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This Author published in this journals
All Journal Startupreneur Business Digital (SABDA Journal) Journal of Computer Science and Technology Application
Daniel Bennet
Eduaward Incorporation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Other

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Consumer Behavior and Brand Loyalty: A Study on Digital Marketing Practices Achmad Hidayat Dwi Saputra; Souza Nurafrianto Windiartono Putra; Daniel Bennet
Startupreneur Business Digital (SABDA Journal) Vol. 3 No. 2 (2024): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v3i2.630

Abstract

In the rapidly evolving digital landscape, understanding consumer behavior is critical for building and sustaining brand loyalty. This study explores the relationship between digital marketing practices and consumer behavior, and how these behaviors influence brand loyalty. Leveraging a comprehensive literature review and empirical analysis, this research employs a mixed-methods approach, combining quantitative surveys with qualitative interviews to gain a holistic view of the consumer-brand interaction in the digital era. Data were collected from a diverse sample of consumers who regularly engage with brands through digital platforms. The quantitative analysis, conducted using statistical tools, revealed a significant correlation between personalized digital marketing efforts and increased consumer loyalty. Qualitative findings further highlighted that consumers value brand transparency and engagement in online spaces, which in turn enhances their loyalty. The findings suggest that digital marketing practices, such as targeted advertising and interactive content, play a crucial role in shaping consumer perceptions and driving brand loyalty. These results provide valuable insights for marketers seeking to optimize their digital strategies to foster deeper connections with their audiences.
Predictive Analysis of Startup Ecosystems: Integration of Technology Acceptance Models with Random Forest Techniques Daniel Bennet; Sheila Aulia Anjani; Ora Pertiwi Daeli; Dedi Martono; Cicilia Sriliasta Bangun
CORISINTA Vol 1 No 1 (2024): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i1.8

Abstract

In the dynamic realm of startup ecosystems, forecasting trends and measuring success pose significant challenges. To tackle this multifaceted issue, a novel research method proposes integrating the Technology Acceptance Model (TAM) with the robust Random Forest algorithm, thereby enhancing predictive accuracy. This innovative approach encompasses various aspects including technical intricacies, financial dynamics, stakeholder interactions, and entrepreneurial challenges. Employing empirical data, such as revenue growth, capital raised, innovation rate, and active users, forms the foundation of this methodology. The model’s efficacy is demonstrated through a process involving training on 80% of the dataset and testing on the remaining 20%, showcasing superior predictive capabilities compared to conventional methods. Comparative analysis with established models like logistic regression further highlights the superiority of the integrated TAM and Random Forest approach, particularly in predicting startup success. These findings offer invaluable insights for entrepreneurs navigating the complexities of the startup landscape, as well as for investors, policymakers, and educators. Understanding and supporting growth dynamics within the startup ecosystem can foster innovation and prosperity. Moreover, in the academic sphere, this research contributes a novel framework for startup prediction, enriching existing knowledge and facilitating informed decision-making. Overall, this research not only provides practical applications for immediate stakeholders but also contributes to advancing the theoretical foundations of startup prognostication, thus serving as a significant milestone in the field. 
Co-Authors Achmad Hidayat Dwi Saputra Bangun, Cicilia Sriliasta Dedi Martono Ora Pertiwi Daeli Sheila Aulia Anjani Souza Nurafrianto Windiartono Putra