This research investigates the impact of brand awareness and online customer reviews on purchase intention regarding Adidas products on the Shopee platform in Indonesia. The research background is founded on the growth of e-commerce and the intense competition present in Indonesia’s fashion industry. A quantitative method employing a survey approach was utilized, engaging 150 Shopee users who are familiar with Adidas products. Data analysis encompassed descriptive tests, classical assumption tests, multiple linear regression, hypothesis testing, and the coefficient of determination, all performed using SPSS version 27. The results demonstrate that brand awareness and online customer reviews have a positive and significant effect on consumer purchase intention, both individually and collectively. These findings imply that greater brand awareness and the quality of reviews are associated with an increase in purchase intention for Adidas products on Shopee. This research offers insights for Adidas Indonesia regarding the significance of enhancing brand awareness and fostering authentic reviews through exclusive promotions, review incentives, and the use of the “Verified Purchase” feature. Future research should be encouraged to integrate additional variables such as price and product quality for a more comprehensive analysis

Purpose: The main objective of this research is optimal use of customer segmentation using the Recency, Frequency and Monetary (RFM) approach so that companies can better understand and comprehend the needs of each customer. By carrying out this segmentation, companies can communicate better and provide services tailored to each customer.
Methods: The K-means algorithm is used as the main method for customer segmentation in this research. This research uses a dataset of online retail customers. Apart from that, this research also uses the elbow method to help determine the best number of clusters to be created by the model.
Result: Based on the elbow method, the most optimal is to use 3 clusters for this case. Thus, in K-means modeling, forming 3 clusters is the best choice. Clusters produce groups of customers who have specific characteristics in each cluster. The analysis shows that quantity and unit price have a significant influence on online retail customer behavior.
Novelty: This research strengthens the trend of using the K-means algorithm for customer segmentation in online retail datasets, which has proven popular in journals from 2018 to 2022. This research creates 3 new variables that will be used by the model to understand the characteristics of customer transaction behavior. This study also emphasizes the importance of exploratory data analysis in understanding data before clustering and the use of the elbow method to determine the most appropriate number of clusters, providing a significant contribution in analyzing customer segmentation.