Predicting Purchases and Personalizing the Customer Journey with Artificial Intelligence

A mastery of consumer behaviour analysis and the ability to personalise the customer experience are prerequisites for success in today's cutthroat business climate. In order to improve the customer experience and make purchase predictions, this research explores the world of artificial intelligence. Using a systematic three-step technique, this study aims to improve consumer purchase prediction and provide individualised customer experience. The dataset is preprocessed using Enhanced Z-score normalisation first. This will ensure uniformity and mitigate the impact of extreme cases. After a better collection of features is found using Recursive Feature Elimination (RFE), the dataset is optimised for classification tasks. Lastly, state-of-the-art technology is used in the research via the usage of Enhanced Long Short-Term Memory (LSTM) networks for classification and prediction. Better LSTM networks may be able to detect nuanced changes in customer behaviour, resulting in more accurate predictions. Our study aims to contribute to the advancement of AI applications in CRM by following this rigorous process. We want to help companies better their strategy, offer more personalised experiences, and boost the possibility of consumers completing a purchase by delivering relevant insights.