CN115131079B - Data processing-based advertisement putting effect prediction method and device - Google Patents
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Abstract
The invention provides an advertisement putting effect prediction method and device based on data processing, and belongs to the technical field of data processing. The method comprises the step S100: acquiring the type of the activated data channel loaded this time, and increasing the statistical times of the data channel loaded this time; s200, acquiring page operation data of a target display page in a preset time period; s300: obtaining a plurality of different statistical times values and page operation data corresponding to a plurality of different types of data channels; s400: fitting the statistical times of the different types of data channels in the next statistical period; s500: and predicting the advertisement putting effect based on the fitted statistical times of the different types of data channels and the page operation data. The device comprises a commodity loading unit, a channel counting unit, a frequency fitting unit and an effect predicting unit. The invention can realize the advertisement putting effect of different data channels and adjust the advertisement putting strategy.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an advertisement putting effect prediction method and device based on data processing.
Background
Most of traditional advertisements are put on traditional media such as public places, televisions, newspapers, books and the like, and once put, the traditional advertisements cannot be updated or changed in a short time. With the gradual popularization of the mobile internet, the mobile terminal advertisement is gradually popular, and personalized advertisement recommendation by means of user data becomes a new advertisement promotion mode. With the advent of a revolution in the manner in which advertising is placed. Most of traditional advertisements are passively displayed and statically displayed in an unchanged display mode, while in the era of mobile internet, advertisements are more focused on interaction with users, and the display modes and the putting modes of the advertisements are rich and colorful, so that the advertisements are actively displayed (actively pushed) and passively requested. Examples of the former are browser page advertisements, browser page keyword hints, etc., which are actively presented but passively received by the user; examples of the latter, such as graphic codes, dynamic codes, pan-tilt, etc., require active user involvement, with the user being more viscid.
The advertisement is a high-risk investment, and before operation, the effect generated by the advertisement investment is accurately judged and predicted; otherwise, blind investment is carried out, and the result is not only waste of investment, but also unexpected effect is not achieved, and sometimes negative effects are generated. Therefore, comprehensive and effective advertisement effect evaluation and prediction are carried out, which are necessary and beneficial for advertisers and advertisers.
Advertisers can usually adopt a plurality of ways to simultaneously carry out advertisement putting, but the putting cost and the putting effect of different advertisement putting ways are different. If the advertisement putting effect of different approaches that have been put earlier is reasonably evaluated under the condition of limited advertisement budget, so as to provide a reference for the advertisement putting strategy of the next period, it becomes one of the technical problems faced by those skilled in the art.
Disclosure of Invention
In order to solve the technical problem, the invention provides an advertisement putting effect prediction method and device based on data processing.
The invention can realize the advertisement putting effect of different data channels and adjust the advertisement putting strategy.
In a first aspect of the present invention, a data processing-based advertisement delivery effectiveness prediction method is provided, where the method includes the following steps:
s100: when the target object is detected to be loaded and displayed, acquiring the type of the data channel which activates the current loading, and increasing the statistical times of the data channel which is loaded at the current time;
the target object is a commodity corresponding to the advertisement;
s200, acquiring page operation data of a target display page in a preset time period, wherein the target display page is a page on which the target object is loaded and displayed, and the preset time period is a time period in which the target display page is displayed in the foreground;
s300: when the preset number of statistical periods is over, acquiring a plurality of different statistical times values and page operation data corresponding to a plurality of different types of data channels;
s400: fitting the statistical times of the different types of data channels in the next statistical period based on the plurality of different statistical times;
s500: and predicting the advertisement putting effect based on the fitted statistical times of the different types of data channels and the page operation data.
The page operation data of the target display page within the preset time period obtained in the step S200 includes one of the following data or any combination thereof:
and the target user browses time to the detailed page of the commodity, browses time to the evaluation page, communicates with the online customer service, clicks to join a shopping cart operation and clicks to buy the operation.
After the step S500, the method further includes:
s600: and adjusting the number distribution and/or the time distribution of the data channels of different types based on the prediction result of the advertisement putting effect, and returning to the step S100.
In the process of executing the method, the data channel is an advertisement putting path; different advertisement putting paths correspond to different data channel types, and each advertisement putting path comprises one of an advertisement link, an advertisement two-dimensional code, an advertisement search keyword, an advertisement picture and an advertisement video or any combination of the advertisement link, the advertisement two-dimensional code, the advertisement search keyword and the advertisement picture;
and when the target user activates the advertisement putting path, entering a display page of the target object through a data channel type corresponding to the advertisement putting path, and loading and displaying the commodity on the display page.
The target user activating the advertisement placement path, including:
when the advertisement putting path is one of an advertisement link, an advertisement picture and an advertisement video, the target user clicks the advertisement link, the advertisement picture or the advertisement video;
when the advertisement putting path is an advertisement two-dimensional code, a target user scans the advertisement two-dimensional code through an image terminal;
and when the advertisement putting path is the advertisement search keyword, inputting the advertisement search keyword by a target user through a search engine.
In a second aspect of the present invention, an advertisement delivery effect prediction apparatus based on data processing is provided, where the apparatus includes a commodity loading unit, a channel statistics unit, a frequency fitting unit, and an effect prediction unit;
the functions realized by each unit are as follows:
the commodity loading unit is used for loading and displaying the commodity corresponding to the advertisement on a target page,
the channel statistical unit comprises a frequency statistical unit and a page data storage unit;
the number counting unit is used for counting the number of activated accumulated times of different types of data channels in a plurality of different counting periods;
the page data storage unit is used for storing page operation data of a target display page within a preset time period, wherein the preset time period is a time period for the target display page to be displayed in the foreground;
the number fitting unit is used for fitting the statistical number value of the different types of data channels in the next statistical period according to the activated cumulative number of the different types of data channels in the different statistical periods counted by the number statistical unit;
the effect prediction unit is used for predicting the advertisement putting effect based on the statistical times of the data channels of different types which are fitted by the times fitting unit and the page operation data stored by the page data storage unit.
The page data storage unit stores page operation data of a target display page in a preset time period, and the page operation data comprises one of the following data or any combination of the following data:
and the target user browses the detailed page of the commodity, the browsing time of the evaluation page, the time of communicating with the online customer service, the operation of clicking to join a shopping cart and the operation of clicking to purchase.
The data channel is an advertisement putting path; different advertisement putting paths correspond to different data channel types, and the advertisement putting paths comprise one of advertisement links, advertisement two-dimensional codes, advertisement search keywords, advertisement pictures and advertisement videos or any combination thereof;
and when the target user activates the advertisement putting path, entering a target page of the target object through a data channel type corresponding to the advertisement putting path, and loading and displaying the commodity on the target page.
As a further improvement, the device further comprises an advertisement putting adjusting unit;
and the advertisement putting adjusting unit adjusts the quantity distribution and/or time distribution of the data channels of different types based on the prediction result of the advertisement putting effect obtained by the effect prediction unit.
The number fitting unit comprises a fitting function based on an R language;
the fitting function based on the R language takes the statistical times of different types of data channels as a dependent variable and takes the activated accumulated times of different types of data channels in a plurality of different statistical periods as an independent variable to carry out fitting.
The method can objectively count the number of times that different types of data channels are activated in a plurality of different counting periods, page operation data of a target display page in a preset time period and other actual operation data closely related to the user advertisement behavior, then fit the number of times that different types of data channels are activated in a plurality of different counting periods based on the number of times that R language is counted by the number counting unit according to the different types of data channels, obtain the statistical number of times of the different types of data channels in the next counting period, and finally predict the advertisement putting effect based on the statistical number of times of the different types of data channels fitted by the number fitting unit and the page operation data stored by the page data storage unit.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for predicting advertisement delivery effectiveness based on data processing according to an embodiment of the present invention;
FIG. 2 is a statistical schematic diagram of a plurality of different statistics corresponding to different types of data channels when the method of FIG. 1 is implemented;
FIG. 3 is a schematic diagram of a further preferred embodiment of the method of FIG. 1;
fig. 4 is a schematic diagram illustrating a unit composition of an advertisement placement effect prediction apparatus based on data processing according to an embodiment of the present invention;
fig. 5-6 are schematic diagrams of further preferred embodiments of the data processing-based advertisement impression prediction device shown in fig. 4.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Fig. 1 is a flowchart illustrating a method for predicting advertisement placement effectiveness based on data processing according to an embodiment of the present invention.
In fig. 1, the method includes steps S100 to S500, each of which is implemented as follows:
s100: when the target object is detected to be loaded and displayed, acquiring the type of the data channel which activates the current loading, and increasing the statistical times of the data channel which is loaded at the current time;
the target object is a commodity corresponding to the advertisement;
s200, acquiring page operation data of a target display page in a preset time period, wherein the target display page is a page on which the target object is loaded and displayed, and the preset time period is a time period in which the target display page is displayed in the foreground;
s300: when the preset number of statistical cycles is over, obtaining a plurality of different statistical times values and page operation data corresponding to a plurality of different types of data channels;
s400: fitting the statistical times of the different types of data channels in the next statistical period based on the plurality of different statistical times;
s500: and predicting the advertisement putting effect based on the fitted statistical times of the different types of data channels and the page operation data.
One of the technical problems to be solved by the present invention is to count the advertisement behavior data of different delivery routes, so that in the first step, it must be identified in which delivery route the current advertisement behavior occurs.
In this embodiment, statistics is performed in a manner that the number of detection times of data channel activation is increased incrementally.
Specifically, the data channel is an advertisement delivery path;
different advertisement putting paths correspond to different data channel types, and the advertisement putting paths comprise one of advertisement links, advertisement two-dimensional codes, advertisement search keywords, advertisement pictures and advertisement videos or any combination thereof;
and when the target user activates the advertisement putting path, entering a display page of the target object through a data channel type corresponding to the advertisement putting path, and loading and displaying the commodity on the display page.
Therefore, there are various advertisement delivery routes related in this embodiment, which can be classified into three categories according to different activation types:
the type one is as follows: when the advertisement putting path is one of an advertisement link, an advertisement picture and an advertisement video, a target user clicks the advertisement link, the advertisement picture or the advertisement video;
type two: when the advertisement putting path is an advertisement two-dimensional code, a target user scans the advertisement two-dimensional code through an image terminal;
type three: and when the advertisement putting path is the advertisement search keyword, inputting the advertisement search keyword by a target user through a search engine.
Thus, referring to FIG. 2, FIG. 2 is a statistical schematic diagram illustrating a plurality of different statistics corresponding to different types of data channels when the method of FIG. 1 is implemented.
In fig. 2, a number of different statistical periods are schematically indicated, including weekday statistical periods T1, T2 and non-weekday statistical periods T3, T4;
figure 2 also schematically separates the ad placement paths into four cases,
the first three cases correspond to types one to three (the first data channel, the second data channel, and the third data channel) of the above three cases, respectively, and types other than the type one, the type two, and the type three are classified as other data channels.
In the figure, ai, bi, ci, di (i =1,2,3,4) correspond to the number of times the corresponding data channel is activated for different statistical periods.
For example, when the target user scans the advertisement two-dimensional code through the image terminal in a certain statistical period, the second data channel of the corresponding statistical period is activated once, and thus bi is incremented by 1.
Obviously, only the number of times of statistics is not enough, and therefore, as one of the core improvements of the present invention, the S200 further obtains page operation data of a target display page within a preset time period, where the target display page is a page on which the target object is loaded and displayed, and the preset time period is a time period in which the target display page is displayed in the foreground;
specifically, the page operation data of the target display page obtained in step S200 in the preset time period includes one of the following data or any combination thereof:
and the target user browses the detailed page of the commodity, the browsing time of the evaluation page, the time of communicating with the online customer service, the operation of clicking to join a shopping cart and the operation of clicking to purchase.
Obviously, the page operation data can really reflect whether the activation operation constitutes a real advertisement putting effect.
A further advantage of the present invention is that step S400 is to fit statistical times values of the different types of data channels in the next statistical period based on the plurality of different statistical times values.
Obviously, the focus of the present invention is on the subsequent policy adjustment, and therefore, a corresponding prediction needs to be performed based on the historical data, that is, step S500, and the advertisement delivery effect prediction is performed based on the fitted statistical times of the different types of data channels and the page operation data.
It can be seen that steps S100 to S500 in the embodiment of the present invention are an integral solution associated in a front-back manner, and are all none, and especially, the page operation data therein and the statistical times of the different types of data channels fitted in the next statistical period make the data acquisition richer and the adjustment of the delivery policy more targeted.
As a further preference, see fig. 3, fig. 3 is a schematic view of a further preferred embodiment of the method described in fig. 1.
In fig. 3, on the basis of fig. 1, after the step S500, the method further includes:
s600: and adjusting the number distribution and/or the time distribution of the data channels of different types based on the prediction result of the advertisement putting effect, and returning to the step S100.
Next, taking the data statistics of fig. 2 as an example, a specific embodiment of the method steps involved in prediction and fitting in the method described in the present application is given.
The existing data set (dependent variable) on which the prediction and fitting is based is as follows:
the number of statistics for different statistical periods of data channel i (i =1,2,3);
in the embodiment of the present invention, only the existing three data channels, i.e., the type one to the type three (the first data channel, the second data channel, and the third data channel) in the aforementioned three cases, are currently considered, and the other data channels except for the type one, the type two, and the type three are not considered for the moment.
Recording the activation times of the data channel i in the statistical time interval Tj as Numij;
counting the time length of the time interval Tj to be | Tj |;
the standard of the non-working day statistical time period is idleF; the standard of the working day statistical time interval is workF;
preferably, idleF =50; workF =100;
in page operation data in a preset time period, a target user respectively endows different weight values W to detailed page browsing time, evaluation page browsing time, time for communicating with online customer service, operation of clicking to join a shopping cart, operation of clicking to purchase and the like of the commodity, and the weight values are increased progressively;
recording the browsing time t1 of the detail page, the browsing time t2 of the evaluation page and the time t3 of communication with the online customer service, when ti is more than 0 (i =1,2, 3),
t1 is weighted w1, t2 is weighted w2, and t3 is weighted w3;
1>w3>w2>w1>0。
supposing that working day statistical time periods are T1, T2, \8230; \ 8230Tn, and non-working day statistical time periods are S1, S2, \8230; \8230Sk; n and k are both greater than 2;
the advertisement placement effectiveness score Scorei for channel i is calculated as follows:
wherein,
A preset effect score threshold Fset;
establishing a fitting relation between the advertisement putting score Scorei and the statistical time intervals Ti, sk and the page operation data weight W:
preferably, the fitting relation is as follows:
Likewise, the fitting may also be performed with the accumulated number Fnum of times that different types of data channels are activated in a plurality of different statistical periods as an argument.
Namely, it is
Numi is the total number of increments of time period Ti, numk is the total number of increments of time period Sk,
preferably, the fitting relation is as follows:
wherein a, b, c and d are parameters to be fitted.
Accordingly, the fitting can be performed by using the R language, the statistical times of different types of data channels as dependent variables and the accumulated times of different types of data channels activated in different statistical periods as independent variables, so that the statistical times of the different types of data channels in the next statistical period are fitted;
when the advertisement putting score Scorei corresponding to the statistical times of the different types of data channels in the next statistical period obtained through fitting is greater than the preset effect score threshold Fset, the advertisement putting can be increased in the data channels and/or the corresponding statistical periods, that is, the quantity distribution and/or the time distribution of the different types of data channels are adjusted based on the prediction result of the advertisement putting effect obtained by the effect prediction unit.
It should be noted that the fitting relation is only illustrative, and in the specific fitting, a polynomial fitting function of the independent variable and the dependent variable may also be established, and other forms may also be adopted, for example, a fitting formula of a hyperbolic function and a parabolic function, which is not specifically limited in the present invention.
However, as one skilled in the art will appreciate, the above prediction process is not limited to R function fitting, and may be implemented by using other prediction models or methods, and may perform other quantization on the raw data to adapt to the corresponding prediction models or methods.
For example, a neural network prediction model can be used, and the input is multidimensional data (working day time period length Ti, non-working day time period length Tj \8230;), the output is also multidimensional data (best delivery period, best delivery duration \8230;) and any model or method or fitting function that can achieve the above predicted output is within the scope of the present invention.
However, the R function is a preferred embodiment of the present invention, because the R language does not require a complex model training and updating process, and can also represent the correlation between different variables, it is also not complex to implement, and meanwhile, the R language can be directly inherited, developed and utilized based on the business software Spus, which reduces the later development cost.
Referring next to fig. 4-5, schematic diagrams of unit compositions of a data processing-based advertisement delivery effectiveness prediction apparatus according to different embodiments of the present invention are respectively shown.
In fig. 4, there is shown an advertisement delivery effect prediction apparatus based on data processing, the apparatus including a commodity loading unit, a channel counting unit, a number fitting unit, and an effect prediction unit;
the commodity loading unit is used for loading and displaying commodities corresponding to the advertisements on a target page, and the channel counting unit comprises a frequency counting unit and a page data storage unit;
the number counting unit is used for counting the activated accumulated number of times of different types of data channels in a plurality of different counting periods;
the page data storage unit is used for storing page operation data of a target display page within a preset time period, wherein the preset time period is a time period for the target display page to be displayed in the foreground;
the number fitting unit is used for fitting to obtain the statistical number value of the different types of data channels in the next statistical period according to the number of the activated accumulated times of the different types of data channels in a plurality of different statistical periods, which is obtained by statistics of the number statistical unit;
the effect prediction unit is used for predicting the advertisement putting effect based on the statistical times of the data channels of different types which are fitted by the times fitting unit and the page operation data stored by the page data storage unit.
The page data storage unit stores page operation data of a target display page in a preset time period, and the page operation data comprises one of the following data or any combination of the following data:
and the target user browses time to the detailed page of the commodity, browses time to the evaluation page, communicates with the online customer service, clicks to join a shopping cart operation and clicks to buy the operation.
The data channel is an advertisement putting path; different advertisement putting paths correspond to different data channel types, and each advertisement putting path comprises one of an advertisement link, an advertisement two-dimensional code, an advertisement search keyword, an advertisement picture and an advertisement video or any combination of the advertisement link, the advertisement two-dimensional code, the advertisement search keyword and the advertisement picture;
and when the target user activates the advertisement putting path, entering a target page of the target object through a data channel type corresponding to the advertisement putting path, and loading and displaying the commodity on the target page.
On the basis of fig. 4, see fig. 5. Fig. 5 shows that the apparatus further comprises an advertisement placement adjustment unit; the advertisement putting adjusting unit adjusts the number distribution and/or the time distribution of the data channels of different types based on the prediction result of the advertisement putting effect obtained by the effect prediction unit.
Further, referring to fig. 6, the degree fitting unit includes a fitting function based on R language; the fitting function based on the R language takes the statistical times of different types of data channels as dependent variables and takes the activated accumulated times of different types of data channels in a plurality of different statistical periods as independent variables to carry out fitting.
As one skilled in the art will appreciate, the above prediction process is not limited to R function fitting based on R language, and may be implemented by using other prediction models or methods, and may also perform other quantization on the raw data to adapt to the corresponding prediction models or methods.
For example, a neural network prediction model can be used, the input is multidimensional data (working day period length Ti, non-working day period length Tj \8230; and the output is also multidimensional data (optimal delivery period, optimal delivery period \8230; 8230; and the like), and any model or method or fitting function capable of realizing the above prediction output is within the protection scope of the present invention.
However, the R function is a preferred embodiment of the present invention, because the R language does not need a complex model training and updating process, and can also represent the correlation between different variables, it is also not complex to implement, and meanwhile, the R language can be directly inherited, developed and utilized based on the business software Splus, which reduces the later development cost.
The method can objectively count the number of times that different types of data channels are activated in a plurality of different counting periods, page operation data of a target display page in a preset time period and other actual operation data closely related to user advertisement behaviors, then fit the number of times that different types of data channels are activated in a plurality of different counting periods based on the number of times that R language is counted by the number counting unit according to the different types of data channels, obtain the number of times of statistics of the different types of data channels in the next counting period, finally predict the advertisement putting effect based on the number of times that the different types of data channels are fitted by the number fitting unit and the page operation data stored by the page data storage unit, and perform independent advertisement data recognition and processing for different dimensions by using the data channels, and then perform corresponding advertisement data prediction, so that the subsequent advertisement putting strategy is more targeted.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The present invention is not limited to the specific module structure described in the prior art. The prior art mentioned in the background section can be used as part of the invention to understand the meaning of some technical features or parameters. The scope of the present invention is defined by the claims.
Claims (8)
1. A method for predicting advertisement putting effect based on data processing is characterized by comprising the following steps:
s100: when the target object is detected to be loaded and displayed, acquiring the type of the data channel which is activated for the current loading, and increasing the statistical times of the data channel which is loaded for the current time;
the target object is a commodity corresponding to the advertisement;
s200, acquiring page operation data of a target display page in a preset time period, wherein the target display page is a page loaded and displayed by the target object, and the preset time period is a time period for the target display page to be displayed in the foreground;
s300: when the preset number of statistical cycles is over, obtaining a plurality of different statistical times values and page operation data corresponding to a plurality of different types of data channels;
s400: fitting the statistical times of the different types of data channels in the next statistical period based on the plurality of different statistical times;
s500: predicting the advertisement putting effect based on the fitted statistical times of the different types of data channels and the page operation data;
the data channel is an advertisement putting path; different advertisement putting paths correspond to different data channel types, and the advertisement putting paths comprise one of advertisement links, advertisement two-dimensional codes, advertisement search keywords, advertisement pictures and advertisement videos or any combination thereof;
and when the target user activates the advertisement putting path, entering a display page of the target object through a data channel type corresponding to the advertisement putting path, and loading and displaying the commodity on the display page.
2. The data processing-based advertisement delivery effect prediction method according to claim 1, wherein:
the target user activating the advertisement placement path, including:
when the advertisement putting path is one of an advertisement link, an advertisement picture and an advertisement video, a target user clicks the advertisement link, the advertisement picture or the advertisement video;
when the advertisement putting path is an advertisement two-dimensional code, a target user scans the advertisement two-dimensional code through an image terminal;
and when the advertisement putting path is the advertisement searching keyword, the target user inputs the advertisement searching keyword through a search engine.
3. The data processing-based advertisement delivery effect prediction method according to claim 1, wherein:
in step S200, the page operation data of the target display page within the preset time period includes one of the following data or any combination thereof:
and the target user browses the detailed page of the commodity, the browsing time of the evaluation page, the time of communicating with the online customer service, the operation of clicking to join a shopping cart and the operation of clicking to purchase.
4. The data processing-based advertisement delivery effect prediction method according to claim 1, wherein:
after step S500, the method further comprises:
s600: and adjusting the number distribution and/or the time distribution of the data channels of different types based on the prediction result of the advertisement putting effect, and returning to the step S100.
5. An advertisement putting effect prediction device based on data processing comprises a commodity loading unit, a channel counting unit, a frequency fitting unit and an effect prediction unit;
the method is characterized in that:
the commodity loading unit is used for loading and displaying the commodity corresponding to the advertisement on a target page,
the channel statistical unit comprises a frequency statistical unit and a page data storage unit;
the number counting unit is used for counting the number of activated accumulated times of different types of data channels in a plurality of different counting periods;
the page data storage unit is used for storing page operation data of a target display page within a preset time period, wherein the preset time period is a time period for the target display page to be displayed in the foreground;
the number fitting unit is used for fitting to obtain the statistical number value of the different types of data channels in the next statistical period according to the number of the activated accumulated times of the different types of data channels in a plurality of different statistical periods, which is obtained by statistics of the number statistical unit;
the effect prediction unit is used for predicting the advertisement putting effect based on the statistical times of the data channels of different types which are fitted by the times fitting unit and the page operation data stored by the page data storage unit;
the data channel is an advertisement putting path; different advertisement putting paths correspond to different data channel types, and the advertisement putting paths comprise one of advertisement links, advertisement two-dimensional codes, advertisement search keywords, advertisement pictures and advertisement videos or any combination thereof;
and when the target user activates the advertisement putting path, entering a target page of the target object through a data channel type corresponding to the advertisement putting path, and loading and displaying the commodity on the target page.
6. The data-processing-based advertisement delivery effect prediction apparatus according to claim 5, wherein:
the page data storage unit stores page operation data of a target display page in a preset time period, and the page operation data comprises one of the following data or any combination of the following data:
and the target user browses the detailed page of the commodity, the browsing time of the evaluation page, the time of communicating with the online customer service, the operation of clicking to join a shopping cart and the operation of clicking to purchase.
7. The data-processing-based advertisement delivery effect prediction apparatus according to claim 5, wherein:
the device also comprises an advertisement putting adjusting unit;
the advertisement putting adjusting unit adjusts the number distribution and/or the time distribution of the data channels of different types based on the prediction result of the advertisement putting effect obtained by the effect prediction unit.
8. The data-processing-based advertisement delivery effect prediction apparatus according to claim 5, wherein:
the number fitting unit comprises a fitting function based on an R language;
the fitting function based on the R language takes the statistical times of different types of data channels as a dependent variable and takes the activated accumulated times of different types of data channels in a plurality of different statistical periods as an independent variable to carry out fitting.
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