General
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Executive Takeaway: Markov models matter because they don’t just show where customers or deals are today—they reveal how groups are likely to move tomorrow—toward growth, retention, or churn. They reveal not just the what (likelihood of churn, revenue forecast) but the pathways—how customers and deals actually flow over time. That creates runway for action, giving…
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The way we do data science has changed forever. GenAI makes coding frictionless—but without structure it gets messy and invites slippage down rabbit holes. I’ve been refining CRISP-AI, a lightweight process (inspired by CRISP-DM) to work smarter with AI.
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Data teams are no longer just service providers—they’re becoming strategic partners in decision-making. But as business fluency rises on one side, a growing data literacy gap is emerging on the other. This shift is creating tension—and opportunity. This isn’t a flaw—it’s the future.
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My latest post explores using a large language model (LLM) to directly control a reinforcement learning (RL) environment for optimizing Marketing spend. The LLM actively makes decisions step by step. Unlike typical RL applications, this setup allows real-time interaction and showcases the LLM’s reasoning process while determining optimal actions.
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Drawing inspiration from Rumi’s timeless insight—“When light returns to its source, it takes nothing from what it has illuminated”—this blog post explores how descriptive analytics reveal historical trends while prescriptive analytics chart future actions. Discover how these complementary approaches drive clarity and remarkably empower effective decision-making in today’s data-driven world.
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The Sleep Training of Data-Driven Decision-Making: How Intermittent Reinforcement Creates Bad Habits
As a new father, I saw an unexpected parallel between sleep training my baby and decision-making in marketing: intermittent reinforcement. Just like a baby learns to keep crying if it sometimes works, businesses fall into the trap of justifying bad habits based on occasional success. This post explores how Decision Sciences breaks the cycle.
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This blog explores how saturation curves and incremental returns can guide optimal marketing spend allocation. Using scenario analysis, I show why relying on averages like ROAS can mislead decision-makers. Learn how to balance spend across channels, avoid saturation, and unlock growth by reallocating budgets to underutilized opportunities for higher returns.
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Data helps, people deliver—but what role does GenAI play in the new economy of insights? Reflecting on my last decade as a data scientist, I explore how Decision Sciences bridges the gap between data, decisions, and outcomes in a rapidly evolving landscape. Read more about the lessons I’ve learned and the road ahead.
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Generative AI (GenAI) is revolutionizing how we generate, distribute, and act on data, fundamentally transforming the metaphorical “economy of insights.” In this new paradigm, data remains the currency of decision-making, but the rules of the game have shifted. From a Decision Scientist’s perspective, this evolution creates both challenges and opportunities, reshaping how we deliver value.
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This latest post emphasizes the importance of shifting from topic-driven analysis, which provides descriptive insights, to decision-driven analysis, which focuses on actionable insights tied to specific decisions. This approach improves clarity in decision-making, quantifies outcomes, and helps leaders navigate complexities effectively, ultimately turning data into a powerful tool for impactful choices.