How to quickly find drop-off causes and improve HPP conversion

“One word on a button” — and the numbers move up

Google has a well-known example from hotel search: they changed the CTA from “Book a room” to “Check availability” and saw a 17% increase in engagement. The reason is simple: “Book” sounds like a commitment, while “Check” feels like a safe next step. ([Smashing Magazine][1])

A payment form is an even more emotionally charged place. It’s not “choosing a hotel” anymore — it’s “money right now”. That’s why any micro-friction (a word on a button, a confusing error, an extra step, a redirect) can quickly turn into lost conversion.

That’s exactly why we built Payment Form Analytics — not as “pretty charts”, but as a radar that shows, in minutes, where exactly you’re losing users.

Why this section matters for business

Payment Form Analytics answers the key question: why users don’t reach a successful payment and which segment is affected.

What you get:

A fast diagnosis of “where it hurts”: device, browser, form domain, language, time zone.

Behavior insight: how many times users return and how long they stay on the form.

An operational support tool: open a segment → find specific payments → review the payment form events log.

White-label control: which domain actually receives traffic.

Data source: all charts are built from the user behavior events log on the payment form (events). The same log is available inside each payment details page.

Why we’re launching this right now

Because this is the first step toward the next level: a merchant-controlled payment form.

Next, you will be able to manage payment page content yourself (texts, hints, CTA buttons) through a simple admin interface — no more releases like “please change one phrase”. The next logical step is A/B testing: change text/order/hints and measure the impact.

For A/B testing to make sense, you need a baseline: understand where friction happens and for whom. That’s exactly what Payment Form Analytics provides.

How to read the charts (as an investigation, not a report)

Imagine you’re a detective. You have 4 key questions:

Did the user leave immediately, or try to pay?

Did they return 3+ times (stumbling), or move linearly?

Is this a problem for everyone, or for a specific segment (browser/device/domain)?

Is it about the audience (language/time zone) or technical constraints (JS)?

Below are the “donuts” that answer those questions.

1) Session duration distribution

The main friction indicator: “instant exits” vs a “deliberate attempt to pay”.

What typically signals an issue:

growth of 0–5 sec → load error / confusing first screen / extra redirect / misclick

growth of Unknown → exit is not captured (often an events tracking/delivery issue)

Emotionally, but honestly: a spike in 0–5 sec is like a store door that suddenly stops opening. People don’t even get inside.

2) Session count distribution (0 / 1 / 2 / 3+)

Shows how often a user returns to the form.

Signals:

growth of 3+ → the user “stumbles” (error, decline, retry, confusion)

lots of 0 → the PayStar form may not open (for example, a direct redirect to a bank/external page)

3) Domain / Device / Browser / Screen size

This is “where it hurts”: which domain/device/browser/resolution converts worse.

4) Language / Time zone / Local time

Helps you spot the “wrong audience”, geo shifts, and real prime-time.

5) JavaScript / Java / Color depth

Technical signals. For conversion, JavaScript is usually the most important.

Stories to inspire your investigations

Story 1. “Get Started” as a trap

Nielsen Norman Group describe a common mistake: the CTA “Get Started” often attracts clicks, but… slows down people who still need details. The person isn’t ready to “start” yet — they want to understand the conditions, pricing, and steps. As a result, the button works like an expensive barrier gate: it seems inviting, but psychologically pressures and confuses. ([Nielsen Norman Group][2])

How this looks on a payment form:
a button or text sounds like a commitment (“Pay now”, “Confirm payment”), while at this step the user still wants to “check”, “understand”, “see details”. The result is higher 0–5 sec or higher 3+ (returns and hesitation).

What Payment Form Analytics does:
it shows that the problem is not “payments in general”, but the “decision moment” — and you go fix words and hints, not rebuild the entire system.

Client Story 2: “Traffic went to the wrong place — and conversion disappeared” (anonymized)

Monday, 09:12. One of our clients (iGaming) panics in the chat:
“Checkout conversion dropped hard. We changed nothing. Providers? Bank? PayStar?”

Marketing says ad clicks are normal. Support gets messages like “the form doesn’t open / I don’t understand where I am”. Finance sees the fact: less money.

Step 1 — open Payment Form Analytics and ask one question, not twenty: where are we sending people?

The client opened Payment form domains first — and saw something odd:
instead of the usual white-label domain, most traffic suddenly went to a different domain (old/test).

Step 2 — confirm the hypothesis via behavior

Two fast symptoms showed up:

Session duration had a spike in 0–5 sec (people bounce almost instantly);

Session count showed more 0 and 3+ (some users never open HPP, others try multiple times).

Step 3 — “evidence in court”: one specific payment

They opened a few failed payments from that segment and checked:

Events log — it’s visible the user landed via a link with the “wrong” domain;

Player — the user literally “looks around”, clicks 1–2 times, and leaves.

What it really was

Not the provider. Not the bank. Not “PayStar is down”.
After a platform update, a test flow with an old link was left active, so traffic went to a domain where the scenario was no longer valid. People ended up “in the wrong place” — and simply left.

What they did

updated the pipeline/configuration and replaced the outdated link;

audited all flows to ensure no test/legacy scenarios were still live.

Result

Conversion returned to normal immediately after fixing the link.
Most importantly, the investigation took minutes, because the analytics didn’t show an “abstract drop” — it showed a clear answer: payment didn’t break; entry to the correct payment page did.

The takeaway the client learned

When conversion drops, the problem is often not “payments in general”, but a small detail: domain, device, browser, copy, an extra step.
Payment Form Analytics helps you find that detail — and fix it precisely, without chaos.

Story 3 (client case). “Safari + 0–5 seconds: users don’t even get to see the form” (anonymized)

Everything looked strange: not many complaints, integrations “green”, providers responding — yet conversion on the form started to slip. Not a crash, but enough to show up in revenue.

The worst part: the symptom was “floating”. For some users everything worked; for others it felt like “nothing happens”.

How they found the reason in 10 minutes

The client opened Payment Form Analytics and did one simple thing: selected Browser = Safari.

They immediately saw the picture that explained almost everything:

in Session duration, the 0–5 sec segment spiked — people were leaving “instantly”, before starting a payment.

That’s the moment it becomes clear:
the user didn’t “change their mind” — they likely never reached a stable, usable form state.

What they did next: “evidence” in real payments

They opened 3–5 payments from that segment and reviewed:

Events log — what actually happened step-by-step on the form.

Player — what the user saw and tried to do.

And they found the classic iOS/Safari scenarios:

the redirect opened “wrong” (new tab / inside webview),

the user went back and hit the same loop again,

or they saw a screen that looked like “nothing is happening” and closed the page.

How they fixed it (no magic)

The fix wasn’t “rewrite the whole frontend”, but targeted changes:

adjusted the redirect/return flow specifically for Safari/iOS,

added a clear state on the form: “Opening your bank app… If it didn’t open, tap again”,

plus a safe fallback for webview/limitations.

What happened after the fix

After release, they checked the same place in analytics:

the 0–5 sec share in Safari started returning to normal,

repeated entries went down,

conversion stabilized.

The key takeaway

If you see a spike in 0–5 sec, it’s almost always an “entry breaks” problem: load, first screen, redirect, webview, blocking.
Payment Form Analytics helps you stop arguing “who broke it” and immediately show: who (Safari), where (0–5 sec), and what to do next (open events + player and find the break point).

Deeper than charts: investigating a single payment

Charts show where it hurts on average. But when you need to understand “why this specific payment failed”, you have two tools:

Events log — the sequence of form events in each deposit/payment details.

Player (Webvisor-like) — a replay of user behavior on the form for that payment.

This turns support and product work into a clear process:
segment → specific payments → events → player → hypothesis → change → conversion lift.

What’s next: from reporting to optimization and Heatmap

Today, Payment Form Analytics aggregates behavior into clear segments. Next:

manage form content via the admin panel (no releases),

A/B testing (change copy/hints/CTA and measure),

Heatmap — aggregated clicks and attention across the form (the next layer above Player).

Payment Form Analytics

“One word on a button” — and the numbers move up#

Why this section matters for business#

Why we’re launching this right now#

How to read the charts (as an investigation, not a report)#

1) Session duration distribution#

2) Session count distribution (0 / 1 / 2 / 3+)#

3) Domain / Device / Browser / Screen size#

4) Language / Time zone / Local time#

5) JavaScript / Java / Color depth#

Stories to inspire your investigations#

Story 1. “Get Started” as a trap#

Client Story 2: “Traffic went to the wrong place — and conversion disappeared” (anonymized)#

Step 1 — open Payment Form Analytics and ask one question, not twenty: where are we sending people?#

Step 2 — confirm the hypothesis via behavior#

Step 3 — “evidence in court”: one specific payment#

What it really was#

What they did#

Result#

The takeaway the client learned#

Story 3 (client case). “Safari + 0–5 seconds: users don’t even get to see the form” (anonymized)#

How they found the reason in 10 minutes#

What they did next: “evidence” in real payments#

How they fixed it (no magic)#

What happened after the fix#

The key takeaway#

Deeper than charts: investigating a single payment#

What’s next: from reporting to optimization and Heatmap#