H-E-B democratized data for better shopping experiences with the help of Knoldus
A few of the results achieved -
About the Organization:
The H-E-B grocery chain has grown from a store to many more, including the Central Market specialty food stores. H-E-B has more than 340 stores throughout the U.S. state of Texas, as well as in northeast Mexico.
As a leading grocery chain, H-E-B has dedicated itself to providing the best possible shopping experience for its customers. With that mission in mind, they trust in Knoldus for data analytics and machine learning — allowing them to build an exciting and highly engaging shopping experience that is personalized to each of their customers.
Challenge: Legacy data warehouse not keeping up with website demand
H-E-B was using a traditional corporate data warehouse, but as its business grew, its inability to scale without intensive DevOps support slowed things down. Furthermore, their legacy systems were not collaborative and created silos as only their data analysts could access the data, most of which was left unused due to the challenges created by data silos. This all had a cumulative effect on their ability to not only innovate with machine learning, but when they did build new features, they were not able to scale them. The team struggled to efficiently build data pipelines that unlocked access to curated data for various data teams and business stakeholders.
Solution: Democratizing data and machine learning
Knoldus helps H-E-B with its Data Analytics expertise and build a unified data warehouse that unifies and streamlines the acquisition and processing of historical data. The unified data platform fostered a collaborative and democratic environment across the entire company, enabling them to ingest large volumes of high-velocity data and develop a powerful image classification and recommendation engine to improve the customer experience.
Results: Enabling a shopping experience that converts
With the Knoldus data analytics solution, anyone in H-E-B can easily access data, to work, display and integrate with other services to make more use of that data. The machine learning use cases have provided tremendous value and a direct impact on revenue.