Customers segmentation can help online shoppers’ behaviors and make business decisions more efficient. LRFS (Length, Recency, Frequency, and Staying Rate of Revenue), and similar traditional models (such as LR, LF and LRF) have been employed extensively as a behavioral segmentation analysis. This study provides an improved customer segmentation model by proposing a new feature called Average Page View (A) by substituting Staying Rate for Revenue (S), LRFA (Length, Recency, Frequency and Average Page View) model. This newly implemented metric is determined by aggregating views for administrative, informational, and product-related pages for a better reflection of what people care about. To prove the efficacy of LRFA, we can use standard scaling and K-Means clustering in order to divide customers into homogeneous behavioral segments. We utilized Elbow Method and Silhouette Coefficient method to determine the optimal number of clusters. The relationship between LRFS and LRFA showed that LRFA gets better revenue-based user segmentation than LRFS, as it takes into account user engagement. The results show how LRFA model improves revenue prediction and drives better insights about the customers which results in more data-oriented marketing strategies. The percentage of revenue generated has been increased from 81% to 99%. Based on the insights generated by this model, businesses will be able to perform their customer segmentation with more accuracy and efficiency, thereby achieving higher levels of targeted marketing and revenue generation respectively and thereby contribute to the field of e-commerce analytics.