Identifying the Sales Patterns of Online Stores with Time Series Clustering
Makkonen, M., & Frank, L. (2018). Identifying the Sales Patterns of Online Stores with Time Series Clustering. In A. Pucihar, M. Kljajić, P. Ravesteijn, J. Seitz, & R. Bons (Eds.), Bled 2018 : Proceedings of the 31st Bled eConference. Digital Transformation : Meeting the Challenges (pp. 491-506). University of Maribor Press. https://doi.org/10.18690/978-961-286-170-4.34
Date
2018Copyright
© University of Maribor Press, 2018.
Electronic commerce, especially in the business-to-consumer (B2C) context, has for years been a popular
research topic in information systems (IS). However, the prior research on the topic has traditionally
been dominated by the consumer focus instead of the business focus of online stores. For example,
whereas various segmentations exist for online consumers based on their purchase behaviour, no such
segmentations have been developed for online stores based on their sales patterns. In this study, our
objective is to address this gap in prior research by identifying the most typical sales patterns of online
stores operating in the B2C context. By using self-organising maps (SOM) to analyse the monthly sales
time series collected from 399 online stores between January 2016 and December 2017, we are able to
identify four approximately equally sized segments, each with its characteristic sales pattern. More specifically,
two of the segments are characterised by a clear upward or downward trend in the sales,
whereas the other two are characterised by strong seasonal sales variation. We also investigate the
differences between the segments in terms of several key business and technical parameters of the stores
as well as discuss more broadly the applicability of SOM to IS.
...
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University of Maribor PressParent publication ISBN
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Bled 2018 : Proceedings of the 31st Bled eConference. Digital Transformation : Meeting the ChallengesKeywords
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https://converis.jyu.fi/converis/portal/detail/Publication/28153142
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