How Retailers Optimize Merchandising and Assortment Planning Strategies with the Snowflake Retail Data Cloud

The lingering effects of the global pandemic are merging with inflation to create a perfect storm for retailers looking to find the right inventory stature for the seasons ahead. Companies are getting squeezed between rising supply chain costs and falling consumer confidence. To succeed in this volatile market, McKinsey suggests that retailers “accelerate decision-making tenfold.” 

To enable faster decisions, retail merchandisers must provide insights to ultimately influence cross-functional teams, to optimize category, dynamic placement, pricing, and promotion decisions. However, without access to granular product sales information and customer insights, merchandisers are forced to influence teams and make decisions based on instincts. As a result, teams are left frozen by indecision and second-guessing, which can lead them to make suboptimal decisions, impacting sales.

With the Snowflake Retail Data Cloud, merchandise leaders can access near real-time product sales insights. They can enrich this data with consumer demographic, foot traffic, category, and other data insights used in predicting consumer needs and buying habits. As a result, they can drive sales growth through timely, data-driven decisions around which products to prioritize, how to price and promote them, and how to lay out stores and ecommerce sites.

Here’s how a few of our retail customers are using Snowflake to optimize merchandising and assortment planning.

Snowflake’s Data Cloud enabled Urban Outfitters (UO) to build a single source of truth that provided a 360-degree view of the enterprise and empowered business leaders to make data-driven decisions. Store managers can now access information to make placement decisions early in the day. Merchants and buyers can add equivalent TY (this year) versus LLY (LY and the year before that) reporting with no delays in delivery, while changing calculations. The volume of pricing history UO can keep in the Data Cloud allows extensive analysis of historical trends to determine the best current path for pricing and promotions. Data from the distribution centers is centralized, updated in real time, and easily accessible, enabling fast and timely supply chain management decisions. If there are delays upstream, UO can temporarily increase virtual warehouse size to keep service levels.

To eliminate data silos and better serve the needs of customers, DoorDash turned to Snowflake’s Data Cloud to employ Customer 360. Snowflake’s multi-cluster shared data architecture scaled to handle DoorDash’s data, users, and workloads with speeds twice as fast as before. Snowflake’s fully managed infrastructure with near-infinite scalability kept the BI team focused on data analytics and modeling. Ingesting DoorDash’s consumer, merchant, and Dasher data into Snowflake provides market managers across the globe with the latest supply and demand insights by 7 a.m. daily. Architecting DoorDash’s merchant portal on Snowflake provides merchants with data-driven reports for managing orders, inventory, and staffing. 

Discover how your organization can optimize its merchandising and assortment planning strategies with Snowflake:

• Learn more about the Snowflake Retail Data Cloud
• Read our Retail Success Guide: 10 Ways Retail and CPG Drive Business Value with the Data Cloud
• Read our blog post: Drive Retail Profitability, Stability with Snowflake’s Retail Data Cloud
• Read our ebook: Build Resilience in Your Retail Supply Chain with Data