Forecasting customer purchase behavior in e-commerce after the COVID-19 pandemic: leveraging predictive analytics for strategic insights.

Abstract

"The COVID-19 pandemic has changed how people shop online, making it important for businesses to find new ways to predict customer buying patterns. This project aims to analyse and predict customer behaviour in e-commerce after the pandemic using data from 2019 onward. This project uses advanced data analytics, including machine learning models, to uncover useful insights from past purchase data. This study focuses on forecasting sales quantities over time to understand customer purchase behaviour better. By looking at sales quantities, it is evident that trends and patterns can be seen in what and how much customers are buying, which helps businesses plan better. Tools such as Python, Orange, and Weka were used, and time series analysis was applied with algorithms such as Long Short Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Holt-Winters, Exponential Smoothing (ETS), Simple Moving Average (SMA) and Weighted Moving Average (WMA). The goal is to provide recommendations for e-commerce businesses to improve customer satisfaction, marketing, and operations. By understanding how sales quantities change over time, businesses can make better decisions and adapt to the evolving digital markets. This research shows how predictive analytics can help businesses grow during these challenging times."