Case Study

H-E-B Product Recommendation

The Challenge

Calligo created a machine learning model for H-E-B to analyze data, make better inventory and pricing decisions, and suggest substitutes.

H-E-B Grocery Company, LP, faced a significant challenge related to sales loss resulting from product scarcity and sub-optimal product placement within their stores. The company recognized the need to address this issue to enhance customer satisfaction and maximize revenue. The challenge was to identify a solution that could leverage historical purchase data to optimize product placement and provide valuable insights into customer preferences.

The Action

Calligo developed a sophisticated data science approach

To tackle the challenge at hand, H-E-B employed a sophisticated data science approach, leveraging historical purchase data and implementing the Apriori algorithm. The Apriori algorithm is a data mining technique that identifies frequent item sets to establish associations between items in a dataset. In this case, the algorithm was applied to customer purchase data, organized by cart, to unveil patterns and relationships among products.

The data science methodology employed was “Data Science by Design,” ensuring a systematic and strategic approach to solving the business problem. Through experimentation, the algorithm generated recommendations for products that might not be obvious or were considered obscure, thus providing a unique advantage in product placement optimization.

To implement and deploy the models, H-E-B utilized Databricks, a unified analytics platform. The results generated by the Apriori algorithm were populated into a database, enabling seamless integration into Tableau Dashboards. This approach allowed for a user-friendly visualization of the insights derived from the data science models.

The Impact

Calligo had a substantial impact on H-E-B’s operations and sales strategy

The implementation of the data-driven solution had a substantial impact on H-E-B’s operations and sales strategy. The insights gained from the Apriori algorithm helped in determining which products should be strategically placed together within the stores. By identifying frequent item sets and associations, H-E-B could enhance the overall shopping experience for customers by ensuring that complementary products were conveniently located near each other.

Moreover, the data-driven approach also played a crucial role in addressing the issue of product scarcity. The models informed H-E-B about suitable product substitutes when certain items were scarce, enabling the company to maintain a consistent product offering and mitigate potential revenue loss.

In summary, H-E-B’s adoption of data science methodologies, specifically the Apriori algorithm, empowered the company to make informed decisions regarding product placement. This not only contributed to increased sales but also enhanced customer satisfaction by ensuring a more intuitive and convenient shopping experience. The impact was not just on the bottom line but also on the overall effectiveness of H-E-B’s retail strategy.




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