CN111625563A - User access behavior analysis method and system based on funnel model - Google Patents
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Abstract
The invention discloses a user access behavior analysis method and system based on a funnel model. The method comprises the following steps: accessing a plurality of real-time data streams forming a link, and cleaning and converting required dimensionality and measurement fields of the real-time data streams; storing the user behavior path information of the current visitor in a database according to a key value pair form; a user self-defines a link rule configuration table and stores the link rule configuration table in a database in advance, and data meeting a path information rule in the link rule configuration table are sent to downstream; filtering out repeatedly transmitted data by adopting a duplicate removal component; and performing aggregation analysis according to different dimensions and dimension combinations to form a funnel model aiming at each page type in the stream output by the processing process, and analyzing the access behavior of the user based on the funnel model. The system includes a user link storage unit, a rule filter, a deduplication component, and a funnel link analysis unit. The invention can form a funnel model based on minute updating, analyze various different link combinations and reduce the data loss rate.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a user access behavior analysis method and system based on a funnel model.
Background
The buyer goes through a plurality of processes from easy purchase after logging in Sunning to successful payment, such as browsing a home page, searching for a commodity, accessing a four-level page of the commodity, joining a shopping cart, submitting an order, paying the order, successfully paying the payment and the like. From browsing the home page to payment success, fewer and fewer users have experienced these key nodes, and the conversion rate of the nodes appears in the shape of a funnel. The operation team can analyze the motivation behind the user behavior by monitoring and managing the conversion rate of the concerned nodes, and can make further analysis and improvement aiming at the links with lower conversion rate.
In the background of the rapid development of the internet, the timeliness requirements of operation teams on data are higher and higher, and the access nodes concerned by different operation teams may be different. The existing user access behavior funnel model mainly analyzes scenes according to specific services, the reusability of programs is difficult to achieve, the existing funnel model mostly cannot provide updating based on minutes, and a part of data which is arrived in a delayed mode is often lost when the user behavior model is integrated from a plurality of real-time streaming links.
Disclosure of Invention
The invention aims to provide a user access behavior analysis method and system based on a funnel model, which are used for analyzing various different link combinations and reducing the data loss rate by forming the funnel model based on minute updating.
The technical solution for realizing the purpose of the invention is as follows: a user access behavior analysis method based on a funnel model comprises the following steps:
accessing a plurality of real-time data streams forming a link, and cleaning and converting dimensionality and measurement fields required in the real-time data streams; storing the user behavior path information of the current visitor in a database according to a key value pair form;
a user self-defines a link rule configuration table and stores the link rule configuration table in a database in advance, filters data which do not accord with the rule according to the link rule configuration table, and sends the data which meet the path information rule in the link rule configuration table to downstream;
filtering out repeatedly transmitted data by adopting a duplicate removal component;
and performing aggregation analysis according to different dimensions and dimension combinations aiming at each page type in the stream output by the processing process, so as to form a funnel model, and analyzing the access behavior of the user based on the funnel model.
Further, the plurality of real-time data streams forming the link include a shopping cart stream, a user access stream, an order page stream, and an order sales information stream.
Further, the user behavior path information of the current visitor is stored in a database according to a key-value pair form, which specifically includes:
and storing the link node information accessed by the user, taking the user code splicing session number splicing page code as a unique key, taking the dimensionality required by the service as a value, and storing the user behavior path information of the current user in a database.
Further, the user-defined link rule configuration table, wherein the link rule storage format is: link coding + page link path.
Further, the filtering out data that does not conform to the rule according to the link rule configuration table specifically includes:
and recording the page type code in the received real-time stream data as a current page, traversing a link rule configuration table, if the current page exists in the link rule configuration table and a page before the current page exists in the link rule configuration table, determining that the current page is a page to be sent, otherwise, determining that the current page is data which does not accord with the rule, and filtering.
Further, the data sent repeatedly is filtered by adopting a deduplication component, wherein the filtering criteria of the deduplication component are as follows: before each piece of data is sent, searching from a database storing key value pairs according to a unique key, if the same key is found, indicating that the piece of data is sent to downstream, and the piece of data is repeated data and is not sent any more; if the same key is not found, the piece of data is stored in the database according to the format of the key-value pair and is sent to the downstream.
Further, the aggregate analysis is performed according to different dimensions and combinations of dimensions, so as to form a funnel model, and a user access behavior is analyzed based on the funnel model, specifically:
according to business requirements, counting is conducted on the streams output in the last step in a set time period according to different page types, the number of independent access users of each page is obtained, the number of independent access users of adjacent page types in a link is divided to obtain a page conversion ratio, a funnel model is formed, and user access behaviors are analyzed based on the funnel model.
A funnel model-based user access behavior analysis system, comprising:
the user link storage unit is used for accessing a plurality of real-time data streams forming a link and storing the user behavior path information of the current visitor in a database according to a key value pair form;
the rule filter filters data which do not accord with the rule according to the link rule configuration table, and sends the link which meets the path information in the link rule configuration table to the duplication elimination component;
the duplicate removal component filters out the data which are repeatedly sent;
and the funnel link analysis unit is used for performing aggregation analysis according to different dimensions and dimension combinations aiming at each page type in the stream output by the recombination removing component to form a user access behavior funnel model.
Further, the user link storage unit adopts a streaming processing framework to access a plurality of real-time data streams from an upstream system, and performs cleaning conversion on the dimension and the measurement field required in the real-time data streams.
Further, the real-time analysis tool used for the aggregation analysis is OLAP.
Compared with the prior art, the invention has the following remarkable advantages: (1) the user is allowed to provide a self-defined link configuration table, so that different business parties can conveniently analyze various different link combinations; (2) using a stream processing technology to correlate the plurality of real-time streams, so that a user behavior funnel model updated based on minutes can be analyzed; (3) and a cache mechanism provided by the database is used for ensuring that data is not lost in the process of associating a plurality of real-time streams.
Drawings
FIG. 1 is a schematic view of a Sunnin easy-to-purchase flow funnel.
FIG. 2 is a flow diagram of a user access behavior funnel.
Detailed Description
The invention relates a plurality of real-time flows by using a flow processing technology and a cache mechanism provided by a database, thereby analyzing and obtaining a user behavior funnel model; the user is allowed to provide a self-defined link configuration table, so that different business parties can conveniently analyze funnel models of various different link combinations.
The invention discloses a user access behavior analysis method based on a funnel model, which comprises the following steps:
accessing a plurality of real-time data streams forming a link, and cleaning and converting dimensionality and measurement fields required in the real-time data streams; storing the user behavior path information of the current visitor in a database according to a key value pair form;
a user self-defines a link rule configuration table and stores the link rule configuration table in a database in advance, filters data which do not accord with the rule according to the link rule configuration table, and sends the data which meet the path information rule in the link rule configuration table to downstream;
filtering out repeatedly transmitted data by adopting a duplicate removal component;
and performing aggregation analysis according to different dimensions and dimension combinations aiming at each page type in the stream output by the processing process, so as to form a funnel model, and analyzing the access behavior of the user based on the funnel model.
Furthermore, dimension and measurement fields required in the real-time data stream are cleaned and converted, different cleaning rules are available according to the business logic of each field, and the different cleaning rules are mainly used for ensuring data consistency and processing invalid values and missing values.
Further, the link meeting the path information in the link rule configuration table is sent to the downstream, and the downstream specifically refers to all other data analysis projects needing to depend on the link;
further, the filtering of the repeatedly transmitted data by using the deduplication component specifically includes: before the data stream to be sent is sent, the visitor code splicing session number splicing page code of each piece of data in the stream is taken out and used as a unique key (the unique key can be changed according to specific service scenes and requirements, for example, a member code is used when a member dimension needs to be seen), and the dimension required by other services is used as a value, so that the user behavior path information of the current visitor is stored in a Redis or other databases. Before each piece of data is sent, searching in a database according to the unique key, if the data is found, indicating that the data is sent to downstream, and if the data is the repeated data, not sending the data; if not, the data is stored in the database according to the format described above and sent downstream.
As a specific embodiment, the multiple real-time data streams forming the link include a shopping cart stream, a user access stream, an order page stream, and an order sales information stream.
As a specific embodiment, the user behavior path information of the current visitor is stored in the database in a form of key-value pairs, which is specifically as follows:
and storing the link node information accessed by the user, taking the user code splicing session number splicing page code as a unique key, taking the dimensionality required by the service as a value, and storing the user behavior path information of the current user in a database.
As a specific embodiment, the user-defined link rule configuration table, wherein the link rule storage format is: link coding + page link path.
As a specific embodiment, the filtering out data that does not meet the rule according to the link rule configuration table specifically includes:
and recording the page type code in the received real-time stream data as a current page, traversing a link rule configuration table, if the current page exists in the link rule configuration table and a page before the current page exists in the link rule configuration table, determining that the current page is a page to be sent, otherwise, determining that the current page is data which does not accord with the rule, and filtering.
As a specific embodiment, the filtering, by using the deduplication component, of the repeatedly transmitted data specifically includes:
if the link information of the user meets the link rule configuration table and is not sent, the link is output, and the filtering criteria of the deduplication component are as follows: before each piece of data is sent, searching from a database storing key value pairs according to a unique key, if the same key is found, indicating that the piece of data is sent to downstream, and the piece of data is repeated data and is not sent any more; if the same key is not found, the piece of data is stored in the database according to the format of the key-value pair and is sent to the downstream.
As a specific embodiment, the aggregate analysis is performed according to different dimensions and combinations of dimensions, so as to form a funnel model, and a user access behavior is analyzed based on the funnel model, specifically:
according to business requirements, counting is conducted on the streams output in the last step in a set time period according to different page types, the number of independent access users of each page is obtained, the number of independent access users of adjacent page types in a link is divided to obtain a page conversion ratio, a funnel model is formed, and user access behaviors are analyzed based on the funnel model.
The invention relates to a user access behavior analysis system based on a funnel model, which comprises:
the user link storage unit is used for accessing a plurality of real-time data streams forming a link and storing the user behavior path information of the current visitor in a database according to a key value pair form;
the rule filter filters data which do not accord with the rule according to the link rule configuration table, and sends the link which meets the path information in the link rule configuration table to the duplication elimination component;
the duplicate removal component filters out the data which are repeatedly sent;
and the funnel link analysis unit is used for performing aggregation analysis according to different dimensions and dimension combinations aiming at each page type in the stream output by the recombination removing component to form a user access behavior funnel model.
In one embodiment, the user link storage unit accesses a plurality of real-time data streams from an upstream system by using a streaming processing framework, and performs cleansing conversion on required dimensions and measurement fields in the real-time data streams.
As a specific example, the real-time analysis tool used for the aggregation analysis is OLAP.
The invention is described in further detail below with reference to the figures and the embodiments.
Examples
The method is mainly used for calculating the conversion rate of the user between the Suningyibuyoukey page nodes and is intended to help the business to analyze the access path of the user using the Suningyibuyout in real time. And can help business calculate the full guiding rate of different pages by using the page type configuration table (with a thick caliber: if the link UV from the landing page to the business detail page needs to be calculated, the UV of the landing page does not go through any steps later, as long as the business detail page is visited finally, the UV of the landing page brought to the business detail page is calculated).
The user access behavior analysis method based on the funnel model comprises the following steps:
(1) multiple real-time data streams (shopping cart stream, user access stream, order page stream, order sales information stream, etc.) making up the link are accessed and de-duplicated according to page type and visitor code.
(2) And performing necessary cleaning conversion on the dimension and the measurement field required by writing into the real-time stream according to specific business requirements.
(3) And taking the visitor code and the session number as unique keys, and storing the user behavior path of the current visitor.
(4) And reading the configured user behavior path configuration table, and if the access path of the current visitor meets the path information in the configuration table, sending the link to the downstream.
(5) And (3) performing aggregation analysis on each page type in the stream output in the step (4) according to different dimensions and dimension combinations in a certain time period by using a batch processing mode according to business requirements, thereby providing data support for the user core link in the figure 1.
This embodiment is described in further detail below with reference to fig. 2.
Stage 1. Access real-time flows and storage of user links
A streaming processing framework is used for accessing a plurality of real-time data streams (shopping cart streams, user access streams, order page streams, order sale information streams and the like) forming a link from an upstream system, and necessary cleaning conversion is carried out on dimension and measurement fields required in the real-time data streams according to specific business requirements. And then storing the page access information in the data stream input from the source end in a database according to the key value pair.
Key is dimension required by member code/visitor code + service.
Value: and storing the link node information accessed by the member or the visitor in a map format. The key in the map is the link node code, and the value is the other information needed (e.g., <10001, info >, <10002, info >, <10009, info >).
And (3) storing the user behavior path information of the current visitor into Redis (each record in the Redis is reserved for 30 minutes) by taking the visitor code splicing session number splicing page code as a unique key and the dimensionality required by other services as a value.
Stage 2, filtering out data which do not accord with the rule according to the configuration table
The business analysis party needs to store the link rule which the business analysis party wants to analyze into a database: link coding, and page link path.
(example: link rules, after SQL processing in the filter, will become:
l0001, 10001_10002_10009_10010, meaning the first link, the viewed user link rules are first page, promotion page, list page, last to fourth level page).
If a piece of real-time stream data is received at the moment, selecting a page type code in the piece of data to be recorded as a current page, dividing the current page by underlining, traversing the regular link, and if the current page exists in the regular link and a page before the current page exists in a user access link information table (which indicates that the current user accesses according to a configured rule), determining that the current page is a page to be sent.
Stage 3. deduplication component, filtering out repeatedly sent data
And (3) duplicate removal treatment: if a link meets a portion of the configuration rules, it will be guaranteed that the link is only output once.
If the link information of the user meets the rule configuration table and is not sent, the link is output (the output in the above example is 3 real-time streams, which are respectively member code/visitor code, dimension information, 10001;
membership code/visitor code, dimension information, 10001_ 10002;
membership code/visitor code, dimension information, 10001_10002_ 10009;
stage 4 visited Link analysis
Inputting the output funnel graph into an OLAP real-time analysis tool, performing count calculation on the stream output by stage3 according to different page types within a certain time period according to service requirements, calculating UV of each page, and dividing UV of adjacent page types in a link to obtain a page conversion ratio, thereby forming a user behavior funnel model. Or generating a corresponding funnel model according to different dimensions and dimension combinations in a cube mode.
Through the mode, the visiting funnel model of the visitor or the member is generated, the UV of each page type in the link and the conversion rate of the core link are generated according to different dimensions and the combination of the dimensions, and the corresponding funnel model can be accurately generated according to the link table of the service configuration.
The embodiment can calculate the UV of each page type which is easy to buy by Sunning and the page arrival conversion rate between the access links, and provides data support for the eagle eye core link.
Claims (10)
1. A user access behavior analysis method based on a funnel model is characterized by comprising the following steps:
accessing a plurality of real-time data streams forming a link, and cleaning and converting dimensionality and measurement fields required in the real-time data streams; storing the user behavior path information of the current visitor in a database according to a key value pair form;
a user self-defines a link rule configuration table and stores the link rule configuration table in a database in advance, filters data which do not accord with the rule according to the link rule configuration table, and sends the data which meet the path information rule in the link rule configuration table to downstream;
filtering out repeatedly transmitted data by adopting a duplicate removal component;
and performing aggregation analysis according to different dimensions and dimension combinations aiming at each page type in the stream output by the processing process, so as to form a funnel model, and analyzing the access behavior of the user based on the funnel model.
2. The funnel model-based user access behavior analysis method of claim 1, wherein the plurality of real-time data streams that make up the link comprise a shopping cart stream, a user access stream, an order page stream, an order sales information stream.
3. The method for analyzing user access behaviors based on the funnel model of claim 1, wherein the user behavior path information of the current visitor is stored in a database in a key-value pair manner, specifically as follows:
and storing the link node information accessed by the user, taking the user code splicing session number splicing page code as a unique key, taking the dimensionality required by the service as a value, and storing the user behavior path information of the current user in a database.
4. The funnel model-based user access behavior analysis method of claim 1, wherein the user-defined link rule configuration table, wherein the link rule storage format is: link coding + page link path.
5. The funnel model-based user access behavior analysis method according to claim 1, 2, 3, or 4, wherein filtering out data that does not comply with rules according to the link rule configuration table specifically comprises:
and recording the page type code in the received real-time stream data as a current page, traversing a link rule configuration table, if the current page exists in the link rule configuration table and a page before the current page exists in the link rule configuration table, determining that the current page is a page to be sent, otherwise, determining that the current page is data which does not accord with the rule, and filtering.
6. The funnel model-based user access behavior analysis method of claim 5, wherein the filtering out of the repeatedly transmitted data is performed by a deduplication component, wherein the filtering criteria of the deduplication component is: before each piece of data is sent, searching from a database storing key value pairs according to a unique key, if the same key is found, indicating that the piece of data is sent to downstream, and the piece of data is repeated data and is not sent any more; if the same key is not found, the piece of data is stored in the database according to the format of the key-value pair and is sent to the downstream.
7. The method for analyzing user access behaviors based on the funnel model according to claim 5, wherein the aggregate analysis is performed according to different dimensions and combinations of dimensions to form the funnel model, and the user access behaviors are analyzed based on the funnel model, specifically:
according to business requirements, counting is conducted on the streams output in the last step in a set time period according to different page types, the number of independent access users of each page is obtained, the number of independent access users of adjacent page types in a link is divided to obtain a page conversion ratio, a funnel model is formed, and user access behaviors are analyzed based on the funnel model.
8. A user access behavior analysis system based on a funnel model is characterized by comprising:
the user link storage unit is used for accessing a plurality of real-time data streams forming a link and storing the user behavior path information of the current visitor in a database according to a key value pair form;
the rule filter filters data which do not accord with the rule according to the link rule configuration table, and sends the link which meets the path information in the link rule configuration table to the duplication elimination component;
the duplicate removal component filters out the data which are repeatedly sent;
and the funnel link analysis unit is used for performing aggregation analysis according to different dimensions and dimension combinations aiming at each page type in the stream output by the recombination removing component to form a user access behavior funnel model.
9. The funnel model-based user access behavior analysis system of claim 8, wherein the user link storage unit employs a streaming framework to access multiple real-time data streams from an upstream system and perform cleansing transformation on dimension and metric fields required in the real-time data streams.
10. The funnel model-based user access behavior analysis system of claim 8, wherein the real-time analysis tool employed for the aggregate analysis is OLAP.
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| CN112070556A (en) * | 2020-09-16 | 2020-12-11 | 贝壳技术有限公司 | Information distribution system and information distribution method based on two-dimensional code |
| CN112650743A (en) * | 2020-12-30 | 2021-04-13 | 咪咕文化科技有限公司 | Funnel data analysis method and system, electronic device and storage medium |
| CN113176980A (en) * | 2021-05-25 | 2021-07-27 | 医声医事(北京)科技有限公司 | Dynamic construction method and system of flow funnel |
| CN113176980B (en) * | 2021-05-25 | 2023-09-12 | 医声医事(北京)科技有限公司 | Dynamic construction method and system of flow hopper |
| CN113609188A (en) * | 2021-07-23 | 2021-11-05 | 恩亿科(北京)数据科技有限公司 | Funnel analysis method, system, device and medium for user behavior steps |
| CN114745722A (en) * | 2022-06-15 | 2022-07-12 | 安徽创瑞信息技术有限公司 | Short message platform security audit verification method and system |
| CN114745722B (en) * | 2022-06-15 | 2022-08-12 | 安徽创瑞信息技术有限公司 | Short message platform security audit verification method and system |
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