Conversion rate benchmarks and how to calculate them
A benchmark is only useful if you know what it is measuring and how yours was calculated. Each guide gives the formula first, then a range to compare against, then what actually moves the number.
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Conversion rate itself
The formula, the common ways it gets miscounted, and what normal looks like.
- 01How to Calculate Conversion Rate: Formula and ExamplesTo calculate conversion rate, divide conversions by visitors and multiply by 100. The two free variables that change the answer, why a correctly calculated rate is still not evidence, and what has to be attached to it before it decides anything.
- 02Conversion Rate Benchmarks by Industry and Funnel StageConversion rate benchmarks by industry and funnel stage: e-commerce averages 2-3%, SaaS trial signups 2-5%, trial-to-paid 15-25%, B2B landing pages 2-5%. Plus the one thing they can honestly tell you, and the segmented baseline that replaces them.
- 03E-Commerce Conversion Rates: 2026 Benchmarks by IndustryThe average e-commerce conversion rate runs 2.5% to 3.0%. Benchmarks by industry, device, channel and geography, what the composite hides, and why the right benchmark still names nothing for you to change.
- 04What Is a Landing Page Conversion? Definition, Formula, and 2026 BenchmarksA landing page conversion is when a visitor takes the page target action: buying, signing up, or booking. Learn the formula, industry benchmarks, and how person-level analytics measures it correctly.
02
Engagement measures
Bounce rate, exit rate and time on site: what each one really counts.
- 01What Is Bounce Rate? Definition, Benchmarks, and How to Reduce ItUnderstand bounce rate, what it really measures, industry benchmarks, and proven strategies to reduce it. Learn why bounce rate alone can be misleading and what metrics to pair it with.
- 02Bounce Rate vs. Exit Rate: What Is the Difference and Why It MattersUnderstand the key difference between bounce rate and exit rate, when each metric matters, and how to use both to diagnose user experience issues and improve your website performance.
- 03Time on Site and Time on Page: Definitions and BenchmarksTime on site measures a whole visit; time on page measures one page within it. Why single-page visits record as zero, why no two tools agree, and why even a perfect measurement would still have no direction.
03
Revenue and lifetime value
What a customer is worth, and the retention that decides it.
- 01How to Calculate Customer Lifetime Value (LTV): Formulas and ExamplesEvery LTV formula is a bet on a lifespan you have not observed. The formulas and where the forecast breaks, why the number is inert until it sits over a cost, and the cohort version that removes the guess.
- 02Maximizing Customer Lifetime Value: 6 Retention StrategiesSix retention strategies for maximizing customer lifetime value: personalized onboarding, proactive churn prevention, expansion revenue, behavior-based loyalty, feedback loops, and re-engagement.
- 03Customer Lifetime Value for E-commerce: How to Calculate and Increase LTVLearn how to calculate customer lifetime value for e-commerce businesses. Covers LTV formulas, segmentation by customer cohort, and strategies to increase it.
- 04Monthly Recurring Revenue (MRR): The Complete Guide for SaaS TeamsLearn how to calculate, track, and grow MRR correctly. Covers new MRR, expansion MRR, churn MRR, and common calculation mistakes.
04
Testing a change
Sample size, significance, and reading a result honestly.
- 01A/B Test Sample Size: How to Calculate It and Why Most Teams Get It WrongHow to calculate A/B test sample size using MDE, baseline conversion rate, and statistical power, plus common mistakes like stopping early and peeking at results.
- 02A/B Testing Statistical Significance: When to Call a WinnerUnderstand statistical significance in A/B testing. Learn sample size calculation, confidence intervals, and why stopping tests early gives false results.
- 03Running Multiple A/B Tests? How to Avoid False Positives From Multiple ComparisonsThe multiple comparisons problem in A/B testing, covering false positive math, Bonferroni correction, Benjamini-Hochberg procedure, sequential testing, and practical testing guidelines.
- 04Introduction to A/B Testing: How to Run Experiments That Actually WorkA beginner-friendly guide to A/B testing covering hypothesis formation, test design, statistical significance, and common mistakes to avoid.