Molo Brands — Product Catalog Tool

Molo Brands is a Valencia-based D2C fashion label selling across Spain and Southern Europe. With a catalog of over 1,200 SKUs, seasonal drops every 8 weeks, and a growing wholesale operation, their internal team was struggling to keep product information consistent across channels.

The Problem

The company managed product data through a combination of Google Sheets, WhatsApp messages, and shared Dropbox folders. When a product launched, the marketing team, the e-commerce manager, and the warehouse team all worked from different versions of the same information. The result: wrong dimensions published, incorrect pricing on the site, and stock conflicts that led to overselling.

• No single source of truth for product attributes (size, material, care label, pricing, imagery)
• Seasonal catalog updates required manually notifying 4+ people — and errors still slipped through
• New team members had no reliable way to find product specs without asking someone directly
• Filtering and searching the spreadsheet took 3–5 minutes per product lookup

My Role

I was brought in to design an internal product information management (PIM) tool — a lightweight web interface their small team could use daily without training, that would replace the spreadsheet workflow entirely.

I spent two days embedded with the team before opening Figma. I observed the e-commerce manager during a catalog update, sat with the warehouse coordinator during stock reconciliation, and interviewed the marketing lead about her biggest pain points when launching new collections.

What I found

• The spreadsheet wasn't the problem — the lack of structure was. People had added columns over time without agreement, so the same field existed under three different names.
• Images were the biggest blocker. The team spent 40% of catalog update time hunting down final approved images across Dropbox, email threads, and WhatsApp.
• Approval was invisible. There was no way to tell if a product was draft, approved, or published — they had to message someone to check.

One product, one record

1. Structured Product Records

I designed a product page with clearly separated sections: Core Info (name, SKU, category, status), Specs (dimensions, materials, care), Pricing (retail, wholesale, margin), and Media (approved imagery, with version history). Each field had a defined owner — no more ambiguity about who controls what.

2. Status System

I introduced a simple 4-state status tag: Draft → Review → Approved → Published. Any team member could see at a glance where a product stood without asking anyone. The marketing team could only push products live once they hit "Approved" — removing a major category of human error.

3. Global Search & Filters

A fast search bar with filters by category, status, season, and material. Lookup time went from 3–5 minutes to under 30 seconds. I also added a "quick view" panel so you could check specs without opening the full product record.

4. Bulk Edit for Seasonal Updates

The most time-consuming task was updating pricing across an entire seasonal collection. I designed a bulk edit mode where the team could select multiple SKUs and update shared fields simultaneously — cutting a 3-hour quarterly process to approximately 20 minutes.

• Scope creep vs. usefulness: The team wanted integrations with Shopify and their ERP. We scoped those out for v1 — the priority was getting accurate data in one place first.
• Adoption risk: Any new tool risks being ignored if it's harder than the old workflow. I kept the interface as close to spreadsheet logic as possible while removing the structural problems.
• Mobile usage: The warehouse team often needed to check product specs on their phones. I designed responsive views for all product records — no separate mobile app needed.

• The team adopted the tool within the first week — it was designed around their existing mental model, so the learning curve was minimal.
• Product launch errors (wrong specs, wrong imagery published) dropped significantly after the status system was introduced.
• The marketing team could now prepare campaign assets with confidence that product data was correct and approved.