Optimize retail margins with AI-driven dynamic pricing
Enables retailers to make faster, more profitable pricing decisions by combining rule-based logic, machine learning, and real-time market, sales, and competitor data. Designed for SAP-centric landscapes with integration to SAP S/4HANA and SAP Customer Activity Repository, leveraging master data, transaction history, and promotions. Pricing governance with centralized controls, approval workflows, and auditability while reducing manual effort.
Benefits
Enhance customer consistency with unified pricing across channels
Delivers consistent and unified pricing across e-commerce and physical stores through a centralized SAP-powered pricing platform. Real-time competitor price inputs and margin-based rules ensure prices remain aligned across all customer touchpoints, while significantly reducing the need for manual price overrides.
Improve pricing control with a centralized governance framework
Provides a rule-based pricing approval framework that routes dynamic pricing decisions through defined approval workflows while leveraging SAP S/4HANA historical data for automated price recommendations. This improves pricing transparency, strengthens audit readiness, and enables localized pricing decisions without compromising central pricing governance.
Enhance margin performance with machine-learning-based pricing
Uses machine learning models trained on SAP S/4HANA sales data to forecast optimal pricing by category and region. Continuous retraining using controlled testing feedback improves pricing accuracy, supports better sell-through of seasonal inventory, and drives margin optimization across product portfolios.
Gain real-time competitive intelligence with dynamic price benchmarking
Continuously captures and compares competitor pricing across online marketplaces with internal SAP S/4HANA master data and current offers. Automated, high-frequency price monitoring reduces manual competitor analysis effort and enables faster responses to market price changes.
Features
Automate competitor price data collection
Extract data from Thunderbit to enable seamless downstream processing by leveraging Thunderbit connectors to efficiently pull both structured and unstructured data, while automating extraction workflows to improve ingestion speed, consistency, and overall data accuracy.
Build and train pricing models with Databricks
Trains pricing models using Databricks to deliver dynamic pricing recommendations at scale. Automated machine learning pipelines support continuous retraining, while optimized algorithms developed and deployed workflows enable ongoing pricing intelligence, adaptability to changing market conditions.
Centralize pricing results in SAP Datasphere
Publishes pricing model outputs to Datasphere to provide centralized access and reporting. By integrating results generated in Databricks, the solution enables unified data consumption and delivers processed pricing insights that support faster, more informed business decision-making.
