CN114266604A - Advertisement putting effect evaluation method based on multiple dimensions and storage medium - Google Patents
Advertisement putting effect evaluation method based on multiple dimensions and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of computers, and provides an advertisement putting effect evaluation method based on multiple dimensions, wherein an advertisement profit value corresponding to each advertisement putting dimension in n preset advertisement putting dimensions is measured and calculated according to a collected consumption behavior data set of an account for an advertisement, the input-output ratio in different putting dimensions is calculated according to user profit data of each putting dimension and cost data of each putting dimension, the putting effect of each advertisement putting dimension is determined according to the input-output ratio corresponding to each advertisement putting dimension and a preset threshold corresponding to each advertisement putting dimension, the input-output ratio of different dimensions is calculated based on user behavior data, and the accuracy of advertisement putting effect evaluation is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a multidimensional advertisement putting effect evaluation method and device, computer equipment and a storage medium.
Background
With the rapid development of internet technology, internet advertisements gradually become a mainstream advertisement medium. The internet advertisement is an advertisement operation mode that advertisements are put on the internet through a network advertisement platform, advertisements are published or released on the internet by using advertisement banners, text links and multimedia on websites, and the advertisements are transmitted to internet users through the network. After the advertisement is put to the advertisement channel on the internet, for example, APP software store, APP home page advertisement, APP page advertisement, etc. through the advertisement channel, obtain corresponding user, judge the effect of advertisement putting according to the user to the download volume of advertisement. When the effect of advertisement putting needs to be evaluated, the putting cost of the advertisement is generally calculated in the dimension of the advertisement putting channel, or the advertisement putting effect is evaluated by calculating the flow in the dimension of the advertisement putting channel, so that the problem that the evaluation index of the advertisement putting effect is single exists when the advertisement putting effect is evaluated in the prior art.
Disclosure of Invention
Therefore, it is necessary to provide a method, an apparatus, a computer device, and a storage medium for evaluating an advertisement delivery effect based on multiple dimensions to solve the problem of a single evaluation index of an advertisement delivery effect.
A first aspect of an embodiment of the present application provides a method for evaluating an advertisement delivery effect based on multiple dimensions, including:
calculating an advertisement revenue value corresponding to each advertisement delivery dimension in preset n advertisement delivery dimensions according to the acquired consumption behavior data set of the account aiming at the advertisement; wherein n is an integer greater than 1;
calculating the input-output ratio corresponding to each advertisement putting dimension based on the advertisement revenue data corresponding to each advertisement putting dimension and the preset putting cost data corresponding to each advertisement putting dimension;
and determining the delivery effect of each advertisement delivery dimension according to the input-output ratio corresponding to each advertisement delivery dimension and the preset threshold corresponding to each advertisement delivery dimension.
A second aspect of the present application provides a method and an apparatus for evaluating an advertisement delivery effect based on multiple dimensions, including:
a collecting unit: calculating an advertisement revenue value corresponding to each advertisement delivery dimension in preset n advertisement delivery dimensions according to the acquired consumption behavior data set of the account aiming at the advertisement; wherein n is an integer greater than 1;
the measuring and calculating unit: calculating the input-output ratio corresponding to each advertisement putting dimension based on the advertisement revenue data corresponding to each advertisement putting dimension and the preset putting cost data corresponding to each advertisement putting dimension;
a determination unit: and determining the delivery effect of each advertisement delivery dimension according to the input-output ratio corresponding to each advertisement delivery dimension and the preset threshold corresponding to each advertisement delivery dimension.
A third aspect of embodiments of the present application provides a computer device, including: a memory, a processor, and computer readable instructions stored in the memory and executable on the processor for causing the computer to perform the steps of a multi-dimensional based advertisement placement effectiveness evaluation method.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium storing a computer program, which is executed by a processor to perform steps of a multidimensional-based advertisement placement effectiveness evaluation method.
The advertisement putting effect evaluation method based on the multiple dimensions provided by the embodiment of the application has the following beneficial effects:
the application relates to the technical field of computers, and provides an advertisement putting effect evaluation method based on multiple dimensions, wherein an advertisement profit value corresponding to each advertisement putting dimension in n preset advertisement putting dimensions is measured and calculated according to a collected consumption behavior data set of an account for an advertisement, the putting-in-output ratio in different putting dimensions is calculated according to user profit data of each putting dimension and cost data of each putting dimension, the putting effect of each advertisement putting dimension is determined according to the putting-in-output ratio corresponding to each advertisement putting dimension and a preset threshold value corresponding to each advertisement putting dimension, the putting-in-output ratio of different dimensions is calculated based on user behavior data, the accuracy of advertisement putting effect evaluation is improved, and the follow-up putting strategy of the advertisement can be more accurately determined.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced 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 that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of an implementation of a multidimensional-based advertisement delivery effect evaluation method according to an embodiment of the present application;
fig. 2 is a flowchart of an implementation of a multidimensional-based advertisement placement effectiveness evaluation method according to another embodiment of the present application;
FIG. 3 is a flowchart illustrating an implementation of a method for evaluating advertisement delivery effectiveness based on multiple dimensions according to yet another embodiment of the present application;
fig. 4 is a block diagram of a device of an advertisement delivery effect evaluation method based on multiple dimensions according to an embodiment of the present application;
fig. 5 is a block diagram of a server-side device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The multi-dimension-based advertisement putting effect evaluation method is applied to the technical field of computers and can be executed by a server side.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a method for evaluating an advertisement placement effectiveness based on multiple dimensions according to an embodiment of the present application.
S11: calculating an advertisement revenue value corresponding to each advertisement delivery dimension in preset n advertisement delivery dimensions according to the acquired consumption behavior data set of the account aiming at the advertisement;
in step S11, the user downloads or browses the advertisement through the advertisement delivery channel, the advertisement terminal collects consumption behavior data and uploads the consumption behavior data to the server, and the server calculates and calculates an advertisement revenue value corresponding to each advertisement delivery dimension of the preset n advertisement delivery dimensions according to the collected consumption behavior data set of the account for the advertisement, where the consumption behavior data set of the account for the advertisement includes the user ID and some operation data of the user on the advertisement. And the preset n advertisement putting dimensions are correlated, and when the putting effect in one advertisement putting dimension is not ideal, the putting effect of the next advertisement putting dimension of the advertisement putting dimension is continuously evaluated. The advertisement profit value is the profit obtained through some functions of the advertisement, for example, some paid advertisements obtain the profit through the payment of the account number to the advertisement, some publicized advertisements obtain the profit through the download amount.
In this embodiment, the preset n advertisement delivery dimensions are an application dimension, a channel dimension, an advertisement group dimension, an advertisement plan dimension and an advertisement creative dimension, and these user consumption behavior operations are recorded and sent to the advertisement deliverer after the user clicks the advertisement page or when the user browses the advertisement content, or if the user downloads the relevant APP of the advertisement, the advertisement page will transfer the user to the server of the advertisement deliverer, and meanwhile, for example, the operation of the user clicking the download link will be recorded and sent to the advertisement deliverer. Further, when a user downloads an advertisement-related APP, the advertiser will know some information about the user, such as client ID, client device address, etc. And uploading the acquired consumption behavior data of each account to a server by the advertisement placer. The server side extracts relevant data in n preset advertisement putting dimensions according to the consumption behavior data set of each account, and calculates and obtains the advertisement profit value of each advertisement putting dimension according to the consumption behavior data extracted by each dimension.
As an embodiment of the present application, step S11 specifically includes:
carrying out standardization processing on data in the consumption behavior data set to obtain the consumption behavior standard data set; obtaining an advertisement putting dimension corresponding to the advertisement according to the advertisement identifier in the consumption behavior standard data set; and calculating the advertisement revenue value corresponding to the advertisement putting dimension according to the consumption behavior standard data set and the revenue strategy corresponding to the preset advertisement putting dimension.
In this embodiment, the data in the consumption behavior data set collected through different channels and different data formats, for example, revenue obtained by recharging an advertisement or revenue obtained by displaying an advertisement on an advertisement slot in an advertisement, may be used as behavior data of advertisement revenue, but the data formats collected by the two are different, so that the data in the collected consumption behavior data set needs to be standardized and the data formats are unified. The transformation and processing of data may be performed in an ETL engine, where data transformation is typically implemented in a componentized manner. Common data conversion components include field mapping, data filtering, data cleaning, data replacement, data calculation, data verification, data encryption and decryption, data merging, data splitting and the like. The components are as a process on a pipeline, are pluggable and can be arbitrarily assembled, and share data among the components through a data bus. And after standardization processing, obtaining the consumption behavior standard data set, wherein data in the consumption behavior standard data set are synonymous heterogeneous fields of different informatization systems and are normalized to be the same expression item, for example, some paid advertisements obtain profits through payment of the advertisements by account numbers, some publicized advertisements obtain profits through downloading amount, and payment data and downloading data are uniformly converted into json data.
It should be noted that json data is a huge array, and includes a plurality of parameters, for example, parameters representing a terminal, parameters representing an advertisement, and the like, and extracts an advertisement identifier from the json data to obtain an advertisement delivery dimension corresponding to the advertisement, for example, the json data includes a terminal type, and the terminal type may be distinguished according to an operating system of the terminal, and includes an iOS (apple mobile operating system) operating system type and an Android (Android) operating system type. Different terminal types may have different advertisement identifiers. For example, the advertisement identifier of the iOS operating system type may be an IDFA (also may be referred to as a first advertisement identifier). The Android operating system type advertisement identifier may be Imei (also referred to as a secondary advertisement identifier). The terminal identifications can be extracted from the json data respectively according to the terminal types. And calculating an advertisement profit value corresponding to the advertisement delivery dimension according to a preset profit strategy corresponding to the advertisement delivery dimension, for example, in the dimension of the advertisement delivery channel, the profit strategy of the thousand-time downloading amount of the advertisement in the Android operating system is a profit strategy one, the profit strategy of the thousand-time downloading amount of the advertisement in the iOS operating system is a profit strategy two, and the advertisement profit value corresponding to the advertisement delivery dimension is calculated according to the profit strategy one and the profit strategy two.
S12: calculating the input-output ratio corresponding to each advertisement putting dimension based on the advertisement revenue data corresponding to each advertisement putting dimension and the preset putting cost data corresponding to each advertisement putting dimension;
in step S12, the input-output ratio, also referred to as a return on investment, corresponding to each advertisement placement dimension is used to measure the amount of benefit obtained by each investment within a period of time after each investment, the preset cost data of each advertisement placement dimension is obtained by a third party, the obtained preset cost data is uploaded to the server, and the input-output ratio corresponding to each advertisement placement dimension is obtained by measuring and calculating the ratio of the preset cost data of each advertisement placement dimension to the preset cost data of each advertisement placement dimension according to the advertisement revenue data corresponding to each advertisement placement dimension and the preset cost data of each advertisement placement dimension.
In the embodiment, by taking game advertisements as an example, in the game industry, especially for the developers of the games of the young and the middle, the popularity of the games is difficult to spread spontaneously by a small number of players, the cognition in the players can be effectively improved in an advertisement buying amount mode, a large number of new user registrations can be quickly acquired, the rhythm of the starting amount of the games at the initial online stage is accelerated, and the input-output ratio of the advertisements is calculated to serve as an index for measuring the game advertisements. For example, in the application dimension, the game APP is downloaded in different application stores, then according to the data in the user consumption data set, the specific application store in which the game APP is downloaded is obtained, the income value obtained by the game APP downloaded in the application store, including the recharging income or the income for purchasing goods, is calculated, and then according to the obtained putting cost data of the game APP in the corresponding application store, the obtained input-output ratio of the application store in the application dimension is calculated.
It should be noted that the preset placement cost data corresponding to the advertisement placement dimension may also be obtained by a third party, and when a specific application store is determined according to the consumption behavior data set, the cost data of the game advertisement in the application store is obtained by calculating the browsing amount, the downloading amount and the registration amount of the game advertisement recorded by the application store.
S13: and determining the delivery effect of each advertisement delivery dimension according to the input-output ratio corresponding to each advertisement delivery dimension and the preset threshold corresponding to each advertisement delivery dimension.
In step S13, the input-output ratio can visually represent the return effect of investment and facilitate comparison with other test schemes. The input-output ratio is widely applied to the Internet industry in recent years due to the intuitiveness and flexibility of the calculation mode, and now becomes one of important indexes for measuring the advertisement putting effect.
In this embodiment, to a plurality of account numbers in every advertisement putting dimension, calculate the input-output ratio of every account number in every advertisement putting dimension, through the input-output ratio mean value of every account number in every advertisement putting dimension and the preset threshold value that corresponds advertisement putting dimension carry out the comparison, according to the comparison result, judge the input effect of advertisement in every advertisement putting dimension to can provide data support for the reasonable formulation of the follow-up advertisement putting scheme of advertiser, and then provide effective reference for the reasonable control advertisement putting cost of advertiser.
As an embodiment of the present application, step S13 specifically includes:
obtaining a first threshold value and a second threshold value corresponding to each advertisement putting dimension through statistical analysis according to historical data of the input-output ratio corresponding to each advertisement putting dimension; and determining the delivery effect of each advertisement delivery dimension according to the first threshold and the second threshold corresponding to each advertisement delivery dimension and the input-output ratio corresponding to each advertisement delivery dimension.
In this embodiment, when the threshold value in each advertisement delivery dimension is set, the threshold value corresponding to each advertisement delivery dimension is obtained through statistical analysis according to the historical data of the input-output ratio corresponding to each advertisement delivery dimension. When the input-output ratio corresponding to each advertisement putting dimension is larger than a first threshold corresponding to each advertisement putting dimension, determining that the putting effect of the advertisement putting dimension is positive; and when the input-output ratio corresponding to each advertisement putting dimension is smaller than a second threshold corresponding to each advertisement putting dimension, determining that the putting effect of the advertisement putting dimension is negative.
It should be noted that when a threshold corresponding to each advertisement delivery dimension is set, the advertisement delivery effects of each account in each advertisement delivery dimension in the historical data are classified, and are respectively a positive effect, a negative effect and an effect to be evaluated, the input-output ratio of each account in the category of the effect to be evaluated is ranked, the input-output ratio at an endpoint of a 95% confidence interval is respectively used as a first threshold and a second threshold, and the first threshold is greater than the second threshold.
It should be noted that the advertisement delivery effect of each account may be an influence of advertisement delivery within a certain time, for example, when the advertisement delivery effect is evaluated, the net profit value may be obtained according to the crowd covered by forwarding of the advertisement and the download amount of the advertisement, and when the advertisement delivery effect is evaluated, the net profit value may also be obtained according to advertisement delivery within a certain time.
When the advertisement putting effect is obtained, the advertisement putting effect data can be obtained through a third-party platform, for example, the number of the APIs of the putting effect data provided by a certain platform is two, namely, the API is requested and the API is obtained through the advertisement downloaded by the certain platform. The request API is used for submitting a calculation task to a certain platform, so that the certain platform counts and generates effect data of a given user and a given date to obtain a report account; the obtaining API is used for inquiring the completion condition of the computing task corresponding to the given report account, and if the completion condition is completed, the downloading link is obtained. After obtaining the download link, the report data (zip format file) can be downloaded, and after decompression, a CSV (comma separated) data file containing given fields is obtained, wherein the data file contains advertisement effect data.
And when the input-output ratio corresponding to each advertisement putting dimension is between the first threshold and the second threshold corresponding to the advertisement putting dimension, calculating the input-output ratio of the next advertisement putting dimension corresponding to the advertisement putting dimension. For example, in the dimension of the advertisement plan, the obtained input-output ratio is between the first threshold and the second threshold in the dimension of the advertisement plan, then in the next dimension of the advertisement plan, the advertisement delivery effect is continuously evaluated in the dimension of the advertisement creatives, the dimension of the advertisement creatives includes different advertisement creatives, for example, the advertisement creatives can be delivered through video delivery, advertisement links or delivery through insertion in a webpage, and the input-output ratios obtained through the different advertisement creatives are respectively judged.
It should be noted that if the input-output ratio in the dimension of the ad creative is smaller than the second threshold in the corresponding dimension, it is indicated that the ad placement in the dimension is a negative effect, and at this time, an alarm can be given to an administrator to remind the ad placer to adjust the ad placement in time, or to stop the ad creative from being placed in time, so as to avoid the loss of the ad placer.
Referring to fig. 2, fig. 2 is a flowchart illustrating an implementation of a method for evaluating an advertisement placement effectiveness based on multiple dimensions according to another embodiment of the present application. With respect to the embodiment corresponding to fig. 1, the method for evaluating the effectiveness of advertisement placement based on multiple dimensions provided by the present embodiment further includes step S21 before step S11. The details are as follows:
s21: and setting a data acquisition buried point in the advertisement by utilizing the integrated user behavior statistical tool of the advertisement.
In step S21, the data collection site is used to collect the consumption behavior data set of the account for the advertisement, and the user behavior of each account is monitored by the collection site.
In this embodiment, according to the SDK tool, a collection data embedding point is set in the delivered advertisement, and the page for setting the embedding point includes a specific common page, such as each reading page, and also includes pages representing successful conversion performed by the user, such as a registration success page, a purchase success page, and a download success page. In addition, system broadcast messages of the client device can be monitored through some SDKs to obtain user behavior data such as applications being installed, upgraded, or uninstalled. When the user operates at the collection buried point, the behavior data of the operation is collected, for example, when the advertisement is a game advertisement, if the collection buried point is arranged at each level of the advertisement, the data of passing each time of each account is recorded, the passing condition of each account can be obtained, and when the advertisement is a recharging advertisement, if the collection buried point is arranged at the recharging interface, the recharging value of each time is recorded when recharging is performed each time.
Referring to fig. 3, fig. 3 is a flowchart illustrating an implementation of a method for evaluating an advertisement delivery effect based on multiple dimensions according to yet another embodiment of the present application. With respect to the embodiment corresponding to fig. 1, the method for evaluating the effectiveness of advertisement placement based on multiple dimensions provided by the present embodiment further includes step S31 before step S12. The details are as follows:
s31: and calculating preset delivery cost data corresponding to each advertisement delivery dimension according to each advertisement delivery dimension and a delivery rule corresponding to each preset advertisement delivery dimension.
In step S31, the placement cost in each advertisement placement dimension is calculated, the advertisement placement operator input and output is objectively reflected in the aspect of the placement cost, and the placement rule corresponding to each advertisement placement dimension may be set according to the CPM (thousands of display costs), CPC (single click cost), CVR (click conversion rate), conversion unit price, and the like of the placed advertisement. For example, the CPM of an advertisement in different application stores may set the CPM cost of the advertisement according to the popularity of the store or the amount of downloads by the store.
In this embodiment, when the placement cost in each advertisement placement dimension is calculated, the advertisement placement dimension corresponding to the advertisement is found from the consumption behavior data, and the advertisement placement cost is calculated in different advertisement placement dimensions. For example, a downloading platform for obtaining an advertisement through an advertisement identifier in consumption behavior data obtains a browsing amount, a downloading amount, a forwarding amount and the like of the advertisement in the downloading platform through a third-party downloading platform, and an advertisement publisher pays corresponding delivery cost including cost of advertisement browsing amount, cost of downloading amount, cost of forwarding amount and the like to the third-party downloading platform according to data related to the advertisement in the obtained third-party downloading platform.
Referring to fig. 4, fig. 4 is a block diagram illustrating an advertisement placement effectiveness evaluation method and apparatus based on multiple dimensions according to an embodiment of the present disclosure. In this embodiment, the server includes 3 units for executing the steps in the embodiment corresponding to fig. 1 to 3, and refer to the description in the embodiments corresponding to fig. 1 to 3 and fig. 1 to 3. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 4, the multidimensional-based advertisement placement effectiveness evaluation method apparatus 40 includes: an acquisition unit 41, a calculation unit 42, a determination unit 43, wherein,
the acquisition unit 41 is configured to measure and calculate an advertisement revenue value corresponding to each advertisement delivery dimension of the preset n advertisement delivery dimensions according to the acquired consumption behavior data set of the account for the advertisement; wherein n is an integer greater than 1;
the calculating unit 42 is configured to calculate, based on advertisement revenue data corresponding to each advertisement delivery dimension and preset delivery cost data corresponding to each advertisement delivery dimension, a input-output ratio corresponding to each advertisement delivery dimension;
a determining unit 43, configured to determine, according to the input-output ratio corresponding to each advertisement placement dimension and a preset threshold corresponding to each advertisement placement dimension, a placement effect of each advertisement placement dimension.
As an embodiment of the present application, the method and device 40 for evaluating advertisement delivery effectiveness based on multiple dimensions further includes:
a setting unit 44, configured to set a data collection buried point in the advertisement by using an integrated user behavior statistical tool of the advertisement; the data acquisition buried point is used for acquiring a consumption behavior data set of the account aiming at the advertisement.
And the calculating unit 45 is configured to calculate preset delivery cost data corresponding to each advertisement delivery dimension according to each advertisement delivery dimension and a delivery rule corresponding to each advertisement delivery dimension configured in advance.
As an embodiment of the present application, the acquisition unit 41 is specifically configured to perform standardization processing on data in the consumption behavior data set to obtain the consumption behavior standard data set; obtaining an advertisement putting dimension corresponding to the advertisement according to the advertisement identifier in the consumption behavior standard data set; and calculating the advertisement revenue value corresponding to the advertisement putting dimension according to the consumption behavior standard data set and the revenue strategy corresponding to the preset advertisement putting dimension.
As an embodiment of the present application, the method and device 40 for evaluating advertisement delivery effectiveness based on multiple dimensions further includes:
a first executing unit 46, configured to calculate, if the input-output ratio corresponding to each advertisement placement dimension is between a first threshold and a second threshold corresponding to the advertisement placement dimension, an input-output ratio of a next advertisement placement dimension corresponding to the advertisement placement dimension.
A second executing unit 47, configured to determine that the advertisement serving dimension has a positive serving effect when the input-output ratio corresponding to each advertisement serving dimension is greater than the first threshold corresponding to each advertisement serving dimension; and when the input-output ratio corresponding to each advertisement putting dimension is smaller than a second threshold corresponding to each advertisement putting dimension, determining that the putting effect of the advertisement putting dimension is negative.
As an embodiment of the present application, the determining unit 43 is specifically configured to obtain, through statistical analysis, a first threshold and a second threshold corresponding to each advertisement delivery dimension according to historical data of a input-output ratio corresponding to each advertisement delivery dimension; and determining the delivery effect of each advertisement delivery dimension according to the first threshold and the second threshold corresponding to each advertisement delivery dimension and the input-output ratio corresponding to each advertisement delivery dimension.
It should be understood that, in the structural block diagram of the apparatus of the method for evaluating an advertisement delivery effect based on multiple dimensions shown in fig. 4, each unit is used to execute each step in the embodiment corresponding to fig. 1 to fig. 3, and each step in the embodiment corresponding to fig. 1 to fig. 3 has been explained in detail in the above embodiment, and specific reference is made to the relevant description in the embodiment corresponding to fig. 1 to fig. 3 and fig. 1 to fig. 3, which is not repeated herein.
In one embodiment, a computer device is provided, the computer device is a server, and the internal structure diagram of the computer device can be as shown in fig. 5. The computer device 50 includes a processor 51, an internal memory 53, and a network interface 54 connected by a system bus 52. Wherein the processor 51 of the computer device is used to provide computing and control capabilities. The memory of the computer device 50 includes a readable storage medium 55, an internal memory 53. The readable storage medium 55 stores an operating system 56, computer readable instructions 57, and a database 58. The internal memory 53 provides an environment for the operation of an operating system 56 and computer readable instructions 57 in the readable storage medium 55. The database 58 of the computer device 50 is used to store data relating to a multi-dimensional based advertisement placement effectiveness evaluation method. The network interface 53 of the computer device 50 is used for communication with an external terminal through a network connection. The computer readable instructions 57, when executed by the processor 51, implement a multi-dimension based advertisement placement effectiveness evaluation method. The readable storage medium 55 provided by the present embodiment includes a nonvolatile readable storage medium and a volatile readable storage medium.
It will be understood by those of ordinary skill in the art that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to computer readable instructions, which may be stored in a non-volatile readable storage medium or a volatile readable storage medium, and when executed, the computer readable instructions may include processes of the above embodiments of the methods. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A multidimensional-based advertisement putting effect evaluation method is characterized by comprising the following steps:
calculating an advertisement revenue value corresponding to each advertisement delivery dimension in preset n advertisement delivery dimensions according to the acquired consumption behavior data set of the account aiming at the advertisement; wherein n is an integer greater than 1;
calculating the input-output ratio corresponding to each advertisement putting dimension based on the advertisement revenue data corresponding to each advertisement putting dimension and the preset putting cost data corresponding to each advertisement putting dimension;
and determining the delivery effect of each advertisement delivery dimension according to the input-output ratio corresponding to each advertisement delivery dimension and the preset threshold corresponding to each advertisement delivery dimension.
2. The method for evaluating advertisement putting effectiveness based on multiple dimensions of claim 1, wherein before calculating the advertisement profit value corresponding to each advertisement putting dimension of the preset n advertisement putting dimensions according to the collected consumption behavior data set of the account for the advertisement, the method further comprises:
setting data acquisition buried points in the advertisement by utilizing an integrated user behavior statistical tool of the advertisement; the data acquisition buried point is used for acquiring a consumption behavior data set of the account aiming at the advertisement.
3. The multidimensional-based advertisement putting effect evaluation method according to claim 1, wherein the calculating an advertisement profit value corresponding to each advertisement putting dimension of the preset n advertisement putting dimensions according to the collected consumption behavior data set of the account for the advertisement comprises:
carrying out standardization processing on data in the consumption behavior data set to obtain the consumption behavior standard data set;
obtaining an advertisement putting dimension corresponding to the advertisement according to the advertisement identifier in the consumption behavior standard data set;
and calculating the advertisement revenue value corresponding to the advertisement putting dimension according to the consumption behavior standard data set and the revenue strategy corresponding to the preset advertisement putting dimension.
4. The method for evaluating advertisement putting effectiveness based on multiple dimensions as claimed in claim 1, wherein before calculating the input-output ratio corresponding to each advertisement putting dimension based on the advertisement profit data corresponding to each advertisement putting dimension and the preset putting cost data corresponding to each advertisement putting dimension, the method further comprises:
and calculating preset delivery cost data corresponding to each advertisement delivery dimension according to each advertisement delivery dimension and a delivery rule corresponding to each preset advertisement delivery dimension.
5. The method for evaluating advertisement putting effect based on multiple dimensions as claimed in claim 1, wherein the determining the putting effect of each advertisement putting dimension according to the input-output ratio corresponding to each advertisement putting dimension and the preset threshold corresponding to each advertisement putting dimension comprises:
obtaining a first threshold value and a second threshold value corresponding to each advertisement putting dimension through statistical analysis according to historical data of the input-output ratio corresponding to each advertisement putting dimension;
and determining the delivery effect of each advertisement delivery dimension according to the first threshold and the second threshold corresponding to each advertisement delivery dimension and the input-output ratio corresponding to each advertisement delivery dimension.
6. The method according to claim 5, wherein the determining the advertisement placement effectiveness of each advertisement placement dimension according to the first threshold and the second threshold corresponding to each advertisement placement dimension and the input-output ratio corresponding to each advertisement placement dimension comprises:
when the input-output ratio corresponding to each advertisement putting dimension is larger than a first threshold corresponding to each advertisement putting dimension, determining that the putting effect of the advertisement putting dimension is positive;
and when the input-output ratio corresponding to each advertisement putting dimension is smaller than a second threshold corresponding to each advertisement putting dimension, determining that the putting effect of the advertisement putting dimension is negative.
7. The method according to claim 5, wherein after determining the advertisement placement effectiveness of each advertisement placement dimension according to the first threshold and the second threshold corresponding to each advertisement placement dimension and the input-output ratio corresponding to each advertisement placement dimension, the method further comprises:
and if the input-output ratio corresponding to each advertisement putting dimension is between the first threshold and the second threshold corresponding to the advertisement putting dimension, calculating to obtain the input-output ratio of the next advertisement putting dimension corresponding to the advertisement putting dimension.
8. The multi-dimensional based advertisement placement effectiveness evaluation method device according to claim 1,
a collecting unit: calculating an advertisement revenue value corresponding to each advertisement delivery dimension in preset n advertisement delivery dimensions according to the acquired consumption behavior data set of the account aiming at the advertisement; wherein n is an integer greater than 1;
the measuring and calculating unit: calculating the input-output ratio corresponding to each advertisement putting dimension based on the advertisement revenue data corresponding to each advertisement putting dimension and the preset putting cost data corresponding to each advertisement putting dimension;
a determination unit: and determining the delivery effect of each advertisement delivery dimension according to the input-output ratio corresponding to each advertisement delivery dimension and the preset threshold corresponding to each advertisement delivery dimension.
9. A computer device comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the computer readable instructions are readable instructions generated by the engine of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing the computer to perform the steps of the method of any of the preceding claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117093801A (en) * | 2023-10-10 | 2023-11-21 | 腾讯科技（深圳）有限公司 | Page evaluation method and device, electronic equipment and storage medium |
| CN117314522A (en) * | 2023-09-26 | 2023-12-29 | 选房宝(珠海横琴)数字科技有限公司 | Advertisement putting effect analysis method, device, equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117314522A (en) * | 2023-09-26 | 2023-12-29 | 选房宝(珠海横琴)数字科技有限公司 | Advertisement putting effect analysis method, device, equipment and storage medium |
| CN117093801A (en) * | 2023-10-10 | 2023-11-21 | 腾讯科技（深圳）有限公司 | Page evaluation method and device, electronic equipment and storage medium |
| CN117093801B (en) * | 2023-10-10 | 2024-02-09 | 腾讯科技（深圳）有限公司 | Page evaluation method and device, electronic equipment and storage medium |
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