Predicting Customer Lifetime Values -- ecommerce use case
This work addresses marketing optimization for ecommerce businesses, but it is incremental as it compares existing methods without introducing new techniques.
The paper tackles the problem of predicting customer lifetime values in noncontractual ecommerce settings by comparing a statistical 'buy-till-you-die' model and a neural network on historical purchase data, with results analyzed quantitatively and qualitatively.
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.