Method and system for screening ToB commercial advertisement audiences based on FACEBOOK social media platform

Abstract

The invention provides a FACEBOOK social media platform-based ToB commercial advertisement audience screening method and system, and relates to the technical field of data analysis, and the method mainly comprises the following four steps: data integration and analysis, an audience segmentation model, a machine learning prediction model, and dynamic optimization and feedback circulation. According to the technical scheme, the efficiency and effect of putting the ToB commercial advertisement on the FACEBOOK platform can be remarkably improved, and powerful technical support is provided for enterprise marketing. The method has the beneficial effects that the ToB advertisement audience screening process on the FACEBOOK platform is optimized through an innovative algorithm and a data analysis technology, the accuracy and conversion rate of advertisement putting are improved, and the marketing cost of an enterprise is reduced. The core different from the prior art is to combine enterprise business characteristics, user behavior data and a machine learning prediction model to realize a highly customized audience screening strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for audience screening of ToB commercial advertisements based on the FACEBOOK social media platform. Background Art

[0002] Current Status of ToB Commercial Advertising on Social Media Platforms With the rapid development of Internet technology, social media platforms have become an important channel for enterprises (ToB, i.e., for businesses) to conduct marketing and advertising. Facebook (now branded as Meta), as one of the world's largest social media platforms, has a huge user base and highly segmented user data, providing ToB advertisers with unprecedented marketing opportunities. The goals of ToB commercial advertising are usually to increase brand awareness, promote potential customer conversion, enhance customer loyalty, etc., and the key to achieving these goals is to accurately locate the target audience.

[0003] In the big technical context, social media advertising platforms such as Facebook have developed a series of advertising management tools that allow advertisers to filter target audiences based on multiple dimensions such as geographic location, age, gender, interests, occupation, etc. These tools have greatly improved the flexibility and targeting of advertising, allowing advertisers to allocate budgets more effectively and achieve the desired marketing results. However, with the intensification of market competition and the increasing complexity of consumer behavior, relying solely on these basic screening conditions can no longer meet the needs of ToB advertisers for highly customized and precise advertising.

[0004] Limitations of existing ToB commercial advertising audience screening technology 1. Insufficient industry segmentation: Although Facebook provides a wide range of user interest and behavior data, existing systems often lack sufficient depth and precision in audience screening for specific industries (such as IT services, manufacturing, etc.), and it is difficult to accurately capture the specific needs and preferences of different roles in the industry (such as decision makers, purchasing managers, technicians, etc.).

[0005] 2. Poor adaptability to dynamic markets: The ToB market is affected by multiple factors such as the macroeconomic environment, policy changes, and technological innovations, and market demand and audience characteristics change rapidly. Existing screening strategies are often based on static data and are difficult to capture these dynamic changes in real time, resulting in the gradual weakening of advertising effects over time.

[0006] 3. Limited data integration and analysis capabilities: Although Facebook has rich internal data, advertisers often need to integrate external data sources (such as CRM systems, market research reports, etc.) to improve audience portraits. The existing system does not provide sufficient support for cross-platform data integration and analysis, which limits advertisers from formulating more refined advertising strategies.

[0007] 4. Lack of personalized experience: The success of B2B advertising often depends on establishing deep connections with potential customers and providing personalized solutions and communication methods. However, existing screening tools still need to be improved in terms of personalized content recommendations and dynamic creative optimization, and it is difficult to fully meet this demand.

[0008] In summary, although social media platforms such as Facebook provide a powerful basic framework for ToB commercial advertising, there are still significant deficiencies in the accuracy of audience screening, market adaptability, data integration capabilities and personalized experience. Therefore, developing a strategy and system that can overcome these limitations and achieve more efficient and accurate ToB commercial advertising audience screening has become an urgent need for current technological development.

[0009] In addition, the prior art still has the following defects: 1. Narrow audience positioning: Problem: Existing technologies often rely on limited audience attributes (such as age and gender) for targeting, resulting in a limited range of ad display.

[0010] Reason: This may be due to insufficient understanding of the target market, or an overly conservative positioning strategy that fails to fully utilize the multi-dimensional data provided by the Facebook platform.

[0011] 2. The advertising content lacks appeal: Problem: Ad content may fail to attract the target audience due to violation of platform regulations or lack of creativity.

[0012] Reason: Advertisers may not have enough knowledge of Facebook’s advertising policies and best practices, resulting in content design that lacks novelty or does not meet platform requirements. 3. Lack of interactivity: Problem: The advertising interaction rate is low and fails to effectively stimulate user participation.

[0013] Reason: The ad format may be too traditional, lack innovative interactive elements, or fail to fully utilize the social attributes of Facebook.

[0014] 4. Improper budget allocation: Problem: Improper budget allocation may limit the optimization space and display frequency of ads due to being too low or not optimized.

[0015] Cause: Advertisers may fail to dynamically adjust budgets based on ad performance or lack an effective budget allocation strategy.

[0016] 5. Insufficient data analysis and strategy adjustment: Problem: The lack of systematic data analysis and strategy adjustment mechanisms makes it difficult to continuously optimize advertising effectiveness.

[0017] Reason: Advertisers may not fully utilize the data analysis tools provided by Facebook, or fail to regularly evaluate and adjust their advertising strategies. Summary of the invention

[0018] The present invention overcomes the shortcomings of the prior art and provides a method and system for audience screening of ToB commercial advertisements based on the FACEBOOK social media platform. Through innovative algorithms and data analysis technologies, the ToB advertisement audience screening process on the FACEBOOK platform is optimized, the accuracy and conversion rate of advertisement delivery are improved, and the marketing costs of enterprises are reduced. The core difference between the present invention and the prior art lies in the combination of enterprise business characteristics, user behavior data and machine learning prediction models to achieve a highly customized audience screening strategy.

[0019] In order to solve the above technical problems, the present invention is achieved through the following technical solutions: A method for screening audiences of ToB commercial advertisements based on the Facebook social media platform comprises the following steps: S1. Data integration and analysis: Integrate basic user information, interest preferences, career information, online behavior data, and enterprise-defined data provided by the Facebook platform to provide a rich, multi-dimensional data foundation for subsequent audience segmentation and precise positioning; S2. Audience segmentation model: audience user data is grouped based on clustering algorithms, and segmentation dimensions are defined in combination with the business characteristics of the enterprise; target audience groups with similar characteristics are identified to facilitate the formulation of targeted advertising strategies; S3. Machine learning prediction model: Build a prediction model based on supervised learning to predict the probability of users responding to corporate advertisements; accurately predict which users are most likely to be interested in advertisements and take actions (such as clicks and inquiries) to optimize advertising delivery strategies; S4. Dynamic optimization and feedback loop: Implement A / B Testing, continuously monitor advertising performance, and adjust audience screening criteria and advertising creativity based on real-time data feedback to ensure the effectiveness of advertising strategies, continuously improve advertising results, and reduce losses from ineffective advertising.

[0020] Furthermore, the basic user information includes but is not limited to age, gender, and geographic location; the online behavior data includes but is not limited to browsing history and interaction records; and the enterprise-defined data includes but is not limited to product keywords, customer database, and purchase history.

[0021] Furthermore, the clustering algorithm includes but is not limited to K-means and DBSCAN.

[0022] Furthermore, the segmentation dimensions defined in combination with the business characteristics of the enterprise include but are not limited to industry, job level, purchase intention stage, and B2B business behavior; the prediction model includes but is not limited to logistic regression, random forest, and neural network.

[0023] Furthermore, the data integration and analysis utilizes an API interface to securely acquire data, and uses data accumulation from multiple commercial advertising accounts and multiple advertising creative groups to perform rapid audience tag data screening and preprocessing.

[0024] Furthermore, in addition to the FACEBOOK API, data from other social media platforms can also be integrated, including but not limited to LinkedIn and Tiktok, to obtain a more comprehensive user portrait.

[0025] Going further, in addition to clustering algorithms, one can also explore the use of deep learning techniques for more refined audience segmentation, including but not limited to feature extraction using autoencoders.

[0026] Going further, in addition to supervised learning, semi-supervised learning or unsupervised learning methods can be tried, especially when data labels are scarce.

[0027] Furthermore, in addition to A / B Testing, you can also use strategies such as Bayesian optimization and multi-armed bandit to adjust advertising strategies more efficiently.

[0028] The invention also requests protection for a system including any of the above-mentioned methods for audience screening of ToB commercial advertisements based on the FACEBOOK social media platform.

[0029] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve the work efficiency of operating (user) personnel and reduce the loss of labor costs; 2. Increase the coverage of target audiences and the effective conversion of results, reduce the ineffective consumption of advertising fees and thus reduce advertising costs; 3. Multiple groups of creative advertisements can be created quickly, which is more conducive to operators to carry out A / B testing. Through multi-dimensional testing, valuable advertisements and ineffective advertisements can be quickly identified, so as to make optimization adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and are used together with the embodiments of the present invention to explain the present invention, but do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a method for screening audiences for ToB commercial advertisements based on the FACEBOOK social media platform described in the present invention. DETAILED DESCRIPTION

[0031] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0032] like Figure 1 As shown, a method for screening audiences for ToB commercial advertisements based on the FACEBOOK social media platform includes the following steps: S1. Data integration and analysis: Integrate basic user information, interest preferences, career information, online behavior data, and enterprise-defined data provided by the Facebook platform to provide a rich, multi-dimensional data foundation for subsequent audience segmentation and precise positioning; S2. Audience segmentation model: audience user data is grouped based on clustering algorithms, and segmentation dimensions are defined in combination with the business characteristics of the enterprise; target audience groups with similar characteristics are identified to facilitate the formulation of targeted advertising strategies; S3. Machine learning prediction model: Build a prediction model based on supervised learning to predict the probability of users responding to corporate advertisements; accurately predict which users are most likely to be interested in advertisements and take actions (such as clicks and inquiries) to optimize advertising delivery strategies; S4. Dynamic optimization and feedback loop: Implement A / B Testing, continuously monitor advertising performance, and adjust audience screening criteria and advertising creativity based on real-time data feedback to ensure the effectiveness of advertising strategies, continuously improve advertising results, and reduce losses from ineffective advertising.

[0033] Among them, the basic user information refers to but is not limited to age, gender, and geographic location; the online behavior data refers to but is not limited to browsing history and interaction records; the enterprise-defined data refers to but is not limited to product keywords, customer database, and purchase history.

[0034] In step S1, data integration and analysis uses the API interface to securely obtain data, and uses data accumulation from multiple commercial advertising accounts and multiple advertising creative groups to quickly screen and preprocess audience tag data.

[0035] Secondly, in addition to the FACEBOOK API, data from other social media platforms can also be integrated, including but not limited to LinkedIn and Tiktok, to obtain a more comprehensive user portrait; this method also falls within the protection scope of the present invention.

[0036] In step S2, clustering algorithms include but are not limited to K-means and DBSCAN.

[0037] The segmentation dimensions defined in combination with the business characteristics of the enterprise include but are not limited to industry, job level, purchase intention stage, and B2B business behavior; the prediction model includes but is not limited to logistic regression, random forest, and neural network.

[0038] In addition to clustering algorithms, one can also explore the use of deep learning techniques for finer audience segmentation, including but not limited to feature extraction using autoencoders.

[0039] In step S3, in addition to supervised learning, semi-supervised learning or unsupervised learning methods can be tried, especially when data labels are scarce; this method also falls within the protection scope of the present invention.

[0040] In step S4, in addition to A / B Testing, strategies such as Bayesian optimization and multi-armed bandit can also be used to more efficiently adjust the advertising strategy; this method also falls within the protection scope of the present invention.

[0041] The English abbreviations and terms mentioned above are explained as follows: FACEBOOK: A world-renowned social media platform that provides social networking services.

[0042] ToB: Business-to-Business, refers to business activities between enterprises.

[0043] API: Application Programming Interface, which allows software applications to communicate with each other.

[0044] K-means: A commonly used clustering algorithm used to divide data into K clusters.

[0045] DBSCAN: A density-based spatial clustering algorithm capable of identifying clusters of arbitrary shapes.

[0046] KOL: Key Opinion Leader, an influential person in a specific field.

[0047] PageRank: The web page ranking algorithm used by Google's search engine, also used to evaluate the influence of users in social networks.

[0048] HITS: Hyperlink-Induced Topic Search, used to identify authoritative pages and central pages in web pages.

[0049] A / B Test: It is an example of multiple groups of advertising tests. Using the same dimensional information, other multi-dimensional content items are launched to find out which dimensional information has the best test effect.

[0050] The following results can be achieved through this audience screening method: 1. Broaden the scope of audience targeting: Purpose: By introducing intelligent screening technology (such as "Similar Audiences"), combined with multi-dimensional data (such as interests, behavior patterns, geographic location, decision-making positions, common platforms, B2B purchasing behavior, product keywords, national markets, etc.), a broader and more accurate audience positioning can be achieved.

[0051] 2. Improve the attractiveness of advertising content: Objective: To gain an in-depth understanding of Facebook’s advertising policies and best practices, design high-quality, eye-catching visual materials, and incorporate emotional elements and storytelling to increase the creativity and appeal of ads.

[0052] 3. Enhance interactivity: Purpose: Use innovative interactive forms (such as carousels, sliding videos, etc.), launch social media challenges, and establish reward mechanisms to encourage users to participate and share advertising content, thereby creating a viral effect.

[0053] 4. Optimize budget allocation: Objective: Use Facebook's automatic bidding function to dynamically adjust budget allocation based on ad performance to ensure efficient use of funds. At the same time, formulate a phased delivery strategy to adapt to changes in product life cycle and marketing goals.

[0054] 5. Strengthen data analysis and strategy adjustment: Purpose: To establish a systematic data analysis and strategy adjustment mechanism, regularly monitor advertising performance data (such as click-through rate, conversion rate and conversion effectiveness, etc.), and timely adjust strategies such as audience positioning, content design and budget allocation based on data feedback to achieve continuous optimization of advertising effectiveness.

[0055] Through the above technical solution, the present invention can significantly improve the efficiency and effect of placing ToB commercial advertisements on the FACEBOOK platform, and provide strong technical support for corporate marketing.

[0056] We also request protection for a system, including any of the above-mentioned methods for audience screening of ToB commercial advertisements based on the FACEBOOK social media platform, so that the system also has innovative algorithms and data analysis technology functions, optimizes the ToB advertising audience screening process on the FACEBOOK platform, improves the accuracy and conversion rate of advertising delivery, and reduces corporate marketing costs.

[0057] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. However, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for screening audiences of ToB commercial advertisements based on the Facebook social media platform, characterized in that: The steps include: S1. Data integration and analysis: Integrate basic user information, interest preferences, career information, online behavior data, and enterprise-defined data provided by the Facebook platform to provide a rich, multi-dimensional data foundation for subsequent audience segmentation and precise positioning; S2. Audience segmentation model: audience user data is grouped based on clustering algorithms, and segmentation dimensions are defined in combination with the business characteristics of the enterprise; target audience groups with similar characteristics are identified to facilitate the formulation of targeted advertising strategies; S3. Machine learning prediction model: Build a prediction model based on supervised learning to predict the probability of users responding to corporate advertisements; accurately predict which users are most likely to be interested in advertisements and take actions, and optimize advertising delivery strategies; S4. Dynamic optimization and feedback loop: Implement A / B Testing, continuously monitor advertising performance, and adjust audience screening criteria and advertising creativity based on real-time data feedback to ensure the effectiveness of advertising strategies, continuously improve advertising results, and reduce losses from ineffective advertising.

2. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 1, characterized in that: The basic user information includes but is not limited to age, gender, and geographic location; the online behavior data includes but is not limited to browsing history and interaction records; the enterprise-defined data includes but is not limited to product keywords, customer database, and purchase history.

3. The method for screening audiences of ToB commercial advertisements based on the FACEBOOK social media platform according to claim 1, characterized in that: The clustering algorithm includes but is not limited to K-means and DBSCAN.

4. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 1, characterized in that: The segmentation dimensions defined in combination with the business characteristics of the enterprise include but are not limited to industry, job level, purchase intention stage, and B2B business behavior; the prediction model includes but is not limited to logistic regression, random forest, and neural network.

5. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 1, characterized in that: The data integration and analysis utilizes an API interface to securely acquire data, and uses data accumulation from multiple commercial advertising accounts and multiple advertising creative groups to perform rapid audience tag data screening and preprocessing.

6. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 5, characterized in that: In addition to the FACEBOOK API, you can also integrate data from other social media platforms, including but not limited to LinkedIn and Tiktok, to obtain a more comprehensive user portrait.

7. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 1, characterized in that: In addition to clustering algorithms, one can also explore the use of deep learning techniques for finer audience segmentation, including but not limited to feature extraction using autoencoders.

8. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 1, characterized in that: In addition to supervised learning, one can try semi-supervised learning or unsupervised learning methods, especially when data labels are scarce.

9. The method for screening audiences of ToB commercial advertisements based on the Facebook social media platform according to claim 1, characterized in that: In addition to A / B Testing, you can also use strategies such as Bayesian optimization and multi-armed bandit to adjust advertising strategies more efficiently.

10. A system, characterized in that: It includes a method for screening audiences for ToB commercial advertisements based on the FACEBOOK social media platform as described in any one of claims 1 to 9.

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