The statistics in a recent Wall Street journal article confirm what is widely known: The retail landscape has transformed dramatically worldwide with major shifts in consumer spending habits, increased store closures, and rising ecommerce sales.1 Consumer data that yields rich insights can help companies keep up with changing consumer demands. According to a recent Economist Intelligence Unit report sponsored by Snowflake, “It has never been more important for retail companies to understand who their customers are, what they want to buy and how they prefer to shop.” 2

Snowflake’s Data Cloud equips both retail and consumer packaged goods (CPG) companies with the power of data to stay on top of changes in the retail landscape. With the Data Cloud, retailers are achieving deep and timely insights, accelerating decision-making, personalizing customer experiences, reducing customer churn, and even finding new areas of revenue. 

Here are some of the business benefits that Snowflake’s retail and CPG customers Yamaha, Pizza Hut, Petco, US Foods, and Sainsbury’s are achieving with the Data Cloud. 

US Foods: Reducing Customer Churn

To track sales performance and anticipate future demand, US Foods ingests and analyzes large amounts of transactional data. US Foods’ legacy, on-premises data warehouse required constant maintenance, experienced frequent resource contention, and could not affordably store more than two years’ worth of data. Business analysts took weeks to prepare a single report due to the system’s counterintuitive user interface, inability to load large data sets, and limited BI features.

Snowflake’s platform scaled to become US Foods’ single analytics repository for transaction data. Thanks to features such as Snowflake Connector for Python, bulk loading from Amazon S3, and Snowflake’s native support for SQL, one report that previously took five hours was executed in three minutes with Snowflake. Ingesting US Foods’ sales and purchase order data into Snowflake and using it to train and run data science models in DataRobot elevates executive reporting and provides richer insights to inform merchandising, marketing, and supply chain decisions. Predictive analytics help US Foods build forecasts and reduce the customer churn rate. Streamlined data ingestion and ad hoc reporting frees up additional resources to explore new data sources, such as weather data and clickstream data from US Foods’ ecommerce portal. 

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Sainsbury’s: Innovating New Services

Sainsbury’s chose the Snowflake Data Cloud to create a single source of truth for business-critical data across all of its brands. Sainsbury’s implemented Data Vault, a method and approach to modeling an enterprise data warehouse that is agile, flexible, and scalable. 

With Snowflake, Sainsbury’s was able to launch a product-matching service that compares its products with competitors’ products. The service is a popular feature on its websites. The company built business models to calculate the business benefits provided by the data science engine on Snowflake. Before Snowflake, handling GDPR requests and generating reports entailed complex processes on multiple systems, including IBM MDM, Oracle, and PostgreSQL. Snowflake helped eliminate layers of duplication and confusion. Instead of seven or eight different systems, engineers now just have to look at one. 

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Petco: Personalizing Customer Experience

Petco’s digital transformation initiative and omnichannel sales strategy led to increased demand for data analytics, but its on-premises data architecture could not elastically scale to support retail reporting cycles. Data processing workloads took hours to run and prevented users from accessing timely insights. Sharing advertising data with Petco’s media agencies took up to 40 hours per transfer. Data engineering challenges diverted the team’s attention from developing data visualizations and drill-down reports. Petco’s legacy data warehouse experienced frequent outages despite constant oversight by two full-time technical staff. Hardware, software, and data center costs represented a major expense. 

Realizing the need for a modern data environment, Petco turned to Snowflake. Snowflake’s multi-cluster shared data architecture scales instantly to eliminate resource contention at Petco. Flexible capacity scaling provides a convenient solution to Petco’s cyclical reporting challenges. Modernizing Petco’s data environment with Snowflake had an immediate impact on operations. Business teams use data from Snowflake to understand and influence customer behavior across Petco’s stores, mobile app, and website. Data visualizations, enabled by Snowflake, provide 360-degree customer views that guide decision-making about personalized service offers, promotional campaigns, and other revenue-generating programs. The near-instant elasticity of Snowflake’s compute aligns with Petco’s cyclical reporting needs without incurring the cost of unused compute. 

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Yamaha: Achieving Deeper, Timely Insights

To deliver timely insights to BI users, Yamaha ingests and analyzes large amounts of transactional and relationship data. Seeking to accelerate the development, deployment, and iteration of data visualizations for BI users across sales, marketing, support, and finance, Yamaha’s IT team began exploring infrastructure enhancements. 

Determined to deliver data visualizations that load in 30 seconds or less, the team turned to Snowflake’s platform. Snowflake ingested a massive data set of roughly 4 million enterprise relationship management (ERP) records in six minutes, 5x faster than other data warehouses. The total data set contains over 910 million records. Expedited report rendering in Tableau boosts productivity and empowers staff to make data-driven decisions in less time. Hourly ingestion of Yamaha’s ERP and Salesforce data into Snowflake provides timely sales insights about dealer order volume, customer credit limits, inventory availability, and pipeline performance. Success teams monitor product support metrics from Salesforce to measure customer satisfaction and decide when to ramp up hiring. Marketing and finance teams rely on data in Snowflake to inform a variety of pricing and budgetary decisions. Snowflake’s near-zero management infrastructure and per-second pricing deliver tangible cost savings. 

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Pizza Hut: Accelerating Decision-Making

Pizza Hut’s on-premises data warehouse could not scale to meet increasing demands. During the U.S. National Football League’s Super Bowl, Pizza Hut’s busiest day of the year, its data volumes can sometimes triple. With its legacy data warehouse, the company did not have unified real-time analytics during the Super Bowl due to limited scalability. 

Pizza Hut implemented a Snowflake Data Cloud proof of concept that rapidly demonstrated positive results. Snowflake’s near-instant elasticity immediately addressed the previous scalability issues. The advanced analytics team can perform ad hoc queries—something that the previous system could not provide. Now, Snowflake enables users to answer business questions with data. During the 2020 Super Bowl, the company created a unified, near real-time view of business analytics, which wasn’t feasible before Snowflake. 

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For more information on how Snowflake’s Data Cloud can help your retail or consumer packaged goods company achieve business benefits, register for our free online Retail and CPG Data Analytics Forum, which will be held on July 21.