Kyvos Insights Case Studies Retail Chain Transforms Customer Experiences with BI Acceleration

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	Retail Chain Transforms Customer Experiences with BI Acceleration Kyvos Insights

Retail Chain Transforms Customer Experiences with BI Acceleration

Kyvos Insights

Technology Category	Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Retail
Applicable Functions	Sales & Marketing Business Operation
Use Cases	Supply Chain Visibility Inventory Management Demand Planning & Forecasting
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	The grocery chain wanted to use their data to understand their customers better and maintain their market-leader position. They decided to align their merchandise mix and store inventory to match each customer’s specific needs. With hundreds of stores, thousands of products, and daily visitors, the store had details on almost 140,000 transactions per day and wanted to use this data to support their business decisions. However, their current environment could not handle the data scale and complexity, making it impossible to conduct Year-over-Year, much less Month-over-Month analysis. They resorted to writing complex queries using Impala to fetch data from their Cloudera platform. But when they tried to join tables with more than one billion cardinalities, Impala usually timed out. Read More
About Customer	The customer is a leading US-based grocery chain that has carved a niche by building a loyal customer base. They have hundreds of stores and thousands of products, with daily visitors resulting in almost 140,000 transactions per day. The company is known for its unique shopping experience and customer loyalty. They wanted to use their data to understand their customers better and maintain their market-leader position. To offer a unique shopping experience to each customer, they decided to align their merchandise mix and store inventory to match each customer’s specific needs. Read More
Solution	The company evaluated several options for almost three years, ultimately choosing Kyvos as their BI acceleration platform. Kyvos created a universal semantic layer on Azure to solve complex use cases and get quick answers to business questions. Smart OLAP™ technology helped them pre-aggregate two years of historical data, leveraging Azure Databricks service and ADLS for storage. Analysts and business users could plug their Tableau and MicroStrategy dashboards into Kyvos and conduct self-service, interactive analysis on enterprise data with 100x faster performance than Impala. Read More Log in to view content
Contents

Technology Category

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Retail

Applicable Functions

Sales & Marketing

Business Operation

Use Cases

Supply Chain Visibility

Inventory Management

Demand Planning & Forecasting

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

The grocery chain wanted to use their data to understand their customers better and maintain their market-leader position. They decided to align their merchandise mix and store inventory to match each customer’s specific needs. With hundreds of stores, thousands of products, and daily visitors, the store had details on almost 140,000 transactions per day and wanted to use this data to support their business decisions. However, their current environment could not handle the data scale and complexity, making it impossible to conduct Year-over-Year, much less Month-over-Month analysis. They resorted to writing complex queries using Impala to fetch data from their Cloudera platform. But when they tried to join tables with more than one billion cardinalities, Impala usually timed out.

About Customer

The customer is a leading US-based grocery chain that has carved a niche by building a loyal customer base. They have hundreds of stores and thousands of products, with daily visitors resulting in almost 140,000 transactions per day. The company is known for its unique shopping experience and customer loyalty. They wanted to use their data to understand their customers better and maintain their market-leader position. To offer a unique shopping experience to each customer, they decided to align their merchandise mix and store inventory to match each customer’s specific needs.

Solution

The company evaluated several options for almost three years, ultimately choosing Kyvos as their BI acceleration platform. Kyvos created a universal semantic layer on Azure to solve complex use cases and get quick answers to business questions. Smart OLAP™ technology helped them pre-aggregate two years of historical data, leveraging Azure Databricks service and ADLS for storage. Analysts and business users could plug their Tableau and MicroStrategy dashboards into Kyvos and conduct self-service, interactive analysis on enterprise data with 100x faster performance than Impala.

Impact #1	The retailer transformed its customer analytics by achieving faster and deeper data insights.
Impact #2	Mapping purchase data with the customer’s profile data provided an in-depth understanding of their behavior and purchase patterns.
Impact #3	Analysts could identify the items customers bought most often, the stores most frequently visited, the day and time when they preferred to buy, whether they purchased a single item or in bulk, etc.

Impact #1

The retailer transformed its customer analytics by achieving faster and deeper data insights.

Impact #2

Mapping purchase data with the customer’s profile data provided an in-depth understanding of their behavior and purchase patterns.

Impact #3

Analysts could identify the items customers bought most often, the stores most frequently visited, the day and time when they preferred to buy, whether they purchased a single item or in bulk, etc.

Benefit #1	Two-second query responses on 12 billion rows and 1 billion distinct count
Benefit #2	Year over Year analysis of two-year history with instant response
Benefit #3	High performance on Tableau dashboards involving Complex LOD calculations on multiple dimensions

Benefit #1

Two-second query responses on 12 billion rows and 1 billion distinct count

Benefit #2

Year over Year analysis of two-year history with instant response

Benefit #3

High performance on Tableau dashboards involving Complex LOD calculations on multiple dimensions

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Overview

Retail Chain Transforms Customer Experiences with BI Acceleration

Operational Impact

Quantitative Benefit