CN112465573B - Multi-channel intelligent advertisement putting method and device and electronic equipment - Google Patents

Multi-channel intelligent advertisement putting method and device and electronic equipment Download PDF

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CN112465573B
CN112465573B CN202110148196.9A CN202110148196A CN112465573B CN 112465573 B CN112465573 B CN 112465573B CN 202110148196 A CN202110148196 A CN 202110148196A CN 112465573 B CN112465573 B CN 112465573B
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delivery
customer
correction value
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CN112465573A (en
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陈博
黎文杰
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Beijing Qilu Information Technology Co Ltd
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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
    • G06Q30/0241—Advertisements
    • G06Q30/0242—Determining effectiveness of advertisements
    • G06Q30/0244—Optimization
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0249—Advertisements based upon budgets or funds
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0277—Online advertisement

Abstract

The invention provides a multi-channel intelligent advertisement delivery method, a multi-channel intelligent advertisement delivery device and electronic equipment. The method comprises the following steps: acquiring multi-channel historical release data, and fitting a calculation relation between the passenger capacity and the average piece cost according to the historical release data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels; determining a target passenger capacity according to user input, and automatically calculating an optimal delivery strategy of each channel by adopting the combined model, wherein the delivery strategy is used for controlling the average cost of each channel; and automatically carrying out multi-channel network advertisement delivery according to the optimal delivery strategy of each channel. The method reduces the trial and error cost of the advertiser, quickly finds the balance point between the channel and the cost target, efficiently controls the delivery cost and improves the delivery efficiency.

Description

Multi-channel intelligent advertisement putting method and device and electronic equipment
Technical Field
The invention relates to the field of computer information processing, in particular to a multi-channel intelligent advertisement putting method and device and electronic equipment.
Background
In recent years, online advertising has gained increasing weight throughout the advertising industry. Real Time Bidding (RTB) advertisements in online advertisements have an increasing weight year by year due to their good conversion effect. In order to achieve the promotion target, advertisers set corresponding promotion plans, and the promotion plans comprise setting information such as target audiences, media resource positions, bids, exhibition creatives and the like. The advertiser can bid in real time aiming at the network flow (the network flow consumed by browsing the creative) meeting the requirements, and the creative set by the advertiser is displayed to the bid network flow, so that the popularization target of the advertiser is achieved, and the advertising effect is greatly improved.
The amount of network traffic for advertising on the current internet is enormous and varies in forms. Due to the limitation of advertisement promotion targets and promotion budgets, advertisers need to select better target network traffic from a plurality of network traffic for bidding and releasing under the rated promotion budget, so that the utilization efficiency of the budget is maximized, and the advertisement effect is improved. Currently, advertisers can know the quality of the delivery effect through continuous testing. Such testing greatly increases the cost and efficiency of placement by advertisers.
In the related art, a method based on effect simulation is provided, which mainly comprises: and simulating the advertisement effect for each option by using historical data, so that the advertiser can select several options with better advertisement effect to promote the advertisement. However, the advertisement effect simulated by the method is greatly different from the actual advertisement effect, and the simulation precision is low.
Therefore, there is a need to provide a more effective intelligent advertisement delivery method.
Disclosure of Invention
In order to reduce the trial and error cost of an advertiser, quickly find a balance point between a channel and a cost target, efficiently control the delivery cost and improve the delivery efficiency, the invention provides a multi-channel intelligent advertisement delivery method, which is used for dynamically determining a multi-channel network advertisement delivery strategy and comprises the following steps: acquiring multi-channel historical release data, and fitting a calculation relation between the passenger capacity and the average piece cost according to the historical release data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels; determining a target passenger capacity according to user input, and automatically calculating an optimal delivery strategy of each channel by adopting the combined model, wherein the delivery strategy is used for controlling the average cost of each channel; and automatically carrying out multi-channel network advertisement delivery according to the optimal delivery strategy of each channel.
Preferably, the calculation model comprises calculating an average cost to acquire an object for a channel using the following formula:
Figure 406067DEST_PATH_IMAGE001
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE002
calculating the average cost of acquiring the clients of the delivery channel by using the CPC index;
Figure 12629DEST_PATH_IMAGE003
releasing cost increase parameters for the customers of the release channel;
Figure DEST_PATH_IMAGE004
delivering a first correction value of a cost increase parameter for a guest of a delivery channel,
Figure 537151DEST_PATH_IMAGE005
a corrected value of the lowest value of the release cost for the guests of the release channel,
Figure DEST_PATH_IMAGE006
=0；
Figure 993278DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure DEST_PATH_IMAGE008
a second correction value of the cost increase parameter is released for the guest;
Figure 871236DEST_PATH_IMAGE009
and obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the calculation model comprises calculating the average cost for obtaining the clients of the delivery channel by using the following formula:
Figure DEST_PATH_IMAGE010
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
calculating the average cost of acquiring the clients of the delivery channel by using the CPM index;
Figure DEST_PATH_IMAGE012
a first growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure DEST_PATH_IMAGE013
is a first correction value for the first growth parameter,
Figure DEST_PATH_IMAGE014
a first correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 801145DEST_PATH_IMAGE005
=0；
Figure 136050DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure DEST_PATH_IMAGE015
a second correction value for the first growth parameter;
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a second growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure DEST_PATH_IMAGE017
for the first correction value of the second growth parameter,
Figure DEST_PATH_IMAGE018
a second correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure DEST_PATH_IMAGE019
=0；
Figure DEST_PATH_IMAGE020
a second correction value for a second growth parameter;
Figure DEST_PATH_IMAGE021
a third growth parameter of the drop cost for the customer of the drop channel;
Figure DEST_PATH_IMAGE022
for the first correction value of the third growth parameter,
Figure DEST_PATH_IMAGE023
a third correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 626943DEST_PATH_IMAGE023
=0；
Figure DEST_PATH_IMAGE024
a second correction value for a third growth parameter;
Figure DEST_PATH_IMAGE025
and obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the method further comprises the following steps: and acquiring the recent passenger capacity and the average cost of the parts of each channel in real time, and updating the parameters of each calculation model in the combined model.
Preferably, the optimal delivery strategy of each channel is updated by adopting the combined model according to the current remaining target passenger capacity.
Preferably, the delivery strategy includes: time and/or frequency of impressions and price of impressions.
In addition, the invention also provides a multi-channel intelligent advertisement delivery device, which is used for dynamically determining a multi-channel network advertisement delivery strategy and comprises the following steps: the acquisition processing module is used for acquiring multi-channel historical release data, and fitting a calculation relation between the passenger capacity and the average piece cost according to the historical release data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels; the calculation module is used for determining target passenger capacity according to user input and automatically calculating the optimal delivery strategy of each channel by adopting the combined model, wherein the delivery strategy is used for controlling the average cost of each channel; and the delivery module automatically delivers the multi-channel network advertisement according to the optimal delivery strategy of each channel.
Preferably, the calculating module further calculates the average cost for obtaining the customer for the channel using the following formula:
Figure 105329DEST_PATH_IMAGE001
，
wherein the content of the first and second substances,
Figure 279958DEST_PATH_IMAGE002
calculating the average cost of acquiring the clients of the delivery channel by using the CPC index;
Figure 349545DEST_PATH_IMAGE003
releasing cost increase parameters for the customers of the release channel;
Figure 750571DEST_PATH_IMAGE004
delivering a first correction value of a cost increase parameter for a guest of a delivery channel,
Figure 360543DEST_PATH_IMAGE005
a corrected value of the lowest value of the release cost for the guests of the release channel,
Figure 592942DEST_PATH_IMAGE006
=0；
Figure 505534DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure 252910DEST_PATH_IMAGE008
a second correction value of the cost increase parameter is released for the guest;
Figure 40475DEST_PATH_IMAGE009
and obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the calculation model comprises calculating the average cost for obtaining the clients of the delivery channel by using the following formula:
Figure 392959DEST_PATH_IMAGE010
，
wherein the content of the first and second substances,
Figure 866666DEST_PATH_IMAGE011
calculating the average cost of acquiring the clients of the delivery channel by using the CPM index;
Figure 976704DEST_PATH_IMAGE012
a first growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure 928480DEST_PATH_IMAGE013
is a first correction value for the first growth parameter,
Figure 135470DEST_PATH_IMAGE014
a first correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 389865DEST_PATH_IMAGE005
=0；
Figure 111834DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure 70562DEST_PATH_IMAGE015
a second correction value for the first growth parameter;
Figure 69742DEST_PATH_IMAGE016
a second growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure 885252DEST_PATH_IMAGE017
for the first correction value of the second growth parameter,
Figure 202838DEST_PATH_IMAGE018
a second correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 699679DEST_PATH_IMAGE019
=0 ；
Figure 412420DEST_PATH_IMAGE020
a second correction value for a second growth parameter;
Figure 539776DEST_PATH_IMAGE021
a third growth parameter of the drop cost for the customer of the drop channel;
Figure 970757DEST_PATH_IMAGE022
for the first correction value of the third growth parameter,
Figure 271288DEST_PATH_IMAGE023
a third correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 979481DEST_PATH_IMAGE023
=0；
Figure 136793DEST_PATH_IMAGE024
second correction value for third growth parameter；
Figure 930437DEST_PATH_IMAGE025
And obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the system further comprises a parameter updating module, wherein the parameter updating module is used for acquiring recent passenger capacity and average piece cost of each channel in real time and updating parameters of each calculation model in the combined model.
Preferably, the system further comprises a strategy updating module, and the strategy updating module adopts the combined model to update the optimal delivery strategy of each channel according to the current remaining target passenger capacity.
Preferably, the delivery strategy includes: time and/or frequency of impressions and price of impressions.
In addition, the present invention also provides an electronic device, wherein the electronic device includes: a processor; and a memory storing computer executable instructions that, when executed, cause the processor to perform the multi-channel smart advertisement delivery method of the present invention.
In addition, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the multi-channel smart advertisement delivery method of the present invention.
Advantageous effects
Compared with the prior art, the multi-channel intelligent advertisement putting method can more accurately simulate the process of getting a customer and effectively reduce the trial and error cost of an advertiser; by simulating the passenger obtaining process of each channel, the calculation relation between the passenger obtaining amount and the average piece cost can be more accurately determined, a quick-calculation combined calculation model is constructed, the balance point between each releasing channel and the cost target can be quickly found, the optimal releasing scale is effectively obtained, the releasing cost is efficiently controlled, and the releasing efficiency is also improved.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive faculty.
FIG. 1 is a flow chart of an example of a multi-channel smart advertisement delivery method of the present invention.
FIG. 2 is a flow chart of another example of a multi-channel smart advertisement delivery method of the present invention.
FIG. 3 is a flow chart of yet another example of a multi-channel smart advertisement delivery method of the present invention.
Fig. 4 is a schematic diagram of an example of a multi-channel smart advertisement delivery apparatus according to embodiment 2 of the present invention.
Fig. 5 is a schematic diagram of another example of the multi-channel smart advertisement delivery apparatus according to embodiment 2 of the present invention.
Fig. 6 is a schematic diagram of still another example of a multi-channel smart advertisement delivery apparatus according to embodiment 2 of the present invention.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention.
Fig. 8 is a block diagram of an exemplary embodiment of a computer-readable medium according to the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
In view of the above problems, the invention provides a multi-channel intelligent advertisement delivery method, which can more accurately simulate a customer acquisition process, and can more accurately determine the calculation relationship between the customer acquisition amount and the per-piece cost by simulating the customer acquisition process of each channel to construct a quick-calculation combined calculation model, so that a balance point between each delivery channel and a cost target can be quickly found, the optimal delivery scale is effectively obtained, the delivery cost is efficiently controlled, and the delivery efficiency is also improved. The process of the present invention will be illustrated below by specific examples.
Example 1
Hereinafter, an embodiment of a multi-channel smart advertisement delivery method of the present invention will be described with reference to fig. 1 to 3.
FIG. 1 is a flow chart of an example of a multi-channel smart advertisement delivery method of the present invention. As shown in fig. 1, a multi-channel smart advertisement delivery method includes the following steps.
Step S101, obtaining multi-channel historical release data, fitting a calculation relation between the passenger capacity and the average piece cost according to the historical release data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels.
And S102, determining target passenger capacity according to user input, and automatically calculating the optimal delivery strategy of each channel by adopting the combined model, wherein the delivery strategy is used for controlling the average cost of each channel.
And step S103, automatically carrying out multi-channel network advertisement delivery according to the optimal delivery strategy of each channel.
In this example, the advertisement delivery method of the present invention is used to dynamically determine a multi-channel network advertisement delivery policy, where the advertisement delivery channels include four types of advertisement delivery channels, namely a first delivery channel, a second delivery channel, a third delivery channel and a fourth delivery channel. Furthermore, the four types of advertisement delivery channels have different charging modes. The invention determines the optimal simulation data, reduces the total cost of the mixed (or combined) channels, determines the average cost of the fixed parts, calculates the new customer scale of the total available increment and determines the optimal multi-channel network advertisement putting strategy by producing and simulating the customer acquisition capacity of each channel. The concrete description is as follows.
First, in step S101, multi-channel historical delivery data is obtained, and a calculation relationship between the passenger volume and the average piece cost is fitted according to the historical delivery data of each channel to establish a combined model, where the combined model includes calculation models of multiple channels.
Specifically, multi-channel historical delivery data, historical passenger capacity and historical delivery cost data of each channel are obtained. In this example, the multiple channels are the four types of advertisement delivery channels with different charging types, where the charging types include two charging types, namely CPC and CPM, and the index parameter in the corresponding calculation model is determined according to a specific charging type.
It should be noted that in other examples, the billing type of advertisement placement further includes cpc (cost Per click), cpm (cost Per mille), cpa (cost Per action), cpi (cost Per install), cps (cost Per sales), and so on. The foregoing is described by way of preferred examples only and is not to be construed as limiting the invention.
Further, the calculation relationship between the passenger capacity and the average piece cost is fitted by using the historical release data of each channel, the historical passenger capacity and the historical release cost data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels, in the example, a first calculation model and a second calculation model.
Specifically, the first calculation model includes calculating an average cost to acquire the customer for the channel using the following formula:
Figure 769080DEST_PATH_IMAGE001
，
wherein the content of the first and second substances,
Figure 721992DEST_PATH_IMAGE002
calculating the average cost of acquiring the clients of the delivery channel by using the CPC index;
Figure 424107DEST_PATH_IMAGE003
releasing cost increase parameters for the customers of the release channel;
Figure 767364DEST_PATH_IMAGE004
delivering a first correction value of a cost increase parameter for a guest of a delivery channel,
Figure 737594DEST_PATH_IMAGE005
for obtaining the lowest cost for the delivery channelThe value of the correction is such that,
Figure 482696DEST_PATH_IMAGE006
=0；
Figure 653914DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure 484467DEST_PATH_IMAGE008
a second correction value of the cost increase parameter is released for the guest;
Figure 337016DEST_PATH_IMAGE009
and obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the number of the customers acquired is increased in a specific number increasing manner, for example, in a number increasing manner of 1000, the production simulates the customer acquisition situation of each delivery channel, and specifically includes generating an operation customer acquisition simulation, generating an application store customer acquisition simulation, generating a CPC, CPM or CPA customer acquisition simulation, and the like. Therefore, the customer obtaining process can be simulated more accurately, and the trial and error cost of the advertiser can be effectively reduced.
As shown in fig. 2, a step S201 of determining whether the guest remaining amount is zero within a predetermined time.
In step S201, it is determined whether the guest remaining amount is zero within a predetermined time.
Specifically, the target passenger capacity is split to form the passenger capacity of each delivery channel, and according to the set passenger capacity (smaller than the target passenger capacity) of each delivery channel, whether the remaining passenger capacity and the total remaining passenger capacity of each delivery channel are zero or not is judged within a predetermined time such as one month.
Further, based on the judgment result of the remaining passenger obtaining amount, the passenger obtaining amount corresponding to the lowest cost of the passenger obtaining pieces of each delivery channel is determined.
Preferably, based on each simulation result, optimal simulation data is determined, and the optimal simulation data comprises the minimum target cost, the maximum passenger capacity or the minimum residual passenger capacity of each delivery channel.
Further, firstlySetting initial customer placement cost growth parameters
Figure 264521DEST_PATH_IMAGE003
And determining a cost increase parameter for the delivery of the guest
Figure 809903DEST_PATH_IMAGE003
The correction coefficient is used for parameter correction, so that each parameter of the calculation model is further optimized, and the calculation accuracy is improved.
Further, based on the determined optimal simulation data, the average cost for acquiring the client for each delivery channel is output (or determined).
In this example, the average cost of acquiring the clients of the first delivery channel and the third delivery channel is calculated by the expression (1).
Further, the second calculation model includes calculating an average cost for obtaining the customer for the delivery channel using the following formula:
Figure 127752DEST_PATH_IMAGE010
，
wherein the content of the first and second substances,
Figure 174205DEST_PATH_IMAGE011
calculating the average cost of acquiring the clients of the delivery channel by using the CPM index;
Figure 798959DEST_PATH_IMAGE012
a first growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure 905456DEST_PATH_IMAGE013
is a first correction value for the first growth parameter,
Figure 710601DEST_PATH_IMAGE014
a first correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 170532DEST_PATH_IMAGE005
=0；
Figure 541470DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure 694234DEST_PATH_IMAGE015
a second correction value for the first growth parameter;
Figure 721096DEST_PATH_IMAGE016
a second growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure 374931DEST_PATH_IMAGE017
for the first correction value of the second growth parameter,
Figure 475743DEST_PATH_IMAGE018
a second correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 861724DEST_PATH_IMAGE019
=0；
Figure 703779DEST_PATH_IMAGE020
a second correction value for a second growth parameter;
Figure 15766DEST_PATH_IMAGE021
a third growth parameter of the drop cost for the customer of the drop channel;
Figure 95718DEST_PATH_IMAGE022
for the first correction value of the third growth parameter,
Figure 590284DEST_PATH_IMAGE023
a third correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 857318DEST_PATH_IMAGE023
=0；
Figure 321797DEST_PATH_IMAGE024
is a third growthA second correction value for the parameter;
Figure 725096DEST_PATH_IMAGE025
and obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the average cost of acquiring the clients of the second delivery channel and the fourth delivery channel is calculated by the expression (2).
It should be noted that, in this example, the first delivery channel and the third delivery channel are both used for delivering interactive advertisements and display advertisements, and the second delivery channel and the fourth delivery channel are both used for delivering effect advertisements. However, the present invention is not limited to the above, and the above description is only a preferable example, and the present invention is not limited thereto.
In yet another example, the cost-per-acquire for a delivery channel is calculated using CPA metrics, CPI metrics, or CPS metrics
Figure DEST_PATH_IMAGE026
、
Figure DEST_PATH_IMAGE027
、
Figure DEST_PATH_IMAGE028
And determining the corresponding other calculation model. Further, a combined model is established by using the first calculation model, the second calculation model and/or other calculation models, and the simulation calculation of the average piece cost is carried out on a plurality of delivery channels so as to simulate the customer obtaining condition of each channel.
Preferably, the recent passenger capacity and the average piece cost of each channel are obtained in real time so as to be used for updating the parameters of each calculation model in the combined model.
Specifically, for example, the passenger capacity and the piece-by-piece cost of each channel in a specific time are acquired, wherein the specific time comprises 7 days, 10 days or 15 days and the like.
Therefore, through the simulation and calculation process, the calculation relation between the passenger capacity and the per-piece cost can be more accurately determined, and a combined calculation model for quick calculation is constructed. Therefore, the balance point between each delivery channel and the cost target can be quickly found, and the optimal delivery scale is effectively obtained.
The above description is given only as a preferred example, and the present invention is not limited thereto.
Next, in step S102, a target passenger capacity is determined according to the user input, and the optimal delivery strategy of each channel is automatically calculated by using the combined model.
In this example, the placement strategy is used to control the piece-by-piece cost for each channel, and includes placement time and/or frequency and placement price.
Specifically, receiving user input to determine a target passenger capacity, and automatically calculating the optimal delivery strategy of each channel by adopting the combined model according to the determined target passenger capacity and the target cost.
For example, the user inputs n target passenger capacities, the target cost is C, the combined model is used for automatic calculation, and the average piece cost of each channel and the simulated passenger capacities of each channel are calculated, for example, the output delivery policy includes: a is
Figure DEST_PATH_IMAGE029
+b
Figure DEST_PATH_IMAGE030
C, and a + b
Figure DEST_PATH_IMAGE031
n；a’
Figure 859406DEST_PATH_IMAGE029
+b’
Figure 518795DEST_PATH_IMAGE011
+c’
Figure DEST_PATH_IMAGE032
= C, and a ' + b ' + C '
Figure 786965DEST_PATH_IMAGE031
n, etc.
Further, the method also comprises the releasing channels, releasing time, releasing frequency and the like corresponding to the expression.
Preferably, an optimal delivery strategy is further selected from the multiple delivery strategies according to the volume business requirements and the types of the advertisements to be delivered.
Next, in step S103, multi-channel web advertisement delivery is automatically performed according to the optimal delivery policy of each channel.
In particular, the delivery strategy is also used to control the average piece cost of each channel.
As shown in fig. 3, the method further includes a step S301 of updating the optimal delivery strategy for each channel.
In step S301, the optimal delivery policy of each channel is updated.
Specifically, when calculating the simulated passenger capacity of each delivery channel, the method further comprises monitoring and judging the current remaining target passenger capacity.
And further, recalculating by adopting the combined model according to the current remaining target passenger capacity so as to update the optimal delivery strategy of each channel.
Further, multi-channel network advertisement delivery is automatically performed on an advertisement delivery platform according to an optimal delivery strategy of each channel. Therefore, the throwing cost can be effectively controlled, and the throwing efficiency is improved.
The above description is only given as a preferred example, and the present invention is not limited thereto.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Compared with the prior art, the multi-channel intelligent advertisement putting method can more accurately simulate the process of getting a customer and effectively reduce the trial and error cost of an advertiser; by simulating the passenger obtaining process of each channel, the calculation relation between the passenger obtaining amount and the average piece cost can be more accurately determined, a quick-calculation combined calculation model is constructed, the balance point between each releasing channel and the cost target can be quickly found, the optimal releasing scale is effectively obtained, the releasing cost is efficiently controlled, and the releasing efficiency is also improved.
Example 2
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
Referring to fig. 4, 5 and 6, the present invention further provides a multi-channel intelligent advertisement delivery apparatus 400 for dynamically determining a multi-channel network advertisement delivery policy, including: the acquisition processing module 401 is configured to acquire multi-channel historical delivery data, and fit a calculation relationship between the acquired passenger volume and the average piece cost according to the historical delivery data of each channel to establish a combined model, where the combined model includes calculation models of multiple channels; a calculating module 402, which determines a target passenger capacity according to user input, and automatically calculates an optimal delivery strategy of each channel by using the combined model, wherein the delivery strategy is used for controlling the average cost of each channel; and a delivery module 403 for automatically delivering the multi-channel network advertisement according to the optimal delivery strategy of each channel.
Preferably, the calculating module 402 further comprises calculating the average cost for obtaining the customer for the channel using the following formula:
Figure 982454DEST_PATH_IMAGE001
，
wherein the content of the first and second substances,
Figure 881140DEST_PATH_IMAGE002
calculating the average cost of acquiring the clients of the delivery channel by using the CPC index;
Figure 653924DEST_PATH_IMAGE003
releasing cost increase parameters for the customers of the release channel;
Figure 866731DEST_PATH_IMAGE004
delivering a first correction value of a cost increase parameter for a guest of a delivery channel,
Figure 979043DEST_PATH_IMAGE005
a corrected value of the lowest value of the release cost for the guests of the release channel,
Figure 110947DEST_PATH_IMAGE006
=0；
Figure 246394DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure 325208DEST_PATH_IMAGE008
a second correction value of the cost increase parameter is released for the guest;
Figure 354344DEST_PATH_IMAGE009
and obtaining the lowest fitting value of the customer cost for the delivery channel.
Preferably, the calculating module 402 further calculates the average cost for obtaining the clients of the delivery channel by using the following formula:
Figure 31051DEST_PATH_IMAGE010
，
wherein the content of the first and second substances,
Figure 981689DEST_PATH_IMAGE011
calculating the average cost of acquiring the clients of the delivery channel by using the CPM index;
Figure 67457DEST_PATH_IMAGE012
a first growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure 685520DEST_PATH_IMAGE013
is a first correction value for the first growth parameter,
Figure 34593DEST_PATH_IMAGE014
a first correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 3686DEST_PATH_IMAGE005
=0；
Figure 96407DEST_PATH_IMAGE007
obtaining a target value of a customer for a delivery channel;
Figure 834556DEST_PATH_IMAGE015
a second correction value for the first growth parameter;
Figure 682426DEST_PATH_IMAGE016
a second growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure 778296DEST_PATH_IMAGE017
for the first correction value of the second growth parameter,
Figure 737024DEST_PATH_IMAGE018
a second correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 860838DEST_PATH_IMAGE019
=0；
Figure 551714DEST_PATH_IMAGE020
a second correction value for a second growth parameter;
Figure 229820DEST_PATH_IMAGE021
a third growth parameter of the drop cost for the customer of the drop channel;
Figure 992239DEST_PATH_IMAGE022
for the first correction value of the third growth parameter,
Figure 580347DEST_PATH_IMAGE023
a third correction value for the lowest value of the drop cost for the guests of the drop channel,
Figure 832336DEST_PATH_IMAGE023
=0；
Figure 138684DEST_PATH_IMAGE024
a second correction value for a third growth parameter;
Figure 970374DEST_PATH_IMAGE025
and obtaining the lowest fitting value of the customer cost for the delivery channel.
As shown in fig. 5, the system further includes a parameter updating module 501, where the parameter updating module 501 is configured to obtain recent passenger capacity and average piece cost of each channel in real time, and update parameters of each calculation model in the combined model.
As shown in fig. 6, the system further includes a policy updating module 601, where the policy updating module 601 updates the optimal delivery policy of each channel by using the combined model according to the current remaining target passenger capacity.
Preferably, the delivery strategy includes: time and/or frequency of impressions and price of impressions.
In embodiment 2, the same portions as those in embodiment 1 are not described.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Compared with the prior art, the multi-channel intelligent advertisement putting device can more accurately simulate the process of getting a guest and effectively reduce the trial and error cost of an advertiser; by simulating the passenger obtaining process of each channel, the calculation relation between the passenger obtaining amount and the average piece cost can be more accurately determined, a quick-calculation combined calculation model is constructed, the balance point between each releasing channel and the cost target can be quickly found, the optimal releasing scale is effectively obtained, the releasing cost is efficiently controlled, and the releasing efficiency is also improved.
Example 3
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. An electronic apparatus 200 according to this embodiment of the present invention is described below with reference to fig. 7. The electronic device 200 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform steps according to various exemplary embodiments of the present invention described in the processing method section of the electronic device described above in this specification. For example, the processing unit 210 may perform the steps as shown in fig. 1.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 230 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: and training the created user risk control model by using APP download sequence vector data and overdue information of the historical user as training data, and calculating the financial risk prediction value of the target user by using the created user risk control model.
As shown in fig. 8, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage 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 storage 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 for aspects of the present invention 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 and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.

Claims (7)

1. A multi-channel intelligent advertisement delivery method is used for dynamically determining a multi-channel network advertisement delivery strategy, and is characterized by comprising the following steps:
acquiring multi-channel historical release data, and fitting a calculation relation between the passenger capacity and the average piece cost according to the historical release data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels;
determining a target passenger capacity according to user input, and automatically calculating the average cost of the acquired passengers of the channels by using any one of the following two formulas by adopting the combined model to obtain the optimal delivery strategy of each channel, wherein the delivery strategy is used for controlling the average cost of each channel:
Figure FDA0002989251870000011
Figure FDA0002989251870000012
wherein, Yq1Calculating the average cost of acquiring the clients of the delivery channel by using the CPC index; a. the1Releasing cost increase parameters for the customers of the release channel;
Figure FDA0002989251870000013
first correction value of cost increase parameter for delivery channel for customer acquisition1Correction of the lowest value of the cost of delivery for the guest of the delivery channel, B1＝0；xmObtaining a target value of a customer for a delivery channel; mu is a second correction value of the guest-obtaining and releasing cost increasing parameter; tau is1Obtaining a lowest fitting value of the customer obtaining cost for the delivery channel; y isq2Calculating the average cost of acquiring the clients of the delivery channel by using the CPM index; a. the1’For obtaining a cost for delivery from a delivery channelA first growth parameter;
Figure FDA0002989251870000014
is a first correction value of a first growth parameter, B1’First correction value of lowest cost for the delivery channel for the customer1’＝0；xmObtaining a target value of a customer for a delivery channel; δ is a second correction value of the first growth parameter; a. the2’A second growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure FDA0002989251870000015
is a first correction value of a second growth parameter, B2’Second correction value of lowest cost for the delivery channel for the customer2’0; gamma is a second correction value of the second growth parameter; a. the3’A third growth parameter of the drop cost for the customer of the drop channel;
Figure FDA0002989251870000016
as a first correction value for a third growth parameter, B3’A third correction value for the lowest value of the cost of delivering the customer to the delivery channel, B3’0; theta is a second correction value of the third growth parameter; tau is1’Obtaining a lowest fitting value of the customer obtaining cost for the delivery channel;
and automatically carrying out multi-channel network advertisement delivery according to the optimal delivery strategy of each channel.
2. The multi-channel smart advertisement delivery method of claim 1, further comprising: and acquiring the recent passenger capacity and the average cost of the parts of each channel in real time, and updating the parameters of each calculation model in the combined model.
3. The multi-channel intelligent advertisement delivery method according to claim 1, wherein the optimal delivery strategy of each channel is updated by using the combined model according to the current remaining target passenger capacity.
4. The multi-channel smart advertisement delivery method of claim 3, wherein the delivery strategy comprises: time and/or frequency of impressions and price of impressions.
5. The utility model provides a multichannel intelligence advertisement puts in device for dynamic definite multichannel network advertisement puts in tactics, its characterized in that includes:
the acquisition processing module is used for acquiring multi-channel historical release data, and fitting a calculation relation between the passenger capacity and the average piece cost according to the historical release data of each channel to establish a combined model, wherein the combined model comprises calculation models of a plurality of channels;
the calculation module is used for determining target passenger capacity according to user input, automatically calculating the average cost of the acquired customers of the channels by using any one of the following two formulas by adopting the combined model, and obtaining the optimal delivery strategy of each channel, wherein the delivery strategy is used for controlling the average cost of each channel:
Figure FDA0002989251870000021
Figure FDA0002989251870000022
wherein, Yq1Calculating the average cost of acquiring the clients of the delivery channel by using the CPC index; a. the1Releasing cost increase parameters for the customers of the release channel;
Figure FDA0002989251870000023
first correction value of cost increase parameter for delivery channel for customer acquisition1Correction of the lowest value of the cost of delivery for the guest of the delivery channel, B1＝0；xmObtaining a target value of a customer for a delivery channel; mu is a second correction value of the guest-obtaining and releasing cost increasing parameter; tau is1Obtaining a lowest fitting value of the customer obtaining cost for the delivery channel; y isq2Computing impressions for use of CPM metricsThe uniform cost of acquiring the clients from the channel; a. the1’A first growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure FDA0002989251870000031
is a first correction value of a first growth parameter, B1’First correction value of lowest cost for the delivery channel for the customer1’＝0；xmObtaining a target value of a customer for a delivery channel; δ is a second correction value of the first growth parameter; a. the2’A second growth parameter of the drop-in cost for the customer obtained from the drop-in channel;
Figure FDA0002989251870000032
is a first correction value of a second growth parameter, B2’Second correction value of lowest cost for the delivery channel for the customer2’0; gamma is a second correction value of the second growth parameter; a. the3’A third growth parameter of the drop cost for the customer of the drop channel;
Figure FDA0002989251870000033
as a first correction value for a third growth parameter, B3’A third correction value for the lowest value of the cost of delivering the customer to the delivery channel, B3’0; theta is a second correction value of the third growth parameter; tau is1’Obtaining a lowest fitting value of the customer obtaining cost for the delivery channel;
and the delivery module automatically delivers the multi-channel network advertisement according to the optimal delivery strategy of each channel.
6. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the multi-channel smart advertisement delivery method of any of claims 1-4.
7. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the multi-channel smart advertisement delivery method of any of claims 1-4.
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