CN109360012B - Advertisement delivery channel selection method and device, storage medium and electronic equipment - Google Patents
Advertisement delivery channel selection method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure belongs to the technical field of big data, and relates to a method and a device for selecting an advertisement delivery channel, a computer-readable storage medium and electronic equipment, wherein the method for selecting the advertisement delivery channel comprises the following steps: acquiring user data and product data respectively corresponding to a plurality of advertisement putting channels; obtaining average income of each user according to the product data, and obtaining user conversion rate according to the user data; obtaining expected delivery values of the advertisement delivery channels according to the average income of each user and the user conversion rate; inputting said average revenue per user, said user conversion rate, and said delivery expectation value into a data model to train said data model; and predicting the target delivery channel through the data model to obtain a predicted expected value, and obtaining the advertisement delivery channel to be delivered according to the predicted expected value. The method and the device can reduce the updating cost and accurately predict the advertising channel.
Description
Technical Field
The disclosure relates to the technical field of big data, in particular to a method for selecting an advertisement delivery channel, a device for selecting the advertisement delivery channel, a computer readable storage medium and electronic equipment.
Background
With the development of computer technology, electronic commerce has emerged. Electronic commerce is an activity of conducting transaction activities and related services on the Internet in an electronic transaction mode, and is the electronization and networking of each link of the traditional business activities. The network traffic is the root of the electronic commerce enterprise, the subsequent order conversion can be carried out only by the traffic, the network traffic is basically divided into two types, one type is the network traffic brought by new users, the other type is the potential of the existing old users, and the access frequency of the old users is improved, so that the network traffic is improved.
Channel delivery is a main means for expanding new users by a platform, and each channel delivery needs to cost, so that the delivery strength of good channels is increased for operators and products, and the delivery of poor channels is reduced or canceled as much as possible, so that the cost benefit is maximized. In the prior art, the total life cycle value LTV (life time value) is usually used as an index for supervising the effect of the channel, but the actual LTV needs to wait for a quite long time to be known, so most of LTVs are predicted, and the predicted value is usually deviated, so that the guidance of the channel and the amount of the channel is not facilitated.
Therefore, it is desirable to provide a new method and apparatus for selecting advertising channels.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure aims to provide a method for selecting an advertisement putting channel, a device for selecting the advertisement putting channel, a computer readable storage medium and electronic equipment, so that the refreshing cost of the putting channel is reduced at least to a certain extent, and the profit of advertisers is improved.
According to one aspect of the present disclosure, there is provided a method for selecting an advertisement delivery channel, comprising:
acquiring user data and product data respectively corresponding to a plurality of advertisement putting channels;
obtaining average income of each user according to the product data, and obtaining user conversion rate according to the user data;
obtaining expected delivery values of the advertisement delivery channels according to the average income of each user and the user conversion rate;
inputting said average revenue per user, said user conversion rate, and said delivery expectation value into a data model to train said data model;
and predicting the target delivery channel through the data model to obtain a predicted expected value, and obtaining the advertisement delivery channel to be delivered according to the predicted expected value.
In an exemplary embodiment of the present disclosure, the product data includes a number of purchases, a purchase price, and a total amount of impressions corresponding to each of the advertising channels.
In an exemplary embodiment of the present disclosure, obtaining average revenue per user from the product data includes:
acquiring a purchase total according to the purchase quantity and the purchase price;
and obtaining the average income per user according to the total purchase amount, the total put amount and the number of purchases.
In an exemplary embodiment of the present disclosure, obtaining a delivery expectation value for each of the advertisement delivery channels according to the average revenue per user and the user conversion rate includes:
calculating the delivery expectation value according to the following formula:
P＝a(M-m)+b(N-n)
wherein P is the expected value of delivery, M is the average income of each user corresponding to each advertisement delivery channel, N is the user conversion rate corresponding to each advertisement delivery channel, M is the set average income standard value of each user, N is the set user conversion rate standard value, a and b are coefficients, and 0< a <1,0< b <1.
In an exemplary embodiment of the present disclosure, training a data model based on the average revenue per user, the user conversion rate, and the delivery expectations includes:
and taking the average income per user and the user conversion rate as input vectors, and taking the corresponding input expected value as output vector to input the input expected value into the data model so as to train the data model.
In an exemplary embodiment of the present disclosure, predicting, by the data model, a target delivery channel to obtain a predicted expected value, and obtaining an advertisement delivery channel to be delivered according to the predicted expected value, including:
acquiring target user data and target advertisement data corresponding to the target delivery channel;
acquiring average income per user and target user conversion rate according to the target user data and the target advertisement data;
and inputting the average income per user of the target and the conversion rate of the target user into the data model to obtain the predicted expected value, and obtaining the advertising channel to be placed according to the predicted expected value.
In an exemplary embodiment of the present disclosure, obtaining an advertisement delivery channel to be delivered according to the predicted expected value includes:
comparing the predicted expected value with a predicted expected standard value;
and when the predicted expected value is higher than the predicted expected standard value, determining the advertisement delivery channel corresponding to the predicted expected value as the advertisement delivery channel to be delivered.
According to one aspect of the present disclosure, there is provided a selection apparatus of an advertisement delivery channel, comprising:
the data acquisition module is used for acquiring user data and product data corresponding to the advertisement delivery channels respectively;
the statistics module is used for acquiring average income of each user according to the product data and acquiring user conversion rate according to the user data;
the expected delivery value calculation module is used for obtaining expected delivery values of the advertisement delivery channels according to the average income of each user and the user conversion rate;
the training module is used for inputting the average income of each user, the user conversion rate and the expected throwing value into a data model so as to train the data model;
and the prediction module is used for predicting the target delivery channel through the data model so as to obtain a predicted expected value and obtaining the target advertisement delivery channel according to the predicted expected value.
According to one aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of selecting an advertising channel as described in any one of the above.
According to one aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of selecting an advertising channel of any one of the above via execution of the executable instructions.
The method for selecting the advertisement putting channel comprises the steps of collecting user data and product data corresponding to the putting channel, so that average income and user conversion rate of each user are obtained according to the user data and the product data; then calculating a delivery expected value of a delivery channel according to the average income of each user and the conversion rate of the user; and training a data model through average income per user, user conversion rate and corresponding release expected values, and finally predicting through the data model to guide advertisers to select release channels with large profit space. On one hand, the method reduces the refreshing cost and improves the profit of advertisers; on the other hand, the prediction result is accurate, and has stronger guiding significance.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a flow chart of a method of selecting an advertising channel;
FIG. 2 schematically illustrates an exemplary diagram of an application scenario for a method of selecting an advertising channel;
FIG. 3 schematically illustrates a block diagram of a selection apparatus of an advertising channel;
FIG. 4 schematically illustrates an example block diagram of an electronic device for implementing the above-described advertising channel selection method;
fig. 5 schematically illustrates a computer-readable storage medium for implementing the above-described advertising channel selection method.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
In this exemplary embodiment, a method for selecting an advertisement delivery channel is provided first, where the method for selecting an advertisement delivery channel may be run on a server, or may be run on a server cluster or a cloud server, or the like, and of course, those skilled in the art may also run the method of the present disclosure on other platforms according to requirements, which is not limited in particular in this exemplary embodiment. Referring to fig. 1, the method of selecting an advertisement delivery channel may include the steps of:
s110, obtaining user data and product data respectively corresponding to a plurality of advertisement delivery channels;
s120, obtaining average income of each user according to the product data, and obtaining user conversion rate according to the user data;
s130, obtaining expected delivery values of the advertisement delivery channels according to the average income of each user and the user conversion rate;
s140, inputting the average income of each user, the user conversion rate and the expected throwing value into a data model so as to train the data model;
s150, predicting the target delivery channel through the data model to obtain a predicted expected value, and obtaining the target advertisement delivery channel according to the predicted expected value.
In the method for selecting the advertisement putting channel, average income and user conversion rate of each user are obtained according to the user data and the product data; calculating expected delivery values of all advertisement delivery channels through average income and user conversion rate of each user, and training a data model according to the average income, the user conversion rate and the expected delivery values of each user; and finally, predicting the target delivery channel through the data model to obtain a predicted expected value. On one hand, the channel quality can be dynamically monitored, the updating cost is reduced, and the profit of advertisers is improved; on the other hand, the prediction result of the method is accurate, and the method can be used for accurately guiding a delivery channel.
Next, each step in the above-described user data authenticity analysis method in the present exemplary embodiment will be explained and described in detail with reference to fig. 2.
In step S110, user data and product data corresponding to a plurality of advertisement delivery channels, respectively, are acquired.
In an exemplary embodiment of the present disclosure, when using an application program in the terminal device 202, a user clicks to browse a plurality of advertisement pages, an access log of the user may be stored in the terminal device 202, the server 201 may obtain the access log of the user from the terminal device 202, and obtain user data from the access log of the user, where the terminal device 202 may be a browser, a smart phone, an iPad, or the like, and the present disclosure does not specifically limit this, the user data may include behavior data, attribute data, and space data of the user, where the behavior data includes a product path of browsing, a page browsing time, a number of page clicks, a number of purchases, and the like, the attribute data includes gender, age, occupation, and the like, and the space data includes a current address, a common address, and the like. While the advertising provider may provide product data including product name, purchase price, purchase quantity, advertising delivery quantity, total amount delivered, and the like.
In step S120, average revenue per user is obtained from the product data, while user conversion rate is obtained from the user data.
In exemplary embodiments of the present disclosure, after obtaining the user data and the product data, average revenue per user may be obtained from the product data, and user conversion rate may be obtained from the user data.
In an exemplary embodiment of the present disclosure, average revenue per user may be obtained according to product data, and a total purchase amount of the advertising channel may be obtained according to a purchase price and a purchase amount of a product placed in the advertising channel, and a calculation formula of the total purchase amount is shown in formula (1):
wherein A is buy To purchase the total amount, P i For each product, r is the purchase amount.
It should be noted that the price of the product may be different for each advertising channel, even though the same channel may be at different times, for example, to increase the activity of the user, the operator may promote various activities during holidays to decrease the price of the product, thus P i The values of (2) are not exactly the same.
In an exemplary embodiment of the present disclosure, after obtaining the total purchase amount, the average revenue per user may be obtained according to the total purchase amount, the total put amount, and the number of purchases, and the calculation formula of the average revenue per user is shown in formula (2):
wherein M is average income of each user corresponding to each advertisement delivery channel, A put To put in the total amount, P n Is the number of buyers.
In an exemplary embodiment of the present disclosure, users may be classified into old users, active users, and new users, and user conversion rates including conversion rates of active users into old users, new users into active users, and unregistered users into new users may be determined according to clicking actions of the users.
In step S130, a desired delivery value of each advertisement delivery channel is obtained according to the average income per user and the user conversion rate.
In exemplary embodiments of the present disclosure, the placement expectations for individual advertising channels may be calculated based on average revenue per user and user conversion rate. The calculation formula of the expected value is shown in formula (3):
P＝a(M-m)+b(N-n) (3)
wherein P is the expected value of delivery, M is the average income of each user corresponding to each advertisement delivery channel, N is the user conversion rate corresponding to each advertisement delivery channel, M is the set average income standard value of each user, N is the set user conversion rate standard value, a and b are coefficients, and 0< a <1,0< b <1.
As can be seen from the formula (3), when the average income of each user is higher than the average income standard value of each user and the conversion rate of the user is higher than the conversion rate standard value of the user, the corresponding delivery channel is the delivery channel with larger profit space, and the delivery channel with the largest profit can be obtained by comparing the expected value of the delivery of each delivery channel.
In step S140, the average revenue per user, the user conversion rate, and the delivery expectation value are input into a data model to train the data model.
In an exemplary embodiment of the present disclosure, after obtaining the input expected value, the average revenue per user and the user conversion rate may be used as input vectors, and the input expected value may be used as output vectors, to be input to the data model to perform machine training on the data model. The data model can be a neural network model, a logistic regression model and other machine learning models, and is trained through average income, user conversion rate and expected throwing value of each user corresponding to a plurality of advertisement throwing channels so as to obtain the data model with excellent performance.
In step S150, the target delivery channel is predicted by the data model, so as to obtain a predicted expected value, and the advertisement delivery channel to be delivered is obtained according to the predicted expected value.
In the exemplary embodiment of the disclosure, after the data model is trained, user data and product data corresponding to a target delivery channel in a preset time period can be collected, average income and user conversion rate of each user are obtained according to the user data and the product data, then the average income and the user conversion rate of each user are input into the data model, the data model is used for processing and outputting a predicted expected value, and an advertisement provider can evaluate the target delivery channel according to the predicted expected value to guide the delivered advertisement quantity. Further, a predicted expected standard value can be set, and when the predicted expected value is higher than the predicted expected standard value, the target delivery channel is good in quality, can be used as an advertisement delivery channel to be delivered, and increases the delivery quantity; when the predicted expected value is lower than the predicted expected standard value, the quality of the target delivery channel is poor, and the advertisement provider should consider reducing the delivery amount or stopping the delivery.
In an exemplary embodiment of the present disclosure, in order to further reduce the pull-up cost, the total amount of advertising may be set to a fixed value, and the average revenue per user and the user conversion rate are detected by changing the amount of advertising in the advertising channel.
By the method for selecting the advertisement delivery channels, the delivery expected value of each delivery channel can be accurately predicted, advertisement suppliers are guided to select the delivery channels with large profit space, and the new drawing cost is reduced.
The disclosure also provides a device for selecting the advertisement delivery channel. Referring to fig. 3, the advertisement delivery channel selecting apparatus may include a data acquisition module 310, a statistics module 320, a delivery expectation calculation 330, a training module 340, and a channel acquisition module 350. Wherein:
a data obtaining module 310, configured to obtain user data and product data corresponding to the multiple advertisement delivery channels respectively;
a statistics module 320, configured to obtain average revenue per user according to the product data, and obtain user conversion rate according to the user data;
a delivery expected value calculation module 330, configured to obtain a delivery expected value of each advertisement delivery channel according to the average income per user and the user conversion rate;
a training module 340, configured to input the average revenue per user, the user conversion rate, and the delivery expectation value into a data model, so as to train the data model;
the channel obtaining module 350 is configured to predict the target delivery channel through the data model, so as to obtain a predicted expected value, and obtain the advertisement delivery channel to be delivered according to the predicted expected value.
The specific details of each module in the above-mentioned advertisement delivery channel selection device are described in detail in the corresponding advertisement delivery channel selection method, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 400 according to such an embodiment of the present disclosure is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 4, the electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: the at least one processing unit 410, the at least one memory unit 420, and a bus 430 connecting the various system components, including the memory unit 420 and the processing unit 410.
Wherein the storage unit stores program code that is executable by the processing unit 410 such that the processing unit 410 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 410 may perform step S110 as shown in fig. 1: acquiring user data and product data respectively corresponding to a plurality of advertisement putting channels; step S120: obtaining average income of each user according to the product data, and obtaining user conversion rate according to the user data; step S130: obtaining expected delivery values of the advertisement delivery channels according to the average income of each user and the user conversion rate; step S140: inputting said average revenue per user, said user conversion rate, and said delivery expectation value into a data model to train said data model; step S150: and predicting the target delivery channel through the data model to obtain a predicted expected value, and obtaining the advertisement delivery channel to be delivered according to the predicted expected value.
The storage unit 420 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The electronic device 400 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 450. Also, electronic device 400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 over the bus 430. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 5, a program product 500 for implementing the above-described method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (9)
1. A method of selecting an advertising channel, comprising:
acquiring user data and product data respectively corresponding to a plurality of advertisement putting channels;
obtaining average income of each user according to the product data, and obtaining user conversion rate according to the user data;
obtaining expected delivery values of the advertisement delivery channels according to the average income of each user and the user conversion rate;
inputting said average revenue per user, said user conversion rate, and said delivery expectation value into a data model to train said data model;
predicting a target delivery channel through the data model to obtain a predicted expected value, and obtaining an advertisement delivery channel to be delivered according to the predicted expected value;
wherein obtaining a desired delivery value for each of the advertising channels based on the average revenue per user and the user conversion rate comprises:
the expected delivery value is calculated according to the following formula:
P＝a(M-m)+b(N-n)
wherein P is the expected value of delivery, M is the average income of each user corresponding to each advertisement delivery channel, N is the user conversion rate corresponding to each advertisement delivery channel, M is the set average income standard value of each user, N is the set user conversion rate standard value, a and b are coefficients, and 0< a <1,0< b <1.
2. The method of claim 1, wherein the product data includes a number of purchases, a purchase price, and a total amount of impressions for each of the advertising channels.
3. The method of selecting an advertising channel according to claim 2, wherein obtaining average revenue per user based on the product data comprises:
acquiring a purchase total according to the purchase quantity and the purchase price;
and obtaining the average income per user according to the total purchase amount, the total put amount and the number of purchases.
4. The method of claim 1, wherein training a data model based on the average revenue per user, the user conversion rate, and the placement expectations comprises:
and taking the average income per user and the user conversion rate as input vectors, and taking the corresponding input expected value as output vector to input the input expected value into the data model so as to train the data model.
5. The method for selecting an advertisement delivery channel according to claim 1, wherein predicting the target delivery channel by the data model to obtain a predicted expected value, and obtaining the advertisement delivery channel to be delivered according to the predicted expected value comprises:
acquiring target user data and target advertisement data corresponding to the target delivery channel;
acquiring average income per user and target user conversion rate according to the target user data and the target advertisement data;
inputting the target average revenue per user and the target user conversion rate into the data model to obtain the predicted expected value;
and acquiring the advertisement putting channel to be put according to the predicted expected value.
6. The method of claim 1, wherein obtaining a targeted advertising channel based on the predicted desired value comprises:
comparing the predicted expected value with a predicted expected standard value;
and when the predicted expected value is higher than the predicted expected standard value, determining the advertisement delivery channel corresponding to the predicted expected value as the advertisement delivery channel to be delivered.
7. A device for selecting an advertising channel, comprising:
the data acquisition module is used for acquiring user data and product data corresponding to the advertisement delivery channels respectively;
the statistics module is used for acquiring average income of each user according to the product data and acquiring user conversion rate according to the user data;
the expected value calculation module for obtaining expected values of the advertisement delivery channels according to the average income per user and the user conversion rate comprises the following steps:
the expected delivery value is calculated according to the following formula:
P＝a(M-m)+b(N-n)
wherein P is the expected value of delivery, M is the average income of each user corresponding to each advertisement delivery channel, N is the user conversion rate corresponding to each advertisement delivery channel, M is the set average income standard value of each user, N is the set user conversion rate standard value, a and b are coefficients, and 0< a <1,0< b <1;
the training module is used for inputting the average income of each user, the user conversion rate and the expected throwing value into a data model so as to train the data model;
and the channel acquisition module is used for predicting the target delivery channel through the data model so as to acquire a predicted expected value and acquiring the advertisement delivery channel to be delivered according to the predicted expected value.
8. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of selecting an advertising channel as claimed in any one of claims 1 to 6.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of selecting an advertising channel of any one of claims 1-6 via execution of the executable instructions.
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