Experimentation Teams Should Operate Like Internal Consultants
How an internal-consulting model changes an experimentation team’s role, with outcomes to measure rather than a promised performance advantage.
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Free Learning · Better Decisions Make Growth Decisions
Make Growth Decisions
You Can Defend
Know what to test, when to trust the result, and what to do next. Practical guides for analysts, growth teams, and founders.
From SVB to NRG: What Enterprise Marketing Analytics Actually Looks Like
Lessons from leading marketing analytics at SVB and NRG, including measurement systems, geo tests, and executive decisions.
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What an SVB OOH Geo Test Did—and Did Not—Prove
SVB OOH geo test: treatment markets grew, but the Seattle control grew faster, so the result did not establish causal lift.
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Jobsolv at 35K Users: What $0 Current Paid Acquisition Means
A July 2026 Jobsolv snapshot: 35K+ users, $80K+ total revenue, $0 current paid acquisition, earlier paid tests, and explicit cost limits.
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Managing a 27-Person Roster as a Solo Founder with a Full-Time Role
How Atticus Li managed a 27-person Jobsolv roster across the build, with up to 22 active in a week, while working full-time at NRG Energy.
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Multi-Brand Experimentation Governance: Running 100+ Tests Across 5 Energy Brands
How Atticus Li governed 100+ annual tests across five NRG Energy retail brands, with explicit rules for stopping, collisions, prioritization, and evidence.
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Stop Solutionizing: Why the Best Hypotheses Start with the Problem
How to write problem-first hypotheses that connect observed user friction, a plausible mechanism, and a measurable business decision.
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The Product Manager's Guide to Working with Experimentation Teams
A practical guide for PMs working with experimentation teams, from test duration and scope to interpretation and rollout decisions.
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Build the Evidence Before You Scale Experimentation
How to connect early experimentation evidence to business decisions without treating annualized models as realized revenue.
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From Services to SaaS: How I Validated Jobsolv Before Writing a Line of Code
A founder-reported Jobsolv cohort: 24 of 26 service clients received at least one interview within 30 days, followed by the transition from service to software.
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The Solo Founder's Playbook for Growth, Experimentation, and Shipping
A practical solo-founder playbook for experimentation, channel choices, and shipping when time, traffic, and budget are constrained.
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Speak the CFO's Language: How to Get Budget for Experimentation
How to present test-window evidence, modeled financial impact, and recognized outcomes so finance can inspect an experimentation investment.
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Standards Are How Experimentation Teams Survive Turnover
How durable templates, decision rules, and ownership standards preserve experimentation quality through growth and turnover.
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What UX Researchers Get Right (And What Breaks in Enterprise Reality)
UX researchers trained in academic rigor often struggle to deliver inside real companies.
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Why Most A/B Tests Fail (And How to Fix Your Experimentation Program)
Five process failures that undermine A/B tests, plus practical fixes for tracking, metrics, sample size, and stakeholder decisions.
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Why Most CRO Advice Breaks When You Apply It to Real Companies
The biggest CRO influencers run programs at companies with Netflix-level traffic. That is not your reality.
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We Made the UX 'Better' and Conversion Dropped — Here's What Happened
How a cleaner homepage, a modal instead of a dedicated page, and a flat primary metric quietly killed enrollment conversions—and what to change in your…
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Atticus Li's PRISM Method: How to Run Revenue-Driven Experiments
A five-step framework for connecting experiment decisions to revenue assumptions, implementation quality, and post-test evidence.
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The Future of A/B Testing: AI, Automation, and What Comes Next
A/B testing is evolving fast. Explore how AI, automation, and new statistical methods will reshape experimentation in the coming years.
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Ethical Considerations in A/B Testing: Where to Draw the Line
A/B testing raises real ethical questions about consent, manipulation, and fairness. Learn where the ethical boundaries are and how to test responsibly.
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Machine Learning and A/B Testing: Using ML to Improve Experiment Design
Machine learning and A/B testing are complementary, not competing. Learn how ML improves experiment design, analysis, and the speed of optimization cycles.
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CUPED in A/B Testing: Reducing Variance to Detect Smaller Effects
CUPED uses pre-experiment data to reduce noise in your A/B tests. Learn how this variance reduction technique works and when it dramatically improves power.
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How to Run Pricing Experiments Without Losing Customers
Pricing experiments are high-stakes and high-reward. Learn the frameworks and safeguards that let you test pricing without damaging trust or revenue.
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How to Handle Low-Traffic A/B Tests: Small Sample Strategies
Low traffic does not mean you cannot experiment. Learn proven strategies for running meaningful A/B tests when your sample size is limited.
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