CN110264244B - Advertisement user trajectory tracking management system and method - Google Patents
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Abstract
The invention discloses an advertisement user track tracking management system and method, relating to the technical field of data monitoring; there is provided an advertising user trajectory tracking management system, including: the information extraction module: the system is used for tracking the track of the visitor, extracting visitor information and uploading the visitor information; an advertisement decision module: the advertisement promotion system is used for receiving the client information, making an advertisement promotion decision after analyzing the information and issuing the decision; the online advertisement promotion module: the advertisement decision module is used for receiving the promotion decision issued by the advertisement decision module and directly implementing the promotion decision; offline advertising promotion module: and the system is used for receiving the promotion decision issued by the advertisement decision module and carrying out simulated layout, and a GIS map is arranged in the system for layout display. The invention monitors visitors in real time, classifies the monitoring results, and divides the monitoring results according to regions and characteristics, thereby adjusting the online and offline advertisement layouts simultaneously.
Description
Technical Field
The invention relates to the technical field of data monitoring, in particular to an advertisement user trajectory tracking management system and method.
Background
The advertising effect is the impact of an advertising campaign or advertising work on the consumer; the narrow advertising effect refers to the economic effect achieved by the advertisement, namely the degree of the advertisement reaching the set target, which is the commonly included spreading effect and selling effect; in a broad sense, the advertisement effect also includes a psychological effect and a social effect; the psychological effect is the influence degree of the advertisement on the psychological cognition, emotion and will of audiences, and is the centralized embodiment of the propagation function, the economic function, the educational function, the social function and the like of the advertisement; the social effect of the advertisement is the influence of the advertisement on social morality, cultural education, ethics and environment; the good social effect can also bring good economic benefits to enterprises;
the evaluation of the advertisement effect generally refers to the evaluation of the advertisement economic effect; the evaluation of the advertising effect is the contact situation of the user to various media, such as newspapers, magazines, radio, television, outdoor advertisements, and the like. The existing user tracking management system usually focuses on an online browsing track and pushes according to the browsing track, and the method is inaccurate in positioning the user's requirements and easily causes the user's dislike. Therefore, a new user tracking system is needed, the demand positioning is more accurate, and the effect is better by combining the online and offline same-step advertisement layout.
Disclosure of Invention
The invention aims to provide an advertising user track tracking management system, which comprises:
the information extraction module: the system is used for tracking the track of the visitor, extracting visitor information and uploading the visitor information;
an advertisement decision module: the advertisement promotion system is used for receiving the client information, making an advertisement promotion decision after analyzing the information and issuing the decision;
the online advertisement promotion module: the advertisement decision module is used for receiving the promotion decision issued by the advertisement decision module and directly implementing the promotion decision;
offline advertising promotion module: and the system is used for receiving the promotion decision issued by the advertisement decision module and carrying out simulated layout, and a GIS map is arranged in the system for layout display.
An advertising user trajectory tracking management method comprises the following steps:
s1, the information extraction module captures the information of the visitor of the online advertisement webpage at regular time;
s2: performing region division in the captured information, performing feature extraction, performing cluster analysis on the visitors in the same region, and classifying, wherein the category is defined as Q1、Q2......Qm；
S3: performing feature analysis and track positioning on each visitor of the specific category;
s4: the advertisement decision module makes an online advertisement promotion decision through characteristic analysis, and the advertisement decision module makes an offline advertisement promotion decision through track positioning and characteristic analysis;
s5: the online advertisement promotion module is used for establishing an advertisement pool and promoting advertisements according to the command of the advertisement decision module;
and the offline advertisement promotion module is used for performing offline advertisement layout according to the command of the advertisement decision module, wherein the offline advertisement layout comprises the layout of advertisement content, the layout of advertisement modes and the layout of advertisement places.
In the foregoing method for tracking and managing track of advertising users, the step S1 is performed by the information extraction module in a time-divided manner in one day when capturing visitor information, and includes: early stage: 6: 00-9: 00; and (3) during the noon period: 10: 00-12: 00; afternoon period 14: 00-16: 00; and (4) a night period: 18: 00-21: 00, more than two information captures are performed in each time period.
In the foregoing method for tracking and managing advertisement user trajectory, step S2 classifies the captured visitor information, and first matches the information to specific visitors to implement one-to-one correspondence between the information and the visitors; the visitors are then classified according to regions, which are in units of cities.
In the foregoing method for tracking and managing advertisement user trajectory, the clustering in step S2
Analyzing visitor information for a single advertisement, comprising the steps of:
firstly, according to the obtained visitor information column matrix; xm＝【xm_1,xm_2,xm_3,......xm_nWherein m represents visitors numbered sequentially m, n represents an advertisement numbered n, and the element is the number of clicks of the n advertisement by the m visitors;
randomly selecting k objects from m data objects as initial clustering centers; for the rest other objects, respectively allocating the other objects to the most similar clusters according to the similarity between the other objects and the cluster centers, wherein new clusters continuously appear in the process, and repeatedly calculating the cluster center of each new cluster until the standard measure function starts to converge;
the clustering center is the average value of all objects in the cluster, and the standard measure function is a mean square error function;
the calculation formula of the clustering center E is as follows:
the mean square error formula is:
finally, a result of clustering for m visitors is formed, including Q1、Q2 ......Qm。
In the foregoing method for tracking and managing advertisement user trajectory, the advertisement decision module is based on Q1、Q2.. specific characteristics of Qm categories, allocating advertisement pools in the online advertisement promotion module at fixed points, and making specific online promotion recommendation schemes;
advertisement decision module according to Q1、Q2.... Qm category, performing region identification, and performing grid division in a single region;
the grid division method comprises the following steps: firstly, integrating specific effective advertisement effect areas by taking a city as a unit, and connecting the areas into slices;
carrying out micro-grid division on the effective area, wherein the grid takes a road river as a boundary; for a particular location, including but not limited to a school, hospital, company dormitory, the particular location is divided into one or several cells and the location is specifically identified;
the area of the grid is controlled to be 1km2The content of the compound is less than the content of the compound;
after grid division, firstly positioning a visitor address;
determining the resident grid of the visitor, combining the captured information in different time periods, determining the track of the visitor and further acquiring the category Q1、Q2.... Qm visitor's trajectory;
and establishing a reflection model on the grids according to the track, defining the grids with high track probability as effective grids, and determining that the effective grids are used for off-line advertisement delivery.
After the offline advertisement delivery grid is determined, the offline advertisement delivery grid is pushed to a GIS map of the offline advertisement promotion module to be displayed.
Compared with the prior art, the invention has the following beneficial effects:
and monitoring the visitors in real time, classifying the monitoring results, and dividing the monitoring results according to regions and characteristics so as to adjust the online and offline advertisement layout simultaneously.
Detailed Description
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.
Example 1: an advertising user trajectory tracking management system, characterized by: the method comprises the following steps:
the information extraction module: the system is used for tracking the track of the visitor, extracting visitor information and uploading the visitor information;
an advertisement decision module: the advertisement promotion system is used for receiving the client information, making an advertisement promotion decision after analyzing the information and issuing the decision;
the online advertisement promotion module: the advertisement decision module is used for receiving the promotion decision issued by the advertisement decision module and directly implementing the promotion decision;
offline advertising promotion module: and the system is used for receiving the promotion decision issued by the advertisement decision module and carrying out simulated layout, and a GIS map is arranged in the system for layout display.
An advertising user trajectory tracking management method comprises the following steps:
s1, the information extraction module captures the information of the visitor of the online advertisement webpage at regular time;
when capturing visitor information, the information extraction module in step S1 is performed in time intervals in one day, and includes: early stage: 6: 00-9: 00; and (3) during the noon period: 10: 00-12: 00; afternoon period 14: 00-16: 00; and (4) a night period: 18: 00-21: 00, more than two information captures are performed in each time period. The advertising department for special goods or services selects according to actual conditions, wherein the early period aims at the information of visitors on the way to work, the noon period is the information of visitors in the leisure time of lunch, the afternoon period is the information of visitors in the leisure time of afternoon, and the night period is the information of visitors at night. The time segmentation system is more scientific and reasonable aiming at common goods or services.
S2: performing region division in the captured information, performing feature extraction, performing cluster analysis on the visitors in the same region, and classifying, wherein the category is defined as Q1、Q2......Qn；
Step S2, the captured visitor information is classified, the visitor information comprises address information, access advertisement content and the like, firstly, the information is matched to specific visitors, and the one-to-one correspondence between the information and the visitors is realized; the visitors are then classified according to regions, which are in units of cities.
The cluster analysis in step S2 is directed to visitor information of a single advertisement, including the steps of:
firstly, according to the obtained visitor information column matrix; xm＝【xm_1,xm_2,xm_3,......xm_nWherein m represents visitors numbered sequentially m, n represents an advertisement numbered n, and the element is the number of clicks of the n advertisement by the m visitors;
randomly selecting k objects from m data objects as initial clustering centers; for the rest other objects, respectively allocating the other objects to the most similar clusters according to the similarity between the other objects and the cluster centers, wherein new clusters continuously appear in the process, and repeatedly calculating the cluster center of each new cluster until the standard measure function starts to converge;
the clustering center is the average value of all objects in the cluster, and the standard measure function is a mean square error function;
the calculation formula of the clustering center E is as follows:
the mean square error formula is:
finally, a result of clustering for m visitors is formed, including Q1、Q2 ......Qm。
S3: performing feature analysis and track positioning on each visitor of the specific category;
the characteristic analysis aims at the characteristics of the visitors and the advertisements browsed by the visitors, and mainly comprises the number of clicks on an advertisement page and the number of clicks on an advertisement picture, and the approval/receiving degree of each visitor to a certain advertisement can be analyzed from the parameters.
Advertisement decision module according to Q1、Q2.. specific characteristics of Qm categories, allocating advertisement pools in the online advertisement promotion module at fixed points, and making specific online promotion recommendation schemes;
advertisement decision module according to Q1、Q2.... Qm category, performing region identification, and performing grid division in a single region;
the grid division method comprises the following steps: firstly, integrating specific effective advertisement effect areas by taking a city as a unit, and connecting the areas into slices;
carrying out micro-grid division on the effective area, wherein the grid takes a road river as a boundary; the roads and rivers have certain division, and particularly the roads have reference function in the aspect of advertisement promotion under later period lines. For example, if a billboard is set up at the side of a road, the range of effect radiation of the billboard may include a plurality of grid regions.
Dividing the special positions into one or several grids according to the special positions including but not limited to schools, hospitals and company dormitories, further dividing the special positions according to the essential properties of the special positions, and specially identifying the positions (color identification or icon identification); there may be a division as follows:
| school | Hospital | Cell |
| Kindergarten | City hospital | Village in town |
| Primary school | Provincial hospital | Middle-grade district |
| Middle school | Oral hospital | High-grade community |
| University | Tumor Hospital | Return cell |
| 。。。。。 | 。。。。。 | 。。。。。。 |
The area of the grid is controlled to be 1km2The content of the compound is less than the content of the compound; ensuring a certain accuracy.
After grid division, firstly positioning a visitor address;
the positioning of the guest address comprises the following scheme:
the method comprises the steps of directly positioning a PC-end visitor through an internet IP address or a GPS, positioning a mobile end through the internet IP address or the GPS, capturing a communication list of the visitor more accurately, positioning a base station where a user has business according to LAC and CI, and analyzing a palace lattice passed by the user and a resident palace lattice by combining the corresponding relation between the interior of the palace lattice and the base station.
Determining a visitor resident grid, determining the track of the visitor by combining the captured information in different time periods, and further acquiring the track of clustered visitors;
at this time, it is further clear that two key parameters are to be integrated, namely, the track of the visitor in each cluster; the parameter can position the visitor for the position of offline advertisement layout, and is the visitor characteristic of each cluster; the parameter shows the acceptance of the visitor to the type of advertisement for selecting the type of advertisement when the advertisement is laid out offline or online.
And establishing a reflection model on the grids according to the track, defining the grids with high track probability as effective grids, and determining that the effective grids are used for off-line advertisement delivery.
After the offline advertisement delivery grid is determined, the offline advertisement delivery grid is pushed to a GIS map of the offline advertisement promotion module to be displayed
S4: the advertisement decision module makes an online advertisement promotion decision through characteristic analysis, and the advertisement decision module makes an offline advertisement promotion decision through track positioning and characteristic analysis;
s5: the online advertisement promotion module is used for establishing an advertisement pool and promoting advertisements according to the command of the advertisement decision module;
the offline advertisement promotion module is used for performing offline advertisement layout according to the command of the advertisement decision module, wherein the offline advertisement layout comprises the layout of advertisement content, the layout of advertisement modes and the layout of advertisement places;
the method comprises the steps of installing positions of an advertisement box or other fixed advertisement platforms and designing pages of the fixed advertisement platforms;
in addition, the flow path of the liquidity advertisement is also included, and the display content of the liquidity advertisement is designed.
Claims (2)
1. An advertising user trajectory tracking management system, characterized by: the method comprises the following steps:
the information extraction module: the system is used for tracking the track of the visitor, extracting visitor information and uploading the visitor information;
an advertisement decision module: the advertisement promotion system is used for receiving the client information, making an advertisement promotion decision after analyzing the information and issuing the decision;
the online advertisement promotion module: the advertisement decision module is used for receiving the promotion decision issued by the advertisement decision module and directly implementing the promotion decision;
offline advertising promotion module: the system is used for receiving the promotion decision issued by the advertisement decision module and carrying out simulated layout, and a GIS map is arranged in the system for layout display;
also comprises the following steps:
s1, the information extraction module captures the information of the visitor of the online advertisement webpage at regular time;
s2: performing region division in the captured information, and performing feature extractionClassifying the visitors in the same area after clustering analysis, wherein the category is defined as Q1、Q2......Qn；
S3: performing feature analysis and track positioning on each visitor of the specific category;
s4: the advertisement decision module makes an online advertisement promotion decision through characteristic analysis, and the advertisement decision module makes an offline advertisement promotion decision through track positioning and characteristic analysis;
s5: the online advertisement promotion module is used for establishing an advertisement pool and promoting advertisements according to the command of the advertisement decision module;
the offline advertisement promotion module is used for performing offline advertisement layout according to the command of the advertisement decision module, wherein the offline advertisement layout comprises the layout of advertisement content, the layout of advertisement modes and the layout of advertisement places;
step S2, the captured visitor information is classified, firstly, the information is matched to a specific visitor, and the one-to-one correspondence between the information and the visitor is realized; then, classifying the visitors according to areas, wherein the areas take cities as units;
advertisement decision module according to Q1、Q2.. specific characteristics of Qm categories, allocating advertisement pools in the online advertisement promotion module at fixed points, and making specific online promotion recommendation schemes;
advertisement decision module according to Q1、Q2.... Qm category, performing region identification, and performing grid division in a single region;
the grid division method comprises the following steps: firstly, integrating specific effective advertisement effect areas by taking a city as a unit, and connecting the areas into slices;
carrying out micro-grid division on the effective area, wherein the grid takes a road river as a boundary; for a particular location, including but not limited to a school, hospital, company dormitory, the particular location is divided into one or several cells and the location is specifically identified;
the area of the grid is controlled to be 1km2The content of the compound is less than the content of the compound;
after grid division, firstly positioning a visitor address;
determining a visitor resident grid, determining the track of the visitor by combining the captured information in different time periods, and further acquiring the track of clustered visitors;
establishing a reflection model of the track on the grids, defining the grids with high track probability as effective grids, and determining that the effective grids are used for putting off-line advertisements;
after determining the offline advertisement delivery grid, pushing the offline advertisement delivery grid to a GIS map of an offline advertisement promotion module for displaying;
when capturing visitor information, the information extraction module in step S1 is performed in time intervals in one day, and includes: early stage: 6: 00-9: 00; and (3) during the noon period: 10: 00-12: 00; afternoon period 14: 00-16: 00; and (4) a night period: 18: 00-21: 00, more than two information captures are performed in each time period.
2. The advertising user trajectory tracking management system of claim 1, wherein: the cluster analysis in step S2 is directed to visitor information of a single advertisement, including the steps of:
firstly, according to the obtained visitor information column matrix; xm＝[xm_1,xm-2,xm-3,......xm-n]Wherein m represents visitors with the label number of m, n represents an advertisement with the label number of n, and the element is the click times of the visitors to the advertisement with the label number of n;
randomly selecting k objects from m data objects as initial clustering centers; for the rest other objects, respectively allocating the other objects to the most similar clusters according to the similarity between the other objects and the cluster centers, wherein new clusters continuously appear in the process, and repeatedly calculating the cluster center of each new cluster until the standard measure function starts to converge;
the clustering center is the average value of all objects in the cluster, and the standard measure function is a mean square error function;
the calculation formula of the clustering center E is as follows:
the mean square error function is:
finally, a result of clustering for m visitors is formed, including Q1、Q2......Qm。
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| CN105516928A (en) * | 2016-01-15 | 2016-04-20 | 中国联合网络通信有限公司广东省分公司 | Position recommending method and system based on position crowd characteristics |
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| CA2619773C (en) * | 2005-08-19 | 2016-01-26 | Biap Systems, Inc. | System and method for recommending items of interest to a user |
| US20170046768A1 (en) * | 2015-08-10 | 2017-02-16 | SVG Media Pvt Ltd | Hybrid recommendation system for recommending product advertisements |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102509170A (en) * | 2011-10-10 | 2012-06-20 | 浙江鸿程计算机系统有限公司 | Location prediction system and method based on historical track data mining |
| CN105516928A (en) * | 2016-01-15 | 2016-04-20 | 中国联合网络通信有限公司广东省分公司 | Position recommending method and system based on position crowd characteristics |
| CN106095841A (en) * | 2016-06-05 | 2016-11-09 | 西华大学 | Method is recommended in a kind of mobile Internet advertisement based on collaborative filtering |
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