Analysis of Customers Purchase Patterns of E-Commmerce Transactions Using Apriori Algoritm and Sales Forecasting Analysis With Weighted Moving Average (WMA) Methods

Abstract

The challenge of today's e-commerce companies is how to extract large data into information for decision making, especially in terms of promoting products to be relevant, effective and efficient. At this time the XYZ company uses product category data as the main parameter in promoting its products to customers, but the method used is not optimal and efficient because promotions are not displayed to potential customers based on customer purchasing patterns. so that the sales target is not achieved, therefore market basket analysis is needed to find and understand the basic patterns of association rules that occur in customer purchase transactions. In this study the algorithm used is the Apriori algorithm, apriori algorithm is chosen because the resulting association rules have higher accuracy than the FP-Growth algorithm. Then the results of the apriori algorithm association rules are used as a reference in determining the items to be promoted, then sales forecasting is carried out with the Weighted Moving Average (WMA) method to predict the estimated total sales. The results of this study are apriori algorithm that has a higher accuracy value of 130.75 accompanied by sales forecasting analysis with a weighted moving average method that can be implemented in association rules generated from the Apriori algorithm so that it can help companies make decisions in the category of products that are sold a lot.