Product Layout Recommendations based on Customer Behavior and Data Mining

Good and effective product structuring in the User Interface (UI) can significantly impact sales. Strategically place products, increase relevant product visibility, and present product recommendations that match customer preferences. In addition, the right UI inspires impulse buying and makes it easier for customers to find the products they seek. However, presenting product recommendations that match customer preferences is a challenge for E-commerce today. This has a significant risk to the continuity and trust of consumers in the products offered. So, presenting product recommendations needs to be built based on customer behavior by optimizing the tendency of a product purchased with other products. This research aims to make recommendations for product locations based on customer behavior and data mining to optimize sales. The Market Basket Analysis method is applied through the association rules algorithm by finding support, confidence, and lift ratio values. The analysis revealed a strong relationship between Xbox products and PlayStation CDs, with a confidence value of 66% and a support value of $\geq 0.6\%$. With these findings, product rules can be generated that can be used to compile product layouts tailored to consumer behavior, aiming to optimize sales.