JP2016184330A - Approach support system, Approach support method and Approach support program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an approach support system, an approach support method and an approach support program for making outbound business efficient.SOLUTION: A support server 20 comprises: a host system 30 in which transaction results of customers are recorded; and a control part 21 connected to an SNS site 10 and a person-in-charge terminal 40. Then the control part 21 acquires, from the SNS site 10, a transmission message that each customer transmits at the period in which the customer makes a transaction with respect to a plurality of customers whose transaction results are recorded in the host system 30, and generates a prediction model consisting of information common to a plurality of acquired transmission messages for each article type in the transaction. Further, when a transmission message matching the prediction model is newly acquired from the SNS site 10, an instruction for outbound business for each article type of the prediction model to the sender of the transmission message is sent to the person-in-charge terminal 40.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an approach support system, an approach support method, and an approach support program for supporting outbound work.

  In the call center, there are an outbound service for making a call to a prospective customer and introducing a new product, and an inbound service for responding to a call from the customer. In the outbound business, call center operators conduct business while interacting with prospective customers. Therefore, a technique for supporting outbound work in a call center has been studied (see, for example, Patent Document 1). In the technique described in this document, the order status for each customer is registered in the database according to the order form, and the outbound request made from the sales department through the network is registered in the database. In the computer terminal operated by the operator, information on the order status and outbound request for each customer is searched from the database, and the search result is displayed in a list on the display device. Call requirements are determined according to the order status for each customer, and the determination result is included in the list display as information for telephone reception to the customer.

  Further, a technique for improving the customer acquisition rate in the outbound business has been studied (for example, see Patent Document 2). The outbound work support apparatus described in this document extracts a set of outbound materials corresponding to accepted work contents from a database. Then, a theme and a customer knowledge level value in which the success rate of the outbound business is a predetermined value or more are extracted from the database. A set of customer IDs corresponding to each extracted theme and customer knowledge level value is extracted from the database, and customer information corresponding to each customer ID is extracted from the database. Then, the extracted outbound document and customer information correspondence are output in association with each other and transmitted to the outbound operator terminal.

  In addition, word-of-mouth sites, blogs, and SNS (Social Networking Service) have been spreading in recent years, and in the advertising method from the corporate side, not only unilateral advertisement transmission by TV CM etc. but also consumers through social media Attention has been focused on marketing through dialogue. Consumers are placing the highest priority on social media word of mouth as a motivation for buying a product. Therefore, in trend extraction from social media, a technique for extracting a trend including users only of mass media and potential users of social media has been studied (for example, see Patent Document 3). In the technology described in this document, the trend analysis device analyzes the metadata collected from the mass media and extracts mass media trend keywords. Keyword diffusion data is calculated from mass media trend keywords and social media data, and trend keywords suitable for the user's business are extracted using the keyword diffusion and the product master.

JP 2004-304243 A JP 2008-84228 A JP 2014-81882 A

  In the above-described outbound business, when approaching a customer, efficient outbound business cannot be realized unless the approach destination is extracted and the timing of the approach is appropriate. On the other hand, there are cases where the needs of potential customers cannot be made clear only by outgoing messages in social media services.

  The present invention has been made to solve the above-described problems, and provides an approach support system, an approach support method, and an approach support program for improving the efficiency of outbound work.

  (1) An approach support system that solves the above problem includes a transaction information storage unit that records customer transaction results, and a control unit that is connected to a social media site and a person-in-charge terminal. And the said control part is the said social media site about the outgoing message which each said customer transmitted at the time when each said customer's transaction was performed about the several customer by which the transaction performance was recorded on the said transaction information storage part. And a learning unit that generates a prediction model including information common to the plurality of acquired outgoing messages for each product type of the transaction. Furthermore, when an outgoing message that matches the prediction model is newly acquired from the social media site, the product of the prediction model is sent to the person in charge of the approach destination that is the sender of the outgoing message. Provided with an approach destination extraction unit that outputs an instruction of an outbound job for a type. Thereby, the approach destination in an outbound business can be specified using social media.

  (2) In the approach support system, the control unit is further connected to a customer information storage unit that records a customer attribute of the customer, and the learning unit uses the customer attribute recorded in the customer information storage unit. Preferably, a prediction model including common information is generated, and the approach destination extraction unit specifies an approach destination using a customer attribute included in the prediction model. Thereby, an approach destination can be specified in consideration of customer attributes.

  (3) In the approach support system, the control unit is further connected to an account information storage unit that records customer account information, and the approach destination extraction unit stores the account information of the approach destination in the account information storage unit. It is preferable to determine whether or not an outbound operation to the approach destination is necessary using the account information. Thereby, an approach destination can be specified in consideration of account information.

  (4) In the approach support system, the control unit obtains the outgoing message of the approach destination from the social media site, and determines timing information for performing the outbound job based on the outgoing time zone of the outgoing message. It is preferable. Thereby, the outbound business can be performed in consideration of the approach destination situation specified based on the information acquired from the social media.

  (5) In the approach support system, the control unit obtains the outgoing message of the approach destination from the social media site, and determines timing information for performing the outbound job based on a transmission place of the outgoing message. Is preferred. Thereby, an outbound work can be performed in consideration of the approach destination location specified based on information acquired from social media.

  (6) In the approach support system, the control unit obtains transaction results of the approach destination from the transaction information storage unit, and determines timing information for performing the outbound operation based on a transaction time zone of the transaction results. It is preferable to do. Thereby, an outbound operation | work can be performed in consideration of the approach destination condition specified based on the transaction time zone of a transaction performance.

  According to the present invention, the efficiency of outbound work can be improved.

Explanatory drawing of the approach assistance system of embodiment of this invention. Explanatory drawing of the process sequence of the learning process of this embodiment. It is explanatory drawing of the process sequence of this embodiment, Comprising: (a) is approach destination extraction processing, (b) is explanatory drawing of approach processing. Explanatory drawing of the prediction model of this embodiment. Explanatory drawing of the prediction model of other embodiment. Explanatory drawing of the prediction model of other embodiment.

  Hereinafter, an embodiment that embodies an approach support system, an approach support method, and an approach support program will be described with reference to FIGS. In this embodiment, the case where the approach destination in an outbound operation | work is specified using the transmission information in social media, customer attribute information, and transaction information is assumed. Here, it is assumed that a social networking service is used as social media.

As shown in FIG. 1, in this embodiment, an SNS site 10, a support server 20, a host system 30, and a person in charge terminal 40 connected via a network are used.
The SNS site 10 is a server computer (social media site) that provides a social networking service. In this SNS site 10, the outgoing message registered by the user is associated with the user and released together with the outgoing date and time.

  The support server 20 is a computer system that supports outbound work. The support server 20 includes a control unit 21, a prediction model storage unit 22, and a transaction condition information storage unit 23.

  The control unit 21 includes a control unit such as a CPU (not shown), and a memory such as a RAM and a ROM, and performs processes described later (each process such as a learning stage, an information acquisition stage, an approach destination extraction stage, and an outbound support stage). Then, by executing the approach support program, the control unit 21 functions as a learning unit 211, an information acquisition unit 212, an approach destination extraction unit 213, and an outbound support unit 214.

  The learning unit 211 executes a process of generating a prediction model for predicting the relevance between the outgoing message registered by the customer at the SNS site 10 and the product type for which the customer has made a transaction. The learning unit 211 holds data related to the product type to be learned. A product (for example, a mortgage loan) handled in the outbound business is used as the product type to be learned. Furthermore, the learning unit 211 holds an evaluation target period table. In this evaluation target period table, an evaluation target period (for example, the number of days) for acquiring outgoing messages in the social networking service is recorded for each product type. Furthermore, the learning unit 211 includes a memory for temporarily storing sample information (product type, customer attribute, outgoing message) used for learning.

The information acquisition unit 212 executes processing for acquiring a message transmitted by the user from the SNS site 10.
The approach destination extraction unit 213 executes processing for specifying an approach destination based on a transmission message in the SNS site 10 and information recorded in the host system 30.
The outbound support unit 214 executes a process of instructing the person-in-charge terminal 40 to perform an outbound work for the identified approach destination.

  The prediction model storage unit 22 stores a prediction model (data) for specifying the approach destination of the outbound work. This prediction model is registered when a learning process described later is performed.

In the present embodiment, as shown in FIG. 4, the prediction model 220 is configured to include data for specifying a product type for performing an outbound job based on an event and a customer attribute.
In the event data area, data related to the customer situation (event) for specifying the customer who is the approach destination is recorded. This event is specified using, for example, a keyword included in a message transmitted in a specific user situation. For this reason, a keyword is recorded in association with each event. Examples of events include byte determination and enrollment.

In the customer attribute data area, data related to customer attributes for specifying a customer who is an approach destination of the outbound business is recorded.
In the product type data area, data relating to an identifier for specifying a product (product type) for which an outbound operation is performed is recorded with respect to the approach destination.

  In the transaction condition information storage unit 23, a transaction condition management record relating to conditions for performing a transaction is recorded. This transaction condition management record is recorded when a new product type is registered. In the transaction condition management record, data relating to the product type and transaction conditions are recorded.

In the product type data area, data relating to an identifier for specifying a product for which an outbound operation is performed is recorded.
In the transaction condition data area, data relating to conditions for performing a transaction for this product type is recorded. As a transaction condition, for example, a condition regarding a transaction status such as a transaction record (presence / absence of transaction) of the product and an account balance necessary for performing the transaction is used.

The host system 30 is a computer system that manages customer information and transaction information. The host system 30 includes a customer information storage unit 32 and a transaction information storage unit 33.
The customer information storage unit 32 stores customer management records for customers of financial institutions. The customer management record is recorded when a customer opens an account with a financial institution. In the customer management record, data related to customer ID, name, account identifier, contact information, address, customer attribute, and access destination are recorded.

In the customer ID data area, data relating to an identifier for identifying each customer of the financial institution is recorded.
In the name data area, data related to the customer's name is recorded.
In the account identifier data area, data relating to an identifier (main branch code, type code, account number, etc.) for specifying an account held by the customer is recorded.

In the contact information and address data areas, data related to the customer's contact information (telephone number and the like) and address are recorded.
In the customer attribute data area, data relating to customer attributes (age, gender, job type, etc.) is recorded.

  In the access destination data area, data on an acquisition destination for acquiring a message transmitted in the social networking service used by the customer is recorded. In this data area, for example, an account of a social networking service used by the user, a URL where a message is disclosed, and the like are recorded.

The transaction information storage unit 33 stores a transaction management record related to a transaction with a customer and an account management record for managing an account opened by the customer. The transaction information storage unit 33 functions as an account information storage unit that records customer account information.
The transaction management record is recorded when a transaction is performed with a customer. In the transaction management record, transaction ID, transaction date and time, product type, customer ID, and data regarding transaction contents are recorded.

Data relating to an identifier for identifying each transaction is recorded in the transaction ID data area.
In the transaction date / time data area, data related to the date and time when the transaction was performed is recorded.

In the product type data area, data relating to an identifier for specifying a product for which a transaction has been performed is recorded.
In the customer ID data area, data relating to an identifier for identifying a customer who has made a transaction is recorded.
In the transaction content data area, data relating to the transaction content (for example, transaction quantity and amount) performed by the customer is recorded.

  The account management record is registered when an account is opened in a financial institution. In this account management record, data relating to an account identifier, an account balance, and a deposit / withdrawal history is recorded.

Data relating to an identifier for identifying each account is recorded in the account identifier data area.
Data relating to the balance of the account is recorded in the account balance data area.
In the deposit / withdrawal history data area, data related to transaction date / time, amount, description, and location is recorded for deposit / withdrawal in this account. In addition, when cash is deposited or withdrawn using an automatic teller machine (ATM), information on the location of the used automatic teller machine is recorded.

  The person-in-charge terminal 40 is a computer terminal used by a person in charge in charge of outbound work. The person-in-charge terminal 40 includes a control unit, a storage unit, an input unit including a keyboard and a pointing device, and an output unit including a display.

  Next, processing performed using the above system will be described with reference to FIGS. In the present embodiment, description will be made in the order of learning processing for learning a prediction model for identifying an approach destination, approach destination extraction processing for extracting an approach destination, and approach processing for outbound to the extracted approach destination.

(Learning process)
First, the learning process will be described with reference to FIG. In this process, a plurality of customers registered in the host system 30 are used as learning subjects for creating a prediction model. Here, a processing target person is specified among a plurality of learning target persons, and the following processing is repeated.

  First, the control unit 21 of the support server 20 executes transaction information acquisition processing (step S1-1). Specifically, the learning unit 211 of the control unit 21 extracts the transaction management record of the processing target person (learning target person) from the transaction information storage unit 33 of the host system 30.

  Next, the control part 21 of the support server 20 performs the determination process about whether there exists a transaction of the goods classification of learning object (step S1-2). Specifically, the learning unit 211 of the control unit 21 confirms whether or not the extracted transaction management record includes a record in which the product type to be learned is recorded.

When it is determined that the transaction of the learning target product type is not included (in the case of “NO” in step S1-2), the processing for this processing target person (learning target person) is terminated.
When it is determined that the transaction of the product type to be learned is included (in the case of “YES” in step S1-2), the control unit 21 of the support server 20 executes the customer attribute information acquisition process (step S1). -3). Specifically, the learning unit 211 of the control unit 21 extracts the customer management record of the person to be learned from the customer information storage unit 32 of the host system 30 and acquires customer attributes. Then, the learning unit 211 temporarily stores the customer attribute in the memory in association with the product type.

Next, the control part 21 of the support server 20 performs the acquisition process of social media transmission information (step S1-4). Specifically, the learning unit 211 of the control unit 21 acquires an access destination from the customer management record of this processing target person (learning target person). And the learning part 211 specifies the SNS site 10 which this process subject uses using the access destination of a customer management record. Next, the learning unit 211 specifies the evaluation target period of this product type from the evaluation target period table. Next, the learning unit 211 acquires a transmission message for the evaluation target period from the SNS site 10 based on the date on which the transaction to be learned is performed. Then, the learning unit 211 temporarily stores the acquired outgoing message in the memory in association with the product type and the customer attribute.
The above processing is repeated until all learning subjects are completed.

  And the control part 21 of the support server 20 performs a prediction model registration process (step S1-5). Specifically, the learning unit 211 of the control unit 21 specifies a common customer attribute for each product type temporarily stored in the memory. Next, a common keyword included in the outgoing message is specified as an event for each product type and common customer attribute. Then, the learning unit 211 generates a prediction model 220 including the identified event, common customer attributes, and product type, and records the prediction model 220 in the prediction model storage unit 22.

(Approach destination extraction process)
Next, the approach destination extraction process will be described with reference to FIG. In the present embodiment, as shown in FIG. 4, the product type is specified using the event specified based on the message acquired from the SNS site 10 and the customer attribute recorded in the customer information storage unit 32. Furthermore, the approach destination that performs the outbound business is specified based on the transaction status.

  First, the control part 21 of the support server 20 performs the acquisition process of social media transmission information (step S2-1). Specifically, the information acquisition unit 212 of the control unit 21 periodically acquires an access destination from a customer management record recorded in the customer information storage unit 32 of the host system 30. And the learning part 211 accesses each SNS site 10 using the access destination of a customer management record, and acquires the transmission message which the customer transmitted.

  Next, the control part 21 of the support server 20 performs a prediction model application process (step S2-2). Specifically, the approach destination extraction unit 213 of the control unit 21 applies the prediction model 220 recorded in the prediction model storage unit 22 to the acquired outgoing message. Here, the approach destination extraction unit 213 checks whether or not the acquired outgoing message includes a keyword corresponding to the event of the prediction model 220 recorded in the prediction model storage unit 22. When an event of the prediction model 220 is detected in the outgoing message, the approach destination extraction unit 213 acquires a customer attribute from the customer management record in the customer information storage unit 32 of the host system 30. Then, the approach destination extraction unit 213 performs matching between the customer attribute acquired from the customer information storage unit 32 and the customer attribute of the prediction model 220.

  Next, the control unit 21 of the support server 20 executes a product specifying process (step S2-3). Specifically, when the approach destination extraction unit 213 of the control unit 21 determines that the customer attribute acquired from the customer information storage unit 32 matches the customer attribute of the prediction model 220 and the prediction model 220 is applicable, The product type of the prediction model 220 is specified.

  Next, the control unit 21 of the support server 20 executes transaction status specifying processing (step S2-4). Specifically, the approach destination extraction unit 213 of the control unit 21 acquires the transaction condition for this product type from the transaction condition information storage unit 23. Next, the approach destination extraction unit 213 acquires a customer ID, a customer account management record that matches the customer account identifier, and a transaction management record from the transaction information storage unit 33.

  Next, the control part 21 of the support server 20 performs the determination process about whether approach is possible (step S2-5). Specifically, the approach destination extraction unit 213 of the control unit 21 compares the transaction conditions with the transaction status specified using the account management record and the transaction management record. Then, the approach destination extraction unit 213 determines that the approach is possible when the transaction status matches the transaction conditions, and determines that the approach is impossible when the transaction status does not match.

When it is determined that the approach is impossible (in the case of “NO” in step S2-5), the control unit 21 of the support server 20 ends the process for this customer.
On the other hand, when it is determined that an approach is possible (“YES” in step S2-5), the control unit 21 of the support server 20 executes an approach process (step S2-6). This process will be described below.

(Approach processing)
Next, the approach process will be described with reference to FIG. Here, the time zone in which the approach to the customer is performed is specified using the transmission status of the outgoing message on social media and the transaction information recorded in the host system 30.

  First, the control unit 21 of the support server 20 executes a social media transmission history acquisition process (step S3-1). Specifically, the outbound support unit 214 of the control unit 21 acquires a transmission history of outgoing messages sent from the SNS site 10 during the most recent predetermined period for the customer specified as the approach destination. And the outbound assistance part 214 specifies the utilization time zone of the SNS site 10 based on a transmission log | history.

  Next, the control unit 21 of the support server 20 executes transaction history acquisition processing (step S3-2). Specifically, the outbound support unit 214 of the control unit 21 acquires the usage history of the automatic teller machine in the account management record of the transaction information storage unit 33 of the host system 30.

  Next, the control unit 21 of the support server 20 executes an approach time zone specifying process (step S3-3). Specifically, the outbound support unit 214 of the control unit 21 predicts a predetermined user situation (for example, at home or going out) based on a keyword included in the outgoing message of the outgoing call history. Then, the outbound support unit 214 specifies a time zone corresponding to the predicted predetermined user situation (for example, at home). Further, the outbound support unit 214 specifies the home absent time zone based on the usage history of the automatic teller machine. And the outbound support part 214 specifies the approach time slot | zone which excluded the absent time slot | zone from the home time slot | zone by the transmission log | history in a social networking service, and the utilization log | history of an automatic teller machine.

  Next, the control part 21 of the support server 20 performs an outbound instruction | indication process (step S3-4). Specifically, the outbound support unit 214 of the control unit 21 gives an outbound instruction to the person-in-charge terminal 40. This outbound instruction includes customer information about the approach destination (sender of the outgoing message), information on the product type specified using the prediction model 220, and the home time zone. In this case, the person in charge makes a telephone call to the approach destination at home time based on the outbound instruction.

As described above, according to the present embodiment, the following effects can be obtained.
(1) In this embodiment, the control unit 21 of the support server 20 executes social media transmission information acquisition processing (step S1-4) and prediction model registration processing (step S1-5). Thereby, the information for extracting an approach destination can be produced | generated based on the transmission information in social media.

  (2) In this embodiment, the control unit 21 of the support server 20 executes social media transmission information acquisition processing (step S2-1) and prediction model application processing (step S2-2). And the control part 21 of the support server 20 performs a goods specific process (step S2-3). As a result, it is possible to specify a product to be proposed to the approach destination in the outbound business.

  (3) In this embodiment, the control unit 21 of the support server 20 executes a transaction status specifying process (step S2-4) and a determination process (step S2-5) as to whether or not an approach is possible. When it is determined that an approach is possible (“YES” in step S2-5), the control unit 21 of the support server 20 executes an approach process (step S2-6). As a result, it is possible to perform the outbound work in consideration of the situation of the approach destination.

  (4) In the present embodiment, the control unit 21 of the support server 20 executes a social media transmission history acquisition process (step S3-1). Thereby, based on the information transmitted in social media, it is possible to take an approach at an appropriate timing in consideration of the customer's situation. Further, the control unit 21 of the support server 20 executes transaction history acquisition processing (step S3-2). Thereby, a customer's condition can be specified based on a transaction situation, and an approach can be performed at an appropriate timing.

In addition, you may change the said embodiment into the following aspects.
In the above embodiment, it is assumed that a social networking service is used as social media. Social media is not limited to this. Any medium that can identify a user can be applied, and for example, an electronic bulletin board, a blog, or the like can be used.

  In the above-described embodiment, the prediction model 220 recorded in the prediction model storage unit 22 is configured to include data for specifying the product type for performing the outbound business based on the event and the customer attribute. Then, the product type is specified using the event specified based on the outgoing message acquired from the SNS site 10 and the customer attribute recorded in the customer information storage unit 32. Furthermore, the approach destination that performs the outbound business is specified based on the transaction status. The prediction model for specifying the approach destination is not limited to the above-described configuration.

  For example, as shown in FIG. 5, based on the event specified by the keyword included in the outgoing message acquired from the SNS site 10 and the transaction status (account balance, transaction results, etc.) from the transaction information storage unit 33, You may make it identify needs. In this case, the control unit 21 of the support server 20 generates a prediction model 221 of a customer who has funding needs based on the outgoing message of the customer who had some transaction and the transaction status. Then, the control unit 21 of the support server 20 identifies a customer who has the funding needs as an approach destination. Furthermore, the control unit 21 of the support server 20 compares the transaction status acquired from the transaction information storage unit 33 with the transaction conditions recorded in the transaction condition information storage unit 23, and specifies the product type that matches the transaction status.

Moreover, the information specified based on the transmission message acquired from the SNS site 10 is not limited to an event, Information which can specify a sender | caller's condition can be used.
As in the prediction model 222 shown in FIG. 6, for example, the living situation may be specified based on the outgoing message. In this case, the living situation is predicted based on keywords (for example, “low income”, “high expenditure”, etc.) included in the outgoing message.

  In the above embodiment, the control unit 21 of the support server 20 performs an outbound instruction process (step S3-4). In this case, the person in charge makes a telephone call to the approach destination at home time based on the outbound instruction. The contact method in the outbound business is not limited to a telephone call. For example, you may make it transmit the message regarding an outbound operation | work via the social networking service which the approach destination uses.

  DESCRIPTION OF SYMBOLS 10 ... SNS site, 20 ... Support server, 21 ... Control part, 211 ... Learning part, 212 ... Information acquisition part, 213 ... Approach destination extraction part, 214 ... Outbound support part, 22 ... Prediction model storage part, 23 ... Transaction conditions Information storage unit, 30 ... host system, 32 ... customer information storage unit, 33 ... transaction information storage unit, 40 ... person-in-charge terminal.

Claims (8)

A transaction information storage unit that records customer transaction results;
An approach support system comprising a social media site and a control unit connected to a person-in-charge terminal,
The control unit is
For a plurality of customers whose transaction results are recorded in the transaction information storage unit, at the time when the transaction of each customer was performed, an outgoing message sent by each customer is acquired from the social media site,
A learning unit that generates a prediction model composed of information common to the plurality of acquired outgoing messages for each product type of the transaction;
When the outgoing message matching the prediction model is newly acquired from the social media site, the product type of the prediction model for the approach destination that is the sender of the outgoing message for the person in charge terminal An approach support system characterized by having an approach destination extraction unit that outputs an instruction of the outbound business of the user. The control unit is further connected to a customer information storage unit that records customer attributes of the customer,
The learning unit generates a prediction model including common information using the customer attribute recorded in the customer information storage unit,
The approach support system according to claim 1, wherein the approach destination extraction unit specifies an approach destination using a customer attribute included in the prediction model. The control unit is further connected to an account information storage unit that records customer account information,
The approach destination extraction unit acquires the account information of the approach destination from the account information storage unit, and determines whether or not an outbound operation to the approach destination is necessary using the account information. The approach support system according to 1 or 2. The control unit acquires the approach destination outgoing message from the social media site,
The approach support system according to any one of claims 1 to 3, wherein timing information for performing the outbound operation is determined based on a transmission time zone of the outgoing message. The control unit acquires the approach destination outgoing message from the social media site,
The approach support system according to any one of claims 1 to 4, wherein timing information for performing the outbound work is determined based on a place where the outgoing message is sent. The control unit obtains the transaction result of the approach destination from the transaction information storage unit,
The approach support system according to any one of claims 1 to 5, wherein timing information for performing the outbound work is determined based on a transaction time zone of the transaction record. A transaction information storage unit that records customer transaction results;
A method for providing approach support using an approach support system including a social media site and a control unit connected to a person-in-charge terminal,
The control unit is
For a plurality of customers whose transaction results are recorded in the transaction information storage unit, at the time when the transaction of each customer was performed, an outgoing message sent by each customer is acquired from the social media site,
For each product type of the transaction, a learning stage for generating a prediction model composed of information common to the plurality of acquired outgoing messages;
When the outgoing message matching the prediction model is newly acquired from the social media site, the product type of the prediction model for the approach destination that is the sender of the outgoing message for the person in charge terminal An approach support method characterized by performing an approach destination extraction step of outputting an instruction of the outbound business of the user. A transaction information storage unit that records customer transaction results;
A program for providing approach support using an approach support system including a social media site and a control unit connected to a person-in-charge terminal,
The control unit
For a plurality of customers whose transaction results are recorded in the transaction information storage unit, at the time when the transaction of each customer was performed, an outgoing message sent by each customer is acquired from the social media site,
A learning unit that generates a prediction model composed of information common to the plurality of acquired outgoing messages for each product type of the transaction,
When the outgoing message matching the prediction model is newly acquired from the social media site, the product type of the prediction model for the approach destination that is the sender of the outgoing message for the person in charge terminal An approach support program characterized by functioning as an approach destination extraction unit that outputs an instruction of an outbound business.

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