CN112862530A - Marketing system based on big data - Google Patents
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- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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Abstract
The invention discloses a marketing system based on big data, which belongs to the technical field of big data marketing and comprises a client, a marketing platform, a client information acquisition module, a product information acquisition module, a data processing module, a data storage module, a big data analysis module and a marketing promotion module; the marketing platform mainly comprises a commodity display unit, an information retrieval unit and a client information recording unit; the big data analysis module comprises a client group classification unit and an association degree division unit; according to the method, the big data information of the user is extracted, accurate division of different customer groups is achieved through machine learning, then the relation analysis is carried out on the different customer groups and the sold products through an association algorithm, three association degree grades of high, medium and low are obtained, and finally marketing promotion is carried out according to the high association degree grade, so that accurate marketing is achieved; thereby being beneficial to improving the marketing efficiency and the marketing return rate.
Description
Technical Field
The invention relates to the technical field of big data marketing, in particular to a marketing system based on big data.
Background
Through retrieval, the Chinese patent number CN103024705A discloses a method for marketing mobile advertisement short messages, and although the invention optimizes the advertisement promotion mode, the advertisement is not accurately sent, the accurate marketing can not be realized, and the marketing return rate is low; big data (bigdata) is a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is massive, high-growth rate and diversified information assets which can have stronger decision-making power, insight discovery power and flow optimization capability only by a new processing mode; at present, with the continuous development of electronic commerce and the continuous improvement of a logistics transportation system, more and more customers select online consumption, but due to the fact that the category attributes, interests, hobbies, income levels and the like of the customers are different, large-range universal marketing advertisements are determined to be sent, and the method is extremely low in efficiency, poor in return rate and high in cost; with the continuous improvement of the big data technology, how to perfectly combine the big data technology with marketing to realize accurate marketing so as to improve the purchasing experience and purchasing loyalty of customers, and the method becomes the key point of current research; therefore, it becomes important to invent a marketing system based on big data.
Most of the existing marketing systems adopt a large-range universal mode to send marketing advertisements, and the mode has the advantages of extremely low efficiency, poor return rate and high cost, and cannot realize personalized accurate marketing for customers; to this end, we propose a big data based marketing system.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a marketing system based on big data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a marketing system based on big data comprises a client, a marketing platform, a client information acquisition module, a product information acquisition module, a data processing module, a data storage module, a big data analysis module and a marketing promotion module;
the marketing platform mainly comprises a commodity display unit, an information retrieval unit and a customer information recording unit; the big data analysis module comprises a customer group classification unit and an association degree division unit; the client is specifically a mobile phone, an Ipad or a computer.
Further, the registration login unit is used for registering the marketing platform account number by a client in a mode of filling in personal information, wherein the personal information comprises name, birthday, hobbies and mobile phone number.
Further, the commodity display unit is used for displaying the information of the sales products; the information retrieval unit is used for the client to retrieve the commodity according to the self requirement; the customer information recording unit is mainly used for recording implicit information and historical transaction record information of customers; the sales product information mainly comprises product price, product type and product use; the implicit information comprises client retrieval keywords and client commodity page retention time.
Further, the customer information acquisition module is used for collecting personal information, implicit information and historical transaction record information and uploading the personal information, the implicit information and the historical transaction record information to the data processing module; the product information acquisition module is used for collecting sales product information and uploading the sales product information to the data processing module; the data processing module is used for cleaning, converting, loading, normalizing, associating, classifying, denoising and correlation analyzing the collected personal information, implicit information, historical transaction record information and sales product information; the data storage module is used for storing the personal information, the implicit information, the historical transaction record information and the information of the sold products which are processed by the data processing module.
Further, the big data analysis module is used for carrying out customer group classification and correlation degree calculation processing by utilizing personal information, implicit information, historical transaction record information and sales product information; the client group classification unit is used for classifying clients by utilizing a machine learning algorithm and combining personal information, implicit information and historical transaction record information, and the specific classification process is as follows:
s1: extracting data characteristics of personal information, implicit information and historical transaction record information to obtain a training set 1, a training set 2 and a training set 3;
s2: constructing a plurality of classifiers, and respectively putting a training set 1, a training set 2 and a training set 3 into the plurality of classifiers to obtain a classification model I, a classification model II and a classification model III;
s3: for each individual client, respectively detecting by a classification model I, a classification model II and a classification model III; obtaining a client quantization feature I, a client quantization feature II and a client quantization feature III;
s4: constructing an integrated classifier, taking the client quantitative feature I, the client quantitative feature II and the client quantitative feature III as input data of the integrated classifier, and obtaining an integrated model through integrated learning training;
s5: dividing the clients into a plurality of homogeneous client groups by the integrated model;
the classifiers are all specifically K-means algorithms, and the formula is as follows:
the integrated classifier is specifically a BP neural network algorithm, and the specific formula is as follows:
further, the relevancy division unit is configured to perform relevancy calculation on a plurality of homogeneous client groups and sales product information by using a relevancy algorithm, and analyze a relationship between a client and a product, where a specific analysis process is as follows:
SS 1: extracting characteristic data of a plurality of homogeneous client groups and sales product information, and calculating the association degree of the characteristic data by using an Apriori algorithm to obtain an association degree data set;
SS 2: carrying out grade division on the association degree data set to obtain three association degree grades of high, medium and low;
the marketing promotion module is used for providing product promotion and product recommendation for the customers according to the high-relevancy grade, so that accurate marketing is realized;
the Apriori algorithm proceeds as follows:
SSS 1: respectively making characteristic data of a plurality of homogeneous client groups and sales product information into databases X and Y, and calculating a minimum support threshold min support;
SSS 2: retrieving all frequent item sets in the database, namely association degree data sets, through continuous iteration;
the minimum support threshold min support calculation formula is as follows:
compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a client group classification unit, and the specific classification process is as follows: firstly, extracting data characteristics of personal information, implicit information and historical transaction record information to obtain a training set 1, a training set 2 and a training set 3; secondly, constructing a plurality of classifiers, and respectively putting the training set 1, the training set 2 and the training set 3 into the plurality of classifiers to obtain a classification model I, a classification model II and a classification model III; thirdly, respectively detecting each individual client by using a classification model I, a classification model II and a classification model III; acquiring a client quantitative feature I, a client quantitative feature II and a client quantitative feature III, constructing an integrated classifier, taking the client quantitative feature I, the client quantitative feature II and the client quantitative feature III as input data of the integrated classifier, and obtaining an integrated model through integrated learning training; fifthly, dividing the clients into a plurality of homogeneous client groups by the integrated model; the accurate division of the client groups is realized, and the success rate of subsequent accurate marketing is improved;
2. the invention is provided with a relevancy division unit and a marketing promotion module; the relevancy division unit calculates the relevancy of a plurality of homogeneous client groups and sales product information by using a relevancy algorithm, and analyzes the relationship between the client and the product to obtain three relevancy grades of high relevancy, medium relevancy and low relevancy; and then the marketing promotion module extracts the high-relevancy grade and provides product promotion and product recommendation for the client according to the high-relevancy grade, so that personalized and accurate marketing for the client is realized, and the marketing efficiency and the marketing return rate are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic overall structure diagram of a big data-based marketing system according to the present invention;
fig. 2 is a schematic overall flow chart of a big data-based marketing system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1, a marketing system based on big data comprises a client, a marketing platform, a client information acquisition module, a product information acquisition module, a data processing module, a data storage module, a big data analysis module and a marketing promotion module;
the marketing platform mainly comprises a commodity display unit, an information retrieval unit and a client information recording unit; the big data analysis module comprises a client group classification unit and an association degree division unit; the client is specifically a mobile phone, an Ipad or a computer.
The registration login unit is used for registering the marketing platform account number by a client in a mode of filling personal information, wherein the personal information comprises name, birthday, hobby and mobile phone number.
The commodity display unit is used for displaying the information of the sold products; the information retrieval unit is used for the client to retrieve the commodity according to the self requirement; the customer information recording unit is mainly used for recording implicit information and historical transaction record information of customers; the sales product information mainly comprises product price, product type and product use; the implicit information includes the customer search keywords and the residence time of the customer goods page.
The client information acquisition module is used for collecting personal information, implicit information and historical transaction record information and uploading the personal information, the implicit information and the historical transaction record information to the data processing module; the product information acquisition module is used for collecting the information of the sold products and uploading the information to the data processing module; the data processing module is used for cleaning, converting, loading, normalizing, associating, classifying, denoising and correlation analyzing the collected personal information, implicit information, historical transaction record information and sales product information; the data storage module is used for storing the personal information, the implicit information, the historical transaction record information and the information of the sold products which are processed by the data processing module.
The big data analysis module is used for carrying out customer group classification and correlation calculation processing by utilizing personal information, implicit information, historical transaction record information and sales product information; the client group classification unit is used for classifying clients by utilizing a machine learning algorithm and combining personal information, implicit information and historical transaction record information, and the specific classification process is as follows:
s1: extracting data characteristics of personal information, implicit information and historical transaction record information to obtain a training set 1, a training set 2 and a training set 3;
s2: constructing a plurality of classifiers, and respectively putting a training set 1, a training set 2 and a training set 3 into the plurality of classifiers to obtain a classification model I, a classification model II and a classification model III;
s3: for each individual client, respectively detecting by a classification model I, a classification model II and a classification model III; obtaining a client quantization feature I, a client quantization feature II and a client quantization feature III;
s4: constructing an integrated classifier, taking the client quantitative feature I, the client quantitative feature II and the client quantitative feature III as input data of the integrated classifier, and obtaining an integrated model through integrated learning training;
s5: dividing the clients into a plurality of homogeneous client groups by the integrated model;
the classifiers are all embodied as K-means algorithm, and the formula is as follows:
the integrated classifier is specifically a BP neural network algorithm, and the specific formula is as follows:
the relevancy division unit is used for calculating relevancy of a plurality of homogeneous client groups and sales product information by using a relevancy algorithm and analyzing the relationship between the client and the product, and the relevancy division unit specifically comprises the following analysis processes:
SS 1: extracting characteristic data of a plurality of homogeneous client groups and sales product information, and calculating the association degree of the characteristic data by using an Apriori algorithm to obtain an association degree data set;
SS 2: carrying out grade division on the association degree data set to obtain three association degree grades of high, medium and low;
the marketing promotion module is used for providing product promotion and product recommendation for the client according to the high-relevancy grade, so that accurate marketing is realized;
apriori algorithm procedure is as follows:
SSS 1: respectively making characteristic data of a plurality of homogeneous client groups and sales product information into databases X and Y, and calculating a minimum support threshold min support;
SSS 2: retrieving all frequent item sets in the database, namely association degree data sets, through continuous iteration;
the minimum support threshold min support calculation formula is as follows:
the working principle and the using process of the invention are as follows: before the marketing system based on big data is used, firstly, a client is required to register an account number of a marketing platform in a mode of filling personal information, and then, the client is required to search commodities through an information search unit according to the requirement of the client; then the commodity display unit displays the information of the sold products to the customers; in the process of user retrieval and browsing, the customer information recording unit can record implicit information and historical transaction record information of a customer; then the customer information acquisition module collects personal information, implicit information and historical transaction record information and uploads the personal information, the implicit information and the historical transaction record information to the data processing module; the product information acquisition module can collect the information of the sold products and upload the information to the data processing module; then the data processing module can carry out cleaning, conversion, loading, normalization, association, classification, denoising and correlation analysis processing on the collected personal information, implicit information, historical transaction record information and sales product information; then the data storage module stores the personal information, the implicit information, the historical transaction record information and the information of the sold products which are processed by the data processing module; then, the client group classification unit classifies the clients by using a machine learning algorithm and combining personal information, implicit information and historical transaction record information to obtain a plurality of homogeneous client groups; then the relevancy division unit calculates the relevancy of a plurality of homogeneous client groups and sales product information by using a relevancy algorithm, and analyzes the relationship between the client and the product to obtain three relevancy grades of high relevancy, medium relevancy and low relevancy; finally, the marketing promotion module can provide product promotion and product recommendation for the client according to the high-relevancy grade, and accurate marketing is achieved; according to the method, a machine learning algorithm is utilized, and personal information, recessive information and historical transaction record information are combined to divide customers into a plurality of homogeneous customer groups, then correlation calculation is carried out on the plurality of homogeneous customer groups and sales product information, the relationship between the customers and products is analyzed, three correlation levels of high correlation level, medium correlation level and low correlation level are obtained, finally, the high correlation level is extracted, product popularization and product recommendation are carried out according to the high correlation level, accurate marketing is favorably realized, marketing popularization efficiency is favorably improved, and marketing popularization return rate is improved; the invention not only improves the customer satisfaction degree and reduces the customer selection time, but also effectively reduces the marketing cost of the marketing company.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. A marketing system based on big data is characterized by comprising a client, a marketing platform, a client information acquisition module, a product information acquisition module, a data processing module, a data storage module, a big data analysis module and a marketing promotion module;
the marketing platform mainly comprises a commodity display unit, an information retrieval unit and a customer information recording unit; the big data analysis module comprises a customer group classification unit and an association degree division unit; the client is specifically a mobile phone, an Ipad or a computer.
2. The big data-based marketing system of claim 1, wherein the registration login unit is used for a customer to register an account number of the marketing platform by filling in personal information, and the personal information comprises name, birthday, hobbies and mobile phone number.
3. The big data-based marketing system of claim 1, wherein the merchandise display unit is configured to display information on products for sale; the information retrieval unit is used for the client to retrieve the commodity according to the self requirement; the customer information recording unit is mainly used for recording implicit information and historical transaction record information of customers; the sales product information mainly comprises product price, product type and product use; the implicit information comprises client retrieval keywords and client commodity page retention time.
4. The big data-based marketing system of claim 1, wherein the customer information collection module is used for collecting personal information, implicit information and historical transaction record information and uploading the information to the data processing module; the product information acquisition module is used for collecting sales product information and uploading the sales product information to the data processing module; the data processing module is used for cleaning, converting, loading, normalizing, associating, classifying, denoising and correlation analyzing the collected personal information, implicit information, historical transaction record information and sales product information; the data storage module is used for storing the personal information, the implicit information, the historical transaction record information and the information of the sold products which are processed by the data processing module.
5. The big data based marketing system of claim 1, wherein the big data analysis module is configured to perform customer group classification and association calculation processing using personal information, implicit information, historical transaction record information, and sales product information; the client group classification unit is used for classifying clients by utilizing a machine learning algorithm and combining personal information, implicit information and historical transaction record information, and the specific classification process is as follows:
s1: extracting data characteristics of personal information, implicit information and historical transaction record information to obtain a training set 1, a training set 2 and a training set 3;
s2: constructing a plurality of classifiers, and respectively putting a training set 1, a training set 2 and a training set 3 into the plurality of classifiers to obtain a classification model I, a classification model II and a classification model III;
s3: for each individual client, respectively detecting by a classification model I, a classification model II and a classification model III; obtaining a client quantization feature I, a client quantization feature II and a client quantization feature III;
s4: constructing an integrated classifier, taking the client quantitative feature I, the client quantitative feature II and the client quantitative feature III as input data of the integrated classifier, and obtaining an integrated model through integrated learning training;
s5: dividing the clients into a plurality of homogeneous client groups by the integrated model;
the classifiers are all specifically K-means algorithms, and the formula is as follows:
the integrated classifier is specifically a BP neural network algorithm, and the specific formula is as follows:
6. the big data-based marketing system of claim 1, wherein the relevancy division unit is configured to perform relevancy calculation on several homogeneous customer groups and sales product information by using a relevancy algorithm, and analyze the relationship between the customers and the products by the following specific analysis processes:
SS 1: extracting characteristic data of a plurality of homogeneous client groups and sales product information, and calculating the association degree of the characteristic data by using an Apriori algorithm to obtain an association degree data set;
SS 2: carrying out grade division on the association degree data set to obtain three association degree grades of high, medium and low;
the marketing promotion module is used for providing product promotion and product recommendation for the customers according to the high-relevancy grade, so that accurate marketing is realized;
the Apriori algorithm proceeds as follows:
SSS 1: respectively making characteristic data of a plurality of homogeneous client groups and sales product information into databases X and Y, and calculating a minimum support threshold min support;
SSS 2: retrieving all frequent item sets in the database, namely association degree data sets, through continuous iteration;
the minimum support threshold min support calculation formula is as follows:
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| CN113850611A (en) * | 2021-08-02 | 2021-12-28 | 上海画龙信息科技有限公司 | Marketing task execution method and device based on response surface and electronic equipment |
| CN113850611B (en) * | 2021-08-02 | 2023-01-13 | 上海画龙信息科技有限公司 | Marketing task execution method and device based on response surface and electronic equipment |
| CN113781117A (en) * | 2021-09-13 | 2021-12-10 | 中国农业银行股份有限公司 | Marketing scheme generation method and device, electronic equipment and computer storage medium |
| CN113781117B (en) * | 2021-09-13 | 2023-08-22 | 中国农业银行股份有限公司 | Marketing scheme generation method and device, electronic equipment and computer storage medium |
| CN113570421A (en) * | 2021-09-23 | 2021-10-29 | 枣庄职业学院 | E-commerce marketing system based on big data analysis |
| CN114387043A (en) * | 2022-03-24 | 2022-04-22 | 南京未迟智能科技服务有限公司 | Flow channel analysis configuration method and system based on internet data analysis |
| CN116934531A (en) * | 2023-07-28 | 2023-10-24 | 重庆安特布鲁精酿啤酒有限公司 | Wine information intelligent management method and system based on data analysis |
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