How to better manage product data, pricing, inventory, orders, store operations and customer experience
Retail business areas
Different product category trade involves distinct assortment, inventory, shelf life, seasonality, delivery, returns and customer service processes, therefore each business area is analysed separately.
Product, price and inventory data differs between channels
Customers see inaccurate information, staff correct errors manually, and orders are cancelled due to products not actually being available.
Biggest opportunity
Unified product, pricing, stock and order data
Integrate product information, pricing, promotions, real-time availability, order management, customer data, returns and store tasks.
Recommended first step
Consolidate master product, pricing and stock data
Define which system creates and modifies each master value, unify identifiers and error controls.
Retail digital maturity is determined not by the number of channels, but by the ability to serve those channels with consistent, reliable data and a coherent order process.
Complete sector analysis
The full digital sector analysis is presented below. A more detailed analysis tailored to the specific operating model is available on the business area pages.
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How this sector operates
The sector encompasses the sale of goods to end consumers through physical stores, e-commerce, marketplaces and other direct channels. Technology requirements depend on assortment size, product lifecycle, store and warehouse network, delivery model and returns intensity.
Operations are high-volume and continuously repeating
Sales, price changes, stock movements, goods receipt, picking, payments and returns occur daily at scale.
Assortment is broad and constantly changing
Product attributes, variants, images, categories, suppliers, cost price and availability must be continuously updated across multiple channels.
Every error quickly impacts margin
Inaccurate purchasing, pricing, promotion or inventory decisions translate into lost sales, excessive discounts and write-offs.
Physical and digital channels are interdependent
A store can be a point of sale, collection, returns and order fulfilment, so its processes cannot be designed in isolation.
The customer expects consistent information everywhere
The buyer expects the same price, loyalty benefits, availability, delivery options and order status across all channels.
Market and technology context
Retail is moving from separate physical and digital channels to unified commerce. The greatest practical value today is created by real product availability, centralised order management, improved inventory planning, consistent pricing and digitalisation of store processes.
Expectation of consistent experience across all channelsThe customer expects that price, loyalty benefits, availability and order status will not vary depending on the channel.
Margin and labour cost pressureRising goods financing, logistics and labour costs increase the importance of more precise inventory and repetitive operations management.
E-commerce and marketplace expansionEach additional channel increases the need for synchronisation of prices, stock levels, orders and returns.
Faster commercial decisions based on dataSales, margin, promotion and stock data are increasingly used for daily rather than periodic decisions.
AI is transforming product search and recommendationsSearch, recommendations and shopping assistants are increasing the importance of structured, accurate product data.
Digital maturity model
0
Channels and data managed separately
POS, accounting, warehouse and e-commerce operate separately, with data transferred manually.
1
Core digital systems in place
Sales, accounting and e-commerce are digital, but product, price and stock data are not consistently aligned.
2
Core channels partially connected
Product, price and order data are integrated, but stock levels, returns and exceptions are still managed in a fragmented way.
3
Multi-channel processes managed Typical current situation
Product, price, stock and order data have clear sources, and core channels operate according to aligned rules.
4
Unified commerce Siektina
All channels use real-time data, centralised order fulfilment, a unified customer profile and operational analytics.
5
Commerce predicts and safely optimises operations
Forecasts, recommendations and automated rules help manage inventory, pricing, offers and fulfilment, whilst maintaining human control.
Key finding
In retail, the weak point is most often not the online shop itself. Problems arise when POS, ERP, warehouse, e-commerce, loyalty and customer service systems have different understandings of product, price, stock or order status.
When stock levels are inaccurate, orders are routed to the wrong fulfilment location, promotions overlap, and returns are managed manually, customer-facing features merely conceal the operational problem.
It is worth first selecting one coherent scenario – for example, online order with in-store collection – and connecting product, price, stock, reservation, picking, customer notification and returns processes.
Related digitalisation topics
Unified commerce and channel integrationProduct information managementOrder management systemInventory planning and forecastingLoyalty and customer data platformDigitalisation of retail processes
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Product, price and inventory data differs between channels
Critical
POS, ERP, e-commerce, marketplaces and warehouse systems receive or update product, price and inventory information at different times.
Consequences
Customers see inaccurate information, staff correct errors manually, and orders are cancelled due to products not actually being available.
Inventory is inaccurately planned and allocated
Critical
Purchasing and replenishment decisions are based on historical sales, delayed reports or general rules, insufficiently accounting for promotions, stock shortages and location differences.
Consequences
Some locations lack in-demand products whilst others accumulate excess, increasing write-offs and working capital requirements.
Orders are fulfilled separately by sales channel
Critical
E-commerce, marketplace, store and customer service orders enter different processes, with fulfilment location often selected manually.
Consequences
Picking takes longer, exceptions multiply, it becomes difficult to offer collection or delivery from the most suitable location and to inform the customer accurately.
Price and promotion rules are difficult to control
High
Discounts, coupons, loyalty tiers, bundles and channel pricing are created in multiple systems, and their interactions are not always validated before launch.
Consequences
Incorrect prices emerge, unplanned discount overlaps occur, margin losses materialise and conflicts between channels arise.
Returns and complaints require significant manual effort
High
Return eligibility, payment method, product condition, sales channel, warehouse movement and refund are verified across multiple systems.
Consequences
The process is slow, the customer lacks clear status, and returned products re-enter sales too late or are written off with insufficient control.
Integrations unreliably support growing channel and order volumes
High
Data between systems is transferred via files, periodic imports or individual interfaces without common error monitoring.
Consequences
As the number of channels, SKUs and orders increases, synchronisation errors become more frequent, and identifying their cause and recovering data becomes difficult.
Customer history is fragmented across channels
Medium
Store, e-commerce, loyalty, marketing and service systems recognise the customer differently or in some situations do not recognise them at all.
Consequences
Service lacks full context, personalisation relies on incomplete information, and loyalty benefits and communication become inconsistent.
Central tasks are executed inconsistently in stores
Medium
Pricing, display, stocktake, stock transfer and order collection tasks are communicated by email, spreadsheets or verbal agreements.
Consequences
Stores execute tasks differently, price and display changes are delayed, and the central team lacks a reliable view of completion.
Commercial decisions are made based on delayed reports
Medium
Sales, margin, promotion, stock, returns and customer data are consolidated periodically and often manually.
Consequences
Response to demand changes, underperforming promotions, stock shortages or declining category margins is slow.
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One coherent omnichannel scenarioVery high impactConnect online order, real-time availability, reservation, picking, collection, statuses and returns for a selected customer and store segment.
Unified product, price, stock and order dataVery high impactEstablish primary data sources and progressively connect sales, accounting, product information, warehouse, e-commerce, loyalty and order management systems.
Centralised omnichannel order fulfilmentVery high impactSelect order fulfilment location based on actual stock, location, delivery timeframe, picking capacity and economic benefit.
Demand, replenishment and stock allocation optimisationVery high impactUse more detailed sales, promotion, seasonality, out-of-stock and location data for forecasting, replenishment and redistribution.
Managed product data and faster assortment launchHigh impactCentralise categories, attributes, variants, content, media and channel requirements.
Digital store task and operations managementHigh impactManage price and display checks, product searches, stocktakes, transfers and order collections via mobile.
Faster commercial control based on actual dataHigh impactConsolidate sales, margin, promotion, stock, returns and fulfilment data into operational KPIs and exception alerts.
Biggest opportunity
Unified product, pricing, stock and order data
Integrate product information, pricing, promotions, real-time availability, order management, customer data, returns and store tasks.
Higher conversion through accurate pricing and availability
Fewer cancelled orders
Lower stock-outs, excess inventory and write-offs
Faster order management
More consistent customer experience across all channels
Fewer manual exceptions and data corrections
More accurate margin and promotion control
Expected impact on operations and financial performance
Increased revenue and conversionMore accurate product information, reliable availability and smoother order processes reduce lost sales.
Better margin controlConsistent pricing and promotion rules reduce unplanned discounts, pricing errors and write-offs.
Lower working capital requirementsMore accurate demand and replenishment management reduces slow-moving stock without sacrificing product availability.
More efficient operationsManual data corrections, order exceptions, price changes, reporting and returns administration are reduced.
More consistent customer experienceCustomers receive reliable information, more flexible delivery or collection, simpler returns and consistent loyalty benefits.
Greater capacity for growthConnected architecture enables faster onboarding of new stores, channels, marketplaces, product categories and partners.
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Problema
Product, pricing and stock data differ across channels
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Sprendimo kryptis
Product information management system
Centrally manages categories, attributes, variants, descriptions, media and channel publishing, and delivers reliable information to ERP, e-commerce, POS and marketplaces.
Problema
Integrations unreliably serve the growing scale of channels and orders
→
Sprendimo kryptis
Product information management system
Centrally manages categories, attributes, variants, descriptions, media and channel publishing, and delivers reliable information to ERP, e-commerce, POS and marketplaces.
Problema
Product, pricing and stock data differ across channels
→
Sprendimo kryptis
Commerce data and integration platform
Establishes master sources for products, pricing, stock, customers and orders, manages real-time exchange, errors and data retransmission.
Problema
Orders are fulfilled separately by sales channel
Picking takes longer, exceptions multiply, it becomes difficult to offer collection or delivery from the most suitable location and to inform the customer accurately.
→
Sprendimo kryptis
Commerce data and integration platform
Establishes master sources for products, pricing, stock, customers and orders, manages real-time exchange, errors and data retransmission.
Problema
Integrations unreliably serve the growing scale of channels and orders
→
Sprendimo kryptis
Commerce data and integration platform
Establishes master sources for products, pricing, stock, customers and orders, manages real-time exchange, errors and data retransmission.
Problema
Orders are fulfilled separately by sales channel
Picking takes longer, exceptions multiply, it becomes difficult to offer collection or delivery from the most suitable location and to inform the customer accurately.
→
Sprendimo kryptis
Order management system
Centrally accepts orders from all channels, reserves items, selects fulfilment location, manages splitting, collection, delivery, statuses and cancellations.
Recommended digital solutions
The implementation sequence should begin with the reliability of product, pricing, stock and order data. A new e-shop feature or customer app should not become yet another separate system.
Product information management system
Centrally manages categories, attributes, variants, descriptions, media and channel publishing, and delivers reliable information to ERP, e-commerce, POS and marketplaces.
Commerce data and integration platform
Establishes master sources for products, pricing, stock, customers and orders, manages real-time exchange, errors and data retransmission.
Order management system
Centrally accepts orders from all channels, reserves items, selects fulfilment location, manages splitting, collection, delivery, statuses and cancellations.
Inventory planning and replenishment platform
Uses sales, promotions, seasonality, stock, lead time and stock-out data for forecasting, replenishment and redistribution.
Centralised pricing and promotions management system
Manages base prices, promotions, coupons, loyalty rules, bundles, channel restrictions, approvals and margin impact checks in one place.
Customer data and loyalty platform
Consolidates customer identity, purchases, returns, service, loyalty, communication and consents into a single managed profile.
Store operations and tasks app
Manages price and display checks, stock takes, transfers, order collection, product search and central tasks via mobile.
Investment priorities
Consolidate master product, pricing and stock dataDefine which system creates and modifies each master value, unify identifiers and error controls.
Centralise order management and availabilityIntegrate reservation, fulfilment location selection, statuses, collection, delivery and returns.
Unify pricing, promotion and margin controlCentralise rules, approvals, publication and promotion impact assessment.
Connect customer profile and store processesManage identity, purchases, returns, service, consents and store tasks in a unified manner.
Only then expand forecasting, personalisation and AIDeploy advanced models only when sufficient quality data, stable processes and measurable impact are in place.
Key implementation conditions
Every data point must have a single source of truth
Product, price, stock, customer and order fields must have a clear system that creates, modifies and distributes them.
Accounting balance is not the same as actual availability
Availability must assess reservations, unconfirmed movements, safety stock, damaged goods and the specific location's ability to fulfil the order.
The system must operate in a controlled manner during peak periods
During promotions, holidays and seasonal peaks, the integration and order process must maintain data consistency and clear fallback states.
Implement in complete scenarios, not partial functions
It is safer to fully implement one purchase scenario in several units than to partially change all channels at once.
Store employee workstation must reduce the number of steps
If a new process requires more clicks or duplicates existing work, employees will circumvent it.
Customer data and personalisation must be used transparently
Customer profile, loyalty and AI scenarios must have a clear legal basis, data minimisation, consent management and explainable use.
Recommended implementation sequence
01
Single critical customer scenario analysis
Identify how the selected purchase or order scenario moves through product, pricing, inventory, order and returns systems.
Customer and operations process map
Systems and integrations map
Master data owners
Initial KPIs
02
Foundation of data and integrations required for the first scenario
Organise only the product, price, inventory, reservation and order status data required for the selected order and collection scenario.
Data model for the selected scenario
Product, price and availability interfaces
Reservation and order status exchange
Synchronisation error monitoring
03
First complete omnichannel commerce scenario
Implement one end-to-end working scenario, for example, order online with collection in store.
Reliable availability
Product reservation
Fulfilment location selection
Store associate tasks
Customer status communication
04
Expansion of channels, returns and store operations
Connect additional stores, warehouses, sales channels, returns and delivery models.
Centralised order management
Returns process
Store operations app
Exception management
05
Inventory, pricing and margin optimisation
Use unified data for faster inventory, promotion, pricing and category decisions.
Operational KPIs
Demand forecasts
Replenishment recommendations
Promotion and margin analysis
06
Personalisation and AI scenarios
Implement only measurable recommendation, shopping assistant and operational exception prioritisation scenarios.
Personalised recommendations
AI shopping assistant pilot
Price recommendation pilot
Automated exception prioritisation
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Recommended KPIs
Inventory accuracy%
Measure what proportion of system stock levels corresponds to the actually available quantity of goods.
Out-of-stock rate% SKU and locations
Assess how often popular products are unavailable at a specific sales location.
Inventory turnovertimes per period
Measure how efficiently inventory is converted into sales.
Slow-moving inventory share% of inventory value
Monitor capital tied up in low-demand products.
On-time and in-full order fulfilment rate%
Assess the reliability of order fulfilment location selection and the entire fulfilment process.
Cancelled orders due to unavailable products rate%
Measure stock availability and reservation quality.
Proportion of manually handled order exceptions%
Monitor automation and process stability progress.
Product data completeness% SKU
Measure how many products have mandatory attributes, content and appropriate channel values.
Number of price discrepanciescases per period
Control differences between shelf, POS, e-commerce and promotion rules.
Proportion of orders started in digital channel and completed in store%
Assess channel integration and click-and-collect scenario usage.
Average returns process durationhrs
Measure time from return acceptance to stock status update and refund completion.
Promotional margin deviation from planpercentage points
Assess pricing and promotional planning control.
Key risks
The system displays quickly updated but inaccurate stock levelsAvailability logic does not account for reservations, losses, unconfirmed movements or store fulfilment capacity.Kaip suvaldyti Clearly define availability formula, monitor discrepancies and improve inventory accuracy in stages.
Attempting to replace the entire commerce architecture in a single projectChannels, systems, pricing, inventory, orders and customer processes are changed simultaneously.Kaip suvaldyti Select one end-to-end scenario, clear KPIs and expand only after live usage validation.
Legacy systems cannot reliably transfer dataERP or POS does not have reliable APIs, real-time events, or a sufficiently detailed data model.Kaip suvaldyti Use an intermediate integration layer, manage synchronisation delay and clearly define system changeover boundaries.
Pricing automation creates incorrect pricesInappropriate rules or data may trigger unplanned discounts, customer distrust or legal risk.Kaip suvaldyti Apply price limits, approvals, experimental groups, audit history and human review.
AI is used without sufficient quality dataModels provide convincing but inaccurate recommendations due to incomplete product, customer or demand data.Kaip suvaldyti Start with limited scenarios, measure accuracy and business impact, and ensure human control.
Shop floor staff do not use the new processThe solution is too slow, inconvenient or does not reflect real shift conditions.Kaip suvaldyti Co-design with staff, pilot in a real environment, measure usage and remove additional steps.
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Advanced solutions create value only when product, pricing, stock, customer and order data are already reliable. AI recommendations and automated pricing solutions must have clear boundaries and human oversight.
Already applied in the sector1
Electronic shelf labels
Relevant
Centrally managed labels enable rapid synchronisation of prices and promotions in physical stores.
How it is applied Greatest value where prices change frequently and manual label replacement causes significant work and discrepancies.
What value can be created
Fewer price discrepancies
Faster promotion launches
Less manual work
What is needed for this to work
Centralised price source
Reliable product and location mapping
Price change controls
Short-term perspectiveCommercial solutions are available
Market expansion3
Demand forecasts and replenishment recommendations
Highly urgent
Models assess demand at specific SKU and location level and help balance stock-outs and holding costs.
How it is applied Particularly relevant for networks managing many stores, seasonal assortment or limited shelf and warehouse capacity.
What value can be created
Fewer stock-outs
Lower excess inventory
More accurate working capital utilisation
What is needed for this to work
Reliable sales and stock history
Promotion and stock-out tagging
Supply lead time and constraint data
Medium-termApplied in practice
Video analysis for shelf and display control
Relevant
Video analysis can detect empty shelf spaces, display discrepancies and other standardised operational events.
How it is applied Most beneficial in larger chains, where manual shelf and display control is frequent and costly.
What value can be created
Faster out-of-stock detection
More consistent display implementation
Fewer manual checks
What is needed for this to work
Clear planograms and product identification
Image data management rules
Process for responding to detected exceptions
Medium-termCommercial solutions are available
Data-driven pricing recommendations
Relevant
Prices or discounts are recommended based on demand, inventory age, competitive position, expiry dates and margin targets.
How it is applied Suitable for categories where prices can be changed frequently enough and the company can clearly define limits, approvals and customer communication.
What value can be created
Lower write-offs
More accurate margin control
Faster response to demand
What is needed for this to work
Reliable pricing, cost and inventory data
Pricing limits and approvals
Experimentation and impact measurement
Medium-termApplied in practice
Early stage1
AI shopping assistants
Relevant
A conversational assistant helps the customer clarify their need, compare products and build their basket.
How it is applied Relevant for complex or large assortments, where the assistant can use reliable product attributes, real prices, stock levels and delivery terms.
What value can be created
Faster product discovery
Higher conversion in complex categories
Reduced service workload
What is needed for this to work
Structured product data
Real-time pricing and stock interfaces
Clear recommendation and safety rules
Medium-termCommercial solutions are available
Being experimented with1
Store or network process modelling
Moderately urgent
Customer flows, space, staff, inventory and order fulfilment are virtually modelled before making changes in the real environment.
How it is applied Valuable for large networks evaluating a new layout, picking model, collection zone or staff allocation.
What value can be created
Lower risk of testing changes
More accurate capacity planning
Better use of space
What is needed for this to work
Detailed operational and flow data
Standardised processes
Model calibration based on actual results
Long-term perspectivePilot projects
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Where to start retail digitalisation?
Select one important customer scenario and identify where product, price, inventory, customer and order data are created. The first stage should eliminate a specific revenue or working time problem, not merely add a new interface feature.
Is a good online shop sufficient for a retailer?
Not if it does not receive reliable pricing, inventory and order statuses or its processes are not linked to stores, warehouses, returns and loyalty. The customer interface cannot compensate for a fragmented operational foundation.
Is it necessary to replace the ERP, POS or e-commerce platform immediately?
Usually not. It is worth first identifying the core data sources and creating a reliable integration and error monitoring layer. Existing systems can be replaced in stages where they actually constrain the process.
What should the first version of the project be?
It should cover one end-to-end scenario, for example an online order with in-store collection: real availability, reservation, picking task, customer notification, collection and possible return.
When is it worth implementing an order management system (OMS) in retail?
When orders are received from multiple channels, can be fulfilled from multiple warehouses or stores, and the customer is offered collection, partial fulfilment, delivery options or returns in another channel.
When is a product information management system (PIM) needed?
When the business manages many SKUs, variants, languages, categories, supplier data or sales channels and product information updates become slow, error-prone or duplicated.
How to assess the return on investment of a project?
Measure inventory accuracy, stockouts, cancelled orders, manual exceptions, write-offs, return duration, promotion margin and employee time spent correcting data. The number of features built or app users alone does not demonstrate financial benefit.
How to reduce stockouts without increasing excess inventory?
It is necessary to distinguish real demand from lost sales, assess promotions, seasonality, supply lead times and location differences, and continuously compare replenishment recommendations against actual results.
When is it worth using AI in retail?
AI is most useful for demand forecasts, replenishment recommendations, product search, personalisation and prioritising operational exceptions by importance. It is only meaningful with reliable product, customer, pricing and inventory data and clear human control rules.
Next step
Assess where the most revenue and working time is lost in the trading process
Review the processes for product, pricing, inventory, order, store, returns and customer data and help select one first stage whose benefits can be clearly measured.