Roberta Bajrami

Type of the article: Research Article

Abstract
The fast development of data availability has altered digital marketing, establishing big data analytics as a vital tool for optimizing decision-making and enhancing campaign performance. This study explores how big data analytics contributes to effective decision-making, targeting precision, and customer engagement among digital marketing professionals. Conducted from June to August 2024 in Kosova, the research polled 250 professionals across varied industries (e.g., retail, banking, technology) and firm sizes (SMEs and major organizations), selected by purposive sampling. An online questionnaire, delivered through SurveyMonkey, achieved a 92% response rate (n = 230), capturing data on tool usage, benefits, and problems. Data pre-processing includes duplicate removal and mean imputation, followed by K-means clustering and logistic regression analysis using Python (scikit-learn, pandas). Results identified four adopter segments: High Adopters (35%) reported a 30% increase in targeting accuracy and 25% efficiency gain; Moderate Adopters (40%) achieved a 15% efficiency boost; Emerging Adopters (15%) noted 70% improved adaptability; and Low Adopters (10%) faced skill shortages (55%) and privacy concerns (65%). Overall, 85% leveraged big data for segmentation, 70% for real-time flexibility, and 60% observed a 20% engagement gain via sentiment analysis. Privacy (65%) and technical intricacy (50%) were important hurdles. These findings show big data’s revolutionary potential, underlining the need for scalable solutions, talent development, and ethical data practices to optimize its impact on digital marketing efficacy and inclusivity.

Type of the article: Research Article

Abstract
This study synthesises findings from 56 research articles published between 2010 and 2024 to examine how digital marketing affects the performance of small and medium-sized enterprises (SMEs). Using a random-effects model estimated with restricted maximum likelihood (REML), the analysis shows a clear and moderately strong positive relationship (r = 0.3715, 95% CI [0.3443, 0.3884], p < 0.0001). The lack of variability across studies is remarkable, with an I² value of only 0.09%. This suggests a strong consistency in the observed relationship, even though the studies used different contexts and research methods.
Mixed-effects moderation analyses show that the impact of digital marketing varies depending on industry structure. Specifically, service-sector companies see greater performance improvements than small and medium-sized manufacturing businesses. In contrast, the geographic setting, the research design, and the type of performance metric used do not significantly change the observed relationship. To assess the possibility of publication bias, the study used Egger’s regression test and funnel plot analysis. Neither method showed any signs of systematic bias.
The combined results suggest that digital marketing is a strong and broadly effective way to improve the performance of small and medium-sized enterprises (SMEs), especially in service-based industries where digital interactions with customers are crucial. However, the lack of diversity requires further investigation, as it could indicate hidden similarities in how the studies were designed or how they measured things. Future research would benefit from more detailed studies over time, as well as a closer look at how different sectors or organizations might affect the results of digital marketing.