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H-E-B democratised data for better shopping experiences with the help of NashTech

H-E-B democratised data for better shopping experiences with the help of NashTech

Introduction

With NashTech 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.

About H-E-B

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 NashTech  for data analytics and machine learning — allowing them to build an exciting and highly engaging shopping experience that is personalised to each of their customers.

Impact

  • 70% Reduction in operational costs.
  • 2X Increase in revenue due to increased customer engagement.
  • 100s of models built per day.

The 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.

  • Massive volumes of data: Data generated by 500,000 visitors and 400,000 products every single day.
  • Data silos and inability to scale: Struggled to scale operations to support data science efforts against huge amounts of data due to data silos and a traditional data warehouse environment. As a result, time-to-insight was slower than they required to drive innovation across their various global websites.
  • Inefficient machine learning: Inability to scale model building and training to meet business needs.
  • Slow time to market: Building new features was slow, taking them over one year to go from ideation to production — impeding their ability to quickly scale regional successes across their global websites.

The solution

Democratising data and machine learning

NashTech helped 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.

  • Fully managed platform on AWS: Automated cluster management simplifies the infrastructure and operations at any scale
  • More efficient data flow: Able to easily integrate with other tools like airflow and Kubernetes, allowing them to build automated data pipelines while establishing CI/CD best practices.
  • Improved cross-team collaboration: Collaborative notebook environment with support for multiple languages (SQL, Scala, Python, R) enables a diverse team of users to work together in their preferred language allowing them to accelerate data science operations and innovation.
  • Streamlined ML lifecycle: Native support for ML flow enables data science teams to easily replicate experiments, track model performance, and rapidly iterate across their models in a systematic fashion.

The outcome 

Enabling a shopping experience that converts

With our 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.

  • Improved data team productivity: With data analysts, scientists, and engineers working together and efficiently, H-E-B broke the data silos, making it easier to use the data. H-E-B has enabled all of their analysts to analyse their data and drive better business decisions.
  • Improved operational efficiency: Features such as auto-scaling clusters and MLflow have improved operations from data ingestion to managing the entire machine learning life cycle — allowing them to build and train hundreds of models per day. In addition, we used Tableau to consume data directly from Delta Lake, enabling analysts to more easily visualise their entire data lake.
  • Reduced operational costs: Migrating to cloud reduced the operational costs by roughly 70%.
  • More data science innovations: Automated display of products with image classification and more personalised shopping experience for customers. Serve 10 different kinds of recommendations at scale and increase customer engagement with more personalised eventually increase their revenue by 2X.
  • Customer Satisfaction: Now they are able to process and prepare data more efficiently and reliably — empowering various groups from analysts and data scientists to executives the insights they need to make smarter business decisions from forecasting consumer demands to increase customer satisfaction.

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