An Analytical Method of Irregular Data to Maximize Customer Satisfaction in e-Commerce—Using the Korean Morpheme Analysis of Mecab with Python

With technological developments of smart devices, an increasing number of customers are using e-commerce to purchase products. With the increase in usage of e-commerce, the effective customer management and site operation became the most important success factor. Now analytic methods are helping e-commerce companies in this respect. However, comments left by existing customers are used as a factor in making a purchase decision and subjective opinions of potential customers or purchasing customers have become important. Therefore the analysis of irregular data such as texts, images or figures that the customers leave on the site to become more important. This paper seeks to analyze effective marketing methods by using Python's Mecab to analyze Korean morphemes to filter irregular data generated by customers of e-commerce. In doing so, the paper can contribute to economical and efficient e-commerce.