CN109034854A - Method and apparatus for distributing sample - Google Patents
Method and apparatus for distributing sample Download PDFInfo
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- CN109034854A CN109034854A CN201710437157.4A CN201710437157A CN109034854A CN 109034854 A CN109034854 A CN 109034854A CN 201710437157 A CN201710437157 A CN 201710437157A CN 109034854 A CN109034854 A CN 109034854A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0203—Market surveys; Market polls
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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]
-
- 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]
- G06Q30/0623—Item investigation
Abstract
This application discloses the method and apparatus for distributing sample.One specific embodiment of the method includes: the sample message for obtaining sample；Based on sample message and preset user portrait model, the probability that each user buys article corresponding with sample is analyzed, above-mentioned user draws a portrait model for characterizing the corresponding relationship between sample message and the purchase probability of user；According to above-mentioned probability and preset user information list, determine that at least one user is target user；Sample is distributed into target user.The embodiment had not only realized effectively management sample, but also may be implemented targetedly to distribute sample, while increasing the probability that user buys the corresponding article of sample after obtaining sample by browsing shopping website.
Description
Technical field
This application involves logistics technology, and in particular to article distribution technique field, more particularly to it is a kind of for distributing
The method and apparatus of sample.
Background technique
With the emergence of 20 A-1. Net's network technologies, electric business industry has welcome swift and violent development.Procurement staff was purchasing
Cheng Zhonghui obtains the sample of many articles, and the type and quantity of these samples are also more and more.
These samples how are systematically managed, while sample is distributed into different users to experience, to improve electric business
The pageview of website is current a urgent problem needed to be solved.
Summary of the invention
The purpose of the application is to propose a kind of method and apparatus for distributing sample, to solve background above technology department
Divide the technical issues of mentioning.
In a first aspect, the embodiment of the present application provides a kind of method for distributing sample, the above method includes: acquisition sample
The sample message of product；Based on above-mentioned sample message and preset user portrait model, each user's purchase and above-mentioned sample are analyzed
The probability of the corresponding article of product, above-mentioned user's portrait model are corresponding between sample message and the purchase probability of user for characterizing
Relationship；According to above-mentioned probability and preset user information list, determine that at least one user is target user；By above-mentioned sample
Distribute to above-mentioned target user.
In some embodiments, above-mentioned sample message includes sample mark and sample size；And it is above-mentioned based on above-mentioned
Sample message and preset user portrait model, analyze the probability that each user buys article corresponding with above-mentioned sample, packet
It includes: above-mentioned sample being identified and above-mentioned sample size inputs above-mentioned user's portrait model, determines that each user buys above-mentioned sample
The probability of the corresponding article of product.
In some embodiments, above-mentioned sample message includes sample type, and above-mentioned user information list includes user's generation
The order of order generates the moment；And it is above-mentioned according to above-mentioned probability and preset user information list, determine at least one use
Family is target user, comprising: in response to above-mentioned sample type be the first kind, determine above-mentioned probability be greater than the first preset value use
The order at family generates the moment；Determining that the above order generates the user that the moment is located in preset time period is above-mentioned target user.
In some embodiments, above-mentioned sample message further includes the first warehouse of above-mentioned sample storage, above-mentioned user information
List include target user generate target order in include article mark；And above-mentioned above-mentioned sample is distributed into above-mentioned mesh
Mark user, comprising: according to above-mentioned article mark, determine the second warehouse of the article storage for including in above-mentioned target order；According to
The distance between above-mentioned first warehouse and above-mentioned second warehouse are determined in above-mentioned target order and are ordered with the matching of above-mentioned sample match
It is single；It determines the matching user for generating above-mentioned match orders, and above-mentioned sample is distributed into above-mentioned matching user.
In some embodiments, above-mentioned sample message includes sample type, sample price and sample size；And it is above-mentioned
According to above-mentioned probability and preset user information list, determine that at least one user is target user, comprising: in response to above-mentioned
Sample type is that Second Type or above-mentioned sample price are greater than the second preset value, according to above-mentioned user portrait model, is determined each
User buys the purchasing power of the corresponding article of above-mentioned sample；According to above-mentioned probability and above-mentioned purchasing power, determine each user's
Buying policy index；Above-mentioned buying policy index is ranked up according to descending, determines that preceding sample size user is in above-mentioned sequence
Above-mentioned target user.
In some embodiments, above-mentioned user information list includes user identifier and shipping address；And it is above-mentioned will be upper
It states sample and distributes to above-mentioned target user, comprising: according to above-mentioned sample message, above-mentioned user identifier and above-mentioned shipping address,
Generate new order；Handle new order.
In some embodiments, the above method further include: detect in above-mentioned target user, bought after receiving above-mentioned sample
The quantity of the conversion user of the corresponding article of above-mentioned sample；According to the quantity of above-mentioned target user and the number of above-mentioned conversion user
Amount, determines the conversion ratio of above-mentioned sample.
Second aspect, the embodiment of the present application provide a kind of for distributing the device of sample, and above-mentioned apparatus includes: that information obtains
Unit is taken, for obtaining the sample message of sample；Probability determining unit, for being based on above-mentioned sample message and preset user
Portrait model, analyzes the probability that each user buys article corresponding with above-mentioned sample, and above-mentioned user draws a portrait model for characterizing
Corresponding relationship between sample message and the purchase probability of user；User's determination unit is used for according to above-mentioned probability and presets
User information list, determine at least one user be target user；Sample allocation unit, for distributing to above-mentioned sample
State target user.
In some embodiments, above-mentioned sample message includes sample mark and sample size；And above-mentioned determine the probability
Unit is further used for: above-mentioned sample being identified and above-mentioned sample size inputs above-mentioned user's portrait model, determines each use
Buy the probability of the corresponding article of above-mentioned sample in family.
In some embodiments, above-mentioned sample message includes sample type, and above-mentioned user information list includes user's generation
The order of order generates the moment；And above-mentioned user's determination unit include: order generate moment determining module, in response to
Stating sample type is the first kind, determines that above-mentioned probability is greater than the order generation moment of the user of the first preset value；First object
User's determining module is above-mentioned target user for determining that the above order generates the user that the moment is located in preset time period.
In some embodiments, above-mentioned sample message further includes the first warehouse of above-mentioned sample storage, above-mentioned user information
List include target user generate target order in include article mark；And above-mentioned sample allocation unit includes: warehouse
Determining module, for determining the second warehouse of the article storage for including in above-mentioned target order according to above-mentioned article mark；Matching
Order determining module, for determining in above-mentioned target order according to the distance between above-mentioned first warehouse and above-mentioned second warehouse
With the match orders of above-mentioned sample match；First sample distribution module, for determining the matching user for generating above-mentioned match orders,
And above-mentioned sample is distributed into above-mentioned matching user.
In some embodiments, above-mentioned sample message includes sample type, sample price and sample size；And it is above-mentioned
User's determination unit includes: purchasing power determining module, for being Second Type or above-mentioned sample valence in response to above-mentioned sample type
Lattice are greater than the second preset value, according to above-mentioned user portrait model, determine that each user buys the purchase of the corresponding article of above-mentioned sample
Buy power；Buying policy index determining module, for determining the buying policy index of each user according to above-mentioned probability and above-mentioned purchasing power；
Second target user's determining module, for above-mentioned buying policy index to be ranked up according to descending, before determining in above-mentioned sequence
Sample size user is above-mentioned target user.
In some embodiments, above-mentioned user information list includes user identifier and shipping address；And above-mentioned sample
Allocation unit includes: order generation module, is used for according to above-mentioned sample message, above-mentioned user identifier and above-mentioned shipping address,
Generate new order；Ordering Module, for handling new order.
In some embodiments, above-mentioned apparatus further include: conversion user's detection unit, for detecting above-mentioned target user
In, the quantity of the conversion user of the corresponding article of above-mentioned sample is bought after receiving above-mentioned sample；Conversion ratio determination unit, is used for
According to the quantity of above-mentioned target user and the quantity of above-mentioned conversion user, the conversion ratio of above-mentioned sample is determined.
The third aspect, the embodiment of the present application provide a kind of server, comprising: one or more processors；Storage device,
For storing one or more programs, when said one or multiple programs are executed by said one or multiple processors, so that on
It states one or more processors and realizes method described in any of the above-described embodiment.
Fourth aspect, the embodiment of the present application provide a kind of computer readable storage medium, are stored thereon with computer journey
Sequence, the program realize method described in any of the above-described embodiment when being executed by processor.
The method and apparatus provided by the above embodiment for distributing sample of the application, first the sample letter of acquisition sample
Breath is then based on above-mentioned sample message and preset user portrait model, and it is corresponding with above-mentioned sample to analyze each user's purchase
The probability of article, wherein above-mentioned user draws a portrait model for characterizing the corresponding pass between sample message and the purchase probability of user
System determines that at least one user is target user, then will then according to determining probability and preset user information list
Sample distributes to target user.The method of the present embodiment had not only realized effectively management sample, but also may be implemented targetedly to divide
With sample, while increasing the probability that user buys the corresponding article of sample after obtaining sample by browsing shopping website.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is that this application can be applied to exemplary system architecture figures therein；
Fig. 2 is the flow chart according to one embodiment of the method for distributing sample of the application；
Fig. 3 is the schematic diagram according to an application scenarios of the method for distributing sample of the application；
Fig. 4 is the process that one embodiment of target user is determined in the method for distributing sample according to the application
Figure；
Fig. 5 is the structural schematic diagram according to one embodiment of the device for distributing sample of the application；
Fig. 6 is adapted for the structural schematic diagram for the computer system for realizing the server of the embodiment of the present application.
Specific embodiment
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to
Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 is shown can be using the method for distributing sample of the application or the implementation of the device for distributing sample
The exemplary system architecture 100 of example.
As shown in Figure 1, system architecture 100 may include terminal device 101,102,103, network 104 and server 105.
Network 104 between terminal device 101,102,103 and server 105 to provide the medium of communication link.Network 104 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 101,102,103 and be interacted by network 104 with server 105, to receive or send out
Send message etc..Various telecommunication customer end applications can be installed on terminal device 101,102,103, such as information input application,
Web browser applications, shopping class application, searching class application, instant messaging tools, mailbox client, social platform software etc..
Terminal device 101,102,103 can be the various electronic equipments with display screen and information input, including but
It is not limited to smart phone, tablet computer, pocket computer on knee and desktop computer etc..
Server 105 can be to provide the server of various services, such as pass through terminal device 101,102,103 to user
The background server that the information of input is handled.Background server can carry out the data such as the information received analyzing etc.
Reason, and processing result (such as user information) is fed back into terminal device 101,102,103.
It should be noted that the method provided by the embodiment of the present application for distributing sample is generally held by server 105
Row, correspondingly, the device for distributing sample is generally positioned in server 105.
It should be understood that the number of terminal device, network and server in Fig. 1 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
With continued reference to Fig. 2, the process of one embodiment of the method for distributing sample according to the application is shown
200.The method for distributing sample of the present embodiment, comprising the following steps:
Step 201, the sample message of sample is obtained.
In the present embodiment, the method for distributing sample runs electronic equipment (such as service shown in FIG. 1 thereon
Device) sample can be received using its terminal for carrying out information input from user by wired connection mode or radio connection
Information.Wherein, above-mentioned sample is a small amount of material object that can represent article quality.It either extracts work from commodity by the gross
Externally to show needed for model and product quality detection；Or according to commodity design and in advance by the producer before mass production
It makes, be process.Above-mentioned sample message may include various information related with sample, for example, sample mark, sample size,
Sample type (product that disappear fastly, new product dress on probation etc.), sample date of manufacture, sample storage position etc..
It should be pointed out that above-mentioned radio connection can include but is not limited to 3G/4G connection, WiFi connection, bluetooth
Connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection and other currently known or exploitations in the future
Radio connection.
Step 202, based on sample message and preset user portrait model, each user's purchase and above-mentioned sample are analyzed
The probability of corresponding article.
In the present embodiment, above-mentioned user's portrait model can be user and characterize between sample message and the purchase probability of user
Corresponding relationship various models.User portrait may include user much information, such as natural quality (gender, the age,
Domain, level of education, date of birth, occupation, constellation), social property (marital status, home background, social channel preference), consumption
Behavior (income, purchasing power, purchasing channel preference, purchased item, liveness), living habit (sport and body-building, the daily schedule,
Shopping time of concentration) and interest characteristics (hobby uses the website APP/, collection content, Brang Preference, product preference).On
Stating user's portrait model can be determined by following steps: the basic behavioral data of user be collected, to above-mentioned basic behavior number
According to being analyzed；According to the portrait for the data building user that analysis obtains.Wherein, above-mentioned basic behavioral data may include user
Transaction data, user preference data, network behavior data etc.；It, can be using cluster when analyzing basic behavioral data
Algorithm, prediction algorithm, machine learning method, natural language processing method, text mining method etc. are realized.Obtaining above-mentioned use
Family is drawn a portrait after model, sample message can be input in user's portrait model, according to the portrait of each user, be determined each use
Buy the probability of article corresponding with above-mentioned sample in family.
In some optional implementations of the present embodiment, above-mentioned sample message may include sample mark and sample
Quantity.Above-mentioned steps 202 can be realized by following steps: by sample mark and sample size input user's portrait mould
Type determines that each user buys the probability of the corresponding article of above-mentioned sample.
Step 203, according to above-mentioned probability and preset user information list, determine at least one user for target use
Family.
After the probability that each user buys the corresponding article of above-mentioned sample has been determined, one or more users can be chosen
As target user.For example, the user that can choose maximum probability can also choose probability greater than preset value as target user
Multiple users as target user.
Step 204, above-mentioned sample is distributed into target user.
After target user has been determined, above-mentioned sample can be distributed into target user.Specifically, can be by above-mentioned sample
It is sent in target user's hand by various forms of transport.
With continued reference to the signal that Fig. 3, Fig. 3 are according to the application scenarios of the method for distributing sample of the present embodiment
Figure.In the application scenarios of Fig. 3, procurement staff is by terminal in 301 input sample information of the information input page, above-mentioned sample letter
Breath may include sample mark, sample size, warehouse mark locating for the date of manufacture of sample and sample.Procurement staff is defeated
After entering above-mentioned sample message, above-mentioned sample message is sent to server, preset user's portrait model is installed in server,
After receiving above-mentioned sample message, determine that each user buys the probability of the corresponding article of each sample, then server according to
Above-mentioned probability and pre-stored user information list, determine target user, and above-mentioned sample is then distributed to target user.
User can be distributed information page 302 by terminal by sample and obtain sample distribution information.
The method provided by the above embodiment for distributing sample of the application obtains the sample message of sample, so first
Afterwards based on above-mentioned sample message and preset user portrait model, analyzes each user and buy article corresponding with above-mentioned sample
Probability, wherein for characterizing the corresponding relationship between sample message and the purchase probability of user, then above-mentioned user draws a portrait model
According to determining probability and preset user information list, determine that at least one user is target user, then by sample point
Dispensing target user.The method of the present embodiment had not only realized effectively management sample, but also may be implemented targetedly to distribute sample
Product, while increasing the probability that user buys the corresponding article of sample after obtaining sample by browsing shopping website.
With continued reference to Fig. 4, it illustrates determine the one of target user in the method for distributing sample according to the application
The process 400 of a embodiment.In the present embodiment, sample message may include sample mark, sample size, sample type, sample
Warehouse locating for price and sample.It may include the order generation moment that user generates order in user information list.Such as Fig. 4
It is shown, in the present embodiment target user can be determined by following steps:
Step 401, determine that each user buys the probability of the corresponding article of sample.
In the present embodiment, it is corresponding can to determine that each user buys sample by the step 202 in embodiment illustrated in fig. 2
The probability of article.
Step 4021, it is the first kind in response to sample type, determines that probability is greater than the order of the user of the first preset value
Generate the moment.
When the sample type of above-mentioned sample is the first kind, the general of the corresponding article of sample is bought according to each user
Rate determines that probability is greater than the user of the first preset value, then determines that the order of these users generates the moment.The above-mentioned first kind can
It to be any type, such as can be disappear fastly category type, including personal-care supplies, food and drink etc..
The above order generation moment can be user at the time of clicking submission order on shopping website or confirming an order.
Step 4031, determining that order generates the user that the moment is located in preset time period is target user.
In the present embodiment, it can generate the moment determines whether order is processed according to order, it can determine in order
Whether article has sorted completion.Such as server can default the time difference between order generation moment and current time less than 30
The order of minute is not processed, then it is not processed can to determine that probability is greater than order in the user of the first preset value for server
User is target user.
In some optional implementations of the present embodiment, above-mentioned sample message can also include the first of sample storage
Warehouse, above-mentioned user information list further include target user generate target order in include article mark.Can then it pass through
Sample is distributed to target user by unshowned following steps in Fig. 4:
According to article mark, the second warehouse of the article storage for including in target order is determined；According to the first warehouse and
The distance between two warehouses determine the match orders in target order with sample match；Determine for generating above-mentioned match orders
Matching user is distributed to user, and by sample.
In this implementation, determine that the order that target user generates is target order, server can be according to target order
In each article article mark, determine the second warehouse of each article storage in target order.Then it is deposited according to sample
The distance between first warehouse and the second warehouse for putting, determine the match orders in target order with sample match.Then it determines
Sample is distributed to above-mentioned matching user by the matching user for generating above-mentioned match orders.
In this implementation, sample can be issued with the article that target user buys, save additional individually hair
Send required cost.
Step 4022, it is that Second Type or sample price are greater than the second preset value in response to sample type, is drawn according to user
As model, determine that each user buys the purchasing power of the corresponding article of sample.
When sample type is Second Type or sample price is greater than the second preset value, can draw a portrait model according to user,
Determine that each user buys the purchasing power of the corresponding article of sample.Above-mentioned Second Type can be different from the various of the first kind
Type, such as can be the new product dress on probation of various articles.Above-mentioned sample price can by the Determination of Value of sample, specifically,
The price of sample can be determined according to the volume or capacity and price of the corresponding article of sample.Purchasing power refers to that user is obtaining
The ability of article is bought after income.Above-mentioned user's portrait model may include the income situation of user, so as to combine sample
The price or sample price of the corresponding article of product determine that user buys the purchasing power of above-mentioned sample.
Step 4032, according to above-mentioned probability and above-mentioned purchasing power, the buying policy index of each user is determined.
In the present embodiment, server can add probability and purchasing power after determining above-mentioned probability and purchasing power
Power calculates, so that it is determined that the buying policy index of each user.It is understood that the target user that server determines should be purchase
The probability of the corresponding article of sample is big and has the user of certain purchasing power.
Step 4042, buying policy index is ranked up according to descending, determines that preceding sample size user is in sequence
Target user.
Server, can be by above-mentioned buying policy index according to descending sequence, then after above-mentioned buying policy index has been determined
Determine that top n user is target user in above-mentioned sequence, wherein N is equal to sample size.This way it is secured that each target
User obtains a sample, improves the popularization rate of sample, improves the conversion ratio of sample.
In some optional implementations of the present embodiment, above-mentioned user information list can also include user identifier with
And shipping address.Then server has been after having determined above-mentioned target user, can be according to following steps unshowned in Fig. 4 come by sample
Product distribute to target user:
According to sample message, user identifier and shipping address, new order is generated；Handle new order.
In this implementation, server is after having determined and being suitble to the target user of above-mentioned sample, available user identifier
And the shipping address of user, new order is generated, the above order is then handled, so that above-mentioned sample is sent to by the personnel of sorting
Target user.Above-mentioned user identifier can be user in the user name etc. of shopping website.
In some optional implementations of the present embodiment, the above method further includes unshowned following steps in Fig. 4:
It detects in target user, the quantity of the conversion user of the corresponding article of sample is bought after receiving sample；According to mesh
It marks the quantity of user and converts the quantity of user, determine the conversion ratio of sample.
After each sample distributes to target user, server can detecte buys sample after receiving sample in target user
The user of corresponding article, these users are referred to as conversion user herein.It then can be according to the quantity and conversion of target user
The quantity of user determines the conversion ratio of sample.
In this way, being conducive to the further optimization algorithm of server, more accurately to determine target user, shopping network is being improved
While the pageview stood, realization accurately distributes sample.
In concrete practice, server, can be by user identifier, sample mark and sample after target user has been determined
Locating warehouse is sent to distributed post and subscribes in message system kafka, wherein kafka is a kind of distribution of high-throughput
Formula distribution subscription message system, the purpose is to be unified on line and at offline message by the loaded in parallel mechanism of Hadoop
Reason.When detecting the target order of target user, server can be by calling a http interface by order information (as generated
Warehouse belonging to the article for including in the article mark and order for including in the user identifier of the order, order) it is synchronized to
In kafka.Server can also monitor the data in kafka using storm task, once new data are received in kafka,
Data in kafka will be analyzed and processed.Wherein, storm provides a kind of generic primitives for distributed calculate in real time,
It can be used to handle message and more new database in real time, can be used for doing continuous-query to data flow, it will knot when calculating
Fruit is exported in the form of data flow to user.
The method provided by the above embodiment for distributing sample of the application, can effectively improve the clear of shopping website
The amount of looking at, while may be implemented more accurately to distribute sample.
With further reference to Fig. 5, as the realization to method shown in above-mentioned each figure, this application provides one kind for distributing sample
One embodiment of the device of product, the Installation practice is corresponding with embodiment of the method shown in Fig. 2, which can specifically answer
For in various electronic equipments.
As shown in figure 5, the device 500 for distributing sample of the present embodiment includes: that information acquisition unit 501, probability are true
Order member 502, user's determination unit 503 and sample allocation unit 504.
Wherein, information acquisition unit 501, for obtaining the sample message of sample.
Probability determining unit 502, for analyzing each user's purchase based on sample message and preset user portrait model
Buy the probability of article corresponding with sample.
Above-mentioned user draws a portrait model for characterizing the corresponding relationship between sample message and the purchase probability of user.
User's determination unit 503, for determining at least one use according to above-mentioned probability and preset user information list
Family is target user.
Sample allocation unit 504, for sample to be distributed to target user.
In some optional implementations of the present embodiment, above-mentioned sample message includes sample mark and sample number
Amount.Then above-mentioned probability determining unit 502 can be further used for: by sample mark and sample size input user's portrait mould
Type determines that each user buys the probability of the corresponding article of sample.
In some optional implementations of the present embodiment, above-mentioned sample message includes sample type, above-mentioned user's letter
Breath list includes the order generation moment that user generates order.Then above-mentioned user's determination unit 503 may further include in Fig. 5
Unshowned order generates moment determining module and first object user's determining module.
Wherein, order generates moment determining module, for being the first kind in response to sample type, determines that probability is greater than the
The order of the user of one preset value generates the moment.
First object user's determining module is target for determining that order generates the user that the moment is located in preset time period
User.
In some optional implementations of the present embodiment, above-mentioned sample message further includes the first storehouse of sample storage
Library, above-mentioned user information list include target user generate target order in include article mark.Then above-mentioned sample distribution
Unit 504 may further include unshowned warehouse determining module, match orders determining module and the first sample point in Fig. 5
With module.
Wherein, warehouse determining module, for determining the second of the article storage for including in target order according to article mark
Warehouse.
Match orders determining module, for determining in target order according to the distance between the first warehouse and the second warehouse
With the match orders of sample match.
Sample for determining the matching user for generating match orders, and is distributed to matching and used by the first sample distribution module
Family.
In some optional implementations of the present embodiment, above-mentioned sample message include sample type, sample price with
And sample size.Then above-mentioned user's determination unit 503 may further include unshowned purchasing power determining module in Fig. 5, purchase
Buy index determining module and second target user's determining module.
Wherein, purchasing power determining module, for being that Second Type or sample price are greater than second in advance in response to sample type
If value, according to user's portrait model, determine that each user buys the purchasing power of the corresponding article of sample.
Buying policy index determining module, for determining that the purchase of each user refers to according to above-mentioned probability and above-mentioned purchasing power
Number.
Second target user's determining module, for buying policy index to be ranked up according to descending, before determining in sequence
Sample size user is target user.
In some optional implementations of the present embodiment, above-mentioned institute's user information list includes user identifier and receipts
Goods address.Then above-mentioned sample allocation unit 504 may further include in Fig. 5 at unshowned order generation module and order
Manage module.
Wherein, order generation module, for generating new order according to sample message, user identifier and shipping address.
Ordering Module, for handling new order.
In some optional implementations of the present embodiment, above-mentioned apparatus can also include unshowned conversion in Fig. 5
User's detection unit and conversion ratio determination unit.
Wherein, user's detection unit is converted, for detecting in target user, the corresponding object of sample is bought after receiving sample
The quantity of the conversion user of product.
Conversion ratio determination unit, for determining turning for sample according to the quantity of target user and the quantity of conversion user
Rate.
The application's is provided by the above embodiment for distributing the device of sample, obtains the sample message of sample first, so
Afterwards based on above-mentioned sample message and preset user portrait model, analyzes each user and buy article corresponding with above-mentioned sample
Probability, wherein for characterizing the corresponding relationship between sample message and the purchase probability of user, then above-mentioned user draws a portrait model
According to determining probability and preset user information list, determine that at least one user is target user, then by sample point
Dispensing target user.The device of the present embodiment had not only realized effectively management sample, but also may be implemented targetedly to distribute sample
Product, while increasing the probability that user buys the corresponding article of sample after obtaining sample by browsing shopping website.
It should be appreciated that for distributing the unit 501 recorded in the device 500 of sample to unit 504 respectively and in reference Fig. 2
Each step in the method for description is corresponding.As a result, above with respect to the operation and feature of the method description for distributing sample
It is equally applicable to device 500 and unit wherein included, details are not described herein.The corresponding units of device 500 can be with server
In unit cooperate to realize the scheme of the embodiment of the present application.
Below with reference to Fig. 6, it illustrates the computer systems 600 for the server for being suitable for being used to realize the embodiment of the present application
Structural schematic diagram.Server shown in Fig. 6 is only an example, should not function and use scope band to the embodiment of the present application
Carry out any restrictions.
As shown in fig. 6, computer system 600 includes central processing unit (CPU) 601, it can be read-only according to being stored in
Program in memory (ROM) 602 or be loaded into the program in random access storage device (RAM) 603 from storage section 608 and
Execute various movements appropriate and processing.In RAM 603, also it is stored with system 600 and operates required various programs and data.
CPU 601, ROM 602 and RAM 603 are connected with each other by bus 604.Input/output (I/O) interface 605 is also connected to always
Line 604.
I/O interface 605 is connected to lower component: the importation 606 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 607 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 608 including hard disk etc.；
And the communications portion 609 of the network interface card including LAN card, modem etc..Communications portion 609 via such as because
The network of spy's net executes communication process.Driver 610 is also connected to I/O interface 605 as needed.Detachable media 611, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 610, in order to read from thereon
Computer program be mounted into storage section 608 as needed.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising carrying is on a machine-readable medium
Computer program, which includes the program code for method shown in execution flow chart.In such implementation
In example, which can be downloaded and installed from network by communications portion 609, and/or from detachable media 611
It is mounted.When the computer program is executed by central processing unit (CPU) 601, limited in execution the present processes upper
State function.
It should be noted that computer-readable medium described herein can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In this application, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In application, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF etc. are above-mentioned
Any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the application, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Being described in unit involved in the embodiment of the present application can be realized by way of software, can also be by hard
The mode of part is realized.Described unit also can be set in the processor, for example, can be described as: a kind of processor packet
Include information acquisition unit, probability determining unit, user's determination unit and sample allocation unit.Wherein, the title of these units exists
The restriction to the unit itself is not constituted in the case of certain, for example, information acquisition unit is also described as " obtaining sample
Sample message unit ".
As on the other hand, present invention also provides a kind of computer-readable medium, which be can be
Included in device described in above-described embodiment；It is also possible to individualism, and without in the supplying device.Above-mentioned calculating
Machine readable medium carries one or more program, when one or more of programs are executed by the device, so that should
Device: the sample message of sample is obtained；Drawn a portrait model based on sample message and preset user, analyze each user's purchase with
The probability of the corresponding article of sample, above-mentioned user draw a portrait model for characterizing pair between sample message and the purchase probability of user
It should be related to；According to above-mentioned probability and preset user information list, determine that at least one user is target user；By sample point
Dispensing target user.
Above description is only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the application, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from foregoing invention design, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed herein
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (16)
1. a kind of method for distributing sample, which is characterized in that the described method includes:
Obtain the sample message of sample；
Based on the sample message and preset user portrait model, analyzes each user and buy object corresponding with the sample
The probability of product, the user draw a portrait model for characterizing the corresponding relationship between sample message and the purchase probability of user；
According to the probability and preset user information list, determine that at least one user is target user；
The sample is distributed into the target user.
2. the method according to claim 1, wherein the sample message includes sample mark and sample number
Amount；And
It is described to be based on the sample message and preset user portrait model, it is corresponding with the sample to analyze each user's purchase
Article probability, comprising:
The sample is identified and the sample size inputs user's portrait model, determines that each user buys the sample
The probability of the corresponding article of product.
3. the user believes the method according to claim 1, wherein the sample message includes sample type
Breath list includes the order generation moment that user generates order；And
It is described according to the probability and preset user information list, determine that at least one user is target user, comprising:
It is the first kind in response to the sample type, when determining that the probability is greater than the order generation of the user of the first preset value
It carves；
Determining that the order generates the user that the moment is located in preset time period is the target user.
4. according to the method described in claim 3, it is characterized in that, the sample message further includes the first of the sample storage
Warehouse, the user information list include target user generate target order in include article mark；And
It is described that the sample is distributed into the target user, comprising:
According to the article mark, the second warehouse of the article storage for including in the target order is determined；
According to the distance between first warehouse and second warehouse, determine in the target order with the sample match
Match orders；
It determines the matching user for generating the match orders, and the sample is distributed into the matching user.
5. the method according to claim 1, wherein the sample message include sample type, sample price with
And sample size；And
It is described according to the probability and preset user information list, determine that at least one user is target user, comprising:
It is that Second Type or the sample price are greater than the second preset value in response to the sample type, is drawn a portrait according to the user
Model determines that each user buys the purchasing power of the corresponding article of the sample；
According to the probability and the purchasing power, the buying policy index of each user is determined；
The buying policy index is ranked up according to descending, determines that the preceding sample size user is institute in the sequence
State target user.
6. according to the method described in claim 5, it is characterized in that, the user information list includes user identifier and receives
Address；And
It is described that the sample is distributed into the target user, comprising:
According to the sample message, the user identifier and the shipping address, new order is generated；
Handle new order.
7. method described in one of -6 according to claim 1, which is characterized in that the method also includes:
It detects in the target user, the number of the conversion user of the corresponding article of the sample is bought after receiving the sample
Amount；
According to the quantity of the target user and the quantity of the conversion user, the conversion ratio of the sample is determined.
8. a kind of for distributing the device of sample, which is characterized in that described device includes:
Information acquisition unit, for obtaining the sample message of sample；
Probability determining unit, for analyzing each user's purchase based on the sample message and preset user portrait model
The probability of article corresponding with the sample, the user draw a portrait model be used to characterize sample message and user purchase probability it
Between corresponding relationship；
User's determination unit, for determining that at least one user is mesh according to the probability and preset user information list
Mark user；
Sample allocation unit, for the sample to be distributed to the target user.
9. device according to claim 8, which is characterized in that the sample message includes sample mark and sample number
Amount；And
The probability determining unit is further used for:
The sample is identified and the sample size inputs user's portrait model, determines that each user buys the sample
The probability of the corresponding article of product.
10. device according to claim 8, which is characterized in that the sample message includes sample type, user's letter
Breath list includes the order generation moment that user generates order；And
User's determination unit includes:
Order generates moment determining module, for being the first kind in response to the sample type, determines that the probability is greater than the
The order of the user of one preset value generates the moment；
First object user's determining module is described for determining that the order generation moment is located at the user in preset time period
Target user.
11. device according to claim 10, which is characterized in that the sample message further includes the of sample storage
One warehouse, the user information list include target user generate target order in include article mark；And
The sample allocation unit includes:
Warehouse determining module, for determining the second of the article storage for including in the target order according to the article mark
Warehouse；
Match orders determining module, for determining the mesh according to the distance between first warehouse and second warehouse
Mark the match orders in order with the sample match；
The sample for determining the matching user for generating the match orders, and is distributed to institute by the first sample distribution module
State matching user.
12. device according to claim 8, which is characterized in that the sample message include sample type, sample price with
And sample size；And
User's determination unit includes:
Purchasing power determining module, for being that Second Type or the sample price are greater than second and preset in response to the sample type
Value determines that each user buys the purchasing power of the corresponding article of the sample according to user portrait model；
Buying policy index determining module, for determining the buying policy index of each user according to the probability and the purchasing power；
Second target user's determining module determines the sequence for the buying policy index to be ranked up according to descending
In the preceding sample size user be the target user.
13. device according to claim 12, which is characterized in that the user information list includes user identifier and receipts
Goods address；And
The sample allocation unit includes:
Order generation module, for generating new order according to the sample message, the user identifier and the shipping address
It is single；
Ordering Module, for handling new order.
14. according to the described in any item devices of claim 8-13, which is characterized in that described device further include:
User's detection unit is converted, for detecting in the target user, it is corresponding that the sample is bought after receiving the sample
Article conversion user quantity；
Conversion ratio determination unit, described in determining according to the quantity of the target user and the quantity of the conversion user
The conversion ratio of sample.
15. a kind of server characterized by comprising
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now method as described in any in claim 1-7.
16. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The method as described in any in claim 1-7 is realized when execution.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710437157.4A CN109034854A (en) | 2017-06-12 | 2017-06-12 | Method and apparatus for distributing sample |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710437157.4A CN109034854A (en) | 2017-06-12 | 2017-06-12 | Method and apparatus for distributing sample |
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|---|---|
| CN109034854A true CN109034854A (en) | 2018-12-18 |
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| CN201710437157.4A Pending CN109034854A (en) | 2017-06-12 | 2017-06-12 | Method and apparatus for distributing sample |
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Application publication date: 20181218 |