TWI692733B - Data analysis method and financial system - Google Patents

Data analysis method and financial system Download PDF

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TWI692733B
TWI692733B TW107130708A TW107130708A TWI692733B TW I692733 B TWI692733 B TW I692733B TW 107130708 A TW107130708 A TW 107130708A TW 107130708 A TW107130708 A TW 107130708A TW I692733 B TWI692733 B TW I692733B
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client
recommended
attribute
recommended client
marketing information
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TW202011317A (en
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吳思緯
林賢仲
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彰化商業銀行股份有限公司
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Abstract

A data analysis method includes following operations. A referring data is collected. The referring data includes a referrer terminal and a referral terminal. A first user attribute of the referrer terminal and a second user attribute of the referral terminal are obtained by analyzing a feature of recommendation process, a feature of user relation and a financial interaction record between the referrer terminal and the referral terminal. Marketing information is sent to the referrer terminal and the referral terminal respectively according to the first user attribute and the second user attribute.

Description

資料分析方法及金融系統 Data analysis method and financial system

本揭示文件係關於一種金融業的資料分析方法及系統,特別是一種推薦因子關聯分析方法及系統。 This disclosure document relates to a data analysis method and system for the financial industry, especially a method and system for correlation analysis of recommendation factors.

在現今強調個人理財重要性的時代,各家金融機構為了增加客戶數量及推廣業務而推出不同活動、優惠或商品吸引客戶購買或使用。 In today's era of emphasizing the importance of personal finance, various financial institutions have launched different activities, offers or products to attract customers to purchase or use in order to increase the number of customers and promote business.

但金融商品種類繁多,活動資訊推陳出新,客戶短時間不易得知適合自己的商品、活動或行銷資訊,因此金融機構藉由各項用戶數據對不同客戶做屬性強弱分析,藉以了解客戶個性及消費習慣,推薦客戶購買或參加適合自己的金融商品及活動。 However, there are many types of financial commodities, and event information is updated. It is not easy for customers to know the commodities, activities or marketing information suitable for them in a short time. Therefore, financial institutions use various user data to analyze the strength of different customers to understand their personality and consumption habits. , Recommend customers to buy or participate in their own financial products and activities.

對於金融機構來說,如何增加用戶數量,並精確分析不同客戶達到精準行銷,實為各金融機構一直以來的核心目標。 For financial institutions, how to increase the number of users and accurately analyze different customers to achieve accurate marketing have been the core goals of various financial institutions.

本揭示內容的一實施例中,一種資料分析方法包含下列操作。收集一推薦資料,推薦資料包含一推薦用 戶端與一被推薦用戶端。該資料分析方法分析推薦用戶端與被推薦用戶端彼此之間的一推薦過程特徵、一用戶關聯特徵或一金融互動紀錄,得到推薦用戶端的一第一客戶屬性以及被推薦用戶端的一第二客戶屬性。根據第一客戶屬性及第二客戶屬性,分別將複數個行銷資訊發送至推薦用戶端與被推薦用戶端。 In an embodiment of the present disclosure, a data analysis method includes the following operations. Collect a recommendation, the recommendation contains a recommendation The client and a recommended client. The data analysis method analyzes a recommendation process feature, a user association feature or a financial interaction record between the recommended client and the recommended client to obtain a first client attribute of the recommended client and a second client of the recommended client Attributes. According to the first client attribute and the second client attribute, a plurality of marketing information is sent to the recommended client and the recommended client, respectively.

本揭示內容的另一實施例中,一種金融系統包含一推薦用戶端、一被推薦用戶端以及一金融伺服器,金融伺服器分別與推薦用戶端及被推薦用戶端通訊連接,金融伺服器用以收集推薦用戶端與被推薦用戶端之間的一推薦資料。分析推薦用戶端與被推薦用戶端彼此之間的一推薦過程特徵、一用戶關聯特徵或一金融互動紀錄,得到推薦用戶端的一第一客戶屬性以及被推薦用戶端的一第二客戶屬性,以及根據第一客戶屬性及第二客戶屬性,分別將複數個行銷資訊發送至推薦用戶端與被推薦用戶端。 In another embodiment of the present disclosure, a financial system includes a recommended client, a recommended client, and a financial server. The financial server is in communication with the recommended client and the recommended client, respectively. In order to collect a recommendation data between the recommended client and the recommended client. Analyze a recommendation process feature, a user association feature, or a financial interaction record between the recommended client and the recommended client to obtain a first client attribute of the recommended client and a second client attribute of the recommended client, and according to The first customer attribute and the second customer attribute send a plurality of marketing information to the recommended client and the recommended client, respectively.

綜上所述,即可根據第一與第二客戶屬性的相似度,發送適合的行銷資訊,達到更加精準的行銷。 In summary, according to the similarity between the attributes of the first and second customers, appropriate marketing information can be sent to achieve more accurate marketing.

100‧‧‧金融系統 100‧‧‧Financial System

120‧‧‧推薦客戶端 120‧‧‧Recommended client

140‧‧‧被推薦客戶端 140‧‧‧Recommended client

160‧‧‧金融伺服器 160‧‧‧Financial Server

162‧‧‧通訊電路 162‧‧‧Communication circuit

164‧‧‧處理器 164‧‧‧ processor

200‧‧‧資料分析方法 200‧‧‧Data analysis method

300‧‧‧資料分析方法 300‧‧‧Data analysis method

M1、M2‧‧‧行銷資訊 M1, M2‧‧‧‧ Marketing information

REC‧‧‧推薦資訊 REC‧‧‧Recommended information

D1a、D1b‧‧‧第一屬性分析圖 D1a, D1b‧‧‧The first attribute analysis chart

D2a、D2b‧‧‧第二屬性分析圖 D2a, D2b‧‧‧Second attribute analysis chart

S210、S220、S221、S222、S223、S224、S230、S231、S232、S233‧‧‧步驟 S210, S220, S221, S222, S223, S224, S230, S231, S232, S233

第1圖繪示一種金融系統的功能方塊圖。 Figure 1 shows a functional block diagram of a financial system.

第2圖繪示本揭示文件之一實施例中資料分析方法的流程圖。 FIG. 2 shows a flowchart of a data analysis method in an embodiment of the disclosed document.

第3圖繪示本揭示文件之一實施例中資料分析方法的詳細流程圖。 FIG. 3 shows a detailed flowchart of the data analysis method in one embodiment of the disclosed document.

第4A圖及第4B圖繪示本揭示文件之一實施例中第一屬性分析圖與第二屬性分析圖。 FIGS. 4A and 4B illustrate a first attribute analysis graph and a second attribute analysis graph in an embodiment of the present disclosure.

第5A圖及第5B圖繪示本揭示文件另一實施例中第一屬性分析圖與第二屬性分析圖。 FIGS. 5A and 5B illustrate a first attribute analysis graph and a second attribute analysis graph in another embodiment of the present disclosure.

在本文中所使用的用詞『包含』、『具有』等等,均為開放性的用語,即意指『包含但不限於』。此外,本文中所使用之『及/或』,包含相關列舉項目中一或多個項目的任意一個以及其所有組合。 The words "including", "having", etc. used in this article are all open terms, which means "including but not limited to". In addition, "and/or" as used in this article includes any one or more of the listed items and all combinations thereof.

於本文中,當一元件被稱為『連結』或『耦接』時,可指『電性連接』或『電性耦接』。『連結』或『耦接』亦可用以表示二或多個元件間相互搭配操作或互動。此外,雖然本文中使用『第一』、『第二』、...等用語描述不同元件,該用語僅是用以區別以相同技術用語描述的元件或操作。除非上下文清楚指明,否則該用語並非特別指稱或暗示次序或順位,亦非用以限定本揭示文件。 In this article, when an element is called "connected" or "coupled", it can be referred to as "electrically connected" or "electrically coupled." "Link" or "Coupling" can also be used to indicate the operation or interaction of two or more components. In addition, although the terms "first", "second", ... are used in this article to describe different components, the terms are only used to distinguish the components or operations described in the same technical terms. Unless the context clearly dictates, the term does not specifically refer to or imply the order or order, nor is it intended to limit the present disclosure.

請參考第1圖,第1圖繪示本揭示文件之一實施例中的金融系統100的功能方塊圖。其中金融系統100包含推薦客戶端120、被推薦客戶端140及金融伺服器160,推薦客戶端120及被推薦客戶端140網路連接金融伺服器160。金融伺服器160包含通訊電路162及處理器164,通 訊電路162與處理器164電性連接。 Please refer to FIG. 1, which illustrates a functional block diagram of the financial system 100 in an embodiment of this disclosure. The financial system 100 includes a recommended client 120, a recommended client 140, and a financial server 160. The recommended client 120 and the recommended client 140 are connected to the financial server 160 via a network. The financial server 160 includes a communication circuit 162 and a processor 164. The communication circuit 162 is electrically connected to the processor 164.

在一實施例中,通訊電路162可以是各種協定介面的網路卡(有線或無線)、無線通訊晶片及/或其它具有通訊功能的元件,但不限於這些。藉以供推薦客戶端120及被推薦客戶端140經網際網路連接,達到各種訊號傳輸。處理器164可以是中央處理器及/或微處理器等處理器實現,但不限於這些。 In an embodiment, the communication circuit 162 may be a network card (wired or wireless) with various protocol interfaces, a wireless communication chip, and/or other components with communication functions, but is not limited to these. In this way, the recommended client 120 and the recommended client 140 are connected via the Internet to achieve various signal transmissions. The processor 164 may be implemented by a processor such as a central processor and/or a microprocessor, but is not limited to these.

應注意到,上述金融系統100中的裝置及元件的實現方式不以上述實施例所揭露的為限,且連接關係亦不以上述實施例為限,凡足以令金融系統100實現下述技術內容的連接方式與實現方式皆可運用於本案。 It should be noted that the implementation of the devices and components in the financial system 100 is not limited to that disclosed in the above embodiment, and the connection relationship is not limited to the above embodiment. Anything sufficient to enable the financial system 100 to implement the following technical content Both the connection method and the implementation method can be applied to this case.

在一實施例中,推薦客戶端120可對被推薦客戶端140發送一推薦資訊REC,被推薦客戶端140藉由推薦資訊REC使自身成為會員後即完成推薦。完成推薦後,金融伺服器160會藉由通訊電路162接收對應於推薦客戶端120與被推薦客戶端140之間的推薦過程特徵、用戶關聯特徵或金融互動紀錄三者中任一組合的資料。處理器164分析通訊電路162接收到的資料後,根據分析後的結果藉由通訊電路162各自發送行銷資訊M1及M2到推薦客戶端120與被推薦客戶端140。 In one embodiment, the recommendation client 120 may send a recommendation information REC to the recommended client 140, and the recommended client 140 completes the recommendation after making itself a member by the recommendation information REC. After the recommendation is completed, the financial server 160 receives the data corresponding to any combination of the recommendation process feature, the user-related feature, or the financial interaction record between the recommendation client 120 and the recommended client 140 through the communication circuit 162. After analyzing the data received by the communication circuit 162, the processor 164 sends marketing information M1 and M2 to the recommendation client 120 and the recommended client 140 through the communication circuit 162 according to the analyzed results.

推薦資訊REC可以是對應於推薦用戶端120的電子序號、網路連結、條碼或其他用以表達一組資訊的標識,但不限於這些。推薦用戶端120發送推薦資訊REC的方式可以是利用無線網路傳輸、藍芽、紅外線等具有傳輸功 能之媒介或藉由金融軟體、社群網站、社群軟體等具有分享功能之網路媒體發送,但不限於這些。 The recommendation information REC may be an electronic serial number corresponding to the recommendation client 120, a network link, a bar code, or other identifiers used to express a set of information, but it is not limited to these. The method for recommending the client 120 to send the recommended information REC may be to use wireless network transmission, Bluetooth, infrared, etc. The media can be sent through financial software, social networking sites, social software and other network media with sharing functions, but not limited to these.

請參考第2圖,第2圖繪示本揭示文件之一實施例中資料分析方法的流程圖。在資料分析方法200的步驟S210中,金融伺服器160中的通訊電路162會收集完成推薦後的推薦客戶端120與被推薦客戶端140的資料。步驟S220中,處理器164會分析推薦用戶端與被推薦用戶端彼此之間的推薦過程特徵、用戶關聯特徵或金融互動紀錄,得到推薦用戶端120的第一客戶屬性以及被推薦用戶端140的第二客戶屬性。步驟S230中,根據在步驟S220中的第一客戶屬性及第二客戶屬性,藉由通訊電路162將適合第一客戶屬性及第二客戶屬性的行銷資訊分別發送到推薦用戶端120與被推薦用戶端140。 Please refer to FIG. 2, which illustrates a flowchart of a data analysis method in an embodiment of this disclosure. In step S210 of the data analysis method 200, the communication circuit 162 in the financial server 160 collects the data of the recommended client 120 and the recommended client 140 after the recommendation is completed. In step S220, the processor 164 analyzes the recommendation process feature, user association feature, or financial interaction record between the recommended client and the recommended client to obtain the first client attribute of the recommended client 120 and the recommended client 140 Second customer attribute. In step S230, according to the first client attribute and the second client attribute in step S220, marketing information suitable for the first client attribute and the second client attribute is sent to the recommended user terminal 120 and the recommended user through the communication circuit 162, respectively端140.

請參考第3圖,第3圖繪示本揭示文件之一實施例中資料分析方法的詳細流程圖。在資料分析方法300中,如第2圖所描述之步驟S210,金融伺服器160中的通訊電路162會收集完成推薦後的推薦客戶端120與被推薦客戶端140的資料。步驟S220包含步驟S221、步驟S222、步驟S223及步驟S224。 Please refer to FIG. 3, which illustrates a detailed flowchart of the data analysis method in one embodiment of the present disclosure. In the data analysis method 300, as in step S210 described in FIG. 2, the communication circuit 162 in the financial server 160 collects data of the recommended client 120 and the recommended client 140 after the recommendation is completed. Step S220 includes step S221, step S222, step S223, and step S224.

在步驟S221中,金融伺服器160中的處理器164會分析推薦用戶端120與被推薦用戶端140之間的推薦過程特徵,推薦過程特徵包含當推薦用戶端120發送推薦資訊REC到被推薦用戶端140,推薦客戶端140藉由推薦資訊REC使自身成為會員後完成推薦時的間隔時間。此間隔時 間之長短會影響推薦用戶端120與被推薦用戶端140之間的關聯性強弱。金融伺服器160中的處理器164可設定一參考時間,當間隔時間小於參考時間時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性高;當間隔時間大於或等於參考時間時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性低。此參考時間可根據所有完成推薦的推薦用戶端120與被推薦用戶端140之平均時間來設定或其他計算方法,但不限於這些。 In step S221, the processor 164 in the financial server 160 analyzes the recommendation process characteristics between the recommended user terminal 120 and the recommended user terminal 140. The recommended process characteristics include when the recommended user terminal 120 sends recommendation information REC to the recommended user At the end 140, the recommendation client 140 uses the recommendation information REC to make itself a member after completing the recommendation interval. At this interval The length of time will affect the correlation between the recommended client 120 and the recommended client 140. The processor 164 in the financial server 160 can set a reference time. When the interval time is less than the reference time, the processor 164 determines that the recommended client 120 and the recommended client 140 have a high correlation; when the interval time is greater than or equal to the reference time The processor 164 determines that the recommended user terminal 120 and the recommended user terminal 140 have a low correlation. The reference time may be set or other calculation methods based on the average time of all the recommended client 120 and the recommended client 140 completing the recommendation, but not limited to these.

在步驟S222中,金融伺服器160中的處理器164會分析推薦用戶端120與被推薦用戶端140之間的用戶關聯特徵,用戶關聯特徵包含推薦用戶端120與被推薦用戶端140的兩者所對應的居住位址或通訊地址之間的間隔距離,此間隔距離之遠近會影響推薦用戶端120與被推薦用戶端140之間的關聯性強弱。金融伺服器160中的處理器164可設定一第一參考距離,當間隔距離小於第一參考距離時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性高;當間隔距離大於或等於第一參考距離時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性低。此第一參考距離可根據所有完成推薦的推薦用戶端120與被推薦用戶端140之平均間隔距離來設定、依照不同縣市設定不同的第一參考距離或其他計算方法,但不限於這些。 In step S222, the processor 164 in the financial server 160 analyzes the user-related features between the recommended client 120 and the recommended client 140. The user related features include both the recommended client 120 and the recommended client 140 The separation distance between the corresponding residential address or communication address, the distance between the separation distance will affect the strength of the correlation between the recommended client 120 and the recommended client 140. The processor 164 in the financial server 160 can set a first reference distance. When the separation distance is less than the first reference distance, the processor 164 determines that the recommended client 120 and the recommended client 140 have a high correlation; when the separation distance is greater than or When equal to the first reference distance, the processor 164 determines that the recommended user terminal 120 and the recommended user terminal 140 have a low correlation. The first reference distance may be set according to the average separation distance between the recommended client 120 and the recommended client 140 that have completed the recommendation, and different first reference distances or other calculation methods may be set according to different counties and cities, but are not limited to these.

在上述步驟S222中,用戶關聯特徵更包含推薦用戶端120與被推薦用戶端140之間的定位間隔距離,用戶端120與被推薦用戶端140的所在位置可以利用GPS或其 他定位系統來獲得,但不限於這些。此定位間隔距離之遠近會影響推薦用戶端120與被推薦用戶端140之間的關聯性強弱。金融伺服器160中的處理器164可設定一第二參考距離,當定位間隔距離小於第二參考距離時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性高;當定位間隔距離大於或等於第二參考距離時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性低。此第二參考距離可根據所有完成推薦的推薦用戶端120與被推薦用戶端140之平均定位間隔距離來設定、依照不同縣市設定不同的第二參考距離或其他計算方法,但不限於這些。 In the above step S222, the user-related feature further includes the positioning distance between the recommended user terminal 120 and the recommended user terminal 140. The location of the user terminal 120 and the recommended user terminal 140 can use GPS or He locates the system to obtain, but not limited to these. The distance between the positioning intervals will affect the strength of the correlation between the recommended user terminal 120 and the recommended user terminal 140. The processor 164 in the financial server 160 can set a second reference distance. When the positioning interval distance is less than the second reference distance, the processor 164 determines that the recommended user terminal 120 and the recommended user terminal 140 have a high correlation; when the positioning interval distance When it is greater than or equal to the second reference distance, the processor 164 determines that the recommended user terminal 120 and the recommended user terminal 140 have a low correlation. The second reference distance may be set according to the average positioning interval distance between the recommended client 120 and the recommended client 140 that have completed the recommendation, and different second reference distances or other calculation methods may be set according to different counties and cities, but not limited to these.

在步驟S223中,金融伺服器160中的處理器164會分析推薦用戶端120與被推薦用戶端140之間的金融歷史紀錄,金融歷史紀錄包含推薦用戶端120與被推薦用戶端140的之間的帳戶資金移轉頻率,帳戶資金移轉頻率包含推薦用戶端120與被推薦用戶端140各自的實體帳戶使用轉帳功能將資金移轉給對方之頻率。此帳戶資金移轉頻率之高低會影響推薦用戶端120與被推薦用戶端140之間的關聯性強弱。金融伺服器160中的處理器164可設定一參考移轉頻率,當資金移轉頻率高於參考移轉頻率時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性高;當資金移轉頻率低於或等於參考移轉頻率時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性低。此參考移轉頻率可根據對應於推薦用戶端120與被推薦用戶端140各自的使用者職業、薪俸、薪資結構、收入來源等項目來設 定,但不限於這些。 In step S223, the processor 164 in the financial server 160 analyzes the financial history record between the recommended client 120 and the recommended client 140, and the financial history record includes between the recommended client 120 and the recommended client 140 The frequency of transfer of account funds. The frequency of transfer of account funds includes the frequency of transfer of funds to the other party using the transfer function of the physical accounts of the recommended client 120 and the recommended client 140 respectively. The frequency of fund transfer in this account will affect the strength of the correlation between the recommended client 120 and the recommended client 140. The processor 164 in the financial server 160 may set a reference transfer frequency. When the fund transfer frequency is higher than the reference transfer frequency, the processor 164 determines that the recommended client 120 and the recommended client 140 have a high correlation; when the funds When the transfer frequency is lower than or equal to the reference transfer frequency, the processor 164 determines that the correlation between the recommended client 120 and the recommended client 140 is low. The reference transfer frequency can be set according to the user occupation, salary, salary structure, income source and other items corresponding to the respective recommendation client 120 and the recommended client 140 Fixed, but not limited to these.

在上述步驟S223中,帳戶資金移轉頻率更包含推薦用戶端120與被推薦用戶端140接觸頻率。接觸頻率包含推薦用戶端120與被推薦用戶端140藉由有線通訊、無線通訊、搖晃感應或掃描方式接觸之頻率,但不限於這些方式。此接觸頻率之高低會影響推薦用戶端120與被推薦用戶端140之間的關聯性強弱。金融伺服器160中的處理器164可設定一參考接觸頻率,當接觸頻率高於參考接觸頻率時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性高;當接觸頻率低於或等於參考接觸頻率時,處理器164判斷推薦用戶端120與被推薦用戶端140關聯性低。此參考接觸頻率可根據對應於推薦用戶端120與被推薦用戶端140各自的使用者職業、薪俸、薪資結構、收入來源等項目來設定,但不限於這些。 In the above step S223, the frequency of transfer of account funds further includes the frequency of contact between the recommended client 120 and the recommended client 140. The contact frequency includes the frequency of contact between the recommended client 120 and the recommended client 140 through wired communication, wireless communication, shaking induction, or scanning, but is not limited to these methods. The level of this contact frequency will affect the correlation between the recommended client 120 and the recommended client 140. The processor 164 in the financial server 160 may set a reference contact frequency. When the contact frequency is higher than the reference contact frequency, the processor 164 determines that the recommended user terminal 120 and the recommended user terminal 140 have a high correlation; when the contact frequency is lower than or When it is equal to the reference contact frequency, the processor 164 determines that the recommended user terminal 120 and the recommended user terminal 140 have a low correlation. The reference contact frequency may be set according to the user's occupation, salary, salary structure, income source and other items corresponding to the recommendation client 120 and the recommended client 140, but is not limited to these.

經由步驟S221、步驟S222及步驟S223中的分析結果得出推薦用戶端120的第一客戶屬性及被推薦用戶端140的第二客戶屬性。處理器164可以分別對步驟S221、步驟S222及步驟S223中的推薦過程特徵、用戶關聯特徵及金融歷史紀錄設定不同的權重,或是更進一步地,對間隔時間、間隔距離、定位間隔距離、帳戶資金移轉頻率及接觸頻率設定不同的權重,而得出不同的關聯強度。 Through the analysis results in step S221, step S222, and step S223, the first client attribute of the recommended client 120 and the second client attribute of the recommended client 140 are obtained. The processor 164 can set different weights for the recommendation process characteristics, user-related characteristics, and financial history records in step S221, step S222, and step S223, or further, for the interval time, interval distance, positioning interval distance, and account Different weights are set for the frequency of fund transfer and the frequency of contact, resulting in different strengths of association.

在步驟S231中,處理器164會比對推薦用戶端120的第一客戶屬性以與被推薦用戶端140的第二客戶屬性是否相似。比對的方法可以例如是判斷是否大於一設定 的參考值或其他比對方法,但不限於此。如果比對結果相似,在步驟S232中,會根據相似的第一與第二客戶屬性分別產生量化的結果,如第4A圖及第4B圖所示的第一屬性分析圖D1a與第二屬性分析圖D2a。 In step S231, the processor 164 compares whether the first client attribute of the recommended client 120 is similar to the second client attribute of the recommended client 140. The method of comparison can be, for example, to determine whether it is greater than a setting Reference value or other comparison methods, but not limited to this. If the comparison results are similar, in step S232, quantitative results will be generated based on the similar first and second customer attributes, respectively, as shown in Figures 4A and 4B, the first attribute analysis graph D1a and the second attribute analysis Figure D2a.

請參考第4A圖及第4B圖,第4A圖及第4B圖繪示本揭示文件之一實施例中第一屬性分析圖D1a與第二屬性分析圖D2a的示意圖。第一屬性分析圖D1a對應於推薦客戶端120,第二屬性分析圖D2a對應於被推薦客戶端140。第一屬性分析圖D1a與第二屬性分析圖D2a有五個指標,分別是資產多寡、自動化接受度、風險承受度、交易頻率及關連強度。 Please refer to FIGS. 4A and 4B. FIGS. 4A and 4B illustrate schematic diagrams of the first attribute analysis graph D1a and the second attribute analysis graph D2a in an embodiment of the present disclosure. The first attribute analysis graph D1a corresponds to the recommendation client 120, and the second attribute analysis graph D2a corresponds to the recommended client 140. The first attribute analysis graph D1a and the second attribute analysis graph D2a have five indicators, which are the amount of assets, automation acceptance, risk tolerance, transaction frequency and connection strength.

在第4A圖及第4B圖中,資產多寡、自動化接受度、風險承受度及交易頻率四項指標為推薦客戶端120及被推薦客戶端140於各自的分析結果,關聯強度為推薦客戶端120及被推薦客戶端140彼此經由步驟S221、步驟S222及/或步驟S223的分析結果。 In Figures 4A and 4B, the four indicators of asset size, automation acceptance, risk tolerance and transaction frequency are the recommended client 120 and the recommended client 140 in their respective analysis results, and the correlation strength is the recommended client 120 The analysis results of the recommended client 140 through step S221, step S222, and/or step S223.

在第4A圖及第4B圖中,資產多寡包含對應推薦客戶端120及被推薦客戶端140各自使用者的實體帳戶或網路帳戶中的存款、銀行貸款、債務或其他金融機構可得知之資產資料,但不限於這些。資產多寡的標準可由處理器164根據對應於推薦客戶端120及被推薦客戶端140的同年齡使用者的資產平均所設定或其他計算方法,但不限於這些。 In Figures 4A and 4B, the amount of assets includes deposits, bank loans, debts, or other assets known to financial institutions in the physical accounts or online accounts of the respective users of the recommended client 120 and the recommended client 140. Information, but not limited to these. The amount of assets may be set by the processor 164 according to the average assets of users of the same age corresponding to the recommended client 120 and the recommended client 140 or other calculation methods, but not limited to these.

在第4A圖及第4B圖中,自動化接受度包含對 應推薦客戶端120及被推薦客戶端140各自使用者對金融機構自動化服務的接收度,包含從傳統臨櫃辦理金融業務轉變到使用網路裝置或行動裝置執行數位銀行或執行金融業務功能,自動化接受度的標準可由處理器164計算使用者使用數位銀行的金融業務功能的頻率、一段時間內數位銀行的使用次數或其他計算方法,但不限於這些。 In Figures 4A and 4B, automation acceptance includes The acceptance of the respective users of the recommended client 120 and the recommended client 140 to the automation services of financial institutions, including the transition from traditional counter-office financial services to the use of network devices or mobile devices to perform digital banking or perform financial business functions, automation The acceptance criteria can be calculated by the processor 164 for the frequency with which the user uses the financial business functions of the digital bank, the number of times the digital bank is used over a period of time, or other calculation methods, but not limited to these.

在第4A圖及第4B圖中,風險承受度包含對應推薦客戶端120及被推薦客戶端140各自使用者對投資金融商品的風險接受程度,風險承受度可由處理器164計算使用者曾經購買的金融商品種類、次數、金額及持有時間等投資紀錄或使用者現有資產、收入、貸款及債務等資產紀錄,但不限於這些。 In FIGS. 4A and 4B, the risk tolerance includes the risk acceptance of investment financial products by the users of the corresponding recommended client 120 and the recommended client 140. The risk tolerance can be calculated by the processor 164 Investment records such as the type, frequency, amount and holding time of financial commodities or the user's existing assets, income, loans and debts and other asset records, but not limited to these.

在第4A圖及第4B圖中,交易頻率包含對應推薦客戶端120及被推薦客戶端140各自使用者將帳戶存款移轉到其他使用者的次數,交易頻率可由處理器164計算一段時間內的移轉次數、平均移轉次數或其他計算方法,但不限於這些。 In FIGS. 4A and 4B, the transaction frequency includes the number of times that the respective users of the recommended client 120 and the recommended client 140 transfer account deposits to other users. The transaction frequency can be calculated by the processor 164 over a period of time Number of transfers, average number of transfers, or other calculation methods, but not limited to these.

在第4A圖及第4B圖中,關聯強度包含對應推薦客戶端120及被推薦客戶端140之間經由步驟S221、步驟S222及/或步驟S223的分析結果所計算出的關聯性強弱,詳細的計算方法及步驟如上所描述,在此不多贅述。 In FIGS. 4A and 4B, the correlation strength includes the correlation strength between the corresponding recommended client 120 and the recommended client 140 calculated through the analysis results of step S221, step S222, and/or step S223, detailed The calculation method and steps are as described above and will not be repeated here.

如第4A圖及第4B圖所示,由於關聯強度是根據推薦客戶端120及被推薦客戶端140之間各項關聯項目所計算,由前述實施例中關聯強度的計算標準可知,當關 聯強度越高(越接近最外框),表示推薦客戶端120及被推薦客戶端140之間較常見面、連絡或互動、居住地距離較近或資金移轉頻率較高。由上述也可能表示對應推薦客戶端120及被推薦客戶端140的使用者之間的關係越親密、個性越相似、生長環境越接近或價值觀越相近,也可能彼此是鄰居、朋友、情人、夫妻、家人、親戚或同事而不是陌生人。所以關聯強度越高,第一客戶屬性與第二客戶屬性相似的機率越高,產生的第一屬性分析圖D1a與第二屬性分析圖D2a也越相似。 As shown in FIG. 4A and FIG. 4B, since the correlation strength is calculated based on each correlation item between the recommended client 120 and the recommended client 140, from the calculation standard of the correlation strength in the foregoing embodiment, The higher the connection strength (closer to the outermost frame), it means that the recommendation client 120 and the recommended client 140 are more common, contact or interact, have a closer residence, or have a higher frequency of funds transfer. From the above, it may also mean that the relationship between the users corresponding to the recommended client 120 and the recommended client 140 is more intimate, the personality is more similar, the growing environment is closer, or the values are closer, or they may be neighbors, friends, lovers, couples. , Family members, relatives or colleagues rather than strangers. Therefore, the higher the correlation strength, the higher the probability that the first customer attribute is similar to the second customer attribute, and the more similar the first attribute analysis graph D1a and the second attribute analysis graph D2a are.

在步驟S232中,根據相似的第一客戶屬性與第二客戶屬性的分析結果,處理器164將相同或相似的行銷資訊M1與M2藉由通訊電路162同時傳送到推薦客戶端120及被推薦客戶端140。於一實施例中,在步驟S232中可以根據相似的第一客戶屬性與第二客戶屬性產生如第4A圖及第4B圖所示第一屬性分析圖D1a與第二屬性分析圖D2a後,處理器164會根據第一客戶屬性與第二客戶屬性以及第一屬性分析圖D1a與第二屬性分析圖D2a的分析結果,也就是根據對應推薦客戶端120及被推薦客戶端140的使用者特質,將相同或相似的行銷資訊M1與M2藉由通訊電路162同時傳送到推薦客戶端120及被推薦客戶端140,推薦客戶端120可以查看符合自己需求的行銷資訊M1,被推薦客戶端140可以查看符合自己需求的行銷資訊M2。 In step S232, based on the analysis results of the similar first client attributes and second client attributes, the processor 164 simultaneously transmits the same or similar marketing information M1 and M2 to the recommendation client 120 and the recommended client through the communication circuit 162端140. In an embodiment, in step S232, the first attribute analysis graph D1a and the second attribute analysis graph D2a shown in FIGS. 4A and 4B may be generated according to the similar first client attributes and the second client attributes, and then processed According to the analysis results of the first client attribute and the second client attribute and the first attribute analysis graph D1a and the second attribute analysis graph D2a, that is, according to the user characteristics of the corresponding recommendation client 120 and the recommended client 140, The same or similar marketing information M1 and M2 are simultaneously transmitted to the recommendation client 120 and the recommended client 140 through the communication circuit 162. The recommendation client 120 can view the marketing information M1 that meets their needs, and the recommended client 140 can view Marketing information M2 that meets your needs.

例如,當處理器164預計發送有關人壽保險商品資訊給推薦客戶端120,此時金融系統100透過上述分 析,判斷推薦客戶端120與被推薦客戶端140相似性較高,因此,也可以將相同的人壽保險商品資訊或同類型的保險商品資訊發送給被推薦客戶端140。另一例子,若預計發送有關優惠貸款利率廣告給被推薦客戶端140,金融系統100也可以將相同的優惠貸款利率廣告或同類型的貸款廣告發送給推薦客戶端120。 For example, when the processor 164 is expected to send information about life insurance products to the recommendation client 120, the financial system 100 then According to the analysis, it is judged that the recommended client 120 and the recommended client 140 are highly similar. Therefore, the same life insurance product information or the same type of insurance product information can also be sent to the recommended client 140. As another example, if it is expected to send the preferential loan interest rate advertisement to the recommended client 140, the financial system 100 may also send the same preferential loan interest rate advertisement or the same type of loan advertisement to the recommending client 120.

在步驟S231中,如果比對結果相異(例如小於或等於設定的參考值),在步驟S233中,會根據相異的第一與第二客戶屬性分別產生量化的結果,如第5A圖及第5B圖所示的第一屬性分析圖D1b與第二屬性分析圖D2b。 In step S231, if the comparison results are different (for example, less than or equal to the set reference value), in step S233, quantized results will be generated according to the different first and second customer attributes, as shown in Figure 5A and The first attribute analysis graph D1b and the second attribute analysis graph D2b shown in FIG. 5B.

請參考第5A圖及第5B圖,第5A圖及第5B圖繪示本揭示文件另一實施例中第一屬性分析圖D2a與第二屬性分析圖D2b。五項指標與第4A圖及第4B圖所示相同。 Please refer to FIGS. 5A and 5B. FIGS. 5A and 5B illustrate the first attribute analysis graph D2a and the second attribute analysis graph D2b in another embodiment of the present disclosure. The five indicators are the same as shown in Figures 4A and 4B.

如第5A圖及第5B圖所示,由前述實施例中關聯強度的計算標準可知,當關聯強度越低(越接近最內框),表示推薦客戶端120及被推薦客戶端140之間較少見面、連絡或互動、居住地距離較遠或資金移轉頻率較低。由上述也可能表示對應推薦客戶端120及被推薦客戶端140的使用者之間的關係越疏遠、個性越不同、生長環境越不同或價值觀越有差異,彼此是鄰居、朋友、情人、夫妻、家人、親戚或同事的機率越低,而彼此是陌生人的機率越高。所以關聯強度越低,第一客戶屬性與第二客戶屬性相似的機率越低,產生的第一屬性分析圖D2a與第二屬性分析圖D2b也越不同。 As shown in FIG. 5A and FIG. 5B, according to the calculation criterion of the correlation strength in the foregoing embodiment, when the correlation strength is lower (the closer to the innermost frame), it indicates that the recommended client 120 and the recommended client 140 are relatively Rarely meet, contact or interact, have a long distance from the place of residence, or have a low frequency of fund transfer. From the above, it may also mean that the more alienated the relationship between the users corresponding to the recommended client 120 and the recommended client 140, the more different the personality, the more different the growth environment or the more different values, each other is a neighbor, friend, lover, couple, The lower the probability of family members, relatives or colleagues, and the higher the probability of being strangers to each other. Therefore, the lower the correlation strength, the lower the probability that the first customer attribute is similar to the second customer attribute, and the more different the first attribute analysis graph D2a and the second attribute analysis graph D2b are.

在步驟S233中,根據相異的第一客戶屬性與第二客戶屬性產生第一屬性分析圖D2a與第二屬性分析圖D2b後,處理器164會根據第一客戶屬性與第二客戶屬性以及第一屬性分析圖D2a與第二屬性分析圖D2b的分析結果,也就是根據對應推薦客戶端120及被推薦客戶端140的使用者特質,將相異或完全不同的行銷資訊M1及M2藉由通訊電路162分別傳送到推薦客戶端120及被推薦客戶端140,推薦客戶端120可以查看符合自己需求的行銷資訊M1,被推薦客戶端140則是可以查看符合自己需求的行銷資訊M2。 In step S233, after generating the first attribute analysis graph D2a and the second attribute analysis graph D2b according to the different first client attributes and second client attributes, the processor 164 will determine the first client attribute and the second client attribute and the second The analysis results of an attribute analysis graph D2a and a second attribute analysis graph D2b, that is, according to the user characteristics of the corresponding recommendation client 120 and the recommended client 140, different or completely different marketing information M1 and M2 are communicated by communication The circuit 162 transmits to the recommendation client 120 and the recommended client 140 respectively. The recommendation client 120 can view the marketing information M1 that meets their needs, and the recommended client 140 can view the marketing information M2 that meets their needs.

例如,當處理器164預計發送有關人壽保險商品資訊給推薦客戶端120,此時金融系統100透過上述分析,判斷推薦客戶端120與被推薦客戶端140相似性較低,不需要或者避免將類似的人壽保險商品或同類型的保險商品資訊發送給被推薦客戶端140。此時,金融系統100可以另外根據被推薦客戶端140的特性改為推薦相異的金融商品,例如連動性債券。 For example, when the processor 164 is expected to send information about life insurance products to the recommendation client 120, the financial system 100 judges that the similarity between the recommendation client 120 and the recommended client 140 is low through the above analysis. Of life insurance products or insurance products of the same type are sent to the recommended client 140. At this time, the financial system 100 may additionally recommend different financial commodities, such as linked bonds, according to the characteristics of the recommended client 140.

例如,如第5A圖及第5B圖所示的第一屬性分析圖D2a與第二屬性分析圖D2b中的資產多寡與風險承受度差異較大,當處理器164預計發送有關高風險基金商品資訊(例如股票型基金)給推薦客戶端120,此時金融系統100透過上述分析判斷被推薦客戶端140的風險承擔能力較低,不需要或者避免將高風險基金相關商品資訊發送給被推薦客戶端140。 For example, as shown in Figures 5A and 5B, the first attribute analysis graph D2a and the second attribute analysis graph D2b differ greatly in the amount of assets and risk tolerance. When the processor 164 is expected to send high-risk fund commodity information (For example, a stock fund) to the recommended client 120. At this time, the financial system 100 determines that the recommended client 140 has a low risk-taking ability through the above analysis, and does not need or avoid sending high-risk fund-related commodity information to the recommended client 140.

上述行銷資訊M1及M2,可以是配合金融機構當前的行銷活動資訊、會員紅利積點活動資訊、金融商品資訊、廣告資訊、會議資訊、展覽資訊或其他資訊,但不限於這些。 The above marketing information M1 and M2 may be in accordance with the current marketing activities information of the financial institution, member bonus point activity information, financial commodity information, advertising information, conference information, exhibition information or other information, but not limited to these.

透過上述一實施例的操作,即可根據不同使用者之間的關聯特質來達到更精準的客戶分析結果,如此一來,讓使用者即時獲得真正需要且有興趣的資訊,節省許多使用者搜尋查看的時間,也能讓使用者免去因為非金融專業的判斷所造成的錯誤。 Through the operation of the above embodiment, more accurate customer analysis results can be achieved according to the correlation characteristics between different users, so that users can obtain the information they really need and are interested in real time, saving many users from searching Checking the time also allows users to avoid errors caused by non-financial professional judgment.

雖然本揭示文件已以實施例揭露如上,然其並非用以限定本揭示文件,任何熟習此技藝者,在不脫離本揭示文件之精神和範圍內,當可作各種之更動與潤飾,因此本揭示文件之保護範圍當視後附之申請專利範圍所界定者為準。 Although this disclosure document has been disclosed as above with examples, it is not intended to limit this disclosure document. Anyone who is familiar with this skill can make various changes and retouching without departing from the spirit and scope of this disclosure document. The scope of protection of the disclosure document shall be deemed as defined by the scope of the attached patent application.

300‧‧‧資料分析方法 300‧‧‧Data analysis method

S210、S220、S221、S222、S223、S224、S230、S231、S232、S233‧‧‧步驟 S210, S220, S221, S222, S223, S224, S230, S231, S232, S233

Claims (8)

一種資料分析方法,包含:收集一推薦資料,該推薦資料包含一推薦用戶端與一被推薦用戶端;分析該推薦用戶端與該被推薦用戶端彼此之間的一推薦過程特徵、一用戶關聯特徵或一金融互動紀錄,得到該推薦用戶端的一第一客戶屬性以及該被推薦用戶端的一第二客戶屬性;以及根據該第一客戶屬性及該第二客戶屬性,分別將複數個行銷資訊發送至該推薦用戶端與該被推薦用戶端,其中該推薦過程特徵包含自該推薦用戶端發送一推薦邀請至該被推薦用戶端接受該推薦邀請之一間隔時間;若該間隔時間小於一參考時間,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端,若該間隔時間大於或等於該參考時間,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 A data analysis method includes: collecting recommended data including a recommended user terminal and a recommended user terminal; analyzing a recommendation process feature and a user association between the recommended user terminal and the recommended user terminal Characteristics or a financial interaction record, obtain a first client attribute of the recommended client and a second client attribute of the recommended client; and send a plurality of marketing information based on the first client attribute and the second client attribute, respectively To the recommended client and the recommended client, wherein the recommendation process feature includes an interval between sending a recommendation invitation from the recommended client to the recommended client accepting the recommendation invitation; if the interval is less than a reference time , The data analysis method determines that the first client attribute is similar to the second client attribute, and sends the same marketing information to the recommended client and the recommended client at the same time. If the interval is greater than or equal to the reference time, the The data analysis method determines that the attributes of the first client and the attributes of the second client are different, and sends differentiated marketing information to the recommended client and the recommended client, respectively. 如請求項1所述之資料分析方法,其中該用戶關聯特徵包含該推薦用戶端與該被推薦用戶端兩者所對應的居住地址或通訊地址之間的一間隔距離,其中當該間隔距離小於一第一參考距離,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同 時發送至該推薦用戶端與該被推薦用戶端;當該間隔距離大於或等於該第一參考距離,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 The data analysis method according to claim 1, wherein the user-related feature includes a separation distance between the residential address or the correspondence address corresponding to the recommended client and the recommended client, wherein when the separation distance is less than A first reference distance, the data analysis method determines that the first customer attribute is similar to the second customer attribute, and the same marketing information Is sent to the recommended client and the recommended client; when the separation distance is greater than or equal to the first reference distance, the data analysis method determines that the attributes of the first client are different from the attributes of the second client. Marketing information is sent to the recommended client and the recommended client respectively. 如請求項1所述之資料分析方法,其中該用戶關聯特徵包含該推薦用戶端與該被推薦用戶端之間的一定位間隔距離,其中當該定位間隔距離小於一第二參考距離,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端;當該定位間隔距離大於或等於該第二參考距離,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 The data analysis method according to claim 1, wherein the user-related feature includes a positioning interval distance between the recommended user terminal and the recommended user terminal, wherein when the positioning interval distance is less than a second reference distance, the data The analysis method determines that the first client attribute is similar to the second client attribute, and sends the same marketing information to the recommended client and the recommended client at the same time; when the positioning interval distance is greater than or equal to the second reference distance, the The data analysis method determines that the attributes of the first client and the attributes of the second client are different, and sends differentiated marketing information to the recommended client and the recommended client, respectively. 如請求項1所述之資料分析方法,其中該金融歷史紀錄包含該推薦用戶端與該被推薦用戶端之間的一帳戶資金移轉頻率,其中該帳戶資金移轉頻率高於一參考移轉頻率,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端;當該帳戶資金移轉頻率低於或等於該參考移轉頻率,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相 異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 The data analysis method according to claim 1, wherein the financial history record includes an account fund transfer frequency between the recommended client and the recommended client, wherein the account fund transfer frequency is higher than a reference transfer Frequency, the data analysis method determines that the first client attribute is similar to the second client attribute, and sends the same marketing information to the recommended client and the recommended client at the same time; when the account fund transfer frequency is less than or equal to The reference transfer frequency, the data analysis method determines that the first client attribute is related to the second client attribute Different, different marketing information is sent to the recommended client and the recommended client. 如請求項1所述之資料分析方法,其中該金融歷史紀錄包含該推薦用戶端與該被推薦用戶端之間一接觸頻率,其中該接觸頻率包含該推薦用戶端與該被推薦用戶端藉由有線通訊、無線通訊、搖晃感應或掃描方式接觸之頻率,其中當該接觸頻率高於一參考接觸頻率,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端;當該接觸頻率低於或等於該參考接觸頻率,該資料分析方法判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 The data analysis method according to claim 1, wherein the financial history record includes a contact frequency between the recommended client and the recommended client, wherein the contact frequency includes the recommended client and the recommended client by Wired communication, wireless communication, shaking induction, or scanning contact frequency, wherein when the contact frequency is higher than a reference contact frequency, the data analysis method determines that the first customer attribute is similar to the second customer attribute, and the same marketing information At the same time, it is sent to the recommended client and the recommended client; when the contact frequency is lower than or equal to the reference contact frequency, the data analysis method judges that the first client attribute is different from the second client attribute, Marketing information is sent to the recommended client and the recommended client respectively. 一種金融系統,包含:一推薦用戶端;一被推薦用戶端;以及一金融伺服器,分別與該推薦用戶端及該被推薦用戶端通訊連接,該金融伺服器用以:收集該推薦用戶端與該被推薦用戶端之間的一推薦資料;分析該推薦用戶端與該被推薦用戶端彼此之間的一推薦過程特徵、一用戶關聯特徵或一金融互動紀 錄,得到該推薦用戶端的一第一客戶屬性以及該被推薦用戶端的一第二客戶屬性;以及根據該第一客戶屬性及該第二客戶屬性,分別將複數個行銷資訊發送至該推薦用戶端與該被推薦用戶端,其中該推薦過程特徵包含自該推薦用戶端發送一推薦邀請至該被推薦用戶端接受該推薦邀請之一間隔時間;若該間隔時間小於一參考時間,該金融伺服器判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端,若該間隔時間大於或等於該參考時間,該金融伺服器判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 A financial system includes: a recommended client; a recommended client; and a financial server, which are respectively in communication with the recommended client and the recommended client. The financial server is used to: collect the recommended client A recommendation profile with the recommended client; analyze a recommendation process feature, a user association feature, or a financial interaction history between the recommended client and the recommended client Record, obtain a first client attribute of the recommended client and a second client attribute of the recommended client; and send a plurality of marketing information to the recommended client according to the first client attribute and the second client attribute, respectively With the recommended client, wherein the recommendation process feature includes an interval from when the recommended client sends a recommendation invitation to when the recommended client accepts the recommended invitation; if the interval is less than a reference time, the financial server It is determined that the first client attribute is similar to the second client attribute, and the same marketing information is sent to the recommended client and the recommended client at the same time. If the interval time is greater than or equal to the reference time, the financial server determines the The first client attribute is different from the second client attribute, and different marketing information is sent to the recommended client and the recommended client, respectively. 如請求項6所述之金融系統,其中該用戶關聯特徵包含該推薦用戶端與該被推薦用戶端兩者所對應的居住地址或通訊地址之間的一間隔距離,或該用戶關聯特徵包含該推薦用戶端與該被推薦用戶端兩者之間的一定位間隔距離,其中當該間隔距離或該定位間隔距離小於一參考距離,該金融伺服器判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端;當該間隔距離或該定位間隔距離大於或等於該參考距離,該金融伺服器判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 The financial system according to claim 6, wherein the user-related feature includes a separation distance between the residential address or correspondence address corresponding to the recommended client and the recommended client, or the user-related feature includes the A positioning interval distance between the recommended client and the recommended client, wherein when the interval distance or the positioning interval distance is less than a reference distance, the financial server determines the first client attribute and the second client attribute Similarly, the same marketing information is sent to the recommended client and the recommended client at the same time; when the separation distance or the positioning separation distance is greater than or equal to the reference distance, the financial server judges the first client attribute and the first The two customers have different attributes, and send the different marketing information to the recommended client and the recommended client respectively. 如請求項6所述之金融系統,其中該金融歷史紀錄包含該推薦用戶端與該被推薦用戶端之間的一帳戶資金移轉頻率,或該金融歷史紀錄包含該推薦用戶端與該被推薦用戶端之間一接觸頻率,其中當該帳戶資金移轉頻率高於一參考移轉頻率或該接觸頻率高於一參考接觸頻率,該金融伺服器判斷該第一客戶屬性與該第二客戶屬性相似,將相同的行銷資訊同時發送至該推薦用戶端與該被推薦用戶端;當該帳戶資金移轉頻率低於或等於該參考移轉頻率或該接觸頻率低於或等於一參考接觸頻率,該金融伺服器判斷該第一客戶屬性與該第二客戶屬性相異,將差異性的行銷資訊分別發送至該推薦用戶端與該被推薦用戶端。 The financial system according to claim 6, wherein the financial history record includes an account fund transfer frequency between the recommended client and the recommended client, or the financial history record includes the recommended client and the recommended A contact frequency between users, wherein when the account funds transfer frequency is higher than a reference transfer frequency or the contact frequency is higher than a reference contact frequency, the financial server determines the first client attribute and the second client attribute Similarly, send the same marketing information to the recommended client and the recommended client at the same time; when the account transfer frequency is lower than or equal to the reference transfer frequency or the contact frequency is lower than or equal to a reference contact frequency, The financial server determines that the first client attribute is different from the second client attribute, and sends the differentiated marketing information to the recommended client and the recommended client, respectively.
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