Predicting Probable Product Swaps in Customer Behaviour: An In-depth Analysis of Forecasting Techniques, Factors Influencing Decisions, and Implications for Business Strategies
DOI:
https://doi.org/10.4108/eetsis.4049Keywords:
Prediction, Product swap, Feature Selection, Random Forest, ranking, chi-square test, Support Vector Machine, Machine Learning, Artificial IntelligenceAbstract
Introduction: Factors influencing product swap requests and predict the likelihood of such requests, focusing on product usage, attributes, and customer behaviour, particularly in the IT industry.
Objectives: Analyse customer and product data from a leading IT company, aiming to uncover insights and determinants of swap requests
Methods: Gather product and customer data, perform data processing, and employ machine learning methods such as Random Forest, Support Vector Machine, and Naive Bayes to discern the variables influencing product swap requests and apply them for classification purposes.
Results: Analysed a substantial dataset, comprising 320K product purchase requests and 30K swap requests from a prominent social media company. The dataset encompasses 520 attributes, encompassing customer and product details, usage data, purchase history, and chatter comments related to swap requests. The study compared Random Forest, Support Vector Machine, and Naïve Bayes models, with Random Forest fine-tuned for optimal results and feature importance identified based on F1 scores to understand attribute relevance in swap requests.
Conclusion: Evaluated three algorithms: support vector machine, naive Bayes, and Random Forest. The Random Forest, fine-tuned based on feature importance, yielded the best results with an accuracy of 0.83 and an F1 score of 0.86.
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