A grocery company faced a challenge in understanding customer buying behavior
H-E-B Grocery Company faced a significant challenge in understanding customer buying behavior across various products and brands. The lack of insights into customer preferences hindered their ability to customize product availability and marketing strategies, resulting in substantial estimates of lost sales.
A comprehensive data science approach was employed
To address this challenge, a comprehensive data science approach was employed. The initial model categorized customers into regular or low-frequency buyers, further distinguishing habitual from non-habitual buyers among the regular customers.
The model then quantified brand and product affinity, providing a nuanced understanding of customer alignment to specific brands and products.
The implementation involved Data Science by Design, Survival Curve Analysis, and a Novel Calculus Solution.
The models developed had a transformative impact on H-E-B’s operations.
They played a pivotal role in informing purchasing contracts with different merchants based on customer affinity and dependability. Additionally, the insights gleaned from the models enabled the implementation of targeted marketing campaigns tailored to specific customer segments.
This data-driven strategy not only optimized product availability but also significantly increased the effectiveness of marketing efforts, thereby mitigating lost sales and fostering a more tailored and responsive customer experience.