  Customer Segmentation is the process of dividing customers into groups based
on common characteristics. An intelligent Customer Segmentation will not only
enable an organization to effectively allocate marketing resources (e.g.,
Recommender Systems in the Banking sector) but also it will enable identifying
the customer cohorts that are most likely to benefit from a specific policy
(e.g., to discover diverse patient groups in the Health sector). While there
has been a significant improvement in approaches to Customer Segmentation, the
main challenge remains to be the understanding of the reasons behind the
segmentation need. This task is challenging as it is subjective and depends on
the goal of segmentation as well as the analyst's perspective. To address this
challenge, in this paper, we present an intelligent vector-based customer
segmentation approach. The proposed approach will leverage feature engineering
to enable analysts to identify important features (from a pool of features such
as demographics, geography, psychographics, behavioral, and more) and feed them
into a neural embedding framework named Customer2Vec. The Customer2Vec combines
the neural network classification and clustering methods as supervised and
unsupervised learning techniques to embed the customer vector. We adopt a
typical scenario in the Banking Sector to highlight how Customer2Vec
significantly improves the quality of the segmentation and detecting customer
similarities.
