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Journal articles on the topic 'Predictive Marketing'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Muhajir, Ali. "Predictive Analytics in Marketing: Contribution to Marketing Performance." Management Studies and Business Journal (PRODUCTIVITY) 1, no. 3 (2024): 447–60. http://dx.doi.org/10.62207/0qan8b95.

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This systematic literature review explores predictive analytics in marketing decision making and its relationship to key concepts in consumer behavior prediction. Drawing on established theories and empirical studies, this study explores the influence of customer-based brand equity, brand attachment, self-concept, perceived value, and other variables on consumer purchasing behavior and intentions. Additionally, this study investigates the impact of social psychology theory, destination image, sustainability in marketing, and marketing practices that align with consumer values on satisfaction

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Modi, Mr Ashish. "The Impact of Predictive Analytics on Modern Marketing Strategies: A Data-Driven Approach." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 662–67. https://doi.org/10.22214/ijraset.2025.66929.

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Predictive analytics has transformed marketing by enabling businesses to anticipate customer behavior, refine strategies, and make more informed decisions. This paper explores the core methodologies, real-world applications, and challenges of predictive analytics in marketing, emphasizing its impact on customer segmentation, churn prediction, personalized marketing, and campaign optimization. By harnessing machine learning, statistical modeling, and big data analytics, companies can gain deeper insights that drive engagement and profitability. However, obstacles such as data accuracy, ethical

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Talekar, P. R. "Predictive Analytics for Market Trends." International Journal of Advance and Applied Research 5, no. 10 (2024): 64–66. https://doi.org/10.5281/zenodo.11299015.

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Predictive analytics as their name implies, tries to predict the things that happen in the future. It supports most of the enterprise activities, but we are focused on marketing. Rather than just describing who, where and when, it helps in predicting the future using many algorithms of regression equations. It not only describes what’s happening but also predicts what will happen in the future, Predictive analytics helps the marketers make better decisions in today’s generation by providing them with insights into what is likely to happen in future and helps in recognizing the patt

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Valli, Latha Narayanan. "A succinct synopsis of predictive analysis applications in the contemporary period." International Journal of Multidisciplinary Sciences and Arts 3, no. 4 (2024): 26–36. http://dx.doi.org/10.47709/ijmdsa.v3i4.4625.

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A potent subset of data analytics called predictive analytics is revolutionizing a number of industries by using historical data, machine learning methods, and statistical algorithms to predict future events and guide strategic choices. The uses and advantages of predictive analytics in the fields of finance, healthcare, manufacturing, energy and utilities, retail, and marketing are highlighted in this thorough overview. Predictive models improve market risk management, fraud detection, and credit risk assessment in the financial sector, promoting stability and confidence. Applications in heal

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Jayashree, R., and S. Mercitha. "Predictive Analytics using AI in Marketing: An Impact." ComFin Research 13, S1-i2 (2025): 131–36. https://doi.org/10.34293/commerce.v13is1-i2.8751.

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This analysis investigates the transformational effects of artificial intelligence (AI) on predictive modelling in digital advertising campaigns. The study aims to examine the effectiveness of the AI-controlled prediction analysis when improving marketing results, to examine the influence on segmentation and personalisation strategies of customers and to assess their contribution to improving Return on Investment (ROI) in marketing campaigns. In addition, research deals with the most important challenges, including data protection concerns, algorithmic distortion and the ethical use of AI. The

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Sahu, Shivam. "Developing Predictive Models for Real Estate Pricing and Marketing Trends." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50903.

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Real estate is a capital-intensive and data-rich sector where accurate price prediction and effective marketing can significantly improve transaction outcomes. Traditional property valuation methods, though useful, fall short in capturing complex, non-linear market dynamics. With the increasing availability of structured and unstructured real estate data and the rise of digital marketing platforms, machine learning (ML) offers promising tools to predict prices and evaluate the effectiveness of property listings. This study applies ML models Linear Regression, Random Forest, and XGBoost to a re

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Pawar, Supriya V., Gireesh Kumar, and Eashan Deshmukh. "Predictive Analytics for High Business Performance through Effective Marketing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 380–82. http://dx.doi.org/10.31142/ijtsrd8385.

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Pawar, Supriya V., Gireesh Kumar, and Eashan Deshmukh. "Predictive Analytics for High Business Performance through Effective Marketing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1046–50. http://dx.doi.org/10.31142/ijtsrd9502.

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Chockalingam, Divya. "Predictive Analytics for Vehicle Purchase Intent." International Journal of Multidisciplinary Research and Growth Evaluation. 3, no. 4 (2022): 606–8. https://doi.org/10.54660/.ijmrge.2022.3.4.606-608.

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Predictive analytics has emerged as a critical tool for businesses across industries, with the automotive sector being no exception. In the context of vehicle sales, predicting the intent of consumers to purchase a vehicle is crucial for optimizing marketing strategies, improving sales processes, and managing inventory efficiently. This research paper investigates the application of predictive analytics to forecast vehicle purchase intent using advanced machine learning algorithms, consumer data, and market trends. By analyzing patterns in customer behavior, this study highlights how predictiv

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GORODETSKY, Yuri. "FUNDAMENTAL PRINCIPLES OF PREDICTIVE ANALYTICS IN MARKETING." Management 38, no. 2 (2024): 9–21. http://dx.doi.org/10.30857/2415-3206.2023.2.1.

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INTRODUCTION. This article highlights the fundamental principles of predictive analytics as they apply to marketing, providing the reader with a thorough understanding of the methodologies and techniques used to predict consumer behavior and optimize marketing strategies. RESEARCH HYPOTHESIS. The article examines the key concepts and approaches underlying predictive analytics, including machine learning, statistical analysis, and forecasting algorithms. THE AIM of particular attention is paid to the application of these methods in various areas of marketing, such as market segmentation, pricin

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Gülter, Erkan, and Muhammed Fatih Cevher. "Evolution of digital marketing campaigns with artificial intelligence and machine learning: Analysing success prediction capabilities." Business & Management Studies: An International Journal 13, no. 2 (2025): 478–93. https://doi.org/10.15295/bmij.v13i2.2498.

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This study aims to investigate the predictive capabilities of machine learning algorithms in forecasting the success of digital marketing campaigns. In addition, the study aims to evaluate the performance of machine learning algorithm classification models and to determine which classification model is more effective in making this prediction. In this direction, a classification analysis was performed with machine learning algorithms using a dataset of 10,001 digital marketing campaigns obtained from the Kaggle platform. The study's theoretical background is based on Attribution Modelling, the

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Agboola, Oluwademilade Aderemi, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, Abiodun Yusuf Onifade, Oyeronke Oluwatosin George, and Remolekun Enitan Dosumu. "Advances in Lead Generation and Marketing Efficiency through Predictive Campaign Analytics." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 1 (2022): 1143–54. https://doi.org/10.54660/.ijmrge.2022.3.1.1143-1154.

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Advances in predictive campaign analytics are transforming lead generation and marketing efficiency by enabling data-driven decision-making and enhanced targeting strategies. Predictive analytics, which uses historical data and advanced algorithms to forecast customer behavior and outcomes, has become a cornerstone of modern marketing efforts. This explores the role of predictive analytics in lead generation, examining its ability to identify high-potential leads early in the sales funnel, improve lead scoring, and personalize marketing efforts for greater impact. By leveraging customer segmen

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Kotras, Baptiste. "Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge." Big Data & Society 7, no. 2 (2020): 205395172095158. http://dx.doi.org/10.1177/2053951720951581.

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This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence t

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Joo Lee, Hong. "Predictive models of intent to repurchase based on customer data." Edelweiss Applied Science and Technology 8, no. 4 (2024): 1174–87. http://dx.doi.org/10.55214/25768484.v8i4.1492.

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As the business environment becomes ever more complex, predicting future buying patterns has become increasingly important. Moreover, there has been growth in the number of consumers focusing on health and self-care given rapid societal and economic changes. This shift is leading to heightened interest in athleisure wear and an increase in sales volume in this domain. It is accordingly necessary for companies in this field to establish marketing strategies based on predicting consumers’ future purchases to maintain continuous growth and competitiveness. This paper surveyed 400 consumers who pu

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HANUSHCHAK-YEFIMENKO, Liudmyla, and Sanja GONGETA. "PREDICTIVE ANALYTICS AND MARKETING MANAGEMENT: IMPLEMENTING BUSINESS STRATEGIES THROUGH INTELLIGENT MODELS." Management 41, no. 1 (2025): 80–94. https://doi.org/10.30857/2415-3206.2025.1.5.

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INTRODUCTION. In today's digital economy and highly competitive market conditions, companies are faced with the need to quickly adapt to changes in consumer behavior, market trends, and technological trends. In these conditions, marketing management ceases to be an exclusively intuitive art and is transformed into a systematic activity based on data analysis, customer behavior prediction, and customer interaction management based on objective patterns. One of the key tools that allows for such a transformation is predictive analytics, which provides companies with the ability to proactively ma

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Sangsawang, Thosporn. "Predicting Ad Click-Through Rates in Digital Marketing with Support Vector Machines." Journal of Digital Market and Digital Currency 1, no. 3 (2024): 225–46. https://doi.org/10.47738/jdmdc.v1i3.20.

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This study investigates the effectiveness of Support Vector Machines (SVM) in predicting click-through rates (CTR) in digital marketing campaigns. Utilizing a dataset comprising user demographic and behavioral data, the research aims to develop a predictive model to forecast ad clicks accurately. The primary objectives include conducting exploratory data analysis (EDA), preprocessing data, training the SVM model, and evaluating its performance using standard metrics. The dataset includes features such as Daily Time Spent on Site, Age, Area Income, Daily Internet Usage, and Gender. Key findings

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Kalaivani, M. "A Study on Predictive Analytics in Marketing." ComFin Research 13, S1-i1-Mar (2025): 208–11. https://doi.org/10.34293/commerce.v13is1-i1-mar.8680.

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The analytic in trading data-driven approach that uses historical data to predict marketing trends and scenarios. It can help marketers create more effective marketing strategies and make better decisions. This research aims to focus on the current usage of predictive analytic tools for engagement within the branch of digital marketing and evaluate the proficiency of marketers in utilizing predictive analytic. It seeks to identify the species digital marketing tools employed in Business-to-Business (B2B) contexts and explores strategies for enhancing brand awareness while transitioning towards

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Altwaijri, Ahmad Saleh. "The effect of marketing 5.0 on marketing performance: The moderating effect of customer resources." Decision Science Letters 14, no. 1 (2025): 113–22. https://doi.org/10.5267/j.dsl.2024.10.009.

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This study aims at exploring the effect of marketing 5.0 as a whole construct on marketing performance and the moderating role of customer resources between these two variables. Moreover, the study aims at examining the effects of marketing 5.0 dimensions, i.e., predictive marketing, contextual marketing, augmented marketing, and agile marketing on marketing performance as well as the moderating role of customer resources in the effect of each dimension on marketing performance. Collecting data by a closed-end questionnaire from a sample consisting of 186 managers and sales persons in clothing

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Ma, Neil. "Analysis of the Impact of Artificial Intelligence on Digital Marketing." Highlights in Business, Economics and Management 19 (November 2, 2023): 625–31. http://dx.doi.org/10.54097/hbem.v19i.12097.

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In today's fast-paced technological landscape, the synergy between Artificial Intelligence (AI) and digital marketing stands out, transforming business operations and influencing customer relationships. Digital marketing, having evolved from the Internet era, is on the brink of another significant evolution, propelled by AI's unmatched data analysis, prediction, and personalization capabilities. The study focuses on exploring the less studied connection between AI's technical aspects and individual digital marketing strategies. By investigating this area, the research discovers that AI can sig

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Choi, Youngkeun, and Jae W. Choi. "Assessing the Predictive Performance of Machine Learning in Direct Marketing Response." International Journal of E-Business Research 19, no. 1 (2023): 1–12. http://dx.doi.org/10.4018/ijebr.321458.

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This paper intends to better understand the pre-exercise of modeling for direct marketing response prediction and assess the predictive performance of machine learning. For this, the authors are using a machine learning technique in a dataset of direct marketing, which is available at IBM Watson Analytics in the IBM community. In the results, first, among all variables, customer lifetime value, coverage, employment status, income, marital status, monthly premium auto, months since last claim, months since policy inception, renew offer type, and the total claim amount is shown to influence dire

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Arvinder Kour Mehta. "AI-Powered Marketing Analytics for Predicting Consumer Purchase Behavior." Journal of Information Systems Engineering and Management 10, no. 17s (2025): 636–43. https://doi.org/10.52783/jisem.v10i17s.2785.

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This research examines machine learning systems that use predictive patterns from consumers to develop electronic marketing strategies. Using regression analysis along with classification algorithms as parts of predictive modeling techniques makes purchasing prediction outcomes more accurate when based on historical consumer data sets. Through logistic and multivariate regression models users can generate forecasts about future purchase numbers and values yet Random Forest and gradient-boosting classification algorithms identify groups of consumers based on projected buying activities. The eva

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Anam Afaq. "Integrating Predictive Analytics for Workforce Planning." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 93–111. https://doi.org/10.52783/jisem.v10i30s.4780.

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Workforce planning helps in aligning human resources with the goals of an organization; accordingly, the present era’s marketing scenario needs widespread workforce planning. In this article, we examine the roles of predictive analytics in marketing workforce planning and offer recommendations for optimizing resource allocation and effective decision-making. The research builds and validates predictive models that help forecast workforce demand, personnel supply-skill gaps, and cost-efficiency using machine learning algorithms and statistical forecasting techniques. The paper takes advantage o

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Calle García, Aldrin Jefferson, Carlos David Urquiza Zambrano, Ariana Vanessa Alvarado Pérez, Paul Artemio Verduga Medranda, Nahin Josue García Castro, and Carlos Alan García Chipre. "PREDICTIVE MARKETING INTELLIGENCE: TRANSFORMANDO DATOS EN DECISIONES ESTRATÉGICAS." Ciencia y Desarrollo 28, no. 1 (2025): 569. https://doi.org/10.21503/cyd.v28i1.2849.

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El estudio aborda la importancia de la inteligencia de marketing predictiva como una herramienta clave para transformar datos en decisiones estratégicas. En un entorno empresarial altamente competitivo, las empresas enfren tan el reto de anticiparse a las tendencias del mercado y optimizar sus estrategias comerciales. La problemática central radica en la dificultad de interpretar y aprovechar grandes volúmenes de datos para la toma de decisiones, lo que puede afectar la identificación de oportunidades, la gestión de riesgos y la satisfacción del cliente. El objetivo del estudio es desarrollar

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Divya, Chockalingam. "The Role of AI in Predictive Marketing Analytics." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 4 (2021): 1–4. https://doi.org/10.5281/zenodo.15086598.

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Artificial Intelligence (AI) has revolutionized the marketing sector by enhancing predictive analytics. AI-based techniques allow businesses to forecast consumer behavior, optimize marketing strategies, and deliver personalized customer experiences. This paper discusses the role of AI in predictive marketing analytics, its applications, and the benefits it brings to organizations in terms of efficiency, targeting accuracy, and return on investment. The paper also highlights challenges and the future trajectory of AI in marketing analytics.

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El-Hajj, Mohammed, and Miglena Pavlova. "Predictive Modeling of Customer Response to Marketing Campaigns." Electronics 13, no. 19 (2024): 3953. http://dx.doi.org/10.3390/electronics13193953.

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In today’s data-driven marketing landscape, predicting customer responses to marketing campaigns is essential for optimizing both engagement and Return On Investment (ROI). This study aims to develop a predictive model using a Decision Tree (DT) to identify key factors influencing customer behavior and improve campaign targeting. The methodology involves building the DT model, initially achieving an accuracy of 87.3%. However, the model faced challenges with precision and recall due to class imbalance. To address this, a resampling technique was applied, which significantly improved model perf

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Lei, Ouyang, Tanjian Liang, Xiuye Xie, and Sonja Rizzolo. "A Logistic Regression Model to Predict Graduate Student Matriculation." Journal of International Education and Practice 4, no. 1and2 (2021): 24. http://dx.doi.org/10.30564/jiep.v4i1and2.2628.

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Higher education institutions invest a significant amount of resources every year to recruit new students. However, higher education administrators have been continuously facing challenges in enrollment management due to the demographic shifts, dramatic increases in educational costs, intense competition among institutions, and the uncertain nature of human selection patterns (Baum, Kurose, &McPherson, 2013).[3] Today's post-baccalaureate applicants are more knowledgeable than in previous years, because they can access information on a specific graduate program, in a given college, at any

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Eido, Warveen Merza, and Subhi R. M. Zeebaree. "Smarter Marketing with AI: How Cloud Technology is Changing Business." Asian Journal of Research in Computer Science 18, no. 4 (2025): 331–59. https://doi.org/10.9734/ajrcos/2025/v18i4623.

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The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing cap

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Alanazi, Tawfeeq Mohammed. "Marketing 5.0: An Empirical Investigation of Its Perceived Effect on Marketing Performance." Marketing and Management of Innovations 13, no. 4 (2022): 55–64. http://dx.doi.org/10.21272/mmi.2022.4-06.

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The study aims to explore the effect of marketing 5.0 on marketing performance. Marketing 5.0 was conceptualized using three dimensions: predictive marketing, contextual marketing, and augmented reality marketing. This study uses a questionnaire to collect data from a sample of employees working in marketing departments in 25 furniture stores. Eight employees were selected based on their managers’ recommendations regarding employee knowledge of digital marketing. The total number of the sample is 200 participants. Data were collected using a questionnaire designed as a five-point Likert scale.

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Borole, Priyal. "A Comprehensive Review on Advancements in Marketing Predictive Analytics and Reporting." International Journal of Science and Research (IJSR) 9, no. 4 (2020): 1796–800. http://dx.doi.org/10.21275/sr24301010649.

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Jama, Iman. "Understanding the Consumer: AI-Driven Predictive Analytics and the Transformation of Purchasing Behavior." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 2174–90. http://dx.doi.org/10.21009/isc-beam.012.157.

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The digital era has revolutionized marketing from a product-centric to a customer-centric approach. Understanding consumer behavior is no longer an indulgence; it is a requirement. Artificial intelligence (AI), with its remarkable predictive analytics features, is a game-changing asset to companies. This article analyzes the implications of utilizing Artificial Intelligence Systems in predicting consumer behavior and its impact on business strategies. The method used in this study is an investigation of actual cases of firms that have effectively used this technology. Case studies were conduct

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Rong, Gong Li. "Predictive-Modeling Technologies in Web Power Engineering." Applied Mechanics and Materials 312 (February 2013): 923–27. http://dx.doi.org/10.4028/www.scientific.net/amm.312.923.

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Web marketing can improve the management level of power-grid enterprises. With the increasing of market-oriented process, it is necessary to classify the customer efficiently to provide special electricity services. The predictive-modeling technologies can carry out classification for customer well. Thus, the four typical predictive-modeling technologies--regression analysis, Bayesian networks, decision tree, and neural networks--are introduced. In addition, the instances in web power marketing are used for illustrating these methods. The application of one or more predictive-modeling technolo

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Adaga, Ejuma Martha, Zainab Efe Egieya, Oluwatoyin Ajoke Farayola, Adekunle Abiola Abdul, and Temitayo Oluwaseun Abrahams. "MACHINE LEARNING ALGORITHMS IN PREDICTIVE MARKETING: A COMPREHENSIVE REVIEW." Information Management and Computer Science 7, no. 1 (2023): 16–27. https://doi.org/10.26480/imcs.01.2024.16.27.

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This review paper presents a comprehensive examination of the application of machine learning algorithms in the realm of predictive marketing. The core objective is to provide a thorough overview of how various machine learning techniques are employed to forecast consumer behavior, market trends, and sales outcomes, thereby enhancing marketing strategies. Our methodology involves a detailed survey of peer-reviewed articles, industry reports, and case studies, categorizing machine learning techniques into supervised, unsupervised, and reinforcement learning, and their respective roles in predic

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Pitri, Tedi, Irman Firmansyah, Raden Rijanto, and Deri Firmansyah. "The Evolving Marketing Mix in the Digital Age and Customer Mix: How Does It Affect Purchase Intention?" International Journal of Business, Law, and Education 6, no. 1 (2025): 345–61. https://doi.org/10.56442/ijble.v6i1.1003.

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The era of technological disruption in the 21st century is increasingly challenging the traditional 4P marketing mix concept to evolve to facilitate the creation of hybrid communication and services in the digital environment. This study aims to explore the causality predictive model between the evolution of the 4P marketing mix model and the customer mix considered in the modern business landscape of the digital age, by analyzing how it affects the purchase intention?, the mediating effect of the customer mix is examined. Quantitative research with a survey type is developed in predictive cau

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Zaman, Khansa. "Transformation of Marketing Decisions through Artificial Intelligence and Digital Marketing." Journal of Marketing Strategies 4, no. 2 (2022): 353–64. http://dx.doi.org/10.52633/jms.v4i2.210.

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Artificial Intelligence (AI) is ornamental to the strategic decisions of consumers and its competitive nature and has rapidly transformed the dynamics of the emerging digital world. The evolution of predictive marketing has increased the understating of consumer decision-making. Moreover, AI has enabled many businesses to predict big consumer data to fulfill customer expectations and provide customized products and services. AI’s role has been increased in operational marketing, such as design and selection of ads, customer targeting and customer analysis. Nevertheless, the role in strategic d

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Shmueli, Galit, Marko Sarstedt, Joseph F. Hair, et al. "Predictive model assessment in PLS-SEM: guidelines for using PLSpredict." European Journal of Marketing 53, no. 11 (2019): 2322–47. http://dx.doi.org/10.1108/ejm-02-2019-0189.

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Purpose Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for a

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García-Guerra, Jazmín Isabel, Héctor Oswaldo Aguilar-Cajas, Heidy Elizabeth Vergara-Zurita, Ana Lucía Rivera-Abarca, Freddy Armijos-Arcos, and José Israel López-Pumalema. "Predictive Analytics in Digital Marketing: A Statistical Modeling Approach for Predicting Consumer Behavior." Data and Metadata 4 (June 3, 2025): 1061. https://doi.org/10.56294/dm20251061.

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Introduction: The evolution of predictive analytics in digital marketing is deeply rooted in the development of statistical modeling and data analytics. Aim: The aim of the present research was to analyze the use of advanced statistical models for predicting consumer behavior in digital marketing environments, highlighting the relevance of predictive analytics in data- driven strategic decision making. Methodology: five machine learning, logistic regression, decision tree, random forest, support vector machines (SVM) and neural networks models were evaluated on a synthetic dataset representati

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Sruthi S Madhavan. "Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 182–98. https://doi.org/10.52783/jisem.v10i32s.5219.

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Managing and utilizing marketing data presents numerous challenges for businesses; this analysis sheds light on a few of these limitations. These obstacles, which range from data fragmentation to the complexities of real-time analytics, must be overcome to allow for marketing insights to reach their full potential. Issues that arise in the management of an organization's marketing records include concerns about scalability, the necessity of real-time processing, the need for trustworthy predictive modeling, and the integration of data from numerous sources. These problems prevent companies fro

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Roy, Bhaskar, Debabrata Bera, Praveen Kumar Tripathi, and S. K. Upadhyay. "Improving Profitability Using Predictive Analytics." Indian Journal of Marketing 51, no. 8 (2021): 8. http://dx.doi.org/10.17010/ijom/2021/v51/i8/165759.

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Kravets, Olha, Uliana Korolova, Oleksandr Nosachenko, Olena Vasiltsova, and Serhii Koberniuk. "AI-Powered Digital Marketing: Enhancing Customer Behaviour Predictions." European Journal of Sustainable Development 14, no. 2 (2025): 84. https://doi.org/10.14207/ejsd.2025.v14n2p84.

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The article explores the current practices of using artificial intelligence (AI) in digital marketing ecosystems to predict customer behaviour. It has been found that using machine learning technologies and AI analytical tools will not only facilitate the automation of marketing processes but also allow the creation of personalised content, increasing the accuracy of predicting consumer decisions, which helps businesses optimise communication with customers and increase customer loyalty. The study analysed various functional AI tools, such as analytical, predictive, generative, communication,

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Supriya, V. Pawar, Kumar Gireesh, and Deshmukh Eashan. "Predictive Analytics for High Business Performance through Effective Marketing." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1046–50. https://doi.org/10.31142/ijtsrd9502.

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With economic globalization and continuous development of e commerce, customer relationship management CRM has become an important factor in growth of a company. CRM requires huge expenses. One way to profit from your CRM investment and drive better results, is through machine learning. Machine learning helps business to manage, understand and provide services to customers at individual level. Thus propensity modeling helps the business in increasing marketing performance. The objective is to propose a new approach for better customer targeting. We'll device a method to improve prediction

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SEKERIN, Vladimir D., and Lyudmila E. GORLEVSKAYA. "Development of marketing communications in the context of process algorithmization." Economic Analysis: Theory and Practice 24, no. 4 (2025): 4–16. https://doi.org/10.24891/ea.24.4.4.

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Subject. Algorithmization of processes has an impact on consumer patterns and requires rethinking current approaches to marketing communications. The article discloses the specifics of the evolution of the marketing communications process. Objectives. The aim is to develop and describe communication marketing models relevant to the modern marketing environment. Methods. The study draws on methods of information analysis and synthesis, graphical and tabular methods. Results. We analyzed marketing communication models and trends in the marketing environment development. The paper describes the e

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Nan, Zhang, Chen Yuping, and Zhu Kongjue. "The Future of Marketing Analytics: Trends and Emerging Technologies." International Journal of Advances in Business and Management Research 01, no. 03 (2024): 23–32. http://dx.doi.org/10.62674/ijabmr.2024.v1i03.003.

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The study gives a summary of marketing analytics, its importance in modern companies, and the changing environment influenced by technological progress. It stresses the need to keep abreast of trends and approaches to improve organisational results. The literature review explores important elements of marketing analytics such as artificial intelligence, machine learning, predictive analytics, personalization, consumer segmentation, and the growing significance of augmented reality and virtual reality. It delves into how these technologies and approaches are changing marketing strategies. The d

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Rinji Goshit Kassem, Akachukwu Obianuju Mbata, Precious Azino Usuemerai, Luqman Adewale Abass, and Eigbokhan Gilbert Ogbewele. "Digital transformation in pharmacy marketing: integrating AI and machine learning for optimized drug promotion and distribution." World Journal of Advanced Research and Reviews 15, no. 2 (2020): 749–62. http://dx.doi.org/10.30574/wjarr.2022.15.2.0792.

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The digital transformation of pharmacy marketing, driven by artificial intelligence (AI) and machine learning, is revolutionizing how drugs are promoted and distributed. This review examines the role of AI and machine learning in enabling more efficient, personalized marketing strategies, while also optimizing the supply chain and distribution processes within the pharmaceutical industry. By leveraging data-driven insights, pharmacies can enhance the reach and effectiveness of their marketing campaigns, targeting specific demographics and predicting patient behaviors. AI tools such as predicti

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Lewis Reynolds, Paul, and Geoff Lancaster. "Predictive strategic marketing management decisions in small firms." Management Decision 45, no. 6 (2007): 1038–57. http://dx.doi.org/10.1108/00251740710762062.

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Poorna Devi, K. "Predictive Analytics in Marketing using for Artificial Intelligence." ComFin Research 13, S1-i1-Mar (2025): 143–46. https://doi.org/10.34293/commerce.v13is1-i1-mar.8670.

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The coming of Fake Insights (AI) has changed the promoting scene, empowering businesses to use prescient investigation for data-driven decision-making. This inquire about investigates the application of prescient investigation in promoting utilizing AI, centering on its potential to improve client bits of knowledge and personalization. By analyzing client information and behavior, AI-powered prescient models can estimate future patterns, inclinations, and needs. This ponder illustrates the viability of prescient examination in showcasing, highlighting its benefits, challenges, and future headi

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PRAKASH,, Dr M., Mr ABHINAV. B, and Mr NISHANTH. V. "A STUDY OF ARTIFICIAL INTELLIGENCE IN DIGITAL MARKETING WITH REFERENCE TO RECENT TRENDS IN AMAZON." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02509.

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The advent of Artificial Intelligence (AI) has revolutionized the digital marketing landscape, enabling businesses to personalize customer experiences, optimize marketing strategies, and enhance consumer engagement. This study examines the role of AI in digital marketing, with a specific focus on recent trends in Amazon. A mixed-methods approach was employed, combining secondary data from Amazon's annual reports and industry reports with primary data from surveys and interviews with Amazon marketers. The findings reveal that AI-powered digital marketing strategies, such as personalized product

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Yusuf Onifade, Abiodun, Remilekun Enitan Dosumu, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, and Uloma Stella Nwabekee. "Advances in Cross-Industry Application of Predictive Marketing Intelligence for Revenue Uplift." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 2301–12. https://doi.org/10.62225/2583049x.2024.4.6.4325.

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Predictive Marketing Intelligence (PMI) has emerged as a transformative approach that leverages advanced analytics, machine learning, and big data to anticipate consumer behaviors and optimize marketing strategies. Its cross-industry application has gained significant traction, providing businesses across sectors with actionable insights that enhance customer engagement, streamline operations, and drive substantial revenue uplift. This explores the evolution and implementation of PMI in various industries, including retail, finance, healthcare, telecommunications, and hospitality. Each sector

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Hartnett, Nicole, Luke Greenacre, Rachel Kennedy, and Byron Sharp. "Extending validity testing of the Persuasion Principles Index." European Journal of Marketing 54, no. 9 (2020): 2245–55. http://dx.doi.org/10.1108/ejm-12-2018-0865.

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Purpose This study aims to independently test the predictive validity of the Persuasion Principles Index (PPI) for video advertisements for low-involvement products with a measure of in-market sales effectiveness. This study follows the inaugural test conducted by Armstrong et al. (2016) for print advertisements for high-involvement utilitarian products with a measure of advertising recall. Design/methodology/approach The method was in line with that developed by Armstrong et al. (2016) for rating advertisements and assessing the reliability of ratings. Consensus PPI scores were calculated for

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Alves Werb, Gabriela, and Martin Schmidberger. "Predictive Modeling in Marketing: Ensemble Methods for Response Modeling." Die Unternehmung 75, no. 3 (2021): 376–96. http://dx.doi.org/10.5771/0042-059x-2021-3-376.

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Ensemble methods have received a great deal of attention in the past years in several disciplines. One reason for their popularity is their ability to model complex relationships in large volumes of data, providing performance improvements compared to traditional methods. In this article, we implement and assess ensemble methods’ performance on a critical predictive modeling problem in marketing: predicting cross-buying behavior. The best performing model, a random forest, manages to identify 73.3 % of the cross-buyers in the holdout data while maintaining an accuracy of 72.5 %. Despite its su

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Narra, Bhumeka, Navya Vattikonda, Anuj Kumar Gupta, Dheeraj Varun Kumar Reddy Buddula, Hari Hara Sudheer Patchipulusu, and Achuthananda Reddy Polu. "REVOLUTIONIZING MARKETING ANALYTICS: A DATA-DRIVEN MACHINE LEARNING FRAMEWORK FOR CHURN PREDICTION." International Journal Of Innovation In Engineering Research & Management 09, no. 01 (2025): 150–57. https://doi.org/10.63665/ijierm.v09i01.01.

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One of the biggest problems for big businesses is customer attrition. Since consumers are the primary source of income for businesses in the rental industry, they are specifically searching for strategies to keep them as clients. This study presents the data-driven machine learning technique for telecom sector churn prediction, addressing challenges of noisy, imbalanced, and high-dimensional data through a comprehensive preprocessing pipeline that includes noise removal, SMOTE-based balancing, feature selection, and outlier detection. In order to create predictive models using Decision Trees a

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