Turn Every Transaction Into a Competitive Advantage
Omnichannel data platforms, real-time inventory visibility, and customer analytics that scale from quiet Tuesdays to Black Friday peaks. Built for retailers and e-commerce brands that want to compete on data, not guesswork.
The Problem
Retail Data Challenges We Solve
Omnichannel Data Fragmentation
Shopify says one thing, Amazon says another, your POS system reports a third number. Website analytics lives in Google Analytics, email in Klaviyo, paid ads across 5 platforms. Nobody has a unified view of the customer or the business.
Real-Time Inventory Blindness
Nightly batch jobs mean your morning stock levels are already stale. Overstock costs you warehouse fees. Stockouts lose you sales. The data to prevent both exists, but by the time your batch job finishes it is already 6 hours old.
Customer 360 Gaps
You know what they bought online but not what they returned in store. Their email engagement lives in one tool, their purchase history in another, and their support tickets in a third. Personalization is impossible when you cannot see the whole customer.
Seasonal Scaling Pain
Your data warehouse handles normal traffic fine, but Black Friday and holiday peaks bring it to its knees. Queries grind to a halt right when leadership needs real-time numbers. You are paying for peak capacity 365 days a year to handle 10 peak days.
Abandoned Cart Black Hole
70% of carts get abandoned and you do not know why. The checkout funnel data lives in one system, the marketing data in another, and the pricing data in a third. You cannot connect the dots to figure out what is actually causing drop-off.
How We Help
Retail Data Engineering That Drives Revenue
Snowflake Retail Data Platform
A single warehouse for all your retail data: transactions, inventory, customer profiles, marketing, and supplier data. Snowflake's multi-cluster auto-scaling means Black Friday traffic gets more compute, not more headaches. Pay for what you use, not peak capacity.
Real-Time Streaming Pipelines
Near-real-time inventory sync across distribution centers, stores, and online channels. CDC pipelines capture every transaction, stock movement, and order change as it happens. Your merchandising team sees current inventory, not yesterday's numbers.
Customer Segmentation with AI/ML
Python and Snowpark models that analyze purchase history, browsing behavior, return patterns, and engagement data to build actionable customer segments. RFM analysis, churn prediction, and next-best-offer recommendations that feed directly into your marketing stack.
Power BI Merchandising Dashboards
Dashboards for inventory health, sell-through rates, category performance, and customer lifetime value. Real-time visibility into what is selling, what is sitting, and what needs markdown. Built for buyers and merchandisers, not just data analysts.
Snowflake Data Sharing for Suppliers
Share real-time inventory and sales data with suppliers through Snowflake's secure data sharing. No file transfers, no APIs to maintain, no stale data. Your suppliers see what they need to see, updated continuously, with governance controls that keep your competitive data private.
Proof It Works
Retail Projects With Real Numbers
E-Commerce Retailer — SQL Server to Snowflake Migration
An online retailer with 500+ employees was stuck on SQL Server with reports taking 4 hours to generate. Every Black Friday, queries ground to a halt. The data team spent 60% of their time patching infrastructure. We migrated everything to Snowflake with multi-cluster auto-scaling, built new Talend pipelines to sync inventory across 3 distribution centers, and deployed Power BI dashboards for merchandising and supply chain teams.
Mid-Size Retailer — Snowflake Migration Cuts Inventory Costs
A 350-employee retailer had nightly batch jobs taking 6+ hours, leaving morning stock levels always stale. Overstock was costing $280K per month in warehousing fees. We moved the warehouse to Snowflake, built near-real-time inventory pipelines, and deployed demand forecasting models using Python and Snowpark that brought forecast accuracy to 97%.
Our Stack
Retail Data Technology
Retail Data Questions
We Hear Every Week
Snowflake's multi-cluster auto-scaling handles this natively. When Black Friday traffic hits, the warehouse spins up additional compute clusters automatically and scales back down when traffic normalizes. You pay for what you use, not for peak capacity year-round. We have managed e-commerce platforms through major sale events with zero query degradation.
Yes. We build omnichannel data pipelines that pull from Shopify, Amazon Seller Central, WooCommerce, point-of-sale systems, and any other sales channel into a single Snowflake warehouse. We use tools like Hevo Data and Fivetran for managed ingestion, then dbt to transform and unify everything into a consistent schema. Your merchandising team gets one dashboard that shows all channels.
For a focused migration from a legacy warehouse like Redshift or SQL Server to Snowflake, expect 7 to 10 weeks. A full retail data platform build with omnichannel integration, customer 360, and merchandising dashboards is closer to 12 to 16 weeks. We have completed e-commerce Redshift-to-Snowflake migrations in as little as 7 weeks, with both systems running in parallel during validation.
Yes. Once your customer data is unified in Snowflake, we build segmentation models using Python and Snowpark. These models analyze purchase history, browsing behavior, return patterns, and engagement data to create actionable segments. The results feed directly into your marketing tools and Power BI dashboards so your team can target campaigns based on actual data, not gut feel.
Ready to Modernize Your Retail Data?
Tell us about your sales channels, inventory challenges, and what insights you wish you had. We will map out a data platform that turns transactions into competitive advantages.
Schedule a Retail Data Assessment