Rebuilding navigation nobody asked me to fix

Anza's navigation and filtering had grown feature by feature rather than being designed as a whole. It wasn't on anyone's list: user feedback had no complaints about filters, so it wasn't close to being a priority.

Filtering was the clearest example. Choosing a filter replaced the whole menu with that one filter and its options. After making a selection, buyers had to click back to the list, pick the next filter, and repeat, one filter at a time. Even a simple yes/no filter like Tier-1 took a trip into its own screen and back.

I started pushing for the overhaul on my first day at Anza. With no complaints to point to, I was going on instinct and experience: people rarely complain about filters, they just work around them or give up. I believed fixing a problem nobody was reporting would bring better results.

I designed the new navigation and filters and ran internal feedback sessions with the sales team, who knew how customers actually used the product. With their input built in, I took the designs to stakeholders. About three months of design and feedback later, the overhaul shipped and filter usage started climbing.

The new filters live in a persistent rail beside the results. Every filter is visible at once, grouped under Most used, Counterparty and Supply chain, and each one expands in place to show its options. Yes/no filters like Commercial Wrap became one-click toggles, and an Applied tab shows everything that's active.

The filter patterns became part of the design system, so every library and product line used the same model.

The top navigation was redesigned as part of the same effort and shipped in fall 2026.

The redesigned filters shipped first, in June 2026. Monthly filter usage more than doubled afterwards, and storage filtering roughly tripled. The new top navigation followed in fall 2026.

What I'd push next. I'd set up filter tracking before the redesign as well as after. Doubling usage is a strong signal, but a clean baseline by product line, along with which filter combinations lead buyers to a product, would show which filters drive decisions and which ones just add noise.

What I learned. Just because there weren't any complaints didn't mean there weren't any problems. Users don't report friction they've learned to live with, or that they don't know could be better, so feedback alone would never have put this on the roadmap. Trusting my instinct, then making the case concrete with designs and sales input, turned a problem nobody was reporting into one of the clearest wins in the data.