Sales & revenue
Gross sales, discounts, net revenue, and margin by day, store, channel, and category — with like-for-like, year-on-year, and trading-calendar comparisons that survive a 53-week year.
Power BI for Retail & E-commerce
Power BI retail sales dashboard for every store and channel
We turn POS, Shopify, marketplace, and ad-platform data into one decision-ready Power BI retail sales dashboard — with inventory, basket, marketing, and returns views beside it — so merchandising, marketing, and finance run on the same numbers.
Representative dashboard — sample data
Power BI for retail
One trusted view of every store, channel, and SKU
Most retail and e-commerce teams are not short on data — they are drowning in it. Sales sit in the POS, online orders in Shopify or WooCommerce, marketplace revenue in Amazon Seller Central, ad spend across Google and Meta, and inventory in a separate ERP or 3PL feed. Every department exports its own spreadsheet, and by the time anyone reconciles them the trading week is over. We build Power BI dashboards that stitch those sources into one governed model, so a buyer, a marketer, and a CFO all see the same gross-to-net revenue, the same margin, and the same stock position.
This is a productized service for omnichannel retailers, direct-to-consumer brands, and multi-store operators. We focus on the metrics that actually move a retail P&L — sell-through, full-price versus markdown mix, contribution margin after returns and shipping, and customer acquisition cost against lifetime value. Every dashboard is built on a clean star schema and DAX measures you can trust during peak trading, and you own the report, the model, and the credentials at go-live.
Sources we unify
- Shopify
- WooCommerce
- Amazon Seller Central
- Square
- Lightspeed
- NetSuite
- Klaviyo
- GA4
Common retail dashboards
Six views that earn their place on the home screen
Each is built around the decisions a retail team makes weekly — not a wall of charts nobody opens.
Inventory & stockouts
Sell-through, weeks of cover, ageing stock, and stockout-risk flags by SKU and location, so buyers reorder before bestsellers go dark and clear slow movers before they tie up cash.
Basket & product affinity
Average order value, units per transaction, attach rates, and which products sell together — the basis for bundles, cross-sell placement, and smarter category adjacencies online and in aisle.
Marketing & attribution
Spend, ROAS, CAC, and new-versus-returning revenue across Google Ads, Meta, email, and GA4, reconciled to real orders so you fund the channels that actually drive profitable demand.
Store & channel performance
Sales per square foot, conversion, traffic, and labor cost by location next to online channel mix — so head office can compare a flagship store and the DTC site on equal footing.
Returns & refunds
Return rate by SKU, channel, and reason code, plus the margin returns destroy. Spot sizing problems, bad product descriptions, and serial returners before they erode a whole category.
The KPIs we model for retail and e-commerce
A dashboard is only as good as the measures behind it. For retail we standardize definitions up front as governed DAX measures (opens in new tab) so “revenue” means the same thing in every tile and nobody argues about whose number is right. The metrics that consistently earn their place include:
- Net revenue & gross margin — gross sales less discounts, returns, and refunds, then less cost of goods, by SKU, category, store, and channel.
- Sell-through & weeks of cover — units sold against units received, with days-of-supply and reorder-point flags for every location.
- Average order value & units per transaction — with full-price versus markdown split to see whether growth is healthy or just discounting.
- ROAS, CAC & new-customer revenue — blended and by channel, with new versus returning cohorts so marketing spend ties to incremental demand.
- Customer lifetime value & repeat rate — first-order versus repeat margin, so you know what a new customer is actually worth.
- Return rate & refund value — by SKU, size, channel, and reason, with the contribution margin returns quietly erase.
The data sources we connect
We meet your stack where it is. Common retail connections include Shopify, WooCommerce, BigCommerce, and Magento for online orders; Amazon Seller Central, Walmart Marketplace, and eBay for marketplaces; Lightspeed, Square, Clover, and NetSuite for point of sale; ERPs such as NetSuite, Microsoft Dynamics 365 Business Central, and Brightpearl for inventory and cost; Google Ads, Meta Ads, Klaviyo, and GA4 for marketing; and 3PL or warehouse feeds for fulfilment. Where a system has no native connector, we ingest via API, scheduled file, or a lightweight database staging layer — and we document every transformation so the lineage is never a black box. Self-hosted stores are the clearest example: our WooCommerce Power BI dashboard page sets out how we read the WordPress order tables and map order statuses to revenue. Counter and mobile point of sale is the same story: our Square Power BI dashboard page sets out the Orders, Payments and Reporting API split, why gross sales, net sales, total collected and net total have to be unstacked into separate measures, and why Square fees can never be attributed to an item category. Power BI’s native retail connectors are documented by Microsoft, and our models are built to refresh cleanly against them. (Power BI data source reference (opens in new tab).)
The pain points this solves
Retailers come to us when the trading meeting runs on a spreadsheet that takes a day to build and is wrong by the time it is presented; when online and store revenue never tie out; when buyers find out about a stockout from an angry customer rather than a report; and when marketing and finance argue about ROAS because they measure it differently. We close those gaps with one model, one definition of every metric, and a refresh that runs on its own. The result is less time assembling numbers and more time acting on them — markdowns taken earlier, reorders placed sooner, and ad budget shifted to channels that earn it. Selling vehicles rather than general merchandise? Gross-per-unit, F&I penetration, and inventory aging off a dealer management system are a different build — see Power BI consultant for car dealerships.
Retail dashboards at a glance
A quick reference for the trading views retail and e-commerce teams reach for, the kinds of metrics each one surfaces, and the systems we typically connect for them.
| Dashboard | Key metrics | Typical source systems |
|---|---|---|
| Sales & revenue | Gross sales, discounts, net revenue, margin, like-for-like and year-on-year by store, channel, and category | POS (Lightspeed, Square, Clover), Shopify, Amazon, ERP |
| Inventory & stockouts | Sell-through, weeks of cover, ageing stock, reorder-point and stockout-risk flags by SKU and location | ERP / inventory (NetSuite, Business Central), POS, 3PL/WMS feeds |
| Basket / affinity | Average order value, units per transaction, attach rates, products that sell together | POS, Shopify / e-commerce order data, marketplaces |
| Marketing & attribution | Spend, ROAS, CAC, new-vs-returning revenue reconciled to real orders | Google Ads, Meta Ads, Klaviyo, GA4 |
Retail dashboards we build
- Sales & revenue
- Inventory & stockouts
- Basket & product affinity
- Marketing & attribution
- Returns & refunds
How it works
From scattered exports to a single trading view
- 1
Scope & connect
On a free demo we map your channels, source systems, and the decisions the dashboard must support. You submit the task, we reply with a scope estimate and the hours needed, and once you approve we connect POS, e-commerce, marketplace, and ad data into a clean, reconciled model — hours deducted only for approved work.
- 2
Build & validate
We design the star schema (opens in new tab), write the DAX for every KPI, and build the sales, inventory, basket, marketing, and returns views. You validate numbers against your own reports until the dashboard is trusted, not just delivered.
- 3
Launch & hand over
We deploy to your workspace, set scheduled refresh and row-level security by store or region, and train your team. You own the report, model, and credentials — with optional ongoing support if you want it.
Representative perspective
Before this we ran the Monday trading meeting off four spreadsheets that never agreed. Now store and online sit on one screen with margin after returns, and we reorder before bestsellers sell out instead of after.Head of e-commerce, multi-store apparel brand
Representative perspective
Reconciling ROAS across Google, Meta, and our actual orders used to be a monthly fight. Having one definition everyone trusts meant we cut spend on two channels and barely felt it in revenue.Marketing director, direct-to-consumer retailer
See your sales, stock, and marketing in one place
Book a free demo. We’ll map your channels and source systems, then scope the task and tell you the hours it needs before any work begins. Your prepaid Flex hours cover this — and they never expire.
Built on the same foundations as the rest of our work
A retail dashboard is only as reliable as the model under it, so every engagement draws on our core services. The data plumbing and DAX come from our data modeling and DAX work; the layout and storytelling from report design; and where you are moving off spreadsheets or a legacy tool, our migration to Power BI service brings your history across cleanly. If your sales and inventory numbers live in QuickBooks or NetSuite, we can wire the dashboard straight to the source — see our QuickBooks Power BI dashboard and NetSuite Power BI dashboard pages. If you need a strategy and governance partner beyond a single build, Power BI consulting covers rollout, workspace structure, and security. You can also see the broader build process and the Flex pricing model. Marketing performance usually starts in GA4 — see the GA4 Power BI marketing dashboard for how sessions, channels, and conversions sit beside your sales data. Sellers trading on the marketplace have a dedicated view on our Power BI Amazon seller dashboard page. Multi-unit restaurant operators have their own version of this — see Power BI restaurant sales dashboards. Selling to trade accounts rather than consumers? See Power BI for wholesale distributors. Where the stores are franchised rather than company-owned, royalty accrual and per-owner data separation take over from basket analysis — that build is covered on our Power BI dashboards for franchise owners page. Licensed cannabis retailers are a different job again, because seed-to-sale reconciliation and the 280E split sit on top of everything here — see our Power BI dashboard for cannabis dispensary operators page. Every task is scoped before work begins from your prepaid block of senior-led hours — no project minimums, no change-order surprises, and hours are deducted only for work you approve. Your hours never expire, so you spend them when you need them.
3–5
business days for a typical task
1
governed model, every channel
100%
yours at go-live — report, model & credentials
FAQ
Retail Power BI questions, answered
Yes. We routinely blend Shopify, Amazon Seller Central, WooCommerce, and POS systems like Lightspeed, Square, and NetSuite into one model. We reconcile SKUs, currencies, and order states so online and in-store sales report on a single, trustworthy revenue number instead of three conflicting exports.
We model gross sales, discounts, returns, and refunds as distinct measures, then net them by SKU, channel, and reason code. That lets you see true net revenue and margin, spot products with high return rates, and stop counting refunded orders as won demand in your sales reports.
Yes. We build sell-through, days-of-supply, and stockout-risk measures that refresh on the schedule your data sources allow — typically several times daily. You see which SKUs will run out before reorder, which stores are overstocked, and where to move inventory before it costs you sales.
We pull spend and conversions from Google Ads, Meta, and GA4, then align them to your order data so ROAS and CAC are calculated consistently. We are clear about attribution windows and where platforms double-count, so finance and marketing finally work from the same numbers.
You buy a block of prepaid senior-led hours that never expire, then spend them on any Power BI task. We scope each request by email and tell you the hours needed before any work begins, and hours are deducted only for work you approve. Most retail tasks are delivered within 3–5 business days of your approval; larger multi-channel builds get a clear timeline up front. See full Flex pricing on our how-it-works page.
Book a free demo
Get senior Power BI help, on demand
Tell us the task. We scope it, you approve, and a senior consultant delivers — typically in 3–5 business days. Prepaid hours that never expire.
- Prepaid hours from $100/hour — and they never expire
- Senior-led delivery — 20+ years on the Microsoft platform
- Typical 3–5 business day turnaround
- Full refund if you’re unhappy in your first 4 hours
The fastest way to reach us is the form — tell us the task and we'll reply within one business day.