Two-Week Retail Data Diagnostic
Suits: brands that suspect the range or the prices are wrong but cannot prove it from their own reports.
You get: a findings memo, quantified gaps and a prioritised plan.
Services
Six services, one method: read the data first, agree the definitions, then build something the team can keep running.
Range architecture, option counts and depth by market, so the buy matches real demand instead of last season’s habit.
Price ladders, entry and exit points, margin and markdown scenarios by category and market.
Sell-through, stock cover, full-price share and returns in one model the commercial team actually opens.
Open-to-buy, seasonal buy plans and margin models built to be maintained by the team, not by a consultant.
One attribute dictionary across markets: clean seasons, colours, compositions and codes in the PLM system.
Reporting calendar, owners, definitions and review rituals, so numbers are agreed once and reused everywhere.
Suits: brands that suspect the range or the prices are wrong but cannot prove it from their own reports.
You get: a findings memo, quantified gaps and a prioritised plan.
Suits: teams with one clear problem to fix — the range plan, the price ladder, the PLM data or the KPI reporting.
You get: working models or dashboards, documentation and team handover.
Suits: growing brands without an in-house analyst who still need a reliable monthly rhythm.
You get: maintained reporting, a monthly review and decisions documented.
“Placeholder recommendation text. Two or three sentences from a colleague on a retail analytics consulting project, quoted with permission once the real recommendation is in place.”
Placeholder note: tell me where the reporting hurts most and I will say which of the six is the right starting point — or that none of them is.
Tatsiana