General
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In ‘The Executive Decision Series,’ I delve into key decision science concepts that empower leaders to make smarter, data-driven decisions. Today, we focus on Monte Carlo Simulations—their importance, how they function, and how they can be applied to campaign forecasting through a clear and practical marketing example. Executives should treat forecasted values with skepticism unless…
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In ‘The Executive Decision Series,’ I delve into key decision science concepts that empower leaders to make smarter, data-driven decisions. Today’s focus is on enhancing the decile table by incorporating critical financial metrics—Revenue, Cost per Acquisition (CPA), and Return on Investment (ROI). This extension bridges the gap between predictions and profitability, enabling leaders to align…
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In ‘The Executive Decision Series,’ I explore key decision science concepts that empower leaders to make smarter, data-driven decisions. Today’s focus is on leveraging decile tables to rank and segment audiences using predictive model scores. In part two, we’ll take this further by calculating ROI per segment to optimize marketing spend and drive maximum impact.
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Drawing on HBR’s ‘Where Data-Driven Decision-Making Can Go Wrong,’ this post connects insights from the authors’ research to strengthen the idea that by defining clear ‘impact paths,’ avoiding ‘decision black holes,’ and resisting the temptation to ‘shoehorn data,’ organizations can make better decisions.
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In Matt Haig’s new novel The Life Impossible, a boy asks, “Who would win in a fight, five hundred small cats or one single tiger?” This analogy highlights a challenge I often encounter when discussing data analysis: we often believe we have a “Tiger”—a single, powerful recommendation ready to drive action—but instead find ourselves with…
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In today’s data-driven world, it’s not enough to simply include charts and metrics in business presentations. The true test of data-driven decision-making lies not in the presence of numbers on a slide, but in the clear and logical bridge between those numbers, the insights they generate, and the decisions they guide. Too often, data suffers…
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In today’s data-driven landscape, many data scientists face the frustration of their hard work disappearing into the “decision black hole,” where insights fail to translate into measurable outcomes. This blog introduces the concept of impact paths—structured, traceable frameworks that connect data-driven insights to business decisions and their “tangible” results. By defining clear objectives, crafting actionable…
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Let me give a quick overview of what Decision Sciences is and why it is increasingly important, including some pretty staggering findings on how poor, ill-informed decisions destroy business value. In the broadest sense, Decision Sciences is an established interdisciplinary approach to research that covers business, public policy, healthcare, non-profit organizations, and beyond. In more…
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A Decision Scientist stands out through unique and defining characteristics that blend technical prowess with other very important skills and intangibles. Essential attributes include Strong Business Acumen, Robust Analytical Mind and Extensive Data Science Proficiency. Similar to a Data Scientist, a Decision Scientist must possess high technical proficiency in coding languages like Python, R, and…