CN117132331B - Method for mall to provide sales promotion integration reminding for user - Google Patents
Method for mall to provide sales promotion integration reminding for user Download PDFInfo
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- CN117132331B CN117132331B CN202311388352.4A CN202311388352A CN117132331B CN 117132331 B CN117132331 B CN 117132331B CN 202311388352 A CN202311388352 A CN 202311388352A CN 117132331 B CN117132331 B CN 117132331B
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- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000010354 integration Effects 0.000 title claims abstract description 12
- 238000010224 classification analysis Methods 0.000 claims abstract description 8
- 238000005065 mining Methods 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000005457 optimization Methods 0.000 claims description 4
- 230000001737 promoting effect Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 230000006872 improvement Effects 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
- 230000006399 behavior Effects 0.000 claims 4
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/0203—Market surveys; Market polls
Abstract
The invention relates to the technical field of electronic commerce, in particular to a method for providing promotion integration reminding for users in a mall, which comprises the steps of collecting information, preprocessing data after statistical data of the users are collected by a system, sequentially performing consumption mode analysis, clustering and classification analysis, commodity preference analysis and preferential form preference analysis, performing association rule mining according to the consumption mode analysis, the clustering and classification analysis, the commodity preference analysis and the preferential form preference analysis, performing preheating analysis on commodities which are interested by the users according to consumption and preferential form habits of the users, and summarizing commodities and preferential forms which meet requirements to form a prediction list. The invention can automatically integrate the preferential information, reduces manual operation and reduces operation cost; different preferential information can be displayed according to the personalized requirements of the user, and user experience is improved.
Description
Technical Field
The invention relates to the technical field of electronic commerce, in particular to a method for providing promotion integration reminding for users in a mall.
Background
Today, the electronic commerce is rapidly developed, and the preferential activities of various malls are endlessly layered. In order to facilitate a user to obtain more offers when purchasing goods, a method for generating a detail page of goods capable of integrating information of mall offers is needed.
Therefore, a method for providing promotion integration reminding for users in a mall is provided for solving the problems.
Disclosure of Invention
The invention aims to provide a method for providing promotion integration reminding for users in a mall so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for a mall to provide promotional integration reminders to a user, comprising the steps of:
step one: the information collection system collects search information, browse page information, click element information and commodity purchasing information of a user in a mall, and performs statistics to form statistical data;
step two: after the system collects the statistical data of the user, data preprocessing is carried out, then consumption pattern analysis, clustering and classification analysis are sequentially carried out, commodity preference analysis and preferential form preference analysis are carried out, association rule mining is carried out according to the consumption pattern analysis, the clustering and classification analysis, the commodity preference analysis and the preferential form preference analysis, preheating analysis is carried out on commodities interested by the user according to consumption and preferential form habits of the user, and commodities and preferential forms meeting requirements are summarized to form a prediction list;
step three: and synchronizing the forecast list to a system front page and a shopping cart page by utilizing mall pushing, and pushing the commodity and preferential form of the user.
In the second step, the data preprocessing operation is as follows: user behavior data is collected through server logs, databases, and other backend systems.
Preferably, in the second step, the consumption mode analysis includes a settlement mode statistics and a settlement habit analysis, the settlement mode statistics includes a mode for collecting payment orders, including single commodity settlement, combined commodity settlement, a purchasing commodity rule and preferential settlement information, and the custom ranking list of the user using the settlement mode is obtained by ranking the purchase modes from high to low.
Preferably, in the second step, clustering and classifying analysis are used for analyzing the commodities browsed by the user and the types of the purchased commodities, extracting commodity keywords, analyzing the preference and the types of the purchased commodities of the user, and classifying and arranging according to the preference and the types of the commodities to obtain a list of the commodities purchased by the user.
Preferably, in the second step, the commodity preference analysis is used for analyzing a list of commodities purchased by the user, extracting commodity keywords, performing preference statistics to obtain a preference list, the preferential form preference analysis is used for analyzing a habit ranking list of the user using a settlement mode in the consumption mode analysis, analyzing preferential forms of high-frequency use of the user, and performing statistics according to the use at the moment to obtain a preferential form preference list.
Preferably, in the second step, the association rule mining synchronizes the habit ranking list of the user using the settlement mode, the list of the commodities purchased by the user and the preferential form preference list, searches for the commodities associated in the mall to collect and classify, and then carries out predictive analysis to calculate the type of the commodities purchased by the user next time and preferential settlement form.
Preferably, in the third step, information pushing is performed for the type and preferential settlement form of the commodity purchased next time by the user, which is obtained in the second step, wherein the pushing of the commodity is combined into a mall pushing rule to perform combination optimization, and meanwhile, a preferential policy applicable to the corresponding commodity is added into the commodity.
Compared with the prior art, the commodity detail page can display different preferential information according to the personalized requirements of the user, so that the user experience is improved, meanwhile, the method can automatically integrate the preferential information, reduces manual operation and reduces operation cost, and has the beneficial effects that:
1. the preferential information can be automatically integrated, so that manual operation is reduced, and the operation cost is reduced;
2. different preferential information can be displayed according to the personalized requirements of the user, so that the user experience is improved;
3. the integration of the preferential information can be adjusted according to specific business requirements, and the method has higher flexibility.
Drawings
FIG. 1 is a schematic diagram of a method for providing promotion integration prompt to a user in a mall according to the present invention.
Detailed Description
The present invention will be further described in detail below with reference to the drawings and examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and the specific embodiments.
Examples:
referring to fig. 1, the present embodiment provides a technical solution:
a method for a mall to provide promotional integration reminders to a user, comprising the steps of:
step one: the information collection system collects search information, browse page information, click element information and commodity purchasing information of a user in a mall, and performs statistics to form statistical data;
step two: after the system collects the statistical data of the user, data preprocessing is carried out, then consumption pattern analysis, clustering and classification analysis are sequentially carried out, commodity preference analysis and preferential form preference analysis are carried out, association rule mining is carried out according to the consumption pattern analysis, the clustering and classification analysis, the commodity preference analysis and the preferential form preference analysis, preheating analysis is carried out on commodities interested by the user according to consumption and preferential form habits of the user, and commodities and preferential forms meeting requirements are summarized to form a prediction list;
wherein:
the data preprocessing operation is as follows: collecting user behavior data through server logs, databases and other backend systems;
the consumption mode analysis comprises settlement mode statistics and settlement habit analysis, wherein the settlement mode statistics comprises a mode for collecting payment orders, including single commodity settlement, combined commodity settlement, commodity purchasing rules and preferential settlement information, and the settlement modes are ordered according to the times of the purchase modes from high to low to obtain a habit ranking list of the user using the settlement modes;
the clustering and classifying analysis is used for analyzing commodities browsed by a user and the types of purchased commodities, extracting commodity keywords, analyzing the preference and the types of the purchased commodities of the user, classifying and arranging according to the preference and the types of the commodities, and obtaining a list of the purchased commodities of the user;
the commodity preference analysis is used for analyzing a list of commodities purchased by a user, extracting commodity keywords, carrying out preference statistics to obtain a preference list, and the preferential form preference analysis is used for analyzing a habit ranking list of a user using a settlement mode in consumption mode analysis, analyzing preferential forms of high-frequency use of the user and carrying out statistics according to the use at the moment to obtain a preferential form preference list;
the association rule mining synchronizes a habit ranking list of a settlement mode used by the user, a list of commodities purchased by the user and a preferential form preference list, searches for related commodities in a mall to collect and classify the commodities, then carries out predictive analysis, and calculates the type of the commodities purchased by the user next time and preferential settlement forms;
step three: synchronizing the forecast list to a system front page and a shopping cart page by utilizing mall pushing to complete pushing of commodities and preferential forms of users;
and the mall pushing and combining step two is used for pushing information aiming at the type and the preferential settlement form of the commodity purchased next time by the user, wherein the pushing of the commodity is combined into a mall pushing rule for combining and optimizing, and meanwhile, a preferential policy which can be used by the corresponding commodity is added into the commodity.
In this embodiment, the implementation steps are as follows:
1. collecting behavior data of a user in a website or an application;
2. analyzing the result, making a corresponding preferential strategy and marketing popularization plan, and precisely pushing;
3. receiving a user request, and acquiring detailed information of the commodity according to the commodity ID in the request;
4. acquiring all preferential information related to the commodity according to the detailed information of the commodity;
5. integrating the preferential information into the commodity detail page according to the personalized setting of the user and the priority of the preferential information;
6. and returning the integrated commodity detail page to the user.
Further, in this embodiment, the specific implementation steps are as follows:
the user can search and inquire the commodity list in the mall, browse commodities, click elements such as head navigation, side navigation and the like, and purchase commodities, and the actions send requests to the server;
collecting user behavior data through server logs, databases and other backend systems;
the system cleans noise, redundancy and error information of the data, and performs de-duplication, conversion and normalization on the data;
and according to the analysis result, personalized setting of the user and priority of the preferential information, the system pushes the commodity and all preferential information of the commodity which is changed first, and integrates the preferential information into the commodity detail page. For example, if the user sets a priority to display the discount offer, the server will place the discount offer information in a salient location on the item detail page;
the server returns the integrated commodity detail page to the user, and the user can check the detailed information and specific preferential information of the commodity in the detail page;
after preferential form optimization, the effectiveness of the new design is verified through an A/B test, and the analysis and strategy improvement are performed.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. A method for a mall to provide a promotional integration reminder to a user, comprising: the method comprises the following steps:
step one: the information collection system collects search information, browse page information, click element information and commodity purchasing information of a user in a mall, and performs statistics to form statistical data;
step two: after the system collects the statistical data of the user, data preprocessing is carried out, then consumption pattern analysis, clustering and classification analysis are sequentially carried out, commodity preference analysis and preferential form preference analysis are carried out, association rule mining is carried out according to the consumption pattern analysis, the clustering and classification analysis, the commodity preference analysis and the preferential form preference analysis, preheating analysis is carried out on commodities interested by the user according to consumption and preferential form habits of the user, and commodities and preferential forms meeting requirements are summarized to form a prediction list; the data preprocessing operation is as follows: collecting user behavior data through server logs, databases and other backend systems; the consumption mode analysis comprises settlement mode statistics and settlement habit analysis, wherein the settlement mode statistics comprises a mode for collecting payment orders, including single commodity settlement, combined commodity settlement, commodity purchasing rules and preferential settlement information, and the settlement modes are ordered according to the times of the purchase modes from high to low to obtain a habit ranking list of the user using the settlement modes; the clustering and classifying analysis is used for analyzing commodities browsed by a user and the types of purchased commodities, extracting commodity keywords, analyzing the preference and the types of the purchased commodities of the user, classifying and arranging according to the preference and the types of the commodities, and obtaining a list of the purchased commodities of the user; the commodity preference analysis is used for analyzing a list of commodities purchased by a user, extracting commodity keywords, carrying out preference statistics to obtain a preference list, and the preferential form preference analysis is used for analyzing a habit ranking list of a user using a settlement mode in consumption mode analysis, analyzing preferential forms of high-frequency use of the user and carrying out statistics according to the use at the moment to obtain a preferential form preference list; the association rule mining synchronizes a habit ranking list of a settlement mode used by the user, a list of commodities purchased by the user and a preferential form preference list, searches for related commodities in a mall to collect and classify the commodities, then carries out predictive analysis, and calculates the type of the commodities purchased by the user next time and preferential settlement forms;
step three: synchronizing the forecast list to a system front page and a shopping cart page by utilizing mall pushing to complete pushing of commodities and preferential forms of users; information pushing is carried out on the type and the preferential settlement form of the commodity purchased next time by the user, wherein the pushing of the commodity is combined into a mall pushing rule to carry out combination optimization, and meanwhile, a preferential policy which can be used by the corresponding commodity is added into the commodity;
the specific operation of the system for promoting integration reminding is as follows:
(1) Collecting behavior data of a user in a website or an application;
(2) Analyzing the result, making a corresponding preferential strategy and marketing popularization plan, and precisely pushing;
(3) Receiving a user request, and acquiring detailed information of the commodity according to the commodity ID in the request;
(4) Acquiring all preferential information related to the commodity according to the detailed information of the commodity;
(5) Integrating the preferential information into the commodity detail page according to the personalized setting of the user and the priority of the preferential information;
the system comprises the following specific implementation steps:
the user searches and inquires a commodity list in the mall, browses commodities, clicks elements and purchases the commodities, and the behaviors send requests to the server;
collecting user behavior data through a server log, a database and a back-end system;
the system cleans noise, redundancy and error information of the data, and performs de-duplication, conversion and normalization on the data;
according to the analysis result, personalized setting of the user and priority of the preferential information, the system pushes the commodity and all preferential information of the commodity which is changed first, the preferential information is integrated into a commodity detail page, and if the preferential display of discount preferential is set by the user, the server places the discount preferential information at a significant position of the commodity detail page;
the server returns the integrated commodity detail page to the user, and the user checks detailed information and specific preferential information of the commodity in the detail page;
after preferential form optimization, the effectiveness of the new design is verified through an A/B test, and the analysis and strategy improvement are performed.
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| CN202311388352.4A CN117132331B (en) | 2023-10-25 | 2023-10-25 | Method for mall to provide sales promotion integration reminding for user |
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| CN202311388352.4A CN117132331B (en) | 2023-10-25 | 2023-10-25 | Method for mall to provide sales promotion integration reminding for user |
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| CN117132331B true CN117132331B (en) | 2024-01-26 |
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| US6049785A (en) * | 1993-12-16 | 2000-04-11 | Open Market, Inc. | Open network payment system for providing for authentication of payment orders based on a confirmation electronic mail message |
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