Online shopping has grown in popularity over the years, mainly because people find it convenient and easy to bargain shop from the comfort of their homes or offices. One of the most enticing factors about online shopping, particularly during the holiday season, is it alleviates the need to wait in long lines or search from store to store for a particular item. Thus, the need to best understand the customers. In this investigation, I look at the features of the superstore that can help us maximize revenue leading to increased profit. The main focus was on customer segments and how they differ in revenue and profit over the years and months.
Also, I developed a model aimed at predicting profit for each of the segement of the superstore. The prediction model was then deployed for easy usability
The dataset includes the purchase pattern of different customer segments, the category of products most preferred by each segment, and total sales and profit for each segment. The dataset covers between 2014 and 2017 and thus is not up-to-date, which means that the insight obtained may not apply to the present situation. However, although collected from a tertiary source, it is complete and consistent and reliable
This work has shown the need for a business to segment its customer and understand how each segment differs. Thus, helping in targeted customer satisfaction delivery, reducing costs and increasing profit. Wale also agreed that market segmentation followed by targeted advertising would likely increase profit and reduce cost.
- Linear Regression
