Filter Redesign for a
B2B Operations Platform

Amazon FBA sourcing merchants are the primary users. Their core task: finding products worth buying for resale, filtered by buy box price, sales rank, estimated margin, and seller competition. Merchants run Product Finder regularly with the same base criteria, refining their filter set session to session. The design needed to be fast for experienced merchants and legible for first-timers.

Heuristic evaluation of Arbitrack's Product Finder, cross-referenced against the third-party tool it was modeled after. The comparison showed what carried over intact, what was adapted, and where the adaptation created new issues. The result: a 10-point list covering onboarding gaps, visual inconsistency, hierarchy, non-neutral defaults, modal constraints, and filter scope.

The 10-point list collapsed into eight root problems and one design challenge: rebuild the Product Finder as a filtering experience that works for new users without slowing down experienced ones, and scales as the filter set grows.

Explored filter ordering, sub-filter placement, and the modal-vs-page question. Chose a full page: the filter density made the modal a dead end at launch, and a scrollable modal degrades with every addition. A full page fits the current filter set and scales as more are added.

The sidebar structure pointed the same direction. Lead Bank, Tracking, and Purchases are management surfaces for existing data. Product Finder is discovery: configure filters, run a search, review results. It feeds Lead Bank directly: a merchant finds a product and adds it as a lead. Separate pages preserve that handoff rather than burying it in a nested UI.

Eight decisions:

1. Lightweight onboarding The page opens with a persistent subtitle: "Build filters and search for products." New users get immediate context; returning merchants tune it out. No modal or guided tour required.
2. Saved Presets at the top Profitable merchants reuse the same filter configurations. Placing presets at the top gives returning merchants direct access before they touch a single filter.Serial Position Effect: items at primacy position are recalled most reliably — presets at the top means returning merchants encounter their most-used shortcut before touching a single filter.
3. Two-column filter layout Left column: categorical inputs (brands, categories, BSR range, seller IDs). Right column: performance metrics (buy box percentage, fulfillment type, hazmat flags, dimensions). Merchants scope by category first, then qualify on metrics. The column split follows that sequence.Hick's Law: splitting filters into two bounded groups reduces decision complexity at each column — users aren't choosing from 15 undifferentiated options, they're choosing within categorical or performance scope.Miller's Law: two bounded columns keep the filter set within working memory limits — scannable without cognitive overload.
4. Active state indicators Each filter row shows a colored dot and its current value when active, without expanding the row. In the reference tool, active filters only appear in a floating card after submission: easy to miss and invisible before first use. Inline indicators let merchants verify their full configuration before running a search.Von Restorff Effect: an active filter row visually isolates itself from inactive ones through the colored dot, making it immediately identifiable in a list of identical-looking items.Zeigarnik Effect: the visible active state keeps unconfigured filters in working memory, motivating a review of the full configuration before running a search.
5. Neutral defaults Every filter opens unset: no pre-selected ranges, no pre-checked options. The previous version shipped several filters with non-neutral defaults that silently narrowed results. Any narrowing in the redesign is intentional.
6. Sort By in a sticky footer Filtering determines which products appear. Sorting determines how to read them. They're sequential, not parallel. Placing Sort By alongside filters conflates the two. In the reference tool, clearing filters requires scrolling to the modal's top, and that option only appears after applying a filter. The sticky footer keeps Sort By, Reset, and Search accessible at all times, and positions Sort By as the last step before running a search.Serial Position Effect: Sort By occupies recency position — the last step before running a search — reinforcing its role as a post-filter action distinct from filtering.
7. Consistent field system Every filter row uses the same label style, input pattern, and placeholder format. The previous version had mismatched fonts, inconsistent placeholder text, and misaligned inputs. A uniform system makes the form scannable and cuts the visual noise.Aesthetic Usability Effect: a visually consistent form is perceived as more usable and trustworthy — reducing noise lowers the threshold to engage with the filters.Law of Uniform Connectedness: rows sharing the same label style, input pattern, and placeholder format are perceived as elements of the same coherent system.
8. Reduced filter set, built to scale The reference tool exposes ~120 filters across 800+ input fields, built for buyers, catalog researchers, and technical analysts. For an FBA sourcing merchant, most of that is noise. The MVP launched with 15 filters covering what this audience uses. The layout absorbs future additions without restructuring: more filters are on the roadmap, and adding them requires no redesign.Hick's Law: reducing the filter set from 120 to 15 directly reduces decision time. Fewer choices, faster configuration.