TWI726981B - Screening method and equipment for information delivery users - Google Patents

Abstract

The present invention discloses a method for screening information delivery users. The method includes: a server obtains user behavior data; the server determines whether the user behavior data meets a preset behavior standard; if so, the server The server determines the comprehensive score of the user according to the preset behavioral data score; the server determines the order in which information is delivered to each user based on the comprehensive score of the user and the comprehensive scores of other users; the server determines the delivery order of the information to each user according to the type of delivery of the information And the preset delivery strategy delivers the information to the user according to the delivery sequence. The invention can realize precise positioning of potential users, thereby realizing the hit rate of information delivery.

Description

Screening method and equipment for information delivery users

The present invention relates to the field of e-commerce, in particular to a method and equipment for screening users of information delivery.

With the rapid development of e-commerce in recent years, more and more businesses choose to open stores online. In order to attract buyers and increase sales, many sellers will issue coupons to stimulate buyers to buy goods in their stores. Sellers generally use two methods to issue coupons. One is to proactively issue them to old customers who have already purchased goods in this store. Since the seller has mastered the online shopping account and mobile phone number of the old customer, he can directly issue the coupon to the old customer’s account and notify by text message; the second is to directly place the coupon in a prominent position of the online store, and then buy the coupon from the store. The home takes the initiative to receive it, and it is directly deducted when the buyer needs to purchase the goods in the store.

Both of these two methods of issuing coupons initiated by the seller themselves have obvious flaws. For the first method, many stores often buy goods once and will not buy them again within a short period of time, such as large-sized goods or electronic products, so the use rate of coupons after the issuance of old customers is extremely low; The second method does not segment the users who enter the store. All buyers are It can be received, and the utilization rate after receiving it is also very low.

The prior art proposes a solution to the above-mentioned problems. The solution includes a parameter configuration module, a receiving module, and a product page file generation module.

The parameter configuration is used to generate an activity page file for displaying coupon activities to specific merchants according to the configured activity range control parameters and activity information display control parameters. Wherein, the activity range control parameter is used to configure the product category participating in the coupon activity, and the activity information display control parameter is used to configure the merchant participating in the coupon activity. The activity information display control parameters corresponding to each merchant can be stored in the form of a database table. By configuring the event information display control parameters, merchants can be set to be able to participate in coupon activities and not to participate in coupon activities, and for merchants that cannot participate in coupon activities, the activity page file is not generated.

The receiving module is used for receiving activity requests submitted by merchants in the specific merchants.

The product page file generation module is used to generate a product page file for the products that meet the review rules in the merchant that submits the activity request, and the product page file includes the product information for participating in the coupon activity and the coupon activity information. The product page file is generated for the merchants in the whitelist, and the whitelist is a preset list of merchants that meet the review rules.

In the process of implementing the prior art, the applicant found that the prior art has at least the following problems: the prior art does not analyze the daily behavior data of consumers, cannot accurately classify users who are interested in store coupons, and is manually set The parameters are not only not smart enough, but also a lot of work. At the same time, determining the users who meet the requirements by means of a whitelist will make the operation not flexible enough when the user changes.

The purpose of the present invention is to provide a method and equipment for screening information delivery users, which can determine potential users who need to deliver information by analyzing user behavior data, which can not only achieve accurate positioning of potential users, but also improve the hit rate of information delivery.

The technical solution of the present invention is as follows: a method for screening information delivery users, the method includes: a server obtains user behavior data; the server determines whether the user behavior data meets a preset behavior standard; if it meets, The server determines the user's comprehensive score according to preset behavior data scores; the server determines the order in which information is delivered to each user based on the user's comprehensive score and the comprehensive scores of other users; the server determines the order in which information is delivered to each user based on the information The delivery type and the preset delivery strategy of delivering the information to the user according to the delivery sequence.

The behavior data includes any combination of one or more of the following: the number of times to browse the target object, the collection status of the target object, the retrieval status of the target object, industry preference and historical purchase information.

The server determines whether the user behavior data meets the preset behavior Is a standard, specifically: when the behavior data is the number of times the target object is browsed, the server determines whether the number of times the user browses the target object within the first preset time exceeds the browsing threshold; when the behavior When the data is the collection of the target object, the server determines whether the user has collected the target object within the second preset time; when the behavior data is the number of times the target object is retrieved, the server Determining whether the number of times the user retrieves the target object within the third preset time exceeds a retrieval threshold; when the behavior data is an industry preference, the server counts the user's industry preference within the fourth preset time; Determine whether the user’s industry preference matches the industry of the target object; when the behavior data is shopping information, the server counts the user’s shopping information within the fifth preset time, and determines the Whether the user's shopping information is related to the target object.

The server determines the user's comprehensive score according to a preset behavior data score, specifically: the server determines the type of the user's behavior data; the server determines the user's behavior data according to the preset behavior data score as the The user's behavior data type is scored; the server determines the user's comprehensive score according to the scoring of the user's behavior data type.

The server is based on the delivery type of the information and the preset delivery policy The delivery of the information to the user slightly in accordance with the delivery sequence is specifically: the server determines the delivery strategy of the information according to the user’s behavior data and the user’s industry unit price through the preset delivery strategy Delivery type; the server delivers the information to the user in the order of delivery according to the delivery type of the information.

A server comprising: an acquisition module for acquiring user behavior data; a judging module for determining whether the user’s behavior data meets a preset behavior standard; and a first determining module, if The user's behavior data meets preset behavior standards, and is used to determine the user's comprehensive score based on the preset behavior data score; the second determination module is used to determine the user's comprehensive score based on the user's comprehensive score and other users' comprehensive scores Determine the delivery order of the information to each user; the delivery module is used to deliver the information to the user according to the delivery order according to the delivery type of the information and the preset delivery strategy.

The behavior data includes any combination of one or more of the following: the number of times to browse the target object, the collection status of the target object, the retrieval status of the target object, industry preference and historical purchase information.

The judging module is specifically configured to: when the behavior data is the number of times to browse the target object, determine whether the number of times the user browses the target object within the first preset time exceeds a browsing threshold; When the behavior data is the collection of the target object, it is determined whether the user has collected the target object within the second preset time; when the behavior data is the number of times the target object is retrieved, the third prediction is determined Set whether the number of times the user retrieves the target object within a time exceeds the retrieval threshold; when the behavior data is an industry preference, count the user’s industry preference within the fourth preset time to determine whether the user’s industry preference Match with the industry of the target object; when the behavior data is shopping information, collect the shopping information of the user within the fifth preset time, and determine whether the shopping information of the user is related to the target object .

The first determination module is specifically configured to: determine the type of the user's behavior data; score the type of the user's behavior data according to a preset behavior data score; and according to the type of the user's behavior data The scoring determines the overall score of the user.

The placement module is specifically configured to: determine the type of placement of the information according to the user’s behavior data and the user’s industry unit price through the preset placement strategy; and according to the type of placement of the information according to the type of placement. The delivery order delivers the information to the user.

The present invention judges whether the user’s behavior data meets the preset behavior standard by analyzing the user’s behavior data. The user’s comprehensive score confirms the order in which the information is delivered to the user, and then the information is delivered according to the delivery order according to the type of information delivery and the preset delivery strategy. The present invention can determine the delivery order of the delivery information. Potential users, by sorting the potential users and delivering information, not only can the accurate positioning of potential users be achieved, but also the hit rate of information delivery can be improved. At the same time, it also ensures that users who are more interested in the delivery information can get priority. The invention still analyzes users directly based on big data, which reduces the workload of operators, and can flexibly make corresponding judgments in response to user changes when user behavior changes.

21: Get the module

22: Judgment module

23: The first confirmation module

24: The second confirmation module

25: Putting the module

In order to more clearly explain the technical solutions of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the present invention or the prior art. Obviously, the drawings in the following description are only some of the present invention. Embodiments, for those of ordinary skill in the art, without creative work, other drawings can be obtained based on these drawings.

FIG. 1 is a flowchart of a method for screening information delivery users in an embodiment of the present invention; FIG. 2 is a schematic structural diagram of a server in an embodiment of the present invention.

In the following, the technical solutions of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are the present invention. Part of the embodiments, but not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art all fall within the protection scope of the present invention.

As described in the background art, the prior art does not analyze consumers’ daily behavior data, cannot accurately classify users who are interested in store coupons, and manually set parameters, which is not only not smart enough, but also requires a lot of work. The way to determine the users who meet the requirements in the way of the list will make the operation not flexible enough when the users change.

Based on this, an embodiment of the present invention proposes a method for screening information delivery users, which determines whether the user meets the information delivery standard by analyzing the user's behavior data, and determines after the user meets the information delivery standard The user’s demand for the information is used to determine the order in which the information is delivered to the user, and then according to the type of information delivery and a preset delivery strategy, it is determined that the delivery information corresponding to the user is in accordance with the The delivery of the information in the order of delivery realizes the precise positioning of the delivery information corresponding to the user and the precise delivery of the delivery information, improves the hit rate of the information delivery, and also ensures that users who are more interested in the delivery information can get priority.

As shown in Fig. 1, it is a schematic flow chart of a method for screening information delivery users according to an embodiment of the present invention. The method includes the following steps:

Step 101: The server obtains user behavior data.

Wherein, the behavior data includes any combination of one or more of the following: the number of times to browse the target object, the collection status of the target object, the retrieval status of the target object, industry preferences, and historical purchase information.

It should be noted that the target object here can refer to a store, or a business object sold in a store, and the business object itself can be a physical product or a service, such as: car washing, maintenance, Massage, cleaning, chef visits, housekeeping, tutoring, entertainment, eating and drinking, travel, hotels, car rental, etc., any content that can fall into the scope of user needs can be used as the analysis object in the embodiment of the present invention to find potential target users Such a change will not affect the protection scope of the present invention. In the subsequent description of the present invention, the meaning of the target object also has the above limitation, and the description will not be repeated in the following.

The server may be a server of an e-commerce platform, and users will leave operation traces on the e-commerce platform during daily online shopping. These operation traces are the user's behavior data, and the user's potential can be analyzed based on the operation traces. The business object needs or likes the business object sold in the store.

For example: Taking the business object as physical goods as an example, if a user browses the goods sold in a certain store many times, it means that the user prefers the goods of the store, but due to some reasons (such as: price or no discount And so on) without purchasing, the information of the number of times the user browses the products of the store can determine which store's products the user is interested in, and can further analyze which products are of the store's interest.

Similarly, if a user collects a store’s goods, or retrieves a certain product for a certain number of times, or the user’s industry preference (for example, a user often purchases some electronic products indicates that the user has a preference for electronic products), and the user There is a clear correlation between the shopping information of and certain products, which can also indicate that users are interested in these products.

By acquiring the user's behavior data, it is possible to analyze which products the user likes, and then to determine whether the user corresponds to the products sold in a certain store, so as to determine whether the user is a potential user of the store.

Of course, if the business object is a specific service content, such as housekeeping service, hotel business, etc., the above situation also exists, and it can be determined whether the user is a potential user of the store that provides these services.

Of course, the user's behavior data can also include other operation traces left by the user on the server (such as the aforementioned e-commerce platform) during the daily consumption process. Any behavior information that can indicate the user's potential business needs belongs to the present invention. protected range.

Step 102: The server determines whether the user's behavior data meets a preset behavior standard.

If it is satisfied, go to step 103, and if it is not satisfied, go to the end.

The server determines whether the user behavior data meets a preset behavior standard, specifically: when the behavior data is the number of times the target object is viewed, the server determines whether the user browses within the first preset time Whether the number of times of the target object exceeds the browsing threshold; when the behavior data is the bookmarking of the target object, the server determines whether the user has bookmarked the target object within the second preset time; When the behavior data is the number of retrievals of the target object, the server determines whether the number of retrievals of the target object by the user within the third preset time exceeds a retrieval threshold; When the behavior data is an industry preference, the server counts the user's industry preference within the fourth preset time, and determines whether the user's industry preference matches the industry preference of the target object; When the behavior data is shopping information, the server counts the user's shopping information within the fifth preset time, and determines whether the user's shopping information is related to the target object.

Specifically, for different specific application scenarios, taking the case where the business object is a physical commodity as an example, the above situations are explained as follows:

Case 1. The behavioral data is the number of times the target object is viewed.

In this case, the basis for determining the target user is: when a user browses a certain store’s merchandise more than a certain number of times, it indicates that the user has a strong purchase demand for the store’s merchandise, but it may be due to price or no discount. If there is no purchase due to other reasons, it can be determined that the user is a potential purchase user of the commodity, or the user is a potential purchase user of the store.

Based on the above-mentioned basis, when a store or the goods sold in the store have promotional activities to put information, it is possible to obtain the number of views of the store or the products in the store by each user within the first preset time, and determine whether the number of views is Exceeds the preset browsing threshold. If it exceeds, the corresponding user meets the behavior standards preset by the store, indicating that the user has a strong demand for the store or the products sold in the store. Therefore, the user can be regarded as the information that the store needs to place when posting information. One of the users. On the contrary, if it does not exceed, the corresponding user does not meet the behavior standards preset by the shop, indicating that the user has a low demand for the shop or the goods sold in the shop Strong, you don’t need to deliver to the user when posting information in the store. Wherein, the first preset time and the browsing threshold may be determined according to actual needs.

Case 2: The behavioral data is the collection of the target object.

In this case, the basis for determining the target user is: when a user has bookmarked a product in a certain store, it indicates that the user has a desire to purchase the product in the store or prefers the product, but it may be due to price or No purchase due to discounts or other reasons, or, when a user has bookmarked a store, it indicates that the user has a desire to purchase the goods in the store or prefers the store, but may not purchase due to price or no discount. Therefore, it can be determined that the user is a potential purchase user of the commodity in the shop, or the user is a potential purchase user of the shop.

Based on the above-mentioned basis, when a store or a product sold in the store has information to be placed in a promotional event, it is determined whether each user has bookmarked the store or the products of the store within the second preset time range. If it has been bookmarked, the corresponding user meets the preset behavior standards of the store, indicating that the user has a strong demand for the store or the products sold in the store. Therefore, the user can be regarded as the store that needs to be posted when posting information One of the users. On the contrary, if there is no collection, the corresponding user does not meet the preset behavior standards of the store, indicating that the user does not have a strong demand for the store or the goods sold in the store, and does not need to be posted to the store when posting information in the store The user. Wherein, the second setting time can be determined according to actual needs.

Case 3. The behavioral data is the number of times the target object is retrieved.

In this case, the basis for determining the target user is: when the number of times a user searches for a product in a certain store exceeds a certain number of times, it indicates that the user There is a strong purchase demand for the goods in the store, but may not be purchased due to price or no discount. Therefore, it can be determined that the user is a potential purchaser of the product, or the user is a potential purchaser of the store. Purchase users.

Based on the above-mentioned basis, when a store or the goods sold in the store have promotional activities to put information, it is possible to obtain the number of times each user has searched the store or the goods in the store within the third preset time, and determine whether the number of searches is Exceeds the preset retrieval threshold. If it exceeds, the corresponding user meets the behavior standards preset by the store, indicating that the user has a strong demand for the store or the products sold in the store. Therefore, the user can be regarded as the information that the store needs to place when posting information. One of the users. On the contrary, if it does not exceed, the corresponding user does not meet the preset behavior standards of the store, indicating that the user does not have a strong demand for the store or the products sold in the store, and does not need to be delivered to the store when posting information in the store. user. Wherein, the third preset time and the retrieval threshold can be determined according to actual needs.

Situation 4. The behavioral data are industry preferences.

Some users have industry preferences. For example, if a user’s industry preference is electronic products, then the user will usually pay attention to or buy a lot of electronic products, or some users have obvious preferences for certain products over a period of time, such as : When a user is doing home improvement, the user has an obvious industry preference for home improvement products. If a user’s industry prefers to correspond to the products in some stores, then the user becomes a potential buyer of these stores.

Since the user’s industry preference may be fixed, it may also be for a period of time There is a certain industry preference in the time, therefore, it is necessary to count the user's industry preference in the fourth preset time range, and judge whether it is consistent with the goods of a certain store according to the statistics of the industry preference. If they match, the behavior standards preset by the store are met, indicating that the user has a strong demand for the store or the products sold in the store. Therefore, the user can be regarded as one of the users that the store needs to publish when posting information. . On the contrary, if they do not match, the corresponding user does not meet the behavior standards preset by the store, indicating that the user does not have a strong demand for the store or the goods sold in the store, and does not need to be posted to the store when posting information in the store. user. Wherein, the fourth setting time can be determined according to actual needs.

Situation 5. The behavior data is shopping information.

The user’s shopping information can reflect what kind of products the user needs. For example, if a user has bought a laptop, then the user may need laptops and laptop-related computer accessories. If a store’s products and laptops Or laptop-related computer accessories, then the user is a potential buyer of the shop.

Based on the above basis, the shopping information of each user can be counted within the fifth preset time, and it can be judged whether it is related to a product in a certain store according to the shopping information of each user. If there is an association, the corresponding user meets the preset behavior standards of the store, indicating that the user has a strong demand for the store or the products sold in the store. Therefore, the user can be regarded as the store that needs to be posted when posting information One of the users. On the contrary, if there is no association, the corresponding user does not meet the behavior standards preset by the store, indicating that the user does not have a strong demand for the store or the goods sold in the store, and puts it in the store The information does not need to be delivered to the user. Wherein, the fifth preset time can be determined according to actual needs.

It should be noted that the above examples are all based on the case where the business object is a physical commodity. If the business object is specifically a service, the corresponding processing flow is similar to the above-mentioned solution, which can be deduced by analogy. This will not be repeated.

Step 103: The server determines the comprehensive score of the user according to a preset behavior data score.

The server determines the user's comprehensive score according to a preset behavior data score, specifically: the server determines the type of the user's behavior data; the server determines the user's behavior data according to the preset behavior data score as the The user's behavior data type is scored; the server determines the user's comprehensive score according to the scoring of the user's behavior data type.

Specifically, set a score for the behavior data in advance, for example, the score for browsing a target object is W1, the score for bookmarking the target object is W2, the score for searching the target object is W3, and the industry preference is consistent with the target object. The score is W4, and the score associated with the shopping information and the target object is W5.

The user may have a variety of behavioral data that meet the behavior standards preset by the store. For example, the number of times that the user browses a shop’s merchandise in the first preset time exceeds a threshold, and the user also has a second preset time. The store’s goods have been collected, then the user’s comprehensive score is W1+W2. Further, if the preset behavior data type also includes shopping information, and the user's industry preference is also consistent with the products of the store, then the user's comprehensive score is W1+W2+W4.

Step 104: The server determines the order in which information is delivered to each user according to the user's comprehensive score and the comprehensive score of other users.

Specifically, the user’s comprehensive score can reflect the size of the user’s and the store’s interest in posting information. The higher the score, the greater the user’s interest in the store’s posting of information, and the user will use the The higher the probability of posting information, it is necessary to give priority to users with high comprehensive scores, so as to avoid the set number of posting information that prevents users with high comprehensive scores from obtaining advertising information.

Step 105: The server delivers the information to the user in the order of delivery according to the type of delivery of the information and the preset delivery strategy.

The server delivers the information to the user according to the delivery order according to the delivery type of the information and the preset delivery strategy, specifically: the server delivers the information to the user according to the user’s behavior data and the user’s The unit price of the industry pen determines the delivery type of the information according to the preset delivery strategy; the server delivers the information to the user according to the delivery order according to the delivery type of the information.

The industry unit price of the user is the average price of each order purchased by the user in a certain industry, which is used to examine the user's purchasing ability in the industry.

When the user's behavior data meets the behavior standards set by the store When the user is a potential user of the shop, the industry to which the shop belongs is determined based on the products of the shop, and then the industry unit price of the user in the industry is determined to determine the user’s purchase ability.

The server compares the purchasing power of the user with the price corresponding to the user behavior data, and determines the type of information delivery according to a preset delivery strategy. Specifically, taking the information as a coupon as an example, the number of coupons to be delivered is full 300 yuan minus 30 and full 200 yuan minus 20 two kinds.

When the user’s behavior data is a product of the store, if the price of the product of the store is 200 yuan, the unit price of the user’s industry pen is 300, and if the preset delivery strategy is Stimulative delivery strategy. Determine the type of information to be delivered as a coupon of 300 yuan minus 30, which can stimulate users to purchase more goods in the store. If the preset delivery strategy is a robust delivery strategy, make sure to The type of information posted is a discount coupon of RMB 20 minus 20, so that the user can directly make purchases and increase the user's probability of using the coupon. When the price of the stored commodity in the store is 200 yuan, and the unit price of the user's industry pen is 100, a coupon of 200 minus 20 is offered to the user. Among them, the delivery is carried out in accordance with the determined user delivery order during delivery.

The present invention judges whether the user’s behavior data meets the preset behavior standard by analyzing the user’s behavior data. If it does, it then confirms the order in which the information is delivered to the user according to the determined comprehensive score of the user, and then according to the information The delivery type and the preset delivery strategy will deliver the information according to the delivery order. The present invention can determine the potential users who need the delivery information. By sorting the potential users and delivering the information, it is not only In order to achieve precise positioning of potential users, it also improves the hit rate of information delivery, and at the same time, it also ensures that users who are more interested in the delivery information can obtain priority. Moreover, the present invention directly analyzes users based on big data, which reduces The workload of the operator, and when the user's behavior changes, it can flexibly make corresponding judgments for the user's changes.

In order to further illustrate the technical idea of the present invention, the technical scheme of the present invention will now be described in combination with specific application scenarios, which are specifically as follows: Taking the business object as a physical product as an example, the server obtains user behavior data, including: the number of times a product in the store is browsed, whether the product is collected, the number of times the product is retrieved, industry preferences, and shopping information, according to all The behavior data determines whether the user is a potential purchase user of the store.

For users who frequently browse a certain product in a shop in a short period of time, if the behavior standard set in the server is 10 days (equivalent to the aforementioned first preset time), the browsing of a certain product in a shop exceeds 5 times (equivalent to the aforementioned browsing threshold), the server determines whether the user has browsed the product in the shop more than 5 times in 10 days, and if it exceeds, the user is listed as the shop or the shop Of potential buyers of this item.

In the case of a user’s collection of goods in a shop, if the behavior standard set in the server is that the goods of a certain shop have been collected in the last 5 days (equivalent to the aforementioned second preset time), the server will determine that the user Whether the store’s merchandise has been bookmarked within 5 days, if so, the user will be listed as a potential purchaser of the store or the merchandise in the store.

For users who frequently search for a certain product in a shop in a short period of time, if the behavior standard set in the server is 10 days (equivalent to the aforementioned third preset time), the search for a certain product in a shop exceeds 5 times (equivalent to the aforementioned retrieval threshold), the server determines whether the user has retrieved the item in the shop more than 5 times within 10 days, and if so, the user is listed as the shop or the shop Of potential buyers of this item.

In another application scenario, the server can determine the user’s industry preference based on the daily purchase behavior of each user within 1 month (equivalent to the aforementioned fourth preset time). Based on the result of such industry preference, the server Determine whether the user’s industry preference is the same as the goods of a certain store, for example: if it is determined that the user’s industry preference is to buy books and the current store’s goods are books, then the user’s industry preference is consistent with the goods of this shop , The user is listed as a potential purchaser of the store or the store.

In another application scenario, the server can count the shopping information of each user within one month. If a user has purchased a laptop within one month (equivalent to the aforementioned fifth preset time), then the server Through the association analysis algorithm, such as: Apriori algorithm (a frequent itemset algorithm for mining association rules), determine whether a store’s goods are associated with the user’s shopping information. If there is an association, the user will be listed as the user. The shop or potential purchasers of the shop.

It should be noted that the above examples are all based on the case where the business object is a physical product. If the business object is specifically a store The corresponding processing flow for itself or service is also similar to the above-mentioned solution, and can be deduced by analogy, and will not be repeated here.

In the specific processing process, the server counts the behavior data of the user that meets the preset behavior standard. When two items of a user's behavior data meet the preset behavior standard, the server performs the calculation for these two behavior data. Score, and determine the user’s comprehensive score, for example: the user’s behavior data for the number of times the shop has been browsed and the goods in the shop meet the preset behavior criteria, then the server will follow the preset The behavioral data score is scored for the two behavioral data of the number of times of browsing the store and the products of the store, and the comprehensive score of the user is obtained.

The server sorts the comprehensive scores of multiple users, and places the coupons prepared by the store according to the sort.

Before placing the coupon, the server must determine the type of the coupon to be placed. Specifically, the server determines the industry of the product in the store, and then determines the industry of the user in the industry The unit price, if the number of times the user browses the store meets the set behavior standard, and the goods the user browses to the store are all around 200 yuan, the goods sold in the store are electronic products, and then the The user’s industry unit price on the electronic product. If the user’s industry unit price on the electronic product is 300 yuan, the server determines the type of coupon placement according to the preset placement strategy, and according to the user’s placement Place the corresponding coupons in order, for example: There are two types of coupons to be placed: 300 yuan minus 30 and 200 yuan minus 20. If the preset placement strategy is a robust placement strategy, make sure that the type of information to be placed is full 200 yuan minus 20 excellent Coupon, if the preset delivery strategy is a stimulative delivery strategy, determine the type of information to be delivered as a coupon of 300 yuan minus 30.

When the industry unit price of the user is less than the price of the commodity browsed by the user in the shop, the server selects a coupon of 200 yuan minus 20 for placement. Wherein, the placing is performed according to the placing order of the user when placing.

The present invention judges whether the user’s behavior data meets the preset behavior standard by analyzing the user’s behavior data. If it does, it then confirms the order in which the information is delivered to the user according to the determined comprehensive score of the user, and then according to the information The delivery type and the preset delivery strategy deliver the information according to the delivery order. The present invention can determine the potential users who need the delivery information. By sorting the potential users and placing the information, the accurate positioning of the potential users can not only be achieved , It also improves the hit rate of information delivery, and at the same time, it ensures that users who are more interested in the delivery information can obtain priority. Moreover, the present invention directly analyzes users based on big data, reducing the workload of operators, and When the user's behavior changes, it can flexibly make corresponding judgments for the user's changes.

Based on the same application concept as the above method, the present invention also proposes a server, as shown in FIG. 2, the server includes: an acquisition module 21 for acquiring user behavior data; a judgment module 22 for Determine whether the user's behavior data meets the preset behavior standard; the first determining module 23, if the user's behavior data meets the preset behavior standard, is used to determine the user according to the preset behavior data score. The second determining module 24 determines the order of information delivery to each user based on the user’s comprehensive score and the comprehensive scores of other users; the delivery module 25 is used to deliver information according to the type of delivery and preset delivery The strategy delivers the information to the user according to the delivery order.

The behavior data includes any combination of one or more of the following: the number of times to browse the target object, the collection status of the target object, the retrieval status of the target object, industry preference and historical purchase information.

The judgment module is specifically configured to: when the behavior data is the number of times the target object is viewed, determine whether the number of times the user browses the target object within the first preset time exceeds a browsing threshold; when the behavior When the data is the collection of the target object, it is determined whether the user has collected the target object within the second preset time; when the behavior data is the number of times the target object is retrieved, it is determined whether the target object is retrieved within the third preset time. Whether the number of times that the user retrieves the target object exceeds the retrieval threshold; when the behavior data is an industry preference, the user’s industry preference is counted within the fourth preset time to determine whether the user’s industry preference is consistent with the target The industry of the object matches; when the behavior data is shopping information, the shopping information of the user within the fifth preset time is counted, and it is determined whether the shopping information of the user is related to the target object.

The first determining module is specifically used for: Determine the type of the user's behavior data; score the type of the user's behavior data according to a preset behavior data score; determine the user's comprehensive score based on the score of the type of the user's behavior data.

The placement module is specifically configured to: determine the type of placement of the information according to the user’s behavior data and the user’s industry unit price through the preset placement strategy; and according to the type of placement of the information according to the type of placement. The delivery order delivers the information to the user.

The present invention judges whether the user’s behavior data meets the preset behavior standard by analyzing the user’s behavior data. If it does, it then confirms the order in which the information is delivered to the user according to the determined comprehensive score of the user, and then according to the information The delivery type and the preset delivery strategy deliver the information according to the delivery order. The present invention can determine the potential users who need the delivery information. By sorting the potential users and placing the information, the accurate positioning of the potential users can not only be achieved , It also improves the hit rate of information delivery, and at the same time ensures that users who are more interested in the delivery information can obtain priority. Moreover, the present invention directly analyzes users based on big data, reducing the workload of operators, and When the user's behavior changes, it can flexibly make corresponding judgments for the user's changes.

Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus the necessary universal hardware platform, of course, it can also be implemented by hardware, but in many cases the former is Better implementation. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions to make a computer A terminal device (which can be a mobile phone, a personal computer, a server, or a network device, etc.) executes the methods described in the various embodiments of the present invention.

The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made. These improvements and modifications The protection scope of the present invention should also be considered.

Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed to be located in one or more devices different from this embodiment. The modules in the above-mentioned embodiments can be integrated or deployed separately; they can be combined into one module, or they can be further split into multiple sub-modules. The sequence numbers of the foregoing embodiments of the present invention are only for description, and do not represent the superiority or inferiority of the embodiments.

The above-disclosed are only a few specific embodiments of the present invention, but the present invention is not limited thereto, and any changes that can be thought of by those skilled in the art should fall into the protection scope of the present invention.

Claims (10)

An information delivery user screening method, the method includes: a server obtains user behavior data; the server determines whether the user behavior data meets a preset behavior standard; if so, the server determines whether the user's behavior data meets a preset behavior standard; The behavior data score determines the user’s comprehensive score; the server determines the order in which information is delivered to each user based on the user’s comprehensive score and the comprehensive scores of other users; the server determines the delivery order of the information to each user based on the type of delivery of the information and the preset The delivery strategy delivers the information to the user according to the delivery order; wherein the server determines whether the user behavior data meets a preset behavior standard, which specifically includes: when the behavior data is a retrieval of a target object When the number of times, the server determines whether the number of times the user retrieves the target object within the third preset time exceeds the retrieval threshold; and when the behavior data is an industry preference, the server counts the fourth preset time In the industry preference of the user, it is judged whether the industry preference of the user matches the industry of the target object. The method according to claim 1, wherein the behavior data includes any combination of one or more of the following: the number of times to browse the target object, the collection status of the target object, the retrieval status of the target object, industry preferences, and historical purchases News. The method according to claim 2, wherein the server determines whether the user behavior data meets a preset behavior standard, and specifically includes: when the behavior data is the number of times the target object is viewed, the server Determine whether the number of times the user browses the target object within the first preset time exceeds the browsing threshold; when the behavior data is the collection of the target object, the server determines the user within the second preset time Whether the target object has been bookmarked; and when the behavior data is shopping information, the server counts the shopping information of the user within the fifth preset time, and determines whether the shopping information of the user is consistent with the target object Related. The method according to claim 1, wherein the server determines the user's comprehensive score according to a preset behavior data score, specifically: the server determines the type of the user's behavior data; the server Score the type of the user's behavior data according to a preset behavior data score; the server determines the user's comprehensive score according to the score of the type of the user's behavior data. The method according to claim 1, wherein the server delivers the information to the user in accordance with the delivery order according to the delivery type of the information and the preset delivery strategy, specifically: the server delivers the information according to the delivery order. The user’s behavioral data and the user’s behavior The unit price of the business pen determines the delivery type of the information according to the preset delivery strategy; the server delivers the information to the user in the order of delivery according to the delivery type of the information. A server comprising: an acquisition module for acquiring user behavior data; a judging module for determining whether the user’s behavior data meets a preset behavior standard; and a first determining module, if The user's behavior data meets preset behavior standards, and is used to determine the user's comprehensive score based on the preset behavior data score; the second determination module is used to determine the user's comprehensive score based on the user's comprehensive score and other users' comprehensive scores Determine the order in which the information is delivered to each user; the delivery module is used to deliver the information to the users in the order according to the delivery type of the information and the preset delivery strategy; wherein the judgment module is specifically used In: when the behavior data is the number of times the target object is retrieved, determine whether the number of times the user retrieves the target object within the third preset time exceeds a retrieval threshold; and when the behavior data is an industry preference, make statistics The industry preference of the user within the fourth preset time period is to determine whether the industry preference of the user matches the industry of the target object. The server according to claim 6, wherein the behavior data includes any combination of one or more of the following: The number of visits to the target object, the collection of the target object, the retrieval of the target object, industry preferences and historical purchase information. For example, the server according to claim 7, wherein the judgment module is specifically used to judge that the user browses the target within the first preset time when the behavior data is the number of times the target has been viewed. Whether the number of times of the object exceeds the browsing threshold; when the behavior data is a collection of the target object, it is determined whether the user has favorited the target object within the second preset time; and when the behavior data is shopping information Calculate the shopping information of the user within the fifth preset time, and determine whether the shopping information of the user is related to the target object. The server according to claim 6, wherein the first determining module is specifically configured to: determine the type of the user's behavior data; and perform processing for the type of the user's behavior data according to a preset behavior data score Scoring: The user's comprehensive score is determined according to the scoring of the type of the user's behavioral data. The server according to claim 6, wherein the delivery module is specifically configured to: determine the delivery of the information according to the user’s behavior data and the user’s industry unit price through the preset delivery strategy Type; according to the delivery type of the information, the delivery order to the user The user posts the information.

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