CN111127099A - E-commerce user analysis system based on big data and analysis method thereof - Google Patents
E-commerce user analysis system based on big data and analysis method thereofDownload PDFInfo
- Publication number
- CN111127099A CN111127099ACN201911344125.5ACN201911344125ACN111127099ACN 111127099 ACN111127099 ACN 111127099ACN 201911344125 ACN201911344125 ACN 201911344125ACN 111127099 ACN111127099 ACN 111127099A
- Authority
- CN
- China
- Prior art keywords
- analysis
- data
- user
- analysis module
- commerce
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- 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
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- 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
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Game Theory and Decision Science (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Technical Field
The invention relates to the technical field of electronic commerce, in particular to an e-commerce user analysis system based on big data and an analysis method thereof.
Background
With the vigorous development of electronic commerce, online shopping has become a necessary product under the background of a new era, the e-commerce platform also generates massive user data in development, and deep user analysis becomes a core point for solving the dilemma under the condition that the traditional advertisement marketing and drainage mode cannot meet the development of the e-commerce platform.
Due to the development of big data technology and artificial intelligence technology, a brand new mode and value are brought to user analysis, the mode gradually develops into a multidimensional mode based on user portrait and machine deep learning from a single mode such as random matching and keyword matching, the goal of accurate marketing is realized through a large data-based e-commerce user analysis system, the supply and demand balance relation is further matched, and the optimal path from a consumer to a supplier is realized.
The accumulated data of the user data warehouse of the existing e-commerce platform is not fully utilized, so that a unified and complete data view facing the whole e-commerce enterprise is lacked, a risk assessment system supporting daily operation of the e-commerce enterprise is lacked, a 360-degree view of a customer of the e-commerce enterprise is lacked, analysis and prediction of customer behaviors cannot be realized, and a key performance index system facing financial business operation management is lacked.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an e-commerce user analysis system based on big data and an analysis method thereof, and solves the problems that the accumulated data of a user data warehouse of the existing e-commerce platform is not fully utilized, so that a unified and complete data view facing to the whole e-commerce enterprise is lacked, a risk assessment system supporting the daily operation of the e-commerce enterprise is lacked, a 360-degree view of a customer of the e-commerce enterprise is lacked, the analysis and prediction of the customer behavior cannot be realized, and a key performance index system facing to the operation management of financial services is lacked.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: the utility model provides an electricity commercial user analytic system based on big data, includes data acquisition unit, the output of data acquisition unit is connected with data storage unit, the output of data storage unit is connected with data analysis unit;
the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
Preferably, the data acquisition unit acquires information through an information system, an application program APP, the Internet, an Internet of things facility and a web program. Fully considering the increase of the user scale and the data scale, and preparing for data asset accumulation; the method comprises the following steps of (1) collecting a plurality of data sources in a full amount by a plurality of methods, and running through the whole life cycle of a product used by a user; collecting sufficient and comprehensive attributes, dimensions and indexes to ensure that the accumulated data assets are better in quality; the timeliness of data acquisition is improved, and therefore the timeliness of subsequent data application is improved.
Preferably, the data storage unit stores data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a secure cloud.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
step three, data analysis, namely, carrying out multi-category analysis on the data step by step, namely order conversion analysis, event analysis, funnel analysis, retention analysis, distribution analysis, attribution analysis, user path analysis, webpage thermal analysis and interval analysis.
Preferably, the order conversion analysis analyzes user behaviors, various charts are summarized and displayed, various indexes are switched, and various data boards are conveniently checked through full-functional multi-dimensional analysis capability.
Preferably, the event analysis is a query analysis based on functions of event index statistics, attribute grouping, condition screening and the like through tracked or recorded user behaviors or business processes.
Preferably, the funnel analysis is used for analyzing the conversion and loss conditions of each step in a multi-step process, and the retention analysis is used for examining how many people perform subsequent behaviors in the user after the initial behaviors are performed by analyzing the user participation conditions and the activity degree of the analysis model, so as to measure the value of the product to the user.
Preferably, the distribution analysis analyzes the user distribution condition of a certain event index by analyzing the dependence of the user on the product, and the attribution analysis analyzes the conversion contribution of a certain advertisement position and a promotion position to a target event by using an attribution analysis model when the business needs to analyze the conversion contribution of the certain advertisement position and the promotion position.
Preferably, the user path analysis is mainly used for analyzing the path distribution situation of the user when the user uses the product, and the webpage thermal analysis is displayed to the user with a visual effect by analyzing the conditions of clicking, reaching depth and the like of the user on the webpage.
Preferably, the interval analysis is used for analyzing the conversion conditions of various services, and the conversion duration distribution of the service conversion link is obtained by calculating the time interval of two events in the user behavior sequence.
(III) advantageous effects
The invention provides an e-commerce user analysis system based on big data and an analysis method thereof. The method has the following beneficial effects:
the E-commerce user analysis system based on the big data realizes data sharing. Data concentration is realized through the data platform, so that all levels of departments of the e-commerce enterprise can use data on the premise of ensuring data privacy and safety, and the business value of the data as important assets of the enterprise is fully exerted. And (5) innovating a sales promotion service. The electric business enterprise business personnel can perform multidimensional analysis and data mining based on detailed and credible data, and create favorable conditions for financial business innovation. And the construction efficiency is improved. The data are centralized through the data platform, a consistent data base is provided for systems such as management analysis and mining prediction, the current situations of multiple data sources and complex data processing of the existing system are changed, and the transformation of the construction mode of the application system is realized. And the data quality is improved. In the middle and long term, the data warehouse integrates and cleans data of the electronic commerce enterprises scattered in each business system, contributes to the improvement of the overall data quality of the enterprises, and improves the practicability of the data. And realizing real-time second-level query of a full-platform full-analysis scene. Data of multiple stages can be derived, including the collected finest granularity data, the stored modeling data and the analyzed result data.
Drawings
FIG. 1 is a block diagram of the overall process of the present invention;
FIG. 2 is a block diagram of a data analysis unit according to the present invention.
In the figure: the system comprises adata acquisition unit 1, adata storage unit 2, adata analysis unit 3, a 301 top conversion analysis module, an 302 event analysis module, a 303 funnel analysis module, a 304 retention analysis module, a 305 distribution analysis module, a 306 attribution analysis module, a 307 user path analysis module, a 308 webpage thermal analysis module and a 309 interval analysis module.
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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, order conversion and analysis, namely analyzing user behaviors, summarizing and displaying various charts, switching various indexes and viewing various data through full-functional multi-dimensional analysis capability, and facilitating viewing.
Example two
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, event analysis, namely query analysis based on functions of event index statistics, attribute grouping, condition screening and the like through the tracked or recorded user behaviors or business processes.
EXAMPLE III
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, funnel analysis, wherein the conversion and loss conditions of each step in a multi-step process are analyzed.
Example four
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, retention analysis, namely, examining how many people can perform subsequent behaviors in the user after the initial behavior is performed by analyzing the user participation condition and the analysis model of the activity degree, and measuring the value of the product to the user.
EXAMPLE five
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, distribution analysis, namely analyzing the dependence of the user on the product and the user distribution condition of a certain event index.
EXAMPLE six
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, attribution analysis, wherein when the conversion contribution of a certain advertisement position and promotion position to the target event needs to be analyzed in service, an attribution analysis model is used for analysis.
EXAMPLE seven
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, user path analysis, wherein the user path analysis is mainly used for analyzing the path distribution condition of a user when the user uses a product.
Example eight
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, webpage thermal analysis, namely analyzing the conditions of clicking, reaching depth and the like of a user on a webpage and displaying the conditions to the user in a visual effect.
Example nine
A big data-based e-commerce user analysis system comprises a data acquisition unit, wherein the output end of the data acquisition unit is connected with a data storage unit, and the output end of the data storage unit is connected with a data analysis unit; the data analysis unit comprises a top end conversion analysis module, an event analysis module, a funnel analysis module, a retention analysis module, a distribution analysis module, an attribution analysis module, a user path analysis module, a webpage thermal analysis module and an interval analysis module. Through the system construction, enterprises can build a unified e-commerce big data sharing and analyzing platform, carry out prospective prediction and analysis on e-commerce users and e-commerce services, simultaneously support full-end acquisition and modeling of business data and third-party data, drive marketing channel effect evaluation and user refined operation improvement, build a user data system, and enable user behavior data to play profound values.
An analysis method of a large data-based e-commerce user analysis system comprises the following steps:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
and step three, analyzing the conversion conditions of various services at intervals, and calculating the time interval of two events in the user behavior sequence to obtain the conversion time length distribution of the service conversion link.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation. The use of the phrase "comprising one of the elements does not exclude the presence of other like elements in the process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An e-commerce user analysis system based on big data comprises a data acquisition unit (1), and is characterized in that: the output end of the data acquisition unit (1) is connected with a data storage unit (2), and the output end of the data storage unit (2) is connected with a data analysis unit (3);
the data analysis unit (3) comprises a top conversion analysis module (301), an event analysis module (302), a funnel analysis module (303), a retention analysis module (304), a distribution analysis module (305), an attribution analysis module (306), a user path analysis module (307), a webpage thermal analysis module (308) and an interval analysis module (309).
2. The big-data-based e-commerce user analysis system according to claim 1, wherein: the data acquisition unit (1) acquires information through an information system, an application program APP, the Internet, an Internet of things facility and a web program.
3. The big-data-based e-commerce user analysis system according to claim 1, wherein: the data storage unit (2) stores data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud.
4. The analysis method of the big-data-based e-commerce user analysis system according to claim 1, comprising the steps of:
acquiring data, namely acquiring information through an information system, an application program APP, the Internet, Internet of things facilities and a web program;
step two, data storage, namely cleaning the acquired data, and storing the data through an IDC data center, a storage resource pool, a computing resource pool, a network resource pool or a security cloud after cleaning;
step three, data analysis, namely, carrying out multi-category analysis on the data step by step, namely order conversion analysis, event analysis, funnel analysis, retention analysis, distribution analysis, attribution analysis, user path analysis, webpage thermal analysis and interval analysis.
5. The analysis method of the big-data-based e-commerce user analysis system according to claim 4, wherein: and the order conversion analysis analyzes user behaviors, various charts are summarized and displayed, various indexes are switched and various data boards through full-functional multidimensional analysis capability.
6. The analysis method of the big-data-based e-commerce user analysis system according to claim 4, wherein: the event analysis is the query analysis of the functions of index statistics, attribute grouping, condition screening and the like based on events through the tracked or recorded user behaviors or business processes.
7. The analysis method of the big-data-based e-commerce user analysis system according to claim 4, wherein: the funnel analysis analyzes the transformation and loss conditions of each step in the multi-step process, the retention analysis examines the user who performs the initial behavior through analyzing the analysis model of the participation condition and the activity degree of the user, and a plurality of people can perform the follow-up behavior to measure the value of the product to the user.
8. The analysis method of the big-data-based e-commerce user analysis system according to claim 4, wherein: the distribution analysis is to analyze the dependence of users on products and the user distribution condition of a certain event index, and the attribution analysis is to analyze by using an attribution analysis model when the conversion contribution of a certain advertisement position and a promotion position to a target event needs to be analyzed in business.
9. The analysis method of the big-data-based e-commerce user analysis system according to claim 4, wherein: the user path analysis is mainly used for analyzing the path distribution condition of a user when the user uses a product, and the webpage thermal analysis is used for analyzing the conditions of clicking, reaching depth and the like of the user on a webpage and displaying the conditions to the user with a visual effect.
10. The analysis method of the big-data-based e-commerce user analysis system according to claim 4, wherein: and the interval analysis is used for analyzing the conversion conditions of various services and obtaining the conversion duration distribution of the service conversion link by calculating the time interval of two events in the user behavior sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911344125.5ACN111127099A (en) | 2019-12-24 | 2019-12-24 | E-commerce user analysis system based on big data and analysis method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911344125.5ACN111127099A (en) | 2019-12-24 | 2019-12-24 | E-commerce user analysis system based on big data and analysis method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111127099Atrue CN111127099A (en) | 2020-05-08 |
Family
ID=70501517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911344125.5AWithdrawnCN111127099A (en) | 2019-12-24 | 2019-12-24 | E-commerce user analysis system based on big data and analysis method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111127099A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113254544A (en)* | 2021-04-29 | 2021-08-13 | 西安交通大学 | Data processing device and method based on dimension modeling |
| CN113469519A (en)* | 2021-06-29 | 2021-10-01 | 平安银行股份有限公司 | Attribution analysis method and device of business event, electronic equipment and storage medium |
| CN114119257A (en)* | 2021-11-16 | 2022-03-01 | 上海镁信健康科技有限公司 | A management system based on insurance data |
| CN116501778A (en)* | 2023-05-16 | 2023-07-28 | 湖北省珍岛数字智能科技有限公司 | Real-time user behavior data analysis method based on ClickHouse |
- 2019
- 2019-12-24CNCN201911344125.5Apatent/CN111127099A/ennot_activeWithdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113254544A (en)* | 2021-04-29 | 2021-08-13 | 西安交通大学 | Data processing device and method based on dimension modeling |
| CN113254544B (en)* | 2021-04-29 | 2023-01-03 | 西安交通大学 | Data processing device and method based on dimension modeling |
| CN113469519A (en)* | 2021-06-29 | 2021-10-01 | 平安银行股份有限公司 | Attribution analysis method and device of business event, electronic equipment and storage medium |
| CN113469519B (en)* | 2021-06-29 | 2025-02-28 | 平安银行股份有限公司 | Business event attribution analysis method, device, electronic device and storage medium |
| CN114119257A (en)* | 2021-11-16 | 2022-03-01 | 上海镁信健康科技有限公司 | A management system based on insurance data |
| CN116501778A (en)* | 2023-05-16 | 2023-07-28 | 湖北省珍岛数字智能科技有限公司 | Real-time user behavior data analysis method based on ClickHouse |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113064866B (en) | Power business data integration system | |
| CN111127099A (en) | E-commerce user analysis system based on big data and analysis method thereof | |
| Khan et al. | Business intelligence: an integrated approach | |
| Rowe et al. | Automated social hierarchy detection through email network analysis | |
| KR101703529B1 (en) | Market analysis service provision method in accordance with market analysis thereof | |
| CN111291076A (en) | Abnormal water use monitoring and alarming system based on big data and construction method thereof | |
| CN112052966A (en) | Power customer satisfaction analysis system and method based on site emergency repair work order | |
| CN114741598A (en) | Marketing big data informatization management cloud platform | |
| Wang et al. | Future development trend of “new retail” and e-commerce based on big data | |
| CN116777508A (en) | Medical supply analysis management system and method based on big data | |
| Wang et al. | Visual Analysis of E‐Commerce User Behavior Based on Log Mining | |
| Yu et al. | Research on situational perception of power grid business based on user portrait | |
| CN118691309A (en) | Logistics electronic waybill price monitoring management method, device, equipment and storage medium | |
| CN110909050A (en) | Data statistical analysis system | |
| Subagja et al. | Measuring success factors for business intelligence: Management perspective | |
| Zhu et al. | Research on Comprehensive Budget Management of Clothing E-Commerce Enterprises under The Background of Big Data: Take H Group as an Example | |
| Chongwen et al. | O2O E-Commerce Data Mining in Big Data Era. | |
| CN114529383A (en) | Method and system for realizing tax payment tracking and tax loss early warning | |
| Xiong et al. | Research on Sales Forecasting Model Based on Linear Regression Algorithm | |
| Fang et al. | Data mining technology and its Application In CRM of Commercial Banks | |
| Galagali et al. | a Design of B2B Marketing Strategy for the Meter Data Management System Software | |
| CN111062793A (en) | Financial risk monitoring method, device and storage medium | |
| CN119359355B (en) | Road transportation market violation behavior situation sensing system and method thereof | |
| Feng | Research of customer relationship management solutions based on data mining | |
| CN109543987A (en) | The subjective and objective Double Data analytic approach of internet and mobile Internet product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | Application publication date:20200508 | |
| WW01 | Invention patent application withdrawn after publication |