JP2018124970A - Marketing device, marketing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform marketing matching needs of a customer by visualizing a sense-of-value of a customer.SOLUTION: A marketing device 100 calculates degree of similarity corresponding to a behavior of a customer on the basis of sample information including behavior information indicating behavior content and financial sense-of-value information indicating a financial sense-of-value corresponding to the behavior content and transaction information of the customer. The marketing device performs a predetermined arithmetic operation to the financial sense-of-value indicated by the financial sense-of-value information included in the sample information on the basis of the degree of similarity, and estimates the financial sense-of-value of the customer, and graphically displaying degree of deviation of the financial sense-of-values between the identified customer and the predetermined number of customers.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a marketing apparatus, a marketing method, and a program.

  In recent years, financial institutions such as banks, for example, when a large amount of money is transferred from a payee to a customer who is 55 years old or older, this matter is recognized as a retirement payment transfer event and An event that detects changes in individual customer attributes or changes in transaction behavior, such as proposing purchases, etc. A marketing technique called “based marketing” (hereinafter, EBM) has been proposed (for example, Non-Patent Document 1).

"IT Management Terms to Know", [Online], [Search January 16, 2017], Internet <URL: http://itpro.nikkeibp.co.jp/article/Keyword/20080731/311890/>

  However, since the marketing method described in Non-Patent Document 1 uses the occurrence of an event as a trigger for product proposal, the product is proposed to the customer every time the event occurs. For this reason, whether or not the proposed product is closed depends on customer values. In other words, customer values were not taken into account, and it was not enough from the viewpoint of marketing in accordance with customer needs.

  The present invention has been made in view of the above circumstances, and provides a marketing apparatus, a marketing method, and a program that can visualize customer values so that marketing can be performed in accordance with customer needs. It is an object.

In order to achieve the above object, a marketing device according to the first aspect of the present invention provides:
Based on pre-stored sample information (for example, the values behavior list 113) and pre-stored customer transaction information (for example, transaction information 111), the similarity corresponding to the customer's behavior is calculated. Similarity calculation means (for example, the process of step S102);
Financial value estimation means for estimating a financial value that is an index value of the customer's view of finance by performing a predetermined calculation based on the similarity calculated by the similarity calculation means (for example, the process of step S103, etc.) )When,
Customer specifying means (for example, processing in step S501 or step S502) for specifying a customer of financial values in accordance with a user's request based on the financial values estimated by the financial values estimating means;
Display means for displaying the customer information specified by the customer specifying means (for example, the processing in step S501 or step S502),
The sample information includes action information indicating action contents and financial value information indicating financial values corresponding to the action contents (for example, FIG. 4).
The similarity calculation means calculates the similarity corresponding to the customer's behavior by comparing the behavior information included in the sample information with the customer's transaction information (for example, processing in step S102),
The financial value estimation means estimates the customer's financial values by performing a predetermined calculation on the financial values information included in the sample information based on the similarity (for example, processing in step S406) Such),
The display means includes a graph display means for graphically displaying a degree of divergence of financial values between the customer specified by the customer specifying means and a predetermined number of customers.
It is characterized by that.

The customer specifying means is a customer having financial values and attributes according to a user request, based on customer attribute information stored in advance indicating customer attributes and the financial values estimated by the financial values estimating means. (For example, extracting only the customer value estimation list 117 of the target attribute in step S601 or step S701).
You may do it.

The display means associates customer information specified by the customer specifying means with previously stored customer attribute information indicating customer attributes, and displays the information as a proposal target display means (for example, step). Including processing of S703),
You may do it.

In order to achieve the above object, a marketing method according to the second aspect of the present invention includes:
Similarity calculation means responds to the customer's behavior based on pre-stored sample information (for example, values behavior list 113) and customer transaction information (for example, transaction information 111) stored in advance A similarity calculation step (for example, the process of step S102) for calculating the similarity to
A financial value estimation step in which financial value estimation means estimates a financial value that is an index value of the customer's concept of finance by performing a predetermined calculation based on the similarity calculated in the similarity calculation step (For example, the process of step S103),
A customer specifying step in which the customer specifying means specifies a customer with financial values in accordance with the user's request based on the financial values estimated in the financial values estimating step (for example, processing in step S501 or step S502). When,
The display means includes a display step (for example, processing in step S501 or step S502) for displaying the customer information specified in the customer specifying step,
The sample information includes action information indicating action contents and financial value information indicating financial values corresponding to the action contents (for example, FIG. 4).
In the similarity calculation step, the behavior information included in the sample information is compared with the customer transaction information to calculate a similarity corresponding to the customer's behavior (for example, processing in step S102),
In the financial value estimation step, the financial value of the customer is estimated by performing a predetermined calculation on the financial value information included in the sample information based on the similarity (for example, processing in step S406) Such),
The display step includes a graph display step of displaying a graph of the degree of divergence of financial values between the customer specified in the customer specifying step and a predetermined number of customers.
It is characterized by that.

In order to achieve the above object, a program according to the third aspect of the present invention provides:
Computer
Based on pre-stored sample information (for example, the values behavior list 113) and pre-stored customer transaction information (for example, transaction information 111), the similarity corresponding to the customer's behavior is calculated. Similarity calculation means (for example, the process of step S102),
Financial value estimation means for estimating a financial value that is an index value of the customer's view of finance by performing a predetermined calculation based on the similarity calculated by the similarity calculation means (for example, the process of step S103, etc.) ),
Customer specifying means (for example, processing in step S501 and step S502) for specifying a customer of financial values in accordance with a user's request based on the financial values estimated by the financial values estimating means;
Function as display means (for example, the process of step S501 or step S502) for displaying the customer information specified by the customer specifying means,
The sample information includes action information indicating action contents and financial value information indicating financial values corresponding to the action contents (for example, FIG. 4).
The similarity calculation means calculates the similarity corresponding to the customer's behavior by comparing the behavior information included in the sample information with the customer's transaction information (for example, processing in step S102),
The financial value estimation means estimates the customer's financial values by performing a predetermined calculation on the financial values information included in the sample information based on the similarity (for example, processing in step S406) Such),
The display means includes a graph display means for graphically displaying a degree of divergence of financial values between the customer specified by the customer specifying means and a predetermined number of customers.
It is characterized by that.

  According to the present invention, since customer values are visualized, it is possible to perform marketing that meets customer needs.

It is a block diagram which shows an example of the marketing apparatus which concerns on embodiment of this invention. It is a figure which shows an example of transaction information. It is a figure which shows an example of customer attribute information. It is a figure which shows an example of a sense of values action list. It is a figure which shows an example of the questionnaire performed in advance. It is a figure which shows an example of an action score list. It is a figure which shows an example of a score distribution table. It is a figure which shows an example of a similarity list. It is a figure which shows an example of a values estimation list. It is a figure which shows an example of the graph of the average value of the financial values in an investment trust contractor. It is a figure which shows an example of a prospective customer list. It is a flowchart which shows an example of a value estimation process. It is a flowchart which shows an example of an action score list creation process. It is a flowchart which shows an example of a similarity calculation process. It is a flowchart which shows an example of a value estimation list creation process. It is a flowchart which shows an example of a proposal destination customer selection process. It is a flowchart which shows an example of a visualization information generation process. It is a flowchart which shows an example of a prospective customer extraction process. It is a figure which shows an example of the similarity list in a modification.

  The marketing apparatus in the present embodiment is a general computer installed in a financial institution such as a bank or a securities company. As shown in FIG. 1, a storage unit 110, a control unit 120, and an input / output unit 130 are used. And a communication unit 140 and a system bus (not shown) for connecting them to each other. In addition, the marketing device is connected to a plurality of ATMs (Automated Teller Machines / automatic teller machines), window terminals, and the like so as to communicate with each other. ATMs are installed in financial institution stores, stations, and convenience stores. The window terminal is a terminal operated by a staff member who performs a window service of a financial institution, for example.

  The storage unit 110 includes a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The ROM stores a program executed by the CPU of the control unit 120 and data necessary for executing the program in advance. The RAM stores data that is created or changed during program execution. The storage unit 110 includes, as main information used by the program executed by the control unit 120, transaction information 111, customer attribute information 112, values behavior list 113, behavior score list 114, score distribution table 115, similarity list table 116, A value estimation list 117 and a prospective customer list 118 are stored.

  The transaction information 111 is information indicating a transaction history for each customer, and is stored for each customer. Specifically, as shown in FIG. 2, the transaction information 111 is a database that stores information related to transactions performed on the customer account, such as that a transfer has been made or a deposit / withdrawal has been made, in time series. is there. The transaction information is stored in association with the date / time information every time any processing (variation) is performed on the customer account. In the illustrated example, the items stored as the transaction information 111 are simplified for easy understanding. However, in the transaction information 111, for example, information on a transfer destination such as where the transfer is made, Detailed information on the transaction such as information on the transfer source such as where the transfer was made, information on the fee, information on the current balance, and the like are stored. The transaction information 111 is received and stored, for example, from an ATM or a window terminal.

  As shown in FIG. 3, the customer attribute information 112 includes account information such as a customer ID (information that can uniquely identify a customer), name and account number, and customer personal information such as an address, telephone number, and age. It is a database. The customer attribute information 112 is stored when an account is opened. Note that the customer attribute information 112, which is the database, is updated every time an account is newly opened. However, the customer attribute information 112 is also updated when changes to the customer occur, such as account cancellation or address change. The In the illustrated example, the items stored as the transaction information 111 are simplified for easy understanding. However, for example, the place of work or family structure, the presence or absence of a mortgage or the presence or absence of a card loan, insurance or investment In addition to the items shown in the figure, a plurality of items are stored in the customer attribute information 112 such as the number of trust purchases and family account numbers. That is, the customer attribute information 112 stores various information (information related to finance) related to the customer indicated by the customer ID.

  As shown in FIG. 4, the values behavior list 113 is information indicating the score of financial values for the customer and the score of the corresponding action content, and is stored in advance for each customer. The financial values are information indicating the customer's view of finance, such as the idea of saving and the management, and the score of the financial value is an index value of the customer's view of finance. Specifically, the values and behaviors list 113 shows the results of a four-choice questionnaire (see FIG. 5) conducted in advance for a plurality of customers over the Internet regarding items related to financial values and items related to behaviors. It is information to show. As shown in FIG. 4, the financial values in this embodiment include “values for savings”, “values for investment”, “values for debt”, “values for consumption”, “values for insurance”. Financial values of classification (major classification) such as “view” are set. Further, as shown in FIG. 4, the questionnaire questions shown in FIG. 5 are assigned correspondingly to each of a plurality of small classifications obtained by further classifying the major classifications. And 1 to 4 points are assigned to each small classification. Note that the number of small categories shown in FIG. 4 and the number of questions in the questionnaire shown in FIG. 5 may or may not be one-to-one (the number of questions corresponding to each small category may be different). .

  In addition, as shown in FIG. 4, the content of the action similarly corresponds to the above-mentioned financial values, “action on saving”, “action on operation”, “action on debt”, “action on consumption”, “ The action content of the classification (major classification) such as “behavior for insurance” is set. Further, as shown in FIG. 4, the questionnaire questions shown in FIG. 5 are assigned correspondingly to each of a plurality of small classifications obtained by further classifying the major classifications. And 1 to 4 points are assigned to each small classification. Note that the number of small categories shown in FIG. 4 and the number of questions in the questionnaire shown in FIG. 5 may or may not be one-to-one (the number of questions corresponding to each small category may be different). . In this embodiment, it is assumed that the value behavior list 113 for 1000 people is stored (assuming that the questionnaire shown in FIG. 5 is conducted for 1000 people).

  FIG. 5 shows the contents of a questionnaire conducted in advance for a plurality of customers via the Internet. As described above, the questionnaire is used when creating the values behavior list 113 shown in FIG. As shown in the figure, the questionnaire in this embodiment is prepared for 50 questions for each of the question items concerning the financial values and the question items concerning the action contents. Further, as shown in the figure, the answer results of questions 1 to 3 regarding the financial values are used when calculating the “value for savings” score in the small classification of FIG. In addition, the answer to question 1 regarding the action content is used to calculate the “savings” score in the subcategory in FIG. 4, and the answer to question 2 is “ATM payout” in the subcategory in FIG. Used when calculating the number of “gold.” As described above, the number of questions used when calculating the small classification in FIG. 4 varies depending on the contents of the small classification.

  For example, if the answer to question 1 regarding financial values is “A”, “4 points”, “B” if “3 points”, “C” if “2 points”, If “D”, “1 point”, “3” if the answer to question 2 is “A”, “4 points” if “B”, “1 point” if “C”, In the case of “D”, “2 points”, etc., a score of 1 to 4 is set in advance according to the answer result for each question, and the average value of each score is shown in FIG. (The average value of the questions 1 to 3 of the questionnaire regarding financial values is the financial values corresponding to the subdivision of “savings” in FIG. 4). In addition, the answer of one question item may be used for calculation of the score of a plurality of small classifications. Since the questionnaire is performed again regularly (for example, every three years), the value behavior list 113 shown in FIG. 5 is updated when the questionnaire is performed again. Further, when a questionnaire is newly conducted for 100 people, a new value behavior list for 100 people is stored, and a value behavior list for 1100 people is also stored.

  The action score list 114 is a list in which the actual actions of individual customers are quantified. As shown in FIG. 6, the action score list 114 is information in which a score of 1 to 4 is assigned to each predetermined action data. is there. Each action data is data having the same contents as the contents classified into the action classification items in the values action list 113 shown in FIG. The action score list 114 is created by an action score list creation unit 121 of the control unit 120 described later. The action score list 114 shown in FIG. 6 is stored for the number of customers indicated by the transaction information 111 (the number of action score lists 114 corresponding to the number of customers is stored). As will be described later, the score corresponding to each action data in the action score list 114 is given by adding the transaction contents corresponding to each action data from the transaction information 111, but the transaction contents corresponding to the action data are If it does not exist, points may not be awarded.

  As shown in FIG. 7, the score distribution table 115 is a table in which 1 to 4 points are assigned in advance according to the result of counting the transaction contents for each action data. The score distribution table 115 is stored in the storage unit 110 in advance, and is referred to in an action score list creation process in the action score list creation unit 121 described later.

  As shown in FIG. 8, the similarity list table 116 is a table in which the degree of similarity of the action contents in all the value action list 113 of the value action list numbers “1” to “1000” is shown to the customer. Yes, stored for each customer. The similarity is a value indicating how similar the behavior (actually performed) indicated by the transaction information corresponding to the customer and the behavior indicated by the small classification of the behavior content in the values behavior list 113 are. It is calculated by a similarity calculation process described later. The similarity list table 116 is created by similarity calculation processing described later.

  The value estimation list 117 is estimation information of the customer's financial values indicated by the transaction information 111. In the values estimation list 117, as shown in FIG. 9, estimated values of 1 to 4 points are set for each financial value data, and the estimated values of the points are calculated by the similarity analysis unit 122 described later. It is set based on the calculated similarity (based on the similarity list 116 shown in FIG. 8). The value estimation list 117 is created by a value estimation list creation process described later. The financial value data is data having the same content as the content classified into the small categories of the financial value items in the value behavior list 113 shown in FIG.

  As shown in FIG. 11, the prospective customer list 118 is a table in which information on customers (prospects) that are targets for product proposals is shown. The prospective customer list 118 is created by a prospective customer list creation process described later.

  The control unit 120 includes a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), and the like. The control unit 120 operates according to a program stored in the storage unit 110 and executes processing according to the program. The control unit 120 operates according to a program stored in the storage unit 110. The control unit 120 includes an action score list creation unit 121, a similarity analysis unit 122, a value estimation list creation unit 123, and visualization information generation as main functional units provided by the program stored in the storage unit 110. Unit 124 and a prospective customer extraction unit 125.

  The action score list creation unit 121 creates a behavior score list 114 shown in FIG. 6 with reference to the transaction information 111 stored in the storage unit 110 (action score list creation processing). Specifically, the action score list creation unit 121 sequentially counts the transaction contents corresponding to each predetermined action data with reference to the transaction information 111, and the score distribution table 115 (preliminarily stored in the storage unit 110). The action score list 114 is generated by assigning points corresponding to each action data based on FIG. For example, in the case where points corresponding to the behavior data of “ATM paid withdrawals” are given as an example, the behavior score list creation unit 121 refers to the transaction information 111 shown in FIG. Aggregate the number of “withdrawal” action details corresponding to the paid time. The “ATM payout count” in this embodiment is the number of withdrawals per month during a time period (including weekends and holidays) when the ATM fee is charged. Then, with reference to the score distribution table shown in FIG. 7, if the counting result is “0” to “1”, “4 points” is assigned, and if “2” to “4”, “3 points” is assigned. If “5” to “9”, “2 points” is given, and if “10 or more”, “1 point” is given. As shown in FIG. 7, the score distribution table 115 is a table to which points 1 to 4 are assigned corresponding to the respective aggregation results, and is stored in the storage unit 110 in advance. The action score list creation unit 121 creates an action score list 114 for all customers in which the transaction information 111 is stored.

  The similarity analysis unit 122 stores the scores of the behavior data in the behavior score list 114 created by the behavior score list creation unit 121 and the behavior contents in all the values behavior lists 113 stored in the storage unit 110 in advance. The similarity is calculated by comparing the score of the small classification (similarity calculation processing). In this embodiment, since the value behavior list for 1000 people is stored as described above, the similarity analysis unit 122 performs 1000 value behavior lists for one behavior score list 114. Each similarity is calculated in comparison with 113. That is, as shown in FIG. 8, a similarity list 116 is created by calculating the similarity of the values behavior list for 1000 people with respect to the behavior of one customer. Specifically, the similarity analysis unit 122 assigns a score of the action data of “saving amount” in the action score list 114 shown in FIG. 6 and a score of a small classification of “save amount” in the values action list 113 shown in FIG. Between the corresponding items such as “ATM paid withdrawal count” in the behavior score list 114 and subclass scores of “ATM paid withdrawal count” in the values behavior list 113. Compare points in order. For example, when there are 40 items to be compared (when there are 40 types of behavior data), the similarity is “100%” if the scores are all the same, and the similarity is “0%” if they are all different. ”And only the scores of 10 items match, and when the remaining 30 items have different scores, the degree of similarity is calculated as“ 10% ”. Instead of calculating one similarity level for all comparison items, for example, the similarity level may be calculated for each item corresponding to the large classification shown in FIG. Note that the method of calculating the similarity is an example, and indicates how similar the actual behavior of the customer indicated by the transaction information 111 and the behavior indicated by the small classification of the behavior content in the values behavior list 113 are. If possible, the calculation method is not limited to the above calculation method, and may be calculated by an arbitrary calculation method. For example, the absolute value of the difference between the total score of each behavior data in the behavior score list 114 and the total score of each small classification of the behavior content in the values behavior list 113 is obtained, and the absolute value is calculated as the score of each behavior data. It is good also considering the value which multiplied by 100 to the value which divided by the sum total and subtracted from 1 as a similarity.

  Based on the similarity calculated by the similarity analysis unit 122, the value estimation list creation unit 123 calculates financial value data (financial value in the value behavior list 113) for each customer for which the similarity is calculated. Scores of items corresponding to each small classification of views (see FIG. 9) are estimated based on the values behavior list 113 to create a values estimation list 117 (value estimation list creation processing). Specifically, the value estimation list creation unit 123 performs calculation by weighting the scores of each of the small values of the financial values in the 1000 values behavior list 113 according to the similarity, thereby calculating the target customer's A score estimation list 117 is created by estimating the score of each financial value data.

  Originally, all of the value behavior list number 1 to the value behavior list number 1000 are targets, but in order to facilitate understanding, the value behavior list number “1”, the value behavior list number “600”, The case where the three values behavior list 113 with the values behavior list number “1000” is taken as an example will be described. The value estimation list creation unit 123 is a customer corresponding to the customer ID “500001001” as follows. The value estimation list 117 is created. As shown in FIG. 8, the behavior of the customer corresponding to the customer ID “500001001” is 80% similar to the content of the behavior of the values behavior list 113 corresponding to the values behavior list number “1”, and the values behavior list number. It is 10% similar to the behavior content of the values behavior list 113 corresponding to “600”, and 30% similar to the behavior content of the values behavior list 113 corresponding to the values behavior list number “1000”. Therefore, the value estimation list creation unit 123 multiplies each value of the small classification of financial values in the value behavior list 113 corresponding to the value behavior list number “1” by 80 / (80 + 10 + 30) and the value. A value obtained by multiplying each score of the small classification of financial values in the values behavior list 113 corresponding to the values behavior list number “600” by 10 / (80 + 10 + 30) and values corresponding to the values behavior list number “1000”. The sum of the same minor classifications obtained by multiplying each score of the financial values in the action list 113 by 30 / (80 + 10 + 30) is estimated as the score of each financial value data of the customer corresponding to the customer ID “500001001”. The value estimation list 117 is created. That is, a value obtained by multiplying the score of the “savings orientation” of the financial values in the values behavior list 113 corresponding to the values behavior list number “1” by 80 / (80 + 10 + 300) and the values behavior list number. A value obtained by multiplying the score of the “savings orientation” of the financial values in the values behavior list 113 corresponding to “600” by 10 / (80 + 10 + 30) and a value corresponding to the values behavior list number “1000” The sum of the value obtained by multiplying the score of the “savings orientation” in the behavioral value list 113 by 30 / (80 + 10 + 30) and the “savings orientation” of the customer corresponding to the customer ID “50000101” A value estimation list 117 with points is created. The numbers after the decimal point shall be rounded off.

  The visualization information generation unit 124 is, for example, a value created by the value estimation list creation unit 123 in response to a request from a user (for example, a bank clerk) who wants to know the tendency of financial values of investment trust contractors. Based on the view estimation list 117, as shown in FIG. 10, the average value of the financial values of the investment trust contractor when the average value of the financial values of all the customers is 0 is graphed (visualization information generation). processing). Specifically, the visualization information generation unit 124, when the user's request is to know the tendency of the financial values of the investment trust contractor, all customers created by the value estimation list creation unit 123 The value estimation list 117 of the investment trust contractor is extracted from the value estimation list 117 for. That is, the visualization information generation unit 124 extracts the value estimation list 117 corresponding to the user's request. And the average value of the score corresponding to each financial value data of the extracted value estimation list 117 is calculated, and each average value is the score of each financial value data of the value estimation list 117 for all customers. The degree of deviation from the average value (reference value) is calculated, and the calculated result is graphed in association with each financial value data.

  The prospective customer extraction unit 125 corresponds to, for example, each financial value data in the value estimation list 117 of the investment trust contractor calculated by the visualization information generation unit 124 from the value estimation list 117 of the investment trust non-contractor. A value estimation list 117 similar to the average score is extracted. Specifically, the prospective customer extraction unit 125 extracts a value estimation list 117 that is equal to or higher than the similarity specified in advance by the user, for example, the similarity is 80% or higher. For example, when there are 40 items of financial value data, the prospective customer extraction unit 125 refers to each financial value data in the value estimation list 117 of the investment trust non-contractor and the corresponding score, and The value estimation list 117 that matches 32 or more items with the score of the financial value data in the value estimation list 117 (similarity is 80% or more) is extracted. Note that the method for calculating the similarity is an example, and the calculation method is not limited to the above calculation method as long as it can indicate how similar (similar values) each financial value data is. May be. For example, the absolute difference between the total score of each financial value data in the value estimation list 117 of the investment trust contractor and the total score of each financial value data in the value estimation list 117 of the investment trust contractor A value obtained by dividing the absolute value by the total score of non-investment trustees and subtracting one from 100 may be used as the similarity. Further, the prospective customer extraction unit 125 refers to the customer IDs in the value estimation list 117 of all the extracted investment trust non-contractors, recognizes the customer indicated by the customer ID as a prospective customer, and shows it in FIG. A prospect list 118 is created based on the customer attribute information 112 (prospect list creation processing).

  The input / output unit 130 includes a keyboard, a mouse, a camera, a microphone, a liquid crystal display, an organic EL (Electro-Luminescence) display, and the like, and is a device for inputting and outputting data.

  The communication unit 140 has a serial interface or an analog interface for receiving an analog signal. The communication unit 140 communicates with ATMs and window terminals under the control of the control unit 120.

    The above is the configuration of the marketing apparatus 100. Next, the operation of the marketing apparatus 100 will be described with reference to FIGS. As described above, the marketing apparatus 100 can execute various processes such as an action score list creation process, a similarity calculation process, a value estimation list creation process, a visualization information generation process, and a prospective customer extraction process. Note that the action score list creation process, the similarity calculation process, and the value estimation list creation process are executed as a series of processes (value estimation process; FIG. 12) according to a user operation. Similarly, the visualization information generation process and the prospective customer extraction process are executed as a series of processes (suggested customer selection process; FIG. 16) according to the operation by the user. Depending on the operation by the user, various processes such as an action score list creation process, a similarity calculation process, a value estimation list creation process, a visualization information generation process, and a prospective customer extraction process may be executed as a series of processes. Good.

  FIG. 12 is a flowchart illustrating an example of the value estimation process. As described above, the value estimation process is started by an operation on the input / output unit 130 by the user (for example, an operation of selecting a value estimation process execution button). When the value estimation process is executed, the control unit 120 executes the action score list creation process by the function of the action score list creation unit 121 (step S101). After executing the processing of step S101, the control unit 120 executes similarity calculation processing by the function of the similarity analysis unit 122 (step S102). After executing the process of step S102, the control unit 120 executes the value estimation list creation process by the function of the value estimation list creation unit 123 (step S103), and ends the value estimation process. By executing the value estimation process, the financial values of each customer are estimated.

  FIG. 13 is a flowchart showing an example of the action score list creation process performed in step S101 of FIG. In the action score list creation process shown in FIG. 12, the behavior score list creation unit 121 first refers to the transaction information 111 corresponding to an arbitrary customer (target customer) among the transaction information 111 stored in the storage unit 110. (Step S201), the transaction contents corresponding to the predetermined action data (data of the same item as the action classification sub-category item in the values action list 113) are aggregated (Step S202). Subsequently, the action score list creation unit 121 refers to the score distribution table 115 stored in advance in the storage unit 110 (step S203), and assigns points corresponding to each action data based on the totaled result obtained in step S202. (Step S204), the action score list 114 corresponding to the target customer is created.

  After executing the process of step S204, the action score list creation unit 121 determines whether or not the process has been performed with reference to the transaction information 111 of all customers stored in the storage unit 110 (step S205). When it determines with having referred the transaction information 111 of all the customers (step S205; Yes), an action score list creation process is complete | finished. On the other hand, when it is determined that the transaction information 111 of all customers has not been referred to (step S205; No), the action score list creation unit 121 returns to the process of step S201, and the transaction information 111 corresponding to an unprocessed customer. The above process is repeatedly executed with reference to FIG.

  In this way, the action score list creation process is repeatedly executed until transaction information 111 of all customers is referred to (until points are assigned to the behavior data of all customers). By the action score list creation process, the action score list 114 shown in FIG. 6 is created for all customers.

  FIG. 14 is a flowchart illustrating an example of similarity calculation processing performed in step S102 of FIG. In the similarity calculation process shown in FIG. 14, the similarity analysis unit 122 first selects an action score list for an arbitrary customer from the action score list 114 created in the action score list creation process in step S <b> 101 of FIG. 12. Reference is made (step S301). Then, the values behavior list 113 stored in advance in the storage unit 110 is referred to (step S302). In this embodiment, as described above, the 1000 value behavior list 113 having the value behavior list numbers “1” to “1000” is stored in advance, and the process of step S302 is performed first. Then, the values behavior list 113 whose value behavior list number is “1” is referred to.

  Subsequently, the similarity analysis unit 122 compares the score of each behavior data in the behavior score list 114 referred to in step S301 with the score of each minor classification of the behavior content in the values behavior list 113 referred to in step S302. The similarity is calculated (step S303). In step S303, as described above, the score of the action data of “save amount” in the action score list 114 shown in FIG. 6 and the score of the small classification of “save amount” in the values action list 113 shown in FIG. The score of “ATM paid withdrawals” in the behavior score list 114 and the subcategory score of “ATM paid withdrawals” in the values behavior list 113, etc. Compare in order. For example, when there are 40 items to be compared (when there are 40 types of behavior data), the similarity is “100%” if the scores are all the same, and the similarity is “0%” if they are all different. ”And only the scores of 10 items match, and when the remaining 30 items have different scores, the degree of similarity is calculated as“ 10% ”. Further, in the process of step S303, the similarity list table 116 shown in FIG. 8 is created by associating the calculated similarity with the values behavior list 113. As described above, the method for calculating the similarity is arbitrary.

  After executing the processing of step S303, the similarity analysis unit 122 refers to all the value behavior lists 113 for the behavior score list 114 (target customer behavior score list 114) that is the target of the similarity calculation. It is determined whether or not (step S304). Specifically, in this embodiment, since the values behavior list 113 for 1000 people is stored, in step S304, all 1000 values from the values behavior list number “1” to “1000” are stored. It is determined whether or not the viewing behavior list 113 has been referred to. When it is determined that not all the value behavior list 113 has been referred to the behavior score list of the target customer (step S304; No), the similarity analysis unit 122 returns to step S302, and refers to the value behavior that has been referenced. With reference to the value behavior list 113 of the next number in the list 113, the above processing is repeatedly executed.

  On the other hand, when it is determined that all the value behavior lists 113 have been referred to the behavior score list 114 of the target customer (step S304; Yes), the similarity analysis unit 122 performs the behavior score list 114 of all customers. Is determined (step S305). When it is determined that the action score list 114 of all customers has been referred to (step S305; Yes), the similarity calculation process ends. On the other hand, when it is determined that the action score list 114 of all customers has not been referred (step S305; No), the similarity analysis unit 122 returns to the process of step S301, and the action score list corresponding to the unprocessed customer. The above process is repeatedly executed with reference to 114.

  In this manner, the similarity calculation processing is performed until the behavior score list 114 of all customers is referred to (the behavior data scores in the behavior score list 114 of all customers are It is repeated (until it is compared with the score of each subclass). By the similarity calculation process, the similarity between the target customer and all the value behavior lists 113 is calculated for all the customers, and a similarity list table 116 as shown in FIG. 8 is created for each customer. .

  FIG. 15 is a flowchart illustrating an example of the value estimation list creation process performed in step S103 of FIG. In the value estimation list creation process shown in FIG. 15, the value estimation list creation unit 123 first refers to the similarity list table 116 (see FIG. 8) corresponding to an arbitrary customer (target customer) (step S401). . Subsequently, the value estimation list creation unit 123 refers to the value behavior list 113 stored in advance in the storage unit 110 (step S402). As described above, in this embodiment, the 1000 values behavior list 113 having the values behavior list numbers “1” to “1000” is stored in advance, and the process of step S402 is performed first. Then, the values behavior list 113 whose value behavior list number is “1” is referred to.

  After executing the process of step S402, the value estimation list creation unit 123 determines the ratio of the similarity of the value behavior list 113 referred to in step S402 to the similarity of the all values behavior list 113 (effect). Ratio) is calculated (step S403). Specifically, in the process of step S403, the degree of similarity corresponding to the value behavior list 113 referred to in step S402 corresponds to each of the value behavior list 113 having the value behavior list numbers “1” to “1000”. The quotient divided by the sum of similarities is calculated as the influence ratio. After executing the processing of step S403, the value estimation list creation unit 123 calculates the product of the score of the financial value minor classification in the value behavior list 113 referred to in step S402 and the influence ratio, as the minor classification. It calculates for every (step S404).

  After executing the process of step S404, the value estimation list creation unit 123 sets all values (for the target customer) in the similarity list 116 (target customer similarity list 116) referred to in step S401. It is determined whether or not the action list 113 has been referred to (step S405). Specifically, in this embodiment, since the values behavior list 113 for 1000 people is stored, in step S405, all 1000 values from the values behavior list number “1” to “1000” are stored. It is determined whether or not the viewing behavior list 113 has been referred to (whether or not the processing up to step S404 has been executed based on all the values behavior lists 113). When it is determined that all the value behavior lists 113 have not been referred to the target customer (step S405; No), the value estimation list creation unit 123 returns to step S402, and refers to the value behavior list 113 that has been referenced. Referring to the value behavior list 113 of the next number, the above process is repeatedly executed.

  On the other hand, when it is determined that all the value behavior lists 113 have been referred to the target customer (step S405; Yes), the value estimation list creation unit 123 performs each small classification of the calculation result in the process of step S404. Is calculated as an estimated value of financial values in the target customer (step S406), and stored as a values estimation list 117 shown in FIG. Originally, there are calculation results of “1” to “1000” for one small classification of financial values, but in order to facilitate understanding, the case of only three calculation results will be described as an example. Among the calculation results of step S404 for the value behavior list number “1” of the target customer (the calculation results of the respective small categories of the financial values in the value behavior list 113 shown in FIG. 4), The calculation result (rounded down to the second decimal place) is “2.66” in the “saving-oriented sub-category”, and the calculation result of step S404 for the value behavior list number “600” in the target customer is Among them, the calculation result in the same small classification (small classification of “savings orientation”) is “0.33”, and the calculation result of step S404 for the value behavior list number “1000” in the target customer Among them, when the calculation result in the same small classification (the “saving-oriented” small classification) is “1.00”, the calculation result in step S <b> 404 in the processing of step S <b> 406 includes the small classification (“saving-oriented”). The estimated value of financial values in the (minor category) is “4”, which is the sum of “2.66”, “0.33”, and “1.00” (3.99) rounded off to the nearest whole number. . Then, the small classification corresponding to the calculated estimated value (small classification of “savings orientation”) is the financial value data (financial value data of “savings orientation”) shown in FIG. 9, and the estimated values are associated with each other. And stored as the value estimation list 117.

  After executing the process of step S405, the values estimation list creation unit 123 determines whether or not the estimated values of financial values have been calculated for all customers (step S407). Specifically, in the process of step S407, it is determined whether or not the similarity list 116 of all customers has been referred to. When it is determined that the estimated value of financial values has been calculated for all customers (step S407; Yes), the average value of the points of the respective financial value data in the value estimation list 117 of all customers is used as the reference value. (Step S408), and the value estimation list creation process is terminated. On the other hand, when it is determined that the estimated values of financial values have not been calculated for all customers (step S407; No), the values estimation list creating unit 123 returns to the process of step S401, and the unprocessed customers The above processing is repeatedly executed with reference to the similarity list 116 corresponding to. In this embodiment, the reference value is calculated for all customers. However, not all customers such as customers (for example, gender, region, age, etc.) that satisfy the user's requirements are selected. A reference value may be calculated as a target.

  In this way, the value estimation list creation process is repeatedly executed until the similarity list 116 of all customers is referred to (until the estimated values of financial values for all customers are calculated). Therefore, the value estimation list 117 as shown in FIG. 9 is created for each customer by the value estimation list creation processing.

  FIG. 16 is a flowchart illustrating an example of a proposal customer selection process. As described above, the proposed customer selection process is started by an operation on the input / output unit 130 by the user. Specifically, the processing is started when a user request such as wanting to know the tendency of the financial values of the investment trust contractor is selected by an operation on the input / output unit 130 by the user. It should be noted that a plurality of types of user requests are prepared in advance, and one of the plurality of user requests may be selected according to an operation by the user. When the proposed customer selection process is executed, the control unit 120 executes the visualization information generation process by the function of the visualization information generation unit 124 (step 501). After executing the process of step S501, the control unit 120 executes a prospective customer extraction process by the function of the prospective customer extraction unit 125 (step S502), and ends the proposed customer selection process. By executing the proposed customer selection process, a prospective customer according to the user's request is selected.

  FIG. 17 is a flowchart illustrating an example of the visualization information generation process performed in the process of step S501 of FIG. In the visualization information generation process illustrated in FIG. 17, the visualization information generation unit 124 includes a value estimation list corresponding to the user's request among the value estimation lists 117 for all customers created in the process of step S <b> 103 in FIG. 12. All 117 are extracted (step S601). And the average value of each financial value data shown by the extracted value estimation list 117 is calculated (step S602). In the process of step S601, for example, if the user's request is to know the tendency of the financial values of the investment trust contractor, the values for all customers created by the value estimation list creation unit 123 are displayed. From the estimation list 117, the value estimation list 117 of the investment trust contractor is extracted, and the average value of each financial value data in the value estimation list 117 of the investment trust contractor extracted in step S602 is extracted. Is calculated.

  Subsequently, the visualization information generation unit 124 calculates the average values of the financial value data calculated in the process of step S602 in the values estimation list 117 for all customers calculated in the process of step S103 of FIG. The degree of deviation from the average value of the financial value data (the reference value calculated in step S408 in FIG. 15) is calculated for each financial value data (step S603). That is, in the process of step S603, the degree to which the financial value data of the investment trust contractor differs from the financial value data of the entire customer is calculated. Specifically, in the process of step S603, the reference value for each financial value data calculated in step S408 in FIG. 15 is compared with the average value of each financial value data calculated in step S602, and the degree of divergence is calculated. calculate.

  After executing the process of step S603, the visualization information generation unit 124 sets the average value (reference value) of each financial value data for all customers calculated in the process of step S103 of FIG. The divergence calculated in the process of S603 is graphed as shown in FIG. 10 (step S604). After executing the process of step S604, the visualization information generation unit 124 ends the visualization information generation process. As described above, since the visualization information generation process displays a graph as shown in FIG. 10, for example, how far the financial value data of the investment trust contractor differs from the financial value data of the entire customer. The user can grasp at a glance. That is, it is possible to grasp at a glance the trend of financial values of customers who are likely to reach an investment trust contract.

  FIG. 18 is a flowchart illustrating an example of a prospective customer extraction process performed in the process of step S502 of FIG. In the prospective customer extraction process shown in FIG. 18, the prospective customer extraction unit 125 is designated in advance from the value estimation list 117 other than the value estimation list 117 extracted in the process of step S601 in FIG. A value estimation list 117 having a similarity or higher is extracted (step S701). For example, if the value estimation list 117 extracted in step S601 in FIG. 16 is the investment trust contractor's value estimation list 117, in the process of step S701, the investment trust non-contractor's value estimation list is obtained. A value estimation list 117 having a degree of similarity or higher specified in advance is extracted from 117. Specifically, when there are 40 items of financial value data, in the process of step S701, the value of each financial value data in the value estimation list 117 of the investment trust non-contractor is referred to and the values of the investment trust contractor are referred to. The value estimation list 117 is extracted that matches 32 or more items with the score of the financial value data in the estimation list 117 (when the similarity specified in advance is 80%). As described above, the method for calculating the degree of similarity is arbitrary, and in the process of step S701, the value estimation list 117 having a degree of similarity or higher calculated by an arbitrary method may be extracted.

  After executing the process of step S701, the prospective customer extraction unit 125 specifies the customer attribute information 112 corresponding to the customer ID based on the customer ID of the value estimation list 117 extracted in step S701 (step S702). . Subsequently, the prospective customer extraction unit 125 extracts various data indicated by the identified customer attribute information 112, creates a prospective customer list 118 as shown in FIG. 11 (step S703), and performs prospective customer extraction processing. finish. In this way, by the prospective customer extraction process, a list of non-contractors of investment trusts with financial values similar to the financial values of customers who are already contracting investment trusts, that is, financial values of customers who have contracts for products. A list of customers having similar financial values is created as the prospective customer list 118. Therefore, the user can improve the possibility of reaching a close by making a product proposal to the customer shown in the prospective customer list 118, and perform marketing that meets the customer's needs. it can.

  The above is the operation of the marketing apparatus 100. As described above, the marketing device 100 according to the present embodiment digitizes the actual behavior of the individual customer by the action score list creation process, and the value stored in advance by the similarity calculation process. Compared with the behavior content of the viewing behavior list 113, the similarity of behavior is calculated. Then, the marketing apparatus 100 creates the value estimation list 117 by estimating the customer's financial values by the value estimation list creation process. In addition, the marketing apparatus 100 displays a graph according to the user's request using the value estimation list 117 created in this way by the visualization information generation process, and the user's request by the prospective customer extraction process. Create a prospect list that is a list of prospects on demand. Therefore, the user can not only grasp at a glance how the customer's financial values satisfying the requested content tend to be for the entire customer, but also display the prospective customer list 118. By making a product proposal to the indicated customer, it is possible to improve the possibility of reaching a contract and to perform marketing that meets the customer's needs.

(Modification)
In addition, this invention is not limited to the said embodiment, A various deformation | transformation and application are possible. For example, the marketing apparatus 100 may not include all the technical features described in the above embodiment, and may be a part described in the above embodiment so that at least one problem in the related art can be solved. It may be provided with the following structure. Further, at least some of the following modifications may be combined.

  In the above embodiment, the value behavior list 113 corresponding to all the value behavior list numbers shown in the similarity list 116 shown in FIG. 8 is targeted in the value estimation list creation processing in step S103 of FIG. An example of calculating an influence ratio and calculating an estimated value of a customer's financial values is shown as an example. For example, the target value behavior list 113 is similar in that the similarity in the similarity list 116 shown in FIG. 8 is “30%” or higher, “40%” or higher (any setting can be changed). Only the values behavior list 113 whose degree is a predetermined value or more may be targeted. In this case, in step S402 of FIG. 15, the target value behavior list 113 may be referred to. According to this, the customer's financial values can be estimated with higher accuracy.

  Furthermore, in the said embodiment, although the similarity calculation process shown in FIG. 14 showed the example which calculates one similarity with respect to all the comparison items of the action score list 114 and the values action list | wrist 113, For example, as shown in FIG. 19, the degree of similarity may be calculated for each item corresponding to the major classification (major classification shown in FIG. 4). In this case, in the value estimation list creation process illustrated in FIG. 15, based on the similarity calculated for each item corresponding to the major classification, the influence ratio for each major classification is calculated, and the estimated value is calculated. Good.

  For example, as shown in FIG. 19, the similarity in the “behavior for saving” is “80%” and the similarity in the “behavior for operation” is “80%” with the values behavior list 113 whose value behavior list number is “1”. “40%” for the “behavior for saving” and “10%” for the “behavior for operation” with the values behavior list 113 with the value behavior list number “1000”. In the subcategory items (items such as “savings orientation” and “long-term savings orientation”) corresponding to “values for savings”, the referenced value behavior list 113 (value behaviors) The similarity (80) of the “behavior for saving” in the values behavior list 113) whose list number is “1” is the sum of the similarities of the “behavior for saving” (80) in all the values behavior list 113 (80・ ・ ・ ・ Multiply by the quotient divided by +40), and subcategory items (items such as “operation planability” and “risk tolerance”) corresponding to “operational values” refer to the referenced values. Similarity (20) of “behavior for operation” in the behavior list 113 (value behavior list 113 whose value behavior list number is “1”), and similarity of “behavior for operation” in all values behavior list 113 Multiply by the quotient divided by the sum of (20+... +10). According to this, since the estimated value is calculated by using different degrees of similarity for each major classification, the customer's financial values can be estimated more accurately.

  Moreover, in the said embodiment, in the process of step S701 in the prospective customer extraction process shown in FIG. 18, the example which extracted the value estimation list | wrist 117 based on the similarity degree made into all the financial value data was shown. But this is an example. For example, as shown in FIG. 10, the financial values of investment trust contractors are lower than the standard values for items such as “restaurant-oriented” and “consumption planning”, and “operation planning” and “risk” Since the number of points corresponding to items such as “tolerance” tends to be higher than the standard value, when extracting prospective customers from non-investment trustees, “eating out”, “consumption planning”, “operation plan” Value estimation list 117 based on the similarity for a part of the financial value data, such that the similarity of items such as “sex” and “risk tolerance” is equal to or greater than the similarity specified in advance. May be extracted. Furthermore, the similarity for all financial value data is “80% or more”, and some (in the above-mentioned example, “restaurant orientation”, “consumption planning”, “operation planning”, “ It is also possible to extract the value estimation list 117 that satisfies a plurality of conditions such that the degree of similarity of “risk tolerance” is “90%” or more. That is, the condition for extracting the value estimation list 117 may be arbitrarily changed. According to such a configuration, it is possible to deal with various proposal methods such as when product proposals are to be made to more customers or when product offerings are to be provided to more core customers.

  Moreover, in the said embodiment, the prospective customer list | wrist 118 is produced for all the customers shown by customer ID of the value estimation list 117 extracted in the process of step S701 in the prospective customer extraction process shown in FIG. An example is shown, but this is an example. For example, a part of the customer ID of the value estimation list 117 extracted in the process of step S701, such as creating a prospective customer list 118 only for men or only living in Tokyo, may be excluded. The prospective customer list 118 to be created may be divided into a plurality according to different age groups. According to this, customers who make product proposals can be classified more finely. Note that, for example, in the visualization information generation process shown in FIG. 17, graph display may be performed for each customer attribute such as only a male or a customer living in Tokyo. In this case, it is only necessary to check the customer attribute information when extracting the value estimation list 117 in the processing of step S601 or step S701, and extract only the customer value estimation list 117 of the target attribute. Also in the visualization information generation process, a plurality of graphs to be displayed may be created according to attributes such as sex, address, and age. According to this, it is possible to grasp at a glance characteristics for each customer attribute such as gender, address, and age. Note that it may be possible to switch and display each attribute. That is, an attribute to be displayed may be selected in advance, the value estimation list 117 corresponding to the selection may be extracted, and the graph may be switched and displayed for each selected attribute.

  Further, for example, not only the customer indicated by the customer ID of the value estimation list 117 extracted in the process of step S701 in the prospective customer extraction process shown in FIG. 18, but also the prospective customer list for the family who lives with the customer. You may make it add to the table | surface 118. FIG. For example, when the customer indicated by the customer ID of the value estimation list 117 extracted by the prospective customer extraction process has a spouse, information corresponding to the spouse is acquired from the customer attribute information, and the prospective customer list is obtained. 118 may be added. According to this, although it is not similar to the target financial values, it is possible to make a product proposal to the whole family sharing the livelihood, which can be used for decision making of the contract.

  Moreover, in the said embodiment, the prospective customer list 118 is produced by a prospective customer extraction process, and the user proposes goods to the said prospective customer by making the customer shown in the said prospective customer list 118 into a prospective customer. Although shown, this is an example. For example, the function of the control unit 120 of the marketing apparatus 100 may have a function of creating a direct mail for product proposals with reference to the created prospective customer list 118. Moreover, you may have the function to propose goods via a network with respect to the customer terminal of the customer shown by the produced prospective customer list 118. FIG. In addition, if the information of the transaction card inserted during the operation of the ATM is acquired and the customer indicated by the information of the transaction card is the customer shown in the created prospective customer list 118, the operation of the ATM You may make it notify the content of a product proposal to ATM so that a product proposal may be made in ATM after the end (it may be before the end). Moreover, you may have the function to perform all of these. According to this, it is possible to make a product proposal without depending on the user's work, it is possible to make a product proposal at a plurality of timings, improve work efficiency, and increase the possibility of closing Can do.

  In the case where the above functions are realized by sharing an OS (Operating System) and an application, or by cooperation between the OS and the application, only a part other than the OS may be stored in the medium.

  It is also possible to superimpose a program on a carrier wave and distribute it via a communication network. For example, the program may be posted on a bulletin board (BBS, Bulletin Board System) on a communication network, and the program may be distributed via the network. Then, the above-described processing may be executed by starting these programs and executing them in the same manner as other application programs under the control of the operating system.

DESCRIPTION OF SYMBOLS 100 Marketing apparatus, 110 Memory | storage part, 111 Transaction information, 112 Customer attribute information, 113 Value behavior list, 114 Action score list, 115 score distribution table, 116 Similarity list, 117 Value estimation list, 118 Prospect list , 120 control unit, 121 action score list creation unit, 122 similarity analysis unit, 123 sense of value estimation list creation unit, 124 visualization information generation unit, 125 prospective customer extraction unit, 130 input / output unit, 140 communication unit

Claims (5)

A similarity calculation means for calculating a similarity corresponding to the behavior of the customer based on the sample information stored in advance and the customer transaction information stored in advance;
Financial values estimation means for estimating financial values that are index values of the customer's view of finance by performing a predetermined calculation based on the similarity calculated by the similarity calculation means;
Customer specifying means for specifying a customer of financial values according to a user's request based on the financial values estimated by the financial values estimating means;
Display means for displaying customer information specified by the customer specifying means,
The sample information includes action information indicating action contents, and financial values information indicating financial values corresponding to the action contents,
The similarity calculation means calculates the similarity corresponding to the behavior of the customer by comparing the behavior information included in the sample information with the transaction information of the customer,
The financial value estimation means estimates the customer's financial values by performing a predetermined calculation based on the similarity to the financial values information included in the sample information,
The display means includes a graph display means for graphically displaying a degree of divergence of financial values between the customer specified by the customer specifying means and a predetermined number of customers.
A marketing device characterized by that. The customer specifying means is a customer having financial values and attributes according to a user request, based on customer attribute information stored in advance indicating customer attributes and the financial values estimated by the financial values estimating means. To identify,
The marketing apparatus according to claim 1, wherein: The display means includes a proposal destination display means for displaying the customer information specified by the customer specifying means as a customer of a product proposal target in association with customer attribute information stored in advance indicating customer attributes.
The marketing apparatus according to claim 1 or 2, wherein A similarity calculation step in which the similarity calculation means calculates the similarity corresponding to the behavior of the customer based on the sample information stored in advance and the customer transaction information stored in advance;
A financial value estimation step in which financial value estimation means estimates a financial value that is an index value of the customer's concept of finance by performing a predetermined calculation based on the similarity calculated in the similarity calculation step When,
A customer identifying step for identifying a customer of financial values according to a user's request based on the financial values estimated in the financial values estimating step;
A display step for displaying information of the customer specified in the customer specifying step;
The sample information includes action information indicating action contents, and financial values information indicating financial values corresponding to the action contents,
In the similarity calculation step, the behavior information included in the sample information is compared with the customer transaction information to calculate a similarity corresponding to the customer behavior,
In the financial value estimation step, the financial values of the customer are estimated by performing a predetermined calculation based on the similarity to the financial values information included in the sample information,
The display step includes a graph display step of displaying a graph of the degree of divergence of financial values between the customer specified in the customer specifying step and a predetermined number of customers.
A marketing method characterized by that. Computer
A similarity calculation means for calculating a similarity corresponding to the behavior of the customer based on the sample information stored in advance and the customer transaction information stored in advance;
Financial value estimation means for estimating a financial value that is an index value of the customer's view of finance by performing a predetermined calculation based on the similarity calculated by the similarity calculation means;
Customer specifying means for specifying a customer of financial values according to a user's request based on the financial values estimated by the financial values estimating means;
Function as display means for displaying customer information specified by the customer specifying means,
The sample information includes action information indicating action contents, and financial values information indicating financial values corresponding to the action contents,
The similarity calculation means calculates the similarity corresponding to the behavior of the customer by comparing the behavior information included in the sample information with the transaction information of the customer,
The financial value estimation means estimates the customer's financial values by performing a predetermined calculation based on the similarity to the financial values information included in the sample information,
The display means includes a graph display means for graphically displaying a degree of divergence of financial values between the customer specified by the customer specifying means and a predetermined number of customers.
A program characterized by that.

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