minimum detectable effect (MDE)
- AI Strategy (315)
- Generative AI (106)
- IT Leadership (33)
- Leadership (76)
- Product Management (344)
- Product Management Leadership (306)
- Uncategorized (32)
-
How to Build a Resilient Experimentation Program at Scale
A practical operating model for producing trustworthy decisions through layered evaluation, governed measurement, and reversible delivery at scale.
-
How a Digital Analytics Visionary Shapes My Product Strategy for Growth, Retention & Monetization
Data is my compass for product decision-making, and I operationalize it through behavioral analytics, rigorous A/B testing, and continuous discovery. I start by mapping user journeys…
-
AI Product Validation: From Promising Demo to Proven Value
A practical evidence ladder for validating AI demand, model quality, user value, safety, and unit economics before expanding production exposure.
-
Stop Drowning in Tasks: How AI Marketing Agents Restore Focus and Maximize Impact
Marketers don’t have a volume problem—they have a focus problem. AI multiplies effort, but without outcome clarity it only accelerates busywork. I outline a practical playbook:…
-
Stop Misleading A/B Tests: Master Sample Size Assumptions for Reliable Results
Sample size calculators are only as reliable as the assumptions behind them. I show how I validate baseline rates, set a meaningful minimum detectable effect (MDE),…
-
Inside Amplitude’s ML Playbook: Practical Strategies for Smarter A/B Tests and Growth
Discover how I translate machine learning into practical product analytics wins. I share how MDE-driven planning and disciplined A/B testing unlock faster, defensible decisions. You’ll see…
-
Unlock Confident Decisions with Bayesian Statistics: Smarter A/B Tests from Small Samples
When experiments stall due to low traffic, I turn to Bayesian statistics to convert sparse data into decision-ready insights. Instead of opaque p-values, I communicate intuitive…
-
Product Experimentation for AI Systems: A Practical Playbook
A practical playbook for testing prompts, retrieval, and policy changes with decision-ready metrics, safe rollouts, and reliable instrumentation.
-
How to Build AI-Ready Product Analytics and Experiments
A practical system for linking AI quality, user behavior, risk, cost, and controlled experiments to customer outcomes before you scale.
-
PMs and Developers Need Different AI Metrics—Here’s How That Builds Faster, Better Products
PMs and developers measure different layers of AI systems—and that’s a feature, not a bug. Developers focus on eval-driven development, latency, and reliability, while PMs focus…
-
My Proven Experimentation Playbook for AI PMs: Faster Learning, Safer Launches, Bigger Wins
Experimentation is not guesswork—it’s a disciplined system. In this playbook, I show how to pair A/B testing with eval-driven development to ship safer AI features and…
-
Retail and Ecommerce Product Benchmarks That Drive Growth
A practical framework for turning retail and ecommerce benchmarks into cleaner funnels, sharper experiments, and growth decisions you can defend.
-
Beyond Accuracy: The Trust-First Evaluation Metrics I Use to Scale High-Impact AI Products
Trust—not accuracy—determines whether AI features earn adoption, retention, and long-term impact. In this piece, I share the layered metrics I use to evaluate model quality and…
-
Evidence-Driven Product Analytics: From Signal to Decision
A practical operating system for turning behavioral signals into sound hypotheses, reliable experiments, and product decisions your team can defend.
-
How to Run AI-Augmented Workflow Experiments That Matter
A practical playbook for testing AI-assisted workflows with clear hypotheses, full-job metrics, guardrails, and evidence-based autonomy decisions.
-
From Activation to Retention: A Practical Experiment System
A practical system for defining activation, proving its link to retention, and running experiments that improve durable product use.
-
How to Build Marketing Analytics That Measures Revenue
Connect campaigns, product activation, CRM stages, retention, and experiments to revenue decisions without mistaking attribution for causality.
-
How to Build a High-Velocity Product Experimentation System
A practical operating system for turning small, safe releases into faster product decisions without sacrificing reliability, trust, or strategic focus.
-
How to Match Experiments to Software Experience Maturity
A practical maturity model for choosing trustworthy experiments, fixing measurement gaps, and turning each result into a clear product decision.
-
Evidence-Driven AI Product Delivery: A Practical Operating Model
A practical operating model for choosing AI bets, defining proof before build, evaluating releases, and scaling only when outcomes and guardrails hold.
Weekly digest
One email a week on AI products, enablement and hiring. No fluff.
Browse topics
- AI Strategy (315)
- Generative AI (106)
- IT Leadership (33)
- Leadership (76)
- Product Management (344)
- Product Management Leadership (306)
- Uncategorized (32)
Work with me
45-minute consultation on AI product strategy, GTM and PM hiring — no charge.