Building upon the preceding chapters, in which the design, implementation, and evaluation of the UCR, TEE, and LLE approaches were presented in detail, this chapter provides a critical reflection of their limitations and unresolved challenges, including constraints arising from the experimental design, methodological choices, and aspects of the implementation that could have been strengthened. While the proposed approaches demonstrate that the integration of privacy-awareness into customer representation learning is feasible, it must be acknowledged that alternative architectures and methods offer complementary capabilities in terms of efficiency, robustness, or adaptability. Furthermore, by discussing theoretical, architectural, and experimental shortcomings a more balanced perspective on the generalizability, scalability, and practical feasibility of the contributions is provided.