Conversion Rate Calculator

Conversion rate is the metric that turns traffic into a business question. Ten thousand visitors is a number; 2.5% of ten thousand visitors converting is a diagnosis, because it tells you whether the problem you have is one of attracting people or of persuading them.

This calculator takes visitors, clicks, and conversions - plus optional spend - and reports the conversion rate, a click-through figure, cost per conversion, and the gap between where you are and a target you set. It draws a three-stage funnel, compares you against a built-in industry reference figure, and exports the whole set as CSV.

Two things in that output need reading carefully: what the click-through figure is actually dividing, and how much weight the benchmark deserves. Both are covered below.

Both percentages here use visitors as the denominator. Conversion rate is conversions over visitors, which is the standard definition. The click-through figure is clicks over visitors - which is not the conventional meaning of CTR, where clicks are divided by impressions. What this tool is measuring is the proportion of visitors who took an intermediate action on the page, which is a useful mid-funnel number under an unfortunate label.

Reading it correctly matters because the two interpretations lead to opposite conclusions. A 15% figure would be an extraordinary ad CTR and is an unremarkable rate of visitors clicking a call to action. If you enter ad impressions in the visitors field and ad clicks in the clicks field, the label becomes accurate and the conversion rate becomes conversions-per-impression, which is a different and much smaller number than a landing page conversion rate.

The way to avoid confusing yourself is to fix what a "visitor" means before you start. Landing-page analysis: visitors are sessions on the page, clicks are call-to-action clicks, conversions are completed actions. Ad analysis: visitors are impressions, clicks are ad clicks, and you should read the conversion rate as click-to-conversion only if you also put clicks in the visitors field. Mixing the two produces figures that look plausible and mean nothing.

The industry selector applies a fixed reference figure - a single round number per category - and tells you whether you are above or below it. Those figures are built-in reference values chosen as rough orientation, not measurements from a dataset held here, and the categories are broad enough that "Ecommerce" spans a business selling £3 phone cases and one selling £3,000 bicycles. Treat the above-or-below verdict as a sanity check on the order of magnitude and nothing more.

The reason to be sceptical is that conversion rate is dominated by factors the category cannot capture: price point, traffic source, brand familiarity, and what counts as a conversion. A newsletter signup and a completed checkout are both "conversions" and differ by an order of magnitude in rate. Comparing your checkout rate against a benchmark built partly from signup rates will always flatter or damn you for the wrong reason.

The target field is the more useful of the two comparisons, because you set it. Entering a target you have a reason to believe in - last quarter’s rate, the rate the unit economics require, the rate a competitor’s public case study claims - turns the gap figure into something actionable. The tool then tells you exactly how many additional conversions that target requires at your current traffic, which converts a percentage into a number of people.

Both rates are computed by dividing and multiplying by 100, with a guard that returns zero when the denominator is zero, so an empty visitors field produces 0.00% rather than an error. Cost per conversion is spend divided by conversions and is displayed only when both are greater than zero, appearing as a dash otherwise. All monetary and percentage outputs are fixed to two decimal places.

The gap is your target minus your actual conversion rate, shown with an explicit plus sign when positive - so a positive gap means you are below target and have that many percentage points to make up. Conversions needed is your target percentage applied to your current visitor count and rounded up, and the conversions gap is that figure minus your actual conversions, floored at zero.

The funnel bars are scaled against the largest of the three stage values rather than against visitors specifically, so they stay legible even if you enter figures in an unusual order. The benchmark verdict compares your rate against the fixed value for the selected category and is suppressed entirely when visitors is zero. The CSV export writes every one of these figures, including the inputs and the benchmark value, so a saved file is self-documenting. Everything runs locally.