Conversion Rate Optimization Cheat Sheet

Conversion rate optimization is not a quick win. It is a system.

Why a 1 percent lift can be worth millions, and how to compute it.

The 7 sections every high-converting landing page has.

What goes above the fold, in what order.

Every extra field costs you 4 percent of submissions.

Find every moment that makes the user hesitate.

Color, copy, size, position. Decades of testing distilled.

Real scarcity works. Fake urgency burns trust.

65 percent of traffic is mobile. Most pages still ignore it.

The cheapest visitor you will ever buy is the one already leaving.

Why most A/B tests are called too early.

Badges, reviews, guarantees: what actually moves trust and what does not.

The 100ms = 1 percent revenue relationship: the data behind fast pages and higher conversions.

When to use which, and why most teams should not use multivariate.

Same toolkit, different mandates.

Segment-specific pages, dynamic copy, and when it actually moves the needle.

Heatmaps, recordings, surveys. The qualitative side of a quantitative discipline.

The highest-revenue square foot on the internet: fields, friction, payment options, and trust.

The 5 SaaS conversion moments: signup, activation, free-to-paid, tier upsell, seat expansion, and how to optimise each.

One-click upsells, loyalty prompts, referral incentives, and account activation onboarding loops.

Map motivation, ability, and prompts to your signup flows, onboarding, and checkout using BJ Fogg's Behavior Model for measurable conversion lift.

Six types of social proof: placement science, review widget design, live signals, and testing frameworks to maximize conversion lift without diluting credibility.

Why revealing complexity only as needed converts better than one long form, and when progressive disclosure backfires.

AI-referred visitors land differently: higher intent, less brand familiarity, and often invisible to your analytics. Here's how to convert them.

App CRO is not web CRO with smaller screens. Learn how App Store listing conversion (ASO) and post-install funnels actually behave, and the testing framework that improves both.

Peeking, multiple comparisons, and Simpson's Paradox, the three ways a test with a correct sample size still lies to you.

How to design a cancellation flow that legitimately saves 10-34% of leaving customers, and where the line sits between a real retention offer and an FTC-flagged dark pattern.

How to find which small actions actually predict a sale, and how to optimize the whole path instead of only the last click.