The expansion of digital commerce and online engagement has resulted in large amounts of customer information; thus allowing and challenging present businesses with managing this data. This paper includes a comprehensive structure for analyzing customer behaviour with Big Data Analytics and Machine Learning (ML). We consider five main problems within marketing: customer lifetime value (CLV) prediction, identification of prospects with a high probability of purchase, selecting the best communication channel, predicting customer churn, and performing sentiment analysis. A practical model to predict CLV has been created and tested against a real world e-commerce dataset consisting of 397,925 transactions for 2,845 unique customers using BG-NBD and Gamma-Gamma probabilistic models. The successfully tested model achieved a prediction accuracy of 91.4 percent and indicated a 23:1 ratio of CLV between the top tier and bottom tier segments of customers. The results from comparative analysis demonstrated that both ensemble methods and probabilistic models are superior to traditional rule-based methods for all five use cases. Overall, these findings provide practical information for marketers and data scientists who wish to utilize big data technology strategically for competitive customer relationship management.

A comprehensive framework aimed at analyzing consumer behaviour based on big data analytics and machine learning was outlined. The framework employs five core marketing challenges that have been addressed through the use of purpose-built machine learning pipelines: methods for estimating customer lifetime value using BG-NBD and gamma-gamma models; methods for calculating propensity scores using logistic regression; methods for optimizing communication channels using random forest classification; methods for predicting customer churn using naive Bayes; and methods for performing sentiment analysis using support vector machines. Experimental validations conducted on an ecommerce dataset (n=2,845 consumers, 397,925 purchases) have been shown to be state-of-art in performance with a customer lifetime value prediction accuracy of 91.4%, an AUC-ROC of 0.94 for churn prediction, and an 87.3% accuracy for sentiment classification. The framework is available to the public through cloud-based machine learning platforms as well as through open source Python libraries and hence suitable for organizations of any size.

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