Website Page Speed Loss Calculator
Your website scenario
Turn slow load times into an estimated business impact.
These are editable planning scenarios, not benchmark claims.
Currency and optimization cost
Modeled monthly revenue opportunity
+$10,622.90 / month
+$127,474.75 / year
≈ 142 additional conversions/month
+5% relative conversion change per second faster
These are editable planning scenarios, not benchmark claims.2.2 seconds faster: 4.2s → 2s.
Under the selected assumptions, this scenario corresponds to +$10,622.90 in modeled monthly revenue. Actual outcomes may differ.
Current vs target scenario
| Metric | Current | Target scenario | Difference |
|---|---|---|---|
| Load time | 4.2s | 2s | -2.2s |
| Conversion rate | 2.5% | 2.7833% | 0.2833 percentage points |
| Conversions | 1,250 | 1,391.64 | 141.64 |
| Monthly revenue | $93,750.00 | $104,372.90 | +$10,622.90 |
| Annualized revenue | $1,125,000.00 | $1,252,474.75 | +$127,474.75 |
Business-impact waterfall
- 50,000 monthly visitors
- Current CVR: 2.5%
- 1,250 current conversions
- Speed scenario: 4.2s → 2s
- Modeled relative CVR change: 11.33%
- Additional / lost modeled conversions: 141.64
- Potential revenue difference: +$10,622.90/month
Illustrative assumption scenarios
Same target load time, different sensitivity assumptions. No scenario is designated most likely. Select Custom to edit the assumption.
| Assumption | Modeled target CVR | Additional / lost conversions | Monthly difference | Annualized difference |
|---|---|---|---|---|
| Conservative scenario (2%/s) | 2.6113% | 55.66 | +$4,174.57 | +$50,094.79 |
| Typical scenario (5%/s) | 2.7833% | 141.64 | +$10,622.90 | +$127,474.75 |
| Aggressive scenario (10%/s) | 3.0832% | 291.61 | +$21,870.59 | +$262,447.09 |
Simple modeled payback period
Enter an optional optimization cost to estimate simple modeled payback.
Ignores timing uncertainty, implementation risk, discounting, recurring costs, attribution uncertainty, taxes and margins. This is revenue arithmetic, not investment advice.
Traffic-growth equivalent
To create the same modeled revenue increase using additional traffic alone, you would need approximately 5,666 more visitors per month at the current conversion rate and order value. This is an arithmetic equivalence only.
Show calculation
- Current conversions: 50,000 × 2.5% = 1,250
- Current revenue: 1,250 × $75.00 = $93,750.00
- Load-time difference: 4.2 − 2 = 2.2 seconds
- Multiplier: (1 + 0.05)^2.2 = 1.113311
- Target modeled CVR: 2.5% × multiplier = 2.7833%
- Target conversions: visitors × target CVR = 1,391.6386
- Revenue difference: (target conversions − 1,250) × $75.00 = +$10,622.90/month. Annualized = monthly × 12 = +$127,474.75.
How is speed impact modeled?
There is no universal conversion penalty per second. A selected scenario describes relative conversion-rate change per second. Replace illustrative assumptions with your own measured analytics or A/B-test evidence where available. A site’s own experiments and historical data are generally more relevant to its economics than generic studies.
How to use
Turn slow load times into an estimated business impact under transparent conversion assumptions.
Enter monthly visitors, current page load time in seconds, current conversion rate, average value per conversion and target load time. Select a visible impact assumption. Purchases, leads, signups or trials may be represented by conversion rate and their average value. Use consistent measurement definitions for current and target load time. Currency formats USD, EUR or GBP without converting values. Optional optimization cost enables simple modeled payback. Copy Result includes the assumption and business case when cost is entered. Reset and refresh clear the scenario.
How the model works
Conversion-impact formula
Current conversions = monthly visitors × current conversion rate (as a fraction). Current revenue = current conversions × average value per conversion. Speed difference d = current load time − target load time. With relative sensitivity s per second, modeled conversion multiplier = (1 + s)^d. Target modeled CVR = current CVR × multiplier. Target conversions = visitors × target CVR. Expected conversions remain unrounded in revenue calculations.
Page speed and conversion assumptions
There is no universal conversion penalty per second. Conservative (2%), Typical (5%) and Aggressive (10%) are illustrative sensitivity scenarios, not empirical findings or likelihood ratings. These are editable planning scenarios, not benchmark claims. Custom replaces them with your own assumption. A site’s own analytics, experiments and historical conversion data are generally more relevant to its economics than generic industry studies. No particular engineering change is assumed to achieve a specific uplift.
Relative conversion change
A relative conversion change multiplies the starting rate. A 10% relative increase applied to 2.5% is 2.5% × 1.10 = 2.75%, not 12.5%. That is a 0.25 percentage-point increase. The selected per-second assumption compounds across the load-time difference; total relative change = (multiplier − 1) × 100%.
Current vs target revenue
Target monthly revenue = target conversions × average value. Modeled monthly revenue difference = target revenue − current revenue. Annualized opportunity = monthly difference × 12, assuming unchanged conditions for twelve months. A negative difference describes a modeled revenue decrease. This is potential revenue under entered assumptions, not an observed loss or a prediction.
Speed scenarios
The target-time slider spans 0.5–10 seconds. The numeric field supports more than 0 to 60 seconds and remains authoritative. Equal current and target times give multiplier 1 and zero modeled change. A slower target gives a negative speed difference; with positive sensitivity it reduces modeled CVR and revenue. Negative sensitivity reverses that assumed relationship. At −100%, a faster target models zero CVR, no change retains current CVR, and a slower target is undefined. Scenarios above 100% CVR are rejected without clamping.
Optimization payback
Simple modeled payback months = optimization cost / positive modeled monthly revenue opportunity. No positive modeled monthly revenue gain exists under a zero or negative scenario, so a simple payback period is not calculated. The calculation ignores timing uncertainty, implementation risk, discounting, recurring optimization costs, attribution uncertainty, taxes and margins. It is revenue arithmetic, not investment advice.
Traffic-growth equivalent
Revenue per current visitor = current CVR × average value per conversion. Equivalent additional visitors = monthly revenue opportunity / revenue per current visitor. This answers how much additional traffic at the current economics corresponds to the same modeled revenue increase. It is an arithmetic equivalence only and says nothing about acquisition cost or which approach is better. At zero CVR or zero conversion value, the equivalent is undefined.
To examine acquisition costs, compare paid-acquisition break-even assumptions in a separate order-economics model. Equivalent visitors and hypothetical speed-related revenue do not establish paid-traffic costs, causal advertising value or a preferred strategy.
Page load vs Core Web Vitals
“Load time” is a simplified user-entered scenario in seconds, not a Core Web Vitals score. LCP measures when the largest visible content element renders; INP measures interaction responsiveness; CLS measures visual stability. TTFB measures time to the first byte of a response. These are different measurements and cannot be substituted for generic load time in this model. Modern performance analysis uses multiple user-experience metrics rather than a single page-load number.
You can check bandwidth assumptions separately using traffic and payload inputs. Extra throughput does not guarantee a particular load time, Core Web Vitals score or conversion gain.
Worked examples
Default: 4.2s → 2.0s
50,000 visitors × 2.5% = 1,250 current conversions. Current revenue = 1,250 × $75 = $93,750.00/month. With the illustrative Typical 5% per-second assumption, d = 2.2 and multiplier = (1.05)^2.2 = 1.11331089. Target modeled CVR = 2.783277%; target conversions ≈ 1,391.638615. Monthly opportunity = +$10,622.90; annualized = +$127,474.75. Display rounding does not affect arithmetic.
No change: 2.0s → 2.0s
0 seconds difference gives multiplier 1, 0% modeled relative CVR change and $0 revenue difference.
Slower target: 2.0s → 4.0s
With the same baseline and 5% sensitivity, d = −2. Target CVR = 2.267574%, conversions ≈ 1,133.786848, monthly difference = −$8,715.99, annualized = −$104,591.84.
Simple modeled payback
With 50,000 visitors, 2.5% CVR, $75 value and 3s → 2s at 5%, the monthly opportunity is $4,687.50. A $4,000 optimization cost / $4,687.50 per month = 0.853333 months. This excludes margins and other stated limitations.
Assumptions and limitations
The model assumes monthly visitors and average conversion value stay constant, the selected conversion relationship is valid across the whole speed range, and no other business conditions change. It does not model traffic seasonality, marketing channel mix, attribution, SEO ranking effects, repeat purchases, LTV, margin, refunds, taxes, device segmentation, Core Web Vitals ranking impact or actual engineering outcomes. Bounce rate is not modeled or inferred. Faster load time does not guarantee more revenue or better Google rankings; no speculative SEO revenue is included. Inputs remain in memory, with no storage, URL state, accounts, site crawling, external performance lookup, network calculations or user-input analytics payloads.
FAQ
How do I estimate the revenue impact of page speed?
Enter traffic, conversion rate, conversion value and current/target load times, then choose a transparent relative conversion sensitivity. Compare modeled revenue while holding traffic and value constant.
Does every extra second reduce conversion by the same amount?
No universal relationship is assumed. This scenario compounds a selected relative rate per second; the absolute change depends on the starting rate and speed range.
What assumption does this calculator use?
The default Typical scenario is an illustrative 5% relative conversion change per second faster. Conservative uses 2% and Aggressive 10%; Custom is editable. These are planning scenarios, not benchmark claims.
What is the difference between relative conversion change and percentage points?
A 10% relative increase takes 2.5% to 2.75%. The percentage-point change is 0.25, not 10. Relative change multiplies the current rate.
How is estimated lost revenue calculated?
Calculate current and target expected conversions without rounding, multiply their difference by average conversion value, and annualize by multiplying by 12. A negative result is a modeled decrease under the assumptions.
How does the target-load-time slider work?
It updates the same target value as the numeric input over 0.5–10 seconds. The numeric field also allows targets outside the slider range, up to 60 seconds, and remains authoritative.
How is the optimization payback period calculated?
Divide entered optimization cost by positive modeled monthly revenue gain. Zero or negative gain has no calculated payback. Costs, margin, risk and timing limitations may materially change real economics.
What does the traffic-growth equivalent mean?
It divides the modeled monthly revenue difference by current revenue per visitor. This is arithmetic equivalence at unchanged conversion and value, not an acquisition-cost comparison. Zero CVR or value makes it undefined.
Is page load time the same as Core Web Vitals?
No. Generic load time is a scenario input. LCP, INP and CLS describe distinct loading, responsiveness and stability experiences. TTFB is another separate metric.
Does faster page speed guarantee more revenue or better search rankings?
No. This sensitivity model cannot establish causation, promise conversion outcomes or predict Google rankings. Use your own experiments and analytics to assess the relationship.
Sources
These sources explain performance metrics; they do not supply or endorse the illustrative conversion presets. No study metrics are mapped into generic per-second assumptions.