AI-Powered Pricing and Analytics for a Global Online Retail Firm

A global online retailer with 90,000+ SKUs and more than 50 billion rows of transaction history was suffering from data silos: separate systems for sales, stock, promotions, and delayed reporting via nightly batches or external extracts. Pricing teams lacked visibility into how discounts impacted demand or margin. To address this, Inferenz built a unified cloud warehouse using Snowflake, Python, and Airflow, enabling near-real-time ingestion of stock and transactional data. A price elasticity engine was introduced to flag margin risks before campaigns, while reporting tools like Tableau and Hyperion were consolidated. This transformation happened in just 100 days, bringing in huge operational clarity.

The results were striking: dashboard creation sped up by 80%, manual data pulls reduced by 90%, and promo margin accuracy improved by 15%. A reusable ingestion framework also made it far easier to plug in new data sources without needing to rebuild pipelines. Decision makers now have the visibility and tools to run margin-aware pricing and promotional programs with much more confidence.

Discover the full case study on Inferenz’s website to learn more about the architecture, challenges, and real-world lessons.