A North American manufacturer of architectural and industrial glass products struggled with scattered sales and inventory data across various systems. Blackstraw created a Snowflake-native sales intelligence platform that included external market signals. This improved the accuracy of sales and demand forecasting from 60% to 94%, allowing for more reliable planning and data-driven sales decisions in the U.S. and Canada.
The client operated across various product lines and regions. Sales, inventory, and logistics data were spread out across different systems. Although the organization consolidated internal data into Snowflake using traditional ETL tools like Informatica, analytics still focused mainly on historical reporting.
Sales and marketing teams didn’t have a combined view that merged internal performance data with external industry trends. Integrating third-party data sources and running advanced analytics on Snowflake was complicated and slow. This operational fragmentation limited the organization’s ability to produce predictive insights.
The client needed a modern approach that was native to Snowflake. They wanted to build advanced sales intelligence products without adding cross-platform complexity.
Snowflake-Native External Data Ingestion: Designed an automated framework to ingest and relate third-party industry and market data directly into Snowflake using Snowpipe.
Python-Based Machine Learning on Snowflake: Developed machine learning models in Python that operated directly on Snowflake data, eliminating the need for data movement across platforms.
Reusable Snowpipe Applications Framework: Built a simplified, reusable Snowpipe Apps framework to standardize ingestion, automation, and analytics workflows across multiple data products.
Advanced Data Harmonization: Applied NLP and predictive modeling techniques to normalize and align internal sales data with heterogeneous external data sources.
High-Accuracy Forecasting: Improved sales and demand forecasting accuracy from 60% to 94% by integrating external industry trends with internal performance data.
Simplified Analytics Architecture: Enabled advanced data science and ML workloads directly on Snowflake without cross-platform integration overhead.
Faster Time to Insight: Reduced complexity in building and scaling new sales and marketing intelligence products using reusable Snowpipe frameworks.
Future-Ready Sales Intelligence: Established a scalable foundation for predictive analytics and data-driven decision-making across sales and marketing functions.
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