Automated Feature Engineering for Customer Journey Event Prediction
Automated Feature Engineering for Customer Journey Event Prediction

Abstract: 

Event prediction across customer journey by leveraging the rich and granular customer interaction data is becoming critical to enable smarter marketing. However, the current ML process has several challenges that prevent us from doing this effectively, efficiently and profitably across problems and domains/ - the process is iterative, manual, hard to scale and heavily reliant on domain expertise. These issues tend to be show-stoppers in some cases, and a solution that can accelerate the iterations, scale to any data volume and transfer to any domain can be key to driving ROI. This session will explore evolutionary techniques, coupled with cutting edge machine learning methods across domains to achieve remarkable results from 5x acceleration while maintaining or improving prediction accuracy by up to 10-20%.

Bio: 

Srinivas is a leader in advanced data science with a focus on helping clients through their AI journeys to realize business value by tailoring, designing and implementing AI solutions for them.
Srinivas’s core expertise areas include automated machine learning, natural language processing, customer omni-channel next best action orchestration, recommender systems and longitudinal patient predictive analytics. He has authored several thought leadership articles (such as Artificial Intelligence: Beyond the glamor) and gives conference talks (e.g. PMSA 2017).
Srinivas joined ZS in 2010 after receiving his MBA from Indian School of Business (ISB) Hyderabad. He holds a B.Tech. in Mechanical Engineering from National Institute of Technology (NIT) Warangal
Prior to joining ZS, Srinivas spent time as a solution architect building expert systems to automate product design and manufacturing across multiple industries viz., automobile, power systems, medical devices and retail.

Open Data Science

 

 

 

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