  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.
