CN110111151A - A kind of accurate advertisement analysis method and system based on crowd's label data - Google Patents
A kind of accurate advertisement analysis method and system based on crowd's label data Download PDFInfo
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- CN110111151A CN110111151A CN201910390215.1A CN201910390215A CN110111151A CN 110111151 A CN110111151 A CN 110111151A CN 201910390215 A CN201910390215 A CN 201910390215A CN 110111151 A CN110111151 A CN 110111151A
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- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
- G06Q30/0271—Personalized advertisement
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Abstract
The invention discloses a kind of accurate advertisement analysis methods based on crowd's label data, comprising the following steps: S1, acquisition demographic data；S2, crowd's tag database is established；S3, corresponding label is added to user according to crowd's tag database；S4, it is exported with the attribute and user behavior data of big data cluster analysis technology parsing user, and by analysis result.The present invention is by analyzing consumer information, determine the information such as gender, age, media preferences, the electric business preference of consumer, it is labelled by establishing label model to consumer, enable consumers to the consumption point of interest of the understanding of the accurate simplicity consumer, effective push product is carried out during marketing, cost of marketing is greatly reduced, is brought convenience to sale.
Description
Technical field
The present invention relates to MultiMedia Fields, it particularly relates to a kind of accurate advertisement analysis based on crowd's label data
Method and system.
Background technique
Currently, traditional marketing lacks the tool and method of analysis customer demand in face of the information data of consumer, it can not
The actual demand for holding client, fails to grip with the consumption point of interest of client, it is therefore desirable to employ a large amount of manpower and financial resources to client
The publicity dispensing that group carries out magnanimity can be only achieved marketing purpose, considerably increases the cost of marketing, brings difficulty to sale.
Summary of the invention
To solve problems of the prior art, the object of the present invention is to provide a kind of essences based on crowd's label data
Quasi- advertisement analysis method, have the advantages that reduce cost of marketing, bring to sale it is convenient.
To achieve the above object, the technical solution adopted by the present invention is that: a kind of accurate advertisement based on crowd's label data
Analysis method, comprising the following steps:
S1, acquisition demographic data；
S2, crowd's tag database is established；
S3, corresponding label is added to user according to crowd's tag database；
S4, the attribute and user behavior data that user is parsed with big data cluster analysis technology, and result will be analyzed
It is exported.
In the technical scheme, businessman can release the interested advertisement type of user by the label of user and be attracted with this
User labels to consumer by establishing label model, enables consumers to the understanding of the accurate simplicity consumer
Consumption point of interest, effective push product is carried out during marketing, cost of marketing is greatly reduced, gives sale band
To facilitate.
Preferably, step S1 the following steps are included:
S11, acquisition demographic data, demographic data includes browsing behavior data, buying behavior data；
The browsing behavior data include: user access webpage, user pay close attention to advertisement classification, user's browse advertisements time,
User's browse advertisements duration, user's browse advertisements number statistics；
When the buying behavior data include: that user buys merchandise classification, user buys commodity price, user buys commodity
Between, user buy commodity amount.
The acquisition of demographic data is the first step to user information analysis.
Preferably, step S2 includes:
S21, crowd's tag database is established, crowd's tag database includes user's gender, age of user, user job row
Industry, the level of consumption, Living city, living habit, shopping hobby, Matrix.
The foundation of crowd's tag database facilitates management convenient for labelling to consumer.
Preferably, step S4 includes:
S41, user's gender and age are directly acquired by demographic data, or by the address URL of customer access network,
User pays close attention to gender and the age that merchandise classification judges user；
S42, the affiliated city that the user is determined by the IP address that user accesses；
S43, user job occupation is determined by user's access webpage；
S44, the Matrix that the user is determined by user's concern advertisement classification, user's browse advertisements duration；
S45, electric business preference and shopping type hobby that merchandise classification determines the user are bought by user；
S46, the level of consumption that commodity price determines user is bought by user；
S47, surf time and the sleep habit that the user is determined by user's access time.
A kind of accurate advertisement analysis system based on crowd's label data, including demographic data acquisition module, crowd's label
Data memory module, label adding module, data analysis module, in which:
Demographic data acquisition module, for acquiring demographic data；
Crowd's label data memory module, for storing crowd's label data；
Label adding module adds crowd's label to demographic data；
Data analysis module parses user property and user behavior label and exports parsing result.
Preferably, the demographic data acquisition module includes browsing behavior data acquisition module and the acquisition of buying behavior data
Module, in which:
Browsing behavior data acquisition module accesses webpage information for acquiring user, user pays close attention to advertisement classification, user is clear
Look at the advertising time, user's browse advertisements duration, user's browse advertisements number statistics；
Buying behavior data acquisition module buys merchandise classification for acquiring user, user buys commodity price, user's purchase
Buy the commodity time, user buys commodity amount information.
Preferably, the data analysis module includes user property analysis module and user behavior analysis module, in which:
User property analysis module, for analyzing gender, the age, residence of user；
User behavior analysis module, for analyzing the level of consumption of user, work industry, living habit, doing shopping and like, extensively
Accuse preference.
The beneficial effects of the present invention are:
(1) big data digging technology is combined with advertisement accurately dispensing, using modern the Internet advertisement technology, solves tradition
Advertisement is launched the problem of user can not give for change, and the present invention establishes virtual crowd library by labelling, and the internet for analyzing user is special
Property, to the secondary marketing of visitor once, the present invention is to visitor's exhaustive division, the reason of combined data analyzes customer churn, improves
The retention efficiency of user；
(2) by analyzing consumer information, gender, age, media preferences, electric business preference of consumer etc. are determined
Information labels to consumer by establishing label model, enables consumers to the understanding of the accurate simplicity consumer
Consumption point of interest, effective push product is carried out during marketing, cost of marketing is greatly reduced, gives sale band
To facilitate.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of the accurate advertisement analysis method based on crowd's label data of the embodiment of the present invention；
Fig. 2 is a kind of schematic diagram of the accurate advertisement analysis system based on crowd's label data of the embodiment of the present invention.
Description of symbols:
1, demographic data acquisition module；11, browsing behavior data acquisition module；12, buying behavior data acquisition module；2,
Crowd's label data memory module；3, label adding module；4, data analysis module；41, user property analysis module；42, it uses
User behavior analysis module.
Specific embodiment
The embodiment of the present invention is described in detail with reference to the accompanying drawing.
Embodiment 1:
As shown in Figure 1, a kind of accurate advertisement analysis method based on crowd's label data, comprising the following steps:
S1, demographic data is acquired by flume monitoring nginx Log Directory in real time, and uses nginx reverse proxy service
The load balancing of device progress demographic data；
S2, crowd's tag database is established；
S3, corresponding label is added to user according to crowd's tag database；
S4, the hadoop big data cluster analysis technology with DASP data great master, parse attribute and the user of user
Behavioral data, and analysis result is exported.
Step S1 the following steps are included:
S11, acquisition demographic data, demographic data includes browsing behavior data, buying behavior data；
The browsing behavior data include: user access webpage, user pay close attention to advertisement classification, user's browse advertisements time,
User's browse advertisements duration, user's browse advertisements number statistics；
When the buying behavior data include: that user buys merchandise classification, user buys commodity price, user buys commodity
Between, user buy commodity amount.
User information is obtained from social networks multiple support channels.
Step S2 includes:
S21, crowd's tag database is established, crowd's tag database includes user's gender, age of user, user job row
Industry, the level of consumption, Living city, living habit, shopping hobby, Matrix.
Internet behavior is collected and has analyzed before user, when access has the main advertisement position in website, is marked at this time using crowd
It signs database and obtains accurate label, when being transmitted to advertisement delivery system acquisition correlation or recommendatory label advertisement after labeling
Respective labels advertisement is launched, primary precisely dispensing advertisement is completed simultaneously, is also collecting user behavior parallel, for next advertisement accurately
Do resource.
Step S4 includes:
S41, user's gender and age are directly acquired by demographic data, or by the address URL of customer access network,
User pays close attention to gender and the age that merchandise classification judges user；When notes such as the Sex, Ages that consumer is not present in consumer resources
When record, analyzed by its browsing record；
S42, the affiliated city that the user is determined by the IP address that user accesses；
S43, user job occupation is determined by user's access webpage；
S44, the Matrix that the user is determined by user's concern advertisement classification, user's browse advertisements duration；
S45, electric business preference and shopping type hobby that merchandise classification determines the user are bought by user；
S46, the level of consumption that commodity price determines user is bought by user；
S47, surf time and the sleep habit that the user is determined by user's access time.
By analyzing consumer information, determine that gender, age, media preferences, electric business preference of consumer etc. are believed
Breath, labels to consumer by establishing label model, and enable consumers to accurate simplicity understands the consumer's
Point of interest is consumed, effective push product is carried out during marketing, cost of marketing is greatly reduced, is brought to sale
It is convenient.
Embodiment 2:
As shown in Fig. 2, a kind of accurate advertisement analysis system based on crowd's label data, including demographic data acquisition module
1, crowd's label data memory module 2, label adding module 3, data analysis module 4, in which:
Demographic data acquisition module 1, for acquiring demographic data；
Crowd's label data memory module 2, for storing crowd's label data；
Label adding module 3 adds crowd's label to demographic data；
Data analysis module 4 parses user property and user behavior label and exports parsing result.
The demographic data acquisition module 1 includes browsing behavior data acquisition module 11 and buying behavior data acquisition module
12, in which:
Browsing behavior data acquisition module 11, for acquiring, user accesses webpage information, user pays close attention to advertisement classification, user
Browse advertisements time, user's browse advertisements duration, user's browse advertisements number statistics；
Buying behavior data acquisition module 12, for acquiring, user buys merchandise classification, user buys commodity price, user
Buy the commodity time, user buys commodity amount information.
The data analysis module 4 includes user property analysis module 41 and user behavior analysis module 42, in which:
User property analysis module 41, for analyzing gender, the age, residence of user；
User behavior analysis module 42, for analyze user the level of consumption, work industry, living habit, shopping hobby,
Matrix.
Big data digging technology is combined with advertisement accurately dispensing, using modern the Internet advertisement technology, is solved traditional wide
It accusing and launches the problem of user can not give for change, the present invention establishes virtual crowd library by labelling, analyzes the Internet characteristics of user,
To the secondary marketing of visitor once, the present invention is to visitor's exhaustive division, the reason of combined data analyzes customer churn, improves user
Retention efficiency.
A specific embodiment of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.
Claims (7)
1. a kind of accurate advertisement analysis method based on crowd's label data, which comprises the following steps:
S1, acquisition demographic data；
S2, crowd's tag database is established；
S3, corresponding label is added to user according to crowd's tag database；
S4, it is carried out with the attribute and user behavior data of big data cluster analysis technology parsing user, and by analysis result
Output.
2. a kind of accurate advertisement analysis method based on crowd's label data according to claim 1, which is characterized in that step
Rapid S1 the following steps are included:
S11, acquisition demographic data, demographic data includes browsing behavior data, buying behavior data；
The browsing behavior data include: that user accesses webpage, user pays close attention to advertisement classification, user's browse advertisements time, user
Browse advertisements duration, user's browse advertisements number statistics；
The buying behavior data include: user buy merchandise classification, user buy commodity price, user buy the commodity time,
User buys commodity amount.
3. a kind of accurate advertisement analysis method based on crowd's label data according to claim 1, which is characterized in that step
Suddenly S2 includes:
S21, establish crowd's tag database, crowd's tag database include user's gender, age of user, user job industry,
The level of consumption, Living city, living habit, shopping hobby, advertisement type preference.
4. a kind of accurate advertisement analysis method based on crowd's label data according to claim 1, which is characterized in that step
Suddenly S4 includes:
S41, user's gender and age are directly acquired by demographic data, or pass through the address URL of customer access network, user
Concern merchandise classification judges gender and the age of user；
S42, the affiliated city that the user is determined by the IP address that user accesses；
S43, user job occupation is determined by user's access webpage；
S44, the Matrix that the user is determined by user's concern advertisement classification, user's browse advertisements duration；
S45, electric business preference and shopping type hobby that merchandise classification determines the user are bought by user；
S46, the level of consumption that commodity price determines user is bought by user；
S47, surf time and the sleep habit that the user is determined by user's access time.
5. a kind of accurate advertisement analysis system based on crowd's label data, which is characterized in that including demographic data acquisition module,
Crowd's label data memory module, label adding module, data analysis module, in which:
Demographic data acquisition module, for acquiring demographic data；
Crowd's label data memory module, for storing crowd's label data；
Label adding module adds crowd's label to demographic data；
Data analysis module parses user property and user behavior label and exports parsing result.
6. a kind of accurate advertisement analysis system based on crowd's label data according to claim 5, which is characterized in that institute
Stating demographic data acquisition module includes browsing behavior data acquisition module and buying behavior data acquisition module, in which:
Browsing behavior data acquisition module accesses webpage information for acquiring user, user pays close attention to advertisement classification, user's browsing is wide
Accuse time, user's browse advertisements duration, user's browse advertisements number statistics；
Buying behavior data acquisition module buys merchandise classification for acquiring user, user buys commodity price, user buys quotient
Product time, user buy commodity amount information.
7. a kind of accurate advertisement analysis system based on crowd's label data according to claim 5, which is characterized in that institute
Stating data analysis module includes user property analysis module and user behavior analysis module, in which:
User property analysis module, for analyzing gender, the age, residence of user；
User behavior analysis module, for analyzing the level of consumption, work industry, living habit, the shopping hobby, commercial paper of user
Type preference.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570251A (en) * | 2019-09-11 | 2019-12-13 | 瞿秋昱 | Big data-based user purchase intention prediction system and method |
| CN110650212A (en) * | 2019-10-17 | 2020-01-03 | 华普通用技术研究（广州）有限公司 | Method and system for realizing analysis of network data packet by large data flow technology |
| CN110853488A (en) * | 2019-10-28 | 2020-02-28 | 广州码石信息科技有限公司 | POI (Point of interest) label display method, device and equipment |
| CN111611477A (en) * | 2020-04-24 | 2020-09-01 | 上海第一财经传媒有限公司 | User data statistics management system |
| CN111882400A (en) * | 2020-07-31 | 2020-11-03 | 平安国际融资租赁有限公司 | Behavior recognition analysis method and device, computer equipment and readable storage medium |
| CN112070546A (en) * | 2020-09-10 | 2020-12-11 | 浪潮软件股份有限公司 | Advertisement putting method based on big data and face recognition |
| CN112116384A (en) * | 2020-09-14 | 2020-12-22 | 北京明略昭辉科技有限公司 | Taxi advertisement targeted delivery method and system based on image processing |
| CN112418905A (en) * | 2020-10-20 | 2021-02-26 | 杭州电子科技大学 | Online advertisement accurate delivery method based on machine learning |
| CN112862565A (en) * | 2021-01-19 | 2021-05-28 | 上海映荷网络科技有限公司 | Cross-border e-commerce multi-platform sales system based on cloud computing |
| CN112887762A (en) * | 2021-01-26 | 2021-06-01 | 广州欢网科技有限责任公司 | Method and system for delivering IPTV (Internet protocol television) advertisement resources according to crowd labels |
| CN113420180A (en) * | 2021-05-18 | 2021-09-21 | 北京达佳互联信息技术有限公司 | Video recommendation method and device, electronic equipment and storage medium |
| CN113469755A (en) * | 2021-09-03 | 2021-10-01 | 广东联讯科技发展股份有限公司 | Intelligent accurate marketing management system based on advertisement pushing |
| CN113781173A (en) * | 2021-09-13 | 2021-12-10 | 微积分创新科技(北京)股份有限公司 | AI robot marketing method and system based on one-object-one-code and storable medium |
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| CN112116384A (en) * | 2020-09-14 | 2020-12-22 | 北京明略昭辉科技有限公司 | Taxi advertisement targeted delivery method and system based on image processing |
| CN112418905A (en) * | 2020-10-20 | 2021-02-26 | 杭州电子科技大学 | Online advertisement accurate delivery method based on machine learning |
| CN112862565A (en) * | 2021-01-19 | 2021-05-28 | 上海映荷网络科技有限公司 | Cross-border e-commerce multi-platform sales system based on cloud computing |
| CN112887762A (en) * | 2021-01-26 | 2021-06-01 | 广州欢网科技有限责任公司 | Method and system for delivering IPTV (Internet protocol television) advertisement resources according to crowd labels |
| CN112887762B (en) * | 2021-01-26 | 2023-07-25 | 广州欢网科技有限责任公司 | Method and system for putting IPTV advertisement resources according to crowd labels |
| CN113420180A (en) * | 2021-05-18 | 2021-09-21 | 北京达佳互联信息技术有限公司 | Video recommendation method and device, electronic equipment and storage medium |
| CN113469755A (en) * | 2021-09-03 | 2021-10-01 | 广东联讯科技发展股份有限公司 | Intelligent accurate marketing management system based on advertisement pushing |
| CN113781173A (en) * | 2021-09-13 | 2021-12-10 | 微积分创新科技(北京)股份有限公司 | AI robot marketing method and system based on one-object-one-code and storable medium |
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