Speaking & Media
Strategy. Ethics. The Reality of Data.
I speak on the intersection of data science, corporate strategy, and the philosophy of decision-making. My goal on stage is the same as in the boardroom: clarity over jargon.
Focused on the transition from legacy ratings to subscriber-centric lifetime value (LTV) models. I lead discussions on synthesizing disparate data from partners like Apple and Amazon into actionable global strategy.
Presented on the "Philosophy of the Control Group," emphasizing that sophisticated modeling is secondary to a rigorous counterfactual for measuring true incremental impact.
Explored the intersection of agentic AI and human initiative, focusing on how generative models shift the discovery landscape while requiring human-led "Decision Rules."
The Outcome: Executives leave with a diagnostic framework to separate genuine innovation from vendor "fairy dust." We move beyond the buzzwords to categorize AI into five distinct functional faces: Rules-Based, Statistical, Machine Learning, Deep Learning, and Generative[cite: 555, 558, 561, 563, 566].
The Outcome: A strategic audit of your experimentation culture. We identify why 80% of corporate testing programs fail—often by testing the wrong things, using irrelevant metrics, or failing to retest as market conditions shift[cite: 615, 616, 685].
The Outcome: A masterclass in content valuation using counterfactual modeling and Shapley values[cite: 510, 524]. Attendees learn to estimate the true value of individual library assets within a bulk-sold environment, moving beyond "hours streamed" to understand true ROI in a subscription economy[cite: 364, 438].
For speaking availability or media commentary, please email:
contact@michaelbagalman.com