AI-driven forecasting is starting to change this picture. 58% of supply chain executives are prioritizing forecasting and risk management improvements in 2026. And the investment is paying off: 91% of retailers are now actively using or evaluating AI, with 89% reporting measurable revenue increases. Organizations applying machine learning to demand planning typically see error reductions of 20–50% and inventory cost savings in the range of 20–30%. 

This article walks through how AI forecasting works, what infrastructure you’ll need, and how to figure out if your organization is ready to make the leap.

AI demand forecasting explained: How machine learning predicts customer demand

AI-powered demand forecasting uses machine learning and predictive analytics to estimate how much product customers will buy. 70% of large organizations will adopt AI-based forecasting by 2030. But many aren’t waiting, 87% of enterprises already use AI for demand forecasting, with companies reporting accuracy improvements of 35% or more.

So what makes AI different from traditional methods? The short answer: scale and adaptability. 

AI models can process enormous datasets simultaneously, pulling in historical sales, weather patterns, social media buzz, economic indicators, and more. Traditional statistical methods tend to rely on historical averages and manual adjustments that get updated weekly or monthly. AI forecasts can adjust dynamically as market conditions shift.

AI forecasting systems typically predict:

• Demand volume: How many units customers will purchase
• Timing: When demand spikes or dips will occur
• Geographic distribution: Where demand concentrates across regions
• Channel patterns: How demand differs between e-commerce, retail, and wholesale

Why traditional forecasting fails: The case for AI demand forecasting

Limited data processing in spreadsheet-based forecasting

Spreadsheet-based planning tools cannot handle the volume and variety of data modern supply chains generate. Point-of-sale transactions, web traffic, social media signals, weather feeds, and competitor pricing all contain demand signals. 

Traditional spreadsheet methods typically work with just 3 to 5 variables, while AI systems can analyze 20 to 50 or more at once. With traditional tools, planners end up working with a narrow slice of what’s available.

How traditional methods miss complex demand patterns

Linear regression and moving averages assume that relationships between variables are fairly straightforward. In practice, demand often follows non-linear patterns. A 10% price cut might boost sales by 5% in one region and 25% in another, depending on local competition and what time of year it is. Traditional methods miss these kinds of interactions entirely.

Slow forecast updates create costly supply chain gaps

Most traditional forecasts update on fixed schedules, usually weekly or monthly. When a competitor launches a flash sale or a viral social media post drives unexpected interest, batch-updated forecasts are already stale. 

AI-based systems can adjust forecasts within hours, detecting demand shifts through real-time POS data and external signals. The lag between market changes and forecast updates in traditional systems creates costly misalignment.

Manual forecasting drives high error rates and planner burnout

Demand planners using traditional methods spend significant time on data entry, reconciliation, and manual overrides. Each touchpoint introduces potential for human error and subjective bias. One misplaced decimal or optimistic adjustment can cascade through the entire supply chain.

How AI improves demand forecasting accuracy

Machine learning pattern recognition for demand signals

Machine learning algorithms identify correlations that human analysts would never spot manually. A model might discover that sales of a particular product spike three days after specific weather patterns in certain zip codes. Combining techniques like LSTM, XGBoost, and Random Forest can reduce forecast error from around 28.76% to 16.43%, a drop of about 42.87%. Those kinds of subtle, multi-dimensional relationships simply aren’t visible through traditional analysis.

AI demand sensing: Using external data to predict shifts early

AI models pull in signals like weather forecasts, economic indicators, social media sentiment, and event calendars to sense demand shifts before they show up in sales data. 

This makes a real difference in practice. Unilever’s ice cream division improved forecast accuracy in Sweden by 10% by analyzing weather patterns, enabling it to position inventory before demand spikes. 

In key markets, this translated to sales increases of up to 30% within a single year. Demand sensing allows for proactive adjustments rather than reactive scrambling.

SKU-level AI forecasting for precise inventory planning

Rather than forecasting at the category level and allocating downward, AI enables bottom-up forecasting at the individual product-location-day level. This precision lets retailers optimize inventory at the store and customer level rather than at a regional level. This granularity dramatically improves replenishment accuracy and reduces the safety stock buffer needed at each distribution point.

How AI models learn and adapt to changing demand

AI models automatically retrain on incoming data, adapting to evolving consumer behavior without requiring manual intervention. When demand patterns shift due to tariff announcements or geopolitical disruptions, as supply chains experienced throughout 2025’s trade policy volatility, AI systems can detect and adjust within days rather than quarters.

How AI-powered forecasting reduces inventory costs

Lower safety stock requirements with accurate AI forecasts

When forecast confidence improves, planners can carry leaner buffer inventory without risking stockouts. By generating SKU-level forecasts with tighter error bands, these models enable leaner safety stocks that free up working capital previously tied to dormant inventory.

In 2025, packaging manufacturer Novolex reduced excess inventory by 16% and shortened planning cycles from weeks to days by combining historical sales data with external market signals. 

Walmart uses AI-powered forecasting to optimize inventory placement decisions across its network, ensuring that safety stock isn’t sitting idle in warehouses while stores face potential shortages.

Unlike static formulas that require manual updates, AI systems continuously adjust safety stock levels based on demand trends, supplier reliability, and market conditions. Businesses using intelligent forecasting reduced excess inventory carrying costs by 20% while simultaneously cutting stockouts by 15%.

Reduced warehousing costs through better demand prediction

Less excess inventory directly reduces warehousing costs, insurance premiums, and material handling expenses. For companies with extensive distribution networks, the savings compound across every facility. Warehousing costs can fall by 5 to 10 percent with AI-driven forecasting in place.

Fewer stockouts: How AI forecasting protects revenue

Better demand sensing prevents out-of-stock situations that send customers to competitors. Lost sales due to stockouts can decrease by up to 65% with AI forecasting. The revenue protection from avoiding stockouts often exceeds the direct cost savings from reduced inventory.

Reducing waste and obsolescence with AI demand planning

Accurate forecasting reduces overproduction and the risk of holding expired or outdated inventory. This matters especially for perishable goods, fashion items, and electronics with short product lifecycles. 

Nestlé’s 90-day AI pilot generated $2.3 million in additional revenue while achieving 176% conversion rate improvement, demonstrating how targeted AI can drive both top-line growth and waste reduction.

Core capabilities of AI-driven forecasting systems

Real-time demand sensing and dynamic forecast updates

Streaming data pipelines let models update predictions as new signals arrive, including social media spikes, competitor price drops, or unexpected weather events. 62% of supply chain leaders say AI agents embedded in operational workflows accelerate speed to action. 70% of executives expect their employees to be able to drill deeper into analytics for real-time analysis as AI agents automate operational processes. This represents a fundamental shift from batch systems that wait for scheduled updates.

What-if scenario planning for supply chain decisions

AI platforms let planners model “what-if” scenarios: 

• What happens to demand if we run a 15% promotion next month? 
• What if a key supplier faces delays? 

67% of companies that deployed agentic AI in supply chain and inventory management in 2025 saw a significant increase in revenue. Scenario planning transforms forecasting from a prediction exercise into a genuine decision-support tool.

Multi-channel inventory optimization across sales channels

AI-driven forecasting supports sophisticated allocation across e-commerce, retail, and wholesale channels. The system can optimize where to position inventory based on predicted demand by channel and location.

Automated reordering connected to AI forecasts

Production-grade systems connect forecasts directly to ERP and ordering systems, automatically generating purchase orders or triggering production schedules. Automation reduces manual effort and speeds the replenishment cycle.

How AI demand forecasting works step by step

1. Data collection and integration

The process begins with aggregating relevant data: historical sales, inventory levels, promotions, and external signals, into a unified data layer. Data quality at this stage determines everything that follows.

2. Feature engineering and preparation

Raw data gets transformed into features the model can actually use: lag variables (past values that help predict future ones), encoded categories, and handled missing values. Feature engineering often consumes more time than model training itself, but it’s where much of the value gets created.

3. Model training and validation

Machine learning models train on historical data, then validate against a holdout period the model hasn’t seen. Validation reveals whether the model generalizes to new situations or merely memorizes patterns from training data.

Current AI models achieve 87% accuracy for 30-day demand forecasts, 76% for 90-day predictions, and 62% for annual planning.

4. Deployment and real-time inference

Validated models deploy to production environments where they generate forecasts on a scheduled or an on-demand basis. The deployment architecture determines whether forecasts update in minutes or hours.

5. Continuous monitoring and retraining

A feedback loop tracks forecast accuracy over time, detecting model drift when performance degrades because market conditions have changed. Fully autonomous forecasting still requires human judgment, which is why continuous monitoring remains essential. Automated retraining on fresh data maintains accuracy as conditions evolve.

Data and infrastructure requirements for AI forecasting

Historical sales and transaction data

Most AI forecasting implementations require two to three years of clean, granular transactional data. The quality and completeness of historical records directly impact model accuracy.

External data sources and APIs

Weather APIs, economic indicators, promotional calendars, and competitor pricing feeds enhance forecast accuracy. The challenge lies in integrating diverse sources reliably and maintaining data freshness.

Real-time data pipeline architecture

Enabling real-time demand sensing requires streaming or micro-batch pipelines built with tools like Apache Kafka, Flink, or managed cloud services. Organizations moving toward autonomous decision-making need infrastructure supporting simultaneous analysis of inventory levels, supplier performance, and market trends. Batch-only architectures limit how quickly you can respond to market changes.

Compute and storage considerations

Training and running AI models at scale requires cloud compute instances, GPU resources for complex models, and scalable storage. Infrastructure costs scale with data volume and model complexity.

How to get started with AI in demand planning

1. Audit your current data quality and sources

Before selecting tools or partners, assess the completeness, accuracy, and accessibility of existing data. A thorough data audit is the most critical first step and often reveals gaps that would undermine any AI initiative.

2. Define forecast granularity and business rules

Determine the level of detail your business requires (SKU, location, day, or hour) and identify constraints the model respects, such as supplier lead times or minimum order quantities.

3. Select build versus buy approach

Evaluate tradeoffs between building custom systems in-house versus purchasing platforms. Consider required flexibility, total cost of ownership, internal expertise, and desired time-to-value.

4. Plan integration with ERP and WMS systems

Create a clear plan for connecting forecast outputs to downstream systems. Key integrations include ERP, order management, warehouse management, and production planning software. By 2030, 50% of cross-functional supply chain solutions will use intelligent agents that operate across these systems autonomously.

5. Establish governance and change management

Develop processes for forecast review, exception handling, and training for demand planners transitioning from manual methods. Technology adoption fails without organizational readiness.

What to look for in an AI forecasting solution

Scalability for high data volumes

The solution handles millions of SKU-location combinations without performance degradation as your business grows. Ask vendors about their largest deployments and how they handle peak loads.

Integration with existing tech stack

Pre-built connectors or flexible APIs for your ERP, WMS, and BI tools prevent data silos. Integration complexity often determines the implementation timeline.

Forecast explainability and transparency

Demand planners trust model outputs when they understand why predictions were made. Look for feature importance explanations, confidence intervals, and anomaly flagging.

Production readiness and ongoing support

Choose enterprise-grade systems built for high uptime and robust monitoring, not prototype-level tools. Ensure the vendor provides ongoing support and model maintenance.

Custom AI forecasting solutions for enterprise supply chains

For organizations that require custom, enterprise-grade AI forecasting systems, partnering with experienced engineers accelerates time-to-value while reducing implementation risk. 

Xenoss specializes in building production-ready AI solutions with robust integration, scalability, and domain expertise across CPG, retail, and manufacturing.

Our teams have delivered forecasting systems that integrate seamlessly with existing data infrastructure, connecting real-time pipelines, ERP systems, and analytics platforms into unified decision-support environments.

Book a consultation to discuss your forecasting challenges →

The post How AI demand forecasting reduces inventory costs and improves accuracy appeared first on Xenoss - AI and Data Software Development Company.

As Gus Trigos, AI Product Engineer at Nuvocargo, explains: 

“Data is abundant, yet siloed across the supply chain. Teams rely on tools built in the 1990s–2010s, designed for manual data entry. This creates bottlenecks, drives errors, and is often ‘solved’ by adding headcount, compounding complexity.”

Traditional statistical forecasting can’t keep pace with consumers’ expectations for delivery speed. 90% of shoppers would like to have items delivered to their doorstep in two to three days, and every third consumer is expecting same-day service. 

Meeting these demands puts pressure on supply chain management teams to stay ahead of weather disruptions, supplier risks, and demand shifts.

This is why leaders are turning to predictive analytics. 

Key layers of predictive analytics for supply chain management

Predictive analytics platforms enable a consistent flow of accurate predictions and actionable decisions by connecting three structural layers: data sources, machine learning models, and consumption-ready interfaces. 

Data layer

To build accurate, timely predictions, data engineering teams combine internal sources: ERPs, WMS systems, sensors, with external feeds. 

Internal data includes sales history, inventory levels, lead times, production output, and transportation events. 

External signals provide visibility into weather patterns, promotions, market trends, and macroeconomic indicators.

Operationalizing these sources requires a modern data stack: ingestion tools to pull from ERPs, WMS, TMS, and external APIs, a centralized warehouse or lake to store and align data, and transformation tools to clean, validate, and version datasets.

Prediction layer

The prediction engine transforms raw data into actionable forecasts and risk signals. It applies statistical and machine-learning models to identify patterns, quantify uncertainty, and estimate outcomes like demand levels, lead-time variability, or disruption risk.

Common approaches include:

• Time-series forecasting (ARIMA, exponential smoothing, Prophet) models historical patterns: trend, seasonality, cyclesto project future demand or volumes.
• Machine-learning regression (gradient boosting, random forests) captures non-linear relationships between demand and drivers like price, promotions, weather, or channel mix.
• Probabilistic models (Monte Carlo simulation) represent uncertainty through ranges of outcomes rather than point forecasts, supporting risk-aware decisions on safety stock and service levels.

Consumption layer

The consumption layer operationalizes through integrations, dashboards, and decision rules.

Integrations into planning systems 

Predictions feed back into core systems: ERP, S&OP, replenishment engines, TMS, where they adjust parameters like reorder points, production quantities, or routing priorities. 

For example, forecasted demand volatility can dynamically modify safety stock, or predicted port congestion can shift freight allocation.

User-facing dashboards 

Dashboards surface key findings for operations managers, translating mathematical forecasts into actionable questions:

• Which SKUs risk stockout in the next two weeks?
• Which suppliers are likely to miss committed lead times?
• Which lanes are trending late against SLA?

Predictive outputs are paired with decision rules that define how the organization responds when risk or opportunity thresholds are crossed, such as dual-sourcing when supplier delay risk exceeds a set probability, or expediting only when cost-to-serve stays below margin limits.

These rules can be automated or semi-automated, depending on criticality and risk:

When decision-making is automated, the system executes predefined actions without intervention, dynamically increasing safety stock when demand volatility spikes, or rerouting shipments when predicted delays breach SLA thresholds.

For semi-automated workflows, predictive insights generate recommendations with quantified trade-offs (cost, service impact, risk), allowing planners to approve, modify, or override decisions where stakes are higher or context matters.

4 high-yield use cases for predictive analytics in supply chain operations

1. Demand forecasting

High market volatility has made reactive planning uncompetitive, pushing organizations to proactively anticipate demand and disruptions.

Marcia D. Williams, founder and managing partner at USM Supply Chain Consulting, argues that predictive analytics and machine learning are becoming essential for demand management.

These tools combine historical sales, real-time signals, and ML models to predict demand shifts and optimize inventory. Compared to traditional statistical methods, predictive demand forecasting delivers long-term value, cutting waste and reducing operational costs by up to 30%. 

How Danone improved its supply chain with demand forecasting

The company adopted advanced predictive analytics, integrating historical sales, promotions, media signals, and seasonality patterns into continuous demand forecasts. Previously, Danone relied on statistical averages that couldn’t incorporate real-time market data.

The new approach brought in real-time indicators and cross-functional inputs from supply chain, sales, marketing, and finance, creating forecasts that accounted for demand volatility, reduced forecast errors by 20%, and recovered 30% of previously lost sales.

Predictive analytics tools for demand forecasting in supply chain management

2. Supplier risk management

McKinsey classifies suppliers into three tiers based on visibility:

Predictive analytics improves visibility into deeper tiers, helping managers spot problems before they disrupt operations. 

These tools continuously analyze supplier performance, delivery patterns, quality trends, and external risk signals to forecast where issues are likely to occur. 

With proactive risk evaluation, supply chain teams can reduce late deliveries, quality failures, and supplier instability by adjusting orders or renegotiating terms before disruptions escalate.

How Pietro Agostini, an Italian industrial engineering company, tapped into predictive analytics to vet suppliers

During the COVID-19 pandemic, the Italian industrial engineering company built a quantitative supplier risk model to improve how it evaluated and monitored suppliers. Previously, evaluation was largely qualitative and didn’t allow engineers to anticipate disruptions or prioritize responses.

The team developed a quantitative-qualitative risk scoring methodology based on FMEA (Failure Mode and Effects Analysis) principles, assessing the probability, severity, and detectability of supplier risk factors. 

The model generated a data-driven risk profile for each supplier and recommended prioritized actions for procurement teams.

Predictive analytics tools for supplier risk management

3. Freight management

Poor route planning, last-minute shipping premiums, detention fees, and inefficient routing increase fuel use and drive up logistics costs. Detention alone affects about 40% of loads, costing teams $50–$100 per hour on average.

AI and predictive analytics are helping supply chain teams address these bottlenecks, cutting transportation costs by up to 30% and reducing disruptions by 15%. 

These tools operationalize real-time and historical data (weather, traffic patterns, port conditions) to dynamically adjust routes and avoid congestion.

How predictive analytics powers reliable freight management at UPS

The company’s ORION system (On-Road Integrated Optimization and Navigation) uses predictive analytics to recommend the most efficient stop sequences and route choices for drivers. 

The model dynamically adjusts based on operational constraints: time windows, pickup/delivery patterns, and facility realities like loading dock availability. After a successful pilot, UPS expanded ORION across tens of thousands of routes and paired it with purpose-built navigation.

Tools that use predictive analytics for freight management

4. Simulating scenarios with predictive digital twins 

Embedding predictive analytics into digital twins gives planners a living, data-driven simulation of their entire network that anticipates disruptions, tests “what-if” scenarios, and evaluates outcomes before they occur in the real world.

As Paul Narayanan, Chief Transformation and Digital Officer at KENCO, explains: 

“Digital twin technology is transforming the supply chain and logistics industry by creating virtual replicas of physical operations that mirror real-time activities, equipment, and workflows. The result is optimized processes and enhanced efficiency.”

Organizations leading in predictive simulations report significant gains: up to 20% improvement in on-time delivery, 10% reduction in labor costs, and 5% uplift in revenue. Access to live data and predictive modeling helps these teams fine-tune distribution center utilization and fulfillment strategies.

How combining digital twins and predictive analytics helped Aliaxis improve supply chain planning

The global piping and fluid-management manufacturer, operating in 40+ countries, built a digital twin of its European network to run simulations and “what-if” analyses before making real-world decisions. 

Teams use the model to test alternative network configurations (e.g., distribution-site consolidation), transportation setups, and make-or-buy options, predicting downstream impacts on cost, stock levels, and service outcomes.

After rollout, Aliaxis reported 9% potential cost reduction in total logistics from network and transportation redesign scenarios. Understanding how consolidation affects stock helped reduce inventory, while the same capability compressed decision cycles from months to days.

Tools that help build digital twins with predictive analytics for simulating operations 

Timeline and cost considerations for predictive analytics adoption in supply chain management

Phase 1: Use-case selection

Project timeline: 0-2 months since kick-off

Steps to take: Quantify the cost and impact of supply chain decisions by translating planning outcomes into clear financial consequences using existing data.

For each decision you want to improve: how many SKUs to order, when to expedite, which supplier to choose, start by measuring historical error: how often the decision went wrong and what it caused (excess inventory, stockouts, late deliveries, premium freight). 

Then attach unit costs: carrying cost per unit per month, lost margin per stockout, expediting cost per shipment, penalty fees, or wasted labor hours.

To estimate the impact of predictive analytics, model a conservative improvement (e.g., 10–15% reduction in forecast error or fewer late supplier deliveries) and convert that delta into annualized savings or revenue protected.

Cost considerations: Primary costs come from internal time: supply chain leaders, planners, finance, and IT aligning on decisions, data availability, and success metrics, with minimal external spend beyond light advisory support if needed. It’s best to avoid software purchases, large data work, or model development at this stage.

When the phase is successful: Phase 1 is successful if you leave with a clear business case, defined owners, and quantified ROI assumptions, without committing capital prematurely.

Phase 2: Building the data foundation

Project timeline: 2-5 months since kick-off

Steps to take: After selecting a high-yield use case, prepare the data that prediction models will use.

Data engineers pull the required data (order history, inventory positions, lead times, shipment events, etc.) and run basic validation, reconciling mismatches across systems, removing noise (outliers, duplicates, missing periods), and reality-checking against event logs.

To operationalize this data, the team sets up a repeatable pipeline with clear ownership and refresh frequency, ensuring inputs can reliably feed pilots and future scaling without manual intervention.

Cost considerations: Most spending comes from data engineering time to extract, reconcile, and reshape data. Infrastructure costs include cloud storage and compute for repeatable pipelines, plus limited tooling for integration or data quality checks.

When the phase is successful: Phase 2 is complete when you can reliably produce a decision-ready dataset that is updated on schedule, requires no manual work, and accurately reflects business operations.

Phase 3: Modeling and pilot execution

Project timeline: 5-10 months since kick-off

Steps to take: Once the team has validated high-quality data, these inputs are transformed into predictions that leaders can trust and test in the real world.

At this stage, machine learning engineers build or configure predictive models for the chosen use case, train them on historical data, and benchmark performance against business-relevant metrics.

The model is then deployed on a small pilot, limited to a specific region, product set, or lane. Before scaling the model, compare predictions against current planning methods, planner actions, and measure their impact on cost, service, or risk. 

Cost considerations: Main expenses include data science and analytics engineering time, compute resources for training and testing, and (if buying rather than building) software licensing for forecasting or ML platforms. 

Costs can rise quickly as pilot scope expands, so limit this phase to a clearly defined segment and avoid over-optimizing before business impact is proven.

When the phase is successful: the pilot stage is complete when predictive models consistently outperform current planning methods on real data and demonstrate measurable impact in a live pilot without increasing planner workload.

Phase 4: Scaling the pilot to deliver organization-wide value

Project timeline: 11-15 months since kick-off

Key steps: While small-scale pilots should generate ROI within months of deployment, the true operational impact emerges when model outputs are embedded into core planning and execution systems (ERP, S&OP, replenishment, TMS).

Once predictive analytics is part of the supply chain stack, it influences parameters like reorder points, production quantities, and routing priorities, creating a measurable impact across the flow.

To ensure standardized deployment, define clear automated and semi-automated decision rules that effectively allocate planner time. Make sure to establish governance, monitoring, and KPIs to ensure the system consistently supports new product lines, regions, and use cases.

Cost considerations:  At this stage, the largest expenses are tied to connecting predictive models to core systems, building workflows and decision rules, and training teams to trust and act on outputs. 

Platform, compute, and model-maintenance costs become recurring. 

This phase also delivers the highest ROI because spend is tied directly to operational adoption and scaled impact, not experimentation.

When the phase is successful: a predictive analytics implementation is a success when insights are automatically embedded into daily planning and execution, drive consistent decisions at scale, and require little to no manual oversight. 

Bottom line

The companies in this article didn’t transform overnight. They picked one problem, proved predictive analytics could solve it, and scaled from there.

Which supply chain decision is costing you the most when it’s wrong? That’s where to start.

The post Predictive analytics in supply chain management: Implementation roadmap appeared first on Xenoss - AI and Data Software Development Company.

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:

1. 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:

1. Build a DSP-like advertising platform that would allow sellers to promote products inside the marketplace ecosystem and beyond
2. 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: 

1. 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. 
2. 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.
3. 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.
4. 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.

The post Architecting real-time retail systems: How to unify live inventory, pricing, and personalization across omnichannel touchpoints appeared first on Xenoss - AI and Data Software Development Company.

With this shift in shopper habits, e-retail sites are becoming the main gateway for brand advertising, providing the opportunity to reach customers at the point of sale. In light of the new privacy policies, which crash existing segmentation and retention scenarios, it is not just a new potent advertising channel but a necessity for many brands.

With the phase-out of cookies and depreciation of MAIDs, it will be harder for digital ad platforms to determine what actions a consumer takes after seeing an ad. Retail media, on the contrary, have the unique advantage of offering closed-loop marketing that directly ties ad spending to digital sales. The retail media networks are a critical part of the post-cookie digital advertising landscape. ​​eMarketer predicts that US Retail Media Networks will exceed $52 billion in ad sales by 2023.

However, most of the retail media have rudimentary advertising capabilities, and to seize the benefits of the moment, retail needs to promptly breach the technological gap. Retail media M&A soared 50% in the last two years, showing the demand for AdTech companies that help merchants sell on third-party marketplaces.

Transformation of the scattered retail media properties into the full-blown retail media networks and connecting them to the programmatic marketplace is the new frontier for AdTech.

In our guide we will cover:

• what retail media advertising is
• what a retail media network is
• how the changes in the privacy landscape benefit retail media networks
• how retail media network functions
• market overview of retail media networks
• programmatic adoption among retail media networks
• benefits of retail media networks
• challenges of building retail media networks
• best practices for building retail media networks

What is retail media advertising? 

Retail media advertising is a form of promotion on the media inventory of the retailer, with advertisers paying a platform or marketplace to showcase their products at or near the point of sale. Let’s see how it looks.

Here’s an example from the Walmart search page. Here you can notice two types of retail media advertising before you even get to your search query, a banner for bug spray and a sponsored product listing by Persil. Walmart disclosed that its ad network had sold $2.1 billion in ads in 2021, a timely transition for this mostly brick-and-mortar retail giant.

There are mainly two types of retail media advertising, let’s review each of them.

Sponsored products

Sponsored products are the main form of retail media advertising. Native ads are the ones that usually appear organically among search results and match retailer sites or apps. These ads are displayed when the shopper searches using similar terms. Different retailers have different relevance criteria for organic and sponsored products and can deliver different search results.

Sponsored product is a lower-funnel marketing activity directed at grabbing the attention of the customer and driving conversation. It can be placed on a variety of pages on the retailer’s site, including:

• search results pages
• homepages
• product detail pages

Sponsored products are assembled from the existing listings in the retailer catalog and do not require a separate creative asset.

Onsite display

The onsite display is another form of retail media advertising. This ad type resembles traditional display advertising formats but is placed on the retailer’s site, instead of a third-party publisher’s.

Those ads are usually placed in highly visible areas, at the top of the search query, or even on the retailer’s home page, or products detail page.

The onsite display is the mid-funnel marketing technique aimed at raising awareness about the product and gaining shopper consideration during browsing.

Offsite display

A retail media network can also run ads outside of the retailer’s website or “offsite.” While browsing a publisher site or the open web, customers can see an ad for the product they recently browsed on the retailer platform. It is an upper-funnel tactic used to steer shoppers who have left the retailer platform back to the website to make a purchase.

This technology, also known as Offsite Audience Extension, was previously based on cookie syncing, but the retail media industry is developing solutions without cookies, for instance, CORE ID by Epsilon that identifies users based on deterministic first-party data elements. These technologies open immense opportunities for retailers allowing them to monetize shopper audiences by enabling brands to target them offsite. With their wealth of first-party data, retail media networks will become the leading power brokers in the post-cookie world.

What is a retail media network?

A retail media network is a unified ecosystem the retailer set up to let brands advertise on their website, app, email distribution, and other digital properties. In a nutshell, it is the digital version of in-store advertising and can have a variety of formats that allow advertisers to hit the shopper with the right message at every point of the buyer’s journey from product consideration to the point of sale.

This type of promotion is not entirely new for wholesale advertisers, who learned a long time ago that shelf placement affects purchasing decisions. They always accommodated merchandising fees and shopper marketing in their ad budgets, and now they are smoothly transitioning to spending on retail media networks. And this ad spending is turning up to be even more beneficial since, with first-party purchase-based data, retail media networks provide better opportunities to analyze attribution and ROI.

How the changes in the privacy landscape benefit retail media networks           

With the tightening of data privacy regulations, the advertising data landscape is going through a paradigm shift. New privacy policies are undermining the two pillars of third-party data targeting – Google and Facebook.

The sheer size and scale of Facebook and Google granted them the perks of the hub-and-spoke model. They operated as data warehouses of behavioral signals, which they converted into targetable audience segments with a clear feedback loop built on the engagement data.

However, the hub-and-spoke model is swiftly turning obsolete, opening a window of opportunity for any owner of a sufficient amount of first-party data to create advertising solutions with targeting in the native environment.

Facebook’s ability to observe those types of purchases is being diminished. Many of the companies that previously transmitted their purchase data to Facebook and other ad platforms very enthusiastically are determining that their data can now be used to power a proprietary ad network. 

Eric Benjamin Seufert, Media Strategist at Mobile Dev Demo 

Retail websites and apps are in a unique position here. They can take advantage of their user profiles and provide incredibly relevant and actionable targeting for brands in various product categories. With the abundant supply of first-party consumer data, they can build advertising networks that can stand competition with the walled gardens that are swiftly losing their efficiency.

How a retail media network operates 

A lot of retail media networks work as closed-loop ecosystems; e-commerce sites provide their advertising partners or agencies they contracted with a self-serve interface to place ads only on their inventory.

However, retail media networks increasingly employ a programmatic approach, where retail media inventory is connected to off-site properties and third-party media for the omnichannel reach in retail campaigns.

For instance, Walmart recently partnered with the Trade Desk to launch its own DSP, allowing its advertisers to serve ads on the programmatic inventory in addition to its retail media placements.

Instead of creating a proprietary network, other retailers partner with existing retail media solutions that group the inventory of different retailers to provide an extensive reach for brands and advertisers.

Similar to programmatic, retail media advertisers use a DSP where they choose the retailer they want to run media on, the products they want to promote, and their budget. Since all activity occurs on a retailer’s site or using a retailer’s data, advertisers can now directly tie spending on their DSP to revenue from a retailer.

Often retail media networks will create a platform dedicated to their retailer partners (a lot like SSP). It reports back the results of the served campaigns, from sales achieved through retail media to the number of ads shown to shoppers over a specific period. This platform consolidates the data and conveys it back to retailers to control yield and ad experience.

Retail media networks overview

There is a significant proliferation of retail media networks, all the major retailers in the Tier 1 countries have already launched some forms of the retail media business. There are retail media networks of major retailers, specialized niche retail media networks that cater to advertisers of specific product categories, and connected retail media networks – AdTech platforms that consolidate inventory from several retailers and provide advertisers with a unified dashboard for targeting. We’re going to have a closer look at all three.

Proprietary media networks of big retail

Retail media networks of the major retail companies provide advertising capabilities for the vast pool of advertisers from wholesale to electroniсs.

Amazon Ads 

Retail and ecommerce giant Amazon is one of the first companies to acknowledge the power of retail media networks and added an ad business called Amazon Ads to its portfolio. A lot of retail media businesses followed the Amazon Ads business model. Amazon advertising provides access to one of the most extensive sets of consumer data and a massive audience of over 200 million users just in the US.

Walmart Connect 

Despite its relatively recent foray into the retail media industry, Walmart is already rivaling Amazon in advertising revenue. With 100 million unique visitors to its site, Walmart offers a range of ads on its own properties, which you can pair with the onsite ads in its physical store, and with the launch of its DSP with the ads on a variety of third-party inventory.

Tesco Media and Insight

The third-largest retail company with a stronghold in the European market rolled out its retail media offering, called “Tesco Media and Insight” at the end of last year. Brands on the platform can utilize data from Tesco’s loyalty scheme Clubcard to target customers. Twenty million households in the UK have Club cards, providing a wealth of data for advertisers.

Roundel 

Formerly known as Target Media Network, Roundel is Target’s rebranded retail media platform. Over a thousand advertising partners use Roundel to reach out to the Target consumer base, which can boast 30 million unique site visitors a week. It is used by big-name brands like Unilever, Coca-Cola, Disney, and Microsoft.

Niche retail media networks

Another pocket of growth in the retail media industry is known as niche networks, ad business retailers dedicated to a specific product offering, like home appliances, apparel, or cosmetics. They can provide more in-depth customer profiles to the advertiser in their product category and narrower audience segments.

Retail Media+

Two years ago, Home Depot, a renowned DIY/home improvement chain, launched its advertising network Retail Media+. To capitalize on its wealth of first-party data, Home Depot offers brands that sell in its offline store to purchase ad placements on its website and social media channels.

Wayfair Media Solutions

Wayfair is an eCommerce company focusing on furniture and home goods. They launched a retail media network a couple of years back called Wayfair Media Solutions. Through a self-service interface, advertisers can bid to promote SKUs in a particular category on this platform. The primary pricing model is pay-per-click.

CMX

CVS retail media network, originally known as the Consumer Value Store, after a serious revamp and rebranding as CVS Media Exchange (CMX), became a major staple in pharmaceutical advertising. CVS is the biggest pharmacy chain in the US, with more than 10,000 locations nationwide and a robust loyalty program that allows it to accumulate a massive data bank of first-party consumer data.

Macy’s Media Network 

Macy’s is one of the oldest and largest department stores in the US, specializing primarily in apparel, furniture, cookware, dishes, and small appliances. It recently became one of the most successful omnichannel retailers.

Macy’s Media Network was released in August 2020 and was able to quickly put together robust retail media capabilities and gain momentum with advertisers. In 2021, the platform generated $105 million in net revenue.

Connected retail media networks

Some retail and ecommerce providers lack the scale or in-house technical expertise to create their own retail media network. In this case, they frequently turn to connected retail media networks. Those retail media networks don’t leverage their own inventory. Instead, they aggregate inventory of several retailers, unify targeting and measurement across different platforms, and provide advertisers with a handy interface for running campaigns.

Criteo Retail Media Platform 

Criteo leads the push toward converting a fragmented supply of retail media into a coherent marketing channel. The Criteo Retail Media Platform is a technology solution that facilitates retail media partnerships between retailers and brands.

With its self-service DSP and solid API service that allows integration of various marketing tools, it grants advertisers ease, transparency, and full control over their retail media campaigns.

PromoteIQ

PromoteIQ is a retail media platform founded in 2012 and acquired by Microsoft in late 2019. The platform helps brands connect to the ecommerce sites of global retailers in grocery, apparel, home improvement, consumer electronics, and other industries and offer awareness and performance marketing opportunities.

PromoteIQ can boast robust data and analytics platforms and a strong automation component that allows optimizing campaigns on the go.

CitrusAd

CitrusAd streamlines media sales and ad-serving for the top 20 retailers in the world. The platform powers more than $2 trillion in annual online sales. The platform partners include ShopRite, Lowe’s, Petco, and other retailers across 22 countries.

Programmatic adoption in the retail media networks

The retail media networks are currently balancing between the open AdTech and walled garden approaches in terms of providing access and data to third-party bidders.

Amazon 

Amazon is notorious for its closed tech approach, directed at creating its own content fortress. To leverage Amazon audience segments and attribute ads to sales, advertisers can only use Amazon’s DSP. In turn, Amazon DSP can be used for targeting Target’s Roundel inventory. Amazon advertisers can tag their Roundel creative and create a retargetable segment of Target shoppers.

Walmart 

Despite its partnership with the Trade Desk, Walmart’s strategy can hardly be called open programmatic. It is using the white label version of TTD without access to its wider infrastructure. As for the supply-side solutions tech, Walmart acquired the SSP Polymorph Labs and the ad server Thunder. With in-housing the entire ad tech stack, Walmart is effectively functioning as a walled garden.

Roundel and Kroger

Target’s Roundel took a different approach. While having an exclusive SSP partner, Index Exchange, it is open to outside DSPs. Target makes its data available through Index Exchange, which orchestrates private marketplaces that outside DSPs can connect to. It currently works with  CitrusAd, the Trade Desk, Criteo, Skai, and Pacvue as its demand partners.

Kroger, the fourth-largest retailer in the US, also chooses an open AdTech approach and leverages an open PMP program providing access to third-party DSPs. Pacvue, Skai, and Flywheel Digital were the first to connect.

Pacvue, Skai and Criteo 

Skai and Pacvue have a special place in this emerging marketplace, acting as agnostic DSPs for the retail media space. They allow advertisers to run campaigns on the major open retail marketplaces and supply this media buying with omnichannel analytics and recommendation on better placements. Criteo also has similar capabilities, but focuses on direct deals with retailers, nourishing its own ecosystem.

Media agencies 

There is an increased interest in the proprietary retail media capabilities from the demand side as well. The major media agencies are betting on retail, which sprung a major wave of acquisition. Publicis Groupe acquired Citrus Ad, together with eCommerce intelligence platform Profitero, while WPP launched its own retail media offering Everymile.

Benefits of retail media networks

Retail media networks are flourishing advertising mediums that provides immense benefits for advertisers, retailers that decide to supplement their business with ad revenues, and AdTech vendors that aim to resolve identity and media buying issues for this new advertising environment. Let’s look closer at those.

Benefits for the advertisers

In light of the fading addressability in other advertising channels, retail media ads are a powerful asset for brands to reach their target audiences, which are susceptible to their messaging.

According to McKinsey data, 90% of ecommerce revenue comes from the first page, while the top 10 products account for roughly 23-27% of all ecommerce revenue.

Better visibility 

Retail media ad placements are especially needed by newer products or smaller brands. Organic searches are often returned based on relevance to the search terms and previous traffic the product has received.

The main advantage is that retail media is the closest to ZMOT of the user, the moment where they do the research. Retail media is more relevant for ZMOT than even search advertising. If you have a competitive product and the price, the sale will be yours; it is just a matter of being above your competitors.

Fernando Siles Martin, Head of Online Marketing, Worten España

New products often have no previous traffic, making them less likely to appear organically. Retail media gives brands a fair opportunity to ensure their products get seen by shoppers and obtain an additional revenue stream.

Ad spend tied to digital sales 

In the coming years, tracking the ROI of the walled gardens ads will be increasingly challenging. In contrast, advertising with retail media ads enables advertising at the point of sale, directly tying the ad spend to digital sales. With the available first-party data and extensive user profiles built on buying preferences, retail media provides a powerful analytics and attribution mechanism.

Benefits for retailers and ecommerce

Ad placements on the first page and promoted products are a highly sought-after inventory for brands and lucrative advertising inventory for ecommerce retailers. Retail media allows retailers to monetize their websites through advertising and provides several nonobvious benefits.

Additional sales 

Retail media is not limited to showing ads to customers. It is a comprehensive tool with potential beyond an additional revenue stream. Due to the built-in data management capabilities, the retail media network engine can analyze the shoppers’ behaviors and suggest relevant products that are good complements to what is already in the cart. This way, retail media networks increase the size of the final check and drive ecommerce sales for the retailer.

Enhanced shopper journey 

By gathering data through the scattered retail media property, the retail media network can enhance relevancy measurement and reinforce its suggestion algorithms. With it, retailers can create a more personalized and engaging experience, helping the customers throughout their journey.

Benefits for AdTech vendors 

The retail media space is in its infancy and provides immense opportunities for AdTech vendors and startups that decide to venture into this industry. Given all the challenges with the availability of data, third-party cookies, MAIDS, and GDPR, the retail media data can become the new life-blood of advertising. The influx of retail data will drive more precise targeting and consumer engagement patterns with new ad formats and define new paths to purchase that will drive better measurement and optimization.

Future-proof advertising

Retail media ads are fundamentally different from previously dominant display ad targeting based on third-party data. Retail media does not track behavior across retailers. Each retailer is an independent entity, and the profile developed for each shopper is unique to that retailer. Retail media provide powerful targeting based on the first-party data, immune from the tightening of privacy legislation.

Breaching the gap between sales and marketing data. 

Retail media networks have the potential to deliver a holy grail of marketing – tracking the ROI on media spend. Additional retail media networks can enrich the advertiser’s customer personas with invaluable purchase intent data. It can be done in a privacy-compliant way via the use of emerging data clean room solutions. The usage of data clean room will only increase, allowing marketers and retailers to extract value from their data in a privacy-compliant way, fostering transparency and better cross-channel measurement.

Challenges of building retail media networks

Despite the booming demand for retail media networks, the industry is still at the beginning of its technological journey. It has yet to resolve the many challenges of the fragmented marketplace, appropriate targeting tools for the buy-side, and integrations with the third-party data and attribution vendors. Let’s dive into the most typical roadblocks the up-and-coming retail media network needs to address to get a competitive advantage in the new AdTech race.

Differentiation

Ecommerce must find its niche and craft a unique market proposition in light of the current proliferation of retail media networks. Instead of releasing another ad network where advertisers can run campaigns, they should develop a genuine solution to address the pain points of a certain segment of media buyers.

To do this, retailers need to upgrade their data management and determine the seasonalities in the platform, the biggest buyers, and the most popular product categories. When the most potent and in-demand audience segments are identified, retail media networks should build ad relevancy algorithms that can effectively monetize this traffic while also cultivating customer loyalty and increasing the size of the final check.

Retail media networks operate in silos

Retail media is currently undergoing the same phase that display advertising was going through during the rise of ad networks. With it comes the lack of standardization and integrations between the vendors. The data landscape in this marketplace is fragmented. The inventory of most retail media networks is limited to the owned website and apps, and they operate similar to walled gardens.

The advertisers that are used to wholesome attribution and omnichannel customer journeys will struggle to consolidate buying through one platform and automate their processes. They would have to utilize more resources to deal with each retail media network individually.

The challenge of the siloed platforms is not immediate. Retailers still possess the quality of purchase and intent data that many advertisers desperately require. This setup obstructs the evolution of the ecosystem, and once market saturation occurs, the industry will have to quickly mature in terms of integrations.

Retailers must strategize to fully integrate into the marketplace, align themselves with standards that enable brands to use automation technology or a unified platform to purchase retail media, and have more holistic measurement and attribution.

Customer data privacy 

Even though the retail media networks are built on first-party data, the further evolution of the industry is predicated on the ability of retail media networks to exchange data with third-party vendors and advertisers to deliver more targeted and personalized ads. And that is where data privacy comes into play.

The retail media network is not the sole owner of the purchase data; it still belongs to the user. It won’t be long before customers and regulators start to question the legitimacy of data governance on the retail media networks. To comply with the data privacy laws, retail media networks have to work in two separate directions. First, devise a data governance strategy that will establish how users give explicit consent to the use of personal data, how the data is stored securely on-premise, and how it can be shared with advertisers.

Second, retail media networks need to find or devise a solution, such as a data clean room, to share the data with advertisers in a privacy-centric way, without personal identifiable information.

Best practices for building a retail media network

If despite the mounting challenges for this new advertising medium you still decided to develop a retail media offering, there are several challenges and industry best practices to consider.

First and foremost, retail has to maintain a healthy balance between catering to advertisers and maintaining the loyalty and engagement of their customer base. To serve ads that are relevant to the customer and high-yielding for the platform, retail media networks need to employ in-demand features, deepen their relationships with advertisers, and develop an identity strategy.

The investment in the retail media networks will only increase, and with it, the competition for customers and the ad spend. To develop a viable solution in light of the current proliferation of retail media networks, new entries should accommodate the most in-demand features, deepen their relationships with advertisers, develop a comprehensive identity management strategy, and implement an ML model on their platform.

Accommodating new features

When it comes to developing a retail media network, having a wealth of purchase data and high-traffic media placements is not enough to stand out from the competition. To implement ads on the platform while driving customer loyalty and higher rates of overall spending, retail media networks need to leverage technology tools that provide more control over the customer experience and better visibility into their actions and trends.

At the same time, they must ensure appropriate data infrastructure, targeting, and attribution capabilities for advertisers. According to the INMAR Intelligence study, new retail media network features like data targeting, self-serve capabilities, attribution, and sales analytics are in high demand. Additionally, the new ad formats are taking hold in retail media and soon will become a necessary prerequisite for entering this ad space.

Deepening advertiser relationships

The purchase and intent data is not a silver bullet for keeping advertisers interested in the retail media offering. Savvy media buyers will demand more data for a holistic persona profile and campaign optimization when the market matures. That is when the data partnership with advertisers will matter the most.

Collaboration with the biggest platform advertisers can do wonders in increasing sales by enriching audience segments with additional data. For instance, transactional data can reinforce customer profiles, allow you to analyze users beyond their actions on the platform, find the underutilized potentials, and enhance targeting capabilities.

Major players such as Amazon, Walmart, Kroger, Target, and Best Buy are building their advertising businesses based on first-party consumer data including SKU-level data on transactions.

However, the retail media networks should exercise caution when proceeding in this direction. Using these data has to abide by the data privacy laws and ideally should be done in a closed-loop system that can match data points and improve attribution without sharing personally identifiable information.

Developing an identity strategy

To get accurate customer insights, effectively collaborate with advertisers, and offer actionable targeting, retail media networks need to take control over their customer identity – distitle multiple touchpoints to a singular view of the customer.

However, it requires solving the challenges of securely matching the data from the fragmented data sources, and creating a strong customer identity from the actions on various media properties.

To create the basis for robust customer identity, retail media networks require a data infrastructure that will enable an identity strategy with the following attributes:

• An interoperable solution that completes existing technology and allows the creation of retailer-owned customer segments.
• Privacy-conscious technology to configure the identity graph and enhance it with second and third-party insights.
• Consistent integrations across an organization, a technology stack, multiple data sources, and any number of marketing activation and measurement partners. The translation should be fast, secure, and interoperable across all channels where the customer is engaged.

Utilizing AI/ML for data management

Making sense of the vast data sets of the retail media network is impossible without a savvy use of ML and AI technologies. Automation in the data processes is critical to support the next generation of insights, real-time analytics, and customer intelligence.

Big players like eBay, Amazon, or Alibaba have successfully integrated AI across the entire sales cycle. Machine learning helps retailers apply data about which items are a rare or one-time purchase (a new TV or an entire dining room set, for instance) as opposed to one that will be refilled (like laundry detergent or bread).

Machine learning algorithms of retail media networks should be able to process data that includes input from three platforms: transactional, operational, and analytical systems.

Data movement from transactional systems to operational systems and finally to analytical systems slow down processing, integration, and timely insights. For this reason, the monitoring and automation tools are critical for saving resources on the operation and maintenance of the data pipelines.

Takeaways

Retail media networks are a thriving new sector of AdTech that has yet to resolve the technological challenges posed by its explosive growth. Due to the shift in the data privacy laws, ecommerce sites with a wealth of first-party data are ideally positioned to start their own ad businesses. There is an enormous space for growth for retailers with their ad inventory and AdTech vendors that can provide media buying capabilities in this environment.

To build a retail media monetization solution, retailers or AdTech vendors have to account for data security, platform scalability, and whether the platforms can easily integrate third-party partners and accommodate the future shift to standardization in this industry.

The post Retail media advertising: How e-commerce is becoming AdTech’s next frontier appeared first on Xenoss - AI and Data Software Development Company.