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How a Retailer Used High-Frequency Data to Improve Strategy in Luxury Retail May 02, 2024 (0 comments)


New York, NY--In April 2024, a leading luxury retailer in North America started working on an initiative to dissect the effectiveness of its various sales and marketing programs.

[Image via Maksym Belchenko/]

Per an article published on Marketscience, the primary aim was to distinguish the contributions of new versus returning customers to revenue, which would help tailor future strategies more precisely. The retailer required comprehensive models that could evaluate a wide spectrum of investments, ranging from those enhancing customer experience to those optimizing fulfillment and performance marketing.

Strategic Data Management and Modeling

The retailer gathered three years of daily data on sales, differentiating between new and returning customers, and detailed operational and marketing activities. The article noted that this data, collected at both the store and market levels, was essential for drilling down into the effectiveness of specific campaigns and messaging.

Despite the complexity of managing and scrubbing data from diverse sources, the retailer needed to develop a structured taxonomy of campaign dimensions to organize and interpret the data better.

A suite of Hierarchical Bayesian models was developed to analyze this information using the Marketscience Studio platform. These models integrated daily data from sales and marketing operations at both the store and market levels and were sensitive to external factors, allowing for an understanding of channel, category, and messaging impacts.

Achieving Measurable Results and Insights

Per the article, the detailed insights from these models enabled the retailer to make strategic adjustments quickly and efficiently. Using a tool by Marketscience, the retailer could optimize spending across various channels.

Key strategic shifts included:

Learn more in the entire article on Marketscience.

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