One of the biggest advantages of online presence is that it is easier to get customer to speak up and offer feedback. Online Review Statistics suggest that 94% of consumers refuse to patronize a business if they come across a negative review. And over 97% of consumers report that customer reviews influence their purchasing decision. As a consequence it puts tremendous pressure on the companies to assess what customers are thinking and what ratings they are leaving behind. Other than the Scores provided by customers, there is a mine of information available in the ‘Comments’ box of Feedback section. But when, there are thousands of visitors who leave behind Comments on an e-commerce website, it becomes almost impossible to manually read and decipher the nature and sentiment of feedback. That is where ML-based sentiment analysis can help.
It is possible to train ML based models to identify the ‘topic’ on which customers have commented and additionally assess sentiment of every comment. To assess the topic, NLP technique can be used. For example, in case of sale of shoes, customers would generally comment about a few fixed set of topics like shoe’s fitting, style, comfort, price, color, brand authenticity, etc. A trained NLP model can peruse through the thousands of comments in the feedback section and highlight which topics customer have generally commented about. Further sentiment of feedback comments can be assed using ML modeling. Historical feedback review comments can be first categorized as positive, negative and neutral. The ML model can then be trained on such historical (training) data and then tested using fresh comments for the model’s accuracy in assessing a sentiment of the comments. Once the model offers good accuracy, ML-based model can offer great efficiency by perusing through thousands of comments, assessing their sentiments and providing a summary of percent positive, negative and neutral comments.
ML and NLP can thus offer great insights on the topic (subject) on which customers are commenting and related sentiment. This can enable e-commerce players to undertake improvements on product, distribution or servicing areas as needed.