HOT OFFERINGSWhere do you shop? For years, the answer has been in the mall or on the High Street (depending on where you’re in the world). But today, it’s pretty much anywhere. On the phone, via Instagram, or through a self-service kiosk.
According to the latest estimates, 90% of US and Chinese consumers shopped at online-only retailers last month. And the same holds for 80% of Brits and Germans.
But as eCommerce adoption accelerates, so do shopper expectations. Online shoppers name delivery speed (72%), product availability (66%), and easy or free returns (63%) as the most influential factors in their shopping journeys.
Meanwhile, retailers scramble to meet next-day shipping requests while offering free returns and competitive pricing. Even more so, many are struggling with inconsistent stock information (cue abandoned purchases), inconsistent promotions (cue revenue erosion), and generic promo offers (cue meager conversion rates).
All of these issues have a common root cause: high system fragmentation and the inability to process real-time retail data.
How data fragmentation undermines retail operations
Margins are thinning, competition’s thickening, and the ground keeps shifting under retail’s feet. The growth looms at 1.5% to 3.5% (depending on the sector) as discretionary spending has gone down. Ongoing trade tensions and tariff wars magnify supply chain pressures, while consumer demands for more convenience erode profit margins.
However, apart from macroeconomic pressures, retailers also face pressures from within. Most retail systems weren’t built for real-time data streaming, which the current landscape demands.
Legacy retail store systems lack the capabilities to ingest, process, and act on data at speed. Batch data uploads, like overnight ERP or end-of-day POS syncs, are incompatible with real-time business intelligence. There’s little to no support for event-driven architectures, API-first integration, or unified data schemas. As a result, retail sub-systems can’t “talk” to each other in real time, which leads to the following:
- Sales forecasting engines are working off historical data that’s already outdated by the time it’s processed.
- Inventory and purchasing software aren’t in sync, leading to stockouts in one location and overstocks in others.
- Personalization retail engines lack real-time context, delivering irrelevant or mistimed offers
- Pricing logic lives in silos — one set for POS, another for eCommerce, none of it unified, which leads to poor results from dynamic pricing models.
Combined, these issues make a sizable dent in retail profits. 81% of US shoppers abandoned their preferred brands last year. The majority (40%) cut ties due to product quality issues. Over a third, due to extended delivery timelines and poor online or in-store experiences.
Put simply, shoppers want great quality and unbreakable convenience, ideally for the lowest price possible. Delivering on those wishes isn’t easy, but doable with the transition to unified commerce.
The anatomy of real-time, unified commerce platforms
Unified commerce is a real-time architecture that connects all retail systems from ERP and POS to eCcommerce platforms and marketing tools into one cohesive ecosystem. Unlike traditional retail architectures, where each system operates in a silo, unified commerce centralizes data flow through a single, low-latency infrastructure that connects data from all customer touchpoints.
Unified retail systems are powered by modern data streaming technologies like Apache Kafka or Pulsar, which can ingest and process high volumes of live data, from POS transactions and eCommerce events to stock movements in ERP systems.
Using Change Data Capture (CDC), even legacy systems can be integrated into this real-time loop, allowing older software to contribute data to modern solutions like retail AI personalization tools.
All of the streaming data flows into a unified data warehouse, from where it can be routed dynamically to the right applications: dynamic pricing engines, inventory systems, assortment management platforms, customer-facing apps, or supply chain platforms.
Why unified commerce is more than omnichannel architecture
What sets a unified commerce apart from omnichannel retail architecture is the level of system integration. Omnichannel often means maintaining separate real-time retail systems with some synchronization between them, i.e., they may “talk”, but with some lags and duplication.
Unified commerce takes it a notch further by establishing a shared data backbone, so every channel relies on the same real-time inputs. Pricing, promotions, inventory, and customer profiles stay in sync because they’re orchestrated from a central logic layer, not patched together with brittle integrations.
By investing in a unified data platform and tight system integration, retailers gain sub-second inventory accuracy across locations, plus context-aware dynamic pricing and personalization. These drive:
- Customer acquisition cost reduction
- Higher conversion rates through recommendations
- Better inventory turnover and less deadstock
- Increased cart size via personalized promotions and upsells
- Lower return rates thanks to accurate availability and product information
- Streamlined omnichannel fulfillment and faster order processing
- Reduced operational overhead from automation and data consistency
- Improved campaign performance in retail media networks
In turn, consumers get delightful, consistent, context-aware experiences no matter where or how they shop.
Sold on the idea? Great, here’s what it takes to architect a unified commerce system.
Architecting for real-time performance
Low‑latency streaming pipelines and a unified data layer
Real-time retail systems run on real-time data — POS transactions, cart updates, inventory movements, price changes, and loyalty program activity. And to deliver this data, you need low-latency streaming pipelines.
Streaming data solutions like Apache Kafka, Apache Pulsar, and Apache Flink are our top recs. They can be architected to capture variable data from any part of the retail stack: POS systems, eCommerce storefronts, mobile apps, ERPs, or even IoT retail solutions.
Walmart is a prime example of how well-architected retail data streaming pipelines can be an operational game-changer. Using Apache Kafka with the Confluent platform, they built a real-time replenishment system that processes tens of billions of messages across nearly 100 million SKUs and does it all in under three hours.
Here’s how it works: Walmart uses the Change Data Capture (CDC) to extract updates from transactional systems like ERP and POS, then streams these into Apache Kafka topics. Instead of fully real-time row-level processing, Walmart applies a micro-batch processing pattern. Events are aggregated into short time windows and then processed into denormalized views. These views serve as input for their forecasting and planning engines, which factor in inventory levels, lead times, forecast demand, and distribution constraints.
Once replenishment plans are computed, they’re published back into Kafka across 20+ topics, each with over 500 partitions to be consumed by downstream teams and applications.
With this unified data model, different business units now have a unified view of the replenishment order plans across their massive supply chain network. They can design replenishment cycles closer to pick time at the distribution center to optimize for speed and accuracy.
System integration: POS, eCommerce, and ERP
When your retail systems communicate freely, you can optimize different aspects of your operations in unison. Inventory and pricing synchronization enables smarter markdown strategies and more accurate fulfillment estimates, which impact conversion rates.
A study of over 840,000 Instacart customers found that when shoppers were warned an item had low availability, they were 25% less likely to purchase it, but also more likely to add other items. This simple nudge drove a 5.3% increase in revenue per customer and a 4.9% boost in order frequency.
POS and ERP integration, in turn, enables better customer behavior prediction through a more complete view of sales trends, purchase frequency, and regional demand signals. You can then use the segmented data to create granular retail personalization experiences with AI.
The Xenoss retail development team has recently helped a major online marketplace in CEE deploy a set of ML models to promote a range of vastly different product classes, from dog food and groceries to consumer electronics and luxury beauty products.
We first classified all products into separate classes, based on statistically defined similarities in consumption behavior patterns. Then, we created a separate model for every user behavior class. Thanks to this auto-monitored multi-model solution, brands can identify high-intent audiences and optimize ad placements accordingly.
Result: An 18% CTR for ads and a 9% overall increase in conversion rates.
To achieve similar gains in click-through rate optimization or excel in real-time inventory management, you’ll need to have three tech elements in place:
- Data adapters and schema harmonization
Most retail systems produce and exchange data in different formats. So your first step is building connectors that can translate and normalize data across platforms. Data adapters like Apache Camel or MuleSoft Anypoint Platform can clean, convert, and align key data structures (like SKU IDs, price fields, or stock units) into a shared format that your unified retail data platform can use.
2. Event-based synchronization pipelines
Rather than relying on batch syncs, you architect your systems to respond to real-time events. When a product is sold in-store, an “inventory update” event is triggered and sent through your streaming data pipeline. That update flows instantly into your online storefront, warehouse system, or pricing engine, ensuring stock levels and offers are updated everywhere, with no lag.
3. Effective data conflict resolution strategies
But achieving the above synergy isn’t simple because of the inevitable data conflicts. What happens when two systems clash over pricing and inventory consistency? Well, one washing machine retailer had to shoulder over $4.2 million in losses due to a pricing error caused by incorrect labelling.
To avoid similar scenarios, you need an effective retail data conflict resolution strategy. Our team recommends using either timestamp-based reconciliation (e.g., the most recent update wins) or source-of-truth hierarchies, where one system (e.g., your ERP) is always treated as authoritative for certain fields.
By uniting and orchestrating every system in your retail stack, you can unlock extra operating gains. No more ghost inventory that frustrates shoppers, pitiful pricing mistakes (that also get companies under regulatory scrutiny), and missed sales opportunities.
Decision-making layer for real‑time retail personalization and dynamic pricing
Once your data is unified and streaming in real time, you’re ready to deploy “value-add” retail solutions — a real-time recommendation engine, dynamic pricing tools, AI-powered demand forecasting, automated markdown optimization, and hyper-targeted promotional campaigns.
AI assortment management
Real-time inventory sync across locations gives you irrefutable data on stock levels, plus data for predictive modeling. ML algorithms can factor in sales velocity, regional demand, and supply chain constraints to suggest optimal assortments for physical and digital shelves. This way, your bestsellers remain in while slow-movers are marked down or rotated out on autopilot.
Take it from Target. Before 2023, the company relied on traditional rule-based software for inventory management and often missed the mark. According to Chief Digital and Product Officer Prat Vemana, the company routinely failed to detect half of its out-of-stock items because legacy systems mistakenly believed inventory was available.
That changed with the introduction of Inventory Ledger, Target’s real-time inventory management system, powered by AI. It aggregates data like supply lead times, transportation costs, current stock levels, and demand signals from all sources to forecast stock-outs with higher accuracy. Today, Target uses AI-powered inventory forecasting for over 40% of its assortment, more than doubling its usage from just two years ago, guiding the company’s re-ordering and replenishment strategies.
Real-time personalization
Similarly, streaming data usage also enables advanced product recommendation strategies. Instead of serving up generic bestsellers or past-purchase lookalikes, recommendation tools can use up-to-the-second data to deliver context-aware, inventory-filtered cross-sells and upsells.
Home improvement retailer Kingfisher launched an AI-powered recommendation engine in 2022 for its portfolio of 55,000 brand-name products and 1.5 million partner products. It relies on a combination of machine learning algorithms and Gen AI to deliver exceptional CX across channels. According to Mohsen Ghasempour, Group AI Director, the recommendation engine drove £100 million in revenue last year and showed a 100% uptick in conversion rates.
Dynamic pricing
Next is pricing, where unified retail systems and AI can drive substantial revenue gains when implemented responsibly. With real-time visibility into demand shifts, stock levels, shelf life, and local buying patterns, you can maintain competitive prices without eroding margins.
An AI-powered dynamic pricing engine can help maximize sell-through rates without eroding margins based on real-time data on product velocity, category elasticity, weather, promotions, or even traffic patterns. Statistically, half of such rule-based promotions end up being unprofitable for retailers. With AI, you can implement segmented promotions across channels (in-store vs mobile app vs online), or even tailor promotions to different customer segments.
Colgate-Palmolive, for instance, uses AI to devise optimal promotional calendars — a task that previously involved loads of strenuous spreadsheets and manual effort. A Dutch grocery retailer, in turn, deployed AI pricing suggestion models to improve the customers’ perception of the offered value-for-money. The system delivered a 0.6% increase in like-for-like growth and a 1.2% gross margin improvement.
That said, dynamic pricing engines must be compliant. You’ll need to have rule-based overrides to protect against predatory pricing, enforce minimum advertised price (MAP) policies, or respect market-specific regulatory constraints. These rules act as guardrails around ML predictions, ensuring promotional agility doesn’t cross into legal risk.
Your dynamic pricing tools will also need to operate within strict latency budgets (often under 50ms) to support real-time decisions at checkout or ad auction time. For that, you’ll need streamlined inference pipelines, fast-access feature stores, and careful trade-offs between model complexity and response time, areas where our retail AI team can advise you.
Retail media advertising capabilities
Retailers aren’t just selling products. They also have their audience’s attention. With 75% of shoppers engaging with in-store ads, retail media networks offer a rare combination of reach, relevance, and point-of-purchase influence. Naturally, brands are hooked. Retail media is expected to stay the fastest-growing ad channel through 2027 (with a projected CAGR of over 20%).
But here’s the catch: retailers need more than just ad space to profit from this opportunity. They need unified operations. Why? Because retail media only performs when it’s powered by real-time, high-fidelity data, the kind you can only unlock when inventory systems, customer touch points, pricing engines, and marketing platforms are fully connected.
Unified commerce systems enable this by providing:
- Live product availability feeds to prevent ads for out-of-stock items
- Dynamic pricing signals to adjust bids based on margin and velocity
- Customer behavior insights to personalize offers across owned and paid channels
- Synchronized fulfillment and inventory systems to ensure smooth delivery after conversion
This tight loop between commerce and advertising makes your ad arm more performant, your targeting more precise, and your customer experience more consistent.
Building a next-gen retail media platform
We’ve seen this in action. A leading online marketplace turned to Xenoss to help them with retail media network development. The challenge was twofold:
- Build a DSP-like advertising platform that would allow sellers to promote products inside the marketplace ecosystem and beyond
- Optimize campaign performance to reduce cost per conversion and improve delivery, without adding operational overhead
Our team delivered a custom DSP solution with an embedded Bid Decision Engine — an AI-powered system that evaluated every inbound ad opportunity in real time and calculated the fair price based on contextual and behavioral signals. This ensured efficient budget allocation, CPC minimization, and consistent delivery against campaign goals.
To keep performance strong as traffic patterns evolved, we implemented an automated ML model retraining retail pipeline. It monitors model health, detects drift or degradation, and auto-triggers retraining.
With the new setup, the retailer achieved
- 27% reduction in CPC, driven by real-time bidding optimization
- 45% reduction in operational overhead, due to automation-first design
- Faster experimentation cycles and safe, controlled model deployment
Unified front-end systems for omnichannel experience
Few customer journeys today are linear. Most retail transactions involve several devices and several dozen touchpoints. About 80% of consumers globally browse retailer websites in-store, and 74% use a retailer’s app, according to eMarketer.
In 2025, digitally influenced sales in the US will exceed 60%, whether through online research, mobile engagement, or personalized ads.
However, to convert the digitally influenced sales, retailers need to offer continuity and consistency across all touchpoints. That kind of fluidity doesn’t happen by itself since no integrated front-end app can handle everything retailers need. Instead, most rely on a patchwork of digital experience platforms, self-checkout kiosks, retail mobile apps, and social selling tools.
But with a strong focus on fulfilling customer needs, many forget that these front-end solutions need a shared infrastructure that orchestrates content, sessions, and logic across all channels.
Until recently, the solution to that was just hard-coding new business logic into existing eCommerce systems — an approach that only made orchestration more complex. But a growing number of retailers are trialing a new approach: MACH.
MACH: the foundation of composable retail experiences
Short for Microservices-based, API-first, Cloud-native SaaS and Headless, MACH is an architecture pattern that prioritizes composability. Retailers are shifting to flexible, API-first architectures by building plug-and-play front-end microservices that can connect to back-end APIs in a decoupled way.
This approach enables faster integrations. Instead of hard-coding new business logic, you only need to expose a new API to your platform. From there, a unified API gateway handles request routing, authentication, rate limiting, and protocol translation — the tech blocks you need for high system performance.
MACH architectures not only improve system maintainability and reduce tech debt but also drive compelling business benefits.
To support seamless experiences across devices and channels, MACH architectures should be paired with additional technical strategies:
- Edge caching with real-time invalidation: To accommodate peak loads and avoid data conflicts (e.g., shoppers seeing an expired promo or sold-out inventory). Instead, your systems immediately invalidate cached pages to prevent outdated content from surfacing.
- Token-based session continuity for seamless cross-device shopping: Each user gets assigned a token with embedded key context (e.g., cart contents, preferred store, and active promos) and stores it in a centralized session store (e.g., a cloud-native session database). The data then travels through API calls when the shopper switches devices. Backend servers retrieve the session state, synchronize it with real-time data (stock, pricing, promotions), and return an updated view to the new shopper’s device.
Together, these capabilities make sure every click, swipe, and scroll adds up to one fluid, seamless customer journey.
DataOps and governance for system security and reliability
Without continuous monitoring and data governance, even the best-designed systems can become a security nuisance. ML models drift, pipelines silently fail, and attackers can exploit gaps in your integrations.
That’s why retailers need strong DataOps practices to keep unified systems reliable, secure, and audit-ready:
- Set up an observability platform. Get your intel on system performance flowing with tools like Grafana, Prometheus, and Datadog. Set up alerts for data conflicts and latency to spot and troubleshoot issues early. Add freshness SLAs for key datasets and continuously validate them. Heartbeat events and checkpointing mechanisms can also be embedded in stream processing jobs (e.g., with Kafka Streams or Apache Flink) to verify pipeline health.
- Configure circuit-breakers to prevent missing streams or model lag. If a model starts receiving incomplete inputs (e.g., outdated price feeds), a circuit-breaker pattern halts inference or downstream processing. Technically, this can be implemented via middleware that checks input data freshness or schema conformance before triggering model execution. If the inputs fail validation, fallback logic kicks in. For example, the system disables dynamic pricing software and defaults to rule-based logic. Such a “kill switch” limits the blast radius of bad data.
- Keep audit trails. You need visibility into who did what and when, especially when it comes to price changes, promotion overrides, or model updates. Amazon CloudTrail or Apache Kafka with a schema registry are suitable options for hosting immutable log stores, which serve as a series of events for quick debugging and regulatory reporting.
- Implement automated retraining pipelines. ML models inevitably decay. Your goal is to catch and correct it fast. Use MLflow, Kubeflow, or SageMaker Pipelines to set up automated retraining pipelines, triggered by performance degradation. Combine them with metrics logging, model versioning, and shadow deployments to ensure smooth and safe system updates.
Overall, data governance becomes even more critical when launching new AI-driven workflows. You’ll need to tackle data consistency and conflict resolution head-on.
At Xenoss, we’ve developed custom logic for data trust and conflict resolution that’s already in use in retail media and inventory optimization platforms. These guardrails ensure every system downstream operates on verified, up-to-date information. No ghost inventory, pricing mismatches, or broken customer journeys.
Final thoughts: Unified data is retail’s next edge
Real-time inventory visibility, personalized promotions, savvy assortment management, and fast fulfillment hinge on accurate, synchronized data. Fragmented systems hold retailers back. Unified commerce pushes them forward.
By re-architecting your stack to include modular APIs, event-driven systems, and real-time data pipelines, you can break the cycle of overstock, delayed market reactions, and “guesswork” decisions.
At Xenoss, we help retailers build the technical backbone for that future. Ready to move from reactive to real-time? Let’s talk.
