Published Paper Details:

In today's world, the number of competitors in the E-commerce market has grown, it is only inevitable that there would be fierce rivalry. Organizations must analyze their consumers in order to stay competitive and operate at their best. Customers may also leave customer reviews, which allow them to express their personal thoughts on the product. Customers have the ability to offer their opinions about the product. Thus, most companies should have sufficient market knowledge to forecast or predict which consumer segments are their most productive, the individuals of those businesses realize that scaling the job is not best left to guesswork or instinct. It is also important to understand how each segment will be targeted and what they need from the organization. Segmentation is a process of dividing customers into groups based on characteristics such as age, income level or gender. Segmentation can be done by using different methods like demographic, psychographics and behavioral factors. This paper analyses 3 prominent clustering algorithms (K-means, Agglomerative and Meanshift) to identify the target audience for segmentation. These algorithms are used in order to identify potential customers are likely to buy the product or service.