THE STANDARD FOR PREDICTIVE, AUTOMATED DATA QUALITY IN MODERN RETAIL
Enterprise Data Quality for Retail & Consumer Goods
Retail and consumer goods companies run on data: inventory levels, product attribution, shopper behavior, supply chain signals, pricing, promotions, loyalty engagement, and omnichannel operations. Yet this data, fragmented across legacy enterprise resource planning systems, ecommerce platforms, point-of-sale networks, marketplace feeds, and supplier portals, is often inconsistent and unreliable. As a result, retailers face persistent data quality issues that erode margins, inflate operational costs, and undermine customer experience.
Anomalo provides an AI-native platform that automatically detects, diagnoses, and prevents data issues without the need for pre-written rules, manual tuning, or fragile pipelines. Retailers gain continuous visibility into the quality and content of SKU, channel, supply chain, and customer data, enabling more confident decision-making, higher-impact analytics, and better business results.
Trusted by Industry Leaders
Key Benefits
Enterprise Data Quality for Retail & Consumer Goods
Protect Revenue with Clean, Consistent Product and Inventory Data
Detect missing or incorrect SKU attributes, catch sudden shifts in product segments (e.g., color/size variants), and validate product inventory levels.
Improve Forecast Reliability and AI/ML Model Accuracy
Automatically detect demand anomalies, channel-level spikes, and seasonality deviations using adaptive time-series ML models to ensure accurate inputs for predictive models.
Strengthen Supply Chain and Vendor Data Quality
Reconcile supplier feeds, detect mismatches in shipments versus purchase orders, and flag missing or invalid advance shipping notices, vendor codes, item hierarchies, and unit-of-measure mappings.
Elevate Customer Experience and Personalization
Spot unusual CRM, loyalty, and behavioral data and ensure segmentation and recommendation signals remain accurate and reliable.
Reduce Operational Cost by Eliminating Manual Data QA
‍Increase operational efficiency by automating profiling, detection, root cause analysis, ticket creation, and alert routing.
Speed Operations and Decision-Making
Catching more issues, sooner, enhances confidence in data and enables fast, “right first time” decision-making.
Why Data Quality Is Essential for Retail
Retailers and consumer goods brands face unique, ongoing data challenges:
75%
of supply-chain/logistics executives rated the quality of data they receive as “average” or “poor”
Supply Chain Inefficiency and Operational Disruptions Caused by Data Errors
Data quality issues across suppliers, carriers, warehouses, and store operations ripple into stockouts, overstocking, delayed replenishment, and increased logistics costs.
Fragmented Omnichannel Experiences Cause Customer Frustration
When customer, loyalty, or behavioral data is incomplete, inconsistent, or stale, retailers lose shopper trust, lowering customer satisfaction and repeat purchase rates.
31%
of shoppers report encountering out-of-stock items far too often, undermining trust
$1.2T
in global retail losses due to supplier issues, theft, and out-of-stock items
Broken Product, Pricing, and Inventory Data Leads to Revenue Loss
Poor-quality SKU, pricing, and inventory data directly hurts digital shelf visibility, search and browse performance, online conversion rates, in-stock accuracy, and promotional execution, leading to lower sales, margin leakage, and poor customer experience.
Unreliable Forecasts Impact Retail Profitability
Retailers and CPG brands rely heavily on models for demand forecasting, labor planning, pricing, promotions, personalization, and recommendations. Poor data quality is one of the top contributors to AI model failure and lowered confidence in analytics outputs.
$562B
in losses due to overstocks based on mis-forecasts or late deliveries
Increase Supply Chain Resilience and Drive Conversions Through Automated Data Quality
Reliable product, inventory, supply chain, and customer data fuels accurate decisions, seamless omnichannel experiences, and valuable retail analytics results.
AI-Driven Monitoring That Learns Your Business
Anomalo automatically learns the normal patterns of retail datasets, including promotions, seasonality, holiday impacts, and channel-specific volume swings to detect issues before they reach dashboards or customers.
Go Beyond Metadata to Deep Data Understanding
Anomalo automatically performs full-table scans for distributional anomaly detection to catch sudden segment-level drops in product variations or sizes available, duplicate records, and schema drift in supplier and retailer data.
SHAP-based machine learning surfaces what changed, where, and why.
Flexible, Retail-Ready Integrations
Retail and consumer goods companies are in constant motion, with goods and data coming from every direction.
Anomalo supports major retail data platforms, including:
Snowflake, Databricks, Google BigQuery, Redshift, Synapse, Oracle, POS systems, ecommerce platforms, and workflow tools such as Jira, Microsoft Teams, Slack, and ServiceNow.
Proven Scale and Enterprise-Grade Trust
Anomalo powers data quality improvement for some of the world’s most demanding retail and consumer goods organizations, monitoring hundreds of thousands of columns, thousands of tables, and petabytes of data.
SOC 2 Type II certification and compliance support your organization’s governance standards. In-VPC deployment means data never leaves your systems. Sophisticated role-based access controls enhance your security posture.
Our Customers
What Customers Say
Boost Forecast Accuracy and AI Performance With Clean, Reliable Retail Data
Request a demo to learn how leading retail and CPG brands catch data problems early to turn accurate data into revenue, efficiency, and AI readiness.