Retail Analytics Dashboard — SKU-Level Truth From the Data Your Tills Already Produce
Your EPOS knows everything; its reports tell you almost nothing. DataHub Pro turns EPOS exports and live Shopify data into a retail analytics dashboard — sales by SKU and store, sell-through, inventory turnover and Holt-Winters seasonal forecasts — without a BI project or a data team.
Updated 10 June 2026
What is a retail analytics dashboard?
A retail analytics dashboard turns raw transaction data into the handful of numbers that drive retail decisions: sales by SKU and by store, sell-through rate (what proportion of received stock actually sold), inventory turnover, margin by category, and the seasonal pattern that tells you what next quarter probably looks like. It is the difference between "trade felt soft this week" and "store 7's footwear sell-through dropped nine points and here is the SKU list".
The raw material already exists. Every EPOS system — from enterprise tills to a tablet at the counter — can export transactions as CSV or Excel, and Shopify stores carry the entire history natively. The problem is the gap between that raw export and a decision: someone has to pivot it by SKU and store, compute sell-through against receipts, calculate stock turn, and compare against last year's season. In most independent and mid-size retailers, that someone is the owner or a head-office manager with a long Sunday and a fragile spreadsheet.
Timing makes the gap expensive. Retail decisions are perishable — a slow-selling line spotted in week 3 gets a markdown that clears it; the same line spotted in week 9 becomes dead stock written off at season end. Seasonality compounds it: buying and staffing decisions are made months ahead, so a wrong guess about the Christmas curve is a wrong order quantity with your cash tied up in it.
DataHub Pro closes the gap without a BI project. Connect your Shopify store directly — it is a live connector, no exports needed — or upload EPOS CSVs, and the platform builds the dashboard, runs Pareto analysis on SKUs, computes sell-through and turnover, flags anomalies by store, and projects the season ahead with Holt-Winters forecasting and proper confidence bands. The weekly trading pack generates itself as an editable document.
The reporting problems retailers run into
Whether you run three stores or thirty, these patterns will be familiar:
- EPOS reports are rigid and shallow. The till system's built-in reports answer the questions its vendor predicted, not yours. Cross-store comparison, SKU-level trends and seasonal analysis usually mean exporting to Excel anyway.
- SKU-level truth is buried in aggregate numbers. Total sales can look fine while a fifth of your range quietly stops selling. Without Pareto-style SKU analysis, dead stock is discovered at stocktake, not in time to act.
- Sell-through is computed too late to matter. Markdown decisions are most valuable early in the season. If sell-through is only calculated when the buyer asks, the discount that would have cleared the line in March happens in June, deeper.
- Seasonality is guessed, not measured. Order quantities and staffing rotas are set on memory and gut feel. Two years of daily sales data contain a measurable seasonal curve — almost nobody extracts it.
- Multi-store comparison is a manual grind. Each store's numbers arrive separately; like-for-like comparison means hours of spreadsheet assembly before the Monday trading meeting.
- Head office drowns in weekly Excel rituals. The trading pack — sales vs last week, vs last year, vs plan, by store and category — gets rebuilt by hand every Monday morning.
DataHub Pro features mapped to retail work
These are the tools, from the platform's 50, that retail operators use weekly:
Shopify connector — live
Connect your Shopify store once and orders, products and customers stay in sync — no export ritual. Brick-and-mortar EPOS data joins via CSV/Excel upload alongside it. Online-first operators should also see DataHub Pro for ecommerce.
Pareto analysis on SKUs
See which 20% of SKUs drive 80% of revenue — and, more usefully, the tail that drives almost none. The dead-stock candidate list generates itself.
Sell-through & inventory turnover
Upload receipts and sales, get sell-through by SKU and stock-turn by category — the metrics behind markdown timing and open-to-buy decisions. Our inventory management in Excel guide covers the manual method.
Holt-Winters seasonal forecasting
Project sales by category or store with 80/95% confidence bands, using a model that actually understands seasonality. Try the method on our free forecasting calculator.
Anomaly detection by store and SKU
Statistical flags on unusual movements — a store whose sales pattern breaks trend, a SKU whose returns spike — each with date, value and an AI explanation.
Auto Report trading packs
The Monday trading pack as an editable Word or PowerPoint document, generated on schedule: sales vs plan and last year, by store and category, with AI-written commentary on what moved.
Worked example: from sales.csv to three trading decisions
You upload sales.csv — twelve months of transactions by SKU and store from your EPOS, alongside a receipts file. Three findings land within minutes:
Top 20% of SKUs = 78% of revenue
Pareto analysis confirms concentration and outputs the other end: 31% of active SKUs sold fewer than five units in the last quarter. That list goes straight to the markdown and range-review meeting.
Store 7 sell-through anomaly
Anomaly detection flags store 7, where footwear sell-through runs nine points below the estate average on identical stock allocation — pointing at a merchandising or local-fit issue, not a buying one.
Q4 category forecast: +34%
Holt-Winters projects the gifting category up 34% for Q4 with confidence bands, based on two years of seasonal pattern — turning the open-to-buy conversation from gut feel into a range.
Each figure traces back to the transaction file via the audit trail, and Auto Report turns the analysis into the weekly trading pack automatically. If you are currently doing this by hand, our sales dashboard in Excel guide shows the manual route — DataHub Pro is that workflow with the labour removed.
How it works — three steps, no implementation project
There is no onboarding call, no integration scoping and nothing for IT to install. The workflow is the same whether you are testing on one file or running scheduled reporting across a team:
- 1. Bring your data. Upload an Excel or CSV export from the systems you already use, or connect Google Sheets, SharePoint or Shopify for sources that should stay live. DataHub Pro auto-detects your columns — no template to conform to.
- 2. Run the analysis. The KPI dashboard builds itself on upload. From there, pick from 50 analysis tools — Pareto, cohort, RFM, anomaly detection, variance, what-if, Holt-Winters forecasting — or ask questions in plain English with Ask Your Data, where every answer shows the operations behind it.
- 3. Ship the report. Auto Report turns the analysis into an editable Word or PowerPoint document with charts and AI-written commentary. Schedule it, and the recurring version arrives without you — same method, fresh data, every time.
Plans that scale from a single file to a whole team
- Free — $0, forever. One user, 3 uploads a month, 8 core analysis tools and watermarked PDF export. No AI features, no credit card — but a genuine way to test the workflow on your real data before paying anything.
- Pro — $14.99/mo, or $9.99/mo billed annually. All 50 analysis tools, Ask Your Data auditable AI, Auto Report DOCX/PPTX export, scheduled reports and team roles. This is the plan most teams on this page run.
- Enterprise — custom pricing. Everything in Pro, plus white-label reporting, SSO, unlimited usage, multi-client workspaces and organisation-level audit-log governance.
Every plan runs on UK/EU infrastructure under GDPR, and uploaded data is never used to train AI models — on any tier.
What DataHub Pro does not do
It is not an EPOS, a stock-management system or a replenishment engine — it will not raise purchase orders or move stock. It is the analysis layer on top of the data those systems produce: it tells you what is happening and what is likely next; the buying and merchandising decisions stay yours.
Frequently asked questions
Does DataHub Pro connect to Shopify directly?
Yes — Shopify is a live, native connector. Connect your store once and orders, products and customer data sync automatically, no CSV exports needed. Excel/CSV upload, Google Sheets and SharePoint are also supported for EPOS and back-office data.
My tills are not Shopify — can I still use it?
Yes. Every mainstream EPOS system exports transactions as CSV or Excel. Upload that export and DataHub Pro auto-detects the columns. Many retailers combine a Shopify connection for online with EPOS uploads for stores.
How does it calculate inventory turnover and sell-through?
Upload sales plus stock or receipts data. Turnover is computed as cost of goods sold over average inventory by category or SKU; sell-through as units sold over units received for the period. Both are standard, auditable calculations — every figure shows its working.
How accurate is the seasonal forecasting?
DataHub Pro uses Holt-Winters exponential smoothing, a well-established method for seasonal retail data, and always shows 80% and 95% confidence bands rather than a single line. With two-plus years of history, seasonal patterns are captured well; the bands keep you honest about uncertainty.
Can it compare performance across multiple stores?
Yes. Include a store column in your export and the dashboard breaks every metric down by store, with anomaly detection flagging outliers automatically — like-for-like comparison without the Monday-morning spreadsheet assembly.
Can it help with markdown decisions?
It provides the evidence: sell-through by SKU, weeks of cover, the slow-moving tail from Pareto analysis, and seasonal forecasts for the remaining weeks. The markdown call remains a trading judgement — made earlier and with better data.
Is there a free version to try on our data?
Yes — the free tier ($0, 1 user, 3 uploads/month, 8 core tools) is enough to test the workflow on a real EPOS export. Pro at $14.99/mo ($9.99/mo annual) unlocks all 50 tools, the Shopify connector workflow, AI and scheduled trading packs.
Is customer transaction data handled under GDPR?
Yes. All data is processed and stored in UK/EU infrastructure, never used to train AI models, and a DPA is available. For customer-level analysis such as RFM segmentation, you control what personal data, if any, is included in uploads.
Find out what your tills already know.
Connect Shopify or upload an EPOS export. Get SKU-level analysis, sell-through and a seasonal forecast in minutes.
See also: For ecommerce · Inventory management in Excel · Sales dashboard in Excel · Forecasting calculator · For small business · Home