Anonymised selected engagement
E-commerce Checkout Conversion Optimisation
How a payment-step diagnosis and three sequential experiments increased checkout conversion from 1.8% to 2.85% over eight weeks.
Problem
Checkout conversion had plateaued at 1.8% despite stable traffic, and the team did not have enough funnel evidence to identify where the largest loss was occurring.
Approach
A funnel audit isolated a high-friction payment step. Three sequential A/B tests then addressed that specific constraint while monitoring completion and revenue rather than clicks alone.
Observed result
Checkout conversion increased from 1.8% to 2.85% during the eight-week test programme. At the client’s traffic volume, the observed change corresponded to approximately €18,000 in additional monthly revenue.
Checkout conversion: observed endpoints
- Before
- 1.8%
- After
- 2.85%
Observed endpoints over the eight-week programme. The headline 58% is rounded relative lift, not percentage points.
How it was measured
The relative lift is calculated as (2.85% − 1.8%) ÷ 1.8%. Revenue impact was estimated using the client’s traffic and order-value data during the measured period.
Limitations
The client is anonymised. The revenue figure is an engagement-period estimate and can vary with traffic quality, seasonality, pricing and implementation quality.
Practical decision guide / Original editorial method
How to use this checkout case in your own investigation
The sections above describe the existing engagement. The following is an editorial transfer guide: it explains what a reader should check next, not additional undisclosed work or newly verified results from that client.
Keep the denominator explicit
The published conversion endpoints are 1.8% and 2.85%. Their difference is 1.05 percentage points; the relative change is approximately 58.3%, rounded in the headline. Before using your own figures, define whether the denominator is sessions, customers or eligible checkout starts. Do not compare ratios built from different populations.
Reproduce the friction before redesigning
Break the payment journey into observable steps and inspect failures by device and payment path. Separate a technical failure from uncertainty about delivery, price or trust. Establish event coverage and a reproducible symptom before selecting the smallest useful intervention. This is a recommended method, not a claim about unreported test variants.
Keep the commercial guardrails
A conversion improvement is not automatically a profit improvement. Review order value, refunds, discounts and variable fulfilment costs. The published additional-revenue figure is an engagement-period estimate; it should not be treated as an audited profit result or used to forecast your business without its own inputs.
Apply it carefully
What cannot be inferred: the public summary does not disclose visitor counts, allocation, test-level results or uncertainty intervals. Those omissions mean a reader cannot independently calculate significance, pool the sequential experiments or attribute every euro to one design change.
Use the case as a reason to investigate a comparable constraint, not as a benchmark or a guaranteed outcome. Ask for a measurement plan suited to your traffic and customer decision cycle.
Capabilities used
- Conversion optimisation
- A/B testing
- Funnel analytics
- Growth engineering