Abstract
During the past decades, electronic commerce, especially in the business-to-consumer (B2C) context, has emerged as a popular research topic in information systems (IS). However, this research has traditionally been dominated by the consumer focus instead of the business focus of online stores. In this explorative study, we aim to address this gap in prior research by identifying the most typical sales patterns of online stores operating in the B2C context. By segmenting the monthly sales time series of 399 online stores with time series clustering, we are able to identify four approximately equally sized segments, of which two are characterised by a clear upward or downward trend in the sales and two are characterised by strong seasonal sales variation. We also investigate the potential segment differences in terms of several key business and technical parameters as well as discuss more broadly the applicability of time series clustering to IS.
Recommended Citation
Makkonen, Markus and Frank, Lauri, "Identifying the Sales Patterns of Online Stores with Time Series Clustering" (2018). BLED 2018 Proceedings. 8.
https://aisel.aisnet.org/bled2018/8