How to Save Tokens With Claude Code Without Making It Dumber
Most token-saving advice for Claude Code quietly makes the agent worse — starve its context or drop to a cheap model and you pay for the retries.
Read Article→
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.
No Fine, Just a Deadline: What the UK Hotel-Booking Sector's Undertakings Reveal About Catching a Pattern Early
Six UK hotel-booking sites — and 25 more that followed — resolved a CMA consumer-protection investigation without paying a single pound in penalties.
Read Article→
One Click to Accept, Five to Refuse: What the CNIL and Sephora Cases Reveal About Consent as Choice Architecture
A compliant-looking cookie banner and a compliant cookie banner aren't the same thing — the difference is measured in click counts and visual weight, not…
Read Article→
Inside the 'Iliad' Flow: What Amazon's Cancellation Design Reveals About Retention Metrics vs. Exit Design
A four-page, six-click, fifteen-option cancellation sequence didn't happen by accident — it happened because a retention metric and a simplicity proposal…
Read Article→
The Friction Was Never Symmetric
A good UX instinct — don't interrupt the flow of the experience — produces a different outcome when the action being smoothed is a real-money purchase…
Read Article→
The A/B Test That Became a Regulatory Case Study
Credit Karma's 'pre-approved' claim wasn't a lie — it was an A/B test winner.
Read Article→
Inside the Trade-Off: How Dark Patterns Actually Get Designed (According to Regulators' Own Evidence)
FTC and international regulators don't just allege dark patterns anymore — their complaints now include the internal emails, A/B test data, and executive…
Read Article→
The Winner’s Curse: Why Big A/B Test Wins Rarely Hold Up
The lift in your test report and the lift finance sees a quarter later rarely match. Winner's curse, novelty decay, and regression all shrink it.
Read Article→
Why Your Analytics Tools Never Agree on the Numbers
GA4, your server-side pipeline, and your BI tool report different numbers, and teams cite whichever fits. Why reconciliation is skipped, and the fix.
Read Article→
Marketing Attribution: When Two Teams Claim the Same Conversion
When three teams each claim the same conversion, attributed revenue exceeds reality and budget follows the best dashboard, not the best channel.
Read Article→
Why Enterprise Experimentation Is So Slow
Enterprises govern reversible A/B tests like irreversible decisions, and velocity dies in the approval queue. The reframe that unlocks it.
Read Article→
Zombie Experiments: The Feature-Flag Debt Nobody Owns
Old experiment flags never get cleaned up, quietly contaminate new tests, and occasionally reactivate dead code. The carrying cost of zombie experiments.
Read Article→
Simpson’s Paradox: When a Winning A/B Test Is Actually Losing
An A/B test can show a clean aggregate win while the variant loses in every real segment. Simpson's paradox, why the topline lies, and the fix.
Read Article→
What Actually Breaks When You Switch A/B Testing Tools
Switching A/B testing tools silently redefines your metrics and breaks historical comparability. What the sales demo never shows, and what to check first.
Read Article→
Why Winning A/B Tests Don’t Get Shipped
A winning A/B test isn't a shipped feature. The gap between the tested variant and what actually reaches production is where the value leaks away.
Read Article→
Non-Inferiority Testing for SaaS Revenue Changes
Some SaaS changes should raise revenue. Others should simply not break it. A billing flow rewrite, navigation cleanup, design system migration, or applied
Read Article→
Channel Cannibalization: When a Marketing Win Isn’t Real Growth
A channel metric can rise while total revenue stays flat — the gain came from somewhere else. How to diagnose cannibalization and measure it.
Read Article→
Factorial Experiments for Low-Traffic B2B SaaS
Most low-traffic SaaS teams do not have a testing problem. They have a math problem. If your pricing page gets 8,000 visits a month, a small A/B testing
Read Article→
Which AI Model Should You Actually Use to Build Software? (Hint: It's not the cheapest one)
Token price and benchmark scores are the wrong scoreboard for choosing an AI coding model.
Read Article→
How to Spot Bot Traffic in A/B Tests
When you are trying to spot bot traffic in A/B tests, the numbers can be alarming. In June 2026, Cloudflare Radar reported that bots made up 57.
Read Article→
Missing Event Data: How I Rescue Experiment Analysis
I have seen six-figure decisions ride on an event that never fired. The dashboard said no lift, but revenue reports told a completely different story.
Read Article→
Sales-Assisted Bias in B2B SaaS Testing
A test can win on paper and still lose money. I see this all the time in B2B SaaS. A page changes, form fills rise, the dashboard looks good, then sales
Read Article→
A/B Test Ramp Plans for Revenue-Risk Experiments
A winning test can still lose you money. I see this a lot in high-stakes A/B testing. The team has a solid hypothesis, clean analytics, and good intent,
Read Article→
How I Re-Run an A/B Test Without Fooling Myself
Most bad reruns do not fail in the stats tool. They fail in the story a team tells itself during A/B testing. A first test comes back weak, messy, or
Read Article→
Better Decisions
Make Growth Decisions
You Can Defend
Know what to test, when to trust the result, and what to do next. Practical decision guides for analysts, growth teams, and founders. Free. Weekly.
Opens Substack to confirm your subscription · Free · Unsubscribe anytime