Personalize.AI: Transforming Businesses Through Personalization


Digitization has led to enormous amounts of content across different channels, significantly impacting customer’s attention span. To improve customer attention, e-commerce organizations are focusing on providing higher user experience during the shopping journey through hyper-personalization across content and offers. These hyper-personalization efforts can improve customer experience and organization top-line revenue by up to ~15%.

In this session, ZS will demo its Personalize.AITM (P.AI) application which integrates directly with an organization’s data ecosystem to provide customer-level recommendations. The P.AI system combines heterogeneous datasets including customer-level interactions, demographic data, loyalty program, marketing offers and item properties to optimize the customer experience by providing personalization. We’ll share how the solution provides a scalable ecosystem with 6 core capabilities: promotion design, auto-feature engineering, advanced micros segmentation, item-offer recommendation, dynamic test-control, and customer targeting.

Learn how P.AI application is applied to the E-commerce industry and how it has helped transform customer experience for firms.


Dr. Prakash (PKS Prakash) has over 12 years of data science experience with a focus on healthcare, manufacturing, pharmaceutical, and e-commerce domain. Prakash leads the advanced data science practice in patient analytics and customer-centric marketing at ZS. He has a doctorate from the University of Wisconsin-Madison, U.S., and also pursued his second doctorate in engineering from the University of Warwick, U.K. He holds a master’s degree from the University of Wisconsin-Madison, U.S., and a bachelor’s degree from the National Institute of Foundry and Forge Technology (NIFFT), India. He has co-founded Warwick Analytics, U.K. (based on his doctoral thesis). He has published widely in research areas of operational research & management, soft computing tools, and advance algorithms in leading journals such as IEEE-Trans, EJOR and IJPR among others. He co-authored “Algorithms and Data Structures using R” and “R Deep Learning Cookbook” published by PACKT.

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