Sharad Varshney
VP, Head of Data Science, Publicis Media-COSMOS
Sharad is a Vice President, Head of Data Science for Publicis-COSMOS, based out of San Francisco and has more than 18 years of unique data science cross-domain experience from different industry verticals leveraging Big data. Sharad has researched and designed various marketing based machine learning models ranging from CLV, Churn, Product Propensity, Affinity, Next Arrival, Next Best Action models and have been exceptional in delivering and productionalizing MML models. Prior to joining Publicis, Sharad was Founding member and Chief Data Scientist of Palo Alto based startup, Peritus AI and was instrumental designing the product offering.
Past sessions
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.
