Reading every drop before it sells out.
Launch, sell-through, unit economics
Overproduction risk flagged early
Sold-out heroes vs slow movers
The problem.
Drops are the whole business, and the whole business was run on instinct. A launch would sell out in hours or sit for weeks, and the team only knew which after the fact. Sell-through by SKU, waitlist conversion, and the margin each drop actually banked were spread across exports nobody had time to reconcile before the next launch shipped.
The approach.
I built a command center around the launch cycle. A launch radar tracks revenue against sell-through for every drop, a sell-through view ranks each SKU and flags overproduction risk below a threshold, and a unit-economics view shows AOV, waitlist conversion, restock ROI, and the contribution each drop banks after COGS and spend.
The outcome.
Restock and production calls now start from evidence instead of instinct. The team can see which SKUs to reorder while demand is hot, which to cut from the next buy, and what each drop actually earns, so sold-out heroes come back faster and slow movers stop tying up cash.
Explore the dashboard.
INTERACTIVE SAMPLE · Illustrative data, not client figures. Switch tabs for launch radar, sell-through, and unit economics.
If your drops sell out or sit and you only find out afterward, I will put launch performance and unit economics on one screen. One slot open for Q3 2026.