Integration of business intelligence into E-commerce industry to forecast the sales

Abstract

" E-commerce has grown exponentially in the last two years and the pandemic is one of the important reasons for the huge growth. Moreover, E-commerce has caused tremendous changes in the transformation of the shopping experience from traditional shopping to digital shopping. This was made possible due to the wide range of technologies and computer networks that connect the countries and continents and synchronize them into a global village. Accurate sales prediction plays an essential role in reducing costs and preventing the merchants from holding huge amounts of cash flow due to the waste of resources, replenishment of stocks and loss of profits caused by goods in short supply. This research study attempts to predict future sales for an E-commerce business based on data of historic sales. Firstly, this research carries out a thorough critical analysis of multiple previous research studies to allow the reader to understand the background and the process of this research. Additionally, it analyses the factors impacting sales forecasting and also, uses the knowledge gained from this and proposes four different machine learning models which can be used to forecast the sales based on the historic sales data. The models which are used here are boosted decision tree regression, neural network regression, decision forest regression and bayesian linear regression. Finally, the research paper analyzes the accuracy measures for the above mentioned machine learning models on the available sales data. This study and the results obtained from this research can help e-commerce businesses to have better management in their future operations. "