Transforming Customer Experiences with Kyvos

About the Customer

A leading U.S. supermarket chain

Operating 440+ stores with 150,000 employees, the top-ranked American retailer serves millions of customers every day. With average annual revenues exceeding $40 billion, it continues to lead the industry—earning the top spot on the Retailer Preference Index for exceptional customer satisfaction and loyalty.

Challenges

Performance bottlenecks and legacy tools slowed retail insights and decisions

Despite upgrading its data infrastructure, the retailer faced slow decision-making, missed opportunities and IT bottlenecks.

Performance issues with large-scale dashboards Complex dashboards with 35 worksheets, 20 cascading filters and more than 100 calculated measures were difficult to manage and scale.

Query failures on massive joins The SQL query engine frequently timed out when joining tables containing over one billion rows.

Limited trend analysis Month-over-month and year-over-year comparisons across large historical datasets was nearly impossible due to analytical limitations.

Business Goals

Unlock personalized retail insights to boost customer loyalty

The company was looking to extract faster insights from 140,000 daily transactions across hundreds of stores and thousands of products, all without changing their BI environment. They wanted to:

Understand customer behavior by analyzing household-level grocery consumption patterns.

Anticipate next store visits and likely purchases for individual customers.

Analyze long-term buying trends to manage seasonal demand and improve forecasting.

How Kyvos helped

Modernized customer analytics with a powerful semantic layer

Kyvos built a high-performance semantic layer on Azure to help analyze billions of transactions, uncover customer buying patterns and deliver instant insights across their BI ecosystem.

AI-powered aggregations for granular insights Enabled instant answers to multi-dimensional queries across 2+ years of purchase history.

Customer behavior mapping Mapped purchase data with demographic profiles to create a unified view of customer behavior.

Product placement and profitability modeling Enabled market basket and rack layout analysis for product placement optimization and revenue growth.

Household-level consumption analysis: Grouped household-level purchase data into family buckets to help monitor grocery consumption and predict future visits.

Intuitive UI: Provided an intuitive interface for building data models, along with an ML-powered recommendations engine to optimize them.

Cost optimization: Reduced query pushdowns to expensive data sources with intelligent cache.

Optimized Tableau performance: Enabled high-speed analytics on Tableau dashboards with complex LOD calculations across multiple dimensions.