Krish is a leader in the digital marketing technology space who has extensive experience with building and delivering digital transformation products and services for organizations ranging from startups to Fortune 500 companies. Krish currently leads the Publicis COSMOS Customer Intelligence Platform development, sales, and delivery within the Publicis Media organization. Prior to this, Krish has played senior technology leadership roles within Razorfish, and Sapient as a technology practice leader within the West Region managing the delivery of digital marketing technology solutions for some of Publicis’ largest client accounts including Hewlett Packard Enterprise, Honda, Microsoft, and Sephora.
As the customer acquisition costs are rising steadily, organizations are looking into ways to optimize their end-to-end customer experience in order to convert prospects into customers quickly and to retain them for a longer period of time. In today's omnichannel environment where non-linear events and micro-moments' drive the customer engagement with brands, the traditional one-size-fits-all customer journey will not be able to deliver true value to the customer and to the organization.
COSMOS customer intelligence platform helps organizations to address this challenge by offering a set of comprehensive and scalable Marketing Machine Learning (MML) Models for recommending the 'next-best-action' based on the customer journey. Trained on one of the largest customer datasets available in the United States, COSMOS MML Models leverage Spark, Databricks, and Delta Lake to stitch and analyze profile-based, behavioral, transactional, financial, and operational data to deliver customer journey orchestration at scale. In this session, we will discuss the business benefits of the dynamic customer journey orchestration, limitations of the classic customer journey models, and demonstrate how COSMOS MML models overcome these limitations. We will also review the global customer journey decision system that is built on top of ensemble machine learning techniques, leveraging Customer Lifetime Value (CLV) as the foundation.