Machine Learning for Predictive Telemarketing: Boosting Campaign Effectiveness in Banking

This study evaluates the effectiveness of social media health promotion campaigns in fostering sustainable behavior change in the United States. With the widespread adoption of digital technologies, health promotion efforts have increasingly shifted to social media platforms, offering the potential to reach diverse audiences quickly and cost-effectively. However, the digital health space faces significant challenges, particularly the spread of misinformation, which can undermine public health efforts and exacerbate health disparities. This research explores the intersection of health promotion, misinformation mitigation, and digital innovation, highlighting the opportunities and challenges of using social media for health campaigns. The study reviews the current state of social media health promotion campaigns, noting their success in achieving short-term behavior changes but identifying difficulties in maintaining long-term sustainability. Factors such as message saturation, algorithmic changes, and shifts in public interests are examined as contributors to these challenges. The research also discusses the role of digital innovations, including artificial intelligence (AI), machine learning, and data analytics, in enhancing the evaluation and effectiveness of health campaigns. These technologies offer real-time data collection, personalized interventions, and tools to combat misinformation, thereby improving engagement and promoting sustained behavior change. The findings suggest that while social media health campaigns show promise, more research is needed to evaluate their long-term impact. Addressing misinformation and leveraging digital innovations are critical for enhancing the effectiveness and sustainability of these campaigns.

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 from the EDA reveal that "Daily Time Spent on Site" and "Daily Internet Usage" are significant predictors of CTR, with notable correlations. The SVM model, trained on this data, demonstrated exceptional performance, achieving an accuracy of 97.65%, a precision of 98.58%, a recall of 96.53%, and an F1-score of 97.54%. These results confirm the model's robustness and reliability, indicating its potential for optimizing digital marketing strategies. The study's significance lies in its contribution to the fields of digital marketing and predictive analytics by showcasing the applicability and advantages of SVM in predicting user behavior. These insights can help marketers optimize ad placements, enhance user engagement, and improve return on investment. Practical implications include strategies for targeted and personalized marketing based on key user demographics and behaviors. Despite the promising results, the study acknowledges limitations such as the dataset size and scope of features. Future research should focus on utilizing larger and more diverse datasets, incorporating additional features, and exploring other advanced machine learning algorithms. This research encourages further exploration of machine learning applications in digital marketing to enhance predictive accuracy and campaign effectiveness. By addressing these aspects, this study aims to advance the academic understanding and practical implementation of predictive analytics in digital marketing, providing a robust framework for future applications.

Parental decision-making for children's uptake of a relatively novel vaccine and a more common vaccine could involve different processes. This study aimed to compare the psychological processes and the relative importance of psychological factors influencing parental decision-making for children's seasonal influenza vaccination (SIV) and COVID-19 vaccination. We adopted mixed-methods approach. Study 1 was a qualitative study involving 29 parents to explore and compare their decision-making processes for children's SIV and COVID-19 vaccination. In Study 2, data from 632 parents were collected longitudinally; then, machine learning was used to quantify the relative importance of factors identified in Study 1 that were relevant to parents' decision-making for childhood vaccination decisions. Alluvial plots were used to compare the predictability of parents' baseline intention for follow-up children's SIV and COVID-19 vaccination. Study 1 revealed that parents used the influenza vaccine as an anchor to assess the COVID-19 vaccine's risks. Decision-making for children's SIV was habitual and rule-based, while for COVID-19 vaccination, it involved more deliberation influenced by negative situational cues like negative news and anecdotal experiences. Study 2 further found that, for COVID-19 vaccination, situation-varying factors including distressed emotional states and affective response to news were significant. While for SIV, past-year vaccination behaviour was a more important factor. Baseline intention reliably predicted children's SIV but not COVID-19 vaccination. The convergence of qualitative and quantitative data highlighted the distinct decision-making strategies for these two vaccines. Targeting key factors in parental decisions can enhance the effectiveness of future vaccination campaigns.

Artificial intelligence (AI) and machine learning (ML) have revolutionized programmatic advertising by enhancing precision targeting, operational efficiency, and consumer engagement. This systematic literature review uses the PRISMA framework to synthesize recent advancements, emphasizing personalization and real-time optimization while addressing challenges such as algorithmic bias, privacy concerns, and ethical considerations. Findings reveal that AI-driven advertising significantly improves campaign effectiveness, though its implementation requires careful management to mitigate cultural and data biases. The study identifies key ethical frameworks and consumer transparency gaps, proposing future research directions in AI-integrated creativity, privacy, ethics, and consumer trust dynamics. These insights contribute to developing sustainable and responsible programmatic advertising ecosystems, aligning technological innovation with ethical and cultural sensitivity.

Population-wide vaccination is critical for containing the SARS-CoV-2 (COVID-19) pandemic when combined with restrictive and prevention measures. In this study we introduce SAIVR, a mathematical model able to forecast the COVID-19 epidemic evolution during the vaccination campaign. SAIVR extends the widely used Susceptible-Infectious-Removed (SIR) model by considering the Asymptomatic (A) and Vaccinated (V) compartments. The model contains several parameters and initial conditions that are estimated by employing a semi-supervised machine learning procedure. After training an unsupervised neural network to solve the SAIVR differential equations, a supervised framework then estimates the optimal conditions and parameters that best fit recent infectious curves of 27 countries. Instructed by these results, we performed an extensive study on the temporal evolution of the pandemic under varying values of roll-out daily rates, vaccine efficacy, and a broad range of societal vaccine hesitancy/denial levels. The concept of herd immunity is questioned by studying future scenarios which involve different vaccination efforts and more infectious COVID-19 variants.

The rapid evolution of digital marketing technologies has transformed the landscape of product marketing, enabling businesses to reach broader audiences and deliver personalized experiences. This paper explores effective product marketing strategies using digital marketing tools and technologies, focusing on their impact on customer engagement, brand awareness, and sales performance. Key strategies include leveraging social media platforms, search engine optimization (SEO), content marketing, and data-driven analytics to enhance campaign effectiveness. Integrating artificial intelligence (AI) and machine learning (ML) in digital marketing allows real-time customer insights, automated responses, and hyper-personalized advertisements. Additionally, omnichannel approaches ensure consistent messaging and seamless experiences across multiple platforms. Case studies highlight the successful implementation of these strategies across various industries, demonstrating measurable customer acquisition and retention improvements. The findings emphasize that adopting digital marketing technologies improves operational efficiency and creates competitive advantages in rapidly changing markets. This research provides actionable insights for marketers seeking to optimize their product marketing strategies in the digital era.

Understanding customer behavior and personality is essential for marketing campaign. It might help huge corporations comprehend their purchasing behavior. Companies directed by their marketers can conduct product marketing activities that are specific to the customer’s personality attributes. This study examines the use of various classification models to evaluate client personality attributes and predict marketing campaign performance by customer response after marketing campaign. Response of the customer was the target feature and it is a binary classification. Feature engineering has also been applied for optimal feature selection. Several machine learning classifier algorithms, including Gradient Boosting (GB), Ada Boost (AB), Cat Boost (CB), Voting Classifier (VC), Stacking Classifier (SC), K Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) were implemented after the dataset had been preprocessed and normalized. Several matrices were employed for comparing the performance of these models. The Stacking Classifier emerged to be the best performing model with accuracy of 0.8988.

In today’s competitive business environment, organizations increasingly rely on multi-channel marketing campaigns to engage diverse customer segments and drive revenue. Evaluating the effectiveness of these campaigns is critical for optimizing marketing strategies, maximizing return on investment (ROI), and ensuring efficient allocation of resources. Multi-channel marketing encompasses a range of platforms, including digital channels such as social media, email, and search engine marketing, as well as traditional channels like television, radio, print, and in-store promotions. The complexity and interaction between these channels necessitate the application of rigorous statistical methods to accurately measure campaign performance and inform strategic decisions. This examines statistical approaches used to evaluate multi-channel marketing effectiveness across different industries, including retail, financial services, healthcare, and technology. Descriptive statistics provide initial insights by summarizing engagement, conversion, and revenue metrics, while inferential statistics, such as t-tests, chi-square tests, and ANOVA, enable comparison of outcomes across channels and industry contexts. Regression analysis, including linear, logistic, and multi-level models, facilitates understanding of relationships between marketing inputs and key performance indicators. Time series analysis is applied to track temporal trends and forecast future campaign outcomes. Additionally, multivariate and machine learning techniques, such as cluster analysis, random forests, and attribution modeling, support customer segmentation, channel optimization, and multi-touch attribution for complex campaigns. This highlights the importance of integrating statistical insights with interactive dashboards and visualization tools to provide actionable intelligence for marketing teams. Challenges such as incomplete data, model bias, and cross-industry comparability are discussed, along with strategies to mitigate these limitations. By adopting robust statistical methods, organizations can enhance evidence-based decision-making, optimize channel allocation, and improve overall campaign performance. This research underscores the critical role of data-driven analytics in managing multi-channel marketing strategies effectively across diverse industrial sectors.