CN109559152A - A kind of network marketing method, system and computer storage medium - Google Patents
A kind of network marketing method, system and computer storage medium Download PDFInfo
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- CN109559152A CN109559152A CN201811247254.8A CN201811247254A CN109559152A CN 109559152 A CN109559152 A CN 109559152A CN 201811247254 A CN201811247254 A CN 201811247254A CN 109559152 A CN109559152 A CN 109559152A
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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
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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 present invention provides a kind of network marketing method, system and computer storage medium, method includes the following steps: S1) user is collected in the public information of internet, the public information includes the behavioural information of user and the essential information of user；S2 category division) is carried out to the user by the public information, to establish class of subscriber label to each user；S3 the mapping relations between the class of subscriber label and service provider) are established；And S4) according to the mapping relations corresponding service provider is matched to the user.The network marketing method provided according to the present invention can be that user is efficient, rapidly matches suitable Engineering Service quotient.
Description
Technical field
The invention belongs to information network orientation grass roots marketing techniques more particularly to a kind of network marketing method, system and computers
Storage medium.
Background technique
Being constantly progressive and rapidly expand with Internet technology, network is brought greatly to people's lives and work
Convenience, for example, more and more users can stay indoors by internet hunt, obtain its needs service.
However, in the related technology, user is in search related service (for example, user needs display industry Engineering Service) on the net
When, what the various users for having different demands were faced is same general industry information provision (such as general display industry supply letter
Breath), can not be according to the particular demands of user, the automatic related service for recommending to be suitble to user, user needs various fuzzy big
Oneself really necessary information is selected in amount information, greatly wastes the time of user.
Summary of the invention
The present invention is at least to solve one of the technical problems existing in the prior art, provides a kind of network marketing method, is
System and computer storage medium can be that user is efficient, rapidly matches suitable Engineering Service quotient according to this method.
For this purpose, the present invention provides a kind of network marketing method comprising following steps:
S1 user) is collected in the public information of internet, and the public information includes behavioural information and the user of user
Essential information；
S2 category division) is carried out to the user according to the public information, to establish class of subscriber mark to each user
Label；
S3 the mapping relations between the class of subscriber label and service provider) are established；And
S4) corresponding service provider is matched to the user according to the mapping relations.
In some embodiments of the invention, the behavioural information includes: the channel that user enters website, user's search
Keyword, the History Order of user, user's probe, the facility information of user to view Internet, the information of the daily concern of user, with
And the APP of user opens frequency information；The essential information includes: the gender of user, the name of user, the age of user, with
And the geographical location information of user.
In some embodiments of the invention, the step S1) include:
S11) public information of user is parsed, to extract the keyword of user behavior；And
S12) keyword of extraction is reported.
In some embodiments of the invention, the category division is according to the industry attribute of user, professional attributes, conclusion of the business
What intention degree carried out.
In some embodiments of the invention, the step S2) it include: to pass through machine learning algorithm model first, according to
The public information for the user being collected into carries out clustering processing to user, then according to operation result, to the affiliated class of user
It is not marked or updates, to establish the class of subscriber label.
In some embodiments of the invention, further include step S5) the storage class of subscriber label, and be periodically based on depositing
The newest class of subscriber label of storage, updates the machine learning algorithm model.
In some embodiments of the invention, the class of subscriber label includes: business premises owner, stadiums visitor
Family, media provider, quotient surpass management, residential property management, restaurant storekeeper, convenience store storekeeper, professional user, screen owner, intention visitor
Family, high-value user, medium value user, low value user, high satisfaction user, middle Equal satisfaction degree user, low satisfaction are used
Family, price-sensitive user, quality priority user and rate service user.
The step S4) include:
S41 the user) is pushed into corresponding service provider according to the mapping relations；Or
S42 corresponding service provider) is pushed to the user according to the mapping relations.
Meanwhile the present invention also provides a kind of network marketing systems comprising:
User information collection module, for collecting user in the public information of internet, the public information includes user
Behavioural information and user essential information；
User's categorization module, for carrying out category division to the user according to the public information, to each user
Establish class of subscriber label；
Mapping relations module, the mapping relations for establishing between the class of subscriber label and service provider；And
Matching module, for matching corresponding service provider to the user according to the mapping relations.
Further, the present invention also provides a kind of computer storage medium, the computer storage medium is stored with calculating
Machine program realizes method described in any of the above item when the computer program is executed by processor.
Network marketing method, system and the computer storage medium provided according to the present invention, by collecting user online
Public information, then classified according to the disclosure information to user, and establish class of subscriber label, so establish user with
Mapping relations between service provider, and be that user matches corresponding service provider, therefore, this hair according to the mapping relations
It is bright to recommend suitable service provider automatically according to the demand of user for user, without user oneself various fuzzy
Information in selected, for user efficiently, rapidly match suitable Engineering Service quotient.
Additional aspect and advantage of the invention will be set forth in part in the description, and will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Fig. 1 is the flow diagram of network marketing method provided by one embodiment of the present invention；And
Fig. 2 is the structural schematic block diagram of network marketing system provided by one embodiment of the present invention.
Specific embodiment
In order to which the technical problems, technical solutions and beneficial effects solved by the present invention is more clearly understood, below in conjunction with
Accompanying drawings and embodiments, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
As shown in Figure 1, a specific embodiment of the invention provides a kind of network marketing method comprising following steps:
S1 user) is collected in the public information of internet, and the public information includes behavioural information and the user of user
Essential information；
S2 category division) is carried out to the user according to the public information, to establish class of subscriber mark to each user
Label；
S3 the mapping relations between the class of subscriber label and service provider) are established；And
S4) corresponding service provider is matched to the user according to the mapping relations.
Specifically, in step S1), users can be collected by various common Method and kit fors on the internet
Public information, it is not specifically limited to this by the present invention.For example, user can be extracted by various web crawlers tools on network
Various actions track, and then obtain user's public information on the internet.Exist it is, of course, understood that collecting user
Public information on internet need to be collected information above in the case where obtaining user's license.
Of course, it is possible to the various public informations of user on the internet be collected, for example, behavioural information, essential information etc..This
Invention does not limit behavioural information and essential information specifically, can be various actions information and various essential informations.
For example, in some embodiments of the invention, the behavioural information may include: the channel that user enters website, user's search
Keyword, the History Order of user, user's probe, the facility information of user to view Internet, the information of the daily concern of user,
And the APP of user opens frequency information；The essential information may include: the gender of user, the name of user, the year of user
The geographical location information of age and user.It is understood that the public information of the user collected is more, that classifies is accurate
Degree is higher,
Further, for convenient for subsequent category division, in some embodiments of the invention, the step S1) include:
S11) public information of user is parsed, to extract the keyword of user behavior；And
S12) keyword of extraction is reported.
That is, in this embodiment, by obtaining the keyword of user behavior, and the keyword is reported to subsequent user
The execution module of category division, for example, user's categorization module, in turn, user's categorization module can be non-according to determining keyword
Category division easily often is carried out to user.
It is understood that can be parsed using various methods to the public information of user, the present invention does not have this
It is specifically limited, as long as the keyword of user behavior can be extracted.For example, behavior rail can be passed through for the behavioural information of user
The methods of mark analysis obtains the interested information of user, and concludes to the information, to extract keyword；For the base of user
This information then can directly extract corresponding keyword.
In step S2), category division can be carried out to the user according to the public information using various methods, this
It is not specifically limited to this for invention.For example, in some embodiments of the invention, the category division is the industry according to user
Attribute, professional attributes, conclusion of the business intention degree carry out.That is, in this embodiment, it is other user can be divided into three categories, it may be assumed that industry class
Not, career field, conclusion of the business intention degree classification.Certainly, different small classifications can also be divided into each classification.For example, the meaning that strikes a bargain
It may include that conclusion of the business intention degree is high, in conclusion of the business intention degree and the low classification of conclusion of the business intention degree into degree classification.
User is being obtained after the public information of internet, in some embodiments of the invention, the step S2) packet
It includes: first by machine learning algorithm model, clustering processing being carried out to user according to the public information for the user being collected into,
Then according to operation result, the generic of user is marked or is updated, to establish the class of subscriber label.That is,
In this embodiment, user is divided by machine learning algorithm model.
It is understood that it is known to those skilled in the art how to establish machine learning algorithm model, the present invention couple
This is repeated no more.It can also be appreciated that present invention spy no for the algorithm logic of specific machine learning algorithm model
Different limitation can be various available algorithm models, as long as corresponding class of subscriber can be calculated according to the algorithm model i.e.
It can.For example, in some embodiments of the invention, after the public information of collection is extracted keyword, first being established with the user
Association carries out classified packets to the keyword of extraction, and using the keyword as the index of class of subscriber, is built according to index weights
The algorithm model that vertical class of subscriber divides.
Certainly, machine learning algorithm model can be adjusted according to actual needs, and it is not specifically limited to this by the present invention.
For example, in some embodiments, the public information of user on the internet is carried out after parsing obtains corresponding keyword, it can be with
The weight proportion of each keyword in machine learning algorithm model is adjusted, and then operation result can be adjusted in real time.
It is understood that the present invention is not particularly limited class of subscriber label, can set according to the actual situation
Fixed multiple class of subscriber labels.For example, in some embodiments of the invention, the class of subscriber label includes: business premises
Owner, stadiums client, media provider, quotient surpass management, residential property management, restaurant storekeeper, convenience store storekeeper, professional user,
Screen owner, intention client, high-value user, medium value user, low value user, high satisfaction user, middle Equal satisfaction degree
User, low satisfaction user, price-sensitive user, quality priority user and rate service user.
Further, in some embodiments of the invention, further include step S5) the storage class of subscriber label, and
The periodically newest class of subscriber label based on storage, updates the machine learning algorithm model.In turn, in this embodiment, exist
After method operation a period of time provided by the invention, its machine learning algorithm model can be regularly updated, and then improve the present invention
The precision of the network marketing method of offer.
It is understood that step S3) in the class of subscriber label and service provider between mapping relations can
To present by various modes, for example, as shown in the table, presentation user's class label can be mentioned with service by way of list
For the mapping relations between quotient.
| Class of subscriber | Service provider |
| Price-sensitive user | Meet the service provider of user price section consumption habit |
| Quality priority user | The high service provider of user satisfaction |
| Rate service user | Geographical location and the preferential service provider of response time |
| Ordinary user | Generic services provider |
It is understood that it is understood that can be established between user and service provider using various methods
Mapping relations, it is not specifically limited to this by the present invention.For example, service provider can oneself in industry service intermediary platform
The preference of oneself is provided, oneself classifies to oneself, in turn, according to service provider oneself provide information, can with it is right
The user of classification is answered to match, to form mapping relations.In another example can also using similar method to service provider into
Row classification, establishes the class label of service provider, in turn, according to the classification mark of the class label of service provider and user
Label can easily establish the mapping relations between user and service provider, and then can be the corresponding clothes of user's matching
Be engaged in provider.
It can be to state not by the mapping relations after the mapping relations being successfully established between user and service provider
Generic user matches engineering quotient.The present invention is not particularly limited matched process and matching result, can adopt
With various common matching process and matching result.For example, in some embodiments of the invention, the step S4) include:
S41 the user) is pushed into corresponding service provider according to the mapping relations；Or
S42 corresponding service provider) is pushed to the user according to the mapping relations.
In turn, in this embodiment, matching process may is that intermediary pushes to and user according to active user's class label
The matched engineering quotient platform of class label (i.e. the corresponding service provider of mapping relations), the intermediary can be computer, people
Work or artificial and computer combine.Matching result can be, advertisement interface display or push in user to view Internet
Matched engineering quotient, user open and show or push matched engineering quotient etc. on the advertisement interface shown when cell phone application.
Certainly, public information of the present invention according to the user of collection on the internet carries out category division to user, because
This, it is to be understood that each user may generate multiple class of subscriber labels, therefore, can match multiple correspondences to user
Service provider.Meanwhile as the case may be, it can also be obtained each by adjusting specific machine learning algorithm model
User generate multiple class of subscriber labels a possibility that ranking, and then according to the ranking give user match service provider.
Meanwhile as shown in Fig. 2, some embodiments of the present invention also provide a kind of network marketing system comprising:
User information collection module, for collecting user in the public information of internet, the public information includes user
Behavioural information and user essential information；
User's categorization module, for carrying out category division to the user according to the public information, to each user
Establish class of subscriber label；
Mapping relations module, the mapping relations for establishing between the class of subscriber label and service provider；And
Matching module, for matching corresponding service provider to the user according to the mapping relations.
Further, the present invention also provides a kind of computer storage medium, the computer storage medium is stored with calculating
Machine program realizes method described in any of the above item when the computer program is executed by processor.
Network marketing method, system and the computer storage medium provided according to the present invention, by collecting user online
Public information, then classified according to the disclosure information to user, and establish class of subscriber label, so establish user with
Mapping relations between service provider, and be that user matches corresponding service provider, therefore, this hair according to the mapping relations
It is bright to recommend suitable service provider automatically according to the demand of user for user, without user oneself various fuzzy
Information in selected, for user efficiently, rapidly match suitable Engineering Service quotient.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " illustrative examples ",
The description of " example ", " specific example " or " some examples " etc. means specific features described in conjunction with this embodiment or example, knot
Structure, material or feature are included at least one embodiment or example of the invention.In the present specification, to above-mentioned term
Schematic representation may not refer to the same embodiment or example.Moreover, specific features, structure, material or the spy of description
Point can be combined in any suitable manner in any one or more of the embodiments or examples.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that: not
A variety of change, modification, replacement and modification can be carried out to these embodiments in the case where being detached from the principle of the present invention and objective, this
The range of invention is defined by the claims and their equivalents.
Claims (10)
1. a kind of network marketing method, which comprises the following steps:
S1 user) is collected in the public information of internet, and the public information includes the behavioural information of user and the base of user
This information；
S2 category division) is carried out to the user according to the public information, to establish class of subscriber label to each user；
S3 the mapping relations between the class of subscriber label and service provider) are established；And
S4) corresponding service provider is matched to the user according to the mapping relations.
2. the method according to claim 1, wherein the behavioural information includes: the channel that user enters website,
The keyword of user's search, the History Order of user, user's probe, the facility information of user to view Internet, the daily pass of user
The information of note and the APP of user open frequency information；The essential information includes: the gender of user, and the name of user is used
The age at family and the geographical location information of user.
3. the method according to claim 1, wherein the step S1) include:
S11) public information of user is parsed, to extract the keyword of user behavior；And
S12) keyword of extraction is reported.
4. the method according to claim 1, wherein the category division is according to the industry attribute of user, specially
Industry attribute, conclusion of the business intention degree carry out.
5. the method according to claim 1, wherein the step S2) it include: to pass through machine learning algorithm first
Model carries out clustering processing to user according to the public information for the user being collected into, then according to operation result, to user
Generic be marked or update, to establish the class of subscriber label.
6. according to the method described in claim 5, it is characterized in that, further include step S5) the storage class of subscriber label, and
The periodically newest class of subscriber label based on storage, updates the machine learning algorithm model.
7. -6 any method according to claim 1, which is characterized in that the class of subscriber label includes: business premises
Owner, stadiums client, media provider, quotient surpass management, residential property management, restaurant storekeeper, convenience store storekeeper, professional user,
Screen owner, intention client, high-value user, medium value user, low value user, high satisfaction user, middle Equal satisfaction degree
User, low satisfaction user, price-sensitive user, quality priority user and rate service user.
8. the method according to claim 1, wherein the step S4) include:
S41 the user) is pushed into corresponding service provider according to the mapping relations；Or
S42 corresponding service provider) is pushed to the user according to the mapping relations.
9. a kind of network marketing system characterized by comprising
User information collection module, for collecting user in the public information of internet, the public information includes the row of user
For information and the essential information of user；
User's categorization module, for carrying out category division to the user according to the public information, to be established to each user
Class of subscriber label；
Mapping relations module, the mapping relations for establishing between the class of subscriber label and service provider；And
Matching module, for matching corresponding service provider to the user according to the mapping relations.
10. a kind of computer storage medium, which is characterized in that the computer storage medium is stored with computer program, described
The method according to claim 1 is realized when computer program is executed by processor.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111209490A (en) * | 2020-04-24 | 2020-05-29 | 深圳市爱聊科技有限公司 | Friend-making recommendation method based on user information, electronic device and storage medium |
| CN111242723A (en) * | 2020-01-02 | 2020-06-05 | 平安科技（深圳）有限公司 | User child and child condition judgment method, server and computer readable storage medium |
| CN111598648A (en) * | 2020-04-16 | 2020-08-28 | 上海源慧信息科技股份有限公司 | Full-link online marketing method based on fast-moving industrial commodities |
| CN112016939A (en) * | 2020-10-19 | 2020-12-01 | 耀方信息技术(上海)有限公司 | Automatic maintenance user system |
| CN114004641A (en) * | 2021-10-29 | 2022-02-01 | 广州智会云科技发展有限公司 | Live broadcast accurate drainage method and system based on big data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108038237A (en) * | 2017-12-27 | 2018-05-15 | 广州市云润大数据服务有限公司 | A kind of information recommendation method and system |
| CN108038251A (en) * | 2017-12-29 | 2018-05-15 | 广州市海捷计算机科技有限公司 | A kind of data analysis push platform based on big data and development of Mobile Internet technology |
| CN108256119A (en) * | 2018-02-14 | 2018-07-06 | 北京方正阿帕比技术有限公司 | A kind of construction method of resource recommendation model and the resource recommendation method based on the model |
-
2018
- 2018-10-24 CN CN201811247254.8A patent/CN109559152A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108038237A (en) * | 2017-12-27 | 2018-05-15 | 广州市云润大数据服务有限公司 | A kind of information recommendation method and system |
| CN108038251A (en) * | 2017-12-29 | 2018-05-15 | 广州市海捷计算机科技有限公司 | A kind of data analysis push platform based on big data and development of Mobile Internet technology |
| CN108256119A (en) * | 2018-02-14 | 2018-07-06 | 北京方正阿帕比技术有限公司 | A kind of construction method of resource recommendation model and the resource recommendation method based on the model |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111242723A (en) * | 2020-01-02 | 2020-06-05 | 平安科技（深圳）有限公司 | User child and child condition judgment method, server and computer readable storage medium |
| CN111598648A (en) * | 2020-04-16 | 2020-08-28 | 上海源慧信息科技股份有限公司 | Full-link online marketing method based on fast-moving industrial commodities |
| CN111209490A (en) * | 2020-04-24 | 2020-05-29 | 深圳市爱聊科技有限公司 | Friend-making recommendation method based on user information, electronic device and storage medium |
| CN112016939A (en) * | 2020-10-19 | 2020-12-01 | 耀方信息技术(上海)有限公司 | Automatic maintenance user system |
| CN114004641A (en) * | 2021-10-29 | 2022-02-01 | 广州智会云科技发展有限公司 | Live broadcast accurate drainage method and system based on big data |
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