Brace yourself—the U.S. e-commerce industry is on a trillion-dollar trajectory. The industry is not just changing but undergoing a full-blown digital revolution. Powered by cloud-first technologies like Snowflake professional services, the way businesses connect with customers is getting faster, smarter, and more data-driven than ever.
The numbers speak for themselves:
- The U.S. e-commerce market revenue was around $1.34 trillion in 2025.
- The revenue is likely to reach $1.83 trillion by 2029, at a CAGR of 8.02%.
- It’s expected to be a whopping 37.16% increase between 2025 and 2029.
Whether you want to deliver personalized customer experiences, optimize supply chains, or make data-driven decisions, Snowflake is the gateway to retail analytics. Let’s dive into the exciting world of Snowflake professional services and understand how they can help retailers unlock real-time data analytics.
The Importance of Real-Time Data Processing in E-Commerce
It’s true that the world’s most valuable resource is no longer oil, but data. And in e-commerce, you can’t overstate the importance of real-time analytics. Since today’s retail and e-commerce businesses operate at lightning speed, it has become more important than ever to invest in real-time analytics.
One of the most significant benefits of leveraging e-commerce analytics is their ability to inform business strategies. Brands that leverage data-driven strategies have seen 5-8 times as much ROI as those who don’t. In fact, by 2026, 65% of B2B companies are likely to make data-driven decisions.
Let’s look at a handful of reasons why retail analytics matter:
1.Enhancing Customer Experience
Want to show customers exactly what they crave—the moment they land on your store? It starts with analyzing their behavior in real time. To offer personalized recommendations, offers, or interactions when a customer lands on your e-commerce store, you need to analyze customer behavior first. Here, analyzing real-time data boosts customer loyalty and encourages repeat business.
Further, real-time data processing is the way to streamline customer support. It can help you address customer inquiries and issues instantly. This capability enhances not only the overall customer experience but also builds trust and improves customer retention.
2. Improving Operational Efficiency
An increasing number of retail businesses monitor stock levels to ensure that their products are available when customers need them. Here, real-time insights help prevent overstocking and stockouts. You can optimize your inventory management with real-time data processing. This reduces costs and maximizes productivity.
Take logistics, for example. Real-time data processing allows retailers to streamline their logistics. They can identify bottlenecks and inefficiencies quickly, resulting in lower operational costs and downtime.
3. Fast Decision-Making
When analyzing market conditions and competitor prices instantly, you need to process a large amount of e-commerce data. While traditional batch processing is slow and mundane, real-time data processing ensures that pricing remains competitive for your customers.
Data processing also provides a competitive edge by keeping retailers informed and agile. You can use real-time data to monitor trends and respond swiftly to changing market conditions. You can also capitalize on emerging opportunities and mitigate potential risks.
4. Measuring Marketing Campaigns
Gone are the days when marketing analytics were accessible to only large enterprises. Today, there are several software solutions to assist you in measuring how successful your digital marketing campaigns are. These solutions not only improve decision-making but also help you gain more omnichannel traction.
For instance, real-time data processing revolutionizes targeted ads. You can analyze customer preferences and behaviors instantly, boosting the delivery of personalized ads to the right audience at the right time. As a result, you can achieve higher conversion rates and a better return on investment.
5. Most Common Challenges with Retail Analytics
It’s true that big data analytics has become a powerful tool for retailers to understand their markets and customers, helping them optimize business operations and drive growth. However, when it comes to implementing and running these analytics, you may face some common challenges, including:
1. Collecting Accurate Data
When collecting business data, you need to target the right information. Low-quality data may result in inaccurate insights. In the context of retail, the variety of data sources can be complex. The retail data may include sales volumes, customer footfall, profit margins, stock levels, and so on. Here, collecting and compiling the data in a meaningful way requires extra training and time investment to implement software development solutions effectively.
2. Complying With Data Protection Laws
Protecting the privacy of customers is a growing movement these days. As data and privacy regulations become more complex, retailers find it challenging to comply with everything. Thus, gathering retail data should be done with careful adherence to the law. Also, you need to handle data in an appropriate way, store it securely, and have a robust company policy.
3. Building Customer Trust
To capture customer data on a large scale, retail businesses need to build customer trust. It can be achieved by providing exclusive benefits, such as discount coupons, for a customer in exchange for their data. Other key strategies include effective branding, marketing, and advertising.
4. Using Advanced Technology
Modern technologies such as machine learning and artificial intelligence play a key role in retail data analytics. To ensure accurate insights are gained from big data, you need to keep up with the latest technological advancements. This can be challenging without the right assistance and investment.
5. Keeping Up with the Competition
Retail is a highly competitive industry. Here, making even a tiny mistake can put retailers at risk of falling behind their competitors. To keep up with the competition in the fast-paced world of retail, whether it’s online or offline, you need proper investment in big data analytics solutions. This is where Snowflake consulting services come to the rescue.
Introduction to Snowflake Professional Services in USA
Snowflake is a cloud-based data platform built for modern enterprise needs. It enables businesses to collaborate, build AI-powered data apps, and unlock valuable insights without silos.
The platform also offers a one-stop solution for data warehousing, data lakes, Snowflake data engineering, and much more.
Major brands like Dropbox, Pfizer, Pizza Hut, and Douglas use Snowflake cloud services to process massive amounts of data.
When it comes to e-commerce and retail, Snowflake links data from online stores, physical locations, and third-party sources into a single unified system. Thus, it gets easier for retailers to track sales, predict market trends, and make smart inventory choices in real time.
They can save money since they only pay for the cloud computing power that they need and use.
Different Purposes of Snowflake Consulting Services in the USA:
- Data Ingestion: With its data ingestion service called Snowpipe, Snowflake helps businesses stage data as quickly as possible. It ensures that the data is available from external storage locations such as Amazon S3 and Azure Blob.
- Business Intelligence (BI) and Analytics: Snowflake is compatible with popular business intelligence tools such as QuickSight, Power BI, and Tableau. It helps extract valuable insights from your organizational data through interactive reporting and analytics.
- Data Sharing and Collaboration: Snowflake is a secure solution for teams to share and collaborate on their data via Snowflake Marketplace, which is a centralized platform where users can discover and access data assets.
- Machine Learning (ML): Snowflake also supports ML use cases, so you can build, train, and deploy ML models within the platform. It includes everything from loading and transforming datasets to managing and integrating them with popular ML libraries such as TensorFlow and PyTorch.
What Makes Snowflake Services Stand Out? Key Advantages
| Category | Benefit | Explanation |
|---|---|---|
| Performance | High-Speed Query Execution | Snowflake’s unique architecture separates storage and computing |
| Scalability | Elastic Scaling | Automatically scales resources up/down based on demand |
| Data Integration | Multi-Source Data Handling | Easily integrates with e-commerce platforms, CRMs, and payment gateways via ETL/ELT |
| Cost Efficiency | Pay-As-You-Use Model | Charges only for the computing and storage actually used, unlike traditional fixed-cost warehousing |
| Security | Advanced Data Security | End-to-end encryption, multi-factor authentication, and robust access controls |
| Compliance | Compliance Ready (e.g. GDPR) | Meets industry compliance standards, important for handling customer and payment data |
| Data Sharing | Secure Data Sharing | Easily share live data with suppliers, vendors, or business partners without any FTPs or duplicating files |
| AI & Automation | Machine Learning Integration | Easily export data to tools like DataRobot, SageMaker, or use Snowpark for custom models |
Why Do Retailers Choose Snowflake Services in USA?
While the US e-commerce market expands, retailer businesses often face challenges when modernizing their data systems. They feel stuck with legacy infrastructures and data in silos. This is where Snowflake professional services come in handy, enabling retailers to seamlessly access, govern, and share data.
Snowflake’s cloud-native architecture, combined with its advanced capabilities like Snowpark, Cortex AI, and Snowpipe, transforms how retailers unify, analyze, and act on data.
Here’s why Snowflake is the platform of choice for data-driven retail:
1. Built for the Cloud
While other data platforms later adapted to the cloud environment, Snowflake was originally created with the cloud in mind. This simply means that Snowflake is more flexible and scalable than other data warehousing solutions.
Snowflake’s unique three-layer architecture (storage, compute, cloud services) decouples resources, allowing retailers to scale compute (virtual warehouses) independently of storage. This elasticity is critical for handling seasonal demand spikes without over-provisioning.
Furthermore, Snowflake’s cross-cloud support (AWS, Azure, GCP) ensures compliance with regional data residency laws while enabling global analytics. There is no need to spend time setting up hybrid cloud systems.
2. A Wide Range of Architecture
Snowflake stands out with its multi-cluster approach and shared data architecture. The Snowflake architecture consists of three different layers that are intentionally designed for independent scalability.
This helps users allocate or expand resources for each layer separately based on their specific requirements. The three layers of Snowflake architecture include:
- Database Storage
- Compute Clusters
- Cloud Services
3. Supporting Unstructured Data
It’s true that retailers generate massive amounts of data from point-of-sale systems, e-commerce platforms, inventory management, and customer interactions. This sort of unstructured business data contains insights that can provide a holistic view of business operations. However, it is often challenging to store, search, and analyze unstructured data.
While traditional data warehouses can’t fully support unstructured data formats, Snowflake is not conventional. It fully supports unstructured data so that organizations can gain a more comprehensive understanding of customer sentiments, market trends, and emerging patterns.
Take Snowpipe (a data ingestion service within Snowflake), for instance. It helps retailers:
- Automate continuous data ingestion from e-commerce platforms, POS systems, and IoT devices into Snowflake’s data lake
- Process streaming clickstream data to adjust pricing or inventory in real time
- Consolidate unstructured (images, customer emails) data to enable computer vision for shelf inventory tracking
4. AI-Powered Personalization and Analytics
With built-in support for integrations and compatibility with tools like Python, R, and ML, Snowflake enables retailers to build and deploy machine learning models for personalization. For example, Snowpark (a developer framework from Snowflake) allows developers to write Python, Java, or Scala code directly in Snowflake, enabling advanced predictive analytics.
Whereas, Cortex AI (Snowflake’s embedded LLM framework) powers chatbots for personalized customer support and analyzes unstructured data (product reviews, social media) for sentiment analysis. This way, you can start leveraging:
- Hyper-targeted product recommendations
- Intelligent loyalty and retention strategies
- Increased conversion rates and customer lifetime value
5.Real-Time Inventory Management
Snowflake enables retailers to maintain accurate, real-time inventory visibility across all channels. This prevents stockouts, reduces overstock situations, and ensures customers can find products when and where they want them.
By consolidating data from online browsing, purchase history, loyalty programs, social media interactions, and in-store behavior, retailers can create comprehensive customer profiles. This drives hyperpersonalization, product recommendations, and improved customer experiences. What else?
- Enable cost optimization with zero-copy cloning
- Clone datasets for testing ML models or scenario analysis
- No need for duplicating storage costs
6. Seamless Data Integration Across Multiple Channels
US retailers operate across e-commerce platforms, brick-and-mortar stores, mobile apps, and social media. This is where Snowflake’s architecture enables them to ingest and unify data from all sources into a centralized data warehouse.
Further, retail businesses are prioritizing omnichannel marketing strategies – buy online, pick up in-store (BOPIS), and same-day delivery. Snowflake provides a single source of truth to optimize these experiences in real time. It results in:
- Smoother customer buying journeys
- Better in-store/online synchronization
- Improved fulfillment accuracy and speed
- Live supplier/vendor data sharing without ETL
7.Seasonal Scaling Excellence
US retail follows predictable seasonal patterns, with the holiday season accounting for nearly 17% of the year’s total sales. This is where Snowflake’s elastic scaling allows retailers to automatically provision additional resources during peak periods, flash sales, and seasonal events.
Snowflake professional services help businesses scale elastically to meet seasonal surges without latency. They can prevent both performance bottlenecks during busy seasons and unnecessary costs during quiet periods. These services are ideal for:
- Black Friday, Cyber Monday, or BFCM campaigns
- Real-time stock and fulfillment monitoring
- High-speed checkout experiences
8. Reduced Total Cost of Ownership
Data center systems spending grew by nearly 35% in 2024 and is expected to continue growing. This growth is led by server sales that are set to almost triple from more than $134 billion in 2023 to $332 billion by 2028. Luckily, Snowflake’s pay-as-you-go model reduces Total Cost of Ownership (TCO) by removing the need for:
- Physical hardware
- Manual provisioning and maintenance
- Overprovisioning of compute resources
Unlike traditional data warehouse solutions that require substantial upfront investments and complex licensing structures, Snowflake’s consumption-based pricing aligns perfectly with retail economics. It allows IT teams to focus on value-generating analytics rather than infrastructure management.
7 Leading Companies Using Snowflake Professional Services for E-Commerce
Snowflake Cloud is revolutionizing e-commerce analytics by empowering top companies to harness the full potential of data, driving smarter decisions and personalized customer experiences. Let’s dive into some of the standout enterprises leveraging Snowflake to transform retail and CPG industries with cutting-edge analytics.
1. Amazon
Headquartered in Seattle, Washington, USA, Amazon is a prominent online retailer and cloud computing giant. Through its e-commerce platform, the company offers a wide range of products, including electronics, books, clothing, accessories, and household items. It also provides Amazon Web Services (AWS) for cloud computing and storage.
Amazon uses Snowflake cloud services for Amazon SageMaker – a cloud-based machine learning (ML) service that provides tools and infrastructure for building, training, and deploying ML models. It also has a strong partnership with Snowflake, AWS recognizing Snowflake as it’s Global ISV Data and Analytics Partner of the Year in 2023. This helps Amazon transform, share, and analyze their data using both Snowflake and AWS consulting services.
2. Walmart
Walmart is a US-based multinational retail corporation known for its large chain of hypermarkets, department stores, and grocery stores. It offers a wide variety of products through both physical stores and online platforms. These products range from groceries and electronics to clothing and home items.
With over 2.1 million employees worldwide, Walmart is among the biggest employers globally. The company brings all its data together on Snowflake’s integrated platform to inform every aspect of its retail strategy.
3. 84.51°
84.51° is a leading retail data science and insights powerhouse that personalizes customer experiences through data collaboration. The company taps into Snowflake professional services to enable secure and seamless data collaboration with CPG clients.
Their platform integrates first-party transaction data from 60 million Kroger loyalty card members to create a collaborative cloud that fuels highly personalized and relevant consumer experiences. This data-driven approach helps their clients tailor marketing and product strategies to individual customer preferences.
4. Albertsons
Albertsons is another grocery retailer that uses Snowflake’s Retail Data Cloud to consolidate data from over 2,200 store locations into a single, unified view. The unified data platform breaks down silos, allowing for real-time insights and agile decision-making across their vast retail network.
This comprehensive data integration enables Albertsons to optimize inventory, streamline operations, and ultimately provide a better shopping experience for customers.
5.Kraft Heinz
Kraft Heinz relies on Snowflake consulting services to build an advanced cloud data platform that accelerates innovation and responsiveness to market changes. Snowflake’s Retail Data Cloud enables them to integrate diverse data sources, from purchase orders to manufacturing.
Snowflake also helps them offer end-to-end visibility across their supply chain. This transparency eliminates ambiguity, helping Kraft Heinz optimize operations during uncertain times while focusing on delivering delicious products to consumers faster.
6. Rakuten
Rakuten is a major player in the e-commerce market. They leverage the Snowflake platform to scale their North American business operations with real-time data sharing. This ensures Rakuten can respond quickly to market demands and optimize its product offerings dynamically.
Snowflake cloud computing companies provide secure data access to Rakuten’s vendors and merchandising partners, facilitating swift collaboration and data-driven decision-making.
7.Tapestry
Tapestry is the parent company of Coach, Kate Spade, and Stuart Weitzman, who have built their own customer analytics platform on Snowflake. This modern data environment allows Tapestry to process billions of data rows daily, doubling data sources while cutting costs compared to legacy systems.
Their self-service analytics platform, Apollo, delivers actionable insights such as identifying which products to market during retail events. Snowflake’s secure data sharing also enhances partner cooperation, paving the way for future supply chain optimization and real-time customer transaction insights.
How to Get Started with Snowflake Services in the USA?
Now that you understand everything about using Snowflake for e-commerce analytics, it’s time to make data accessible, intelligent, and useful. Wondering how to get started? Venturing into cloud territory without a map is like setting off on a hike with no trail markers. That’s why partnering with a certified Snowflake expert like SoftProdigy gives you a critical edge.
SoftProdigy has years of experience in helping e-commerce and retail companies modernize their data architecture. They clearly understand that each business has its own DNA, so they tailor Snowflake solutions accordingly.
What SoftProdigy brings to the table:
- Data lake modernization (migrate from Hadoop to Snowflake)
- Streamlit dashboards for real-time inventory tracking
- Snowpark optimizations to reduce query costs
- Certified Snowflake data migration services
- Advanced analytics techniques like ML and predictive analytics
So, if your retail game is stuck in the fog, maybe it’s time to make the switch. Start your journey with Snowflake professional services and bring cloud clarity to your retail data. Because in retail, the future belongs to those who can process data. Get started today!
FAQ’s
Divya Chakraborty is the COO and Director at SoftProdigy, driving digital transformation with AI and Agile. She partners with AWS and Azure, empowers teams, and champions innovation for business growth.
