Predicting future brand value: The role of machine learning monitoring

  • 52 Views
  • 14 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Data-driven strategies have become essential for brand valuation optimization in today’s rapidly evolving virtual economy, where organizations face increasing pressure to gain real-time, accurate insights to maintain a competitive edge. The purpose of the study examine the impact of machine learning in monitoring key market factors to predict future brand value, addressing the growing need among industry professionals for tools that enhance strategic decision-making. From April to September 2024, a purposive sample of 350 upper-level brand managers and sales marketing directors from various Jordanian companies targeted due to their direct involvement in brand evaluation and marketing strategy. 229 completed and valid responses were collected through a self-administered questionnaire. The data analyzed using AMOS software and Structural Equation Modeling (SEM) to test the research hypotheses. Results indicated that all proposed factors significantly influenced the prediction of future brand value, with purchase frequency (β = 2.681), industry trend monitoring (β = 2.228), consumer behavior (β = 0.353), and social media metrics (β = 0.345) showing statistically significant effects (p < 0.05). These findings demonstrate the effectiveness of machine learning in identifying predictive patterns relevant to brand performance and provide a practical framework for leveraging digital tools to enhance brand valuation strategies. The study concludes that integrating machine learning with key performance monitoring enables organizations to make more informed, timely, and impactful branding decisions in a dynamic digital landscape.

Acknowledgment
We would like to thank the Business School at Al Ahliyya Amman University, Jordan. Specifically, many thanks go to the Department of E-marketing and Digital Communications.

view full abstract hide full abstract
    • Figure 1. Research model
    • Figure 2. Structural model
    • Table 1. Distribution of respondents
    • Table 2. Measurement model convergent validity
    • Table 3. Correlation analysis
    • Table 4. Hypotheses testing
    • Table A1. Questionnaire
    • Table A2. Statements
    • Conceptualization
      Ahmad Al Adwan, Ghaiath Altrjman
    • Data curation
      Ahmad Al Adwan
    • Formal Analysis
      Ahmad Al Adwan, Ghaiath Altrjman
    • Investigation
      Ahmad Al Adwan
    • Methodology
      Ahmad Al Adwan
    • Project administration
      Ahmad Al Adwan, Ghaiath Altrjman
    • Supervision
      Ahmad Al Adwan
    • Writing – original draft
      Ahmad Al Adwan
    • Writing – review & editing
      Ahmad Al Adwan, Ghaiath Altrjman, Lu’ay Al-Mu’ani
    • Validation
      Ghaiath Altrjman
    • Visualization
      Ghaiath Altrjman