  It is of high interest for a company to identify customers expected to bring
the largest profit in the upcoming period. Knowing as much as possible about
each customer is crucial for such predictions. However, their demographic data,
preferences, and other information that might be useful for building loyalty
programs is often missing. Additionally, modeling relations among different
customers as a network can be beneficial for predictions at an individual
level, as similar customers tend to have similar purchasing patterns. We
address this problem by proposing a robust framework for structured regression
on deficient data in evolving networks with a supervised representation
learning based on neural features embedding. The new method is compared to
several unstructured and structured alternatives for predicting customer
behavior (e.g. purchasing frequency and customer ticket) on user networks
generated from customer databases of two companies from different industries.
The obtained results show $4\%$ to $130\%$ improvement in accuracy over
alternatives when all customer information is known. Additionally, the
robustness of our method is demonstrated when up to $80\%$ of demographic
information was missing where it was up to several folds more accurate as
compared to alternatives that are either ignoring cases with missing values or
learn their feature representation in an unsupervised manner.
