TWI722774B - Marketing object decision-making method and marketing system using mobile phone number - Google Patents

Marketing object decision-making method and marketing system using mobile phone number Download PDF

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TWI722774B
TWI722774B TW109101770A TW109101770A TWI722774B TW I722774 B TWI722774 B TW I722774B TW 109101770 A TW109101770 A TW 109101770A TW 109101770 A TW109101770 A TW 109101770A TW I722774 B TWI722774 B TW I722774B
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mobile phone
target
phone number
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marketing
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TW202129576A (en
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許少峰
蘇昭宇
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智泓科技股份有限公司
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Abstract

一種行銷對象決策方法包含:將一初始資料輸入一第一預測模型,初始資料包含一個目標手機門號及多個特徵欄位,每一特徵欄位用於填入一門號特徵,且尚未被填入門號特徵的每一特徵欄位被作為一缺漏特徵欄位;第一預測模型根據目標手機門號的數字組成方式產生預測結果以作為門號特徵填入每一缺漏特徵欄位,以使初始資料成為一筆不具有缺漏特徵欄位的待評估資料;將待評估資料輸入一第二預測模型;第二預測模型根據待評估資料的該等門號特徵之間的關聯性產生並輸出一決策結果,決策結果指示出目標手機門號是否為一適於接受行銷的目標行銷對象。A marketing object decision-making method includes: inputting an initial data into a first predictive model. The initial data includes a target mobile phone number and multiple feature fields. Each feature field is used to fill in a feature of the door number and has not yet been filled in. Each feature field of the entry number feature is regarded as a missing feature field; the first prediction model generates a prediction result according to the digital composition of the target mobile phone number to fill in each missing feature field as a feature of the door number, so that the initial The data becomes a piece of data to be evaluated without missing feature fields; the data to be evaluated is input into a second prediction model; the second prediction model generates and outputs a decision result based on the correlation between the door number features of the data to be evaluated , The decision result indicates whether the target mobile phone number is a target marketing object suitable for marketing.

Description

利用手機門號的行銷對象決策方法與行銷系統Marketing object decision-making method and marketing system using mobile phone number

本發明是有關於一種行銷對象決策方法,特別是指一種涉及機器學習的行銷對象決策方法。本發明還有關於能實施該行銷對象決策方法的一種行銷系統。The invention relates to a marketing object decision-making method, in particular to a marketing object decision-making method involving machine learning. The invention also relates to a marketing system capable of implementing the marketing target decision method.

自智慧型手機普及以來,透過行銷系統向手機傳送行銷內容已成為一種較傳統媒體更加直接且即時的行銷方式。Since the popularization of smart phones, delivering marketing content to mobile phones through marketing systems has become a more direct and instant marketing method than traditional media.

然而,隨著持有手機的人口不斷增長,手機的數量也跟著愈來愈多,因此,若行銷系統僅是將行銷內容無差別地傳送至資料庫中的所有手機號碼,則勢必須耗費更多的流量及更長的時間來傳送行銷內容,如此的作法使得行銷系統的運作效率不佳且無法鎖定目標客群,因此使得行銷的轉化率低落而提高了流量成本。However, as the population with mobile phones continues to grow, the number of mobile phones is increasing. Therefore, if the marketing system only transmits marketing content to all mobile phone numbers in the database indiscriminately, it will be more expensive. More traffic and longer time to deliver marketing content. Such an approach makes the marketing system inefficient and unable to target the target customer group. As a result, the marketing conversion rate is low and the traffic cost is increased.

因此,如何改善現有之行銷系統的不足之處,便成為一個值得探討的議題。Therefore, how to improve the shortcomings of the existing marketing system has become a topic worthy of discussion.

本發明的其中一目的,在於提供一種能克服現有技術之不足的行銷對象決策方法。One of the objectives of the present invention is to provide a marketing target decision-making method that can overcome the shortcomings of the prior art.

本發明行銷對象決策方法由一行銷系統實施,該行銷對象決策方法包含:(A)獲得多筆訓練資料,每一訓練資料包含一個由多個數字組成的參考手機門號,以及多個各自與該參考手機門號對應的參考特徵,且每一參考特徵與該參考手機門號所對應的一使用者或一手機裝置相關;(B)訓練一第一預測模型及一第二預測模型,該第一預測模型是藉由分析每一參考手機門號的數字組成方式與該參考手機門號所對應之每一參考特徵之間的關聯性進行機器學習所訓練而成;(C)將一初始資料輸入該第一預測模型,該初始資料包含一個由多個數字組成的目標手機門號,以及多個各自與該目標手機門號對應的特徵欄位,每一特徵欄位與該目標手機門號所對應的一使用者或一手機裝置相關,而用於填入一與該目標手機門號對應的門號特徵,並且,該等特徵欄位中尚未被填入門號特徵的每一特徵欄位被作為一缺漏特徵欄位;(D)對於每一缺漏特徵欄位,該第一預測模型根據該目標手機門號的數字組成方式產生一對應該缺漏特徵欄位的預測結果,並將該預測結果作為一對應該目標手機門號的門號特徵填入該缺漏特徵欄位,以使得該初始資料成為一筆不具有缺漏特徵欄位的待評估資料;(E)將該待評估資料輸入該第二預測模型;(F)該第二預測模型根據該待評估資料的該等門號特徵之間的關聯性產生並輸出一決策結果,該決策結果指示出該目標手機門號是否為一適於接受行銷的目標行銷對象。The marketing target decision-making method of the present invention is implemented by a marketing system. The marketing target decision-making method includes: (A) Obtaining multiple training data, each training data includes a reference mobile phone number composed of multiple numbers, and multiple respective and The reference feature corresponding to the reference mobile phone number, and each reference feature is related to a user or a mobile phone device corresponding to the reference mobile phone number; (B) training a first prediction model and a second prediction model, the The first prediction model is trained by machine learning by analyzing the relationship between the number composition of each reference mobile phone number and each reference feature corresponding to the reference mobile phone number; (C) Data is input into the first predictive model. The initial data includes a target mobile phone number composed of multiple numbers, and a plurality of feature fields corresponding to the target mobile phone number. Each feature field corresponds to the target mobile phone number. It is related to a user or a mobile phone device corresponding to the number, and is used to fill in a door number feature corresponding to the target mobile phone number, and each feature field that has not yet been filled with the door number feature in the feature fields The bit is regarded as a missing feature field; (D) For each missing feature field, the first prediction model generates a prediction result corresponding to the missing feature field according to the digital composition of the target mobile phone number, and then The prediction result is filled in the missing feature field as the door number feature corresponding to the target mobile phone number, so that the initial data becomes a piece of data to be evaluated without missing feature fields; (E) the data to be evaluated is entered into the missing feature field. The second prediction model; (F) The second prediction model generates and outputs a decision result based on the correlation between the door number features of the data to be evaluated, and the decision result indicates whether the target mobile phone number is an appropriate Target marketing objects for accepting marketing.

在本發明行銷對象決策方法的一些實施態樣中,在步驟(A)中 ,每一訓練資料還包含一對應該參考手機門號的參考行銷結果,該參考行銷結果指示出該參考手機門號是否為一適於接受行銷的目標行銷對象;在步驟(B)中 ,該第二預測模型是藉由分析每一訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習所訓練而成。In some implementation aspects of the marketing target decision method of the present invention, in step (A), each training material also contains a pair of reference marketing results that should refer to the mobile phone number, and the reference marketing result indicates the reference mobile phone number Whether it is a target marketing object suitable for marketing; in step (B), the second predictive model performs machine learning by analyzing the correlation between the reference features of each training data and the reference marketing result By training.

在本發明行銷對象決策方法的一些實施態樣中,在步驟(A)中,每一訓練資料之該等參考特徵的其中一者作為一目標參考特徵,並且,在步驟(B)中,對於該等訓練資料的該等目標參考特徵,該第一預測模型進行機器學習的方式包含:針對每一參考手機門號之該等數字中的一第一部分,分析該等參考手機門號之該等第一部分的數字組成方式與該等目標參考特徵之間的關聯性,以產生一對應該等第一部分及該等目標參考特徵的第一關聯性資料;針對每一參考手機門號之該等數字中的一第二部分,分析該等參考手機門號之該等第二部分的數字組成方式與該等目標參考特徵之間的關聯性,以產生一對應該等第二部分及該等目標參考特徵的第二關聯性資料;至少根據該第一關聯性資料及該第二關聯性資料統計出該等參考手機門號的數字組成方式與該等目標參考特徵之間的關聯性,以完成對於該等目標參考特徵的機器學習。In some implementation aspects of the marketing target decision method of the present invention, in step (A), one of the reference features of each training material is used as a target reference feature, and, in step (B), for For the target reference features of the training data, the machine learning method of the first prediction model includes: for a first part of the numbers of each reference mobile phone number, analyzing the reference mobile phone numbers The relationship between the number composition method of the first part and the target reference features to generate the first correlation data corresponding to the first part and the target reference features; these numbers for each reference mobile phone number A second part of the second part of the reference mobile phone number, the analysis of the relationship between the digital composition of the second part of the reference mobile phone number and the target reference characteristics, to generate a correspondence between the second part and the target reference The second relevance data of the feature; at least the relevance between the digital composition of the reference mobile phone numbers and the target reference characteristics is calculated based on the first relevance data and the second relevance data, so as to complete the comparison These targets refer to machine learning of features.

在本發明行銷對象決策方法的一些實施態樣中,在步驟(D)中,該第一預測模型產生該預測結果的方式包含:針對該目標手機門號之該等數字中的一第一部分的數字組成方式進行分析,以產生一對應該第一部分的第一推測資料,該第一推測資料包含一第一推測結果,以及一指示出該第一推測結果之預估準確率的第一信心值;針對該目標手機門號之該等數字中的一第二部分的數字組成方式進行分析,以產生一對應該第二部分的第二推測資料,該第二推測資料包含一第二推測結果,以及一指示出該第二推測結果之預估準確率的第二信心值;至少根據該第一信心值與該第二信心值之間的大小關係,而至少從該第一推測結果及該第二推測結果中決定出該預測結果。In some implementation aspects of the marketing target decision method of the present invention, in step (D), the method for generating the prediction result of the first prediction model includes: a first part of the numbers of the target mobile phone number Analyze the digital composition to generate the first speculation data corresponding to the first part. The first speculation data includes a first speculation result and a first confidence value indicating the estimated accuracy of the first speculation result ; Analyze the digital composition of a second part of the numbers of the target mobile phone number to generate a second guess data corresponding to the second part, the second guess data includes a second guess result, And a second confidence value indicating the estimated accuracy of the second prediction result; at least according to the magnitude relationship between the first confidence value and the second confidence value, and at least from the first prediction result and the second confidence value 2. The prediction result is determined from the prediction result.

在本發明行銷對象決策方法的一些實施態樣中,該行銷對象決策方法還包含一位於步驟(F)之後的:(H)若該決策結果指示出該目標手機門號為該目標行銷對象,傳送一行銷訊息至該目標手機門號所對應的該手機裝置,以供該目標手機門號所對應的該手機裝置將該行銷訊息輸出,若該決策結果指示出該目標手機門號非為該目標行銷對象,則不傳送該行銷訊息至該目標手機門號所對應的該手機裝置。In some implementation aspects of the marketing target decision-making method of the present invention, the marketing target decision-making method further includes a step (F) after step (F): (H) if the decision result indicates that the target mobile phone number is the target marketing target, Send a marketing message to the mobile phone device corresponding to the target mobile phone number for the mobile phone device corresponding to the target mobile phone number to output the marketing message, if the decision result indicates that the target mobile phone number is not the same The target marketing target does not send the marketing message to the mobile phone device corresponding to the target mobile phone number.

在本發明行銷對象決策方法的一些實施態樣中,在步驟(A)中,每一訓練資料的該等參考特徵至少包含一地區參考特徵、一性別參考特徵及一作業系統參考特徵,該地區參考特徵指示出對應之該參考手機門號所對應的該使用者的居住地區,該性別參考特徵指示出對應之該參考手機門號所對應的該使用者的性別,該作業系統參考特徵指示出對應之該參考手機門號所對應的該手機裝置的作業系統種類;在步驟(C)中,該等特徵欄位至少包含一地區特徵欄位、一性別特徵欄位及一作業系統特徵欄位,並且,用於被填入該地區特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該使用者的居住地區的地區特徵,用於被填入該性別特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該使用者的性別的性別特徵,用於被填入該作業系統特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該手機裝置的作業系統種類的作業系統特徵。In some implementation aspects of the marketing target decision method of the present invention, in step (A), the reference features of each training data include at least a region reference feature, a gender reference feature, and an operating system reference feature. The region The reference feature indicates the residential area of the user corresponding to the reference mobile phone number, the gender reference feature indicates the gender of the user corresponding to the reference mobile phone number, and the operating system indicates the reference feature Corresponding to the operating system type of the mobile phone device corresponding to the reference mobile phone number; in step (C), the feature fields include at least a region feature field, a gender feature field, and an operating system feature field And, the door number feature used to be filled in the area feature field is an area feature indicating the residential area of the user corresponding to the target mobile phone number, which is used to be filled in the gender feature field The door number feature of is a gender feature indicating the gender of the user corresponding to the target mobile phone number, and the door number feature used to be filled in the operating system feature field is a gender feature indicating the target mobile phone door The operating system characteristics of the operating system type of the mobile device corresponding to the number.

在本發明行銷對象決策方法的一些實施態樣中,在步驟(A)中,該等訓練資料分別作為多筆第一訓練資料,且每一第一訓練資料的該參考手機門號作為一第一參考手機門號;該行銷對象決策方法包含位於步驟(B)之前的:(G)獲得多筆第二訓練資料,每一第二訓練資料包含一由多個數字組成的第二參考手機門號、多個各自與該第二參考手機門號對應的參考特徵,以及一對應該第二參考手機門號的參考行銷結果,每一第二訓練資料的每一參考特徵與該第二參考手機門號所對應的一使用者或一手機裝置相關,且該參考行銷結果指示出該第二參考手機門號是否為一適於接受行銷的目標行銷對象;在步驟(B)中,該第二預測模型是藉由分析每一第二訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習所訓練而成。In some implementation aspects of the marketing target decision method of the present invention, in step (A), the training data is used as a plurality of first training data, and the reference mobile phone number of each first training data is used as a first training data. A reference mobile phone number; the marketing target decision-making method includes before step (B): (G) obtaining a plurality of second training data, and each second training data includes a second reference mobile phone door composed of multiple numbers Number, multiple reference features each corresponding to the second reference mobile phone number, and a reference marketing result corresponding to the second reference mobile phone number, each reference feature of each second training material and the second reference mobile phone The door number corresponding to a user or a mobile phone device is related, and the reference marketing result indicates whether the second reference mobile phone number is a target marketing target suitable for marketing; in step (B), the second The prediction model is trained by machine learning by analyzing the correlation between the reference features of each second training data and the reference marketing result.

本發明的另一目的,在於提供能實施該行銷對象決策方法的一種行銷系統。Another object of the present invention is to provide a marketing system capable of implementing the method for decision-making of marketing objects.

本發明行銷系統能執行下列步驟:(A)獲得多筆訓練資料,每一訓練資料包含一個由多個數字組成的參考手機門號,以及多個各自與該參考手機門號對應的參考特徵,且每一參考特徵與該參考手機門號所對應的一使用者或一手機裝置相關;(B)訓練一第一預測模型及一第二預測模型,該第一預測模型是藉由分析每一參考手機門號的數字組成方式與該參考手機門號所對應之每一參考特徵之間的關聯性進行機器學習所訓練而成;(C)將一初始資料輸入該第一預測模型,該初始資料包含一個由多個數字組成的目標手機門號,以及多個各自與該目標手機門號對應的特徵欄位,每一特徵欄位與該目標手機門號所對應的一使用者或一手機裝置相關,而用於填入一與該目標手機門號對應的門號特徵,並且,該等特徵欄位中尚未被填入門號特徵的每一特徵欄位被作為一缺漏特徵欄位;(D)對於每一缺漏特徵欄位,該第一預測模型根據該目標手機門號的數字組成方式產生一對應該缺漏特徵欄位的預測結果,並將該預測結果作為一對應該目標手機門號的門號特徵填入該缺漏特徵欄位,以使得該初始資料成為一筆不具有缺漏特徵欄位的待評估資料;(E)將該待評估資料輸入該第二預測模型;(F)該第二預測模型根據該待評估資料的該等門號特徵之間的關聯性產生並輸出一決策結果,該決策結果指示出該目標手機門號是否為一適於接受行銷的目標行銷對象。The marketing system of the present invention can perform the following steps: (A) Obtain multiple training data, each training data includes a reference mobile phone number composed of multiple numbers, and a plurality of reference features corresponding to the reference mobile phone number, And each reference feature is related to a user or a mobile phone device corresponding to the reference mobile phone number; (B) training a first prediction model and a second prediction model, the first prediction model is analyzed by each The relationship between the digital composition of the reference mobile phone number and each reference feature corresponding to the reference mobile phone number is trained by machine learning; (C) an initial data is input into the first prediction model, and the initial The data includes a target mobile phone number composed of multiple numbers, and a plurality of feature fields corresponding to the target mobile phone number, and each feature field corresponds to a user or a mobile phone corresponding to the target mobile phone number Device-related, and used to fill in a door number feature corresponding to the target mobile phone number, and each feature field in the feature fields that has not yet been filled in with the entry number feature is regarded as a missing feature field;( D) For each missing feature field, the first prediction model generates a prediction result corresponding to the missing feature field according to the digital composition of the target mobile phone number, and uses the prediction result as a corresponding target mobile phone number Fill in the missing feature field with the door number feature of, so that the initial data becomes a piece of data to be evaluated without missing feature fields; (E) input the data to be evaluated into the second prediction model; (F) the first data The second prediction model generates and outputs a decision result based on the correlation between the door number characteristics of the data to be evaluated, the decision result indicating whether the target mobile phone number is a target marketing object suitable for marketing.

在本發明行銷系統的一些實施態樣中,在步驟(A)中 ,每一訓練資料還包含一對應該參考手機門號的參考行銷結果,該參考行銷結果指示出該參考手機門號是否為一適於接受行銷的目標行銷對象;在步驟(B)中 ,該第二預測模型是藉由分析每一訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習所訓練而成。In some implementations of the marketing system of the present invention, in step (A), each training material also includes a pair of reference marketing results that should refer to the mobile phone number, and the reference marketing result indicates whether the reference mobile phone number is A target marketing object suitable for marketing; in step (B), the second predictive model is trained by machine learning by analyzing the correlation between the reference features of each training data and the reference marketing result Become.

在本發明行銷系統的一些實施態樣中,在步驟(A)中,每一訓練資料之該等參考特徵的其中一者作為一目標參考特徵,並且,在步驟(B)中,對於該等訓練資料的該等目標參考特徵,該第一預測模型進行機器學習的方式包含:針對每一參考手機門號之該等數字中的一第一部分,分析該等參考手機門號之該等第一部分的數字組成方式與該等目標參考特徵之間的關聯性,以產生一對應該等第一部分及該等目標參考特徵的第一關聯性資料;針對每一參考手機門號之該等數字中的一第二部分,分析該等參考手機門號之該等第二部分的數字組成方式與該等目標參考特徵之間的關聯性,以產生一對應該等第二部分及該等目標參考特徵的第二關聯性資料;至少根據該第一關聯性資料及該第二關聯性資料統計出該等參考手機門號的數字組成方式與該等目標參考特徵之間的關聯性,以完成對於該等目標參考特徵的機器學習。In some implementation aspects of the marketing system of the present invention, in step (A), one of the reference features of each training data is used as a target reference feature, and, in step (B), for the reference features For the target reference features of the training data, the machine learning method of the first prediction model includes: for a first part of the numbers of each reference mobile phone number, analyzing the first parts of the reference mobile phone number The relationship between the number composition method and the target reference features to generate a pair of first correlation data corresponding to the first part and the target reference features; for each of the numbers in the reference mobile phone number A second part analyzes the correlation between the digital composition of the second parts of the reference mobile phone numbers and the target reference features to generate a correspondence between the second part and the target reference features The second relevance data; at least based on the first relevance data and the second relevance data, calculate the relevance between the digital composition of the reference mobile phone numbers and the target reference characteristics, so as to complete the Machine learning of target reference features.

在本發明行銷系統的一些實施態樣中,在步驟(D)中,該第一預測模型產生該預測結果的方式包含:針對該目標手機門號之該等數字中的一第一部分的數字組成方式進行分析,以產生一對應該第一部分的第一推測資料,該第一推測資料包含一第一推測結果,以及一指示出該第一推測結果之預估準確率的第一信心值;針對該目標手機門號之該等數字中的一第二部分的數字組成方式進行分析,以產生一對應該第二部分的第二推測資料,該第二推測資料包含一第二推測結果,以及一指示出該第二推測結果之預估準確率的第二信心值;至少根據該第一信心值與該第二信心值之間的大小關係,而至少從該第一推測結果及該第二推測結果中決定出該預測結果。In some implementation aspects of the marketing system of the present invention, in step (D), the method for the first prediction model to generate the prediction result includes: a first part of the numbers for the target mobile phone number The method is analyzed to generate the first guess data corresponding to the first part. The first guess data includes a first guess result and a first confidence value indicating the estimated accuracy of the first guess result; The digital composition of a second part of the numbers of the target mobile phone number is analyzed to generate second guess data corresponding to the second part. The second guess data includes a second guess result and a A second confidence value indicating the estimated accuracy of the second prediction result; at least according to the magnitude relationship between the first confidence value and the second confidence value, and at least from the first prediction result and the second prediction The prediction result is determined from the result.

在本發明行銷系統的一些實施態樣中,該行銷系統還執行位於步驟(F)之後的:(H)若該決策結果指示出該目標手機門號為該目標行銷對象,傳送一行銷訊息至該目標手機門號所對應的該手機裝置,以供該目標手機門號所對應的該手機裝置將該行銷訊息輸出,若該決策結果指示出該目標手機門號非為該目標行銷對象,則不傳送該行銷訊息至該目標手機門號所對應的該手機裝置。In some implementation aspects of the marketing system of the present invention, the marketing system also executes after step (F): (H) if the decision result indicates that the target mobile phone number is the target marketing target, send a marketing message to The mobile phone device corresponding to the target mobile phone number is used for the mobile phone device corresponding to the target mobile phone number to output the marketing message. If the decision result indicates that the target mobile phone number is not the target marketing object, then Do not send the marketing message to the mobile phone device corresponding to the target mobile phone number.

在本發明行銷系統的一些實施態樣中,在步驟(A)中,每一訓練資料的該等參考特徵至少包含一地區參考特徵、一性別參考特徵及一作業系統參考特徵,該地區參考特徵指示出對應之該參考手機門號所對應的該使用者的居住地區,該性別參考特徵指示出對應之該參考手機門號所對應的該使用者的性別,該作業系統參考特徵指示出對應之該參考手機門號所對應的該手機裝置的作業系統種類;在步驟(C)中,該等特徵欄位至少包含一地區特徵欄位、一性別特徵欄位及一作業系統特徵欄位,並且,用於被填入該地區特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該使用者的居住地區的地區特徵,用於被填入該性別特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該使用者的性別的性別特徵,用於被填入該作業系統特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該手機裝置的作業系統種類的作業系統特徵。In some implementation aspects of the marketing system of the present invention, in step (A), the reference features of each training data include at least a region reference feature, a gender reference feature, and an operating system reference feature. The region reference feature Indicate the residential area of the user corresponding to the reference mobile phone number, the gender reference feature indicates the gender of the user corresponding to the reference mobile phone number, and the operating system refers to the feature to indicate the corresponding The operating system type of the mobile phone device corresponding to the reference mobile phone number; in step (C), the feature fields include at least a region feature field, a gender feature field, and an operating system feature field, and , The door number feature used to be filled in the area feature field is an area feature indicating the residential area of the user corresponding to the target mobile phone number, used for the gender feature field filled in The door number feature is a gender feature that indicates the gender of the user corresponding to the target mobile phone number. The door number feature used to be filled in the operating system feature field is a gender feature that indicates the target mobile phone door number. The operating system characteristics corresponding to the operating system type of the mobile device.

在本發明行銷系統的一些實施態樣中,在步驟(A)中,該等訓練資料分別作為多筆第一訓練資料,且每一第一訓練資料的該參考手機門號作為一第一參考手機門號;該行銷系統還執行位於步驟(B)之前的:(G)獲得多筆第二訓練資料,每一第二訓練資料包含一由多個數字組成的第二參考手機門號、多個各自與該第二參考手機門號對應的參考特徵,以及一對應該第二參考手機門號的參考行銷結果,每一第二訓練資料的每一參考特徵與該第二參考手機門號所對應的一使用者或一手機裝置相關,且該參考行銷結果指示出該第二參考手機門號是否為一適於接受行銷的目標行銷對象;在步驟(B)中,該第二預測模型是藉由分析每一第二訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習所訓練而成。In some implementation aspects of the marketing system of the present invention, in step (A), the training data is used as a plurality of first training data, and the reference mobile phone number of each first training data is used as a first reference Mobile phone number; the marketing system also executes before step (B): (G) to obtain multiple second training data, each second training data contains a second reference mobile phone number composed of multiple numbers, multiple Each reference feature corresponding to the second reference mobile phone number, and the reference marketing result corresponding to the second reference mobile phone number, each reference feature of each second training material is associated with the second reference mobile phone number. Corresponding to a user or a mobile phone device, and the reference marketing result indicates whether the second reference mobile phone number is a target marketing target suitable for marketing; in step (B), the second prediction model is It is trained by machine learning by analyzing the correlation between the reference features of each second training data and the reference marketing result.

本發明之功效在於:該行銷系統能夠根據該目標手機門號的數字組成方式預測該目標手機門號是否為適於接受行銷的目標行銷對象,如此一來,相較於現有技術中對所有手機號碼傳送行銷內容的做法,本實施例的行銷系統能助於有效鎖定目標行銷對象以進行效率更佳的精準行銷,藉此縮短行銷所需的流量及時間成本,並提高行銷的轉化率。值得一提的是,即使該初始資料的門號特徵並不齊全而有所缺漏,該第一預測模型也能利用預測分析而將該初始資料缺漏的部分補齊,甚至,即便該初始資料中的所有特徵欄位全都是缺漏特徵欄位,該第一預測模型也依然能預測出對應每一個缺漏特徵欄位的門號特徵,而確保該第二預測模型能根據完整的待評估資料進行「是否為目標行銷對象」的預測分析,也就是說,在極端的應用情況下,即便該初始資料僅包含該目標門號而未包含任何一個門號特徵,該行銷系統仍然能夠先預測出對應該目標門號的所有門號特徵,再依據所預測出的該等門號特徵產生對應該初始資料的決策結果。The effect of the present invention is that the marketing system can predict whether the target mobile phone number is a target marketing target suitable for marketing according to the digital composition of the target mobile phone number. As a result, compared with the prior art for all mobile phones With the method of transmitting marketing content by number, the marketing system of this embodiment can effectively target target marketing targets for more efficient and accurate marketing, thereby reducing the traffic and time cost required for marketing, and increasing the conversion rate of marketing. It is worth mentioning that even if the initial data’s house number features are not complete and there are deficiencies, the first prediction model can also use predictive analysis to fill in the missing parts of the initial data, even if the initial data All feature fields in are all missing feature fields. The first prediction model can still predict the door number feature corresponding to each missing feature field, so as to ensure that the second prediction model can be based on the complete data to be evaluated. "Is it a target marketing target". That is to say, in extreme applications, even if the initial data only contains the target number and does not contain any characteristics of the number, the marketing system can still predict the corresponding All the door number characteristics of the target door number, and then based on the predicted door number characteristics to produce the decision result corresponding to the initial data.

在本發明被詳細描述之前,應當注意在以下的說明內容中,類似的元件是以相同的編號來表示。另外,本專利說明書中所述的「電連接」是泛指多個電子設備/裝置/元件之間透過導電材料相連接而達成的有線電連接,以及透過無線通訊技術進行無線信號傳輸的無線電連接。並且,本專利說明書中所述的「電連接」亦泛指兩個電子設備/裝置/元件之間直接相連而形成的「直接電連接」,以及兩個電子設備/裝置/元件之間還透過其他電子設備/裝置/元件相連而形成的「間接電連接」。Before the present invention is described in detail, it should be noted that in the following description, similar elements are denoted by the same numbers. In addition, the "electrical connection" mentioned in this patent specification generally refers to a wired electrical connection between multiple electronic devices/devices/components connected through conductive materials, and a radio connection through wireless communication technology for wireless signal transmission. . Moreover, the “electrical connection” mentioned in this patent specification also refers to the “direct electrical connection” formed by the direct connection between two electronic equipment/devices/components, and the “direct electrical connection” between the two electronic equipment/devices/components. "Indirect electrical connection" formed by connecting other electronic equipment/devices/components.

參閱圖1,本發明行銷系統1的一第一實施例例如被實施為一台伺服器,且例如包含一儲存單元11,以及一電連接該儲存單元11的處理單元12。在本實施例中,該儲存單元11可例如被實施為一或多個硬碟,另一方面,該處理單元12可例如被實施為一控制電路主機板以及一設置於該控制電路主機板上的處理器,但並不以此為限。Referring to FIG. 1, a first embodiment of the marketing system 1 of the present invention is implemented as a server, for example, and includes a storage unit 11 and a processing unit 12 electrically connected to the storage unit 11. In this embodiment, the storage unit 11 can be implemented as one or more hard disks. On the other hand, the processing unit 12 can be implemented as a control circuit motherboard and a control circuit motherboard. Processor, but not limited to this.

該儲存單元11儲存有尚未經過訓練的一第一預測模型M1及一第二預測模型M2。在本實施例中,該第一預測模型M1及該第二預測模型M2可例如分別被實施為兩個類神經網路(Artificial Neural Network,簡稱ANN),然而,在其他的實施例中,該第一預測模型M1及該第二預測模型M2也可以是利用其他種類的機器學習(Machine Learning)技術達成,例如支援向量機(Support Vector Machine,簡稱SVM)或隨機森林(Random Forest),而並不以本實施例為限。The storage unit 11 stores a first prediction model M1 and a second prediction model M2 that have not been trained. In this embodiment, the first prediction model M1 and the second prediction model M2 can be implemented as two neural networks (Artificial Neural Network, ANN for short), for example, respectively. However, in other embodiments, the The first prediction model M1 and the second prediction model M2 can also be achieved by using other types of machine learning (Machine Learning) technologies, such as Support Vector Machine (SVM) or Random Forest (Random Forest), and It is not limited to this embodiment.

同時參閱圖1及圖2,以下示例性地詳細說明本實施例的該行銷系統1如何實施一行銷對象決策方法。1 and 2 at the same time, the following exemplarily describes in detail how the marketing system 1 of this embodiment implements the method for decision-making of marketing objects.

首先,在步驟S1中,該處理單元12獲得多筆訓練資料,並將該等訓練資料儲存於該儲存單元11。First, in step S1, the processing unit 12 obtains a plurality of training data, and stores the training data in the storage unit 11.

在本實施例中,該等訓練資料例如分別為多筆針對特定種類商品(例如:化妝品)的歷史行銷紀錄,但並不以此為限。並且,在本實施例中,每一訓練資料例如包含一個由多個數字組成的參考手機門號、多個各自與該參考手機門號對應的參考特徵,以及一對應該參考手機門號的參考行銷結果。其中,每一參考特徵與該參考手機門號所對應的一使用者或一手機裝置相關,而該參考行銷結果則例如以「是」或「否」指示出該參考手機門號是否為一適於接受行銷的目標行銷對象,但並不以此為限。In this embodiment, the training data are, for example, multiple historical marketing records for specific types of commodities (such as cosmetics), but it is not limited to this. Moreover, in this embodiment, each training data includes, for example, a reference mobile phone number composed of multiple numbers, a plurality of reference features corresponding to the reference mobile phone number, and a pair of references that should refer to the mobile phone number. Marketing results. Among them, each reference feature is related to a user or a mobile device corresponding to the reference mobile phone number, and the reference marketing result is, for example, "Yes" or "No" to indicate whether the reference mobile phone number is a suitable one. It is the target marketing target who accepts marketing, but it is not limited to this.

更具體地說,在本實施例中,每一參考手機門號例如是由10個數字所組成(例如「0912345678」)。More specifically, in this embodiment, each reference mobile phone number is, for example, composed of 10 numbers (for example, "0912345678").

另一方面,在本實施例中,每一訓練資料例如是包含三個參考特徵,且該三個參考特徵例如分別為一地區參考特徵、一性別參考特徵,以及一作業系統參考特徵,但並不以此為限。On the other hand, in this embodiment, each training data includes three reference features, and the three reference features are, for example, a region reference feature, a gender reference feature, and an operating system reference feature, but not Not limited to this.

更詳細地說,該地區參考特徵例如指示出對應之該參考手機門號所對應的該使用者的居住地區,例如「北部」、「中部」、「南部」或「東部」。另一方面,該性別參考特徵例如指示出對應之該參考手機門號所對應的該使用者的性別,例如「男」或「女」。再一方面,該作業系統參考特徵例如指示出對應之該參考手機門號所對應的該手機裝置安裝的作業系統種類,例如「Android」或「iOS」。In more detail, the area reference feature indicates, for example, the residential area of the user corresponding to the reference mobile phone number, such as "Northern", "Central", "Southern" or "Eastern". On the other hand, the gender reference feature indicates, for example, the gender of the user corresponding to the reference mobile phone number, such as "male" or "female". In another aspect, the operating system reference feature, for example, indicates the type of operating system installed on the mobile device corresponding to the reference mobile phone number, such as "Android" or "iOS".

為了便於理解,在此以下表1示出一筆示例性的訓練資料。 [表1] 參考 手機門號 地區 參考特徵 性別 參考特徵 作業系統 參考特徵 參考 行銷結果 0912345678 北部 Android For ease of understanding, Table 1 below shows an exemplary training material. [Table 1] Reference mobile phone number Regional reference characteristics Gender reference characteristics Operating system reference features Reference marketing results 0912345678 North male Android Yes

需注意的是,本實施例中的該地區參考特徵、該性別參考特徵及該作業系統參考特徵是為了便於理解,故利用直觀、白話的方式示例性地呈現,應當理解的是,在實際的實施態樣中,該等參考特徵可被實施為預先定義的編號或電腦代碼,而並不以前述之示例態樣為限。It should be noted that the region reference feature, the gender reference feature, and the operating system reference feature in this embodiment are for ease of understanding, so they are presented exemplarily in an intuitive and vernacular way. It should be understood that in actual In the implementation aspect, the reference features can be implemented as a predefined number or computer code, and are not limited to the aforementioned example aspect.

補充說明的是,在本步驟S1中,該處理單元12可例如是先與一台圖式未示出的資料儲存裝置(可例如是硬碟、隨身碟或者另一台電腦裝置)建立電連接,並從該資料儲存裝置獲得該等訓練資料,或者,該處理單元12也可例如是透過網路下載的方式而從雲端獲得該等訓練資料。由於該等訓練資料的獲得方式並非本發明之重點,故本實施例對該處理單元12獲得該等訓練資料的方式並不作特別限定。It is added that in this step S1, the processing unit 12 may first establish an electrical connection with a data storage device not shown in the figure (for example, a hard disk, a flash drive or another computer device). , And obtain the training data from the data storage device, or, the processing unit 12 may also obtain the training data from the cloud, for example, by downloading from the Internet. Since the method for obtaining the training data is not the focus of the present invention, the method for obtaining the training data by the processing unit 12 is not particularly limited in this embodiment.

在該處理單元12獲得該等訓練資料後,流程進行至步驟S2。After the processing unit 12 obtains the training data, the flow proceeds to step S2.

在步驟S2中,該處理單元12根據該等訓練資料訓練儲存於該儲存單元11的該第一預測模型M1及該第二預測模型M2。並且,在本實施例中,該第一預測模型M1及該第二預測模型M2的訓練方式例如是根據該等訓練資料進行深度學習(Deep Learning),但並不以此為限。In step S2, the processing unit 12 trains the first prediction model M1 and the second prediction model M2 stored in the storage unit 11 according to the training data. Moreover, in this embodiment, the training method of the first prediction model M1 and the second prediction model M2 is, for example, deep learning based on the training data, but it is not limited to this.

以下針對該第一預測模型M1的訓練方式進行說明。The following describes the training method of the first prediction model M1.

在本實施例中,該第一預測模型M1是藉由分析每一參考手機門號的數字組成方式與該參考手機門號所對應之每一參考特徵之間的關聯性進行機器學習(在本實施例中為深度學習)所訓練而成。In this embodiment, the first predictive model M1 performs machine learning by analyzing the correlation between the numeric composition of each reference mobile phone number and each reference feature corresponding to the reference mobile phone number (in this In the embodiment, it is trained by deep learning).

為了便於進一步說明該第一預測模型M1的訓練方式,在此先以下表2示出多筆示例說明用的訓練資料,補充說明的是,表2中的部分數字是以「*」的符號表示。 [表2] 參考 手機門號 地區 參考特徵 性別 參考特徵 作業系統 參考特徵 參考 行銷結果 0912345678 北部 Android 0910****** 中部 iOS 0956****** 北部 Android 0917****** 南部 Android 0922****** 東部 iOS 0935****** 南部 iOS 0912****** 東部 Android In order to further explain the training method of the first prediction model M1, the following Table 2 shows the training data used for several examples. It is supplemented that some numbers in Table 2 are represented by "*" symbols. . [Table 2] Reference mobile phone number Regional reference characteristics Gender reference characteristics Operating system reference features Reference marketing results 0912345678 North male Android no 0910****** Central Female iOS Yes 0956****** North male Android no 0917****** South male Android no 0922****** east male iOS no 0935****** South Female iOS Yes 0912****** east Female Android no

配合參閱上表2所示例的該等訓練資料,並以該等訓練資料的該等地區參考特徵為例,本實施例的該第一預測模型M1在進行機器學習的過程中,可例如是利用推論統計學(Inferential Statistics)或描述統計學(descriptive statistics)來進行統計分析,以歸納出該等參考手機門號的數字組成方式與該等地區參考特徵之間的關聯性。藉此,該第一預測模型M1能夠學習哪些特定數字組成方式的參考手機門號有較大的概率是對應哪一種態樣的地區參考特徵,也就是學習每一種地區參考特徵所對應的該等參考手機門號在數字組成上有較大概率出現的共同特徵,例如:前五碼為「09111」的參考手機門號所對應的地區參考特徵有較大的概率(例如70%)為「北部」,但並不以此為限。With reference to the training data exemplified in Table 2 above, and taking the regional reference features of the training data as an example, the first prediction model M1 of this embodiment can be used in the process of machine learning. Inferential statistics or descriptive statistics are used to perform statistical analysis to summarize the relationship between the digital composition of the reference mobile phone numbers and the reference characteristics of the regions. In this way, the first prediction model M1 can learn which specific digital composition mode of the reference mobile phone number has a greater probability of corresponding to which type of regional reference feature, that is, learn the corresponding regional reference features of each type of regional reference feature. The reference mobile phone number has a greater probability of common features in the number composition. For example: the reference mobile phone number with the first five digits of "09111" corresponds to the regional reference feature with a greater probability (for example, 70%) of "North ", but not limited to this.

配合參閱圖3,更詳細地說,該第一預測模型M1例如會先將每一訓練資料的該地區參考特徵作為該訓練資料之所有參考特徵中的一目標參考特徵,並且,對於該等訓練資料的該等目標參考特徵,該第一預測模型M1進行機器學習的方式包含下列子步驟。With reference to FIG. 3, in more detail, the first prediction model M1, for example, will first use the region reference feature of each training data as a target reference feature among all the reference features of the training data, and for the training data For the target reference features of the data, the machine learning method of the first prediction model M1 includes the following sub-steps.

首先,進行子步驟S21。First, proceed to sub-step S21.

在子步驟S21中,針對每一參考手機門號之該等數字中的一第一部分,該第一預測模型M1分析該等參考手機門號之該等第一部分的數字組成方式與該等目標參考特徵(在此例中為該等地區參考特徵)之間的關聯性,以產生一對應該等第一部分及該等目標參考特徵的第一關聯性資料。In sub-step S21, for a first part of the numbers of each reference mobile phone number, the first prediction model M1 analyzes the digital composition method of the first parts of the reference mobile phone number and the target reference The correlation between the features (in this case, the reference features of the regions) to generate a pair of first correlation data that should equal the first part and the target reference features.

更明確地說,在本實施例中,每一參考手機門號的該第一部分例如為該參考手機門號的前五個數字。舉例而言,對於「0912345678」的參考手機門號,其第一部分即是指其中的「09123」。換句話說,在本實施例的子步驟S21中,該第一預測模型M1會針對該等參考手機門號的前五碼的數字組成方式與該等地區參考特徵之間的關聯性進行統計分析,而該第一關聯性資料即為描述該等參考手機門號之前五碼與該等地區參考特徵之間的關聯性的統計分析結果。More specifically, in this embodiment, the first part of each reference mobile phone number is, for example, the first five digits of the reference mobile phone number. For example, for the reference phone number of "0912345678", the first part refers to "09123". In other words, in the sub-step S21 of this embodiment, the first prediction model M1 will perform a statistical analysis on the relationship between the number composition of the first five codes of the reference mobile phone numbers and the reference features of the regions. , And the first relevance data is the statistical analysis result describing the relevance between the five codes before the reference mobile phone numbers and the reference features of the regions.

接著,進行子步驟S22。Next, proceed to sub-step S22.

在子步驟S22中,針對每一參考手機門號之該等數字中的一第二部分,該第一預測模型M1分析該等參考手機門號之該等第二部分的數字組成方式與該等目標參考特徵(在此例中為該等地區參考特徵)之間的關聯性,以產生一對應該等第二部分及該等目標參考特徵的第二關聯性資料。In sub-step S22, for a second part of the numbers of each reference mobile phone number, the first prediction model M1 analyzes the digital composition of the second parts of the reference mobile phone numbers and the The correlation between the target reference features (in this example, the regional reference features), to generate a second correlation data corresponding to the second part and the target reference features.

更明確地說,在本實施例中,每一參考手機門號的該第二部分例如為該參考手機門號的第六至八個數字。舉例而言,對於「0912345678」的參考手機門號,其第二部分即是指其中的「456」。換句話說,在本實施例的子步驟S22中,該第一預測模型M1會針對該等參考手機門號的第六至八碼的數字組成方式與該等地區參考特徵之間的關聯性進行統計分析,而該第二關聯性資料即為描述該等參考手機門號之第六至八碼與該等地區參考特徵之間的關聯性的統計分析結果。More specifically, in this embodiment, the second part of each reference mobile phone number is, for example, the sixth to eight digits of the reference mobile phone number. For example, for the reference phone number of "0912345678", the second part refers to "456". In other words, in the sub-step S22 of this embodiment, the first predictive model M1 will be based on the correlation between the digital composition of the sixth to eighth codes of the reference mobile phone numbers and the reference features of the regions. Statistical analysis, and the second correlation data is the statistical analysis result describing the correlation between the sixth to eighth codes of the reference mobile phone numbers and the reference features of the regions.

接著,進行子步驟S23。Next, proceed to sub-step S23.

在子步驟S23中,針對每一參考手機門號之該等數字中的一第三部分,該第一預測模型M1分析該等參考手機門號之該等第三部分的數字組成方式與該等目標參考特徵(在此例中為該等地區參考特徵)之間的關聯性,以產生一對應該等第三部分及該等目標參考特徵的第三關聯性資料。In sub-step S23, for a third part of the numbers of each reference mobile phone number, the first prediction model M1 analyzes the digital composition of the third parts of the reference mobile phone numbers and the The correlation between the target reference features (in this example, these regional reference features) is used to generate third correlation data corresponding to the third part and the target reference features.

更明確地說,在本實施例中,每一參考手機門號的該第三部分例如為該參考手機門號的最後兩個數字。舉例而言,對於「0912345678」的參考手機門號,其第三部分即是指其中的「78」。換句話說,在本實施例的子步驟S23中,該第一預測模型M1會針對該等參考手機門號的末兩碼的數字組成方式與該等地區參考特徵之間的關聯性進行統計分析,而該第三關聯性資料即為描述該等參考手機門號之末兩碼與該等地區參考特徵之間的關聯性的統計分析結果。More specifically, in this embodiment, the third part of each reference mobile phone number is, for example, the last two digits of the reference mobile phone number. For example, for the reference phone number of "0912345678", the third part refers to "78". In other words, in the sub-step S23 of this embodiment, the first prediction model M1 will perform a statistical analysis on the relationship between the digital composition of the last two codes of the reference mobile phone numbers and the reference features of the regions. , And the third correlation data is the statistical analysis result describing the correlation between the last two codes of the reference mobile phone numbers and the reference features of the regions.

最後,進行子步驟S24。Finally, proceed to sub-step S24.

在子步驟S24中,該第一預測模型M1將該第一關聯性資料、該第二關聯性資料及該第三關聯性資料彼此合併,從而產生一綜合分析結果,以完成對於該等目標參考特徵(在此例中為該等地區參考特徵)的機器學習。並且,該綜合分析結果指示出該等參考手機門號的數字組成方式與該等目標參考特徵之間的關聯性。In sub-step S24, the first predictive model M1 merges the first relevance data, the second relevance data, and the third relevance data with each other to generate a comprehensive analysis result to complete the reference to the targets Machine learning of features (in this case, reference features of these regions). Moreover, the comprehensive analysis result indicates the correlation between the digital composition mode of the reference mobile phone numbers and the target reference features.

綜上所述,在本實施例的子步驟S21至子步驟S24中,該第一預測模型M1在針對該等參考手機門號與該等目標參考特徵(在前述說明中是以該等地區參考特徵為例)之間的關聯性進行機器學習時,是將每一參考手機門號拆分為多個部分(在本實施例中即為前述的該第一部分、該第二部分及該第三部分),並分別針對該等參考手機門號的各個部分依序與該等目標參考特徵進行多次統計分析,最後再將多次統計分析的結果(在本實施例中即為前述的該第一關聯性資料、該第二關聯性資料及該第三關聯性資料)彼此合併,以完成針對該等目標參考特徵的機器學習。也就是說,本實施例的該第一預測模型M1是根據該等參考手機門號及該等目標參考特徵以「分層訓練」的方式進行深度學習所訓練而成的。To sum up, in the sub-steps S21 to S24 of this embodiment, the first prediction model M1 is based on the reference mobile phone numbers and the target reference features (in the foregoing description, reference is made to the regions For example, when performing machine learning, each reference mobile phone number is divided into multiple parts (in this embodiment, the first part, the second part, and the third part). Part), and perform multiple statistical analyses on each part of the reference mobile phone number in sequence and the target reference features. Finally, the results of multiple statistical analyses (in this embodiment are the aforementioned first A relevance data, the second relevance data and the third relevance data) are combined with each other to complete the machine learning for the target reference features. That is to say, the first prediction model M1 of this embodiment is trained by deep learning in a “layered training” manner based on the reference mobile phone numbers and the target reference features.

值得一提的是,若利用k折交叉驗證(k-fold cross-validation)對該第一預測模型M1的學習成果進行測試,相較於「不」將該等參考手機門號拆分為多個部分,而直接以每一參考手機門號的「整體」進行關聯性分析的方式,本實施例以「分層訓練」進行機器學習的方式能夠令該第一預測模型M1具有更佳的預測準確度。進一步地,透過利用k折交叉驗證進行測試,相較於將每一參考手機門號以其他方式拆分,本實施例將每一參考手機門號拆分為該第一部分(前五碼)、該第二部分(第六至八碼)及該第三部分(末兩碼)分別進行關聯性分析的方式,能令該第一預測模型M1在訓練完成後具有較佳的預測準確度。然而,應當理解的是,即便將參考手機門號以其他方式拆分(例如拆分成前四碼、第五至七碼及末三碼),也仍屬本發明的可實施態樣。It is worth mentioning that if k-fold cross-validation (k-fold cross-validation) is used to test the learning results of the first predictive model M1, the reference mobile phone number is divided into more than "no". In this embodiment, the method of machine learning with “layered training” can make the first prediction model M1 have better predictions by directly using the “wholes” of each reference mobile phone number for correlation analysis. Accuracy. Further, by using k-fold cross-validation for testing, compared to splitting each reference mobile phone number in other ways, this embodiment splits each reference mobile phone number into the first part (the first five codes), The second part (the sixth to the eighth code) and the third part (the last two codes) are respectively subjected to correlation analysis, so that the first prediction model M1 has better prediction accuracy after the training is completed. However, it should be understood that even if the reference mobile phone number is split in other ways (for example, split into the first four codes, the fifth to seventh codes, and the last three codes), it is still an implementable aspect of the present invention.

以上即為該第一預測模型M1以該等地區參考特徵為目標參考特徵進行機器學習的說明。The above is the description of the machine learning of the first prediction model M1 using these regional reference features as the target reference features.

並且,在完成針對該等地區參考特徵的機器學習後,該第一預測模型M1例如會改將每一訓練資料的該性別參考特徵作為新的目標參考特徵,並根據該等參考手機門號及該等性別參考特徵,以相同於前述子步驟S21至子步驟S24中的分層訓練方式,進行針對該等性別參考特徵的機器學習。In addition, after the machine learning for the reference features of the regions is completed, the first prediction model M1 will, for example, change the gender reference feature of each training data as the new target reference feature, and based on the reference mobile phone numbers and The gender reference features are subjected to machine learning for the gender reference features in the same hierarchical training method as in the aforementioned sub-step S21 to sub-step S24.

接著,在完成針對該等性別參考特徵的機器學習後,該第一預測模型M1例如會改將每一訓練資料的該作業系統參考特徵作為新的目標參考特徵,並根據該等參考手機門號及該等作業系統參考特徵,以相同於前述子步驟S21至子步驟S24中的分層訓練方式,進行針對該等作業系統參考特徵的機器學習。Then, after the machine learning for the gender reference features is completed, the first prediction model M1 will, for example, change the operating system reference feature of each training data as a new target reference feature, and then use the reference mobile phone number And these operating system reference features, the machine learning for the operating system reference features is performed in the same hierarchical training method as in the aforementioned sub-step S21 to sub-step S24.

如此一來,該第一預測模型M1便完成了該等參考手機門號與該等地區參考特徵、與該等性別參考特徵,以及與該等作業系統參考特徵之間的關聯性的機器學習。In this way, the first prediction model M1 completes the machine learning of the correlations between the reference mobile phone numbers and the regional reference features, the gender reference features, and the operating system reference features.

接著,以下針對該第二預測模型M2的訓練方式進行說明。Next, the training method of the second prediction model M2 will be described below.

在本實施例中,該第二預測模型M2則是藉由分析每一訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習(在本實施例中為深度學習)所訓練而成。In this embodiment, the second prediction model M2 performs machine learning (in this embodiment, deep learning) by analyzing the correlation between the reference features of each training data and the reference marketing result. Trained.

配合參閱前述表2所示例的該等訓練資料,本實施例的該第二預測模型M2在進行機器學習的過程中,可例如亦是利用推論統計學或描述統計學來進行統計分析,以歸納出該等訓練資料的該等參考特徵(亦即該等地區參考特徵、該等性別參考特徵及該等作業系統參考特徵)與該等參考行銷結果之間的關聯性。藉此,該第二預測模型M2便能夠學習哪幾種參考特徵的特定組成方式所對應的參考行銷結果有較大的概率為「是」,也就是學習參考行銷結果為「是」的訓練資料在參考特徵的組成方式上有較大概率出現的共同特徵,例如:在同一筆訓練資料中,若該地區參考特徵、該性別參考特徵及該作業系統參考特徵分別為「北部」、「女」及「iOS」,則該筆訓練資料的參考行銷結果有較大的概率(例如70%)為「是」,但並不以此為限。With reference to the training data exemplified in Table 2 above, the second predictive model M2 of this embodiment may, for example, also use inferential statistics or descriptive statistics to perform statistical analysis in the process of machine learning, so as to summarize Find out the correlation between the reference features of the training materials (that is, the geographic reference features, the gender reference features, and the operating system reference features) and the reference marketing results. In this way, the second predictive model M2 can learn which reference marketing results corresponding to the specific composition of reference features have a greater probability of being "Yes", that is, learning the training data for which the reference marketing result is "Yes" There is a common feature with greater probability in the composition of the reference features. For example, in the same training data, if the region reference feature, the gender reference feature, and the operating system reference feature are "Northern" and "Female" respectively And "iOS", the reference marketing result of the training data has a higher probability (for example, 70%) to be "Yes", but it is not limited to this.

在完成該第一預測模型M1及該第二預測模型M2的訓練後,流程進行至步驟S3。After completing the training of the first prediction model M1 and the second prediction model M2, the process proceeds to step S3.

同時參閱圖1、圖2及圖4,在步驟S3中,該處理單元12將一筆初始資料D(示於圖4)輸入該第一預測模型M1。Referring to FIGS. 1, 2 and 4 at the same time, in step S3, the processing unit 12 inputs a piece of initial data D (shown in FIG. 4) into the first prediction model M1.

在本實施例中,如圖4所示,該初始資料D包含一個由多個數字組成的目標手機門號(如圖4中所示的「0987654321」),以及多個各自與該目標手機門號對應的特徵欄位,每一特徵欄位與該目標手機門號所對應的一使用者或一手機裝置相關,而用於填入一與該目標手機門號對應的門號特徵,並且,該等特徵欄位中尚未被填入門號特徵的每一特徵欄位被作為一缺漏特徵欄位。換句話說,若該初始資料D中有任一個特徵欄位為缺漏特徵欄位,即代表該初始資料D本身並不齊全。In this embodiment, as shown in FIG. 4, the initial data D includes a target mobile phone number consisting of multiple numbers ("0987654321" as shown in FIG. 4), and a plurality of mobile phone numbers that are connected to the target mobile phone. The feature field corresponding to the target mobile phone number, each feature field is related to a user or a mobile phone device corresponding to the target mobile phone number, and is used to fill in a house number feature corresponding to the target mobile phone number, and, In these feature fields, each feature field that has not been filled in with the entry number feature is regarded as a missing feature field. In other words, if any feature field in the initial data D is a missing feature field, it means that the initial data D itself is not complete.

具體而言,在本實施例中,如同該等參考手機門號,該目標手機門號例如亦是由10個數字所組成,並且,該初始資料D例如是包含三個特徵欄位,且該三個特徵欄位例如分別為圖4所示出的一地區特徵欄位C1、一性別特徵欄位C2,以及一作業系統特徵欄位C3。Specifically, in this embodiment, like the reference mobile phone numbers, the target mobile phone number is, for example, composed of 10 numbers, and the initial data D includes, for example, three characteristic fields, and the The three feature fields are, for example, a region feature field C1, a gender feature field C2, and an operating system feature field C3 shown in FIG. 4, respectively.

更詳細地說,該地區特徵欄位C1例如是用於填入被作為一地區特徵f1的門號特徵,且該地區特徵f1例如是用於指示出該目標手機門號所對應之該使用者的居住地區(例如「北部」、「中部」、「南部」或「東部」)。另一方面,該性別特徵欄位C2例如是用於填入被作為一性別特徵f2的門號特徵,且該性別特徵f2例如是用於指示出該目標手機門號所對應之該使用者的性別(例如「男」或「女」)。再一方面,該作業系統特徵欄位C3例如是用於填入被作為一作業系統特徵f3的門號特徵,且該作業系統特徵f3例如是用於指示出該目標手機門號所對應之該手機裝置安裝的作業系統種類(例如「Android」或「iOS」),但並不以此為限。In more detail, the area feature field C1 is, for example, used to fill in the door number feature that is used as a area feature f1, and the area feature f1 is, for example, used to indicate the user corresponding to the target mobile phone number. Where you live (e.g. "North", "Central", "South" or "East"). On the other hand, the gender feature field C2 is, for example, used to fill in the door number feature that is used as a gender feature f2, and the gender feature f2 is, for example, used to indicate the user corresponding to the target mobile phone number. Gender (e.g. "male" or "female"). On the other hand, the operating system feature field C3 is, for example, used to fill in a feature of the operating system feature f3, and the operating system feature f3, for example, is used to indicate the target mobile phone number corresponding to the feature The type of operating system installed on the mobile device (such as "Android" or "iOS"), but it is not limited to this.

並且,以圖4所示出的該初始資料D舉例來說,該地區特徵欄位C1尚未被填入地區特徵f1而處於空白狀態,因此,該地區特徵欄位C1會被作為本實施例中的一個缺漏特徵欄位(在此作為圖4中所示出的一個第一缺漏特徵欄位Cm1)。另一方面,該性別特徵欄位C2已被填入了呈現為「女」的性別特徵f2,因此,該性別特徵欄位C2並不會被作為缺漏特徵欄位。再一方面, 該作業系統特徵欄位C3尚未被填入作業系統特徵f3,因此,該作業系統特徵欄位C3會被作為本實施例中的另一個缺漏特徵欄位(在此作為圖4中所示出的一個第二缺漏特徵欄位Cm2)。And, taking the initial data D shown in FIG. 4 as an example, the area feature field C1 has not yet been filled in the area feature f1 and is in a blank state. Therefore, the area feature field C1 will be used as in this embodiment A missing feature field of (here as a first missing feature field Cm1 shown in Figure 4). On the other hand, the gender feature field C2 has been filled in with the gender feature f2 that appears as "female". Therefore, the gender feature field C2 will not be regarded as a missing feature field. On the other hand, the operating system feature field C3 has not yet been filled in the operating system feature f3. Therefore, the operating system feature field C3 will be used as another missing feature field in this embodiment (here as shown in Figure 4). A second missing feature field is shown Cm2).

在將該初始資料D輸入該第一預測模型M1後,流程進行至步驟S4。After inputting the initial data D into the first prediction model M1, the flow proceeds to step S4.

在步驟S4中,對於該初始資料D的每一缺漏特徵欄位,該第一預測模型M1根據該目標手機門號的數字組成方式進行預測分析(predictive analytics),並藉此產生一對應該缺漏特徵欄位的預測結果,接著,該第一預測模型M1將該預測結果作為一對應該目標手機門號的門號特徵填入該缺漏特徵欄位,以使得該初始資料D成為一筆不具有任何缺漏特徵欄位的待評估資料D’。換句話說,該初始資料D是作為該第一預測模型M1的輸入資料,而該待評估資料D’則是作為該第二預測模型M2的輸出資料。如此一來,雖然該初始資料D的門號特徵有所缺漏,但該第一預測模型M1能夠以其進行機器學習的成果,而利用預測分析的方式將該初始資料D缺漏的部分補齊。In step S4, for each missing feature field of the initial data D, the first predictive model M1 performs predictive analytics based on the digital composition of the target mobile phone number, and generates a corresponding missing feature. Then, the first prediction model M1 fills the missing feature field with the prediction result as the door number feature corresponding to the target mobile phone number, so that the initial data D becomes a piece without any Data to be evaluated D'in the missing feature field. In other words, the initial data D is used as the input data of the first prediction model M1, and the data to be evaluated D'is used as the output data of the second prediction model M2. In this way, although the gate number feature of the initial data D is missing, the first prediction model M1 can use its machine learning results to fill in the missing parts of the initial data D by means of predictive analysis.

以圖4中所示出的該初始資料D舉例來說,對於該第一缺漏特徵欄位Cm1,該第一預測模型M1會根據該目標手機門號的數字組成方式產生一對應該第一缺漏特徵欄位Cm1的預測結果(在此作為一個地區預測結果,例如「北部」),接著,該第一預測模型M1將該地區預測結果填入該第一缺漏特徵欄位Cm1,以使該地區預測結果被加入於該初始資料D,並且被作為對應於該目標手機門號的地區特徵f1。Taking the initial data D shown in FIG. 4 as an example, for the first missing feature field Cm1, the first prediction model M1 will generate a corresponding first missing according to the digital composition of the target mobile phone number The prediction result of the feature field Cm1 (here as a regional prediction result, such as "Northern"), and then the first prediction model M1 fills the prediction result of the region into the first missing feature field Cm1, so that the region The prediction result is added to the initial data D, and is taken as the area feature f1 corresponding to the target mobile phone number.

配合參閱圖5,更詳細地說,該第一預測模型M1產生該地區預測結果的方式包含下列子步驟。In conjunction with FIG. 5, in more detail, the method for generating the prediction result of the region by the first prediction model M1 includes the following sub-steps.

首先,進行子步驟S41。First, proceed to sub-step S41.

在子步驟S41中,針對該目標手機門號之該等數字中的一第一部分,該第一預測模型M1對該第一部分的數字組成方式進行預測分析,以產生一對應該第一部分的第一推測資料。該第一推測資料包含一第一推測結果,以及一指示出該第一推測結果之預估準確率的第一信心值。舉例來說,該第一推測結果可例如為「北部」,而該第一信心值則可例如為「72%」。In sub-step S41, for a first part of the numbers of the target mobile phone number, the first prediction model M1 performs a predictive analysis on the number composition of the first part to generate a first part corresponding to the first part. Speculate information. The first prediction data includes a first prediction result and a first confidence value indicating the prediction accuracy of the first prediction result. For example, the first guess result may be "Northern", and the first confidence value may be "72%", for example.

更詳細地說,在本實施例中,該目標手機門號的該第一部分例如為該目標手機門號的前五個數字。舉例而言,對於「0987654321」的參考手機門號,其第一部分即是指其中的「09876」。換句話說,在本實施例的子步驟S41中,該第一預測模型M1是針對該目標手機門號的前五碼的數字組成方式進行預測分析,藉此推測該目標手機門號所對應的地區特徵為何。In more detail, in this embodiment, the first part of the target mobile phone number is, for example, the first five digits of the target mobile phone number. For example, for the reference phone number of "0987654321", the first part refers to "09876". In other words, in the sub-step S41 of this embodiment, the first predictive model M1 performs predictive analysis on the digital composition of the first five digits of the target mobile phone number, thereby inferring the target mobile phone number corresponding to the What are the characteristics of the region?

在產生該第一推測資料後,接著進行子步驟S42。After the first estimation data is generated, the sub-step S42 is then performed.

在子步驟S42中,針對該目標手機門號之該等數字中的一第二部分,該第一預測模型M1對該第二部分的數字組成方式進行預測分析,以產生一對應該第二部分的第二推測資料。該第二推測資料包含一第二推測結果,以及一指示出該第二推測結果之預估準確率的第二信心值。舉例來說,該第二推測結果可例如為「南部」,而該第二信心值則可例如為「45%」。In sub-step S42, for a second part of the numbers of the target mobile phone number, the first predictive model M1 performs a predictive analysis on the digital composition of the second part to generate a corresponding second part The second speculation data. The second prediction data includes a second prediction result and a second confidence value indicating the prediction accuracy of the second prediction result. For example, the second guess result may be "Southern", and the second confidence value may be "45%", for example.

更詳細地說,在本實施例中,該目標手機門號的該第二部分例如為該目標手機門號的第六至八個數字。舉例而言,對於「0987654321」的參考手機門號,其第二部分即是指其中的「543」。換句話說,在本實施例的子步驟S42中,該第一預測模型M1是針對該目標手機門號的第六至八碼的數字組成方式進行預測分析,藉此推測該目標手機門號所對應的地區特徵為何。In more detail, in this embodiment, the second part of the target mobile phone number is, for example, the sixth to eight digits of the target mobile phone number. For example, for the reference mobile phone number of "0987654321", the second part refers to "543". In other words, in the sub-step S42 of this embodiment, the first predictive model M1 performs predictive analysis on the digital composition of the sixth to eighth digits of the target mobile phone number, thereby inferring the location of the target mobile phone number. What are the corresponding regional characteristics?

在產生該第二推測資料後,接著進行子步驟S43。After the second estimation data is generated, the sub-step S43 is then performed.

在子步驟S43中,針對該目標手機門號之該等數字中的一第三部分,該第一預測模型M1對該第三部分的數字組成方式進行預測分析,以產生一對應該第三部分的第三推測資料。該第三推測資料包含一第三推測結果,以及一指示出該第三推測結果之預估準確率的第三信心值。舉例來說,該第三推測結果可例如為「東部」,而該第三信心值則可例如為「34%」。In sub-step S43, for a third part of the numbers of the target mobile phone number, the first prediction model M1 performs a predictive analysis on the number composition of the third part to generate a corresponding third part The third speculation data. The third prediction data includes a third prediction result, and a third confidence value indicating the prediction accuracy of the third prediction result. For example, the third guess result may be “East”, and the third confidence value may be “34%”, for example.

更詳細地說,在本實施例中,該目標手機門號的該第三部分例如為該目標手機門號的最後兩個數字。舉例而言,對於「0987654321」的參考手機門號,其第三部分即是指其中的「21」。換句話說,在本實施例的子步驟S43中,該第一預測模型M1是針對該目標手機門號的末兩碼的數字組成方式進行預測分析,藉此推測該目標手機門號所對應的地區特徵為何。In more detail, in this embodiment, the third part of the target mobile phone number is, for example, the last two digits of the target mobile phone number. For example, for the reference phone number of "0987654321", the third part refers to "21". In other words, in the sub-step S43 of this embodiment, the first predictive model M1 performs predictive analysis on the digital composition of the last two codes of the target mobile phone number, thereby inferring the target mobile phone number corresponding to the What are the characteristics of the region?

在產生該第三推測資料後,接著進行子步驟S44。After the third estimation data is generated, the sub-step S44 is then performed.

在子步驟S44中,該第一預測模型M1根據該第一信心值、該第二信心值與該第三信心值之間的大小關係,而從該第一推測結果、該第二推測結果及該第三推測結果中決定出該地區預測結果。In sub-step S44, the first prediction model M1 uses the first prediction result, the second prediction result, and the relationship between the first confidence value, the second confidence value, and the third confidence value. The prediction result of the region is determined from the third prediction result.

綜上所述,在本實施例的子步驟S41至子步驟S44中,該第一預測模型M1在針對該目標手機門號進行預測分析時,是先分別針對該目標手機門號的該第一部分、該第二部分及該第三部分進行三次預測分析,以分別產生該第一推測資料、該第二推測資料及該第三推測資料,接著,該第一預測模型M1再根據該第一至第三信心值之間的大小關係,而從該第一至第三推測結果中決定出該地區預測結果。以此例來說,該第一信心值的「72%」是該第一至第三信心值中最高的一者,因此,該第一預測模型M1在此例中例如會將該第一推測結果(亦即圖4中示出的「北部」)作為對應該目標手機門號的地區預測結果。To sum up, in the sub-steps S41 to S44 of this embodiment, the first prediction model M1 firstly focuses on the first part of the target mobile phone number when performing predictive analysis on the target mobile phone number. , The second part and the third part perform three predictive analyses to generate the first inferred data, the second inferred data, and the third inferred data, respectively. Then, the first predictive model M1 is based on the first to The size relationship between the third confidence value, and the prediction result of the region is determined from the first to third prediction results. In this example, the "72%" of the first confidence value is the highest among the first to third confidence values. Therefore, the first prediction model M1 in this example will, for example, predict the first The result (that is, the "Northern" shown in Figure 4) is used as the regional prediction result corresponding to the target mobile phone number.

值得注意的是,呼應於前述子步驟S21至子步驟S24中所述的分層訓練方式,對於目標手機門號,該第一預測模型M1亦是針對該目標手機門號的該第一至第三部分以「分層預測」的方式分別進行分析預測,並根據該第一至第三信心值的大小關係決定出該預測結果,透過利用k折交叉驗證的測試,前述的「分層預測」方式能使該第一預測模型M1具有較佳的預測準確度。It is worth noting that, in response to the hierarchical training method described in the aforementioned sub-steps S21 to S24, for the target mobile phone number, the first prediction model M1 is also for the first to the first to the first to the target mobile phone number. The three parts analyze and predict separately in the way of "stratified prediction", and determine the prediction result according to the magnitude relationship of the first to third confidence values. Through the test of k-fold cross-validation, the aforementioned "stratified prediction" The method can enable the first prediction model M1 to have better prediction accuracy.

接著,對於圖4中所示出的該第二缺漏特徵欄位Cm2,該第一預測模型M1會根據該目標手機門號的數字組成方式產生另一對應該第二缺漏特徵欄位Cm2的預測結果(在此作為一個作業系統預測結果,例如「iOS」),接著,該第一預測模型M1將該作業系統預測結果填入該第二缺漏特徵欄位Cm2,以使該作業系統預測結果被加入於該初始資料D,並且被作為對應於該目標手機門號的作業系統特徵f3。補充說明的是,該第一預測模型M1亦是以分層預測的方式對該目標手機門號進行預測分析以產生該作業系統預測結果,而類似於前述子步驟S41至子步驟S44中產生該地區預測結果的方式,故在此不再重述。Next, for the second missing feature field Cm2 shown in FIG. 4, the first prediction model M1 will generate another prediction corresponding to the second missing feature field Cm2 according to the digital composition of the target mobile phone number Result (here as an operating system prediction result, such as "iOS"), and then the first prediction model M1 fills the operating system prediction result into the second missing feature field Cm2, so that the operating system prediction result is It is added to the initial data D, and is taken as the operating system feature f3 corresponding to the target mobile phone number. It is supplemented that the first predictive model M1 also performs predictive analysis on the target mobile phone number in a hierarchical predictive manner to generate the operating system predictive result, which is similar to that generated in the aforementioned sub-step S41 to sub-step S44. The method of regional forecast results will not be repeated here.

在該第一預測模型M1於每一缺漏特徵欄位中填入對應的預測結果,而使得該初始資料D成為不具任何缺漏特徵欄位的該待評估資料D’後,流程進行至步驟S5。After the first prediction model M1 fills in the corresponding prediction result in each missing feature field, so that the initial data D becomes the data to be evaluated D'without any missing feature field, the process proceeds to step S5.

在步驟S5中,該處理單元12將該待評估資料D’輸入該第二預測模型M2。接著,流程進行至步驟S6。In step S5, the processing unit 12 inputs the to-be-evaluated data D'into the second prediction model M2. Then, the flow proceeds to step S6.

在步驟S6中,該第二預測模型M2根據該待評估資料D’的該等門號特徵(亦即圖4所示的該地區特徵f1、該性別特徵f2及該作業系統特徵f3)之間的關聯性進行預測分析,而產生並輸出一決策結果。並且,該決策結果指示出該目標手機門號是否為一適於接受行銷的目標行銷對象。In step S6, the second prediction model M2 is based on the relationship between the house number features of the data to be evaluated D'(that is, the area feature f1, the gender feature f2, and the operating system feature f3 shown in FIG. 4). Predictive analysis is performed on the relevance, and a decision result is generated and output. Moreover, the decision result indicates whether the target mobile phone number is a target marketing object suitable for receiving marketing.

在該第二預測模型M2產生該決策結果後,流程進行至步驟S7。After the second prediction model M2 generates the decision result, the process proceeds to step S7.

在步驟S7中,該處理單元12根據該決策結果決定是否傳送一行銷訊息至該目標手機門號所對應的該手機裝置,在本實施例中,該行銷訊息可例如被實施為一行銷簡訊,但並不以此為限。In step S7, the processing unit 12 determines whether to send a marketing message to the mobile phone device corresponding to the target mobile phone number according to the decision result. In this embodiment, the marketing message can be implemented as a marketing message, for example, But it is not limited to this.

更具體地說,若該決策結果指示出該目標手機門號為該目標行銷對象,則該處理單元12便會傳送該行銷訊息至該目標手機門號所對應的該手機裝置,以供該目標手機門號所對應的該手機裝置將該行銷訊息輸出,並供該目標手機門號所對應的該使用者參考。另一方面,若該決策結果指示出該目標手機門號非為該目標行銷對象,則該處理單元12便不傳送該行銷訊息至該目標手機門號所對應的該手機裝置,也就是不對該目標手機門號所對應的該使用者進行行銷。More specifically, if the decision result indicates that the target mobile phone number is the target marketing target, the processing unit 12 will send the marketing message to the mobile phone device corresponding to the target mobile phone number for the target The mobile phone device corresponding to the mobile phone number outputs the marketing message for reference by the user corresponding to the target mobile phone number. On the other hand, if the decision result indicates that the target mobile phone number is not the target marketing target, the processing unit 12 does not send the marketing message to the mobile phone device corresponding to the target mobile phone number, that is, it is not the target mobile phone number. The user corresponding to the target mobile phone number performs marketing.

上述的步驟S1至步驟S7即為本實施例之行銷系統1所實施的行銷對象決策方法。The above-mentioned steps S1 to S7 are the marketing target decision-making method implemented by the marketing system 1 of this embodiment.

補充說明的是,為了便於描述,本實施例在步驟S3中僅以單一筆初始資料D進行說明,然而,應當理解的是,在實際的實施態樣中,該處理單元12在步驟S3也可以是將多筆初始資料D批次性地同時輸入該第一預測模型M1,藉此,該第一預測模型M1在步驟S4中即能對該等初始資料D進行批次性地處理而輸出多筆分別對應該等初始資料D的待評估資料D’,而該第二預測模型M2在步驟S6中亦可批次性地產生多個分別對應該等待評估資料D’的決策結果。It is supplemented that, for ease of description, this embodiment only uses a single piece of initial data D in step S3 for description. However, it should be understood that in an actual implementation aspect, the processing unit 12 can also perform step S3. Is to input multiple pieces of initial data D into the first prediction model M1 simultaneously in batches, so that the first prediction model M1 can process the initial data D in batches in step S4 to output multiple pieces of initial data D. The pens respectively correspond to the data to be evaluated D′ of the initial data D, and the second prediction model M2 can also generate multiple decision results corresponding to the data to be evaluated D′ in step S6 in batches.

並且,本實施例將該行銷系統1實施為單一台伺服器僅是示例性的實現方式,應當理解的是,在實際的實施態樣中,該行銷系統1亦可被實施為多台伺服器,例如,該行銷系統1也可以是包含一用於訓練及運行該第一預測模型M1以輸出該待評估資料D’的第一伺服器、一電連接該第一伺服器且用於訓練及運行該第二預測模型M2以輸出該決策結果的第二伺服器,以及一電連接該第二伺服器且用於接收該決策結果以決定是否傳送該行銷訊息的第三伺服器,而並不以本實施例為限。Moreover, the implementation of the marketing system 1 as a single server in this embodiment is only an exemplary implementation. It should be understood that, in an actual implementation, the marketing system 1 can also be implemented as multiple servers. For example, the marketing system 1 may also include a first server for training and running the first prediction model M1 to output the data D'to be evaluated, and a first server electrically connected to the first server and used for training and A second server that runs the second predictive model M2 to output the decision result, and a third server that is electrically connected to the second server and used to receive the decision result to determine whether to send the marketing message, and not It is limited to this embodiment.

另外,在與本實施例類似的另一種實施態樣中,每一訓練資料除了該地區參考特徵、該性別參考特徵及該作業系統參考特徵之外,還可以進一步包含其他多個參考特徵,並且,所述的其他該等參考特徵可例如包含一年齡參考特徵、一感興趣商品參考特徵、一手機種類參考特徵、一簡訊網址點擊時間參考特徵,以及一網頁瀏覽行為參考特徵。In addition, in another implementation aspect similar to this embodiment, each training material may further include multiple other reference features in addition to the region reference feature, the gender reference feature, and the operating system reference feature, and The other reference features may include, for example, an age reference feature, a product of interest reference feature, a mobile phone type reference feature, a short message URL click time reference feature, and a web browsing behavior reference feature.

更具體地說,該年齡參考特徵例如指示出對應之該參考手機門號所對應之使用者的年齡範圍,例如「18歲以下」、「18~25歲」、「25~35歲」等。該感興趣商品參考特徵例如指示出對應之該參考手機門號所對應之使用者曾經瀏覽或購買過的商品種類或型號。該手機種類參考特徵例如指示出對應之該參考手機門號所對應之手機裝置的品牌及/或型號。該簡訊網址點擊時間參考特徵例如指示出:對應之該參考手機門號所對應之手機裝置在接收一包含一特定網址的簡訊後,該特定網址受到點擊操作的時間範圍,更具體地說,該簡訊網址點擊時間參考特徵可例如指示出「早上」、「下午」及「晚上」的其中一者,或者也可指示出「星期一」至「星期日」的其中一者。該網頁瀏覽行為參考特徵例如指示出對應之該參考手機門號所對應之手機裝置是否透過該特定網址連線至一特定網站,以及該手機裝置連線至該特定網站的持續時間長度。More specifically, the age reference feature indicates, for example, the age range of the user corresponding to the reference mobile phone number, such as "under 18 years old", "18-25 years old", "25-35 years old" and so on. The reference feature of the product of interest, for example, indicates the type or model of the product that the user corresponding to the reference mobile phone number has browsed or purchased. The mobile phone type reference feature, for example, indicates the brand and/or model of the mobile phone device corresponding to the corresponding reference mobile phone number. The short message URL click time reference feature indicates, for example, that the mobile device corresponding to the reference mobile phone number receives a short message containing a specific URL, and the time range during which the specific URL is clicked, more specifically, the The click time reference feature of the SMS URL can indicate one of "morning", "afternoon" and "evening", or one of "Monday" to "Sunday", for example. The web browsing behavior reference feature indicates, for example, whether the mobile phone device corresponding to the reference mobile phone number is connected to a specific website through the specific URL, and the duration of time that the mobile phone device is connected to the specific website.

藉此,在另該實施態樣中,該處理單元12在該行銷對象決策方法的步驟S2中還根據上述的其他該等參考特徵訓練該第一預測模型M1及該第二預測模型M2。並且,在另該實施態樣的步驟S3中,該初始資料D例如更包含其他多個特徵欄位,且所述的其他該等特徵欄位包含一用於填入一年齡特徵的年齡特徵欄位、一用於填入一感興趣商品特徵的感興趣商品特徵欄位、一用於填入一手機種類特徵的手機種類特徵欄位、一用於填入一簡訊網址點擊時間特徵的簡訊網址點擊時間特徵欄位,以及一用於填入一網頁瀏覽行為特徵的網頁瀏覽行為特徵欄位。如此一來,該第一預測模型M1在步驟S4中能根據該目標手機門號並針對所述的其他該等特徵欄位進行分析預測,而該第二預測模型M2亦能在步驟S6中進一步根據填入於所述的其他該等特徵欄位的門號特徵產生該決策結果。Therefore, in another embodiment, the processing unit 12 further trains the first prediction model M1 and the second prediction model M2 according to the other reference features mentioned above in step S2 of the marketing target decision-making method. In addition, in step S3 of this embodiment, the initial data D, for example, further includes a plurality of other characteristic fields, and the other characteristic fields include an age characteristic field for filling in an age characteristic. Position, one is used to fill in the feature field of the product of interest, one is used to fill in the feature field of the type of mobile phone, and one is used to fill in the text URL of the short message URL click time feature Click the time feature field, and a web browsing behavior feature field for filling in a web browsing behavior feature. In this way, the first prediction model M1 can analyze and predict the other feature fields according to the target mobile phone number in step S4, and the second prediction model M2 can also perform further analysis and prediction in step S6. The decision result is generated based on the house number characteristics filled in the other characteristic fields.

接著,以下針對本發明行銷系統1的一第二實施例與第一實施例之間的差異進行說明。Next, the following describes the difference between a second embodiment of the marketing system 1 of the present invention and the first embodiment.

第二實施例與第一實施例之間的差異主要在於:在第二實施例中,該第一預測模型M1及該第二預測模型M2是分別以不同的兩匹訓練資料進行訓練的。The difference between the second embodiment and the first embodiment is mainly that: in the second embodiment, the first prediction model M1 and the second prediction model M2 are trained with two different training data.

更具體地說,在第二實施例的步驟S1中,該處理單元12所獲得的該等訓練資料是分別作為多筆第一訓練資料,且每一第一訓練資料的該參考手機門號作為一第一參考手機門號。並且,在第二實施例中,每一第一訓練資料並不需要包含該參考行銷結果。More specifically, in step S1 of the second embodiment, the training data obtained by the processing unit 12 are respectively used as a plurality of first training data, and the reference mobile phone number of each first training data is used as A first reference mobile phone number. Moreover, in the second embodiment, each first training material does not need to include the reference marketing result.

並且,在第二實施例的步驟S1中,該處理單元12還獲得多筆第二訓練資料,但並不一定要與該等第一訓練資料同時獲得。每一第二訓練資料包含一個由多個數字組成的第二參考手機門號、多個各自與該第二參考手機門號對應的參考特徵,以及一對應該第二參考手機門號的參考行銷結果。其中,每一第二訓練資料的每一參考特徵與該第二參考手機門號所對應的一使用者或一手機裝置相關,而該參考行銷結果則例如以「是」或「否」指示出該第二參考手機門號是否為一適於接受行銷的目標行銷對象。In addition, in step S1 of the second embodiment, the processing unit 12 also obtains multiple pieces of second training data, but it does not necessarily have to be obtained at the same time as the first training data. Each second training material includes a second reference mobile phone number consisting of multiple numbers, a plurality of reference features corresponding to the second reference mobile phone number, and a reference marketing corresponding to the second reference mobile phone number result. Wherein, each reference feature of each second training data is related to a user or a mobile device corresponding to the second reference mobile phone number, and the reference marketing result is indicated as "Yes" or "No", for example Whether the second reference mobile phone number is a target marketing object suitable for receiving marketing.

進一步地,在第二實施例的步驟S2中,該處理單元12是以該等第一訓練資料訓練該第一預測模型M1,並以該等第二訓練資料訓練該第二預測模型M2。另外,該第一預測模型M1及該第二預測模型M2進行機器學習的方式與第一實施例相同,故在此不再重述。Further, in step S2 of the second embodiment, the processing unit 12 trains the first prediction model M1 with the first training data, and trains the second prediction model M2 with the second training data. In addition, the machine learning method of the first prediction model M1 and the second prediction model M2 is the same as that of the first embodiment, so it will not be repeated here.

綜上所述,藉由實施該行銷對象決策方法,該行銷系統1能夠根據該目標手機門號的數字組成方式預測該目標手機門號是否為適於接受行銷的目標行銷對象,並在預測的結果為是的情況下才傳送該行銷訊息至該目標手機門號所對應的該手機裝置,如此一來,相較於現有技術中對所有手機號碼傳送行銷內容的做法,本實施例的行銷系統1能有效鎖定目標行銷對象以進行效率更佳的精準行銷,藉此縮短行銷所需的流量及時間成本,並提高行銷的轉化率。值得一提的是,即使該初始資料D的門號特徵並不齊全而有所缺漏,該第一預測模型M1也能利用預測分析而將該初始資料D缺漏的部分補齊,甚至,即便該初始資料D中的所有特徵欄位全都是缺漏特徵欄位,該第一預測模型M1也依然能預測出對應每一個缺漏特徵欄位的門號特徵,而確保該第二預測模型M2能根據完整的待評估資料D’進行「是否為目標行銷對象」的預測分析,也就是說,在極端的應用情況下,即便該初始資料D僅包含該目標門號而未包含任何一個門號特徵,該行銷系統1仍然能夠先預測出對應該目標門號的所有門號特徵,再依據所預測出的該等門號特徵產生對應該初始資料D的決策結果,故確實能達成本發明之目的。In summary, by implementing the marketing target decision-making method, the marketing system 1 can predict whether the target mobile phone number is a target marketing target suitable for marketing according to the digital composition of the target mobile phone number, and predict whether the target mobile phone number is suitable for marketing. The marketing message is sent to the mobile phone device corresponding to the target mobile phone number only when the result is yes. As a result, compared to the prior art method of transmitting marketing content to all mobile phone numbers, the marketing system of this embodiment 1 It can effectively lock target marketing objects for more efficient and accurate marketing, thereby reducing the traffic and time costs required for marketing, and increasing the conversion rate of marketing. It is worth mentioning that even if the initial data D’s house number features are not complete and are missing, the first prediction model M1 can also use predictive analysis to fill in the missing parts of the initial data D, even if the initial data D is missing. All the feature fields in the initial data D are all missing feature fields. The first prediction model M1 can still predict the house number feature corresponding to each missing feature field, so as to ensure that the second prediction model M2 can be based on the complete The data to be evaluated D'is subjected to the predictive analysis of "whether it is the target marketing target", that is to say, in extreme applications, even if the initial data D only contains the target door number and does not contain any door number characteristics, the The marketing system 1 can still predict all the house number characteristics corresponding to the target house number first, and then generate the decision result corresponding to the initial data D based on the predicted house number characteristics, so it can indeed achieve the purpose of the invention.

惟以上所述者,僅為本發明之實施例而已,當不能以此限定本發明實施之範圍,凡是依本發明申請專利範圍及專利說明書內容所作之簡單的等效變化與修飾,皆仍屬本發明專利涵蓋之範圍內。However, the above are only examples of the present invention. When the scope of implementation of the present invention cannot be limited by this, all simple equivalent changes and modifications made in accordance with the scope of the patent application of the present invention and the content of the patent specification still belong to This invention patent covers the scope.

1:行銷系統 11:儲存單元 12:處理單元 M1:第一預測模型 M2:第二預測模型 D:初始資料 D’:待評估資料 C1:地區特徵欄位 C2:性別特徵欄位 C3:作業系統特徵欄位 Cm1:第一缺漏特徵欄位 Cm2:第二缺漏特徵欄位 f1:地區特徵 f2:性別特徵 f3:作業系統特徵 S1~S7:步驟 S21~S24:子步驟 S41~S44:子步驟 1: Marketing system 11: storage unit 12: Processing unit M1: The first predictive model M2: second predictive model D: Initial information D’: Materials to be evaluated C1: Area feature field C2: Gender characteristics field C3: Operating system feature field Cm1: The first missing feature field Cm2: The second missing feature field f1: Regional characteristics f2: gender characteristics f3: Operating system characteristics S1~S7: steps S21~S24: Sub-step S41~S44: Sub-step

本發明之其他的特徵及功效,將於參照圖式的實施方式中清楚地呈現,其中: 圖1是本發明行銷系統之一第一實施例的一方塊示意圖; 圖2是一流程圖,示例性地說明該第一實施例如何實施一行銷對象決策方法; 圖3是另一流程圖,示例性地說明該第一實施例的一第一預測模型如何進行機器學習; 圖4是一示意圖,示例性地表示該第一預測模型如何使一具有缺漏特徵欄位的初始資料成為一完整的待評估資料;及 圖5是再一流程圖,示例性地說明該第一實施例如何根據一目標手機門號的數字組成方式產生一預測結果。 Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: Figure 1 is a block diagram of a first embodiment of the marketing system of the present invention; Figure 2 is a flowchart illustrating how the first embodiment implements the decision-making method for marketing objects; Fig. 3 is another flowchart illustrating how a first prediction model of the first embodiment performs machine learning; Fig. 4 is a schematic diagram illustrating how the first prediction model makes an initial data with missing feature fields a complete data to be evaluated; and Fig. 5 is another flowchart illustrating how the first embodiment generates a prediction result according to the digital composition of a target mobile phone number.

S1~S7:步驟 S1~S7: steps

Claims (8)

一種行銷對象決策方法,由一行銷系統實施,該行銷對象決策方法包含:(A)獲得多筆訓練資料,每一訓練資料包含一個由多個數字組成的參考手機門號,以及多個各自與該參考手機門號對應的參考特徵,且每一參考特徵與該參考手機門號所對應的一使用者或一手機裝置相關;(B)訓練一第一預測模型及一第二預測模型,該第一預測模型是藉由分析每一參考手機門號的數字組成方式與該參考手機門號所對應之每一參考特徵之間的關聯性進行機器學習所訓練而成;(C)將一初始資料輸入該第一預測模型,該初始資料包含一個由多個數字組成的目標手機門號,以及多個各自與該目標手機門號對應的特徵欄位,每一特徵欄位與該目標手機門號所對應的一使用者或一手機裝置相關,而用於填入一與該目標手機門號對應的門號特徵,並且,該等特徵欄位中尚未被填入門號特徵的每一特徵欄位被作為一缺漏特徵欄位;(D)對於每一缺漏特徵欄位,該第一預測模型根據該目標手機門號的數字組成方式產生一對應該缺漏特徵欄位的預測結果,並將該預測結果作為一對應該目標手機門號的門號特徵填入該缺漏特徵欄位,以使得該初始資料成為一筆不具有缺漏特徵欄位的待評估資料;(E)將該待評估資料輸入該第二預測模型;及 (F)該第二預測模型根據該待評估資料的該等門號特徵之間的關聯性產生並輸出一決策結果,該決策結果指示出該目標手機門號是否為一適於接受行銷的目標行銷對象。 A marketing target decision-making method implemented by a marketing system. The marketing target decision-making method includes: (A) Obtaining multiple training data, each training data contains a reference mobile phone number composed of multiple numbers, and multiple respective and The reference feature corresponding to the reference mobile phone number, and each reference feature is related to a user or a mobile phone device corresponding to the reference mobile phone number; (B) training a first prediction model and a second prediction model, the The first prediction model is trained by machine learning by analyzing the relationship between the number composition of each reference mobile phone number and each reference feature corresponding to the reference mobile phone number; (C) Data is input into the first predictive model. The initial data includes a target mobile phone number composed of multiple numbers, and a plurality of feature fields corresponding to the target mobile phone number. Each feature field corresponds to the target mobile phone number. It is related to a user or a mobile phone device corresponding to the number, and is used to fill in a door number feature corresponding to the target mobile phone number, and each feature field that has not yet been filled with the door number feature in the feature fields The bit is regarded as a missing feature field; (D) For each missing feature field, the first prediction model generates a prediction result corresponding to the missing feature field according to the digital composition of the target mobile phone number, and then The prediction result is filled in the missing feature field as the door number feature corresponding to the target mobile phone number, so that the initial data becomes a piece of data to be evaluated without missing feature fields; (E) the data to be evaluated is entered into the missing feature field. The second prediction model; and (F) The second prediction model generates and outputs a decision result based on the correlation between the door number characteristics of the data to be evaluated, the decision result indicating whether the target mobile phone number is a target suitable for marketing Marketing target. 如請求項1所述的行銷對象決策方法,其中:在步驟(A)中,每一訓練資料還包含一對應該參考手機門號的參考行銷結果,該參考行銷結果指示出該參考手機門號是否為一適於接受行銷的目標行銷對象;及在步驟(B)中,該第二預測模型是藉由分析每一訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習所訓練而成。 The marketing target decision-making method according to claim 1, wherein: in step (A), each training material also includes a pair of reference marketing results that should refer to the mobile phone number, and the reference marketing result indicates the reference mobile phone number Whether it is a target marketing object suitable for marketing; and in step (B), the second predictive model is performed by analyzing the correlation between the reference features of each training data and the reference marketing result Learned and trained. 如請求項1所述的行銷對象決策方法,其中,在步驟(A)中,每一訓練資料之該等參考特徵的其中一者作為一目標參考特徵,並且,在步驟(B)中,對於該等訓練資料的該等目標參考特徵,該第一預測模型進行機器學習的方式包含:針對每一參考手機門號之該等數字中的一第一部分,分析該等參考手機門號之該等第一部分的數字組成方式與該等目標參考特徵之間的關聯性,以產生一對應該等第一部分及該等目標參考特徵的第一關聯性資料;針對每一參考手機門號之該等數字中的一第二部分,分析該等參考手機門號之該等第二部分的數字組成方式與該等目標參考特徵之間的關聯性,以產生一對應該等第二部分及該等目標參考特徵的第二關聯性資料;及 至少根據該第一關聯性資料及該第二關聯性資料統計出該等參考手機門號的數字組成方式與該等目標參考特徵之間的關聯性,以完成對於該等目標參考特徵的機器學習。 The marketing target decision method according to claim 1, wherein in step (A), one of the reference features of each training material is used as a target reference feature, and, in step (B), for For the target reference features of the training data, the machine learning method of the first prediction model includes: for a first part of the numbers of each reference mobile phone number, analyzing the reference mobile phone numbers The relationship between the number composition method of the first part and the target reference features to generate the first correlation data corresponding to the first part and the target reference features; these numbers for each reference mobile phone number A second part of the second part of the reference mobile phone number, the analysis of the relationship between the digital composition of the second part of the reference mobile phone number and the target reference characteristics, to generate a correspondence between the second part and the target reference The second relevance data of the feature; and Calculate the correlation between the digital composition method of the reference mobile phone numbers and the target reference features at least according to the first correlation data and the second correlation data, so as to complete the machine learning of the target reference features . 如請求項1所述的行銷對象決策方法,其中,在步驟(D)中,該第一預測模型產生該預測結果的方式包含:針對該目標手機門號之該等數字中的一第一部分的數字組成方式進行分析,以產生一對應該第一部分的第一推測資料,該第一推測資料包含一第一推測結果,以及一指示出該第一推測結果之預估準確率的第一信心值;針對該目標手機門號之該等數字中的一第二部分的數字組成方式進行分析,以產生一對應該第二部分的第二推測資料,該第二推測資料包含一第二推測結果,以及一指示出該第二推測結果之預估準確率的第二信心值;至少根據該第一信心值與該第二信心值之間的大小關係,而至少從該第一推測結果及該第二推測結果中決定出該預測結果。 The marketing target decision method according to claim 1, wherein, in step (D), the method for generating the prediction result by the first prediction model includes: a first part of the numbers of the target mobile phone number Analyze the digital composition to generate the first speculation data corresponding to the first part. The first speculation data includes a first speculation result and a first confidence value indicating the estimated accuracy of the first speculation result ; Analyze the digital composition of a second part of the numbers of the target mobile phone number to generate a second guess data corresponding to the second part, the second guess data includes a second guess result, And a second confidence value indicating the estimated accuracy of the second prediction result; at least according to the magnitude relationship between the first confidence value and the second confidence value, and at least from the first prediction result and the second confidence value 2. The prediction result is determined from the prediction result. 如請求項1所述的行銷對象決策方法,還包含一位於步驟(F)之後的:(H)若該決策結果指示出該目標手機門號為該目標行銷對象,傳送一行銷訊息至該目標手機門號所對應的該手機裝置,以供該目標手機門號所對應的該手機裝置將該行銷訊息輸出,若該決策結果指示出該目標手機門號非為該目標行銷對象,則不傳送該行銷訊息至該目標手機門號所對應的該手機裝置。 The marketing target decision-making method as described in claim 1, further comprising a step (F) after step (F): (H) if the decision result indicates that the target mobile phone number is the target marketing target, sending a marketing message to the target The mobile phone device corresponding to the mobile phone number is used for the mobile phone device corresponding to the target mobile phone number to output the marketing message. If the decision result indicates that the target mobile phone number is not the target marketing target, it will not be sent The marketing message to the mobile phone device corresponding to the target mobile phone number. 如請求項1所述的行銷對象決策方法,其中:在步驟(A)中,每一訓練資料的該等參考特徵至少包含一地區參考特徵、一性別參考特徵及一作業系統參考特徵,該地區參考特徵指示出對應之該參考手機門號所對應的該使用者的居住地區,該性別參考特徵指示出對應之該參考手機門號所對應的該使用者的性別,該作業系統參考特徵指示出對應之該參考手機門號所對應的該手機裝置的作業系統種類;及在步驟(C)中,該等特徵欄位至少包含一地區特徵欄位、一性別特徵欄位及一作業系統特徵欄位,並且,用於被填入該地區特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該使用者的居住地區的地區特徵,用於被填入該性別特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該使用者的性別的性別特徵,用於被填入該作業系統特徵欄位的該門號特徵為一指示出該目標手機門號所對應之該手機裝置的作業系統種類的作業系統特徵。 The marketing target decision-making method according to claim 1, wherein: in step (A), the reference features of each training data include at least a region reference feature, a gender reference feature, and an operating system reference feature, and the region The reference feature indicates the residential area of the user corresponding to the reference mobile phone number, the gender reference feature indicates the gender of the user corresponding to the reference mobile phone number, and the operating system indicates the reference feature Corresponding to the operating system type of the mobile phone device corresponding to the reference mobile phone number; and in step (C), the feature fields include at least a region feature field, a gender feature field, and an operating system feature field In addition, the door number feature used to be filled in the area feature field is an area feature indicating the residential area of the user corresponding to the target mobile phone number, and is used to be filled in the gender feature field The door number feature of the bit is a gender feature indicating the gender of the user corresponding to the target phone number, and the door number feature used to be filled in the operating system feature field is a gender feature indicating the target phone The operating system characteristics of the operating system type of the mobile device corresponding to the door number. 如請求項1所述的行銷對象決策方法,其中:在步驟(A)中,該等訓練資料分別作為多筆第一訓練資料,且每一第一訓練資料的該參考手機門號作為一第一參考手機門號;該行銷對象決策方法包含位於步驟(B)之前的:(G)獲得多筆第二訓練資料,每一第二訓練資料包含一由多個數字組成的第二參考手機門號、多個各自與該第二參考手 機門號對應的參考特徵,以及一對應該第二參考手機門號的參考行銷結果,每一第二訓練資料的每一參考特徵與該第二參考手機門號所對應的一使用者或一手機裝置相關,且該參考行銷結果指示出該第二參考手機門號是否為一適於接受行銷的目標行銷對象;及在步驟(B)中,該第二預測模型是藉由分析每一第二訓練資料的該等參考特徵與該參考行銷結果之間的關聯性進行機器學習所訓練而成。 The marketing target decision-making method according to claim 1, wherein: in step (A), the training data is used as a plurality of first training data, and the reference mobile phone number of each first training data is used as a first training data. A reference mobile phone number; the marketing target decision-making method includes before step (B): (G) obtaining a plurality of second training data, and each second training data includes a second reference mobile phone door composed of multiple numbers Number, multiple each and the second reference hand The reference feature corresponding to the phone number, and the reference marketing result corresponding to the second reference phone number. Each reference feature of each second training data corresponds to a user or a user or a corresponding to the second reference phone number. The mobile phone device is related, and the reference marketing result indicates whether the second reference mobile phone number is a target marketing object suitable for marketing; and in step (B), the second prediction model is performed by analyzing each 2. The correlation between the reference features of the training data and the reference marketing result is trained by machine learning. 一種行銷系統,包含一儲存單元及一電連接該儲存單元的處理單元,且該處理單元能實施如請求項1至7其中任一項所述的行銷對象決策方法。 A marketing system includes a storage unit and a processing unit electrically connected to the storage unit, and the processing unit can implement the marketing target decision method according to any one of claim items 1 to 7.
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