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Zabi Ulla: Knowledge Elicitation & Application

Marketing Science Conference 2019 Conference

At the 2019 BayesiaLab Conference, Zabi Ulla from Course5 Intelligence presented a Bayesian network approach to marketing optimization.

 

Zabi Ulla S, Sr. Director Advanced Analytics, Course5 Intelligence

Presentation Materials

Abstract

The focus of the discussion is to talk about Knowledge Elicitation in causal modelling especially talking about Transfer learning and how Course5 is using these concepts to solve complex problem in marketing measurement and optimization. Course5 marketing applications are primarily build around Bayesian Science leveraging BayesiaLab at various level in its development. In the specific application of BayesiaLab in its solution ‘Integrated Marketing Measurement’, I demonstrate how Course5 is inferring knowledge from one model and transferring it into other related models to handle various challenges in marketing measurement today. The solution is exclusively built leveraging Bayesian Inference engine from BayesiaLab and will talk about flexibility and agility of the technology along with robustness of Bayesian Inference Modeling.

Presenter Biography

Zabi UllaZabi comes with 15 years of experience in data analytics, machine learning and applied artificial intelligence primarily in business consulting domain. He has worked for marque clients such as Lenovo, Intel, Microsoft, YouTube, Del Monte, Wrigley, T-Mobile etc. solving complex business problems related to customer monetization and marketing optimization.

Zabi in his current role, leads advanced analytics and data science practice with Course5 Intelligence. In his previous stint with other companies he has experience in designing and executing machine learning models and developed teams to develop niche solutions.

Zabi comes from applied statistics background. He has masters in statistics and recently selected as Top 40 data scientists in India by Analytics India Magazine. Zabi carries acute interest in machine reasoning, causal inference, experimental designs along with machine learning and data science.

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