Digital delivery adjustment method and system based on multi-platform data analysis

Abstract

The invention discloses a digital putting adjustment method and system based on multi-platform data analysis, and relates to the technical field of advertisement putting, and the method comprises the steps: obtaining platform advertisement putting information, classifying put advertisements according to the platform advertisement putting information, obtaining put advertisement classification information, and obtaining advertisement effect data according to the platform advertisement putting information. According to the method, the advertisements with the same product type are divided into the same group, the follow-up classification of the advertisements is facilitated, the advertisement classification efficiency is improved, the advertisements in the advertisement group are further divided by taking the classification reference advertisement as the reference, the accuracy and similarity of advertisement classification are ensured, and the advertisement classification efficiency is improved. The advertisement effect is analyzed through the advertisement effect index on the basis of considering coincidence of users on different platforms, the accuracy of advertisement effect evaluation is ensured, and the stability of the advertisement effect is ensured by adjusting the digital putting of the advertisement through the advertisement sequence information.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising delivery, and in particular to a digital delivery adjustment method and system based on multi-platform data analysis. Background Art

[0002] With the rapid development of Internet technology, the advertising industry has rapidly shifted from the traditional offline model to the online model. Digital advertising has the advantages of wide coverage, relatively low cost, and high accuracy, and has gradually become the main choice of advertisers. For corresponding advertisements, the advertising delivery strategy greatly affects the advertising effect. Therefore, advertising delivery has become a crucial step in data advertising.

[0003] Currently, there are still problems with digital advertising, such as the inability to classify ads according to their content and the inability to conduct centralized analysis of similar ads, which reduces the efficiency of ad placement adjustments. Ad effects are often evaluated directly through the click-through rate and conversion rate of ads, without considering the impact of the association between different platforms on ad placement. Ads with poor results are often directly replaced without further analyzing the reasons for the abnormal advertising results, making it impossible to adjust ad placement in a timely manner. Summary of the invention

[0004] In order to solve the above technical problems, a digital delivery adjustment method and system based on multi-platform data analysis are provided. This technical solution solves the problems raised in the above background technology that the delivered advertisements cannot be classified according to the advertisement content, and similar advertisements cannot be centrally analyzed, which reduces the efficiency of advertisement delivery adjustment. The advertisement effect is often directly evaluated by the click-through rate and conversion rate of the advertisement, without considering the impact of the correlation between different platforms on the advertisement delivery. Advertisements with poor advertising effects are often directly replaced without further analyzing the reasons for the abnormal advertising effects, and the advertisement delivery cannot be adjusted in time.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: Digital delivery adjustment methods based on multi-platform data analysis include: Obtaining platform advertising information, wherein the platform advertising information includes advertising information, platform information, and advertising effect data; According to the platform advertising information, the advertisements are classified and the classified information of the advertisements is obtained; Acquire advertising effect data based on platform advertising delivery information, wherein the advertising effect data includes click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered; Based on the needs of advertising effect analysis, different weights are set for click-through rate and conversion rate; According to the classified information of the advertisements, the information of the delivery platform corresponding to each advertisement is obtained; Obtain the advertising effectiveness index based on advertising effectiveness data, click-through rate and conversion rate weights and delivery platform information; Based on the classified information of the advertisements placed, the advertisements of the same category are sorted in descending order according to the advertisement effect index to obtain the advertisement sequence information; Adjust the digital delivery of advertisements based on the advertisement sequence information.

[0006] Preferably, the step of classifying the delivered advertisements according to the platform advertisement delivery information and obtaining the delivered advertisement classification information specifically includes: According to the platform advertising information, obtain the product category information corresponding to the advertising; Based on product categories, advertisements with the same product categories are divided into the same group to obtain multiple advertisement groups; Based on the delivered advertisement group, obtaining target user information corresponding to each advertisement in the same advertisement group, wherein the target user information includes target user age group information; Use any advertisement in each delivery ad group as the featured advertisement of the delivery ad group; According to the target user information, select the advertisements in the same group of advertisements that have no overlap in the target user age group corresponding to the feature advertisements; The selected advertisements and characteristic advertisements are used as classified benchmark advertisements of the delivery advertisement group; Based on the classified benchmark advertisements, the advertisements in the delivered advertisement group are further divided to obtain the delivered advertisement classification information.

[0007] Preferably, the step of further dividing the advertisements in the delivered advertisement group based on the classified benchmark advertisements to obtain the delivered advertisement classification information specifically includes: According to the platform advertisement delivery information, platform information is obtained, wherein the platform information includes multiple platform name information and user data corresponding to each platform; According to the platform information, the platform user age ratio information corresponding to each platform is obtained, wherein the platform user age ratio information indicates the proportion of users of different ages in the platform; Based on platform information, obtain the number of daily active users corresponding to each platform; According to the platform information, the user age ratio information and the daily active user number information corresponding to each platform, the user age ratio information is obtained, where the user age ratio information indicates the proportion of the daily active user number of users of different ages in the total number of daily active users of all platforms; Taking the classified benchmark advertisement as a benchmark, obtain the age group of target users that overlaps with the classified benchmark advertisement for any advertisement in the delivery advertisement group; According to the user age ratio information, the overlapping age group of the target users and the classified benchmark advertisement, it is determined whether the advertisement and the classified benchmark advertisement belong to the same category. If not, the advertisement is used as the classified benchmark advertisement. If so, the advertisement and the classified benchmark advertisement are classified as the same category of advertisements, and the classification information of the delivered advertisement is obtained; Among them, if the target user overlaps the user age ratio of all age users in the age group and exceeds half of the user age ratio of the target user age group corresponding to the classified benchmark advertisement, that is, , then the ad belongs to the same category as the classified benchmark ad: ; In the formula, Indicates the age ratio of all users in the target user's overlapping age group and, The age ratio and age group of the target user age group corresponding to the classified benchmark advertisement. Indicates the age ratio of the i-th user in the target user's overlapping age group, Represents the user age ratio of the jth user age in the target user age group corresponding to the classified benchmark advertisement.

[0008] Preferably, the step of obtaining the advertising effect index according to the advertising effect data, the weights of the click rate and the conversion rate and the delivery platform information specifically includes: Obtain the target user age group information for each advertisement and the user age ratio information for each platform; According to the target user age group information and delivery platform information corresponding to each advertisement, obtain the proportion of the target user age group on each delivery platform; The platform with the largest proportion of the target user age group of each advertisement in the corresponding user age ratio of the platform is used as the benchmark delivery platform for the advertisement; Conduct a user overlap survey on the delivery platform corresponding to each advertisement to obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement; Obtain the advertising effectiveness index based on advertising effectiveness data, click-through rate, conversion rate weights, and user overlap; The calculation formula of the advertising effect index is: ; In the formula, Q is the advertising effect index, The click-through rate of ads on the benchmark delivery platform, The advertising conversion rate of the benchmark delivery platform, is the click-through rate of the advertisement on the sth delivery platform, is the advertising conversion rate of the sth delivery platform, is the user overlap between the sth delivery platform and the benchmark delivery platform, h is the number of delivery platforms corresponding to the advertisement, and are the weights of click-through rate and conversion rate respectively.

[0009] Preferably, the step of adjusting the digital delivery of advertisements according to the advertisement sequence information specifically includes: Based on the analysis of advertising cost, set the advertising click rate threshold and conversion rate threshold; According to the advertising effect data, the advertising click rate threshold and the conversion rate threshold, based on the advertising sequence information, abnormal advertising information is obtained, wherein the abnormal advertising information indicates a digital advertisement whose click rate corresponding to each platform when each advertisement in the advertising sequence information is delivered is lower than the advertising click rate threshold or whose conversion rate is lower than the advertising conversion rate threshold; Adjust abnormal advertising delivery based on abnormal advertising information; According to the advertisement order information and based on the ratio of the advertisement effect index, the advertisement delivery budget is adjusted; Among them, the advertising budget ratio and advertising effect index ratio in the same type of advertisements placed in the advertising classification information are the same.

[0010] Preferably, the step of obtaining advertising effect data corresponding to the abnormal advertisement based on the abnormal advertisement information; According to the advertising effect data, the average advertising conversion rate of the delivery platform corresponding to the abnormal advertisement is used as the advertising conversion rate of the abnormal advertisement; According to the advertisement conversion rate and the advertisement conversion rate threshold, determine whether to adjust the advertisement content of the abnormal advertisement; if the advertisement conversion rate is lower than the advertisement conversion rate threshold, adjust the creative content of the abnormal advertisement; If the advertisement conversion rate is higher than the advertisement conversion rate threshold, the delivery platform of the abnormal advertisement is adjusted until the click rate of the abnormal advertisement is higher than the advertisement click rate threshold. The adjustment of the delivery platform includes reselecting the delivery platform and adjusting the advertisement delivery time of each delivery platform.

[0011] Furthermore, a digital delivery adjustment system based on multi-platform data analysis is proposed to implement the above adjustment method, including: A main control module, the main control module is used to determine whether the advertisement and the classified benchmark advertisement belong to the same category according to the user age ratio information, the overlapping age group of the target user and the classified benchmark advertisement, determine whether to adjust the advertisement content of the abnormal advertisement according to the advertisement conversion rate and the advertisement conversion rate threshold, divide the advertisements with the same product category into the same group, obtain multiple advertisement groups, further divide the advertisements in the advertisement group based on the classified benchmark advertisement, obtain the advertisement classification information, and adjust the digital delivery of the advertisement according to the advertisement sequence information; An information acquisition module, the information acquisition module is used to obtain platform advertising information, advertising information, platform information and advertising effect data, and according to the platform advertising information, obtain advertising effect data, click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered, obtain product category information corresponding to the delivered advertisement according to the platform advertising information, and obtain platform information, multiple platform name information and user data corresponding to each platform according to the platform advertising information; An evaluation module, the evaluation module is used to obtain user age ratio information based on platform information, user age ratio information corresponding to each platform, and daily active user number information, conduct a user overlap survey on the delivery platform corresponding to each advertisement, obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and obtain an advertising effect index based on advertising effect data, click-through rate and conversion rate weights and user overlap; The display module interacts with the main control module and is used to output and display advertising classification information, advertising effect data, advertising effect index and advertising sequence information.

[0012] Optionally, the main control module specifically includes: A control unit, the control unit is used to divide the advertisements with the same product category into the same group, obtain multiple advertisement groups, further divide the advertisements in the advertisement groups based on the classified benchmark advertisements, obtain advertisement classification information, and adjust the digital delivery of the advertisements according to the advertisement sequence information; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit is used to judge whether the advertisement and the classification benchmark advertisement belong to the same category based on user age ratio information, overlapping age groups of target users and classification benchmark advertisements, and to judge whether to adjust the advertisement content of the abnormal advertisement based on the advertisement conversion rate and the advertisement conversion rate threshold.

[0013] Optionally, the information acquisition module specifically includes: A first acquisition unit, the first acquisition unit is used to acquire platform advertising delivery information, delivery advertising information, platform information and advertising effect data, and acquire advertising effect data, click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered according to the platform advertising delivery information; The second acquisition unit is used to obtain product category information corresponding to the advertisement according to the platform advertisement delivery information, and to obtain platform information, multiple platform name information and user data corresponding to each platform according to the platform advertisement delivery information.

[0014] Optionally, the evaluation module specifically includes: A first evaluation unit, the first evaluation unit is used to obtain user age ratio information according to platform information, user age ratio information corresponding to each platform, and daily active user number information; The first evaluation unit is used to conduct a user overlap survey on the delivery platform corresponding to each advertisement, obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and obtain the advertising effect index according to the advertising effect data, the weights of the click rate and the conversion rate and the user overlap.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a digital delivery adjustment method and system based on multi-platform data analysis. By dividing delivery advertisements of the same product category into the same group, it is convenient to subsequently classify the delivery advertisements, thereby improving the efficiency of advertisement classification. By further dividing the advertisements in the delivery advertisement group based on the classified benchmark advertisements, the accuracy and similarity of advertisement classification are ensured. The advertisement effect is analyzed based on the overlap of users on different platforms through the advertisement effect index, thereby ensuring the accuracy of advertisement effect evaluation. The digital delivery of advertisements is adjusted through the advertisement sequence information, thereby ensuring the stability of the advertisement effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of the digital delivery adjustment method based on multi-platform data analysis proposed by the present invention; Figure 2 A flowchart for obtaining classified information for placing advertisements in the present invention; Figure 3 A flowchart for obtaining the advertising effect index in the present invention; Figure 4 This is a flowchart for obtaining abnormal advertising information in the present invention; Figure 5 This is a structural block diagram of the digital delivery adjustment system based on multi-platform data analysis proposed by the present invention. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 - Figure 4 As shown, the digital delivery adjustment method based on multi-platform data analysis in an embodiment of the present invention includes: Obtaining platform advertising information, wherein the platform advertising information includes advertising information, platform information, and advertising effect data; According to the platform advertising information, the advertisements are classified and the classified information of the advertisements is obtained; Specifically, according to the platform advertising information, the advertisements are classified and the classified information of the advertisements is obtained, including: According to the platform advertising information, obtain the product category information corresponding to the advertising; Based on product categories, advertisements with the same product categories are divided into the same group to obtain multiple advertisement groups; Based on the delivered advertisement group, obtaining target user information corresponding to each advertisement in the same advertisement group, wherein the target user information includes target user age group information; Use any advertisement in each delivery ad group as the featured advertisement of the delivery ad group; According to the target user information, select the advertisements in the same group of advertisements that have no overlap in the target user age group corresponding to the feature advertisements; The selected advertisements and characteristic advertisements are used as classified benchmark advertisements of the delivery advertisement group; Based on the classified benchmark advertisements, the advertisements in the delivered advertisement group are further divided to obtain the delivered advertisement classification information.

[0019] In this solution, advertisements are grouped according to product types, and advertisements of the same product type are grouped into the same group, which can quickly classify and sort a large number of advertisements. This helps advertisers understand the delivery of advertisements of different product types from a macro perspective, which is convenient for subsequent management and analysis. For example, the number of advertisements, budget and other information of a certain product type (such as food, electronic products, daily necessities, etc.) can be counted to provide basic data support for the overall advertising delivery strategy. By obtaining the target user information corresponding to each advertisement in the same group of advertisements, especially the target user age group information, it is helpful to have a deep understanding of the audience group of each advertisement. By taking any advertisement in each delivery advertising group as a feature advertisement, and selecting advertisements that do not overlap with the target user age group of the feature advertisement as classification benchmark advertisements, it is convenient to classify the remaining advertisements and divide the advertisements more clearly. The classification efficiency of advertisements is improved, ensuring that key indicators such as the click-through rate and conversion rate of advertisements are more targeted and representative when the advertising effect is analyzed later.

[0020] Specifically, based on the classified benchmark advertisements, the advertisements in the delivered advertisement group are further divided to obtain the delivered advertisement classification information, which specifically includes: According to the platform advertisement delivery information, platform information is obtained, wherein the platform information includes multiple platform name information and user data corresponding to each platform; According to the platform information, the platform user age ratio information corresponding to each platform is obtained, wherein the platform user age ratio information indicates the proportion of users of different ages in the platform; Based on platform information, obtain the number of daily active users corresponding to each platform; According to the platform information, the user age ratio information and the daily active user number information corresponding to each platform, the user age ratio information is obtained, where the user age ratio information indicates the proportion of the daily active user number of users of different ages in the total number of daily active users of all platforms; Taking the classified benchmark advertisement as a benchmark, obtain the age group of target users that overlaps with the classified benchmark advertisement for any advertisement in the delivery advertisement group; According to the user age ratio information, the overlapping age group of the target users and the classified benchmark advertisement, it is determined whether the advertisement and the classified benchmark advertisement belong to the same category. If not, the advertisement is used as the classified benchmark advertisement. If so, the advertisement and the classified benchmark advertisement are classified as the same category of advertisements, and the classification information of the delivered advertisement is obtained; Among them, if the target user overlaps the user age ratio of all age users in the age group and exceeds half of the user age ratio of the target user age group corresponding to the classified benchmark advertisement, that is, , then the ad belongs to the same category as the classified benchmark ad: ; In the formula, Indicates the age ratio of all users in the target user's overlapping age group and, The age ratio and age group of the target user age group corresponding to the classified benchmark advertisement. Indicates the age ratio of the i-th user in the target user's overlapping age group, Represents the user age ratio of the jth user age in the target user age group corresponding to the classified benchmark advertisement.

[0021] In this solution, by obtaining platform information (including platform name, user data, etc.), platform user age ratio information, and daily active user number information, we can understand the age distribution and activity of users from a macro perspective on multiple platforms. Calculating the proportion of daily active users of users of different ages in the total number of daily active users on all platforms (i.e., user age ratio information) provides a more comprehensive and objective basis for advertising classification. Based on classified benchmark ads, combined with these platform data to determine whether ads belong to the same category, ads can be classified more accurately, avoiding inaccurate classification problems caused by simply considering the age group of target users, making advertising classification more scientific and reasonable. Based on accurate advertising classification information, advertisers can make more targeted placement adjustments on different platforms based on the characteristics of different classified ads and target user groups.

[0022] Acquire advertising effect data based on platform advertising delivery information, wherein the advertising effect data includes click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered; Based on the needs of advertising effect analysis, different weights are set for click-through rate and conversion rate; According to the classified information of the advertisements, the information of the delivery platform corresponding to each advertisement is obtained; Obtain the advertising effectiveness index based on advertising effectiveness data, click-through rate and conversion rate weights and delivery platform information; Specifically, the advertising effect index is obtained based on the advertising effect data, the weights of click-through rate and conversion rate, and the delivery platform information, including: Obtain the target user age group information for each advertisement and the user age ratio information for each platform; According to the target user age group information and delivery platform information corresponding to each advertisement, obtain the proportion of the target user age group on each delivery platform; The platform with the largest proportion of the target user age group of each advertisement in the corresponding user age ratio of the platform is used as the benchmark delivery platform for the advertisement; Conduct a user overlap survey on the delivery platform corresponding to each advertisement to obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement; Obtain the advertising effectiveness index based on advertising effectiveness data, click-through rate, conversion rate weights, and user overlap; The calculation formula of the advertising effect index is: ; In the formula, Q is the advertising effect index, The click-through rate of ads on the benchmark delivery platform, The advertising conversion rate of the benchmark delivery platform, is the click-through rate of the advertisement on the sth delivery platform, is the advertising conversion rate of the sth delivery platform, is the user overlap between the sth delivery platform and the benchmark delivery platform, h is the number of delivery platforms corresponding to the advertisement, and are the weights of click-through rate and conversion rate respectively.

[0023] In this solution, by using the delivery platform with the largest proportion of the target user age group of each advertisement in the user age ratio corresponding to the platform as the benchmark delivery platform for the advertisement, a core reference platform can be provided for subsequent advertising effect evaluation and delivery adjustment. The benchmark delivery platform is usually the platform with the highest degree of fit with the target audience of the advertisement. Analyzing and making decisions based on it can make the advertising delivery strategy more focused and effective. A user overlap survey is conducted on the delivery platform corresponding to each advertisement to obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and accurate analysis of the advertising effect can be achieved through user overlap.

[0024] It is understandable that if the user overlap of multiple delivery platforms is high, it means that advertising on these platforms may result in repeated exposure to the same group of users, which will not only waste advertising resources, but also cause certain errors in advertising effect data. By considering the user overlap to adjust the advertising effect evaluation results, advertisers can be reminded to pay attention to this repeated delivery when evaluating advertising effects, and avoid overestimating advertising effects due to false high exposure. For example, if the user overlap of two platforms is extremely high, even if the click-through rate and conversion rate of the ads on these two platforms are high, it may actually be just multiple interactions of the same group of users, and it does not really expand the audience range and influence of the ads. At this time, adjusting the advertising effect index by user overlap can more accurately reflect the true effect of the ads.

[0025] Based on the classified information of the advertisements placed, the advertisements of the same category are sorted in descending order according to the advertisement effect index to obtain the advertisement sequence information; Adjust the digital delivery of advertisements based on the advertisement sequence information.

[0026] Specifically, the digital delivery of advertisements is adjusted based on the advertisement sequence information, including: Based on the analysis of advertising cost, set the advertising click rate threshold and conversion rate threshold; According to the advertising effect data, the advertising click rate threshold and the conversion rate threshold, based on the advertising sequence information, abnormal advertising information is obtained, wherein the abnormal advertising information indicates a digital advertisement whose click rate corresponding to each platform when each advertisement in the advertising sequence information is delivered is lower than the advertising click rate threshold or whose conversion rate is lower than the advertising conversion rate threshold; Adjust abnormal advertising delivery based on abnormal advertising information; According to the advertisement order information and based on the ratio of the advertisement effect index, the advertisement delivery budget is adjusted; Among them, the advertising budget ratio and advertising effect index ratio in the same type of advertisements placed in the advertising classification information are the same.

[0027] Specifically, according to the abnormal advertising information, the abnormal advertising delivery is adjusted, including: According to the abnormal advertisement information, the advertisement effect data corresponding to the abnormal advertisement is obtained; According to the advertising effect data, the average advertising conversion rate of the delivery platform corresponding to the abnormal advertisement is used as the advertising conversion rate of the abnormal advertisement; According to the advertisement conversion rate and the advertisement conversion rate threshold, determine whether to adjust the advertisement content of the abnormal advertisement; if the advertisement conversion rate is lower than the advertisement conversion rate threshold, adjust the creative content of the abnormal advertisement; If the advertisement conversion rate is higher than the advertisement conversion rate threshold, the delivery platform of the abnormal advertisement is adjusted until the click rate of the abnormal advertisement is higher than the advertisement click rate threshold. The adjustment of the delivery platform includes reselecting the delivery platform and adjusting the advertisement delivery time of each delivery platform.

[0028] In this solution, abnormal advertising information is obtained through advertising effect data, advertising click-through rate thresholds, conversion rate thresholds, and advertising order information, which can accurately find out which ads in the advertising sequence have unsatisfactory effects on which platforms (click-through rate is lower than the threshold or conversion rate is lower than the threshold). This allows advertisers to clearly understand the problem and focus the optimization work on these abnormal ads, avoid blind adjustments, and improve optimization efficiency. According to the advertising order information, the advertising budget is adjusted based on the ratio of the advertising effect index, and the advertising budget ratio and advertising effect index ratio are the same for the same type of ads. This ensures the scientificity and rationality of advertising budget allocation. The advertising effect index is obtained by comprehensively considering multiple factors such as click-through rate, conversion rate, and platform user overlap, and can fully reflect the actual effect of the advertisement. Linking the budget with the effect index enables advertisers to allocate more resources to ads with good effects, improve the efficiency of budget use, and maximize the benefits of advertising.

[0029] It should be noted that the thresholds for ad click-through rate and ad conversion rate for different product types are also quite different and need to be set according to specific conditions. For example, the threshold for click-through rate in the e-commerce industry is generally between 1% and 3%. For some popular categories, such as clothing and beauty products, user demand is relatively strong and the click-through rate is relatively high, which may reach 2% to 3%; while for some niche or highly professional categories, such as industrial equipment and special chemicals, the click-through rate is 1%. The conversion rate threshold is approximately between 0.5% and 3%. Well-known e-commerce brands, products with large discounts or unique selling points, have a higher conversion rate of 2% to 3%; new brands or products with weaker competitiveness may have a conversion rate of 0.5% to 1%.

[0030] For the gaming industry, the click-through rate threshold is usually between 2% and 5%. For popular game types, such as role-playing games (RPGs) and action games, the click-through rate is high, reaching 3% to 5%, due to their wide audience and the strong appeal of game promotional materials; while for some relatively niche game types, such as strategy simulation business games, the click-through rate is 2%. The conversion rate threshold is between 0.3% and 2%. New games launched by large and well-known game manufacturers, or games with unique gameplay and strong marketing promotion, have a high conversion rate of close to 1% to 2%; products of small game studios or games with insufficient competitiveness have a conversion rate of 0.3% to 0.5%.

[0031] It can be seen that the thresholds corresponding to different industries and different product types are also different, so specific settings need to be made according to the product type.

[0032] Reference Figure 5 As shown, further, in combination with the above-mentioned digital delivery adjustment method based on multi-platform data analysis, a digital delivery adjustment system based on multi-platform data analysis is proposed, including: A main control module, the main control module is used to determine whether the advertisement and the classified benchmark advertisement belong to the same category according to the user age ratio information, the overlapping age group of the target user and the classified benchmark advertisement, determine whether to adjust the advertisement content of the abnormal advertisement according to the advertisement conversion rate and the advertisement conversion rate threshold, divide the advertisements with the same product category into the same group, obtain multiple advertisement groups, further divide the advertisements in the advertisement group based on the classified benchmark advertisement, obtain the advertisement classification information, and adjust the digital delivery of the advertisement according to the advertisement sequence information; An information acquisition module, the information acquisition module is used to obtain platform advertising information, advertising information, platform information and advertising effect data, and according to the platform advertising information, obtain advertising effect data, click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered, obtain product category information corresponding to the delivered advertisement according to the platform advertising information, and obtain platform information, multiple platform name information and user data corresponding to each platform according to the platform advertising information; An evaluation module, the evaluation module is used to obtain user age ratio information based on platform information, user age ratio information corresponding to each platform, and daily active user number information, conduct a user overlap survey on the delivery platform corresponding to each advertisement, obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and obtain an advertising effect index based on advertising effect data, click-through rate and conversion rate weights and user overlap; The display module interacts with the main control module and is used to output and display advertising classification information, advertising effect data, advertising effect index and advertising sequence information.

[0033] Main control module, specifically including: A control unit, the control unit is used to divide the advertisements with the same product category into the same group, obtain multiple advertisement groups, further divide the advertisements in the advertisement groups based on the classified benchmark advertisements, obtain advertisement classification information, and adjust the digital delivery of the advertisements according to the advertisement sequence information; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit is used to judge whether the advertisement and the classification benchmark advertisement belong to the same category based on user age ratio information, overlapping age groups of target users and classification benchmark advertisements, and to judge whether to adjust the advertisement content of the abnormal advertisement based on the advertisement conversion rate and the advertisement conversion rate threshold.

[0034] Information acquisition module, specifically including: A first acquisition unit, the first acquisition unit is used to acquire platform advertising delivery information, delivery advertising information, platform information and advertising effect data, and acquire advertising effect data, click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered according to the platform advertising delivery information; The second acquisition unit is used to obtain product category information corresponding to the advertisement according to the platform advertisement delivery information, and to obtain platform information, multiple platform name information and user data corresponding to each platform according to the platform advertisement delivery information.

[0035] Assessment modules include: A first evaluation unit, the first evaluation unit is used to obtain user age ratio information according to platform information, user age ratio information corresponding to each platform, and daily active user number information; The first evaluation unit is used to conduct a user overlap survey on the delivery platform corresponding to each advertisement, obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and obtain the advertising effect index according to the advertising effect data, the weights of the click rate and the conversion rate and the user overlap.

[0036] In summary, the advantages of the present invention are as follows: by dividing the advertisements with the same product category into the same group, a plurality of advertisement groups are obtained, which facilitates the subsequent classification of the advertisements and improves the efficiency of advertisement classification; by further dividing the advertisements in the advertisement group based on the classified benchmark advertisements, the accuracy and similarity of advertisement classification are ensured; the advertisement effect index is obtained through the weights of advertisement effect data, click-through rate and conversion rate and delivery platform information; the advertisement effect is analyzed based on the advertisement effect index taking into account the overlap of users on different platforms, thereby ensuring the accuracy of advertisement effect evaluation; the digital delivery of advertisements is adjusted through the advertisement sequence information, thereby ensuring the stability of the advertisement effect.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A digital delivery adjustment method based on multi-platform data analysis, characterized in that: include: Obtaining platform advertising information, wherein the platform advertising information includes advertising information, platform information, and advertising effect data; According to the platform advertising information, the advertisements are classified and the classified information of the advertisements is obtained; Acquire advertising effect data based on platform advertising delivery information, wherein the advertising effect data includes click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered; Based on the needs of advertising effect analysis, set different weights for click-through rate and conversion rate; According to the classified information of the advertisements, the information of the delivery platform corresponding to each advertisement is obtained; Obtain the advertising effectiveness index based on advertising effectiveness data, click-through rate and conversion rate weights and delivery platform information; Based on the classified information of the advertisements placed, the advertisements of the same category are sorted in descending order according to the advertisement effect index to obtain the advertisement sequence information; Adjust the digital delivery of advertisements based on the advertisement sequence information.

2. The digital delivery adjustment method based on multi-platform data analysis according to claim 1 is characterized in that: The step of classifying the placed advertisements according to the platform advertisement placement information and obtaining the placed advertisement classification information specifically includes: According to the platform advertising information, obtain the product category information corresponding to the advertising; Based on product categories, advertisements with the same product categories are divided into the same group to obtain multiple advertisement groups; Based on the delivered advertisement group, obtaining target user information corresponding to each advertisement in the same advertisement group, wherein the target user information includes target user age group information; Use any advertisement in each delivery ad group as the featured advertisement of the delivery ad group; According to the target user information, select the advertisements in the same group of advertisements that have no overlap in the target user age group corresponding to the feature advertisements; The selected advertisements and characteristic advertisements are used as classified benchmark advertisements of the delivery advertisement group; Based on the classified benchmark advertisements, the advertisements in the delivered advertisement group are further divided to obtain the delivered advertisement classification information.

3. The digital delivery adjustment method based on multi-platform data analysis according to claim 2 is characterized in that: The step of further dividing the advertisements in the delivery advertisement group based on the classified benchmark advertisements to obtain the delivery advertisement classification information specifically includes: According to the platform advertisement delivery information, platform information is obtained, wherein the platform information includes multiple platform name information and user data corresponding to each platform; According to the platform information, the platform user age ratio information corresponding to each platform is obtained, wherein the platform user age ratio information indicates the proportion of users of different ages in the platform; Based on platform information, obtain the number of daily active users corresponding to each platform; According to the platform information, the user age ratio information and the daily active user number information corresponding to each platform, the user age ratio information is obtained, where the user age ratio information indicates the proportion of the daily active user number of users of different ages in the total number of daily active users of all platforms; Taking the classified benchmark advertisement as a benchmark, obtain the age group of target users that overlaps with the classified benchmark advertisement for any advertisement in the delivery advertisement group; According to the user age ratio information, the overlapping age group of the target users and the classified benchmark advertisement, it is determined whether the advertisement and the classified benchmark advertisement belong to the same category. If not, the advertisement is used as the classified benchmark advertisement. If so, the advertisement and the classified benchmark advertisement are classified as the same category of advertisements, and the classification information of the delivered advertisement is obtained; Among them, if the target user overlaps the user age ratio of all age users in the age group and exceeds half of the user age ratio of the target user age group corresponding to the classified benchmark advertisement, that is, , then the ad belongs to the same category as the classified benchmark ad: ； In the formula, Indicates the age ratio of all users in the target user's overlapping age group and, The age ratio and age group of the target user age group corresponding to the classified benchmark advertisement. Indicates the age ratio of the i-th user in the target user's overlapping age group, Represents the user age ratio of the jth user age in the target user age group corresponding to the classified benchmark advertisement.

4. The digital delivery adjustment method based on multi-platform data analysis according to claim 1 is characterized in that: The advertising effect index is obtained according to the advertising effect data, the weights of the click rate and the conversion rate and the delivery platform information, specifically including: Obtain the target user age group information for each advertisement and the user age ratio information for each platform; According to the target user age group information and delivery platform information corresponding to each advertisement, obtain the proportion of the target user age group on each delivery platform; The platform with the largest proportion of the target user age group of each advertisement in the corresponding user age ratio of the platform is used as the benchmark delivery platform for the advertisement; Conduct a user overlap survey on the delivery platform corresponding to each advertisement to obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement; Obtain the advertising effectiveness index based on advertising effectiveness data, click-through rate, conversion rate weights, and user overlap; The calculation formula of the advertising effect index is: ； In the formula, Q is the advertising effect index, The click-through rate of ads on the benchmark delivery platform, The advertising conversion rate of the benchmark delivery platform, is the click-through rate of the advertisement on the sth delivery platform, is the advertising conversion rate of the sth delivery platform, is the user overlap between the sth delivery platform and the benchmark delivery platform, h is the number of delivery platforms corresponding to the advertisement, and are the weights of click-through rate and conversion rate respectively.

5. The digital delivery adjustment method based on multi-platform data analysis according to claim 1 is characterized in that: The adjusting of the digital delivery of advertisements according to the advertisement sequence information specifically includes: Based on the analysis of advertising cost, set the advertising click rate threshold and conversion rate threshold; According to the advertising effect data, the advertising click rate threshold and the conversion rate threshold, based on the advertising sequence information, abnormal advertising information is obtained, wherein the abnormal advertising information indicates a digital advertisement whose click rate corresponding to each platform when each advertisement in the advertising sequence information is delivered is lower than the advertising click rate threshold or whose conversion rate is lower than the advertising conversion rate threshold; Adjust abnormal advertising delivery based on abnormal advertising information; According to the advertisement order information and based on the ratio of the advertisement effect index, the advertisement delivery budget is adjusted; Among them, the advertising budget ratio and advertising effect index ratio in the same type of advertisements placed in the advertising classification information are the same.

6. The digital delivery adjustment method based on multi-platform data analysis according to claim 5 is characterized in that: The adjusting of abnormal advertisement delivery according to abnormal advertisement information specifically includes: According to the abnormal advertisement information, the advertisement effect data corresponding to the abnormal advertisement is obtained; According to the advertising effect data, the average advertising conversion rate of the delivery platform corresponding to the abnormal advertisement is used as the advertising conversion rate of the abnormal advertisement; According to the advertisement conversion rate and the advertisement conversion rate threshold, determine whether to adjust the advertisement content of the abnormal advertisement; if the advertisement conversion rate is lower than the advertisement conversion rate threshold, adjust the creative content of the abnormal advertisement; If the advertisement conversion rate is higher than the advertisement conversion rate threshold, the delivery platform of the abnormal advertisement is adjusted until the click rate of the abnormal advertisement is higher than the advertisement click rate threshold. The adjustment of the delivery platform includes reselecting the delivery platform and adjusting the advertisement delivery time of each delivery platform.

7. A digital delivery adjustment system based on multi-platform data analysis, used to implement the adjustment method according to any one of claims 1 to 6, characterized in that: include: A main control module, the main control module is used to determine whether the advertisement and the classified benchmark advertisement belong to the same category according to the user age ratio information, the overlapping age group of the target user and the classified benchmark advertisement, determine whether to adjust the advertisement content of the abnormal advertisement according to the advertisement conversion rate and the advertisement conversion rate threshold, divide the advertisements with the same product category into the same group, obtain multiple advertisement groups, further divide the advertisements in the advertisement group based on the classified benchmark advertisement, obtain the advertisement classification information, and adjust the digital delivery of the advertisement according to the advertisement sequence information; An information acquisition module, the information acquisition module is used to obtain platform advertising information, advertising information, platform information and advertising effect data, and according to the platform advertising information, obtain advertising effect data, click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered, obtain product category information corresponding to the delivered advertisement according to the platform advertising information, and obtain platform information, multiple platform name information and user data corresponding to each platform according to the platform advertising information; An evaluation module, the evaluation module is used to obtain user age ratio information based on platform information, user age ratio information corresponding to each platform, and daily active user number information, conduct a user overlap survey on the delivery platform corresponding to each advertisement, obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and obtain an advertising effect index based on advertising effect data, click-through rate and conversion rate weights and user overlap; The display module interacts with the main control module and is used to output and display advertising classification information, advertising effect data, advertising effect index and advertising sequence information.

8. The digital delivery adjustment system based on multi-platform data analysis according to claim 7, characterized in that: The main control module specifically includes: A control unit, the control unit is used to divide the advertisements with the same product category into the same group, obtain multiple advertisement groups, further divide the advertisements in the advertisement groups based on the classified benchmark advertisements, obtain advertisement classification information, and adjust the digital delivery of the advertisements according to the advertisement sequence information; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit is used to judge whether the advertisement and the classification benchmark advertisement belong to the same category based on user age ratio information, overlapping age groups of target users and classification benchmark advertisements, and to judge whether to adjust the advertisement content of the abnormal advertisement based on the advertisement conversion rate and the advertisement conversion rate threshold.

9. The digital delivery adjustment system based on multi-platform data analysis according to claim 7, characterized in that: The information acquisition module specifically includes: A first acquisition unit, the first acquisition unit is used to acquire platform advertising delivery information, delivery advertising information, platform information and advertising effect data, and acquire advertising effect data, click-through rate data and conversion rate data corresponding to each platform when each advertisement is delivered according to the platform advertising delivery information; The second acquisition unit is used to obtain product category information corresponding to the advertisement according to the platform advertisement delivery information, and to obtain platform information, multiple platform name information and user data corresponding to each platform according to the platform advertisement delivery information.

10. The digital delivery adjustment system based on multi-platform data analysis according to claim 7, characterized in that: The evaluation module specifically includes: A first evaluation unit, the first evaluation unit is used to obtain user age ratio information according to platform information, user age ratio information corresponding to each platform, and daily active user number information; The first evaluation unit is used to conduct a user overlap survey on the delivery platform corresponding to each advertisement, obtain the user overlap between the benchmark delivery platform and the delivery platform for each advertisement, and obtain the advertising effect index according to the advertising effect data, the weights of the click rate and the conversion rate and the user overlap.

Images