Unveiling the types of growth patterns of mobile startups: do business models matter?

ABSTRACT

It is important to understand the growth of startups because they are a driver of economic prosperity and wealth. However, due to their short history and high failure rate, research measuring startup growth has been limited and has relied on qualitative data. This study uses monthly active user (MAU) data of mobile-based startups (n = 266) collected by InnoForest, a startup growth analysis platform in South Korea, to classify the patterns of their growth curves using GA-NLS-based Bass model estimation. We also investigate how growth patterns differ depending on several popular business characteristics across mobile apps. The Bass model was used to categorise the pattern of the growth curve, resulting in the four growth patterns: Stealthy Influencer, Rapid Scaler, Late Bloomer, and Niche Dominator. Furthermore, there were differences in growth patterns between the platform and non-platform businesses, and between fixed-fee and pay-per-transaction services. This study reveals that the growth of mobile-based startups, as measured by MAUs using Bass model, follows different patterns depending on the attitudes of the users of startups’ apps and the speed at which apps’ growth peaks. The results can be useful for developing startup growth strategies, making investment decisions, and formulating policies for startup growth.

KEYWORDS:

• Startup
• growth pattern
• MAU (monthly active users)
• business model

Acknowledgement

The authors would like to thank Hyunmyung Cho, Mark & Company Inc., for providing research data and practical implications.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).

Notes on contributors

Saerom Lee

Saerom Lee is a Ph.D. Candidate in the Department of Data Science at Seoul National University of Science and Technology, Seoul, Republic of Korea. She received the degree of MS in Industrial and Information systems from Seoul National University of Science and Technology. Her research interests are business analytics and digital business models.

Jongdae Kim

Jongdae Kim is an assistant professor of marketing in the College of Business Administration at Chonnam National University, Republic of Korea. He received Ph.D. in marketing from Seoul National University. His research interests include the empirical modelling of consumer behaviour, digital marketing and entertainment marketing.

Hakyeon Lee

Hakyeon Lee is currently a Full Professor in Department of Industrial Engineering at Seoul National University of Science and Technology, Seoul, Republic of Korea. He received the B.Sc and Ph.D. degrees in Industrial Engineering from Seoul National University, Seoul, Republic of Korea. His research interests are technology forecasting & planning, digital innovation strategy, and digital business models. He has authored more than 40 published papers in leading journals of technology management and information science including Technology Analysis & Strategic Management, Technological Forecasting and Social Change, Technology in Society, Journal of Engineering and Technology Management, Journal of Technology Transfer, Scientometrics, Information Processing & Management, and Telematics & Informatics.