Measurement
-
How do you test an enterprise AI system when performance is distributed across a chain of interdependent components? In this post, I outline a layered evaluation framework for RAG, prompts, GenAI models, deterministic rules, and end-to-end workflows—using a Decision Contract to define what “good” looks like. The goal is not a single AI accuracy score,…
-
Strategy maps and balanced scorecards are powerful tools—but they’re not causal models. They encode assumptions about how value is created, yet most organizations stop at measurement instead of validation. In this post, I explore why decision-driven analytics is the missing link between strategy frameworks and real business impact—and how it helps insights finally get past…
-
This post explores how Expected Pipeline Value—a probability-driven, risk-adjusted view of the pipeline—transforms B2B sales pipeline management by applying Decision Science principles to evaluate both deal probability and deal value. By moving beyond simple deal counts and nominal (face) value, this approach delivers a clearer view of pipeline quality, improves resource allocation, strengthens forecast credibility,…
-
Executive Takeaway: Decision Debt exists in every organization—it’s only a question of degree. It exposes the hidden costs of unclear or poorly grounded choices: confidence built on assumptions that becomes misdirection, wasted effort, delay, rework, and lost trust. While the metrics and framework introduced is conceptual, it helps leaders see where risk hides—thin evidence, heavy…
-
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…
-
We are producing more insights than ever—but are we making better decisions? This blog introduces the Decision Science Pre-Analysis Framework, a structured approach to overcoming the Last-Mile Problem in analytics, ensuring insights don’t just inform but actively drive decision-making and meaningful action.
-
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.
-
I propose a new working definition of the Decision Science function within Marketing. One where the driving force behind all efforts is a relentless pursuit of the understanding and optimization of incrementality and lift. Incrementality measures the actual impact that a marketing activity) has on a result or key performance indicator (KPI). It refers to…