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.
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Writing · Lean Experiments
Revenue-First
Revenue-First
Experimentation
Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
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
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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.
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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.
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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.
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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
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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,
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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
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Sticky Bucketing in Logged-Out SaaS A/B Tests
If the same visitor sees variant A on Monday and variant B on Wednesday, your A/B testing efforts are not measuring behavior. They are measuring confusion.
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A/B Testing Seasonality for B2B SaaS Teams
I have seen teams ship the wrong variant because week three landed on quarter end, and buyers stopped moving. The test looked clean, but the revenue impact
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Server-Side vs Client-Side Testing in B2B SaaS
Most teams pick an A/B testing method based on who can ship faster this sprint. That is how bad bets get dressed up as experimentation.
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Survival Analysis SaaS: Managing Tests With Delayed Revenue
You ran the test. Signups moved. Activation moved. Revenue did not. At least not yet. This is where many SaaS teams make an expensive mistake.
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CUPAC Explained for Low-Traffic SaaS Teams
Most low-traffic SaaS teams do not have a testing problem. They have a waiting problem. If you only get a few thousand meaningful users a month, a clean
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Switchback Experiments in SaaS With Network Effects
Most bad product tests don't fail because the idea was weak. They fail because the test assigned treatment to the wrong unit.
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Cookie Consent Bias in A/B Tests, and How I Correct It
Your test can look clean and still be wrong. If analytics starts only after consent, you are not measuring visitors. You are measuring the subset willing
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Cluster Randomized Tests for B2B SaaS Accounts
When I review a B2B SaaS test plan, I start with one question: can people inside the same account affect each other? If the answer is yes, user-level A/B
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How I Handle Revenue Outliers in A/B Tests
A test can look like a winner because three customers showed up with a corporate card. I've seen teams ship bad changes, celebrate the lift, then spend a
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Experiment Naming Conventions for Search and Reporting
If your team can't find a winning test in 30 seconds, the name is broken. I care about experiment naming conventions because I've watched bad names slow
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SaaS Checkout Optimization: A/B Tests That Cut Friction
Most SaaS checkouts do not fail because the buyer suddenly stops wanting the product. They fail at the last minute when doubt beats momentum, which is why
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Ratio Metrics in A/B Testing Without Misreading Revenue Lift
A test can lift conversion and still hurt revenue. I have watched teams ship winners that looked great in the dashboard but weak in the finance review.
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Triggered Analysis for SaaS A/B Tests With Partial Exposure
You can run a clean test and still make the wrong call. I see it all the time in SaaS. The experiment is randomized, the stats look fine, and the
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Exposure Logging Attribution Makes A/B Test Results Trustworthy
Most bad A/B test calls are not statistics problems. They are measurement problems. I see the same mistake over and over.
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The Decision Latency Metric for Experimentation Teams
A test doesn't create value when the chart turns green. It creates value when somebody decides. I've seen teams run clean experiments, get solid analytics,
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Activation Metrics: How to Pick the One That Predicts Retention
Most teams track the wrong activation metric. A practitioner's guide to choosing an activation metric that statistically predicts retention, instrumenting…
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