Predicting customer future purchases and lifetime value is a key metrics for managing marketing campaigns and optimizing marketing spend. This task is specifically challenging when the relationships between the customer and the firm are of a noncontractual nature and therefore the future purchases need to be predicted based mostly on historical purchases. This work compares two approaches to predict customer future purchases, first using a 'buy-till-you-die' statistical model to predict customer behavior and later using a neural network on the same dataset and comparing the results. This comparison will lead to both quantitative and qualitative analysis of those two methods as well as recommendation on how to proceed in different cases and opportunities for future research.
OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction
Tencent（腾讯）；McGill University（麦吉尔大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Renmin University of China（中国人民大学）
A predict-and-optimize approach to profit-driven churn prevention
University of Seville（塞维利亚大学）；Instituto de Matemáticas de la Universidad de Sevilla (IMUS)（塞维利亚大学数学研究所（IMUS））；University of Chile（智利大学）；Instituto Sistemas Complejos de Ingeniería (ISCI)（工程复杂系统研究所（ISCI））；Universidad de los Andes, Chile（智利安第斯大学）
