Analyzing ecology of Internet marketing in small- and medium-sized enterprises (SMEs) with unsupervised-learning algorithm

• Published in: Journal of marketing analytics Vol. 6; no. 2; pp. 53 - 61
• Main Authors: Yau, Hon Keung, Tang, Ho Yi Horace
• Format: Journal Article
• Language: English
• Published: London Palgrave Macmillan UK 01.06.2018; Palgrave Macmillan
• Subjects: Algorithms; Business and Management; Cluster analysis; Clustering; Cost analysis; Ecological monitoring; Internet; Machine learning; Market analysis; Marketing; Original Article; Personal computers; Self organizing maps; Small & medium sized enterprises-SME; Small business; Unsupervised learning; Vector quantization; SOM; Internet marketing; K-mean clustering; SMEs; Unsupervised-learning algorithm
• ISSN: 2050-3318; 2050-3326
• DOI: 10.1057/s41270-018-0030-1

Internet marketing is a business imperative due to the irrevocable and unstoppable trend of Internet. In this paper, we propose the methodology of unsupervised learning algorithm to apply on the survey data of Internet marketing. Hidden relationships among critical factors in the model are examined using PC-algorithm. To analyze the ecology of Internet marketing in small-and-medium enterprises, similar commercial organizations are grouped into segments by K-means clustering for studying their characteristics. The last methodology is to perform two levels of clustering using self-organizing map and K-means algorithm to save computational cost for massive data instances. The analytical result describes the overview of the Internet usage under stiff-competition business environment which is beneficial to management level to make a more appropriate decision to upload the existing marketing activities online.