CN111127052A - Method for realizing store member label extraction based on Internet platform - Google Patents
Method for realizing store member label extraction based on Internet platform Download PDFInfo
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- CN111127052A CN111127052A CN201811275909.2A CN201811275909A CN111127052A CN 111127052 A CN111127052 A CN 111127052A CN 201811275909 A CN201811275909 A CN 201811275909A CN 111127052 A CN111127052 A CN 111127052A
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- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0226—Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
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Abstract
The invention relates to the field of Internet retail industry, in particular to a method for realizing store member tag extraction based on an Internet platform. The method of the invention is characterized in that: the method comprises the steps of establishing member information in a store system, recording all historical consumption data and related behavior data of members, classifying all generated data related to the members, counting according to different data types and corresponding data, extracting key words of member features, and identifying the feature tags as the member features. The invention solves the problems that the store system can not carry out characteristic classification management on members and label identification, and can be widely applied to the Internet retail industry.
Description
Technical Field
The invention relates to the field of Internet retail industry, in particular to a method for realizing store member tag extraction based on an Internet platform.
Background
The web Tag (Tag) is an internet content organization mode, is a keyword with strong relevance, and helps people to easily describe and classify content so as to facilitate retrieval and sharing.
The member label is used for describing the characteristics, behaviors, states and the like of the member in the form of keywords or keywords;
under the condition, the store system can only record consumption data and other related data of the members, can only inquire historical data, cannot analyze and count the related data of the members, and cannot describe the characteristics of the members in a keyword form. The invention records, classifies and counts all the data related to the members, and summarizes the characteristics of various data in the form of keywords, thereby solving the problems that the store system can not carry out characteristic classification management on the members and label identification.
Disclosure of Invention
The invention solves the technical problem of providing a method for realizing store member label extraction based on an internet platform; the problem that the store system cannot perform characteristic classification management on the members and label identification is solved.
The technical scheme for solving the technical problems is as follows:
the method is characterized in that: the method comprises the steps of establishing member information in a store system, recording all historical consumption data and related behavior data of members, classifying all generated data related to the members, counting according to different data types and corresponding data, extracting keywords of member features, and identifying the keywords as feature tags of the members.
The method for extracting the store member label based on the Internet platform comprises the step one of creating member information in a store system, wherein the member information comprises a member account number, a name, a mobile phone, a contact address, a micro signal, an identity card number, gender, age, registration time and the like.
In the second step, all data related to the members to be generated are classified, wherein the data include member consumption data, member activity data, point data, health data and the like.
The detailed recording of member data of all categories includes:
(1) consumption data: including consumption variety, variety brand, consumption quantity, amount, consumption date, payment mode, whether group purchase is available or not and the like;
(2) activity data: including the subject, date, reaction result after the activity, etc. of the participated activity;
(3) integration data: the method comprises the steps of point number, point time, point source, point exchange and point consumption details;
(4) health data: the store clerk inquires about the records and consults about the records each time the medicine is purchased.
In the third step, according to different data categories and corresponding data statistics, keyword extraction is carried out on the data of the category according to the statistical result, and label extraction is carried out from the following four aspects:
firstly, living habits:
(1) dividing the member into gender and age groups according to the gender and the age of the member, and extracting a gender label (male, female or unknown) and an age group label (young, middle and old);
(2) extracting information of frequent purchase stores of the members according to the data statistical ranking of the consumption stores of the members;
(3) and extracting member consumption time period information and activity information according to member consumption time statistics.
Secondly, consumption habit:
(1) according to the brand statistics of consumer varieties, labels (first-line brands, second-line brands, three-line and four-line brands and the like) of brand grades purchased by members are extracted;
(2) and counting and extracting whether the member is a regular group purchasing user or a general purchasing user according to the consumption data.
Thirdly, behavior characteristics:
(1) according to the statistical ranking of the payment tests, extracting common payment mode labels (WeChat, Paibao, cash, medical insurance, Unionpay card swiping and the like) of the users;
(2) and extracting the activities frequently attended by the user, the activity times and the activity response rate according to the data statistics of the attended activities.
Fourthly, health diseases:
(1) and extracting the health condition information, the disease information and the chronic disease information of the user according to the statistical analysis of the health data and the inquiry records of the purchased medicines.
Fifthly, member value:
(1) and counting and calculating the unit price of the customers according to the consumption amount and times, and extracting the consumption contribution degree labels (small single customers, single customers and large single customers) of the users.
And in the fourth step, the extracted member feature keywords are recorded in a member tag library of the database and are identified as corresponding member feature tags. And carrying out statistical analysis on all relevant data of the members at regular time every month, extracting the characteristics of the member tags at regular time, updating the member tag library and keeping the dynamics of the member tag library.
The invention solves the problems that the store system can not carry out characteristic classification management on members and label identification, and can be widely applied to the Internet retail industry.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a block flow diagram of the method of the present invention.
Detailed Description
As shown in fig. 1, the method of the present invention includes: the method comprises the steps of establishing member information in a store system, recording all historical consumption data and related behavior data of members, classifying all generated data related to the members, counting according to different data types and corresponding data, extracting keywords of member features, and identifying the keywords as feature tags of the members.
In the first step, member information is created in a store system, wherein the member information comprises a member account number, a name, a mobile phone, a contact address, a micro signal, an identity card number, gender, age, registration time and the like.
And in the second step, classifying the data related to all the members to be generated, wherein the data comprises member consumption data, member activity data, point data, health data and the like.
The detailed recording of member data of all categories includes:
(1) consumption data: including consumption variety, variety brand, consumption quantity, amount, consumption date, payment mode, whether group purchase is available or not and the like;
(2) activity data: including the subject, date, reaction result after the activity, etc. of the participated activity;
(3) integration data: the method comprises the steps of point number, point time, point source, point exchange and point consumption details;
(4) health data: the store clerk inquires about the records and consults about the records each time the medicine is purchased.
In the third step, according to different data categories and corresponding data statistics, keyword extraction is carried out on the data of the category according to the statistical result, and label extraction is carried out from the following four aspects:
firstly, living habits:
(1) dividing the member into gender and age groups according to the gender and the age of the member, and extracting a gender label (male, female or unknown) and an age group label (young, middle and old);
(2) extracting information of frequent purchase stores of the members according to the data statistical ranking of the consumption stores of the members;
(3) and extracting member consumption time period information and activity information according to member consumption time statistics.
Secondly, consumption habit:
(1) according to the brand statistics of consumer varieties, labels (first-line brands, second-line brands, three-line and four-line brands and the like) of brand grades purchased by members are extracted;
(2) and counting and extracting whether the member is a regular group purchasing user or a general purchasing user according to the consumption data.
Thirdly, behavior characteristics:
(1) according to the statistical ranking of the payment tests, extracting common payment mode labels (WeChat, Paibao, cash, medical insurance, Unionpay card swiping and the like) of the users;
(2) and extracting the activities frequently attended by the user, the activity times and the activity response rate according to the data statistics of the attended activities.
Fourthly, health diseases:
(1) and extracting the health condition information, the disease information and the chronic disease information of the user according to the statistical analysis of the health data and the inquiry records of the purchased medicines.
Fifthly, member value:
(1) and counting and calculating the unit price of the customers according to the consumption amount and times, and extracting the consumption contribution degree labels (small single customers, single customers and large single customers) of the users.
And step four, recording the extracted member feature keywords in a member tag library of the database, and identifying the member feature keywords as corresponding member feature tags. And carrying out statistical analysis on all relevant data of the members at regular time every month, extracting the characteristics of the member tags at regular time, updating the member tag library and keeping the dynamics of the member tag library.
Claims (6)
1. A method for realizing store member label extraction based on an Internet platform is characterized by comprising the following steps: establishing member information in a store system, classifying data related to all members to be generated, recording all historical consumption data and related behavior data of the members, counting according to different data types and corresponding data, extracting key words of member characteristics, and identifying the member characteristics as a characteristic label of the member.
2. The method for realizing store member tag extraction based on the internet platform as claimed in claim 1, wherein: the member information is created in the store system and comprises a member account number, a name, a mobile phone, a contact address, a micro signal, an identity card number, gender, age, registration time and the like.
3. The method for realizing store member tag extraction based on the internet platform as claimed in claim 1, wherein: all data relating to the member to be generated is categorized, including member consumption data, member activity data, points data, health data, etc.
4. The method for realizing store member tag extraction based on the internet platform as claimed in claim 3, wherein the detailed recording of all categories of member data comprises:
(1) consumption data: including consumption variety, variety brand, consumption quantity, amount, consumption date, payment mode, whether group purchase is available or not and the like;
(2) activity data: including the subject, date, reaction result after the activity, etc. of the participated activity;
(3) integration data: the method comprises the steps of point number, point time, point source, point exchange and point consumption details;
(4) health data: the store clerk inquires about the records and consults about the records each time the medicine is purchased.
5. The method for realizing member label extraction of the store based on the Internet platform as claimed in claim 4, wherein: according to different data categories and corresponding data statistics, carrying out keyword extraction on the data of the category according to a statistical result, and carrying out label extraction from the following four aspects:
firstly, living habits:
(1) dividing the member into gender and age groups according to the gender and the age of the member, and extracting a gender label (male, female or unknown) and an age group label (young, middle and old);
(2) extracting information of frequent purchase stores of the members according to the data statistical ranking of the consumption stores of the members;
(3) extracting member consumption time period information and activity information according to member consumption time statistics;
secondly, consumption habit:
(1) according to the brand statistics of consumer varieties, labels (first-line brands, second-line brands, three-line and four-line brands and the like) of brand grades purchased by members are extracted;
(2) counting and extracting whether the members are labels of frequent group purchasing users and general purchasing users according to the consumption data;
thirdly, behavior characteristics:
(1) according to the statistical ranking of the payment tests, extracting common payment mode labels (WeChat, Paibao, cash, medical insurance, Unionpay card swiping and the like) of the users;
(2) extracting activities frequently attended by the user, activity times and activity response rate according to data statistics of the attended activities;
fourthly, health diseases:
(1) according to the health data and the inquiry records of the purchased medicines, performing statistical analysis, and extracting the health condition information, the disease information and the chronic disease information of the user;
fifthly, member value:
(1) and counting and calculating the unit price of the customers according to the consumption amount and times, and extracting the consumption contribution degree labels (small single customers, single customers and large single customers) of the users.
6. The method for realizing member label extraction of the store based on the internet platform as claimed in claim 5, wherein: recording the extracted member feature keywords in a member tag library of a database, and identifying the member feature keywords as corresponding member feature tags; and carrying out statistical analysis on all relevant data of the members at regular time every month, extracting the characteristics of the member tags at regular time, updating the member tag library and keeping the dynamics of the member tag library.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811275909.2A CN111127052A (en) | 2018-10-31 | 2018-10-31 | Method for realizing store member label extraction based on Internet platform |
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| CN201811275909.2A CN111127052A (en) | 2018-10-31 | 2018-10-31 | Method for realizing store member label extraction based on Internet platform |
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| CN111127052A true CN111127052A (en) | 2020-05-08 |
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| CN201811275909.2A Withdrawn CN111127052A (en) | 2018-10-31 | 2018-10-31 | Method for realizing store member label extraction based on Internet platform |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085521A (en) * | 2020-08-19 | 2020-12-15 | 深圳前海托克马克科技有限公司 | System for managing and serving store members |
| CN114119068A (en) * | 2021-10-28 | 2022-03-01 | 武汉海云健康科技股份有限公司 | Intelligent analysis method and management platform for pharmacy enterprise WeChat customer group |
| CN115563185A (en) * | 2022-10-11 | 2023-01-03 | 广东智科信息技术发展有限公司 | One-card system for user information statistics |
-
2018
- 2018-10-31 CN CN201811275909.2A patent/CN111127052A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085521A (en) * | 2020-08-19 | 2020-12-15 | 深圳前海托克马克科技有限公司 | System for managing and serving store members |
| CN114119068A (en) * | 2021-10-28 | 2022-03-01 | 武汉海云健康科技股份有限公司 | Intelligent analysis method and management platform for pharmacy enterprise WeChat customer group |
| CN115563185A (en) * | 2022-10-11 | 2023-01-03 | 广东智科信息技术发展有限公司 | One-card system for user information statistics |
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Application publication date: 20200508 |