Sales per store, per square foot, per labour hour
Store ranking on the denominators that matter — selling area, trading hours and scheduled labour — so a small high-street unit is judged against its footprint rather than against a mall flagship.
Power BI consulting for stores, merchandising and e-commerce
Store performance, POS and inventory dashboards, built by a senior Power BI consultant for retail companies
A fixed-fee build for chains, specialty retailers and omnichannel brands whose numbers already sit in the POS but never on one screen: sales per store and per square foot, basket size and average transaction value, inventory turnover and GMROI, stockouts and shrink, markdown effectiveness, customer segmentation and omnichannel reporting. One written price, typical delivery in 2–4 weeks, 30 days of hypercare included.
Representative dashboard — sample data
What you get
The numbers merchandising, store ops and finance argue about
Every build covers the measures that decide what gets bought, marked down or pulled from a shelf — not a generic KPI template.
Basket and POS analysis
Average transaction value, units per transaction, attach and affinity between lines, discount depth and return behaviour — read at register, daypart and store level rather than as one chain-wide average.
Inventory turnover, weeks of supply and GMROI
How hard the stock is working by SKU, category and location, with gross margin return on inventory investment so a fast-turning low-margin line and a slow high-margin one can be compared honestly.
Stockouts, in-stock rate and shrink
Which SKUs went to zero in which stores, how long they stayed there, and where known loss ends and unknown shrink begins — ranked by the sales the gap most plausibly cost you.
Markdown and promotion effectiveness
Sell-through against plan, margin given away per point of lift, and cannibalisation of full-price lines, so the next promo calendar is argued from last season’s record instead of from habit.
Customer segmentation and omnichannel
Loyalty spend, repeat rate and new-versus-returning mix alongside online, in-store, click-and-collect and ship-from-store volume, reconciled so one order is never counted twice.
How it works
Three steps from register receipts to a Monday trading view
- 1
Walk the trading week, then scope
A free consultation covers how you define a comparable store, where markdowns and promotions are recorded, which SKUs and locations carry the margin, and what the buyer and the store manager each check first on a Monday. We reply with a written scope, one fixed fee and a delivery date.
- 2
Connect, model, reconcile
We pull baskets and tenders from the POS, on-hand and receipts from merchandising, orders from your e-commerce platform and member identification from loyalty, then reconcile net sales and margin against the daily flash and month-end close your finance team already signs off.
- 3
Deploy and stand behind it
We publish to your governed workspace with row-level security by store, region or banner, walk buyers, store managers and finance through their own views, hand over documentation, and stay on for 30 days of included hypercare.
What a retail reporting build actually covers
Retailers are not short of records. The POS logs every basket down to the line and the tender, the merchandising system knows what was received and what is on hand, the e-commerce platform knows every session and order — and the Monday trade meeting still runs on a workbook somebody rebuilt from four exports over the weekend. The work is engineering the layer underneath: joining sales, inventory, promotion and customer data on SKU, store and date keys that actually match, agreeing what each measure means, and modelling it so it survives a buyer disputing a sell-through figure. Microsoft’s own Power BI documentation (opens in new tab) describes the data, model, report and service layers involved, and a retail dashboard that survives peak trading needs all four built deliberately.
Retail data is unusually well suited to a disciplined dimensional model, because almost every question is a sales or inventory fact sliced by product, store, date, promotion and customer. Building it that way — along the lines Microsoft sets out in its star schema guidance (opens in new tab) — is what lets one model answer “which categories are over-stocked in the south-east” and “which members bought the promo and nothing else” without a new extract each time. It also means a product hierarchy change or a store re-banner is handled once, in the model, rather than in every report that mentions it.
Deliverables for a typical engagement: the report files and semantic model in your own tenant, tested measures for sales and margin, basket metrics, turnover and GMROI, sell-through and markdown, in-stock and shrink, workspace and row-level security configuration, a refresh schedule with monitoring notes, and written documentation. Timeline is typically 2–4 weeks from kickoff, stated in your scope before you commit, with 30 days of post-delivery hypercare included.
The retail measures we settle before we build
Definitions decide the headline number here more than anywhere. The same week of baskets produces very different figures depending on choices nobody writes down, so we agree them with your merchandising and finance leads first and encode them once in DAX measures (opens in new tab) every page reuses. The table below is the conversation we actually have during scoping.
| Retail measure | What it answers | The definition we settle first |
|---|---|---|
| Sales per square foot | Whether a store earns the footprint and rent it occupies | Whether the denominator is selling area or gross leasable area, and whether online orders fulfilled from that store count toward its sales |
| Comparable-store (like-for-like) sales | Whether growth is real trading or just new doors | How many trading months qualify a store, what a remodel or temporary closure does to its status, and when a re-bannered store re-enters the base |
| Average transaction value and units per transaction | Whether baskets are getting bigger or merely more frequent | Whether returns, exchanges, gift-card sales and zero-value lines belong in the basket count, and whether a split tender is one transaction or two |
| Conversion rate | How much of the traffic through the door actually buys | Whether the denominator is door-counter footfall or identified visits, and how staff movements and repeat entries are excluded from it |
| Inventory turnover and weeks of supply | How hard the stock investment is working | Whether turnover is computed at cost or at retail, and whether the denominator is average inventory across the period or the closing snapshot |
| GMROI | What each dollar tied up in inventory returns in gross margin | Which costs sit inside gross margin — inbound freight, vendor allowances, markdown — and whether inventory is valued at cost or at retail |
| Sell-through and markdown effectiveness | Whether the buy, and then the markdown plan, actually worked | When the sell-through clock starts — first receipt, first sale or season start — and whether promotional discount counts as markdown or as a price event |
| In-stock rate and stockouts | How much sale is being lost to an empty shelf or facing | Whether zero on-hand or below presentation minimum counts as out of stock, and whether SKUs a store never ranged are excluded from the calculation |
| Shrink | What disappears between receipt and sale | Whether known loss (damages, recalls, staff discount errors) is separated from unknown shrink, and whether it is reported at cost or at retail value |
| Repeat rate and spend per member | Whether loyalty is genuinely building repeat purchase | The lookback window that defines a repeat, and whether unidentified baskets are excluded from the denominator or assumed to be new customers |
None of these has a universally correct answer — they have a correct answer for your business, agreed once and then applied consistently. For an outside reference point when you sanity-check a category trend, the US Census Bureau’s Monthly Retail Trade surveys (opens in new tab) publish national retail sales and inventory data your internal numbers can be read against.
Where retail data actually lives
Nearly every retail build draws on the same five or six systems, and each one carries a familiar snag. Naming them early is what keeps a scope honest — this is the checklist we walk through during a consultation, not a promise that every source is reachable on day one.
| Source | What it carries | What it feeds | Common snag |
|---|---|---|---|
| POS / transaction log | Baskets, line items, tenders, discounts, voids, returns, register and associate IDs | Net sales by store and daypart, ATV and UPT, basket affinity, discount depth, return rate | Returns post to the store that processed them rather than the store that sold the item, so store-level net sales are wrong until the original sale is matched |
| Merchandising / inventory management | On-hand and on-order by SKU and location, cost, receipts, transfers, cycle counts, ranging | Turnover, weeks of supply, GMROI, in-stock rate, shrink, transfer and receipt exceptions | On-hand is snapshotted weekly rather than nightly, so a turnover or in-stock figure silently lags the shelf by several days |
| E-commerce platform | Online orders, sessions, carts, fulfilment method, shipping and channel attribution | Omnichannel sales, click-and-collect and ship-from-store volume, online versus in-store margin | Online order IDs do not match POS transaction IDs, so a collected online order double-counts unless one system is declared the owner of the sale |
| Loyalty / CRM | Member IDs, enrolment, points and rewards, tender-level customer identification | Customer segmentation, repeat rate, spend per member, new versus returning mix | Only a share of baskets are identified, so segment figures must be reported against identified sales rather than total sales or they read as decline |
| Promotion and price calendar | Planned markdowns, promo start and end dates, price changes, planned sell-through | Markdown effectiveness, promo lift, sell-through against plan, full-price versus promo mix | It usually lives in a spreadsheet with no SKU or store key, so the calendar has to be mapped to product data before any lift can be measured |
| Traffic counters and labour scheduling | Footfall by store and hour, scheduled versus worked hours by daypart | Conversion rate, sales per labour hour, payroll as a share of sales, staffing against demand | Counters register staff and repeat entries unless calibrated, and hours are kept by store while sales are kept by register — the grain has to be agreed |
Where a source cannot be reached directly, we agree a reliable export path during scoping rather than assuming one, and we say so in the scope. Restricting what each store manager, region or franchise partner can see is handled with row-level security (opens in new tab), so one store never opens a report and finds another store’s payroll in it.
Who this is for
It fits specialty chains, independents with a handful of doors, grocery and convenience operators, franchise groups and direct-to-consumer brands running both a storefront and a website — wherever the trade meeting still opens with a workbook somebody assembled over the weekend. A buyer who wants sell-through by SKU and store before the reorder deadline. A store operations lead who suspects two regions are carrying the comp number but cannot show it. A controller who needs margin to move with markdown and shrink rather than a month-end estimate. A marketing lead who wants to know whether the promo brought new customers or discounted the ones already coming. If your reporting is accurate but lands after the markdown decision is made, that is the gap this closes.
The sell side is deliberately not the make side or the move side. If your question is what happened on the production line — OEE, downtime reasons, scrap and throughput out of MES and ERP data — that work is scoped on our Power BI consultant for manufacturing page. If it is what happened between the warehouse and the door — on-time delivery, cost per mile, lane profitability and fleet utilization out of TMS, WMS and telematics data — that belongs with our Power BI consultant for logistics companies instead. This page is for what happens at the shelf and the register: baskets, SKUs, stores, markdowns and members, modelled from POS, merchandising, e-commerce and loyalty data. Vertically integrated retailers who make or haul their own goods often want two of the three, and we scope them as separate, connected builds rather than pretending one model answers every question.
This page is the retail-shaped version of our wider fixed-fee Power BI dashboard development service, so nothing here is a different engineering standard — only a different vocabulary. Two questions usually come up before a scope is signed, and we have written both up honestly: what a Power BI dashboard build actually costs, with the factors that move a fixed fee up or down, and how to choose a Power BI consultant, including the questions worth asking before you hand anyone your margin and customer data. Retailers who want the count sheet or the markdown request form replaced at source often pair the build with a custom Power App for store staff, and many add Power Automate alerts when a line goes out of stock or a store misses plan so the dashboard pushes rather than waits to be opened.
Fixed-fee project tiers
Three common shapes cover most retail requests. Every fee is fixed and put in writing after your free consultation — the table shows scope and typical timeline, not prices, because an honest price requires seeing your sources first.
| Tier | Scope | Typical timeline | Fee |
|---|---|---|---|
| Sales and basket starter | One POS as the single source: net sales by store, day and daypart, ATV and UPT, discount and return rate, category and SKU ranking, and refresh configured | About 2 weeks | Fixed fee — scoped after your free consultation |
| Merchandising and inventory pack | POS with the merchandising system alongside it: turnover, weeks of supply and GMROI, sell-through and markdown effectiveness, in-stock and stockout reporting, and role-based views for buyers and store managers | 2–3 weeks | Fixed fee — scoped after your free consultation |
| Omnichannel governed rollout | E-commerce, loyalty and promotion data blended with POS and merchandising across banners: channel-reconciled sales, customer segmentation, comparable-store rules encoded, row-level security by store or region, certified datasets and admin documentation | 3–4 weeks | Fixed fee — scoped after your free consultation |
Whatever the tier, you own every artifact — report files, semantic model, measure definitions and workspace settings live in your Microsoft tenant from the first day.
Related services
Other ways we make Microsoft easy
Power BI Dashboards
The wider fixed-fee dashboard service: semantic models, DAX, governed reporting and Fabric migration for any industry.
Power Apps Consulting
Store apps that replace the count sheets, markdown requests and paper checklists feeding your reports.
Power Automate Consulting
Alerts and workflows for stockouts, price change approvals and stores tracking behind plan, so exceptions chase people.
What a dashboard really costs
An honest fixed-fee cost breakdown and the factors that move the number on a Power BI build.
Outgrown your Excel reports?
The warning signs that a weekly trading workbook has stopped being reporting and started being risk.
How to choose a consultant
The questions to ask before you hand anyone your margin, cost price and customer data.
FAQ
Retail reporting questions, answered
Every build includes discovery with your merchandising, store operations and finance leads, connections to POS, inventory and e-commerce sources, a semantic model, validated sales, basket, turnover, margin and sell-through measures, role-based pages for store managers and head office, governed workspace deployment, and written documentation.
Usually several at once. Baskets and tenders come from the POS, on-hand and receipts from the merchandising or inventory system, online orders from the e-commerce platform, and member identification from loyalty or CRM. Where a system has no usable connector, we agree a reliable export path during scoping.
We settle the rule before we build. How many trading months qualify a store as comparable, what a remodel closure does to its status, and whether transferred or ship-from-store volume counts all change the number. We agree one rule with your finance lead and label it on every page.
Yes, once fulfilment is modelled honestly. A buy-online-pickup-in-store order exists in both the e-commerce platform and the POS, so we agree which system owns the sale, which store gets credited, and how returns are attributed, then encode that once so channel totals reconcile to the ledger.
The fee reflects how many operating systems we connect, how consistently SKUs, stores and promotions are coded across them, and how many role-based views merchandising, store operations and finance need. After a free consultation you receive a written scope with one price, and that price moves only if the scope does.
Typical delivery is two to four weeks from kickoff. A single sales and basket starter from one POS lands at the short end; a multi-banner rollout blending merchandising, e-commerce and loyalty data with row-level security by store sits at the longer end. Your scope states the delivery date first.
Book a free consultation
Get more from the Microsoft tools you already pay for
Tell us what you’re trying to fix — a report, an approval process, an intranet, a Copilot rollout. We scope it as a fixed-fee project, you approve, and a senior engineer delivers in 2–4 weeks.
- Fixed-fee scope agreed before any work starts
- Senior, US-based Microsoft engineers — no handoffs
- Typical 2–4 week delivery
- 30 days of post-delivery hypercare included
The fastest way to reach us is the form — tell us the task and we’ll reply within one business day.