CN110619546A - Implementation scheme for solving high throughput of directional ticket issuing - Google Patents
Implementation scheme for solving high throughput of directional ticket issuing Download PDFInfo
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- CN110619546A CN110619546A CN201910856468.3A CN201910856468A CN110619546A CN 110619546 A CN110619546 A CN 110619546A CN 201910856468 A CN201910856468 A CN 201910856468A CN 110619546 A CN110619546 A CN 110619546A
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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
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
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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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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Abstract
An implementation of solving high throughput for directed instrument issuance, the implementation comprising the steps of: s1, collecting order information of the user in a historical time period; s2, collecting evaluation information of the user in a historical time period; s3, judging the risk level of the user by combining the order and the poor evaluation information; s4, determining the shopping tendency of the user according to the real-time operation behavior of the user in the platform; s5, recommending commodities to users meeting shopping requirements; and S6, pushing the relevant coupons to the users meeting the shopping requirements. According to the method, the shopping tendency and the risk of the user group are accurately judged, so that the credit of a merchant is not influenced, and the UV and order quantity of the platform are increased.
Description
Technical Field
The invention relates to the technical field of E-commerce promotion, in particular to an implementation scheme for solving the problem of high throughput of directional ticket issuing.
Background
In an electronic commerce system, it is often necessary to promote the flow of websites and promote the sale of goods through various promotion benefits. In enterprise-level e-commerce systems, the promotional function is often its short board. The e-commerce software system usually adopts a personalized customized development mode to realize the promotion policy of e-commerce, and for example, for presenting a commodity coupon, the e-commerce software system needs to push corresponding commodity coupon information to a target user group to prompt users to generate more behaviors, such as browsing shop commodities, consulting commodity prices or placing orders and purchasing. In the existing coupon pushing, a user group cannot be accurately positioned, the amount of UV and orders of a platform can be increased only through a large number of sending modes, the risk of the user group cannot be well judged, and the normal reputation of a shop is affected due to the fact that risk orders are generated.
In order to solve the above problems, the present application proposes an implementation solution for solving the high throughput of directional ticket issuing.
Disclosure of Invention
Objects of the invention
In order to solve the technical problems in the background technology, the invention provides an implementation scheme for solving the high throughput of directional coupon issuing, and by accurately judging the shopping tendency and the risk of a user group, the credit of a merchant is not influenced, and the UV and order quantity of a platform are increased.
(II) technical scheme
To solve the above problem, the present invention provides an implementation solution for solving high throughput of directional ticket issuing, which includes the following steps:
s1, collecting order information of the user in a historical time period;
s2, collecting evaluation information of the user in a historical time period;
s3, judging the risk level of the user by combining the order and the poor evaluation information;
s4, determining the shopping tendency of the user according to the real-time operation behavior of the user in the platform;
s5, recommending commodities to users meeting shopping requirements;
and S6, pushing the relevant coupons to the users meeting the shopping requirements.
Preferably, the step S1 collects the order information of the user in the historical time period, including browsing information, shopping order information and completed transaction order information.
Preferably, the step S2 collects evaluation information of the user in a historical time period, including total evaluation information, good evaluation information, bad evaluation information, and evaluation change information.
Preferably, the step S3 judges the risk level of the user according to the order and the bad comment information, and sets three risk level users of high-medium-low for the user population by comparing the comment information with the order information.
Preferably, the step S4 is to determine the shopping tendency of the user according to the real-time operation behavior of the user in the platform, and includes browsing information judgment, purchasing information and comment information.
Preferably, in step S5, the user who meets the shopping requirement is recommended the commodity, and the user with low risk level is recommended according to the shopping tendency.
Preferably, in the step S6, in the process of pushing the relevant coupons to the users meeting the shopping requirements, the maximum amount coupons are screened according to the shopping tendency of the users, and the coupons are pushed to the users in a platform message or a mobile phone short message manner.
The technical scheme of the invention has the following beneficial technical effects: the method comprises the steps of determining a low-risk user group meeting shopping requirements by purchasing order information in a user historical time period and judging user risk levels according to orders and evaluation information, judging the shopping requirements and the shopping tendencies of users according to the real-time operation behaviors of the user group in a platform, recommending commodities for the users according to the shopping tendencies and the shopping requirements of the users, and pushing preferential ways and preferential papers of the commodities to potential user groups so as to improve the UV and order quantity of the platform.
Drawings
Fig. 1 is a schematic structural diagram of an implementation scheme for solving the high throughput of directional coupon issuing proposed by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, the present invention provides an implementation solution for solving high throughput of directional coupons, which includes the following steps:
s1, collecting order information of the user in a historical time period;
s2, collecting evaluation information of the user in a historical time period;
s3, judging the risk level of the user by combining the order and the poor evaluation information;
s4, determining the shopping tendency of the user according to the real-time operation behavior of the user in the platform;
s5, recommending commodities to users meeting shopping requirements;
and S6, pushing the relevant coupons to the users meeting the shopping requirements.
According to the invention, a low-risk user group meeting shopping requirements is determined by purchasing order information in a user historical time period and judging the risk level of the user according to the order and the evaluation information, the shopping demand and the shopping tendency of the user are judged according to the real-time operation behavior of the user group in a platform, the commodity is recommended to the user according to the shopping tendency and the demand of the user, and the preferential manner and the preferential paper of the commodity are pushed to a potential user group, so that the UV and the order quantity of the platform are improved.
In an alternative embodiment, the step S1 of collecting the order information of the user in the historical time period includes browsing information, shopping order information and completed transaction order information.
It should be noted that the shopping information of the user is judged through the information of the commodity browsed by the user, the shopping tendency and frequency of the user are obtained through the comparison of the browsing information of the user and the shopping order information, and the risk shopping of the user is identified according to the shopping frequency, so that the identification accuracy is improved; shopping orders carried out by a user in a historical time period comprise orders which are completed in transaction and orders which are returned and cancelled, the shopping transaction completion rate of the user is judged through information collection of the orders which are completed in transaction, and risk transactions are identified through the level of the transaction completion rate of the orders.
In an alternative embodiment, the step S2 collects the evaluation information of the user in the historical time period, including total evaluation information, good evaluation information, bad evaluation information, and evaluation change information.
It should be noted that, because the reputation level of the merchant is affected by the quality of the evaluation, malicious users can be discriminated by collecting the evaluation information, and risk analysis of the users is facilitated by collecting the total number of the evaluations; the number of good reviews of shopping and the proportion of the good reviews of the shopping to the total number of reviews are judged by collecting the good review information of the user, so that the risk analysis of the user is facilitated; poor evaluation is an important factor of merchant credit influence, which is evaluation which is usually pursued to be avoided, and whether a user is malicious shopping is judged by the number of the poor evaluations for the shopping of the user and the proportion of the number of the poor evaluations to the total number of the reviews; the user evaluation can be modified in a later period, whether the user has malicious shopping or not is judged by evaluating and changing the data of the information in the historical time period, for example, the user can change the good evaluation by asking for illegal preferential evaluation after performing poor evaluation for multiple times, so that the risk analysis of the user is facilitated, the discrimination of the bad risk user is improved, and the credit of a merchant is ensured.
In an alternative embodiment, the step S3 judges the risk level of the user according to the order and the poor rating information, and determines three high-medium-low risk level users for the user population by comparing the rating information with the order information.
It should be noted that, whether the user has a malicious shopping situation is judged by analyzing the user order information and the bad comment information; the user types are analyzed and judged, different types of users are classified into high, medium and low levels, malicious users can be conveniently screened, and damage to merchant credit caused by bad users is prevented.
In an alternative embodiment, the step S4 of determining the shopping tendency of the user according to the real-time operation behavior of the user in the platform includes browsing information judgment, purchasing information and comment information.
It should be noted that, the products with the most browsing types of the users are analyzed to judge the current shopping tendency of the users through the browsing of the users to the products in the platform; the shopping tendency of the user is judged according to the products purchased by the user through the platform, so that the required products are conveniently pushed to the user; the shopping tendency of the user is obtained through the comment information of the user, so that the product sales can be conveniently carried out on the user, and the product sales probability is improved.
In an alternative embodiment, the step S5 is to recommend the goods to the user meeting the shopping requirement, and recommend the low risk level user according to the shopping tendency.
It should be noted that, the merchant sends commodity recommendation information to the user, risk orders can be generated for some users with bad risks, so that the normal sales credit of the merchant is influenced, the user group risk level is judged through the mailbox, the shopping tendency commodity recommendation is performed for the users with low risks, and the merchant credit is not adversely influenced while the commodity sales is improved.
In an optional embodiment, in the step S6, in the process of pushing the relevant coupons to the users meeting the shopping requirements, the maximum amount coupons are screened according to the shopping tendency of the users, and the coupons are pushed to the users in a platform message or a short message.
It should be noted that after the user risk, the shopping demand and the shopping tendency are determined, the coupon quota of the shopping commodity which the user tends to, is screened, and the best coupon mode or coupon is pushed to the user through platform information or a mobile phone short message, so that the user is ensured to receive the coupon information, and the platform UV and the order quantity are improved.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (7)
1. An implementation for addressing high throughput of directed coupons, the implementation comprising the steps of:
s1, collecting order information of the user in a historical time period;
s2, collecting evaluation information of the user in a historical time period;
s3, judging the risk level of the user by combining the order and the poor evaluation information;
s4, determining the shopping tendency of the user according to the real-time operation behavior of the user in the platform;
s5, recommending commodities to users meeting shopping requirements;
and S6, pushing the relevant coupons to the users meeting the shopping requirements.
2. The implementation solution for solving the high throughput of directional issue recited in claim 1, wherein said step S1 of collecting order information of the user in the historical time period comprises browsing information, shopping order information and order information for completing trade.
3. The implementation scheme for solving the high throughput of the directional coupon is characterized in that the step S2 is used for collecting the evaluation information of the user in the historical time period, wherein the evaluation information comprises total evaluation information, good evaluation information, poor evaluation information and evaluation change information.
4. The implementation scheme of claim 1, wherein the step S3 is implemented by combining the order and the bad comment information to judge the risk level of the user, and comparing the comment information with the order information to determine the users with three risk levels, namely high-medium-low risk level, for the user population.
5. The implementation solution of claim 1, wherein the step S4 of determining the shopping tendency of the user according to the real-time operation behavior of the user in the platform includes browsing information judgment, purchasing information and comment information.
6. The implementation solution of claim 1, wherein the step S5 is implemented by recommending merchandise for users meeting shopping requirements and recommending merchandise for users with low risk level according to shopping tendency.
7. The solution of claim 1, wherein in the step S6, in the process of pushing the relevant coupons to the users meeting the shopping requirements, the highest credit coupons are screened according to the shopping tendency of the users, and the coupons are pushed to the users through platform messages or short messages.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910856468.3A CN110619546A (en) | 2019-09-11 | 2019-09-11 | Implementation scheme for solving high throughput of directional ticket issuing |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910856468.3A CN110619546A (en) | 2019-09-11 | 2019-09-11 | Implementation scheme for solving high throughput of directional ticket issuing |
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| CN110619546A true CN110619546A (en) | 2019-12-27 |
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| CN201910856468.3A Pending CN110619546A (en) | 2019-09-11 | 2019-09-11 | Implementation scheme for solving high throughput of directional ticket issuing |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112101989A (en) * | 2020-08-31 | 2020-12-18 | 深圳市元征科技股份有限公司 | Coupon management method, device and equipment based on car rental platform and storage medium |
| CN112581223A (en) * | 2020-12-15 | 2021-03-30 | 口碑(上海)信息技术有限公司 | Information interaction method, device and equipment |
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| CN108648377A (en) * | 2018-04-10 | 2018-10-12 | 合肥美的智能科技有限公司 | Automatically vending system based on unmanned retail units and method |
| CN109711955A (en) * | 2019-02-18 | 2019-05-03 | 杭州跨境邦信息技术有限公司 | Difference based on current order comments method for early warning, system, blacklist library method for building up |
| CN110163670A (en) * | 2019-05-10 | 2019-08-23 | 达疆网络科技(上海)有限公司 | A kind of automation marketing method based on user's displaying live view behavior |
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2019
- 2019-09-11 CN CN201910856468.3A patent/CN110619546A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103853948A (en) * | 2012-11-28 | 2014-06-11 | 阿里巴巴集团控股有限公司 | User identity recognizing and information filtering and searching method and server |
| CN108648377A (en) * | 2018-04-10 | 2018-10-12 | 合肥美的智能科技有限公司 | Automatically vending system based on unmanned retail units and method |
| CN109711955A (en) * | 2019-02-18 | 2019-05-03 | 杭州跨境邦信息技术有限公司 | Difference based on current order comments method for early warning, system, blacklist library method for building up |
| CN110163670A (en) * | 2019-05-10 | 2019-08-23 | 达疆网络科技(上海)有限公司 | A kind of automation marketing method based on user's displaying live view behavior |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112101989A (en) * | 2020-08-31 | 2020-12-18 | 深圳市元征科技股份有限公司 | Coupon management method, device and equipment based on car rental platform and storage medium |
| CN112581223A (en) * | 2020-12-15 | 2021-03-30 | 口碑(上海)信息技术有限公司 | Information interaction method, device and equipment |
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Application publication date: 20191227 |
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