Abstract
The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics. RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level. For the purchase prediction model, XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their. For the product recommendation model, the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes. Moreover, some suggestions are proposed for the marketing department according to the outcomes. Overall, the XGBoost model achieved better performance and better accuracy with F1-score around 0.789. The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness. Furthermore, it recommend specific products to new customers. This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.
Highlights
Rapid developments in the field of machine learning (ML) and advances in computational power have enabled the possibility of applying implementation and optimization of machine learning in all types of industries [1,2]
We proposed a novel data science life-cycle and process model with RFM analysis method and the combination of various analytics algorithms are utilized for sales prediction and product recommendation through user behavior analytics
In order to propose a sales prediction and product recommendation model, we reviewed the important part and process of traditional store business transformation
Summary
Rapid developments in the field of machine learning (ML) and advances in computational power have enabled the possibility of applying implementation and optimization of machine learning in all types of industries [1,2]. The retail industry tried to optimize sales forecasting engine. CMC, 2022, vol., no.2 and recommendation engine using advanced algorithms Improved prediction and recommendation models based on user behavior analysis (UBA) provide many benefits for the retail industry. A start-up E-commerce company can find customers’ favorites, electronic equipment, books, or clothes from the historical shopping data. It is beneficial for a company to optimize its inventory, which is a meaningful way to decrease overstocking. A start-up E-commerce company must build and implement a system for predicting sales and goods recommendation
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