Korean DTC Beauty Brand
Diagnosing and rebuilding a stalled US storefront
An active engagement with a Korean DTC beauty brand: diagnosing a conversion problem, then rebuilding the storefront headless to fix it.
Anonymized engagement
Context
A Korean direct-to-consumer beauty brand running an international Shopify storefront aimed at US customers. After a strong start, conversion had stagnated and declined over several months while traffic held steady — meaning the same number of visitors were producing fewer orders.
The challenge
The store was converting roughly half of its trailing-twelve-month baseline, and the loss was concentrated at checkout — a mobile problem, since mobile is the overwhelming majority of traffic but converts at about half the desktop rate. A dormant email list of several thousand was driving effectively none of the sessions. The diagnosis pointed at the mobile purchase and checkout experience as the highest-leverage fix.
By the numbers
Conversion rate, trailing 12 months
A strong start eroding to roughly half the baseline — the decline the engagement is built to reverse.
Latest
Target
Sessions by channel
Email drives effectively none of the traffic despite a list of several thousand — the clearest near-term opportunity.
Where the funnel breaks
Few sessions reach checkout, and most that do never complete — an ~87% abandonment rate.
All visitors
~2.7% of sessions get this far
~13% of checkouts convert
What we did
- Ran a full conversion-funnel diagnosis from analytics — isolating where sessions leaked and confirming mobile checkout as the priority surface.
- Rebuilt the product purchase flow: clearer, repositioned add-to-cart, a sticky mobile buy-bar, and handled pre-order / sold-out states.
- Refreshed brand typography and color consistently across header, footer, cart, and collection pages.
- Moved the storefront to a headless Next.js + React rebuild rather than patching the legacy theme.
- Designed email back in as a core conversion channel — capture plus welcome, abandoned-cart, post-purchase, and win-back flows.
- Stood up UTM tagging and an analytics plan so channel attribution and behavior carry into the new build.
- Recommended a city-level US targeting strategy concentrated on the metros that drive most orders.
Results
Conversion-rate target set against a ~0.35% trailing baseline.
Checkout drop-off identified vs a ~70% industry norm — the central lever.
Traffic is mobile-dominant at ~half desktop CVR, so the rebuild is mobile-first.
A dormant ~5,000-person email list rebuilt into an owned conversion channel.