Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
The experimentation team that treats itself as an internal consulting group outperforms the one that treats itself as a test execution shop. Here is how to reposition your team and why it matters for every stakeholder relationship.
Atticus Li shares lessons from leading marketing analytics at Silicon Valley Bank and NRG Energy — covering Google Analytics vs Adobe Analytics, data storytelling for executives, geo-incrementality testing, and what it means to be a consultant, not just an analyst.
Atticus Li designed a geo-incrementality experiment at Silicon Valley Bank to measure billboard and OOH advertising impact on digital demand, proving +97.8% web traffic lift in Miami and +94.4% in Austin versus the Seattle control market.
Atticus Li built Jobsolv from zero to 30,000+ users and $80K+ revenue without paid advertising. Here's the unglamorous playbook of product-led growth, channel diversification, and obsessive unit economics that actually worked.
Atticus Li built and managed a 27-member cross-functional team for Jobsolv while working full-time as experimentation lead at NRG Energy — coordinating a dev agency in Egypt, a UX/UI design agency, and freelancers across Upwork and Fiverr.
Atticus Li built the governance framework for running 100+ A/B tests per year across NRG Energy's five retail brands, serving 7M+ customers in 24 states.
Most experimentation teams write hypotheses that are really just disguised solutions. Here is why problem-first hypothesis design produces better tests — and how to stop solutionizing before you understand the leak.
Most product managers treat A/B tests like a deploy step. Here is how the best PMs actually work with experimentation teams — from duration negotiation to result interpretation to shipping decisions.
The fastest way to scale an experimentation program is to prove dollar-value impact on the first few wins. Here is how to make experimentation a revenue function instead of a cost center.
Before building Jobsolv's AI platform, Atticus Li validated the market by offering done-for-you resume services at $2,000-$3,000 per client, serving 26 clients with a 92.3% interview success rate, then scaling to 30K+ users and $80K+ revenue.
You do not need a team, a budget, or a full CRO function to run a disciplined growth operation. Here is how solo founders should think about experimentation, channel strategy, and shipping when every hour counts.
The single biggest unlock for experimentation programs is reporting results in the language finance actually cares about. Here is how to tie every A/B test to dollar value and get the budget to scale your program.
When people come and go, only the process stays. Here is how to build experimentation standards that survive turnover, enable scaling from 20 to 100+ tests a year, and onboard new team members without losing rigor.
UX researchers trained in academic rigor often struggle to deliver inside real companies. Here is how to preserve rigor while working within the resource, time, and political constraints that actually determine whether research ships.
Most A/B tests fail because the process is broken, not because the ideas are bad. Five failure modes that kill experimentation programs, and the consultant mindset that fixes them.
The biggest CRO influencers run programs at companies with Netflix-level traffic. That is not your reality. Here is how to think about experimentation advice when you operate under real-world constraints.
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 experimentation strategy.
Atticus Li's PRISM Method is a five-step experimentation framework — Probe, Revenue Rank, Implement, Score, Multiply — that ties every A/B test to projected revenue impact, achieving a 24%+ win rate at NRG Energy.
A/B testing is evolving fast. Explore how AI, automation, and new statistical methods will reshape experimentation in the coming years.
A/B testing raises real ethical questions about consent, manipulation, and fairness. Learn where the ethical boundaries are and how to test responsibly.
Machine learning and A/B testing are complementary, not competing. Learn how ML improves experiment design, analysis, and the speed of optimization cycles.
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.
Pricing experiments are high-stakes and high-reward. Learn the frameworks and safeguards that let you test pricing without damaging trust or revenue.
Low traffic does not mean you cannot experiment. Learn proven strategies for running meaningful A/B tests when your sample size is limited.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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