Abstract:
"Analyzing consumer transactional data is crucial for businesses to strengthen customer
relationships and attain sustainability in a volatile market. Customer transaction predictions
currently heavily depend on human intuition and are considered an intellectual task,
formulating and implementing this intuition as a data-driven approach can be valuable for
companies in improving customer satisfaction and retaining their customer base. Although
there are techniques available to analyze transaction trends, predicting the next transaction day
has not been achieved to a satisfactory level. In this study, the author propose a novel machine
learning-based algorithm to predict the next transaction day of customers based on their
customer lifetime value (CLV)."
