Storefront revenue engine
I co-owned the apparel storefront's P&L and rebuilt how it allocated attention and how it governed supply — two systems, one outcome: the storefront's discovery lifted sharply.
The result
One star, two systems
The problem
A surface nobody had engineered
- Lowest GMV contribution in the category. The storefront was deprioritized, and buyers defaulted to search instead of curated browsing.
- No performance tracking. Decisions ran on a visits-based proxy, not actual engagement — so nothing could be tuned with intent.
- Top brands leaking value. Price drift against offline distributors, recurring stockouts, and listing gaps eroded conversion and margin.
What I built
Two systems, layered
1 · Allocation architecture
Decide what gets seen — by category, by intent, by pool.
- Separated storefront real estate by category (apparel vs footwear), then by buyer intent mode (brand-led vs category-led), then across reliability, value and freshness pools.
- Multiple collection types, each with metric-based eligibility, removal guardrails, a target segment, and one success KPI.
- A multi-cohort buyer-segmentation engine, with primary / secondary / fallback routing via CRM deep-links.
- A/B-tested layouts — which callouts, creative, and positioning convert for which brand and category — with the winners fed back into the architecture.
Artifact · allocation architecture
2 · Pareto governance
Decide what's allowed to be seen — and protect the margin behind it.
- Product lifecycle — Trial → Optimize → Scale → Throttle, each with a minimum visibility runway.
- Drift detection — price benchmarking against offline, stockout monitoring, new-arrival competitiveness, offer parity.
- Compliance-based visibility — premium real estate and guaranteed traffic tied to price, stock and listing standards.
- Non-negotiable guardrails — margin floor enforced, drift flags pull visibility immediately.
The outcome
The storefront started converting attention
- Star — collection CTR ~1.5×, the joint result of allocation and governance.
- CRM-routed CTR 0.9% → 2.4% from the multi-cohort routing.
- Revenue leakage from top brands −30%, with price competitiveness improved.
- Deployment ~30% faster, so the system could be tuned, not just shipped; led 4 direct + ~12 indirect, +30–40% team efficiency.
The trade-off I made: rule-based governance over ML in v1, and I pulled visibility on margin-negative brands despite their GMV. I protected conversion quality and margin over short-term volume — and documented it, because that call is the judgment, not a side note.