To maintain its market-leader position, a leading US-based grocery chain wanted to use its data to understand its customers better. They wanted to align their merchandise mix and store inventory to match their customerâ€™s specific needs and offer a unique shopping experience.
With a massive network of stores, thousands of products, and daily visitors, they were tracking every purchase and logging almost 140,000 transactions per day. However, their current BI environment was unable to support complex analysis of massive data for conducting year-over-year and month-over-month retail analytics.
Searching for solutions, they resorted to using Impala for writing complex queries to fetch data from their big data platform. However, when the data increased to more than one billion cardinalities, Impala failed to perform. They moved to the cloud to overcome these challenges, but even a cloud-based solution could not help them meet their BI performance expectations.
Kyvosâ€™ OLAP on Azure solution helped them conduct self-service, interactive analysis on 12 billion rows of data along with the flexibility to scale limitlessly in the future and 100x faster performance than Impala.
In this case study, you will learn how they use Kyvos to:
- Map buying patterns with customer profile data to offer better experiences
- Analyze buying patterns over two years to improve forecast accuracy
- Perform year-over-year analysis on all historical data to cater to seasonal demand fluctuations