CN103377436A - System and method for recommending sales promotions - Google Patents
System and method for recommending sales promotionsDownload PDFInfo
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- CN103377436A CN103377436ACN2012101295646ACN201210129564ACN103377436ACN 103377436 ACN103377436 ACN 103377436ACN 2012101295646 ACN2012101295646 ACN 2012101295646ACN 201210129564 ACN201210129564 ACN 201210129564ACN 103377436 ACN103377436 ACN 103377436A
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Abstract
Description
Technical field
The present invention relates to a kind of advertising campaign commending system and method, particularly relate to a kind of advertising campaign commending system and a kind of advertising campaign recommend method that utilizes this advertising campaign commending system to realize that can recommend according to the keyword of user's input advertising campaign for the user.
Background technology
Along with the fast development of internet, increasing on-line shop has appearred on the net, because convenient and swift in the extreme, a lot of users select on-line shop to buy extensive stock.Now a lot of on-line shops all to have released a lot of advertising campaigns and advertisement, and have not all carried out commodity shopping guide's setting in traditional on-line shop for the user in order to attract clients, when causing the user to login on-line shop's selection commodity, in the face of a feast for the eyes commodity have no way of selecting.If the user wants to buy the commodity of the discounting in the advertising campaign, also need to check many advertising campaigns and advertisement after, just might find satisfied commodity, this shopping that can have influence on undoubtedly the user is experienced, and has also reduced user's shopping efficient.
Summary of the invention
The technical problem to be solved in the present invention is the recommendation that does not all have to carry out according to user's demand advertising campaign in order to overcome on-line shop in the prior art, the defective of user's shopping efficient is experienced, has been reduced in the shopping that causes having influence on the user, and a kind of advertising campaign commending system and a kind of advertising campaign recommend method that utilizes this advertising campaign commending system realization that can recommend according to the keyword of user's input advertising campaign for the user is provided.
The present invention solves above-mentioned technical matters by following technical proposals:
The invention provides a kind of advertising campaign commending system, its characteristics are that it comprises:
One server is used for setting up a tabulation of showing all advertising campaigns, includes the commodity classification under the commodity of the commodity of sales promotion and sales promotion in each advertising campaign;
One analysis module is used for analyzing the keyword that a targeted customer inputs, to export one or more commodity classifications;
One advertising campaign enquiry module is used for inquiring at least one advertising campaign that comprises a commodity classification these one or more commodity classifications that this analysis module exports from this tabulation, and the advertising campaign that goes out to this targeted customer's recommendation query.
When the targeted customer logins on-line shop's purchase commodity, the present invention can analyze the keyword of targeted customer's input, obtain the targeted customer and may need commodity classification under the commodity bought, again all advertising campaigns that comprise this commodity classification are all checked out, and the advertising campaign that goes out to targeted customer's recommendation query, thereby so that the user can buy the commodity that oneself needs fast, shortened user's shopping-time, the shopping that has promoted the user is experienced.
Preferably, this advertising campaign commending system also comprises browses logging modle, is used for the keyword that all users of record inputted and inputs commodity classification under the commodity of browsing behind each keyword;
This analysis module also is used for browsing logging modle from this and inquires the maximum one or more commodity classifications of number of visits behind this keyword that all users input this targeted customer's input, and should exporting by one or more commodity classifications of will inquiring.
Add up by keyword and input keyword historical viewings record afterwards that all users to login on-line shop inputted, and inquire the maximum one or more commodity classifications of number of visits behind this keyword that all users input this targeted customer input, just can judge more accurately the affiliated commodity classification of commodity that the user needs; And behind these one or more commodity classifications that this analysis module output inquires, this advertising campaign enquiry module just inquires at least one advertising campaign of a commodity classification in these one or more commodity classifications that comprise the output of this analysis module from this tabulation, and the advertising campaign that goes out to this targeted customer's recommendation query.Like this, utilize the means of data mining, so that the advertising campaign of recommending for this targeted customer is more accurate, also more satisfy user's demand.
Preferably, the quantity of the commodity classification of this analysis module output is two or three.
Preferably, this advertising campaign enquiry module also is used for recommending from big to small advertising campaign according to the merchandise discount dynamics.
Preferably, when the merchandise discount dynamics was identical, this advertising campaign enquiry module also was used for recommending from high to low advertising campaign according to conversion ratio.Conversion ratio herein refers to the ratio of the total degree browsed by all users by the order numbers that produces behind the commodity in the advertising campaign of user's actual purchase and this advertising campaign, such as the viewed mistake of acertain advertising campaign 100 times, thereby produced three orders and there are three commodity to be bought by the user in this advertising campaign, the conversion ratio of this advertising campaign is exactly 3 percent so.Therefore, conversion ratio is higher, illustrates that the commodity in the advertising campaign are also larger by the probability that the user buys, and the commodity in this advertising campaign are liked by the user also more.
Preferably, this analysis module is used for utilizing content matching technology and classification matching technique to come the keyword of analysis user input, with these one or more commodity classifications of output.
The present invention also aims to provide a kind of advertising campaign recommend method, its characteristics are that it utilizes above-mentioned advertising campaign commending system to realize that this advertising campaign recommend method may further comprise the steps:
S1, this server sets up this tabulation;
S2, this analysis module analyzes a keyword of targeted customer input, to export one or more commodity classifications;
S3, this advertising campaign enquiry module inquires at least one advertising campaign of a commodity classification in these one or more commodity classifications that comprise the output of this analysis module from this tabulation, and the advertising campaign that goes out to this targeted customer's recommendation query.
Preferably, this advertising campaign commending system also comprises and browses logging modle, step S2Also comprise a recording step: this is browsed logging modle and records the keyword that all users inputted and input commodity classification under the commodity of browsing behind each keyword before;
Step S2In this analysis module browse from this and inquire the maximum one or more commodity classifications of number of visits behind this keyword that all users input this targeted customer input logging modle, and should exporting by one or more commodity classifications of will inquiring.
Preferably, step S2In the quantity of commodity classification of this analysis module output be two or three.
Preferably, step S3In this advertising campaign enquiry module also recommend from big to small advertising campaign according to the merchandise discount dynamics.
Preferably, when the merchandise discount dynamics was identical, this advertising campaign enquiry module was also recommended advertising campaign from high to low according to conversion ratio.
Preferably, step S2In this analysis module utilize content matching technology and classification matching technique to come the keyword of analysis user input, with these one or more commodity classifications of output.
Positive progressive effect of the present invention is: the present invention can analyze the keyword of user's input, obtain the affiliated commodity classification of commodity that the user may need, and recommend to comprise the advertising campaign of this commodity classification to the user, thereby so that the user can buy the commodity that oneself needs fast, shortened user's shopping-time, the shopping that has promoted the user is experienced.
Description of drawings
Fig. 1 is the structural drawing of the advertising campaign commending system of a preferred embodiment of the present invention.
Fig. 2 is the process flow diagram of the advertising campaign recommend method of a preferred embodiment of the present invention.
Embodiment
Provide preferred embodiment of the present invention below in conjunction with accompanying drawing, to describe technical scheme of the present invention in detail.
As shown in Figure 1, the advertising campaign commending system of preferred embodiment of the present invention comprises a server 1, an analysis module 2, an advertising campaign enquiry module 3.
Wherein this server 1 is set up a tabulation of showing advertising campaigns all in the on-line shop, also show the commodity of sales promotion and the affiliated commodity classification of commodity of sales promotion in each advertising campaign, this analysis module 2 will be analyzed the keyword of targeted customer input, to export one or more commodity classifications, this advertising campaign enquiry module 3 just inquires at least one advertising campaign of a commodity classification in these one or more commodity classifications that comprise 2 outputs of this analysis module from this tabulation, and the advertising campaign that goes out to this targeted customer's recommendation query.
When the targeted customer logins on-line shop's purchase commodity, big city finds out the commodity that oneself need by the input keyword, therefore this analysis module 2 just can be analyzed the keyword of this targeted customer's input, thereby obtains the affiliated commodity classification of commodity that the user need to buy.As shown in Figure 1, for so that this analysis module 2 more accurately analyzes the classification under the commodity that the user needs, this advertising campaign commending system can also add browses logging modle 4, this browses keyword that logging modle 4 can be first input all users of login on-line shop and the historical viewings record after the input keyword is added up, so just can set up the corresponding relation of each keyword and at least one commodity classification, and can count the number of visits that all users input the commodity classification of browsing behind some keywords.
All users input the maximum commodity classification of number of visits behind the keyword, just prove the common choice of most people, therefore also just very might be the commodity classifications under the commodity that need of this targeted customer.For example, after after the targeted customer logins on-line shop, inputting " Pepsi " this keyword, if this analysis module 2 is directly analyzed according to " Pepsi " this keyword, will obtain simultaneously " Pepsi Cola " and " Pepsi cold-proof underwear " these two commodity classifications, but just can learn by this record of browsing logging modle 4, overwhelming majority users are behind input " Pepsi " this keyword, and actual what want to buy is " Pepsi Cola " in the soda.Therefore, utilize this record of browsing logging modle 4, this analysis module 2 just can more accurately find out the user and want type of merchandize under the commodity bought.
So, after this targeted customer inputs a keyword, this analysis module 2 just can be browsed from this and inquire the maximum one or more commodity classifications of number of visits behind this keyword that all users input this targeted customer input logging modle 4, and these one or more commodity classification outputs that will inquire, and advertising campaign be inquired about and be recommended to this advertising campaign enquiry module 3 just can according to the commodity classification of output.
Owing to browse in the logging modle 4 at this, the commodity classification that some keywords are corresponding may have a lot, and for the relatively less commodity classification of those numbers of visits, substantially all be the selection of a few users, therefore a plurality of commodity classifications of these analysis module 2 outputs just can be maximum two or three of number of visits, so also just can obtain more accurately the affiliated commodity classification of commodity of user's needs.
Certainly, after inquiring at least one advertising campaign, this advertising campaign enquiry module 3 can also be recommended according to the discount dynamics order from big to small of the commodity in each advertising campaign, and when the discount dynamics was identical, this pin activity query module 3 can also be recommended advertising campaign according to conversion ratio order from high to low.
Wherein this analysis module 2 can utilize content matching technology and classification matching technique to analyze, and these all belongs to the known technology of this area, just repeat no more at this when the keyword of analysis user input.
As shown in Figure 2, the present invention utilizes the advertising campaign recommend method of the advertising campaign commending system realization of present embodiment may further comprise the steps:
Although more than described the specific embodiment of the present invention, it will be understood by those of skill in the art that these only illustrate, protection scope of the present invention is limited by appended claims.Those skilled in the art can make various changes or modifications to these embodiments under the prerequisite that does not deviate from principle of the present invention and essence, but these changes and modification all fall into protection scope of the present invention.
Claims (12)
1. advertising campaign commending system is characterized in that it comprises:
One server is used for setting up a tabulation of showing all advertising campaigns, includes the commodity classification under the commodity of the commodity of sales promotion and sales promotion in each advertising campaign;
One analysis module is used for analyzing the keyword that a targeted customer inputs, to export one or more commodity classifications;
One advertising campaign enquiry module is used for inquiring at least one advertising campaign that comprises a commodity classification these one or more commodity classifications that this analysis module exports from this tabulation, and the advertising campaign that goes out to this targeted customer's recommendation query.
2. advertising campaign commending system as claimed in claim 1, it is characterized in that, this advertising campaign commending system also comprises browses logging modle, is used for the keyword that all users of record inputted and inputs commodity classification under the commodity of browsing behind each keyword;
This analysis module also is used for browsing logging modle from this and inquires the maximum one or more commodity classifications of number of visits behind this keyword that all users input this targeted customer's input, and should exporting by one or more commodity classifications of will inquiring.
3. advertising campaign commending system as claimed in claim 1 or 2 is characterized in that, the quantity of the commodity classification of this analysis module output is two or three.
4. advertising campaign commending system as claimed in claim 1 or 2 is characterized in that, this advertising campaign enquiry module also is used for recommending from big to small advertising campaign according to the merchandise discount dynamics.
5. advertising campaign commending system as claimed in claim 4 is characterized in that, when the merchandise discount dynamics was identical, this advertising campaign enquiry module also was used for recommending from high to low advertising campaign according to conversion ratio.
6. advertising campaign commending system as claimed in claim 1 is characterized in that, this analysis module is used for utilizing content matching technology and classification matching technique to come the keyword of analysis user input, with these one or more commodity classifications of output.
7. an advertising campaign recommend method is characterized in that, it utilizes advertising campaign commending system as claimed in claim 1 to realize that this advertising campaign recommend method may further comprise the steps:
S1, this server sets up this tabulation;
S2, this analysis module analyzes a keyword of targeted customer input, to export one or more commodity classifications;
S3, this advertising campaign enquiry module inquires at least one advertising campaign of a commodity classification in these one or more commodity classifications that comprise the output of this analysis module from this tabulation, and the advertising campaign that goes out to this targeted customer's recommendation query.
8. advertising campaign recommend method as claimed in claim 7 is characterized in that, this advertising campaign commending system also comprises browses logging modle, step S2Also comprise a recording step: this is browsed logging modle and records the keyword that all users inputted and input commodity classification under the commodity of browsing behind each keyword before;
Step S2In this analysis module browse from this and inquire the maximum one or more commodity classifications of number of visits behind this keyword that all users input this targeted customer input logging modle, and should exporting by one or more commodity classifications of will inquiring.
9. such as claim 7 or 8 described advertising campaign recommend methods, it is characterized in that step S2In the quantity of commodity classification of this analysis module output be two or three.
10. such as claim 7 or 8 described advertising campaign recommend methods, it is characterized in that step S3In this advertising campaign enquiry module also recommend from big to small advertising campaign according to the merchandise discount dynamics.
11. advertising campaign recommend method as claimed in claim 10 is characterized in that, when the merchandise discount dynamics was identical, this advertising campaign enquiry module was also recommended advertising campaign from high to low according to conversion ratio.
12. advertising campaign recommend method as claimed in claim 7 is characterized in that step S2In this analysis module utilize content matching technology and classification matching technique to come the keyword of analysis user input, with these one or more commodity classifications of output.
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| CN2012101295646ACN103377436A (en) | 2012-04-27 | 2012-04-27 | System and method for recommending sales promotions |
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| CN2012101295646ACN103377436A (en) | 2012-04-27 | 2012-04-27 | System and method for recommending sales promotions |
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105138680A (en)* | 2015-09-14 | 2015-12-09 | 郑州悉知信息科技股份有限公司 | Keyword classification method and device and product search method and device |
| CN106127504A (en)* | 2016-06-12 | 2016-11-16 | 北京三快在线科技有限公司 | The method for pushing of electronic article certificate, generation method, device, user terminal and server |
| CN106294342A (en)* | 2015-05-12 | 2017-01-04 | 阿里巴巴集团控股有限公司 | A kind of generation method and apparatus of pushed information |
| CN106372131A (en)* | 2016-08-25 | 2017-02-01 | 广西小草信息产业有限责任公司 | Travel information processing system and processing method |
| CN106469403A (en)* | 2015-08-14 | 2017-03-01 | 腾讯科技（深圳）有限公司 | A kind of information displaying method and device |
| CN106504068A (en)* | 2016-11-03 | 2017-03-15 | 北京挖玖电子商务有限公司 | marketing system |
| CN106651531A (en)* | 2016-12-29 | 2017-05-10 | 江西博瑞彤芸科技有限公司 | Configuration method for page display information |
| CN107133358A (en)* | 2017-05-27 | 2017-09-05 | 郑州悉知信息科技股份有限公司 | A kind of keyword classification method and device |
| CN107403359A (en)* | 2017-07-20 | 2017-11-28 | 义乌洞开网络科技有限公司 | A kind of accurate commending system of electric business platform commodity and its method |
| CN108027245A (en)* | 2016-04-22 | 2018-05-11 | 深圳市赛亿科技开发有限公司 | The position query method and device of commodity |
| CN108229997A (en)* | 2016-12-21 | 2018-06-29 | 天脉聚源（北京）科技有限公司 | A kind of method and system of internet shopping promotion |
| CN110020131A (en)* | 2017-10-31 | 2019-07-16 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus arranging commodity |
| CN110570256A (en)* | 2019-09-16 | 2019-12-13 | 中国联合网络通信集团有限公司 | Sales promotion activity publishing method and equipment |
| CN111415182A (en)* | 2019-01-07 | 2020-07-14 | 北京京东尚科信息技术有限公司 | A kind of information push method and apparatus, equipment, storage medium |
| CN115187177A (en)* | 2022-09-07 | 2022-10-14 | 国连科技(浙江)有限公司 | Method and device for managing warehouse of goods of merchants in sales promotion activities |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102110267A (en)* | 2009-12-24 | 2011-06-29 | 艺墨文化创意管理股份有限公司 | Method and system for providing discount information |
- 2012
- 2012-04-27CNCN2012101295646Apatent/CN103377436A/enactivePending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102110267A (en)* | 2009-12-24 | 2011-06-29 | 艺墨文化创意管理股份有限公司 | Method and system for providing discount information |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106294342A (en)* | 2015-05-12 | 2017-01-04 | 阿里巴巴集团控股有限公司 | A kind of generation method and apparatus of pushed information |
| CN106469403B (en)* | 2015-08-14 | 2023-04-18 | 腾讯科技（深圳）有限公司 | Information display method and device |
| CN106469403A (en)* | 2015-08-14 | 2017-03-01 | 腾讯科技（深圳）有限公司 | A kind of information displaying method and device |
| CN105138680A (en)* | 2015-09-14 | 2015-12-09 | 郑州悉知信息科技股份有限公司 | Keyword classification method and device and product search method and device |
| CN108027245A (en)* | 2016-04-22 | 2018-05-11 | 深圳市赛亿科技开发有限公司 | The position query method and device of commodity |
| CN106127504A (en)* | 2016-06-12 | 2016-11-16 | 北京三快在线科技有限公司 | The method for pushing of electronic article certificate, generation method, device, user terminal and server |
| CN106372131A (en)* | 2016-08-25 | 2017-02-01 | 广西小草信息产业有限责任公司 | Travel information processing system and processing method |
| CN106504068A (en)* | 2016-11-03 | 2017-03-15 | 北京挖玖电子商务有限公司 | marketing system |
| CN108229997A (en)* | 2016-12-21 | 2018-06-29 | 天脉聚源（北京）科技有限公司 | A kind of method and system of internet shopping promotion |
| CN106651531A (en)* | 2016-12-29 | 2017-05-10 | 江西博瑞彤芸科技有限公司 | Configuration method for page display information |
| CN107133358A (en)* | 2017-05-27 | 2017-09-05 | 郑州悉知信息科技股份有限公司 | A kind of keyword classification method and device |
| CN107403359A (en)* | 2017-07-20 | 2017-11-28 | 义乌洞开网络科技有限公司 | A kind of accurate commending system of electric business platform commodity and its method |
| CN110020131B (en)* | 2017-10-31 | 2022-06-07 | 北京京东尚科信息技术有限公司 | Method and device for arranging commodities |
| CN110020131A (en)* | 2017-10-31 | 2019-07-16 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus arranging commodity |
| CN111415182A (en)* | 2019-01-07 | 2020-07-14 | 北京京东尚科信息技术有限公司 | A kind of information push method and apparatus, equipment, storage medium |
| CN110570256A (en)* | 2019-09-16 | 2019-12-13 | 中国联合网络通信集团有限公司 | Sales promotion activity publishing method and equipment |
| CN115187177A (en)* | 2022-09-07 | 2022-10-14 | 国连科技(浙江)有限公司 | Method and device for managing warehouse of goods of merchants in sales promotion activities |
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