Using K-Means Clustering and Data Visualization for Monetizing logistics Data

Abstract

Logistics companies possess collect large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. They can extract useful market knowledge by using data mining technologies such as visualization and clustering. The detailed results of such big data analytics methods can also be monetized under certain circumstances. We studied the data on the transactions of a logistics company in the Middle East. K-Means clustering of their data proved to generate deeper insight into several clusters of customers having different profiles. The results propose a best fit model for the clustering. Since the clustering and visualization results are relevant, reliable and anonymous they fit the monetization criteria as well. Improved data driven marketing applications are possible for the customers.