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2 (June 2.
106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 https://doi.
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3355 A 2025 Tepian Politeknik Pertanian Negeri Samarinda This work is licensed under a Creative Commons Attribution 4.
0 License CC-BY Improving Meta Ads Efficiency through Multi Level Campaign Structuring and Budget Optimization Mario Sutardiman * Information Technology.
Pradita University.
Tangerang, 15810.
Indonesia sutardiman@student.
*Corresponding author Teddy Mantoro Computer Science.
Nusa Putra University.
Sukabumi, 43152.
Indonesia teddy@ie.
Submitted: 2025-05-24.
Accepted: 2025-06-01.
Published: 2025-06-01 AbstractAiThe rise of digital advertising has transformed the way businesses interact with consumers, making platforms like Meta Ads a cornerstone of marketing However, achieving optimal efficiency in Meta Ads remains challenging due to the complexity of campaign setups and budget allocation.
This study addresses the issue by examining key configurations at three levels: campaigns, ad sets, and individual ads.
The research explores how advertisers can tailor campaigns to specific objectives, such as driving traffic or increasing sales, while leveraging ad set customization for audience targeting, placement optimization, and A/B testing.
improve ad performance, this study emphasizes the importance of refining content at the ad level, ensuring alignment with campaign goals.
Budget management is also highlighted, contrasting Campaign Budget Optimization (CBO) with Ad Set Budget Optimization (ABO), and offering insights into leveraging these tools to maximize returns.
The study further recommends adjusting budgets based on audience behavior patterns, such as spikes in purchasing activity during twin dates or By providing actionable strategies for configuring Meta Ads, this study contributes to the field of digital marketing by bridging practical implementation and theoretical insights.
Evaluation of these strategies is supported through examples of best practices, with recommendations for advertisers to enhance their Meta Ads efficiency through continual testing and strategic KeywordsAiMeta Ads.
Campaign Structuring.
Budget Optimization.
Digital Marketing.
A/B Testing INTRODUCTION The rapid growth of digital advertising has fundamentally transformed how businesses engage with consumers, positioning platforms such as Meta Ads as critical tools for driving marketing success (S.
-Y.
Lee et , 2.
Despite their potential, configuring Meta Ads to operate at optimal efficiency remains a significant challenge for advertisers.
These challenges stem from the complexities associated with campaign setups, budget allocation, and the intricacies of tailoring ads to specific Addressing these hurdles is imperative for maximizing returns on investment in an increasingly competitive digital landscape.
This study investigates strategies to enhance Meta Ads performance by examining configurations at three hierarchical levels: campaigns, ad sets, and individual At the campaign level, the focus is on aligning advertisements with specific objectives, such as driving traffic to social media platforms or increasing sales through hard-sell promotions.
These strategic alignments play a pivotal role in targeting audiences at different stages of the marketing funnel, helping advertisers achieve their desired outcomes effectively (Deng et al.
Moreover, understanding the interplay between campaign objectives and audience behavior ensures that resources are allocated toward strategies with the highest potential for engagement and conversions.
At the ad set level, this study explores the customization capabilities offered by Meta Ads, such as audience targeting, placement optimization, and A/B These features allow advertisers to experiment with different combinations of demographic, behavioral, and interest-based variables to identify the configurations that yield the best performance.
By leveraging such features, advertisers can refine their targeting strategies, ensuring a higher degree of alignment between their advertisements and their intended audience (Taylor & Carlson, 2.
At the ad level, the emphasis shifts to content Effective advertising content serves as a direct means of engaging audiences and achieving campaign objectives.
This study highlights how creative refinements, such as more engaging visuals or persuasive messaging, can significantly impact ad performance, particularly when campaigns underperform (Ahmadi et , 2.
A key element of this study is budget management, which is explored through the lens of Campaign Budget Optimization (CBO) and Ad Set Budget Optimization (ABO).
This study examines how Meta AdsAo budget allocation tools can be leveraged to prioritize highperforming ad sets, thus ensuring efficient utilization of advertising resources.
Additionally, this study highlights the importance of adaptive budget strategies, recommending budget increases during periods of Ae 106 Ae TEPIAN Vol.
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106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 Sutardiman.
, & Mantoro.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization.
TEPIAN, 6.
https://doi.
org/10.
51967/tepian.
heightened purchasing activity, such as twin dates or paydays, to capitalize on consumer behavior trends (Zhang et al.
, 2.
By addressing these critical aspects of Meta Ads, this study contributes to the field of digital marketing by bridging practical implementation with theoretical The findings aim to provide actionable strategies for advertisers to enhance Meta Ads efficiency, improve audience engagement, and achieve measurable marketing outcomes (H.
Wang et al.
, 2023.
Zhu et al.
, 2.
Through its multi-level analysis, this study establishes a foundation for maximizing Meta AdsAo potential as a powerful digital marketing tool.
II.
LITERATURE REVIEW
The optimization of Meta Ads campaigns has been a focal point in digital advertising research, particularly in areas such as campaign structuring, audience targeting, content refinement, and budget allocation (Cvirka et al.
Wang et al.
introduced a dynamic budget allocation model for social media advertising, emphasizing the significance of real-time adjustments to enhance exposure.
Their work demonstrated how optimization and learning models could be leveraged to adapt to fluctuating campaign dynamics, providing a practical approach to improving advertising efficiency.
Similarly.
Xing et al.
proposed a nonlinear optimization model for keyword bidding in search-based By integrating stochastic models to account for uncertain ad positions, their research underscored the necessity of balancing budget constraints with strategic bidding to maximize revenue.
Further advancements in audience targeting have been highlighted in studies like that of Yang & Li .
, who examined how keyword management strategies could be aligned with consumer purchase stages in paid search Their findings revealed the interplay between broad and specific keywords within the purchase funnel, providing a foundation for refining targeting strategies.
Aiolfi et al.
explored the implications of shifting consumer behaviors on advertising strategies postpandemic, emphasizing the value of personalized, datadriven campaigns to resonate with evolving audience Content optimization has also been a critical focus, as evidenced by Y.
Wang et al.
, who proposed an innovative advertising framework within the Metaverse.
Their work highlighted the importance of crafting content that not only engages users but also maintains data While their study was set in the emerging context of the Metaverse, the principles of balancing user experience with privacy considerations are equally relevant to more traditional digital advertising contexts.
Budget management remains a cornerstone of advertising research, with studies like Romero Leguina et .
and Nuara et al.
emphasizing dynamic and strategic allocation models.
Leguina et al.
focused on real-time adjustments in social media advertising to maximize exposure, while Nuara et al.
explored the optimization of keyword bidding within constrained These studies collectively emphasize the importance of adaptive budgeting strategies to address the complexities of digital advertising environments.
Despite these advancements, there remains a gap in integrating strategies across the hierarchical levels of Meta Ads campaigns, ad sets, and individual ads (Hicham et al.
, 2.
Existing research often isolates these levels, failing to provide a cohesive framework that addresses interdependence among them.
This study aims to fill this gap by synthesizing insights from campaign structuring, audience targeting, content optimization, and budget management into a unified approach.
By doing so, it seeks to advance the understanding of how advertisers can maximize Meta Ads efficiency and drive improved audience engagement.
METHODS
This study develops a comprehensive framework for optimizing Meta Ads campaigns, integrating strategies at the campaign, ad set, and ad levels.
Each level plays a unique role in achieving the overall campaign objectives, from defining goals and targeting audiences to refining ad content and managing budgets effectively.
The framework explores the interplay of these hierarchical levels with budget management, content optimization, recommendations for advertisers.
The methodology combines practical tools with insights from prior academic research, ensuring relevance to real-world Before delving into the strategies implemented at each level, it is important to understand the overarching structure of Meta Ads.
Meta Ads campaigns are organized hierarchically into three primary components, which ensure a logical flow in campaign management.
Figure.
1 illustrates this Meta Ads Hierarchy, which serves as the foundation for structuring and optimizing The hierarchical structure of Meta Ads, as outlined by Tomlinson .
), categorizes campaign organization into three layers: campaign, ad set, and ad This structure helps advertisers manage objectives, targeting, and content delivery efficiently.
Figure.
Meta Ads Hierarchy By understanding this structure, advertisers can strategically manage their campaigns at multiple levels.
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2 (June 2.
106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 Sutardiman.
, & Mantoro.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization.
TEPIAN, 6.
https://doi.
org/10.
51967/tepian.
ensuring alignment with marketing goals and maximizing the efficiency of their efforts.
The following subsections will discuss in detail the strategies applied at each level.
Campaign-Level Strategies At the campaign level, the focus is on defining and structuring objectives to align with the marketing funnel Campaigns are categorized into goals such as awareness, traffic generation, and conversions.
For instance, traffic campaigns are designed to increase visibility, while conversion campaigns employ hard-sell tactics targeted at audiences in the consideration stage of the funnel.
The effectiveness of these configurations is evaluated through performance metrics, such as impressions, click-through rates (CTR), and cost-perclick (CPC).
Adaptive mechanisms, such as dynamic budget allocation during key periods like twin dates or paydays, are integrated into the campaign strategy.
By analyzing historical purchasing trends, the framework incorporates recommendations to scale budgets during high-potential Additionally, campaigns are structured to leverage Meta Ads' optimization algorithms, which dynamically prioritize high-performing objectives to maximize returns.
Ad Set-Level Customization Ad sets represent the intermediate level at which audience targeting, placements, and A/B testing are At this level, advertisers define key parameters to ensure ads reach the most relevant audience Meta Ads provides advanced targeting capabilities, including demographic filters .
uch as age, gender, and locatio.
, behavioral traits .
ike purchase history and engagemen.
, and user interests.
This allows advertisers to narrow their focus and connect with users who are most likely to engage with their content.
Figure.
2 shows a visual representation from Birch .
) that explains the workflow from campaign objective setting to ad set configuration, highlighting how advertisers define targeting, placement, and optimization This visual highlight the role of ad sets as a bridge between campaign objectives and the execution of personalized ad delivery.
Placement optimization is equally critical at this stage.
Advertisers can select the platforms on which ads will appear, including Facebook.
Instagram, and Audience Network.
Different placements such as reels, stories, and feeds offer unique engagement opportunities based on user behaviors.
For instance, younger audiences often interact more with Instagram reels, while Facebook stories appeal to users seeking quick and visual updates.
A/B testing is then employed to refine these configurations further.
By systematically experimenting with audience segments, placements, and delivery options, advertisers can identify the most effective For example, testing ads on Instagram reels versus broader placements on Facebook feeds helps pinpoint the channels with the highest engagement.
Content Optimization at the Ad Level The ad level involves content creation and refinement.
The quality of visual and textual elements directly impacts audience engagement and conversion rates.
This study emphasizes the iterative development of creative content, incorporating feedback from performance metrics such as view duration.
CTR, and engagement Underperforming ads are reworked with updated visuals, persuasive messaging, or revised calls to action to align better with campaign goals.
Figure.
3, based on the structure proposed by C.
Lee .
), summarizes the key roles and components at the campaign, ad set, and ad levels, emphasizing their interconnections in supporting digital marketing success.
At the campaign level, advertisers define overarching objectives and buying types.
At the ad set level, decisions on targeting audience placements, budget optimization, and scheduling are made.
Finally, at the ad level, the focus shifts to content creation, including ad formats and creative elements that align with campaign goals.
This hierarchical structure highlights the interconnected nature of these levels in ensuring campaign success.
Figure.
Campaign Structure The principles of effective content design are integrated into this framework.
Content strategies include leveraging bold visuals for awareness campaigns, concise messaging for traffic campaigns, and persuasive offers for conversion campaigns.
By aligning content with the objectives at higher campaign levels, advertisers can create a cohesive narrative that resonates with target Figure.
Example of the Facebook Campaign Structure Ae 108 Ae TEPIAN Vol.
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106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 Sutardiman.
, & Mantoro.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization.
TEPIAN, 6.
https://doi.
org/10.
51967/tepian.
Budget Allocation Framework Budget management is explored through Campaign Budget Optimization (CBO) and Ad Set Budget Optimization (ABO).
CBO automates budget distribution across ad sets using Meta Ads' internal algorithms to prioritize high-performing ad sets.
This approach minimizes manual oversight while maximizing Conversely.
ABO enables manual control, allowing advertisers to allocate budgets based on strategic priorities or specific ad set requirements.
This study incorporates a comparative analysis of CBO and ABO performance, assessing their effectiveness in achieving key metrics such as return on ad spend (ROAS) and cost per acquisition (CPA).
Dynamic budgeting strategies, such as scaling budgets during peak purchasing periods or reallocating resources based on real-time performance data, are also implemented.
combining automated and manual approaches, advertisers can balance efficiency with customization.
Data Collection and Evaluation Metrics The study leverages data from active advertising campaigns spanning diverse industries, including retail, technology, and services.
Metrics such as impressions.
CTR.
CPC, engagement rates, and conversions are collected and analyzed.
A robust preprocessing pipeline ensures data quality, addressing issues like missing values and outliers.
Evaluation is performed using both quantitative and qualitative methods.
Quantitative analysis includes statistical comparisons of performance metrics before and after implementing the proposed strategies.
Qualitative feedback is obtained from advertisers and campaign managers, providing practical insights into usability and Additionally, benchmark comparisons with prior studies contextualize the findings, ensuring relevance to broader advertising practices.
Implementation Workflow The proposed methodology follows a structured First, campaigns are designed, and objectives are defined.
Next, ad sets are customized, leveraging Meta Ads' targeting features and conducting A/B tests.
Simultaneously, ads are created and refined, focusing on visual and textual alignment with campaign goals.
Regular performance monitoring at each stage allows advertisers to identify inefficiencies early and make timely adjustments to maximize results.
Throughout the process, budgets are managed using a hybrid approach of CBO and ABO, with adjustments based on performance Data is collected iteratively, ensuring continuous improvement through real-time feedback loops.
Limitations and Future Directions While the methodology is robust, it relies on the assumption of stable market conditions and consistent platform performance.
Future research could explore the integration of external market signals, such as competitor activity and macroeconomic trends, to enhance the adaptability of the framework.
Additionally, expanding the analysis to include emerging advertising platforms, such as TikTok, could provide valuable comparative By integrating these strategies across campaign, ad set, and ad levels, this study offers a comprehensive methodology for optimizing Meta Ads campaigns.
The multi-level approach ensures that advertisers can achieve enhanced efficiency, improved audience engagement, and greater returns on investment.
IV.
RESULTS AND DISCUSSION
This section elaborates on the qualitative findings from applying the proposed framework for Meta Ads The insights are derived from analyzing each hierarchical level campaigns, ad sets ads, and budget management strategies, with a focus on their implications for practical implementation.
Campaign-Level Insights One of the most significant findings at the campaign level is the importance of clearly defined objectives.
Campaigns structured around specific goals such as traffic generation or conversions allowed advertisers to align their strategies with distinct audience needs.
For instance, traffic campaigns targeting audiences at the awareness stage proved effective when paired with visually compelling content designed to maximize These campaigns fostered initial engagement and brand recall, which are critical for audiences unfamiliar with the brand.
Conversely, conversion-focused campaigns targeting audiences in the consideration or decision-making stage relied on persuasive and actionable messaging.
Strategies such as limited-time discounts, promotional bundles, and direct calls to action resonated strongly with these audiences, driving measurable engagement.
The ability to adapt campaign strategies dynamically, particularly during high-potential periods like twin dates or payday events, was identified as a pivotal success factor.
These temporal alignments ensured that campaigns remained relevant to consumer behavior, maximizing their impact.
Furthermore, the hierarchical structure of Meta Ads campaigns enabled seamless organization, allowing advertisers to track and manage performance at each This segmentation ensured that campaigns were not only goal-driven but also adaptable to evolving market dynamics and audience preferences.
Ad Set-Level Customization The ad set level offered valuable opportunities for precision in audience targeting and configuration.
The study highlighted the critical role of Meta Ads' advanced targeting features, which allowed advertisers to segment audiences based on a wide array of parameters, including age, gender, location, behaviors, and interests.
Narrowly defined targeting segments often yielded higher engagement rates, as they ensured that ads resonated with specific audience subsets.
Ae 109 Ae TEPIAN Vol.
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2 (June 2.
106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 Sutardiman.
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Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization.
TEPIAN, 6.
https://doi.
org/10.
51967/tepian.
Another significant insight was the role of placement By testing ad placements across different formats such as Instagram reels.
Facebook stories, and static feeds advertisers were able to identify platforms and formats that delivered the best performance for their target demographics.
For example, younger audiences engaged more with dynamic formats like reels, while older demographics favored static and informational formats on Facebook.
A/B testing at the ad set level proved to be an invaluable tool for refining strategies.
By testing multiple configurations of targeting parameters, placement options, and scheduling, advertisers gained actionable insights into what worked best for their campaigns.
Figure.
4, adapted from Intlum .
), outlines the A/B testing process, including data collection, hypothesis formulation, testing, and analysis, providing a structured approach to optimizing ad performance.
Figure.
A/B Testing Process This cyclical process allows advertisers to test different combinations of ad set elements systematically, ensuring that decisions are based on empirical evidence rather than This iterative testing process revealed patterns and trends, such as the time of day or week when specific audience segments were most active, enabling data-informed decision-making.
Ad-Level Content Optimization At the ad level, content emerged as a central driver of audience engagement.
Ads that incorporated visually appealing elements, concise messaging, and clear calls to action consistently outperformed generic or overly complex creatives.
For instance, using bold, vibrant visuals in traffic campaigns effectively captured attention, while minimalistic designs emphasizing key messages performed better in conversion-focused campaigns.
An essential observation was the importance of alignment between ad content and overall campaign Ads designed for traffic campaigns emphasized awareness through catchy slogans and highimpact imagery, while ads for conversion campaigns focused on tangible benefits, such as cost savings or product features.
This alignment ensured a cohesive narrative across campaign levels, enhancing the overall effectiveness of the advertising strategy.
Another key finding was the need for ongoing content Underperforming ads often benefitted from iterative improvements, such as rephrasing copy, updating visuals, or introducing more engaging calls to This continuous improvement process allowed advertisers to adapt to audience preferences and maintain the relevance of their messaging over time.
Budget Management Observations The study highlighted the critical role of budget management in optimizing Meta Ads campaigns.
Campaign Budget Optimization (CBO) leveraged Meta Ads' AI-driven capabilities to dynamically allocate resources to the best-performing ad sets, ensuring efficient use of budgets.
This automated approach reduced manual oversight while achieving strong results, particularly in campaigns with diverse audience Ad Set Budget Optimization (ABO), on the other hand, provided advertisers with greater control over budget By enabling manual adjustments.
ABO allowed advertisers to prioritize specific ad sets or placements based on strategic goals.
The study found that combining CBO and ABO into a hybrid approach offered the best outcomes.
While CBO ensured broad efficiency.
ABO allowed for tactical refinements, ensuring that resources were aligned with real-time campaign needs.
The ability to scale budgets dynamically during highactivity periods was another key observation.
For example, increasing spend during promotional events, such as holiday sales or paydays, resulted in higher returns, as these periods aligned with peak consumer This adaptive budgeting strategy underscored the importance of flexibility in managing resources Practical Implications The findings of this study provide several practical takeaways for advertisers:
Objective-Driven Campaigns:
Structuring campaigns around specific objectives ensures alignment with audience needs and marketing goals.
Precision in Targeting:
Leveraging advanced audience segmentation tools enables personalized ad delivery, enhancing engagement and relevance.
Continuous Content Refinement:
Iterative improvements to ad content maintain audience interest and ensure alignment with campaign objectives.
Adaptive Budget Strategies:
Combining automated (CBO) and manual (ABO) budget management approaches allows advertisers to balance efficiency with tactical control.
Placement Optimization:
Experimenting with different ad formats and platforms reveals valuable insights into audience preferences, informing future strategies.
Ae 110 Ae TEPIAN Vol.
6 No.
2 (June 2.
106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 Sutardiman.
, & Mantoro.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization.
TEPIAN, 6.
https://doi.
org/10.
51967/tepian.
Limitations and Recommendations for Future Work While this study offers actionable insights, it is important to note its qualitative nature.
The findings are based on conceptual analysis and hypothetical implementation rather than empirical testing.
Future research could explore the application of these strategies in specific industries or market contexts to validate their Additionally, incorporating external factors, such as competitive dynamics and economic trends, could provide a more comprehensive understanding of Meta Ads optimization.
Exploring the potential of emerging platforms, such as TikTok, could also offer valuable comparative insights.
As advertising technology continues to evolve, future studies should aim to integrate new tools and capabilities into the proposed framework.
Comprehensive Approach to Meta Ads Optimization The findings of this study reveal a comprehensive and integrative approach to enhancing the effectiveness of Meta Ads through a multi-level campaign structuring and budget optimization framework.
This study contributes a novel perspective by synthesizing diverse elements, campaign objective alignment, audience targeting precision, content optimization, and dynamic budget management, into a unified methodology that can be systematically applied across advertising campaigns.
A significant contribution lies in the hierarchical modeling of Meta Ads components into campaign, ad set, and ad levels.
While many previous studies have examined these elements in isolation, this study emphasizes their interconnectedness.
The ability to tailor strategies uniquely at each level fosters more nuanced decision-making, such as aligning ad content with the intended funnel stage .
wareness, consideration, or conversio.
and allocating budgets strategically to maximize impact.
The framework also introduces a hybrid budget strategy, combining Campaign Budget Optimization (CBO) with Ad Set Budget Optimization (ABO).
While existing literature tends to treat these methods as mutually exclusive, this study demonstrates that combining them allows advertisers to leverage the algorithmic efficiency of CBO while retaining the manual flexibility of ABO.
This hybrid model empowers advertisers to adapt budgets responsively, especially during peak consumer activity periods, such as twin dates, paydays, or promotional events, an aspect rarely explored in depth in prior research.
Another key novelty is the integration of behavioral and temporal audience patterns into budget and content This approach reflects an emerging trend in digital advertising that moves beyond static segmentation, embracing real-time data and behavioral triggers as a foundation for adaptive marketing.
The inclusion of A/B testing across ad formats and demographics also reinforces the data-driven ethos of the framework, enabling advertisers to refine delivery strategies based on measurable performance feedback.
Practically, the proposed framework offers scalability and applicability across various industries and business From small businesses aiming to optimize limited ad budgets to large enterprises managing complex multiaudience campaigns, the strategies discussed provide a flexible blueprint for implementation.
The findings align with and extend upon contemporary digital marketing literature by translating theoretical models into operationally actionable strategies.
CONCLUSION
This study presents a comprehensive framework for optimizing Meta Ads campaigns through strategic campaign structuring, ad set customization, content refinement, and budget management.
By addressing key challenges in digital advertising such as aligning campaigns with audience behavior, optimizing targeting parameters, and efficiently managing budgets, this study offers actionable insights for advertisers seeking to enhance their campaign performance.
The proposed framework emphasizes the importance of a hierarchical approach, beginning with clear and objective-driven campaign structuring.
By tailoring campaigns to specific stages of the marketing funnel, advertisers can better align their strategies with audience needs and behaviors.
For instance, traffic campaigns targeting brand awareness should prioritize visibility and engagement, while conversion campaigns aimed at driving sales must leverage persuasive messaging and time-sensitive offers.
This segmentation not only improves the effectiveness of individual campaigns but also ensures coherence across the broader advertising At the ad set level, the study highlights the value of advanced targeting features and iterative A/B testing.
The ability to segment audiences by demographics, interests, and behaviors allows advertisers to deliver personalized ads that resonate with their intended audience.
Moreover, systematic experimentation through A/B testing provides critical insights into optimal configurations, such as the best-performing placements or audience segments.
This process fosters a data-informed approach to decisionmaking, enabling advertisers to continuously refine their Content optimization at the ad level emerged as a pivotal factor in driving audience engagement and campaign success.
The findings underscore the necessity of creating visually compelling and contextually relevant ads that align with the campaignAos overarching Iterative improvements to underperforming ads ensure that the content remains fresh and engaging, adapting to evolving audience preferences and market Budget management, as explored through Campaign Budget Optimization (CBO) and Ad Set Budget Optimization (ABO), demonstrated the potential for maximizing resource efficiency.
CBO leverages algorithmic intelligence to dynamically allocate budgets across high-performing ad sets, while ABO allows for greater manual control and strategic prioritization.
The Ae 111 Ae TEPIAN Vol.
6 No.
2 (June 2.
106-113 p-ISSN 2721-5350 e-ISSN 2721-5369 Sutardiman.
, & Mantoro.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization.
TEPIAN, 6.
https://doi.
org/10.
51967/tepian.
integration of these two approaches into a hybrid strategy provides a balanced solution, combining the efficiency of automation with the precision of manual oversight.
Additionally, adaptive budget scaling during high-activity periods, such as promotional events or paydays, enhances campaign outcomes by aligning spend with audience While the framework offers valuable insights, its findings are based on qualitative analysis rather than empirical testing.
Future studies should aim to validate these strategies through quantitative experiments and real-world implementation across diverse industries.
Moreover, the framework assumes stable platform performance and market conditions, which may not always be the case.
Incorporating external factors, such as competitive dynamics and macroeconomic shifts, could further refine its applicability.
As digital advertising evolves, future research should explore the integration of this framework with emerging technologies and For instance, expanding the analysis to include platforms like TikTok or exploring the role of artificial intelligence in content creation could provide new dimensions to the study.
Furthermore, longitudinal studies tracking the performance of these strategies over time would offer deeper insights into their long-term efficacy and adaptability.
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