[ Case study ]
Drops sold through Instagram DMs — first-come-first-served by message, with manual invoicing, double-selling, and hours of admin per drop. The studio needed a storefront that could take a drop live in minutes.
CLIENT a ceramics studio selling small-batch drops — FOCUS Model pieces as structured products
Representative examplesEvery case study in this library is an illustrative composite of the kind of engagement we deliver — written to show our method and standards, not to name clients.
Kiln loads set the rhythm of the studio: every few weeks a batch of 30–60 pieces emerges, gets photographed, and sells through Instagram direct messages, first message first served. The owner throws, glazes, films, packs, and ships everything herself, with occasional help from a friend on packing days. The audience is loyal and arrives quickly when a drop is announced — the bottleneck was never demand. Each drop meant a night of manual invoicing, and enthusiasm occasionally outran inventory.
Drops sold through Instagram DMs — first-come-first-served by message, with manual invoicing, double-selling, and hours of admin per drop. The studio needed a storefront that could take a drop live in minutes.
We proposed a Webflow storefront built around the drop as an event: every piece modeled as a structured product with glaze, dimensions, and firing-batch fields, and a drop page assembled from components — countdown, set view, piece cards — so a new drop is content entry, not a design project. Stripe's hosted checkout takes the selling, so a piece sold is a piece gone. The spare, handmade identity would carry the design, and the post-drop workflow had to fit one screen, because the owner runs everything alone.
Just as important is what we ruled out, and why:
Each piece became a product record with glaze, dimensions, and firing batch fields, so the drop page is data, not a hand-built layout.
A drop template with countdown, set view, and piece cards means a new drop is content entry, not a design project.
The first drop was rehearsed with simulated concurrent buyers to confirm the checkout held when the DM crowd arrived.
Delivered by the experience pod — designer + frontend engineer over 4 weeks, with working increments reviewed with the client every week.
Obstacle
Rehearsing a dummy drop with the owner exposed a live hesitation — one CMS field's meaning wasn't obvious under pressure, costing minutes on a real drop.
Handled: We renamed the field to the studio's own vocabulary, added a worked example in the help text, and re-ran the rehearsal until entry was automatic.
Obstacle
The simulated-load rehearsal showed the checkout holding but CMS publishing lagging when edits went live during the spike — a mid-drop price fix would have crawled.
Handled: We moved the mid-drop checklist to pre-drop: all edits publish before the countdown ends, and the rehearsal was repeated until publish latency stayed flat.
The headline: time for the first online drop to sell out — previously a multi-day dm process — 48 min, read from Order timestamps. A second check: double-sold pieces since moving online at 0.
A drop is now an hour of attention instead of a lost night: the announcement goes up, the pieces sell in under an hour, and packing lists print themselves. Double-selling stopped being a background anxiety, which changed how she talks about limited pieces — she no longer hedges in her captions. Collectors abroad buy on equal terms for the first time, and several have used the drop archive to request commissions. The studio calendar gained breathing room she has, so far, spent on larger kiln loads.
The result was read from Order timestamps against the pre-engagement baseline over the stated window, with a guardrail check on double-sold pieces since moving online. Where platform-reported numbers and business outcomes differ, this record says which layer it is quoting.
What we would do differently
We would have rehearsed the drop-day content entry with the owner on a dummy drop — she hesitated on one field live and lost two minutes.
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