Sales Prediction and Product Recommendation Model Through User Behavior Analytics

Xian Zhao · Pantea Keikhosrokiani

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Customer churn and segmentation Forecasting Techniques and Applications Data Mining and Machine Learning Applications

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.

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References

2017Forecasting at Scale 2017A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction 2008Database Marketing: Analyzing and Managing Customers 2019Machine-Learning Models for Sales Time Series Forecasting 2019An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain 2004A purchase-based market segmentation methodology 1998A practical yet meaningful approach to customer segmentation 2017Analysing customer behaviour in mobile app usage

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2022An RFM Model Using K-Means Clustering to Improve Customer Segmentation and Product Recommendation 2024Customer purchase prediction in B2C e-business: A systematic review and future research agenda 2022Predicting Consumer Behavior Change Towards Using Online Shopping in Nigeria

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