Connect the data behind revenue, customers, products and fulfilment

The e-commerce data problem

Every platform has its own version of performance

• Shopify, WooCommerce, Magento, or other storefront platforms
• Marketplaces
• Google / Meta / other advertising platforms
• Payment gateways
• Inventory / warehouse systems
• Fulfilment and shipping platforms
• CRM / email marketing tools
• Finance and accounting systems
• Spreadsheets and manually exported reports

When those systems are reviewed separately, teams can see individual channel metrics but struggle to connect customer acquisition, product performance, inventory, fulfilment, returns, and real financial outcomes.

Typical e-commerce challenges

• Revenue totals differ between storefront, payment, and finance systems
• Advertising performance is disconnected from margin and fulfilment cost
• Product performance is hard to compare across channels
• Inventory and online availability are not aligned
• Returns and cancellations are not included consistently in reporting
• Customer acquisition data is separated from repeat-purchase behaviour
• Marketplace and direct-to-consumer reporting use different product structures
• Management reporting requires manual exports from multiple platforms
• Data refreshes are slow or unreliable
• Leaders have plenty of metrics but no trusted commercial view

What Datazeb can deliver

From commerce platforms to one trusted view

1. Store / Marketplaces
2. Ads / CRM
3. Inventory / Finance
4. Trusted Model
5. Power BI / Actions

The important work often sits between the platforms and the dashboard: order-state logic, refunds, cancellations, SKU mapping, channel classification, customer identity, cost allocation, and financial reconciliation.

Revenue and order analytics

Understand sales after cancellations, returns and channel complexity

• Gross and net revenue
• Order volume
• Average order value
• Units per order
• Discount activity
• Returns and cancellations
• Revenue by channel
• Revenue by region
• Period-on-period trends
• Order-status analysis

The exact revenue definition should be agreed with finance so commercial reporting aligns with the organisation’s accounting logic.

Product and SKU analytics

See which products are creating demand – and which are tying up inventory

• Revenue by product / SKU
• Units sold
• Sell-through
• Margin where cost data is available
• Return rate
• Stock availability
• Sales velocity
• Slow-moving products
• Category / brand performance
• Channel-specific product performance

Product master data should be aligned across storefront, marketplaces, inventory, and finance before cross-channel reporting is treated as reliable.

Customer and retention analytics

Look beyond first purchase

Where customer-level data is available and appropriate, Datazeb can support analysis around:

• New vs returning customers
• Repeat purchase
• Purchase frequency
• Cohort behaviour
• Customer segments
• Average customer value
• Geographic patterns
• Product affinity
• Retention indicators

Marketing and acquisition reporting

Connect advertising activity to commercial outcomes

Advertising platforms are useful for campaign optimisation, but management reporting should also consider actual orders, returns, margin, and customer behaviour.

• Spend by channel / campaign
• Traffic and conversion
• Cost per acquisition where definitions are aligned
• Revenue linked to acquisition source where attribution is available
• New vs returning customer mix
• Product / category performance by channel
• Campaign trend analysis

Inventory and availability

Growth is constrained when the right products are unavailable

• Stock on hand
• Available inventory
• Stockouts
• Days of stock
• Sales velocity
• Slow-moving stock
• Ageing
• Product availability by channel
• Replenishment indicators
• Inventory vs demand trends

Outcome: commercial teams can connect product demand with what the business can actually fulfil.

Fulfilment, shipping and returns

The sale is not complete when the order is placed

• Order-to-dispatch time
• Fulfilment status
• Shipping status
• Delivery performance where available
• Cancellations
• Returns
• Refunds
• Exception reasons
• Fulfilment provider / warehouse comparison

These measures help explain why top-line sales performance may not translate into equally strong customer or financial outcomes.

Marketplace and multi-channel reporting

Standardise reporting across channels without losing channel-specific detail

• Direct website sales
• Marketplaces
• Retail / POS where relevant
• Wholesale or B2B
• Social commerce
• Regional storefronts

Datazeb can create common product, channel, customer, and order structures while retaining the detail required to understand each channel.

Finance and margin alignment

Commercial dashboards should reconcile with finance

Where the source data supports it, reporting can connect:

• Net sales
• Discounts
• Refunds
• Cost of goods
• Gross margin
• Payment fees
• Fulfilment costs
• Advertising spend
• Marketplace fees

This helps leadership move beyond revenue-only reporting toward a more complete view of commercial performance.

Automation opportunities

Remove repetitive exports, checks and notifications

• Scheduled cross-platform reporting
• Inventory / stockout alerts
• Daily commercial summaries
• Failed data-load notifications
• Marketplace file processing
• Product-feed validation
• Order / fulfilment exception workflows
• Automated KPI commentary where appropriate

Explore Business Process Automation

AI opportunities

Use AI where it helps customers or teams access information faster

• Customer support assistants using approved policies and product information
• Internal product / operations knowledge assistants
• Conversational analytics over approved commercial data
• AI-assisted review of customer feedback
• Document and email classification
• AI-generated exception summaries
• Product-content enrichment with human review

AI should be grounded in approved information and connected to defined workflows, not used as a substitute for trusted commerce data.

Explore AI & Agentic Solutions

Data quality and reconciliation

• Duplicate orders
• Mismatched SKU / product IDs
• Cancelled orders still counted as sales
• Refunds recorded differently across systems
• Missing customer or channel mappings
• Late marketplace feeds
• Inventory balances that do not reconcile
• Inconsistent date / timezone handling
• Payment totals that differ from storefront totals

Automated validation can surface these issues before they distort management decisions.

Why Datazeb for e-commerce analytics

• Business-first reporting across revenue, products, customers, channels, inventory, and fulfilment
• Strong API, database, Power BI, Python, SQL, and data-engineering capability
• Focus on reconciliation before visualisation
• Ability to combine analytics, automation, alerts, and AI where appropriate
• Flexible fixed-scope and managed-support models
• Senior-led delivery
• Global remote collaboration

Engagement options

• E-commerce reporting assessment
• Executive / commercial dashboard project
• Storefront / marketplace data integration
• Marketing and revenue reporting
• Product / inventory analytics
• Customer / retention analytics
• Automated reporting and alerts
• Managed analytics support

How we work

1. Understand
   Clarify the business model, channels, customer journey, operational flow, commercial questions, and reporting pain points.
2. Map
   Document storefronts, marketplaces, ad platforms, inventory, payments, CRM, finance, and the identifiers that connect them.
3. Reconcile
   Align products, orders, customers, channels, statuses, refunds, dates, and finance definitions.
4. Build
   Develop integrations, trusted data models, Power BI reporting, alerts, automation, and access controls.
5. Validate & Improve
   Reconcile outputs with commercial, operations, and finance teams, then evolve the model as channels and platforms change.

FAQ