Retail Insight.
Analytics, for operators.
Store-level KPIs, cohort views, and merchandising signal — made readable for the people who actually run a retail floor, not just the analysts who report to them.
- Sector
- Retail · Analytics
- Users
- Operators · merchandisers
- Scope
- Dashboard · data UX
- Surface
- Web app
• 01 · The brief
The brief.
The brief: rebuild a retail dashboard so the people running stores actually open it. The signal that mattered usually arrived in a tab nobody opened.
• 02 · Context
Context.
Retail dashboards are usually built for analysts and then resold to operators — the people running stores and merchandising lines — who don't have the time or training to wrangle a pivot table to find an answer.
• 03 · The approach
The approach.
We rebuilt the surface around five operator-level questions, each given its own screen: how is today versus the same day last year, which stores are off-pace, which lines are performing above forecast, where is staffing under pressure, and what should I look at first thing tomorrow.
• 04 · What we shipped
What we shipped.
- Today — live KPIs against forecast.
- Stores — ranked, off-pace flagged, drill-in to a store's day.
- Lines — merchandising performance, with cohort context.
- Tomorrow — the morning brief, generated from the data.
• 05 · Outcome
What changed.
Dashboard usage moved from weekly to daily across the operations team. Decisions that used to require a request to the analytics team — “is this line off-pace yet?” — now happened on the floor.
