Abstract
ABSTRACTPurpose. This study explores the application of big data technologies within Customer Relationship Management (CRM) in startup e-commerce enterprises, using Mossy Life as a case study. The research examines how big data enhances CRM practices, the challenges startups encounter during implementation, and the strategies used to address those barriers.
Theoretical Framework. Grounded in theories of technology adoption and CRM evolution, the study draws on models by Gupta and George (2016), Wamba et al. (2015, and Gandomi and Haider (2015). The study links big data’s 6Vs with CRM effectiveness, proposing that agility, platform leverage, and targeted analytics are central to overcoming startup limitations.
Methodology. A qualitative single-case study design was employed. Mossy Life represents a rapidly growing Chinese e-commerce startup navigating the competitive digital retail space. Data were collected over three months through semi-structured interviews (n=20), internal documentation, and field observations. Thematic coding and semantic analysis were conducted using Atlas.ti to uncover patterns, challenges, and success factors in the firm's big data-driven CRM initiatives.
Findings. Mossy Life leveraged third-party platforms such as Taobao and Alibaba Cloud to apply big data for customer segmentation, targeted advertising, and personalized engagement. These efforts significantly improved customer acquisition, retention, and loyalty. Despite these gains, the company faced persistent challenges, including technological dependency, data fragmentation, talent shortages, and financial constraints. Key success factors included organizational agility, strategic ecosystem integration, and a focused, ROI-driven analytics approach. The findings underscore that big data plays a significant role rather than a supplementary one in CRM effectiveness for startups, although its practical application differs significantly from large-enterprise models.
Conclusion and Recommendations. This study contributes to theory by advancing understanding of big data adoption in resource constrained contexts. Recommendations include leveraging platform-based analytics, implementing ROI-driven strategies, improving internal data literacy, and adopting basic data governance early. The study calls for further research on scalable data solutions, analytics models, and supportive policy frameworks for startup ecosystems.