Director of Product Growth · ablefy · 2025

Checkout, +6.47% Net Take Rate Increase

Four targeted improvements to ablefy's checkout flow — reducing cognitive load, increasing conversion, and cutting dispute rates through clearer payment communication.

Overview

The checkout page is the single highest-leverage surface for a monetisation platform. At ablefy, creators sell digital products — courses, memberships, subscriptions — each with unique pricing structures. I led a series of focused optimisations to reduce friction and improve clarity at every step of the purchase flow.

This was cross-functional work, delivered with Design, Data, Product, and Engineering.

Challenge
  • High cognitive load — the original checkout presented all payment methods, pricing plans, and legal details in one dense page
  • Complex pricing models — one-time payments, installments, subscriptions, limited subscriptions, and combinations all need distinct UX treatment
  • DACH compliance — German payment regulations require detailed tax breakdowns and cancellation terms, adding information density
  • Conversion leakage — buyers dropped off at payment selection and order summary due to confusion and lack of trust signals
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Improvements shipped
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Sessions tested
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Net take rate increase
€0K
EBITDA uplift
The Original Checkout

The starting point: a single-page checkout with four key problem areas identified through funnel analysis and session recordings.

Original checkout — four areas identified for optimisation: express checkout (1), payment method selector (2), pricing plan options (3), and order summary (4)
Improvement 1 — Express Checkout

Goal: Reduce checkout friction, increase CVR, increase net take rate.

Added an express checkout option at the top of the page — letting returning buyers complete purchases in one click via PayPal or Google Pay, bypassing the full form entirely.

Express checkout — one-click purchase via PayPal or Google Pay for returning buyers
Improvement 2 — Payment Method Selector

Goal: Reduce cognitive load, increase CVR, increase net take rate.

Payment methods were displayed in random order. Nothing guided buyers toward the methods that actually worked better — so we saw more failed payments and higher processing costs.

Sellers control which methods are offered, and we couldn't remove any. So the only lever was order and presentation.

The Data Approach

We partnered with the data team to score every payment method on three axes:

  • Popularity — how often buyers select it
  • Success rate — share of payments completed without failure
  • Take rate — net profitability per method

That produced an optimised ranking, with the four strongest methods first:

  • Surfaced first — PayPal, Klarna, SEPA, Apple Pay
  • Behind the fold — Bank Wire, Google Pay, Credit Card, iDeal, Przelewy24, Pay Later
Experiment 1 — Order Alone Wasn't Enough

Before changing any UI, we tested the new ordering on its own. This isolated the ranking logic from the visual design.

Result: no meaningful signal. Statsig showed no conversion change, and the take-rate gain wasn't strong enough to ship.

A useful negative result — it told us ranking alone doesn't shift behaviour, and pushed us to test the ranking inside a new UI pattern.

Experiment 2 — Two Hypotheses

We kept the optimised order and changed only how the options were presented.

H1 · Take rateValidated

Showing four reordered methods in a horizontal selector will steer buyers toward higher-margin options and lift net take rate.

Variant B shifted the payment-method mix and produced the strongest net take rate of the three arms.

H2 · ConversionNot validated — but not harmed

Showing only four options makes the choice easier, leading to more buy-button clicks and completed payments.

No conversion uplift appeared in Statsig. Critically, there was also no statistically significant drop across the primary funnel metrics — which made Variant B safe to roll out on its economics alone.

Experiment Design

A three-arm test with a staged rollout, measured in Statsig.

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Days running
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Sessions
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Variants tested
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Rollout
The Three Arms
  • Control — traditional vertical list, all 10 methods stacked, no hierarchy, legacy random order
  • Variant A — horizontal selector, all 9 methods visible at once
  • Variant B — horizontal selector, 4 methods visible, remaining 6 behind an accordion (desktop)

Mobile was identical in B and C: a horizontal scroll with a swipe indicator, in the optimised order. The desktop accordion was the only real difference between them.

Variant A's fuller list came at a cost — +14.3% buy-button errors versus Variant B.

Payment selector redesign — Variant A (full grid) vs. Variant B (horizontal selector with accordion on desktop, selected)
Results

Variant B won on unit economics, not conversion.

Statsig primary funnel metrics at 95% CI with CUPED — every delta sits within noise, with and without the accordion. No CVR uplift, but no meaningful downside either, which made Variant B safe to roll out.
VariantNet take rateEBITDAΔ vs control
Control~2.91%€727,684—
Variant A~3.06%€753,900+4.51%
Variant BShipped~3.10%€774,772+6.47%
Statsig confirmed rollout safety on conversion. Net take rate and EBITDA came from a separate follow-up analysis with the data team.
Why It Worked
  • Pay Later: −30% clicks — intentionally. We added friction to a low-margin method to protect net take rate
  • Apple Pay rose significantly in both clicks and completed orders, with or without the accordion opened
  • Progressive disclosure beat the full list. Four options upfront outperformed nine — Variant A's extra visibility produced more errors, not more revenue
  • Design moved unit economics. Net take rate went from ~2.91% to ~3.10%, worth +€47,088 EBITDA
Improvement 3 — Pricing Plan Selector

Goal: Reduce cognitive load, reduce cancellation and dispute rate.

The original pricing options displayed dense legal text for each plan — trial periods, billing dates, minimum terms. Redesigned to show clean summary cards with a visual payment timeline, making cost expectations immediately clear and reducing post-purchase disputes.

Result: No significant impact on checkout conversion in A/B testing; increased customer satisfaction.

Pricing plan selector — before (verbose text blocks) vs. after (clean cards with visual payment timeline)
Improvement 4 — Payment Summary

Goal: Reduce cognitive load, reduce cancellation and dispute rate.

Replaced the minimal order summary with a detailed breakdown showing what's due today, upcoming payments, and recurring charges. Added an expandable "Price overview" for multi-item carts with different billing cycles — giving buyers full transparency before committing.

Result: No significant impact on checkout conversion in A/B testing; increased customer satisfaction.

Payment summary — before (basic total) vs. after (detailed timeline with expandable price overview)
Edge Case Complexity

These improvements had to work across five distinct pricing models — each with its own edge cases for trials, billing cycles, and tax display. The pricing plan variation audit ensured every combination rendered correctly and clearly.

Pricing plan variations — one-time payment, installments, combinations, subscriptions, and limited subscriptions
Outcome
  • 4 improvements shipped — express checkout, payment selector, pricing plan clarity, and order summary
  • Reduced cognitive load — progressive disclosure and visual timelines replaced dense text
  • Improved trust — transparent cost breakdowns reduce post-purchase disputes and cancellations
  • Measured impact — net take rate ~2.91% → ~3.10% (+6.47%), worth +€47,088 EBITDA, validated over 42 days and 790K sessions
  • A negative result worth keeping — reordering alone moved nothing; the gain came from reordering plus progressive disclosure