Retail Analytics
Datazeb helps retailers combine POS, ecommerce, inventory, customer, supplier, and finance data into trusted analytics and reporting.
Track revenue, units, transactions, customers, products, stores, channels, targets, and trends.
Analyse product, SKU, category, brand, sales velocity, margin, returns, and stock context.
Compare stores, branches, regions, or formats using shared KPIs and consistent business logic.
Understand new vs returning customers, purchase patterns, segments, repeat activity, and customer value where data allows.
Bring POS, ecommerce, marketplaces, wholesale, and other sales channels into a common reporting model.
Connect sales with cost and finance data to understand gross margin and commercial mix where reliable cost data exists.
Connect POS, ecommerce, ERP, inventory, CRM, finance, supplier, API, database, and file-based data sources.
Reduce manual exports, spreadsheet consolidation, daily reporting, and repetitive management packs.
Measures
Store, product and stock performance on one model.
Store
Sales, traffic and conversion by location.
POS
Transactions, basket size and attach rate.
SKU
Sell-through and contribution by product.
Inventory
Stock on hand, ageing and shrink.
Margin
Gross margin after discounts and returns.
Promotion
Uplift and margin impact.
Replenishment
Stock-outs and reorder needs.
Related work
Retail · United States
Mobile and electronics retailer — 50+ stores in 10 states
The business had grown to more than 50 locations across 10 states, but it had no shared view of how those stores were performing.
What was built
Client names are withheld. Specific commercial metrics are not published for confidentiality.
The most important work is usually the mapping and reconciliation layer: products, stores, customers, channels, returns, discounts, dates, costs, and inventory must line up before the dashboard can be trusted.
In detail
Sales reporting should use one agreed commercial definition across POS, ecommerce, and finance wherever possible.
Product analytics becomes significantly more useful when sales, stock, cost, and product master data are aligned.
A common KPI model allows fair comparison while still preserving local drill-down.
Datazeb can standardise product, customer, store, and channel reporting while preserving channel-specific detail.
Cost and margin definitions should be validated with finance before they are used as management KPIs.
Datazeb can build reference logic and validation so the same product, customer, or store is not reported differently across systems.
FAQ
Yes. Datazeb can bring multiple sales channels into a common model where products, stores, customers, and transactions can be mapped reliably.
Yes. Sales velocity, stock, availability, ageing, stockouts, and product performance can be combined where the required data exists.
Yes, where reliable revenue and cost data is available and the finance definitions are agreed.
Yes. Shared KPI definitions and hierarchy mappings can support consolidated reporting with local drill-down.
Yes. Customer, repeat-purchase, segment, cohort, and product-affinity analysis can be supported where appropriate customer data is available.
If retail performance is still spread across POS reports, ecommerce dashboards, inventory systems, finance exports, and spreadsheets, tell us how reporting works today. Datazeb can help create one more reliable view of commercial performance – from the underlying data to the management decision.
