Website Conversion Optimization: Find Where Leads Drop Off, Fix the Cause

There is no universal figure, and a "normal" number from someone else's article tells you little. Conversion depends on the niche, the price, the traffic source and what exactly is being counted: a store selling cheap accessories and an industrial equipment site live in different orders of magnitude. It is more useful to compare your site with itself, step by step through the funnel and across channels: that shows where you are losing people.

As the share of visitors who completed a target action: an order, a lead, a call. The hard part isn't the formula, it is the numerator. If the event fires both on form submission and on the thank-you page, the report shows twice as many leads as there really are. If calls and messengers aren't tracked, some inquiries disappear. So the first step of any conversion optimization is reconciling the report with real leads.

With tracking, then with the funnel. First we make sure the numbers are true, then we see which step has the biggest drop-off and go there. The first findings are usually mundane: a form that won't accept the phone number format, shipping shown only at the end, a checkout button hidden under a banner on mobile. These get fixed right away, without tests, because there is no doubt about them.

A UX audit is a one-time report: we walk the shopper's path and point out where it breaks. Conversion optimization goes further. It relies on your data, includes the fixes and checks whether each change had an effect. If the site has no analytics or it is broken, it is more honest to start with an audit or a tracking setup and optimize once there is something to rely on.

No. An A/B test is for debatable changes, when it is unclear which option is better. For obvious defects it is pointless: you don't test a broken form, you fix it. A test also only makes sense with enough visitors and orders. With low traffic, the difference between variants drowns in randomness, and then it is more honest to compare before and after, keeping that method's limits in mind.

Yes, but a different kind. With a few hundred visits a month, it is almost impossible to prove a difference between variants statistically, so we don't run tests there. Obvious defects, however, are visible without a big sample: walking the path by hand, session recordings if Hotjar or Clarity is connected, and reconciliation with leads show where people get stuck. Those can be fixed right away, and the effect assessed once more data comes in.

Nobody knows that in advance, and anyone quoting a percentage before the work starts is making it up. We can tell you what stops people from ordering right now and in what order we will remove it. Your analytics will show the result of each change, and we report it as it is, including the cases where there turned out to be no difference.

The stages are listed above as ranges, and the full cycle from tracking check to findings depends on your traffic. The diagnosis is short; verifying changes takes the longest, because a test can't be stopped the moment a number looks good, it has to collect enough data. After that you can work in cycles until the next constraint sits outside the website, for example in pricing or in how fast a manager replies.