Publication

Abstract : Insurance firms must employ cost-effective client engagement tactics in order to maximize the effectiveness of their marketing campaigns in the ever-changing business landscape of today. This work uses machine learning to solve the problem of effectively identifying future insurance subscribers. To build a prediction model, we utilize past marketing data from a modern insurance provider. The model makes use of historical campaign data, such as call duration, frequency of contact, and previous campaign outcomes, as well as client variables including age, job type, marital status, educational background, and contact communication style. Through the analysis of these characteristics, our machine learning model predicts the probability of a customer subscribing to the insurance product, allowing the business to focus on high-potential customers and reduce the expense of telemarketing.In addition to offering insightful information about consumer behavior, the research helps to improve the general effectiveness of marketing tactics used by the insurance sector.