Relevant bibliographies by topics / Real-time campaign optimization

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Academic literature on the topic 'Real-time campaign optimization'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Real-time campaign optimization"

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Shafeeq, Ur Rahaman. "Real-Time Campaign Optimization: Using Analytics to Adapt Marketing Strategies on the Fly." International Journal on Science and Technology 14, no. 4 (2023): 1–10. https://doi.org/10.5281/zenodo.14471845.

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The marketing environment, one cannot long pitch on a single strategy; anything that works today may well prove ineffective tomorrow. This paper looks at how advanced analytics can be used to optimize campaigns in real time and pinpoints some key lessons that will enable the marketer to effectively shift mid-campaign. The marketer stands a chance to immediately gain insight into consumer behavior, emergent market trends, and campaign performance by leveraging real-time data. It looks at various analytical tools and methodologies that support on-the-fly strategy adjustments, showing how each ha
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Sanasam, Pingky Devi. "Implementing my SQL in Real Time Marketing Campaign." Journal of Research and Review in Digital Marketing and Communications 2, no. 2 (2025): 56–64. https://doi.org/10.5281/zenodo.14799939.

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<em>In today world real time, successful marketing has become very crucial part of any organization. This paper focuses on the application of SQL in the real-time marketing campaigns, addressing the potential ways of using it for extracting valuable information from a range of campaigns, and enhancing their performance based on analysis of real time analytics. It includes data extracted from behavior- based customer segmentation, estimation of engagement rates and determination of relevant kpis. It helps in the management of marketing databases; it also enables the organization to segment cust
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Paturi, Manideep. "AI-Driven Sentiment Analysis for Real-Time Product Positioning and Adaptive Marketing Campaign Optimization." International Journal of Research Publication and Reviews 6, no. 6 (2025): 5822–38. https://doi.org/10.55248/gengpi.6.0625.21104.

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Dr., Sk. Mahaboob Basha, Sai Kiran T., Hemanth Sai Ch., and Lavanya K. "Reinforcement Learning-based Approach for Click-Through Rate Optimization in Real-Time." International Journal of Innovative Science and Research Technology 8, no. 4 (2023): 1429–34. https://doi.org/10.5281/zenodo.7902000.

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A popular metric for assessing the success of online advertising campaigns is click-through rate (CTR), particularly in the context of pay-per-click (PPC) marketing. Ad tracking is a crucial piece of technology for monitoring click-through rates (CTRs) in online advertising campaigns. With the aid of ad monitoring solutions, marketers can monitor the effectiveness of their advertising across various platforms and gadgets, including click-through rates. An advertising campaign&#39;s efficacy is assessed using this data, and it is subsequently improved over time to increase click-through rates a
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Zeng, Anxiang, Han Yu, Hualin He, et al. "Enhancing E-commerce Recommender System Adaptability with Online Deep Controllable Learning-To-Rank." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15214–22. http://dx.doi.org/10.1609/aaai.v35i17.17785.

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In the past decade, recommender systems for e-commerce have witnessed significant advancement. Recommendation scenarios can be divided into different type (e.g., pre-, during-, post-purchase, campaign, promotion, bundle) for different user groups or different businesses. For different scenarios, the goals of recommendation are different. This is reflected by the different performance metrics employed. In addition, online promotional campaigns, which attract high traffic volumes, are also a critical factor affecting e-commerce recommender systems. Typically, prior to a promotional campaign, the
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Rahul Gupta. "Real-time bidding optimization in AdTech using edge-embedded systems." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 216–25. https://doi.org/10.30574/wjarr.2025.26.1.1057.

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Integrating edge-embedded systems into real-time bidding workflows represents a transformative advancement in programmatic advertising. This architectural paradigm significantly reduces latency and enhances decision-making speed in the time-sensitive digital advertising ecosystem by decentralizing computations to local edge nodes positioned closer to data sources. Traditional RTB architectures relying on centralized data centers face inherent limitations that negatively impact campaign performance, particularly during high-volume periods and for geographically distant users. Edge-embedded appr
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Gokhale, Ameya. "Revolutionary AI-Driven Bid Optimization in Retail Media: A Technical Deep Dive." European Journal of Computer Science and Information Technology 13, no. 50 (2025): 27–33. https://doi.org/10.37745/ejcsit.2013/vol13n502733.

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The future of retail advertising is being revolutionized by AI-driven dynamic bid pricing, leveraging optimized algorithmic real-time bidding (RTB) to maximize advertiser efficiency, retailer profitability, and consumer engagement. Traditional bid pricing strategies in retail advertising have relied on static rules and manual optimization, failing to effectively target specific business goals such as awareness, consideration, clicks, or conversions, which results in inefficiencies and an uneven competitive landscape. This technical analysis explores how AI-powered real-time bid optimization of
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Damilola Azeez Bolarinwa, Chikezie Paul-Mikki Ewim, and Abbey Ngochindo Igwe. "Developing a crowdfunding optimization model to bridge the financing gap for small business enterprises through data-driven strategies." International Journal of Scholarly Research and Reviews 5, no. 2 (2024): 052–69. http://dx.doi.org/10.56781/ijsrr.2024.5.2.0048.

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This study explores the development of a crowdfunding optimization model aimed at addressing the financing gap faced by small business enterprises (SBEs). Crowdfunding has emerged as a significant alternative funding source for SBEs, especially those unable to access traditional financing. However, the success of crowdfunding campaigns remains inconsistent, highlighting the need for optimized, data-driven strategies to improve funding outcomes. This research proposes a model that leverages data analytics to identify key factors influencing the success of crowdfunding campaigns and provides rec
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Sravan Yella. "Algorithmic Campaign Orchestration: A Framework for Automated Multi-Channel Marketing Decisions." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 323–31. https://doi.org/10.32996/jcsts.2025.7.2.33.

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This article examines the paradigm shift from traditional rule-based marketing automation to continuous experience optimization enabled by AI-driven decision engines. The article presents an architectural framework for real-time campaign orchestration systems that leverage predictive analytics, reinforcement learning, and natural language processing to dynamically personalize customer interactions across channels. Through multiple case studies across different industry sectors, the article demonstrates how these systems process multi-source data streams to make intelligent decisions in millise
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Sravan Yella. "Algorithmic Campaign Orchestration: A Framework for Automated Multi-Channel Marketing Decisions." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 165–73. https://doi.org/10.32996/jcsts.2025.7.2.15.

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This article examines the paradigm shift from traditional rule-based marketing automation to continuous experience optimization enabled by AI-driven decision engines. The article presents an architectural framework for real-time campaign orchestration systems that leverage predictive analytics, reinforcement learning, and natural language processing to dynamically personalize customer interactions across channels. Through multiple case studies across different industry sectors, the article demonstrates how these systems process multi-source data streams to make intelligent decisions in millise
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Book chapters on the topic "Real-time campaign optimization"

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Naved, Mohd, S. Dhivya, Keerti Sheetal Mahajan, Pradeep Sharma, Nargiza Kuzieva, and M. Gurusamy. "Predictive Analytics for Risk Reduction in Vehicle Supply Chain Management." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0442-7.ch025.

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The use of machine learning for customer profile, predictive analytics, and cluster analysis, AI-powered audience segmentation is revolutionizing campaigns to raise awareness of car safety. By identifying target demographics, driving patterns, and risk variables, this strategy guarantees highly customized marketing campaigns. AI can send customized safety messages by grouping audiences according to safety concerns using behavioral modeling and clustering algorithms. Proactive outreach is made possible by predictive analytics, which forecasts engagement levels and accident risk probability. By
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Tas, Nurullah, Farid Huseynov, and Büşra Özdenizci Köse. "Optimizing Marketing Campaigns With AI-Driven Insights on Mobile User Behavior." In AI and Data Engineering Solutions for Effective Marketing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3172-9.ch010.

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Technology is crucial in our daily lives, enabling us to communicate, access information, and engage in various activities through devices like smartphones, tablets, and laptops. Social media platforms facilitate global connectivity and information sharing. The internet has revolutionized access to limitless information, online shopping, education, and job opportunities. Artificial intelligence (AI) advancements have brought innovative solutions to sectors like healthcare, automotive, and finance. This study aims to emphasize the significance of technology and AI in analyzing mobile user behav
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Verma, Amitabh. "Analytics and Data-driven Marketing." In Marketing in a Digital World: Strategies, Evolution and Global Impact. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305456125010012.

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The chapter “Analytics and Data-Driven Marketing” is an essential exploration of how contemporary businesses utilize data in shaping and refining their digital marketing strategies. It underlines the transformative impact of analytics in understanding customer behavior, optimizing user experience, and evaluating the effectiveness of marketing campaigns. Central to this discussion are two primary areas: understanding user behavior and measuring campaign performance. In understanding user behavior, the chapter delves into how analytics tools offer insights into user interactions across digital p
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Vats, Ritu, and Reeta Clonia. "Utilizing AI for Optimizing Brand Campaigns." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9954-5.ch003.

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Artificial Intelligence (AI) is revolutionizing how brands design, implement, and optimize their marketing campaigns. By leveraging AI-powered tools, brands can benefit from real-time insights, automation, and personalization, surpassing traditional methods reliant on manual data processing. AI processes large datasets and uses advanced algorithms for real-time decisions, allowing dynamic optimization across multiple channels. It helps brands understand consumer behavior by analysing trends, preferences, and sentiments. Machine learning enhances audience segmentation and improves targeting, wh
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Sharma, Khushboo. "FUTURISTIC TRENDS IN DIGITAL MARKETING." In Futuristic Trends in Management Volume 3 Book 7. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bhma7p2ch11.

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In our interconnected and fast-paced world, businesses are continually exploring innovative ways to connect with their target audience and promote their products or services. Digital marketing has emerged as a powerful and dynamic strategy, utilizing the potential of digital technologies to effectively reach, engage, and convert customers in the online realm. This paper delves into the fundamental principles of digital marketing, highlighting its evolution from the early stages of the internet to the complex ecosystem it is today. The historical journey of digital marketing, from the precursor
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Balamurugan, P., K. Gopi, Sankar Ganesh R., T. Srividhya, Sriram Ananthan, and Anil Basappa Malali. "Agile Marketing and Its Practices in the Current Business Landscape." In Advances in Logistics, Operations, and Management Science. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6274-7.ch007.

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Agile Marketing is a transformative approach that emphasizes adaptability, collaboration, and customer-centricity, enabling marketing teams to swiftly respond to evolving market conditions. Derived from Agile project management methodologies, Agile Marketing focuses on iterative work cycles, known as sprints, to implement continuous feedback and real-time optimization of marketing strategies. This flexible approach is designed to help teams manage complex marketing initiatives by breaking them into smaller, manageable tasks that can be rapidly executed and adjusted. In an era characterized by
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Varalakshmi, S., Anupam Sharma, Latifa Saud Al Habsi, and Yasmeen Sultana. "Quantum Machine Learning (QML) Techniques for Enhancing Online Marketing and Business." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6225-9.ch009.

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This chapter discusses the means whereby QML can revolutionize and transform online marketing and business practices. QML is a derivative from the principles of quantum computation and machine learning, such that tremendous volumes of data may be processed at unprecedented velocities, hence advanced analytics in terms of customer behavior being made possible, and also that of hyper-personalized marketing campaigns. Some of the key QML techniques discussed in terms of increasing the efficiency of marketing, reducing costs, and improving return on investments include techniques in quantum algori
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Rathi, Snehal Rahul, Narendra Jadhav, Abhishek Raut, Abhishek Navhal, and Manas Patil. "Predictive Analytics Using Machine Learning for Enhanced Online Advertising." In Advances in Educational Technologies and Instructional Design. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6705-6.ch011.

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Forecasting analytics in online marketing serves as the foundation for enhanced user interaction, accurate targeting, and optimization of marketing tactics in a constantly evolving digital environment. By utilizing predictive analytics to customize campaigns, marketers can improve the efficiency of their strategies by targeting particular interests and demographics. This research examines the application of machine learning models in forecasting user ad clicks, incorporating variables like age, internet usage, and daily site engagement time in the datasets. The effectiveness of decision tree a
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Arora, Suchita, and Neha Jain. "Brain-Computer Interfarentlces in Customer Engagement for Digital Marketing." In Concepts and Applications of Brain-Computer Interfaces. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7427-6.ch026.

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One creative way to increase consumer engagement with digital marketing is to incorporate brain-computer interfaces or BCIs. This strategy offers a better understanding of consumer preferences and behaviour. This study investigates how real-time neural data collection using brain-computer interfaces (BCIs) could transform digital marketing strategies by enabling more effective and personalized client engagements. By using brain-computer interfaces (BCIs), marketers can gain a deeper understanding of cognitive engagement, emotional responses, and decision-making processes. In the end, this lead
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Conference papers on the topic "Real-time campaign optimization"

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Vikkiramapandian, D., M. Ajay, R. Chowmyan, and A. Ganesh Kumar. "Real-Time Blood Donor Prediction and Campaign Optimization using DNN-Autoencoder and MERN Architecture." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11080959.

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Zhang, Yanfen, Paul Bovet, Lorelea Samano, et al. "Surveillance, Analysis, and Optimization (SA&O) During Active Drilling Campaign." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215119-ms.

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Abstract Deepwater drilling is an expensive complex operation. Real-time surveillance data and analysis for drilling operations are very important for ensuring safety and cost control. Due to the high production rate and high expense of deepwater wells, there are usually not many wells planned for developing a deepwater field. Therefore, the results of each well hold particular significance as additional reservoir surveillance data and are crucial for optimizing field development and production forecasts. The subject field of this paper is WRB (pseudonym) which is a deepwater field located in
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Black, Michael, Carsten Heinks, and Ron Cramer. "Real-Time Well Performance Measurement Using Non-Intrusive Clamp-On Measurement Technique." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210126-ms.

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Abstract The ability to quantify well performance individually and real-time across most if not all wells in a given field is a capability that bridges the gap between production and reservoir management. Having real-time oil, gas and water flow measurements from each well distributed across a reservoir has the potential to enable significant improvements in data driven and scalable production/reservoir surveillance and optimization. Traditionally, test separators offer the ability to perform well testing but the process is discontinuous, relatively infrequent and labor intensive. Over the las
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Robertson, Ryan, Aidan Deans, Kriti Singh, et al. "Field Test Results for Real-time ROP Optimization Using Machine Learning and Downhole Vibration Monitoring - A Case Study." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212568-ms.

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Abstract A case study for a real-time field test of a machine learning (ML) ROP prediction and optimization algorithm and a vendor-neutral vibration monitoring system is presented for ten wells in Northeastern British Columbia, Canada. A novel auto-calibration feature adjusts the ML model in real-time to account for prediction bias. The paper is of interest to operators and service companies seeking to accelerate uptake of Artificial Intelligence (AI) by rig personnel. A ten-well campaign in three target formations was drilled from one rig in the lateral sections to test the ML ROP prediction/
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Maafri, Mbarek, Mahmoud AlGaiar, and Moustafa Farhat. "Delivering Successful Quadlateral Wells Campaign in the Middle East: Milestones & Lessons Learned." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23784-ms.

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Abstract This paper examines the successful execution of multilateral drilling and completion of complex oil wells in the Middle East, a region known for its abundant hydrocarbon resources and unique set of challenges, including wellbore stability, stuck pipe, trajectory control, drilling vibrations, downhole tool failure, and low penetration rates. The objective is to provide an in-depth analysis of the challenges faced, the best practices employed, the lessons learned, and the value added to the industry through these efforts. The challenges of drilling and completing multilateral wells requ
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Hassan, Marwa. "Aerial Multi-Component Emissions Monitoring: Bridging Regulatory Compliance and Operational Optimization." In SPE Europe Energy Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225626-ms.

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Abstract In the face of tightening climate regulations and evolving operational demands, scalable and high-resolution emissions monitoring is increasingly vital for the oil and gas industry. This paper presents a technical evaluation of Aeromon’s mobile, drone-deployable BH-12 platform, which facilitates multi-component emissions quantification through a single, integrated campaign. The BH-12 system detects and quantifies over 17 gas components—including methane (CH4), carbon dioxide (CO2), volatile organic compounds (VOCs), sulphur oxides (SOx), and nitrogen oxides (NOx)—in real time, using a
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Abd Hamid, M., Z. Awang @ Mohamed, A. Zeidan, N. Nazkuliyev, Y. Tan, and B. Bahar. "Real Time Gaslift Optimization: Lessons from Field Implementation of Smart Completions for Future Redevelopment Projects Offshore Peninsular Malaysia." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24246-ea.

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Abstract "G" field is a mature field that has been producing for 30 years from 4 platforms. 90% of the wells are on gaslift. Well integrity issues including tubing leaks are common because of corrosion on account of contaminants (CO2 60% and H2S 10 to 100 ppm) and erosion due to sand production. These problems are observed particularly in older wells, which were completed without corrosion resistant tubulars and any downhole sand control. Optimizing gaslift for hundreds of wells is challenging due to robbing effect in dual-string wells, months of lead time to secure slickline for gaslift valve
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Chia, B. S., J. Hemaprasertsuk, M. K. Hamdan, et al. "Lessons Learned and Best Practices from High-Rate HPHT Well Testing Campaign, Lang Lebah Field, Offshore Malaysia." In International Petroleum Technology Conference. IPTC, 2025. https://doi.org/10.2523/iptc-24909-ms.

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Abstract Well-testing for reservoir characterization and production assessment has always been critical, especially in offshore high-pressure, high-temperature (HPHT) environments. A limitation of the technology available for HPHT environments in conjunction with strict safety and environmental constraints elevates the operational challenges over the conventional well-test operations. Three HPHT exploration and appraisal DST well tests were successfully performed in high H2S and CO2 environments offshore Malaysia. A unique approach was adopted that was based on the combination of new-generatio
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Yu, Jingfeng, Diao Zhou, Bo Zhang, et al. "Horizontal Well Drilling and Geosteering Optimization with Integrated Innovation Technologies: Case Studies from the World Largest Conglomerate Reservoir in West China." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21482-ms.

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Abstract MH oilfield is a fan delta deposited unconventional tight oil reservoir with complex lithology of volcanic rocks, metamorphic rocks, conglomerate, and claystone. The drilling efficiency was optimized by using the first-generation boundary mapping technology with Rotary Steering System (RSS) during the first batch drilling campaign (H2-2016∼H1-2017), which was mentioned in IADC/SPE-190998-MS. But with the development going further, more and more wells drilled into shale interbed causing low pay zone exposure, long drilling duration, and numerous drilling hazards. The overall drilling p
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M. Nazri, Amir Badzly, W. M. Anas W. Khairul Anuar, Lucas Ignatius Avianto Nasution, Hayati Turiman, Shar Kawi Hazim Shafie, and Mohamad Mustaqim Mokhlis. "A Success Story in Managing and Optimising Gas Lift Wells in Matured Oil Field: Automated Workflows in Digital Fields as Enablers to Accelerate Opportunities Creation and Production Optimisation." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205576-ms.

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Abstract Field S located in offshore Malaysia had been producing for more than 30 years with nearly 90% of current active strings dependent on gas lift assistance. Subsurface challenges encountered in this matured field such as management of increasing water-cut, sand production, and depleting reservoir pressure are one of key factors that drive the asset team to continuously monitor the performance of gaslifted wells to ensure better control of production thereby meeting target deliverability of the field. Hence, Gas Lift Optimization (GLOP) campaign was embarked in Field S to accelerate shor
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