Conversion Rate Lift Calculator
Put a dollar figure on CRO before you run the test. Compare 0.5 to 3 percentage-point lifts on your traffic and AOV.
100% client-side. Store metrics stay in your browser (ons-cvr-lift-inputs).
Revenue impact
+1% conversion rate = $32,500/month in additional revenue
Current ROAS: 13.00x at $5,000 ad spend
Current conversions
1,000
Current revenue
$65,000
Current gross profit
$35,750
55% margin
Lift scenarios
Four percentage-point lifts compared side by side. Annual lift is monthly times 12.
| Metric | +0.5pp2.5% CVR | +1pp3% CVR | +2pp4% CVR | +3pp5% CVR |
|---|---|---|---|---|
| Revenue lift / mo | $16,250 | $32,500 | $65,000 | $97,500 |
| Profit lift / mo | $8,938 | $17,875 | $35,750 | $53,625 |
| Annual lift | $195,000 | $390,000 | $780,000 | $1,170,000 |
| New ROAS | 16.25x | 19.50x | 26.00x | 32.50x |
Revenue by scenario
How this tool works
Conversion rate lift measures the percentage increase between your current conversion rate and a target rate. A jump from 2% to 3% is a 50% relative lift, even though it is only 1 percentage point in absolute terms. This distinction matters because a small absolute change in conversion rate can translate to significant revenue when multiplied by high traffic volumes. The calculator takes your monthly visitors and multiplies them by both your current and target conversion rates to show the raw difference in conversions. Then it multiplies by your average order value to show the dollar impact.
Worked example
E-commerce store profile: Monthly visitors: 200,000. Current conversion rate: 2.1%. Target conversion rate: 2.8%. Average order value: $65. Relative lift: ((2.8 - 2.1) / 2.1) x 100 = 33.3%. Current monthly conversions: 4,200. Target monthly conversions: 5,600. Additional conversions: 1,400. Monthly revenue lift: 1,400 x $65 = $91,000. Annual revenue lift: $91,000 x 12 = $1,092,000. A 0.7 percentage point improvement in conversion rate generates over $1M in additional annual revenue.
Frequently asked questions
What is a good conversion rate?
It depends on the industry. E-commerce averages 2% to 3%. B2B SaaS landing pages average 3% to 5%. Lead generation forms average 5% to 10%. The question is not whether your rate is 'good' in absolute terms, but whether improving it would generate enough revenue to justify the investment.
How much can I realistically improve my conversion rate?
A/B testing programs typically produce 5% to 20% relative lifts per successful test. Most tests fail (60% to 80% show no significant change). A sustained CRO program running 2 to 4 tests per month might produce 2 to 4 winning tests per quarter, each contributing 5% to 15% relative lift.
What is the difference between absolute and relative lift?
Absolute lift is the raw difference: from 2% to 3% is 1 percentage point of absolute lift. Relative lift is the percentage change: from 2% to 3% is 50% relative lift. Relative lift is more useful for comparing across different baseline rates.
Does this account for statistical significance?
No. This calculator shows the projected impact of a confirmed conversion rate change. To determine whether a measured difference is statistically significant, use an A/B test sample size calculator before running your test, and a significance calculator after. Test both approaches with real data from your business before committing to a single strategy.
Should I focus on conversion rate or traffic?
Both matter, but conversion rate improvements compound with all future traffic. If you double traffic, you double revenue once. If you double conversion rate, every future visitor converts at the higher rate. Conversion improvements also do not have the ongoing cost of paid traffic.
How do diminishing returns work?
Each additional point of conversion rate improvement is harder to achieve than the previous one. The visitors who did not convert despite your current experience are increasingly difficult to persuade. Once you are above 5% in e-commerce or 10% in lead gen, gains slow down. Run the calculation monthly to track trend direction rather than relying on a single data point.