For decades, the quest to build the ultimate model that helps understanding the hearts and minds of customers has been on the top of the agenda for both business decision makers and data scientists. The exponential growth of the quantities of data available as well as the ever-increasing sophistication of customers made this model a moving target and a rather difficult one to achieve. Finally, technology has caught up and we’re ready to move into the era of the Customer Model – a complex, powerful, and comprehensive approach that promises to open a new chapter in the way we model customer behavior.
Starting from hundreds or thousands of features capturing a wide range of aspects related to this behavior, advanced deep learning models can be trained to encode and measure it in a highly efficient way. Easy and cost-effective access to thousands or tens of thousands of GPU cores through services like Azure Machine Learning or Azure Databricks enables such complex deep learning models to become a viable option. The journey to reach the ultimate customer model is not without difficulties though. From “simple” problems like encoding categorical features with thousands of distinct values up to the difficult task of designing efficient deep learning encoders, there are many challenges out there.
I'm excited to talk tomorrow about the fascinating topic of applying Deep Learning techniques to customer data at NDR - The Artificial Intelligence Conference in Bucharest. If you're in the business of figuring out the behavior of your customers, this is a talk you should not miss :)