Abstract
Context: The continuous proliferation of data nowadays has inspired several companies to make data-informed decisions through analytics. Despite the widespread recognition of analytics in both scientific and academic literature, software startup companies are not witnessed utilizing analytics. These young and innovative companies are inherently well-suited to utilize analytics as their processes should be data-driven. However, there is a persistent question about how companies, especially software startup companies with distinguishing characteristics, can effectively create value from it and what constitutes analytics for software startups.
Objective: In this Ph.D. thesis, we aim to bridge the knowledge gap by eliciting an understanding of the analytics that software startup companies hold. Moreover, we also intend to identify the benefits realized, challenges faced, and key analytics practices startups implement, leveraging data to raise odds of success.
Method: We employed multi-method research to meet the research objectives. In particular, we utilized a multiple-case study, case survey, document analysis, and a gray literature review. Accordingly, we collected data through semi-structured interviews with eight software startups at different life-cycle stages, collected failed software startup case postmortem, collected documentation of major analytics platforms, and finally elicited practitioner-oriented data. We analyzed the data using thematic analysis.
Results: Our results firstly revealed a divergent understanding of analytics by software startups, based on which we reported essential characteristics of analytics perceived by these companies. Later, we presented analytics set-up inside these companies. In addition, we identified major analytics challenges faced by startups, key analytics benefits realized or experienced, and key analytics practices employed by these companies to raise the odds of success.
Conclusions: Our findings contribute to an understanding of analytics in software startups and the identification of critical challenges faced by these startups across different stages, benefits, and key practices. The understanding lays the foundation for comprehending what constitutes analytics for software startups, while the identification of challenges anticipates critical barriers to the adoption and implementation of analytics. To address these challenges, we demonstrated numerous key analytics practices. In addition, the list of our presented benefits can motivate potential software startups to introduce or strengthen their analytics set-up. Lastly, we also provide practical implications to both researchers and practitioners.