The value of raw data is unlocked by converting it into information and knowledge that drives decision-making. Machine Learning (ML) algorithms are capable of analysing large datasets and making accurate predictions. Market segmentation, client lifetime value, and marketing techniques have all made use of machine learning. This article examines marketing machine learning techniques such as Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. ML is used to analyse consumer behaviour, propose items, and make other customer choices about whether or not to purchase a product or service, but it is seldom used to predict when a person will buy a product or a basket of products. In this paper, the survival models Kernel SVM, DeepSurv, Survival Random Forest, and MTLR are examined to predict tine-purchase individual decisions. Gender, Income, Location, PurchaseHistory, OnlineBehavior, Interests, PromotionsDiscounts and CustomerExperience all have an influence on purchasing time, according to the analysis. The study shows that the DeepSurv model predicted purchase completion the best. These insights assist marketers in increasing conversion rates.

翻译：原始数据的价值通过转化为驱动决策的信息和知识得以释放。机器学习算法能够分析大规模数据集并进行准确预测。市场细分、客户生命周期价值以及营销技术均已应用机器学习。本文研究了支持向量机、遗传算法、深度学习及K-Means等营销机器学习技术。机器学习被用于分析消费者行为、推荐商品，并辅助客户做出是否购买产品或服务的其他决策，但很少用于预测个人何时购买某一产品或一篮子产品。本文通过考察核支持向量机、DeepSurv、生存随机森林及MTLR等生存模型，预测个体购买决策的时间。分析显示，性别、收入、位置、购买历史、在线行为、兴趣、促销折扣及客户体验均对购买时间产生影响。研究表明，DeepSurv模型在预测购买完成方面表现最佳。这些见解有助于营销人员提升转化率。