A Predictive Models for Advertisement Campaign Budget Allocation
This study explores the role of predictive models in optimizing advertisement campaign budget allocation. As digital marketing grows more complex, predictive models offer data-driven insights that help advertisers allocate budgets more efficiently. These models use machine learning to analyze past...
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| Format: | Article |
| Language: | English |
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Faculty of Science, The University of Azad Jammu & Kashmir, Muzaffarabad
2025-03-01
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| Series: | Kashmir Journal of Science |
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| Online Access: | https://www.kjs.org.pk/index.php/kjs/article/view/75 |
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| author | Iqra kousar |
| author_facet | Iqra kousar |
| author_sort | Iqra kousar |
| collection | DOAJ |
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This study explores the role of predictive models in optimizing advertisement campaign budget allocation. As digital marketing grows more complex, predictive models offer data-driven insights that help advertisers allocate budgets more efficiently. These models use machine learning to analyze past performance, predict trends, and optimize resource distribution across channels, improving campaign outcomes and return on investment (ROI). Techniques such as real-time bidding (RTB), customer segmentation, and multi-touch attribution have enhanced budget allocation. However, challenges like data quality, model interpretability, and integration complexity limit widespread use. Predictive models are integrated into platforms like Google Ads and Facebook Ads Manager, optimizing cost-per-click (CPC) and conversion rates. Balancing automation with human oversight remains crucial. Research should focus on real-time data integration and ethical concerns around data privacy to ensure responsible use. Refining these models will empower advertisers to make better data-driven decisions, improving budget allocation and campaign success.
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| format | Article |
| id | doaj-art-0eab02e861a0462bb674112492d54d13 |
| institution | Kabale University |
| issn | 2958-7832 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Faculty of Science, The University of Azad Jammu & Kashmir, Muzaffarabad |
| record_format | Article |
| series | Kashmir Journal of Science |
| spelling | doaj-art-0eab02e861a0462bb674112492d54d132025-08-20T03:59:25ZengFaculty of Science, The University of Azad Jammu & Kashmir, MuzaffarabadKashmir Journal of Science2958-78322025-03-0140110.63147/krjs.v4i01.75A Predictive Models for Advertisement Campaign Budget AllocationIqra kousar This study explores the role of predictive models in optimizing advertisement campaign budget allocation. As digital marketing grows more complex, predictive models offer data-driven insights that help advertisers allocate budgets more efficiently. These models use machine learning to analyze past performance, predict trends, and optimize resource distribution across channels, improving campaign outcomes and return on investment (ROI). Techniques such as real-time bidding (RTB), customer segmentation, and multi-touch attribution have enhanced budget allocation. However, challenges like data quality, model interpretability, and integration complexity limit widespread use. Predictive models are integrated into platforms like Google Ads and Facebook Ads Manager, optimizing cost-per-click (CPC) and conversion rates. Balancing automation with human oversight remains crucial. Research should focus on real-time data integration and ethical concerns around data privacy to ensure responsible use. Refining these models will empower advertisers to make better data-driven decisions, improving budget allocation and campaign success. https://www.kjs.org.pk/index.php/kjs/article/view/75Predictive Modelsdigital marketingMachin LearningReturn on investment |
| spellingShingle | Iqra kousar A Predictive Models for Advertisement Campaign Budget Allocation Kashmir Journal of Science Predictive Models digital marketing Machin Learning Return on investment |
| title | A Predictive Models for Advertisement Campaign Budget Allocation |
| title_full | A Predictive Models for Advertisement Campaign Budget Allocation |
| title_fullStr | A Predictive Models for Advertisement Campaign Budget Allocation |
| title_full_unstemmed | A Predictive Models for Advertisement Campaign Budget Allocation |
| title_short | A Predictive Models for Advertisement Campaign Budget Allocation |
| title_sort | predictive models for advertisement campaign budget allocation |
| topic | Predictive Models digital marketing Machin Learning Return on investment |
| url | https://www.kjs.org.pk/index.php/kjs/article/view/75 |
| work_keys_str_mv | AT iqrakousar apredictivemodelsforadvertisementcampaignbudgetallocation AT iqrakousar predictivemodelsforadvertisementcampaignbudgetallocation |