Kiehl's cart and checkout test program: ~$1.6M extra revenue
Test run bySplitBaseCRO agency
Commentary byLiftArchiveOur analysis, not theirsA program of cart and checkout tests on the Kiehl's Canada site cut abandonment at every checkout step and added about $1.6M in revenue.
SplitBase ran the program for Kiehl's, part of L'Oréal, and published it as its own case study.
The figure is a program total from many changes, and the individual tests are not described.
- Abandonment went down at every step of the checkout flow, per SplitBase.
- About $1.6M in additional revenue across the program.
- SplitBase reports a significant rise in conversion rate but gives no figure.
- This is a bundle of many tests and rollouts. The result can't be credited to any single change.
Control
A new checkout flow that Kiehl's was piloting on its Canadian site.
Variant
SplitBase researched the site, then tested and rolled out cart and checkout improvements step by step. The specific changes were not shared.
Why it likely won
Our reading of the result, not the practitioner's claim.
- Starting with research meant tests went after problems shoppers actually hit, not guesses.
- Checkout is where buyers already intend to purchase, so small cuts in friction there turn straight into orders.
- Rolling out wins one at a time lets gains stack across steps of the flow.
Caveats: Agency-reported program total from several bundled changes, with no sample sizes, durations or individual test results shared. The case study is undated.
Who ran it
SplitBase ran this test for Kiehl's and published the results (SplitBase case study). The numbers are theirs; the summary and commentary on this page are ours.
Read the original case study on SplitBase →
How to run this test on your store
- Hypothesis
- If we research where shoppers drop out of our cart and checkout and test fixes step by step, then abandonment will fall at each step, because each test targets a known friction point.
- Primary metric
- Checkout completion rate, tracked step by step
- Guardrails
- Average order value
- Payment error rate
- Revenue per visitor
- Who should test it
- Stores with enough checkout traffic to run several tests in sequence, especially after a checkout rebuild. Small stores may not have the volume to measure each step separately.
Questions
Where should a checkout testing program start?
Start with research: step-by-step funnel data, session recordings and customer surveys.
That shows which step loses the most buyers, so tests go where they matter most.
Can a checkout program's revenue be traced to single tests?
Only if each test is reported on its own.
Here SplitBase gave only a program total, so no single change can claim the $1.6M.
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