Pre-Lander Metrics That Actually Matter (Not Just Pageviews)

The metrics that actually matter on a pre-lander are click-through rate to the offer, dwell time, scroll depth, and where readers drop off before they click. Pageviews and raw traffic tell you almost nothing on their own; a pre-lander's whole job is to move a cold click into a warm, pre-sold one, so every metric worth tracking measures some part of that transition.

Most media buyers who are new to advertorial funnels default to watching the same three numbers they'd watch on a landing page: visits, time on site, bounce rate. Those aren't wrong, exactly, they're just not built for what a pre-lander does. A pre-lander isn't trying to hold attention for its own sake. It's trying to build enough context and trust in 20 to 60 seconds that the reader clicks through to the actual offer already convinced. That means the metric that matters most isn't "did they stay," it's "did they leave through the door I built for them."

Click-through rate to the offer#

This is the number. Every pre-lander has one exit that counts: the link, button, or "Continue Reading" click that pushes the visitor into the actual offer or checkout page. Everything else on the page exists to move readers toward that exit.

Track it as a percentage of pre-lander visits that click through, not as a percentage of ad clicks. If your ad CTR is healthy but your pre-lander CTR is weak, the leak is in the story, not the hook. A common pattern across the funnels in OpenAdLibrary's index is that winning pre-landers push 60 to 85% of visitors through to the offer; anything meaningfully under that on a page that's had a few days to gather data usually means the angle promised on the ad doesn't match what the pre-lander delivers, or the CTA is buried below the fold.

Dwell time (and why average is the wrong stat)#

Dwell time, how long a visitor stays before clicking through or leaving, tells you whether people are actually reading or just skimming past. But the average is misleading on its own. A pre-lander with a 45-second average dwell time could mean everyone reads for 45 seconds, or it could mean half the traffic bounces in 3 seconds and the other half reads for 90. Those are two completely different problems.

Where your tracker supports it, look at the distribution, not just the mean. A cluster of very short visits usually points to a mismatch between the ad's promise and the pre-lander's opening line. If people are landing and leaving in under 5 seconds, the headline or hero image on the pre-lander doesn't continue the story the ad started; the visual and copy need to pick up exactly where the ad creative left off, not restart the pitch from scratch.

Scroll depth and the drop-off curve#

Scroll depth shows you where the story loses people. Most trackers or heatmap tools will give you percentage-of-page markers (25%, 50%, 75%, 100%), and the shape of that curve is more useful than any single number.

If you see a cliff right before the call to action, that's usually not a copy problem, it's a design problem: the CTA either looks like another native ad unit (banner blindness) or the transition into it is too abrupt after a slow, story-driven build.

Time-to-click and its relationship to trust#

Time-to-click, the gap between page load and the click that sends someone into the offer, is a rough proxy for how much persuading the page had to do. A very short time-to-click on a page that's designed to run 800+ words of story usually means people are skipping straight to the CTA without reading, which is fine for warm or highly motivated traffic but a red flag if your creative angle depends on building context first. A long time-to-click paired with a healthy click-through rate is often a sign of a well-built advertorial: people are reading the whole thing and converting anyway.

Setting up tracking without guessing#

None of this works without a tracker that can separate the pre-lander event from the offer event. At minimum you need:

• A distinct click ID for the ad click, the pre-lander view, and the click-through to the offer, so you can chain the three and calculate true funnel conversion at each stage.
• Scroll and dwell events fired from the pre-lander itself, not just the offer page. Most trackers used by affiliates and media buyers (Voluum, RedTrack, and similar) support custom postbacks for this; the postback URL needs to fire on the pre-lander's own exit event, not just the final conversion.
• Segmentation by traffic source and device. A pre-lander that converts at 70% on mobile Taboola traffic can convert at 40% on desktop Outbrain traffic with the exact same copy, because reading behavior and screen real estate differ enough to change how the scroll curve plays out.

What longevity tells you that a single day's metrics can't#

A single day of pre-lander metrics is noisy. Traffic quality shifts hour to hour, and a bad afternoon doesn't mean a bad funnel. The more reliable signal, especially when you're researching what's working rather than testing your own page, is how long a creative has stayed live. Advertisers don't keep paying for traffic into a pre-lander that leaks conversions; a funnel still running after two or three weeks has, almost by definition, cleared whatever CTR and dwell bar the advertiser set for it.

That's the angle OpenAdLibrary's ad intelligence tooling is built around: instead of guessing at someone else's internal metrics, you watch how long their exact creative-to-landing-page combination survives in the wild. A pre-lander pattern that keeps reappearing across different advertisers in the same vertical, with long observed run times, is a much stronger signal than any single funnel's dashboard.

Reading these metrics alongside creative fatigue#

Pre-lander metrics don't live in isolation from the ad creative that feeds them. A funnel with a strong click-through rate in week one can quietly decay by week three even if you haven't touched a word of copy, because the ad creative driving traffic to it has hit creative fatigue: the same audience segment has now seen the ad enough times that the initial curiosity that made the hook work has worn off. When you see dwell time and scroll depth holding steady but click-through slowly sliding, check the ad side of the funnel before you touch the pre-lander itself. Swapping in a fresh image or headline on the ad, while keeping the exact same pre-lander, is often enough to reset the numbers, which tells you the page was never the problem.

The reverse also happens. A pre-lander can start strong and then degrade because the offer page it's feeding into changed, a price went up, a form got longer, without anyone updating the pre-lander's promises to match. If click-through to the offer stays healthy but your downstream conversion rate drops, look at whether the pre-lander is still making an accurate promise about what's on the other side of that click.

Common mistakes when reading these numbers#

The biggest mistake is optimizing dwell time upward as if it were a goal on its own. Longer isn't better; a pre-lander that holds attention for two minutes but converts worse than one that holds it for 30 seconds is the worse page, full stop. Dwell time is diagnostic, not a target.

The second mistake is judging a funnel off day-one data. New pre-landers need at least a few hundred visits, ideally across more than one day-part, before the click-through and scroll numbers stabilize enough to act on. Killing a page after 40 visits because the CTR looks soft is how buyers throw away pages that would have worked with one more day of data.

The third mistake is comparing pre-lander CTR across different traffic sources as if it were apples to apples. A pre-lander fed by curiosity-driven content discovery traffic will behave differently from one fed by more intent-driven search or shopping traffic, even with identical copy. Benchmark against your own history on that source, not against a number you read somewhere else.