Predicting e-commerce company success by mining the text of its publicly-accessible website

We analyze the impact of textual information from e-commerce companies’ websites on their commercial success. The textual information is extracted from web content of e-commerce companies divided into the Top 100 worldwide most successful companies and into the Top 101 to 500 worldwide most successful companies. It is shown that latent semantic concepts extracted from the analysis of textual information can be adopted as success factors for a Top 100 e-commerce company classification. This contributes to the existing literature concerning e-commerce success factors. As evaluation, a regression model based on these concepts is built that is successful in predicting the commercial success of the Top 100 companies. These findings are valuable for e-commerce websites creation.

► Use web mining to extract latent semantic concepts from e-commerce companies’ websites.
► Assign latent semantic concepts to e-commerce success factors.
► Use success factors to predict e-commerce companies’ success.
► Evaluate the success of e-commerce success factors.