Techniques are presented herein that support an intelligent system for grouping customers by leveraging exactly those things that make it impossible for a person to perform effective clustering. That is, the presented techniques support clustering based on a customer’s vastly complicated network profiles. Aspects of the presented techniques encompass a smart customer grouping framework with highly accurate deep learning modeling, utilize domain-specific machine learning (ML) to unravel the nonlinear latent representations with a deep autoencoder, provide a flexible feature weighting capability to focus on certain features based on customer personas and business, and support a highly usable system for sales and marketing professionals. Use of the presented techniques allows for the grouping of network customers, considering their complex hierarchical structure, using ML.
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Jung, Doosan; Shao, Qihong; Zhou, Belanna; Arnowitz, Jonathan; Singh, Gurvinder; and Yedavalli, Kiran, "SMART GROUPING – GOING BEYOND DOMAIN SIMILARITIES IN PEER RECOGNITION BY LEVERAGING COMPLEX HIERARCHIES USING MACHINE LEARNING", Technical Disclosure Commons, (April 26, 2022)