Know what is selling, what is available, and what needs attention

The retail data problem

The numbers often exist – just not in one reliable place

Retail performance is usually spread across systems designed for different purposes.

• POS platforms
• Inventory and warehouse systems
• ERP / accounting software
• E-commerce platforms
• Supplier files and portals
• CRM / loyalty systems
• Manual spreadsheets
• Marketplace or third-party sales channels

When sales, stock, product, supplier, and financial data are not aligned, teams spend time reconciling reports instead of deciding what to buy, move, promote, or stop carrying.

Typical retail and inventory challenges

• POS sales do not reconcile cleanly with finance or inventory
• Stock levels differ between systems
• Product codes and descriptions are inconsistent across sources
• Store managers receive different versions of performance reports
• Replenishment depends on spreadsheets or manual judgement
• Slow-moving and dead stock is identified too late
• Supplier performance is difficult to measure consistently
• Margins are unclear by product, category, channel, or location
• Online and physical-store reporting is separated
• Daily or weekly reporting requires repetitive manual preparation

What Datazeb can deliver

From retail systems to one trusted view

1. POS / Commerce
2. Inventory / ERP
3. Transform & Reconcile
4. Trusted Retail Model
5. Power BI / Alerts

The important work often happens between the source systems and the dashboard: product mapping, store mapping, date logic, transaction rules, stock movements, unit conversions, and reconciliation.

Sales performance analytics

See performance by the dimensions that matter

• Revenue and unit sales
• Average transaction value
• Transactions and basket indicators
• Product / SKU performance
• Category and brand performance
• Store / location performance
• Channel performance
• Daily, weekly, monthly, and seasonal trends
• Target vs actual
• Promotion analysis where the required data is available

Outcome: a consistent commercial view that helps management move beyond manually assembled sales reports.

Inventory intelligence

Stock visibility should explain more than what is currently on hand

Useful inventory reporting combines current availability with movement and demand context.

• Stock on hand
• Available vs committed stock where available
• Days of stock / cover
• Sales velocity
• Stock ageing
• Slow-moving products
• Dead stock
• Stockouts
• Overstock indicators
• Warehouse / store comparison
• Inventory movement history

Outcome: faster identification of inventory problems before they become larger commercial issues.

Replenishment and reorder analytics

Support buying decisions with better context

Datazeb can help create replenishment indicators based on the data and business rules available in the client’s environment.

• Recent sales velocity
• Current stock
• Expected demand
• Supplier lead time
• Minimum / maximum stock rules
• Safety-stock logic
• Open purchase orders where available
• Seasonality and promotional context

Product and SKU master-data challenges

Retail analytics breaks quickly when products are not mapped consistently

The same product may appear with different SKUs, descriptions, barcodes, pack sizes, supplier codes, or channel-specific identifiers.

Datazeb can help establish mapping and reference logic around:

• SKU and product identifiers
• Barcodes
• Product categories
• Brands
• Supplier codes
• Pack / unit conversions
• Store-specific references
• Ecommerce vs POS product identifiers

This work is often essential before sales and inventory reports can be trusted.

Store and location performance

Compare performance consistently across locations

• Sales and margin
• Transaction activity
• Product mix
• Inventory availability
• Stockouts
• Slow-moving stock
• Performance vs target
• Period-on-period trends
• Store ranking by selected KPI

Role-based views can give central management a consolidated picture while allowing store or regional users to see only the information relevant to them.

Supplier and purchasing analytics

Connect buying decisions to supplier and product performance

• Purchase value and volume
• Supplier concentration
• Lead-time trends
• Order / receipt comparison
• Product availability
• Supplier-linked stockouts
• Price changes
• Supplier / category performance

The exact metrics depend on the purchasing and receiving data available in the client’s systems.

Margin and profitability

Revenue alone does not show whether the mix is healthy

Where cost and pricing data is available, Datazeb can help analyse:

• Gross margin by product
• Margin by category
• Margin by store / location
• Margin by channel
• High-revenue / low-margin products
• Discount and promotion effects
• Customer or segment profitability where appropriate

Definitions should be agreed with finance before margin measures are published as management KPIs.

Omnichannel and ecommerce visibility

Bring online and physical-channel performance into a common model

For businesses selling through multiple channels, Datazeb can help align reporting across:

• Physical stores
• Ecommerce platforms
• Marketplaces
• Wholesale / B2B channels
• Click-and-collect or fulfilment workflows
• Shared inventory pools where available

The objective is to avoid separate reporting realities for different channels.

Alerts and exception reporting

Do not make managers search every dashboard for the problem

Datazeb can build alerts or exception views around defined operational rules.

• Low-stock / out-of-stock exceptions
• Unusual sales movement
• Slow-moving stock
• Data refresh failures
• Missing product mappings
• Inventory reconciliation differences
• Store performance exceptions
• Supplier or purchasing exceptions

Explore Business Process Automation

AI opportunities in retail

Apply AI where it helps interpret information or support a workflow

• Internal product / policy knowledge assistants
• Customer support chatbots using approved information
• Natural-language questions over curated sales data
• Document or supplier-file classification
• AI-assisted exception summaries
• Product information enrichment with human review

AI should sit on top of a trusted data and process foundation, not compensate for poor product or inventory data.

Explore AI & Agentic Solutions

Data quality and reconciliation

Retail reporting needs repeatable checks

• Missing SKUs
• Duplicate products
• Negative or unexpected stock
• Sales with no matching product
• Store / location mismatches
• Missing cost data
• Late source feeds
• Transaction totals that do not reconcile
• Inventory movement gaps

Automated validation can surface these issues before they reach executive reporting.

Why Datazeb for retail analytics

• Strong BI and data-engineering capability in one team
• Experience working across APIs, POS-style data, inventory, databases, and Power BI
• Focus on product and source reconciliation before dashboard design
• Ability to combine reporting, integration, alerts, automation, and ongoing support
• Practical commercial focus on sales, stock, margin, and operational visibility
• Flexible project and retainer models
• Global remote delivery

Engagement options

• Retail reporting assessment
• Sales / inventory dashboard project
• POS and inventory data integration
• Product master-data cleanup and mapping
• Multi-store reporting implementation
• Replenishment / exception analytics project
• Automated reporting and alerting
• Managed analytics support

How we work

1. Understand
   Clarify how sales, inventory, products, stores, suppliers, and finance are managed today.
2. Map
   Document the source systems, keys, product hierarchies, locations, transaction logic, and reporting definitions.
3. Reconcile
   Resolve the mapping and business-rule differences that create inconsistent numbers.
4. Build
   Develop integrations, trusted models, Power BI reporting, alerts, and automation.
5. Validate & Support
   Confirm results with business users, document the logic, and improve the solution as operations evolve.

FAQ