Conversion rate optimization means raising the share of visitors who do what your page is there for: buy, enquire or sign up. That one action is the conversion, and the conversion rate is its share: conversions divided by visits, times 100. The process has five steps: set the goal, measure the baseline, write down a hypothesis, test, evaluate. The usual levers are the offer, the first screen, the form, load time and trust. An A/B test shows whether a change works, but only with enough visitors, and you work out how many before you start. On small sites, often only big changes are measurable in reasonable time.
How to build the page is covered in Create a Landing Page: Guide and Tools. This article is about what comes after: measuring whether it works, and changing it for a reason. To have it built instead, see Hiring a Landing Page Designer: Steps, Cost. In my business, Sebastian, my AI employee for SEO, GEO and blogging, does the measuring; more below.
What a conversion is and how to calculate the conversion rate
You decide what counts as a conversion. Google's Ads help: "You choose what you identify as valuable, like a purchase, sign-up, or phone call." In Google Analytics the same thing is now called a key event: "an event that measures an action that's particularly important to the success of your business."
Conversion rate = conversions ÷ visits × 100
An example: suppose an offer page gets 1,500 visits a month and 45 enquiries. The conversion rate is 45 ÷ 1,500 = 3%.
Two decisions come first:
- What goes in the denominator? Visits, visitors or sessions give different rates; pick one and stick to it, in the numerator as in the denominator: with visits, count visits with at least one conversion; with visitors, count visitors. Ten presses of the same button count once.
- Which stage are you measuring? Between visit and enquiry there is usually an intermediate step, such as the click on the button to the form. In the example, 120 clicks from 1,500 visits are 8%, and 45 of those 120 submit the form, 37.5%. Now you see where people drop out.
The usual levers: where to look first
- Offer. If the offer doesn't fit, no button colour helps. Say clearly what someone gets, who it is for and what it costs, before touching any other lever.
- First screen. Google's Ads help advises putting important information "towards the top of the page", and the landing page should mirror the call-to-action in the ad. Whoever clicked on "free check" looks for exactly that, at the top.
- Form. Every field takes effort. Ask only what the next step needs, and check the failure case: are entries lost when something goes wrong?
- Load time. Google sets fixed thresholds for the Core Web Vitals: the largest visible element (LCP) within 2.5 seconds, response to input (INP) of 200 milliseconds or less, layout shift (CLS) of 0.1 or less, each measured at the 75th percentile of page loads, split by mobile and desktop. Tools to check it: SEO Audit Tools Compared.
- Trust. Real proof instead of claims, a process that says what happens after submitting, easy-to-find legal details. Invented reviews or numbers don't belong on the page.
Start where your numbers point: drop-outs before the button suggest the offer and first screen, drop-outs in the form suggest the form.
A/B test: work out sample size and run time yourself
There is no general minimum number of visitors; it depends on your baseline and the difference you want to detect. In their survey of online experiments, Kohavi and colleagues give a formula for the minimum sample per variant at 95% confidence and 80% power:
n = 16 × p × (1 - p) ÷ Δ²
Here, p is the original's conversion rate and Δ the difference to detect, as a fraction (0.6 percentage points = 0.006). Using the example: 3% baseline, 50 visits a day, split evenly.
The formula is an approximation. The full two-proportion formula many calculators use gives 2,514 instead of 2,069 per variant in the middle case, so treat the table as a lower bound.
Run time = total visits ÷ visits per day, rounded up to whole weeks. Kohavi and colleagues recommend at least a week or two, then multiples of a week, because weekend visitors often differ from weekday ones, and a 50/50 split: the smaller the variant's share, the longer the test.
When you have few visitors
At 50 visits a day, +20% is practically unmeasurable. What still works:
- Test big changes, not fine-tuning. Only big differences fit your run time, so test changes that could do a lot: a new offer, a completely different headline, a form half as long.
- Measure the nearer step. Clicks on the button are more frequent than enquiries. At an 8% click rate, detecting +50% needs 736 visits per variant by the formula, 5 full weeks instead of 12.
- Before and after, with open eyes. Without a simultaneous comparison group, an increase is no proof. Compare periods of equal length, check whether the whole website moved too, and note what else ran: ads, newsletters, season.
- Watch. Let a few people from your audience use the page and say aloud what they look for. No rate, but hypotheses.
Three mistakes that make a test worthless
Stopping too early. Keep checking and stop at the first "significant", and you find winners that aren't. Johari and colleagues call the usual p-values "wholly unreliable" then. A simulation for this article shows how much: an A/A test, two identical variants at 3% each, 25 visits a day per variant, 12 weeks, 20,000 runs. Evaluated once at the end, 5.1% of runs declared a winner, as expected at 95% confidence. Stopping at the first hit, it was 20.5% with weekly checks and 32.6% with daily checks. With identical variants, every such winner is chance.
Too many variants. At 95% confidence, each comparison with the original has a 5% false-alarm rate. With three variants, the chance of at least one false winner is 1 - 0.95³ = 14%. Each extra variant also splits your visitors further. With few visitors: one variant against the original.
The wrong metric. More clicks are not automatically more enquiries, nor more enquiries more orders. Removing the price may bring more enquiries, but more that don't fit. Decide beforehand on the metric closest to the goal that is reachable within the run time, and watch the other stages as a check. If the visitors come from ads, revenue per ad dollar belongs in the picture too, see ROAS Formula: Calculation and Worked Examples.
How I measure on kevinwelter.com
Since 19 September 2026 I measure, cookie-free, which button on which page gets clicked on kevinwelter.com. Every button into my community carries an event named after its placement, and the same name is in the link. That shows me per page whether the button at the top or the one in the closing section produced the click: the intermediate stage from the first section. Where that stage sits on the whole path to the community is shown in Marketing Funnel Examples: Stages in Practice.
Measuring is Sebastian's job in my business, in a fixed chain: measurement, finding, action, remeasurement with the same setup. Every figure comes from a saved tool result in his own project folder. He measures load times and Core Web Vitals with Lighthouse and Unlighthouse and pulls real-world field data through the PageSpeed Insights API. He puts nothing live himself; I approve that. How he works is on AI for SEO and Blogging: How Sebastian Works.
Frequently asked questions
What is a good conversion rate?
One better than your own previous rate, measured the same way. A general target helps little, because the rate depends on offer, price, traffic source and what you count as a conversion: a first consultation and a newsletter sign-up are not comparable.
How do I calculate the conversion rate?
Conversions divided by visits in the same period, times 100: 45 enquiries from 1,500 visits are 3%. Numerator and denominator must match: same page, same period, without your own test visits.
How many visitors do I need for an A/B test?
Work it out: with n = 16 × p × (1 - p) ÷ Δ², a 3% baseline needs about 520 visits per variant to detect a doubling, almost 13,000 for +20%. Divide the total by your daily visits to see whether the test fits your time frame.
Which tool do I need for A/B tests?
Only once the calculation shows you have enough visitors. Google Optimize has not been available since 30 September 2023; Google names AB Tasty, Optimizely and VWO as providers with a Google Analytics integration. For ads, Meta has its own A/B test, described in How to Run Ads on Meta: A Guide.
What to do next
Write three lines for your most important page: the metric, its current value and the smallest difference worth having. Then work out the run time. If it doesn't fit into three months, test a bigger change or measure a nearer step. If you'd rather not build the measuring yourself: there's a finished package for Sebastian in my community AI Employee Framework, with the tool chain and the project folder structure ready to rebuild.
