The impact of big data analytics on digital marketing decision-making: A comprehensive analysis

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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.

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    • Figure 1. Elbow method for optimal number of clusters
    • Figure 2. Customer segmentation with k-means clustering
    • Table 1. Characteristics of identified clusters
    • Table 2. Overall usage and benefits
    • Table 3. Reported challenges
    • Table 4. Silhouette scores for clustering algorithms
    • Conceptualization
      Roberta Bajrami, Adelina Gashi
    • Data curation
      Roberta Bajrami, Etleva Namligjiu
    • Formal Analysis
      Roberta Bajrami
    • Methodology
      Roberta Bajrami
    • Project administration
      Roberta Bajrami
    • Software
      Roberta Bajrami
    • Supervision
      Roberta Bajrami
    • Writing – original draft
      Roberta Bajrami
    • Writing – review & editing
      Roberta Bajrami, Adelina Gashi, Kaltrina Bajraktari
    • Resources
      Adelina Gashi, Kaltrina Bajraktari, Etleva Namligjiu
    • Validation
      Adelina Gashi, Etleva Namligjiu
    • Investigation
      Adelina Gashi
    • Funding acquisition
      Kaltrina Bajraktari, Etleva Namligjiu
    • Visualization
      Kaltrina Bajraktari