If you lead sales, supply chain, or analytics for a retail brand, a lack of data is unlikely to be your issue. The real challenge is translating that data into business and decision-making value.
Even when you’ve centralized data in a warehouse or BI tool, teams may still be left asking: How do we get ready-to-use sales and inventory data from our retailers? Why do we still have to pull manual reports? Which promotions actually drove sales lift? What data can we use to build our AI strategy?
That’s the gap the SPS Commerce and Snowflake partnership closes.
By combining retail data and insights from SPS Commerce's intelligent supply chain with Snowflake's AI Data Cloud, you can move your business beyond static reports and siloed data into a single, trusted environment where your data is both accessible and actionable.
The Issue: Data Outside Your Walls Is Slowing Decisions
Many brands are heavily invested in their data. You may already have:
An enterprise data warehouse or data cloud platform
BI tools and dashboards across functions
Internal shipment, finance, and marketing data flowing reliably
In most organizations, that means multiple stakeholders are involved: business leaders who own the results, frontline users who need answers fast, and data teams who build and maintain the Snowflake environment.
However, data integration is only half the story; the real value comes when that integrated data is structured, normalized, and enriched with business context so it’s easy to turn into decisions.
On top of that, the data that often matters most for strategic and operational decisions sits outside your four walls:
Point-of-sale and inventory data in retailer portals
Order, shipment, and invoice data scattered across trading partners
Promotion performance buried in retailer dashboards
This challenge is especially visible for brands operating across large retail ecosystems with multiple channels, partners, and data sources. For companies like TaylorMade, understanding what is happening at the point-of-sale across retailers directly impacts promotional strategies, sell-through visibility, as well as the ability to quickly respond to changing demand patterns.
If your team spends hours logging into portals, exporting spreadsheets, and building one-off integrations just to answer basic questions about what sold, where, and why, then you’re leaving time and money on the table.
These manual data practices can quickly turn into:
Slow decision-making: By the time data is pulled, cleaned, and aligned, the window to act on an issue or opportunity may have passed.
Data inconsistencies: Different teams may use different extracts and logic, leading to competing “sources of truth.”
Stalled AI adoption: Your team may struggle to access clean, consistent retail data to train models or build agentic capabilities.
That’s where SPS Commerce and Snowflake come together.
The Fix: See Every Data Point with SPS Commerce + Snowflake
Together, SPS Commerce and Snowflake can turn your retail data into a living decision engine. SPS delivers clean, POS and supply chain data and insights; Snowflake makes that data available alongside other enterprise sources.
With SPS Commerce and Snowflake, your teams can move from manual reports and reactive decision-making to answering bigger, more precise questions, like:
Are we seeing a gap between what we ship and what actually sells through?
Based on POS velocity and lead times, when will we hit critical days of supply, and where should we trigger replenishment?
Did this promotion actually create incremental sales lift, or did it just pull volume forward from another period?
How can we walk into buyer meetings with a normalized view of POS, costs, and margins that anchors the conversation in numbers the retailer can’t easily dispute?
All of these questions, and the answers, depend on having POS and supply chain data from SPS Commerce available in Snowflake.
Make Better, Data-Backed Decisions: SPS Commerce + Snowflake Use Cases
So, what does this actually look like in practice? Here are just a few of the ways brands can use SPS Commerce and Snowflake together to drive better outcomes:
1. Identify missed revenue opportunities
With POS and supply chain data and insights in Snowflake, you can quickly see where sales are underperforming by retailer, region, or SKU. This visibility can help you spot gaps between your true performance and benchmarks, and focus your team on the specific customers, products, or locations that require immediate attention.
2. Improve trade spend efficiency and promotion ROI
With sell-through data in Snowflake, you can separate real incremental lift from noise. You can compare baseline and promoted performance to understand which promotions and retailers are delivering the strongest ROI. This insight can help you confidently scale high-performing efforts and cut the ones that just shift volume around.
3. Optimize pricing based on demand trends
SPS and Snowflake can give you a clearer picture of how consumers actually respond to price. You can see where prices have drifted from your intended architecture, where underpriced items are driving volume without profit, and where strategic price moves could protect or grow margin.
4. Improve demand forecasts with real sell-through data
Build your forecasts with a complete data picture. With clear visibility into POS data, you can separate slow movers from fast movers, spot early seasonality shifts, and reduce bias in your planning models. This lets you ground your inventory and production decisions in what shoppers are actually buying, instead of relying solely on shipment data.
Make the Most of Your Data
Ready to use retail data insights in a more strategic and higher-value way across your business and supply chain relationships? It’s time to put SPS Commerce and Snowflake to work for your team. Find us on Snowflake Marketplace, or connect with us to learn how SPS Commerce and Snowflake can drive better, faster decisions across your business.
Headed to Snowflake Summit?
Join us at Snowflake Summit 2026 at Moscone Center in San Francisco from June 1–4. We’ll also be hosting a breakout session on June 1 at 1:00 p.m. PST, where we’ll explore how connected retail data can drive faster, smarter decisions across the business.
We’d love to connect in person at our co-sponsored happy hour with Sigma Computing on June 3 from 4–6 p.m. at Dirty Habit, 12 4th St, San Francisco, CA 94103.
