Two-Week Retail Data Diagnostic
Suits: brands that suspect the range, prices or reporting are wrong but cannot prove it from their own reports.
You get: a findings memo, quantified gaps and a prioritised plan.
Warsaw, Poland · EN / PL / RU
I help fashion and retail brands plan the assortment, set prices that hold across markets, clean up product data and build Power BI reporting the team actually uses.

Range architecture, size and colour curves, open-to-buy and in-season monitoring, so the buy follows real demand rather than last season’s habit.
Price architecture by category, good-better-best tiers, cross-market price alignment and markdown scenarios tested before the season.
One Power BI model for sell-through, stock cover, margin and returns that the commercial team actually opens.
Open-to-buy, buy plan and margin models in Excel, built for your planners to maintain rather than a consultant.
One attribute dictionary across markets: consistent seasons, colours, compositions and categories in the PLM system.
A reporting calendar, KPI owners, agreed definitions and weekly reviews, so numbers are agreed once and reused everywhere.

I have spent about ten years in international fashion retail, working between the commercial teams and the systems behind them: assortment, pricing, product data and the reporting that has to agree with all three.
Today I am Global Brand Analyst and Product Data Lead at an international fashion brand, where I also lead a team of product specialists. From Warsaw I take on consulting projects in English, Polish and Russian.
Career timeline
sell-through on core categories after the range rebuild
markdown spend across two seasons
monthly reporting time removed by one Power BI model
product attribute completeness in PLM after standardisation
Suits: brands that suspect the range, prices or reporting 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 architecture, the PLM data or the KPI reporting.
You get: working models or dashboards, documentation and a team handover.
Suits: growing brands without an in-house analyst that still need a reliable monthly reporting rhythm.
You get: maintained reports, a monthly review and documented decisions.
Two weeks with your sales, stock and product data. You get a short memo that names where margin and time are being lost.
A written plan: what changes in the range, pricing, data and reporting, in which order, and who owns each change.
Models, attribute rules and dashboards built on your live data, checked against the numbers your team already trusts.
Definitions, documentation and training sessions, so the process keeps running after the project ends.

Most projects start with one honest look at the data.
One data model per brand for sell-through, stock cover and margin, refreshed without manual work.
Buy plans, open-to-buy and margin scenarios your planners can open, read and keep using.
Attribute dictionaries, season and colour structures, and rules that keep product data clean at entry.
A fashion brand selling in Europe and Asia replaced local naming habits with one PLM attribute dictionary and validation at entry.
Fewer options, more depth in proven sizes and colours, and a new open-to-buy plan for a womenswear distributor.
Clear entry, core and top price points per category, one set of pricing rules for all markets and a weekly margin report.
Two free downloads sent by email: an open-to-buy planning model in Excel and a checklist for a Power BI retail KPI dashboard that a commercial team will actually open.
Short answers to the questions brands ask before the first call.
A retail analytics consultant turns a brand’s sales, stock, price and product data into decisions about what to buy, how to price it and how to report on it. In practice that means assortment and pricing analysis, agreed KPI definitions and models or dashboards the team can maintain. My focus is fashion retail: range plans, size curves, price architecture, PLM product data and Power BI.
Retail analytics is the analysis of sales, stock, pricing, customer and product data to improve buying, pricing, allocation and store or online performance. It covers KPIs such as sell-through, stock cover, margin and returns, and the models used to plan the next season. Good retail analytics ends in a decision, not in a report.
For a fashion brand, the five KPIs I would track weekly are sell-through rate, gross margin, stock cover (weeks of supply), full-price sales share and returns rate. GMROI and stock turn complete the picture at season level. The exact list matters less than having one written definition for each KPI that every team uses.
A data diagnostic takes two weeks. A focused project, such as a range plan, a pricing review or a Power BI reporting model, usually takes four to ten weeks depending on the number of categories and markets. Ongoing support runs month by month.
Sales, stock, receipts and returns at SKU and week level, plus product attributes and prices, ideally for two seasons. Standard exports from your ERP, PLM or e-commerce platform are enough to start. An NDA can be signed before any data is shared.
Yes. I am based in Warsaw and work remotely with brands across Europe and beyond, in English, Polish or Russian. On-site workshops are possible when a project needs them.
The weeks of cover formula, why the averaging window changes the answer, a DAX measure that counts only in-stock weeks, and how cover converts to stock turn.
The four pages of a weekly trading report in Power BI, the measures each one needs, the retail calendar underneath it, and the mistakes that make it useless.
The columns a 4-5-4 retail calendar needs, DAX that builds the table, why Power BI time intelligence breaks on it, and what the 53rd week does.
I read every enquiry myself. Tell me what is not working — the range, the prices, the product data or the reporting — and I will reply within one working day with the first questions I would ask. If the project is not a good fit for me, I will say so.