Identifying Patterns of Mergers and Acquisitions in Startup: An Empirical Analysis Using Crunchbase Data

Abstract

With the rise of startup industries, mergers and acquisitions (M&A) have recently gained prominence as a successful exit strategy. To address the limitation of previous research that focused solely on post-M&A performance, this study focuses on identifying M&A patterns by providing a taxonomy on startup M&A cases. This study gathers M&A transaction data from the Crunchbase website, as well as information on both acquired and acquiring firms. We then calculate the similarities and differences between the two to characterize the M&A types. K-means clustering has been used to identify patterns, along with extensive exploratory data analysis and visualizations. As a result, five clusters have been identified: technology-focused field expansion, location-based, needs-based & scale up, for synergy, and potential-focused M&As. Our study can contribute to existing literature in that it is, to our knowledge, the first to provide data-driven taxonomy for M&A patterns, attempting to provide general understanding of how M&As occur in practice.

Keywords

• M&A
• Crunchbase
• taxonomy
• patterns
• data analytics
• startup