CN117593055A - Advertisement effect intelligent analysis method and system based on big data - Google Patents
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Abstract
The invention discloses an intelligent analysis method and system for advertisement effect based on big data, which relate to the technical field of advertisement promotion and comprise the following steps: acquiring user data of an advertisement putting platform; obtaining an online consumption ratio-time change curve; determining the advertisement delivery purposes, and determining the optimal delivery time of advertisements with different delivery purposes on an advertisement delivery platform; determining actual playing data of the advertisement; determining the effect benefit brought by each advertisement; screening high-adaptation advertisements based on the effect benefits brought by each advertisement; extracting at least one advertisement feature from the high-fit advertisement, and marking the advertisement feature as the most-fit advertisement feature; an advertisement characteristic of advertisements with different delivery purposes is adjusted. The invention has the advantages that: and analyzing the advertisement putting platform and the advertisement putting effect by adopting a big data analysis means, optimizing the characteristics of advertisements with different putting purposes according to the effect analysis result, and further improving the advertisement putting effect.
Description
Technical Field
The invention relates to the technical field of advertisement popularization, in particular to an intelligent advertisement effect analysis method and system based on big data.
Background
Internet advertising is a form of advertising presented on mobile or stationary devices, which is typically presented in short videos, in order to attract the attention of users and encourage them to click or interact.
The existing advertisement delivery system focuses on how to deliver advertisements, does not analyze network data and the advertisement which is required to be delivered by a user, obtains where the advertisements are delivered, cannot determine the advertisement delivery analysis according to the attribute of an advertisement delivery platform, cannot monitor and analyze the advertisement delivery effect of the delivered advertisements in the advertisement delivery platform, and is difficult to realize the maximum effect benefit of advertisement popularization.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides an intelligent analysis method and an intelligent analysis system for advertisement effect based on big data, which solve the problems that how to put advertisements is focused on the prior advertisement putting system, the analysis of the advertisement itself is not needed to put the advertisement according to network data and users, what kind of advertisements are put in the advertisement is obtained, the advertisement putting analysis cannot be determined according to the attribute of the advertisement putting platform, the advertisement putting effect of the advertisement put in the advertisement putting platform is not monitored and analyzed, the advertisement is optimized pertinently, and the maximum effect benefit of advertisement popularization is difficult to realize.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent analysis method for advertisement effect based on big data comprises the following steps:
acquiring user data of an advertisement putting platform, wherein the user data at least comprises user browsing habits and user consumption habits;
analyzing the user data of the advertisement putting platform to obtain an online consumption ratio-time change curve;
determining the advertisement delivery purpose, and determining the optimal delivery time of advertisements with different delivery purposes on the advertisement delivery platform based on the advertisement delivery purpose and an online consumption ratio-time change curve of the advertisement delivery platform;
collecting playing data of advertisements on the advertisement putting platform in real time based on the big data platform, and determining actual playing data of the advertisements based on the playing data of the advertisements;
determining the effect benefits brought by each advertisement based on the actual playing data of the advertisement;
screening advertisements which are most suitable for the effect benefits and the delivery purposes of the advertisements delivered in the advertisement delivery platform based on the effect benefits brought by each advertisement, and marking the advertisements as high-fit advertisements;
extracting at least one advertisement feature from the high-fit advertisement, and recording the advertisement feature as the optimal fit advertisement feature, wherein the advertisement feature at least comprises an advertisement file, an advertisement video and an advertisement picture;
and adjusting the advertisement characteristics of advertisements with different delivery purposes based on the optimal advertisement characteristics.
Preferably, the analyzing the user data of the advertisement delivery platform to obtain the online consumption ratio-time variation curve specifically includes:
determining a user online quantity-time change curve of the advertisement delivery platform in an analysis period based on a user browsing habit;
determining a consumption amount-time change curve of the advertisement delivery platform in an analysis period based on user consumption habits;
determining a user online index-time change curve based on the user online quantity-time change curve;
determining a consumption index-time variation curve based on the consumption amount-time variation curve;
determining an online consumption ratio-time change curve based on the user online indicator-time change curve and the consumption indicator-time change curve;
the expression of the user online index-time change curve is as follows:
in the expression of the user online index-time change curve, Z (T) is the user online index at the moment T, T is the analysis period duration, and Y (T) is the user online quantity at the moment T;
the expression of the consumption index-time change curve is as follows:
in the expression of the consumption index-time change curve, X (t) is the consumption index at the time t, and J (t) is the consumption amount at the time t;
the expression of the online consumption ratio-time change curve is as follows:
wherein, the consumption ratio is the online consumption ratio at the time t.
Preferably, determining the advertisement delivery purpose, and determining the optimal delivery time of the advertisement with different delivery purposes on the advertisement delivery platform based on the advertisement delivery purpose and the online consumption ratio-time change curve of the advertisement delivery platform specifically includes:
determining a weight ratio of an advertising effect to an advertising promotion effect based on the advertising purpose;
constructing a fitting index-time change curve based on the weight ratio of the advertising effect to the advertising promotion effect and the online consumption ratio-time change curve;
screening out a plurality of moments of which the fitting indexes are larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and marking the moments as first statistical moments;
screening out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption quantity exceeds the preset consumption quantity value in the first statistical moment, and recording the moments as the optimal putting time on the advertisement putting platform;
wherein, the expression of the fitting index-time change curve is:
N(t)＝|B(t)-Q|
wherein N (t) is a fitting index at the moment t, and Q is a weight ratio of the advertising effect to the advertising promotion effect.
Preferably, the determining the effect benefit brought by each advertisement based on the actual playing data of the advertisement specifically includes:
based on the actual playing data of the advertisement, extracting a plurality of effect parameters of the advertisement, wherein the effect parameters at least comprise advertisement watching times, advertisement average watching duration and advertisement ordering quantity;
determining advertisement effect indexes of each effect parameter, wherein the advertisement effect indexes are specifically advertisement effect proportion brought by the effect parameter and promotion effect proportion brought by the effect parameter;
and respectively calculating the advertising effect benefit and the promotion effect benefit of the advertisement.
Preferably, the calculation formula of the benefit of the propaganda effect is as follows:
wherein C is advertising effect benefit, n is total number of advertising effect parameters, Z i Is the statistical index of the ith effect parameter of the advertisement, alpha i The unit index effect coefficient, P, being the ith effect parameter of the advertisement i The publicity effect proportion of the ith effect parameter of the advertisement;
the calculation formula of the promotion effect income is as follows:
wherein M is the benefit of the promotion effect of the advertisement, Q i The proportion of promotion effect which is the ith effect parameter of the advertisement;
wherein P is i +Q i ＝1。
Preferably, the screening the at least one advertisement of which the effect gain of the advertisement put in the advertisement putting platform is most suitable for the putting purpose based on the effect gain brought by each advertisement, and recording as the high-fit advertisement specifically includes:
calculating the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement;
calculating an adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
screening out the advertisement with the maximum adaptation index as a high adaptation advertisement;
the calculation formula of the adaptation index is as follows:
wherein S is an adaptation index of the advertisement and the throwing purpose, and F is a promotion ratio of the advertisement.
Further, an intelligent advertisement effect analysis system based on big data is provided, which is used for implementing the intelligent advertisement effect analysis method based on big data, and the intelligent advertisement effect analysis system based on big data comprises:
the platform analysis module is used for acquiring user data of the advertisement delivery platform, wherein the user data at least comprises user browsing habits and user consumption habits, and analyzing the user data of the advertisement delivery platform to obtain an online consumption ratio-time change curve;
the delivery time determining module is electrically connected with the platform analysis module and is used for determining the delivery purpose of the advertisement and determining the optimal delivery time of the advertisement with different delivery purposes on the advertisement delivery platform based on the delivery purpose of the advertisement and an online consumption ratio-time change curve of the advertisement delivery platform;
the playing data acquisition module is used for acquiring actual playing data of all advertisements put on the advertisement putting platform and determining effect benefits brought by each advertisement based on the actual playing data of the advertisements;
the advertisement adjusting module is electrically connected with the play data acquisition module, and is used for screening advertisements with the most adaptive advertisement of the effect gains and the throwing purposes of advertisements thrown in the advertisement throwing platform based on the effect gains brought by each advertisement, marking the advertisements as high-adaptive advertisements, extracting at least one advertisement characteristic from the high-adaptive advertisements and marking the advertisement characteristic as the most adaptive advertisement characteristic, wherein the advertisement characteristic at least comprises advertisement text, advertisement videos and advertisement pictures, and simultaneously adjusting the advertisement characteristics of advertisements with different throwing purposes based on the most adaptive advertisement characteristic.
Optionally, the delivery time determining module includes:
the first computing unit is used for determining the weight ratio of the advertising effect to the advertising promotion effect based on the advertising purpose, and constructing a fitting index-time change curve based on the weight ratio of the advertising promotion effect to the advertising promotion effect and the online consumption ratio-time change curve;
the first screening unit is used for screening out a plurality of moments of which the fitting index is larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and recording the moments as first statistical moments;
and the second screening unit is used for screening out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption amount exceeds the preset consumption amount value in the first statistical moment, and recording the moments as the optimal throwing time on the advertisement throwing platform.
Optionally, the advertisement adjustment module includes:
the adaptation calculation unit is used for calculating the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement, and calculating the adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
the third screening unit is used for screening out the advertisement with the maximum adaptation index and taking the advertisement as a high adaptation advertisement;
and the adjusting unit is used for adjusting the advertisement characteristics of advertisements of different delivery purposes based on the optimal advertisement characteristics.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an intelligent analysis method for advertisement effect based on big data, which is used for analyzing an advertisement putting platform, further obtaining the use habits of users in different time periods of different platforms, intelligently planning the advertisement putting period based on the use habits of the users in different time periods of different platforms and combining the advertisement putting purpose, simultaneously adopting big data analysis means to analyze the putting effect of the advertisement put on the advertisement putting platform, optimizing the characteristics of the advertisement in different putting purposes according to the result of the effect analysis, and further improving the advertisement putting effect.
Drawings
FIG. 1 is a flow chart of an intelligent analysis method for advertisement effect based on big data;
FIG. 2 is a flow chart of a method for obtaining an online consumption ratio-time variation curve according to the present invention;
FIG. 3 is a flow chart of a method for determining an optimal delivery time based on an online consumption ratio-time variation curve in the present invention;
FIG. 4 is a flow chart of a method of determining the resulting benefit of each advertisement in accordance with the present invention;
FIG. 5 is a flow chart of a method for screening high-fit advertisements according to the present invention;
FIG. 6 is a block diagram of an intelligent advertisement effect analysis system based on big data according to the present invention; .
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, an intelligent analysis method for advertisement effect based on big data includes:
acquiring user data of the advertisement putting platform, wherein the user data at least comprises user browsing habits and user consumption habits;
analyzing the user data of the advertisement putting platform to obtain an online consumption ratio-time change curve;
determining the advertisement delivery purpose, and determining the optimal delivery time of advertisements with different delivery purposes on the advertisement delivery platform based on the advertisement delivery purpose and an online consumption ratio-time change curve of the advertisement delivery platform;
collecting playing data of advertisements on the advertisement putting platform in real time based on the big data platform, and determining actual playing data of the advertisements based on the playing data of the advertisements;
determining the effect benefits brought by each advertisement based on the actual playing data of the advertisement;
screening advertisements which are most suitable for the effect benefits and the delivery purposes of the advertisements delivered in the advertisement delivery platform based on the effect benefits brought by each advertisement, and marking the advertisements as high-fit advertisements;
extracting at least one advertisement feature from the high-fit advertisement, and recording the advertisement feature as the optimal advertisement feature, wherein the advertisement feature at least comprises an advertisement file, an advertisement video and an advertisement picture;
and adjusting the advertisement characteristics of advertisements with different delivery purposes based on the optimal advertisement characteristics.
According to the scheme, the advertisement delivery platform is analyzed, so that the use habits of users in different time periods of different platforms are obtained, the intelligent advertisement delivery period is planned based on the use habits of the users in different time periods of different platforms and the advertisement delivery purpose, meanwhile, the big data analysis means are adopted to analyze the delivered advertisement effect of the advertisement delivery platform, the characteristics of the advertisements in different delivery purposes are optimized according to the effect analysis result, and the advertisement delivery effect is further improved.
Referring to fig. 2, analyzing user data of the advertisement delivery platform to obtain an online consumption ratio-time variation curve specifically includes:
determining a user online quantity-time change curve of the advertisement delivery platform in an analysis period based on a user browsing habit;
determining a consumption amount-time change curve of the advertisement delivery platform in an analysis period based on user consumption habits;
determining a user online index-time change curve based on the user online quantity-time change curve;
determining a consumption index-time variation curve based on the consumption amount-time variation curve;
determining an online consumption ratio-time change curve based on the user online indicator-time change curve and the consumption indicator-time change curve;
the expression of the user online index-time change curve is as follows:
in the expression of the user online index-time change curve, Z (T) is the user online index at the moment T, T is the analysis period duration, and Y (T) is the user online quantity at the moment T;
the expression of the consumption index-time variation curve is:
in the expression of the consumption index-time change curve, X (t) is the consumption index at the time t, and J (t) is the consumption amount at the time t;
the expression of the online consumption ratio-time variation curve is:
wherein, the consumption ratio is the online consumption ratio at the time t.
Referring to fig. 3, determining the advertisement delivery purpose, and determining the optimal delivery time of advertisements with different delivery purposes on the advertisement delivery platform based on the advertisement delivery purpose and the online consumption ratio-time variation curve of the advertisement delivery platform specifically includes:
determining a weight ratio of an advertising effect to an advertising promotion effect based on the advertising purpose;
constructing a fitting index-time change curve based on the weight ratio of the advertising effect to the advertising promotion effect and the online consumption ratio-time change curve;
screening out a plurality of moments of which the fitting indexes are larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and marking the moments as first statistical moments;
screening out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption quantity exceeds the preset consumption quantity value in the first statistical moment, and recording the moments as the optimal putting time on the advertisement putting platform;
the expression of the fitting index-time change curve is as follows:
N(t)＝|B(t)-Q|
wherein N (t) is a fitting index at the moment t, and Q is a weight ratio of the advertising effect to the advertising promotion effect.
It can be understood that different advertisements have different delivery purposes, for example, in the initial stage of product propaganda, the delivery purposes of the advertisements are mainly propaganda, the purposes of the advertisements are mainly to improve the popularity of the products, the different delivery purposes have different user data requirements on the advertisement delivery platform, for example, the popularity of the products is improved, and the number of online people who need the advertisement delivery platform is large;
in the scheme, the advertisement delivery purpose is divided into the advertisement propaganda effect and the advertisement promotion effect, the advertisement propaganda effect requires the user online quantity of the advertisement delivery platform, the advertisement promotion effect requires the user consumption amount of the advertisement delivery platform, the user online index and the consumption index of the advertisement delivery platform are analyzed to determine the delivery time period which is most suitable for the current advertisement delivery purpose, and the user online quantity and the consumption amount of the advertisement delivery platform are consistent with the advertisement delivery purpose requirement in the most suitable delivery time period, so that the advertisement delivery purpose can be realized to the maximum extent.
Referring to fig. 4, determining the effect benefit of each advertisement based on the actual playing data of the advertisement specifically includes:
based on the actual playing data of the advertisement, extracting a plurality of effect parameters of the advertisement, wherein the effect parameters at least comprise advertisement watching times, advertisement average watching duration and advertisement ordering quantity;
determining advertisement effect indexes of each effect parameter, wherein the advertisement effect indexes are specifically publicity effect proportion brought by the effect parameter and promotion effect proportion brought by the effect parameter;
and respectively calculating the advertising effect benefit and the promotion effect benefit of the advertisement.
The calculation formula of the propaganda effect income is as follows:
wherein C is advertising effect benefit, n is total number of advertising effect parameters, Z i Is the statistical index of the ith effect parameter of the advertisement, alpha i The unit index effect coefficient, P, being the ith effect parameter of the advertisement i The publicity effect proportion of the ith effect parameter of the advertisement;
the calculation formula of the promotion effect income is as follows:
wherein M is the benefit of the promotion effect of the advertisement, Q i The proportion of promotion effect which is the ith effect parameter of the advertisement;
wherein P is i +Q i ＝1。
It can be understood that the effect benefits brought by different effect parameters are different, for example, the advertisement watching times and the average advertisement watching time length have great influence weights on improving the propaganda effect benefits, and the advertisement ordering amount has great influence weights on improving the sales promotion effect benefits;
for example, in some embodiments, the specific gravity of the advertising effect of the average viewing time period of the advertisement is set to 0.75, the specific gravity of the advertising effect of the average viewing time period of the advertisement is set to 0.25, and if the average playing time period of the collected advertisement is 1.5 minutes, the advertising effect gain brought by the average viewing time period of the advertisement is alpha 1 X 1.5x0.75, the advertising average viewing time length brings promotion effect gain alpha 1 ×1.5×0.25，α 1 Promotion coefficient for advertising effectiveness per minute of viewing duration.
In the scheme, a big data analysis mode is adopted to analyze each advertisement put on the advertisement putting platform, and the advertising effect benefit and the promotion effect benefit brought by the advertisement are intelligently analyzed by combining the actual playing data of the advertisement put on the advertisement, so that the advertising benefits brought by the advertisements with different style characteristics in the advertisement putting platform are obtained.
Referring to fig. 5, based on the effect benefit brought by each advertisement, screening at least one advertisement of which the effect benefit and the delivery purpose of the advertisement delivered in the advertisement delivery platform are optimally adapted, wherein the advertisement is recorded as a high-adaptation advertisement specifically comprising:
calculating the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement;
calculating an adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
screening out the advertisement with the maximum adaptation index as a high adaptation advertisement;
the calculation formula of the adaptation index is as follows:
wherein S is an adaptation index of the advertisement and the throwing purpose, and F is a promotion ratio of the advertisement.
It can be understood that the advertisement is usually composed of characteristic factors such as advertisement text, advertisement video and advertisement pictures, and the like, in the scheme, the advertisement is analyzed in the advertisement delivery platform based on big data, the characteristic of the advertisement to be delivered is optimized by collecting the characteristic of the advertisement with high adaptation, the advertisement to be delivered is further adapted to the advertisement delivery purpose in the aspects of content, audience, form and the like, and the advertisement in the advertisement delivery platform is subjected to comprehensive data analysis and processing, so that the advertisement delivery effect can be further improved.
Further, referring to fig. 6, based on the same inventive concept as the above-mentioned intelligent analysis method for advertisement effect based on big data, the present disclosure further provides an intelligent analysis system for advertisement effect based on big data, including:
the platform analysis module is used for acquiring user data of the advertisement delivery platform, wherein the user data at least comprises user browsing habits and user consumption habits, and analyzing the user data of the advertisement delivery platform to obtain an online consumption ratio-time change curve;
the delivery time determining module is electrically connected with the platform analysis module and is used for determining the delivery purpose of the advertisement and determining the optimal delivery time of the advertisement with different delivery purposes on the advertisement delivery platform based on the delivery purpose of the advertisement and an online consumption ratio-time change curve of the advertisement delivery platform;
the playing data acquisition module is used for acquiring actual playing data of all advertisements put on the advertisement putting platform and determining effect benefits brought by each advertisement based on the actual playing data of the advertisements;
the advertisement adjusting module is electrically connected with the play data acquisition module, and is used for screening advertisements with the best fit of the effect benefits and the throwing purposes of advertisements thrown in the advertisement throwing platform based on the effect benefits brought by each advertisement, marking the advertisements as high-fit advertisements, extracting at least one advertisement feature from the high-fit advertisements, marking the advertisement feature as the best fit advertisement feature, wherein the advertisement feature at least comprises advertisement text, advertisement videos and advertisement pictures, and adjusting the advertisement features of advertisements with different throwing purposes based on the best fit advertisement feature.
The delivery time determination module comprises:
the first calculation unit is used for determining the weight ratio of the advertising effect to the advertising promotion effect based on the advertising purpose, and constructing a fitting index-time change curve based on the weight ratio of the advertising promotion effect to the advertising promotion effect and the online consumption ratio-time change curve;
the first screening unit is used for screening out a plurality of moments of which the fitting index is larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and marking the moments as first statistical moments;
and the second screening unit is used for screening out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption amount exceeds the preset consumption amount value in the first statistical moment, and recording the moments as the optimal throwing time on the advertisement throwing platform.
The advertisement adjustment module comprises:
the adaptation calculating unit is used for calculating the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement, and calculating the adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
the third screening unit is used for screening out the advertisement with the maximum adaptation index as a high adaptation advertisement;
the adjusting unit is used for adjusting the advertisement characteristics of advertisements of different delivery purposes based on the optimal advertisement characteristics.
The using process of the intelligent advertisement effect analysis system based on big data comprises the following steps:
step one: the method comprises the steps that a platform analysis module obtains user data of an advertisement delivery platform, wherein the user data at least comprise user browsing habits and user consumption habits, and analyzes the user data of the advertisement delivery platform to obtain an online consumption ratio-time change curve;
step four: the first calculation unit determines the weight ratio of the advertising effect to the advertising promotion effect based on the advertising purpose, and constructs a fitting index-time change curve based on the weight ratio of the advertising promotion effect to the advertising promotion effect and the online consumption ratio-time change curve;
step five: the first screening unit screens out a plurality of moments of which the fitting indexes are larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and marks the moments as first statistical moments;
step six: the second screening unit screens out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption quantity exceeds the preset consumption quantity value in the first statistical moment, and records the moments as the optimal putting time on the advertisement putting platform;
step seven: the playing data acquisition module acquires actual playing data of all advertisements put on the advertisement putting platform, and determines effect benefits brought by each advertisement based on the actual playing data of the advertisements;
step eight: the adaptation calculating unit calculates the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement, and calculates the adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
step nine: and the third screening unit screens out the advertisement with the maximum adaptation index as the high adaptation advertisement.
In summary, the invention has the advantages that: and analyzing the advertisement putting platform and the advertisement putting effect by adopting a big data analysis means, optimizing the characteristics of advertisements with different putting purposes according to the effect analysis result, and further improving the advertisement putting effect.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. An intelligent advertisement effect analysis method based on big data is characterized by comprising the following steps:
acquiring user data of an advertisement putting platform, wherein the user data at least comprises user browsing habits and user consumption habits;
analyzing the user data of the advertisement putting platform to obtain an online consumption ratio-time change curve;
determining the advertisement delivery purpose, and determining the optimal delivery time of advertisements with different delivery purposes on the advertisement delivery platform based on the advertisement delivery purpose and an online consumption ratio-time change curve of the advertisement delivery platform;
collecting playing data of advertisements on the advertisement putting platform in real time based on the big data platform, and determining actual playing data of the advertisements based on the playing data of the advertisements;
determining the effect benefits brought by each advertisement based on the actual playing data of the advertisement;
screening advertisements which are most suitable for the effect benefits and the delivery purposes of the advertisements delivered in the advertisement delivery platform based on the effect benefits brought by each advertisement, and marking the advertisements as high-fit advertisements;
extracting at least one advertisement feature from the high-fit advertisement, and recording the advertisement feature as the optimal fit advertisement feature, wherein the advertisement feature at least comprises an advertisement file, an advertisement video and an advertisement picture;
and adjusting the advertisement characteristics of advertisements with different delivery purposes based on the optimal advertisement characteristics.
2. The intelligent analysis method for advertisement effect based on big data according to claim 1, wherein the analyzing the user data of the advertisement delivery platform to obtain the online consumption ratio-time variation curve specifically comprises:
determining a user online quantity-time change curve of the advertisement delivery platform in an analysis period based on a user browsing habit;
determining a consumption amount-time change curve of the advertisement delivery platform in an analysis period based on user consumption habits;
determining a user online index-time change curve based on the user online quantity-time change curve;
determining a consumption index-time variation curve based on the consumption amount-time variation curve;
determining an online consumption ratio-time change curve based on the user online indicator-time change curve and the consumption indicator-time change curve;
the expression of the user online index-time change curve is as follows:
in the expression of the user online index-time change curve, Z (T) is the user online index at the moment T, T is the analysis period duration, and Y (T) is the user online quantity at the moment T;
the expression of the consumption index-time change curve is as follows:
in the expression of the consumption index-time change curve, X (t) is the consumption index at the time t, and J (t) is the consumption amount at the time t;
the expression of the online consumption ratio-time change curve is as follows:
wherein, the consumption ratio is the online consumption ratio at the time t.
3. The intelligent analysis method for advertisement effect based on big data according to claim 2, wherein the determining the advertisement delivery purpose, based on the advertisement delivery purpose and the online consumption ratio-time change curve of the advertisement delivery platform, determining the optimal delivery time of the advertisement with different delivery purposes on the advertisement delivery platform specifically comprises:
determining a weight ratio of an advertising effect to an advertising promotion effect based on the advertising purpose;
constructing a fitting index-time change curve based on the weight ratio of the advertising effect to the advertising promotion effect and the online consumption ratio-time change curve;
screening out a plurality of moments of which the fitting indexes are larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and marking the moments as first statistical moments;
screening out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption quantity exceeds the preset consumption quantity value in the first statistical moment, and recording the moments as the optimal putting time on the advertisement putting platform;
wherein, the expression of the fitting index-time change curve is:
N(t)＝|B(t)-Q|
wherein N (t) is a fitting index at the moment t, and Q is a weight ratio of the advertising effect to the advertising promotion effect.
4. The intelligent analysis method for advertisement effect based on big data according to claim 3, wherein the determining the effect benefit brought by each advertisement based on the actual playing data of the advertisement specifically comprises:
based on the actual playing data of the advertisement, extracting a plurality of effect parameters of the advertisement, wherein the effect parameters at least comprise advertisement watching times, advertisement average watching duration and advertisement ordering quantity;
determining advertisement effect indexes of each effect parameter, wherein the advertisement effect indexes are specifically advertisement effect proportion brought by the effect parameter and promotion effect proportion brought by the effect parameter;
and respectively calculating the advertising effect benefit and the promotion effect benefit of the advertisement.
5. The intelligent analysis method for advertisement effect based on big data according to claim 4, wherein the calculation formula of the benefit of the propaganda effect is:
wherein C is advertising effect benefit, n is total number of advertising effect parameters, Z i Is the statistical index of the ith effect parameter of the advertisement, alpha i The unit index effect coefficient, P, being the ith effect parameter of the advertisement i The publicity effect proportion of the ith effect parameter of the advertisement;
the calculation formula of the promotion effect income is as follows:
wherein M is the benefit of the promotion effect of the advertisement, Q i The proportion of promotion effect which is the ith effect parameter of the advertisement;
wherein P is i +Q i ＝1。
6. The intelligent analysis method for advertisement effect based on big data according to claim 5, wherein the screening the at least one advertisement with the best fit between the effect benefit and the delivery purpose of the advertisement delivered in the advertisement delivery platform based on the effect benefit brought by each advertisement specifically comprises:
calculating the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement;
calculating an adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
screening out the advertisement with the maximum adaptation index as a high adaptation advertisement;
the calculation formula of the adaptation index is as follows:
wherein S is an adaptation index of the advertisement and the throwing purpose, and F is a promotion ratio of the advertisement.
7. A big data based advertisement effect intelligent analysis system, for implementing the big data based advertisement effect intelligent analysis method according to any one of claims 1-6, comprising:
the platform analysis module is used for acquiring user data of the advertisement delivery platform, wherein the user data at least comprises user browsing habits and user consumption habits, and analyzing the user data of the advertisement delivery platform to obtain an online consumption ratio-time change curve;
the delivery time determining module is electrically connected with the platform analysis module and is used for determining the delivery purpose of the advertisement and determining the optimal delivery time of the advertisement with different delivery purposes on the advertisement delivery platform based on the delivery purpose of the advertisement and an online consumption ratio-time change curve of the advertisement delivery platform;
the playing data acquisition module is used for acquiring actual playing data of all advertisements put on the advertisement putting platform and determining effect benefits brought by each advertisement based on the actual playing data of the advertisements;
the advertisement adjusting module is electrically connected with the play data acquisition module, and is used for screening advertisements with the most adaptive advertisement of the effect gains and the throwing purposes of advertisements thrown in the advertisement throwing platform based on the effect gains brought by each advertisement, marking the advertisements as high-adaptive advertisements, extracting at least one advertisement characteristic from the high-adaptive advertisements and marking the advertisement characteristic as the most adaptive advertisement characteristic, wherein the advertisement characteristic at least comprises advertisement text, advertisement videos and advertisement pictures, and simultaneously adjusting the advertisement characteristics of advertisements with different throwing purposes based on the most adaptive advertisement characteristic.
8. The intelligent advertisement effectiveness analysis system according to claim 7, wherein the delivery time determining module comprises:
the first computing unit is used for determining the weight ratio of the advertising effect to the advertising promotion effect based on the advertising purpose, and constructing a fitting index-time change curve based on the weight ratio of the advertising promotion effect to the advertising promotion effect and the online consumption ratio-time change curve;
the first screening unit is used for screening out a plurality of moments of which the fitting index is larger than a preset fitting degree value in an analysis period based on the fitting index-time change curve, and recording the moments as first statistical moments;
and the second screening unit is used for screening out a plurality of moments when the online quantity of the user exceeds the preset online quantity value and the consumption amount exceeds the preset consumption amount value in the first statistical moment, and recording the moments as the optimal throwing time on the advertisement throwing platform.
9. The intelligent analysis system for advertisement effectiveness based on big data of claim 8, wherein the advertisement adjustment module comprises:
the adaptation calculation unit is used for calculating the advertising promotion ratio of the advertisement based on the advertising total effect index and the promotion effect index of the advertisement, and calculating the adaptation index of the advertisement and the throwing purpose based on the advertising promotion ratio of the advertisement, the advertising effect income and the promotion effect income of the advertisement;
the third screening unit is used for screening out the advertisement with the maximum adaptation index and taking the advertisement as a high adaptation advertisement;
and the adjusting unit is used for adjusting the advertisement characteristics of advertisements of different delivery purposes based on the optimal advertisement characteristics.
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