code stored in the interstitial code column 5504 stored in the introducer relationship table 5500 and the points are associated with each other.
As shown in FIG. 5, the interstitial table 5510 includes a interstitial code column 5511 for storing a interstitial code, a content column 5512 for storing interstitial content data corresponding to the interstitial code, and a point column for storing point data corresponding to the interstitial content data 5513.

FIG. 6 shows a table configuration of a single point table 5100 that stores data related to calculation points possessed by each introducer in the human network DB 51.
As shown in FIG. 6, the single point table 5100 includes a customer ID column 5101 for storing each introducer's customer ID, a distance column 5102 for storing the shortest relationship distance data with the sales staff, and introduceability of each introducer. A calculation point column 5103 for storing points representing the position, a rank column 5104 for storing rank data in the order of calculation points, and a key person for storing determination data on whether or not a key person candidate has calculation points equal to or greater than a preset threshold value. It consists of a candidate column 5105.

FIG. 7 shows a table configuration of the interstitial variation point table 5110 in which data related to interstitial variation points related to other introducers is stored in the human network DB 51.
As shown in FIG. 7, the interstitial variation point table 5110 includes a customer ID column 5111 that stores customer IDs of each introducer, and a variation number column that stores the number of variations of interstitial relationships with other introducers related to each introducer. 5112, an additional point to be added to a single point stored in the single point table 5100 of FIG. 6 is calculated according to the number of variations, an additional point column 5113 for storing the data, and a rank column 5114 for storing rank data in the order of the additional points. It is composed of

FIG. 8 is a sum of the single points stored in the single point table 5100 in FIG. 6 and the additional points stored in the interstitial variation point table 5110 in FIG. 7, and introducer data (key person candidate) data corresponding to the calculated point order. The table configuration of the key person candidate ranking table 5120 for storing
As shown in FIG. 8, the key person candidate ranking table 5120 stores a rank column 5121 for storing rank data in the order of the summed points, and type data for points that are the target for calculating the summed points, or single points and variations. It consists of a point type column 5122, a combined point column 5123 for storing point data relating to a single unit and a variation, and a customer ID column 5124 for storing a customer ID of the corresponding introducer.

FIG. 9 shows a table configuration of a customer model categorization table 5200 that stores data obtained by categorizing introducers (key person candidates) corresponding to the same customer model in the customer model DB 52.
As shown in FIG. 9, the customer model categorization table 5200 categorizes key person candidates by the customer model construction process, stores a customer model code when the customer model is generated, and stores a customer model code column 5201 in each customer model. It consists of a customer ID column 5202 for storing the customer ID of the corresponding introducer (key person candidate).

FIG. 10 shows a table configuration of a customer model ranking table 5210 that stores data ranking customer models based on the key person candidate ranking table 5120 of FIG.
As shown in FIG. 10, the customer model ranking table 5210 includes a model ranking column 5211 for storing customer model ranking data and a customer model code column 5212 for storing customer model codes corresponding to each ranking.

FIG. 11 shows a table configuration of a customer model attribute table 5220 that stores attribute contents for each customer model.
As shown in FIG. 11, an item column 5221 for storing customer model attribute item data and a content column 5222 for storing specific data of attributes corresponding to each item are configured.

FIG. 12 shows a table configuration of a base customer list table 5310 that stores data for displaying a customer list including key persons for each base in the customer DB 53.
As shown in FIG. 12, the base customer list table 5310 includes the base code column 5311 for storing the code data for each base, the customer ID column 5312 for storing the customer ID, and the data of the introduction possibility level from the order of the customer model code. Referral possibility level column 5313, customer model code column 5314 for storing the corresponding customer model code, and key person determination column 5315 for storing determination data as to whether the person is a key person (customer with high possibility of introduction). It is composed of

FIG. 13 shows a table configuration of a base customer attribute table 5320 for storing attribute data for each customer at the base in the customer DB 53.
As shown in FIG. 13, the site customer attribute table 5320 includes a site code column 5321 for storing a site code to which the site belongs, a customer ID column 5322 for storing a customer ID, a name column 5323 for storing name data of the customer, An age field 5324 for storing age data, an address field 5325 for storing address data of the corresponding customer, and the like are included.

Next, an example of a flowchart for explaining main processing functions of the present embodiment is shown in FIG.
In step 100, the introducer data stored in the customer relationship information DB 55 and the input of introducer relationship data are accepted.
In step 200, an order relationship between introducers is set from the inputted introducer relationship data, and a human network having a graph structure in which introducers are connected as nodes is generated.

  Details of step 200 will be described later with reference to FIG.

In step 300, for each introducer (node) of the generated human network, the points of introduction possibility are calculated from the introduction source, the connection of the introduction destination, and the variation of the relationship.
Details of step 300 will be described later with reference to FIGS. 16a and 16b.

In step 400, key person candidates are ranked in the order of introduction possibility points calculated in step 300.
Details of step 400 will be described later with reference to FIG.
In step 500, key person candidates are grouped by key persons having similar attribute data, and a customer model is generated.
Details of step 500 will be described later with reference to FIG.
In step 600, customer data for each base stored in the customer DB 53 is input.
In step 700, a customer that matches the customer model generated in step 500 is selected from the customer data input in step 600.

In step 800, the list of customer data selected in step 700 is output to the display device 63 of the customer relationship information registration / management device 60.
In step 900, a customer visit order is set based on the customer data selected in step 600.
In step 1000, the sales representative data stored in the grade management DB 54 is read, and the sales representative who visits each customer is set.
In step 1100, after the customer visit, the customer data input by the sales representative is registered in the customer DB 53.
In step 1200, introducer relation data related to the introduction is extracted from the customer data registered in step 1100.
In step 1300, it is determined whether there is new introducer relationship data. If it is determined that there is introducer-related data, the process returns to step 100 to repeatedly generate a human network. On the other hand, if there is no introducer relationship data, the process is terminated.
This is the end of the description of the flowchart example describing the main processing functions (overall functions) of the present embodiment.

Next, details of the processing in step 200 shown in FIG. 14 will be described with reference to FIG.
As shown in FIG. 15, in step 210, based on the referral source and referral data stored in the referrer relationship table 5500 based on the sales representative, the introducer ( Set the relationship direction between nodes.

In step 220, the contract presence / absence data stored in the introducer relationship table 5500 is read, and the type of the node of the human network corresponding to the introducer is set.
In step 230, the relationship code stored in the introducer relationship table 5500 is read, and the type of link between the corresponding nodes is set.

In step 240, a human network is generated with a graph structure based on the nodes set in step 220 and step 230 and the link data. An image of the generated human network will be described later with reference to FIG.
In step 250, it is determined whether or not the generated human network is displayed on the display device 73 of the human network analysis device 70 of FIG. As a result of the determination, when displaying, go to step 260. On the other hand, when not displaying, this processing is finished.
In step 260, the human network generated in step 240 is displayed on the display device 73 of FIG.
This is the end of the detailed description of step 200.

Next, details of the processing in step 300 shown in FIG. 14 will be described with reference to FIGS. 16a and 16b.
As shown in FIG. 16a, in step 310, in order to calculate the calculation points of the single point table 5100, point calculation regarding the relationship continuity between nodes is performed, or the number of variations of the interstitial variation point table 5110 is calculated. Determine whether to calculate points. As a result of the determination, if point calculation regarding the relationship continuity between nodes is to be performed, go to Step 311. On the other hand, when the point calculation related to the inter-node variation is performed, the process goes to step 331 in FIG.

In step 311, numbers are assigned to the introduction destination nodes in ascending order, starting from the node of the customer who the sales representative first operated.
In step 312, the minimum number assigned to the node among the numbers assigned in step 311 is set.
In step 313, the distance n between the sales representative and the node is set to n = 1, and the node calculation point = 0 is set. The distance here refers to the number of links connecting the sales representative and the node through the other nodes in the shortest time.

In step 314, the node corresponding to the node number assigned in step 311 is set as a point calculation node.
In step 315, the points of the nodes directly connected to the point calculation node set in step 314 are read and added to the points stored in the relationship table 5510 of FIG. 5 and divided by the distance n. The calculation result is set as a provisional point.
In step 316, provisional points are added to the calculated points to newly set the calculated points.
In step 317, the distance n = n + 1 is calculated, and n is incremented by one.
In step 318, it is determined whether distance n ≦ maximum distance. As a result of the determination, if n ≦ maximum distance, go to step 314. On the other hand, if n> maximum distance, go to step 319.
In step 319, 1 is added to the number assigned to the node.
In step 320, it is determined whether there are other point calculation nodes. As a result of the determination, if there is a point calculation node, go to step 313. On the other hand, if there is no point calculation node, the process goes to step 321.
In step 321, the calculated points are stored in the single point table 5100.

Next, as shown in FIG. 16b, in step 331, numbers are assigned to the introduction destination nodes in ascending order, starting from the node of the customer who the sales representative first operated.
In step 332, node addition point = 0 is set.
In step 333, the node corresponding to the number assigned to the node is set as a point calculation node.
In step 334, the number of variations of the relationship between the point calculation node set in step 333 and the directly connected node is set as an additional point.
In step 335, the additional points are stored in the interstitial variation point table 5110.
In step 336, it is determined whether another point calculation node exists. As a result of the determination, if there is a point calculation node, go to Step 333. On the other hand, when there is no point calculation node, this process is finished.
This is the end of the detailed process of step 300.

Next, details of the processing in step 400 shown in FIG. 14 will be described with reference to FIG.
As shown in FIG. 17, in step 410, it is determined whether the corresponding node has an additional point stored in the interstitial variation point table 5110. As a result of the determination, if there are additional points, the process goes to step 420. On the other hand, if there are no additional points, go to step 430.
In step 420, the corresponding nodes are ranked in the order of additional points in the interstitial variation point table 5110.
In step 430, the nodes are ranked in the order of the points of the nodes set by calculating the relationship continuity points between the nodes.
In step 440, a threshold is set in order to set a key person candidate having a high possibility of introduction among the nodes ranked in step 440.
In step 450, among the calculation points set in each node, a node having a calculation point equal to or greater than the set threshold is set as a key person candidate and set in the single point table 5100.

  In step 460, it is determined whether the set key person candidate has an additional point of interstitial variation. If there are additional points as a result of the determination, the process goes to step 470. On the other hand, if there are no additional points, go to Step 480.

In step 470, the additional points of the interstitial variations and the calculated points are added together.
In step 480, the key person candidates are ranked based on the calculated points and stored in the key person candidate ranking table 5120.
This is the end of the detailed process of step 400.

Next, details of the processing in step 500 shown in FIG. 14 will be described with reference to FIG.
As shown in FIG. 18, in step 510, the key person candidate set in step 400 is read.
In step 520, attribute data of each key person candidate is read from the introducer table 5300.
In step 530, categorization (grouping) is set using attribute data of key person candidates as elements.
In step 540, key person candidates that match the categorization set in step 530 are searched.
In step 550, it is determined whether there are other key person candidates that do not fall into categorization. As a result of the determination, if there is another, the process goes to Step 530. On the other hand, when there is no key person candidate that does not apply, this processing ends.
This is the end of the detailed processing in step 500.

Next, FIG. 19 shows an example of a human network in which nodes representing customers are connected by links and represented by a graph structure.
As shown in FIG. 19, the human network 2000 includes nodes including existing contractors 2010 and non-contractors 2020, links 2030 having a direction from the referral source to the referral destination that connect the nodes, and sales representatives. The distance 2040 is represented by the shortest number of links as a base point. The human network of FIG. 19 periodically changes the human network structure whenever data related to nodes and links is newly added or changed. The history of the structural change can be stored in chronological order, and the extension of the network link can be visualized including differences in step 260 of FIG.

Next, FIG. 20 shows an example of the distinction based on the interstitial variation of links connecting the nodes.
As illustrated in FIG. 20, the interstitial variation 3000 represents a node connected to a certain node 3010 by a plurality of different interstitials (links 3020).
Next, FIG. 21 shows an example of an introducer customer list screen (10000) for bases to be displayed using the display device 63 of the customer relationship information registration / management device 60. The customer list list includes a person-in-charge column 10010, a customer ID column 10020, a name column 10030, an age column 10040, an address column 10050, and the like. For customers who are highly likely to be introduced, as indicated by 10060, the column color is changed and displayed.
The list (shown in the screen example 10000) is scrolled and displayed by the horizontal scroll bar 12010 and the vertical scroll bar 12010 when one screen cannot be fully displayed.

Data displayed in the list can be selected using the cursor 11000 for each customer.
When a certain customer is selected with the cursor 11000, detailed data of the customer is displayed on a pop-up screen 13000. Furthermore, for customers who are highly likely to be introduced, a “customer model” button 13010 for viewing the corresponding customer model is set, and the customer model categorized by clicking the button with the cursor 11000 is set. (Introducer image) 13020 is displayed.
This is the end of the description of the embodiment of the present invention.

10 Customer Model Building Unit 20 Introducer List Building Unit 30 Visit Result Feedback Unit 51 Human Network DB
52 Customer Model DB
53 Customer DB
54 Grade Management DB
55 Customer Relationship Information DB
60 Customer Relationship Information Registration / Management Device 70 Human Network Analysis Device 80 Data Management Device

Claims (7)

Introducer key person candidates are extracted according to the importance of the introducer from the network related to the past introducers, the introducer key person candidates are categorized, and a customer model is generated. An introducer candidate corresponding to the customer model is extracted from the customer list of the introducer.
It is an introducer candidate extraction system that consists of a customer relationship information registration / management device, a human network analysis device, and a data management device, and extracts introducer candidates that may introduce new customers from existing customers.
The human network analyzer is
Through the customer relationship information registration / management device, based on the relationship information of existing customers stored in the data management device, a customer model construction unit that constructs a customer model with a high possibility of introduction;
Based on the built customer model, an introducer list construction section that lists candidate introducers that match the customer model at each site from existing customers;
A visit result feedback unit for registering the introduction result after the visit to the customer candidate introduced by the introducer candidate included in the listed introducer list,
The introducer key person candidate is extracted from the human network information stored in the data management device according to the importance of the introducer, the introducer key person candidate is categorized, and a customer model is generated. An introducer candidate extraction system that extracts and outputs introducer candidates corresponding to the customer model from a customer list of unintroduced customers. In the customer candidate extraction system according to claim 1,
The introducer candidate extraction system characterized in that the human network analysis device itself generates the human network information and displays the generated human network information. In the customer candidate extraction system according to claim 1 or 2,
The human network analyzer itself generates the human network information, stores the generated human network in the data management device in time series, visualizes and displays the extension of the human network information, Feature introducer candidate extraction system. In the introducer candidate extraction system according to any one of claims 1 to 3,
The human network analyzer is
The human network information is characterized by calculating introduction possibility points from the two viewpoints of relationship continuity between nodes representing introducers and inter-node relationship variations, and ranking key person candidates. Introducer candidate extraction system. In the introducer candidate extraction system according to claim 4,
From the relationship continuity of the node, calculate the point of introduction possibility,
An introducer candidate extraction system characterized by identifying a node having a point equal to or greater than a set threshold as a key person candidate. In the introducer candidate extraction system according to claim 4,
Calculate referral points according to the number of pattern variations between the nodes,
The introducer candidate extraction system characterized by adding the points of the interstitial variations for the corresponding nodes to the points calculated from the relationship continuity between the nodes. In the introducer candidate extraction system according to any one of claims 1 to 6,
An introducer candidate extraction system that extracts new introducer-related data from customer data input by a sales representative after a customer visit and repeatedly generates a human network.

Images