From Insights to Actions – 4 Data-driven Strategies for Retailers Powered by Snowflake

04.26.24 By

How can analytics help uncover powerful data-driven strategies for success in the retail landscape? Let’s explore how Snowflake, the leading cloud data platform, is revolutionizing the way retailers can leverage data to drive tangible results.

Understanding and catering to individual customer preferences is paramount in today’s competitive retail market. While the power of advanced analytics in business decision-making has been well-established for years, many retailers have yet to leverage this potential fully. Brick-and-mortar giants are embracing online sales, while digital natives are expanding their physical presence. Customer behavior and psychology around personalization, speed, and cost are increasing. The challenge lies in deciphering vast amounts of data to tailor experiences accordingly.

The recent downfall of The Body Shop, once a pioneer of ethical toiletries on the British high street, serves as a stark reminder for retailers. FRP Advisory, overseeing its restructuring, has announced the closure of 75 stores within the next six weeks, resulting in 489 job losses. Dan Hocking, COO of an advertising agency, comments1, “It was a concept that rightly earned success in the 80s and 90s, but The Body Shop didn’t adapt to changing consumer habits and preferences.”

Numerous retailers struggle with similar challenges of not utilizing data well enough to understand their customers and position brands accordingly. However, with the right strategies and by harnessing the full potential of data analysis, retailers can transform this hurdle into a significant growth opportunity. To thrive in this dynamic environment, retailers need an agile, scalable, and secure data strategy. That’s where Snowflake, the cloud-native data platform, steps in.

Understanding Snowflake: Your Cloud-Based Data Hub

Picture consolidating your company’s information from sales metrics to customer interactions within a single secure, user-friendly interface. Snowflake precisely offers that. Serving as a comprehensive platform for data warehousing, data lakes, data engineering, data science, and secure data sharing, Snowflake acts as a centralized repository for all your data-related requirements. Unlike traditional data warehouses, Snowflake for retail offers a unique architecture that allows companies to:

Unify Data Silos: Break down data silos across stores, online platforms, loyalty programs, and marketing campaigns. Snowflake consolidates all your data into a single, secure cloud location, giving you a holistic view of your business.

Unlock Real-Time Insights: Gone are the days of waiting for data reports overnight. Snowflake’s architecture enables near real-time data analysis, allowing you to react quickly to market trends, customer behavior, and competitor activity.

Scale with Ease: Whether your business is experiencing rapid growth or seasonal fluctuations, Snowflake scales seamlessly to meet the needs. No more upfront infrastructure investments or the risk of being outgrown by your data storage solution.

“In today’s retail landscape, data is no longer an afterthought. The challenge lies in harnessing the full potential of the collected data. Fragmented data silos can lead to lost sales and missed opportunities. Snowflake’s Retail Data Cloud is a transformative solution that simplifies data architecture and empowers retailers to capitalize on the power of AI and machine learning. To implement a sound data-driven strategy, one requires a reliable partner to help you through the process, ensuring that you unlock the true power of data to drive tangible business results,” says Nandakumar Sivaraman, AVP – Data Engineering & Enterprise Architecture, Bridgenext.

Now that we understand the initial capabilities of Snowflake in consolidating and managing diverse datasets, let’s explore four data strategies to drive impactful outcomes within the retail sector.

Strategy 1: Enable Personalization through Data Segmentation

Consumers expect a seamless, personalized shopping experience across all channels. By utilizing advanced analytics and visualization tools, retailers can tap into the power of data lakes, which act as a central storage location for all raw data, regardless of format or structure. This eliminates data silos and facilitates comprehensive analysis. Targeted marketing campaigns and personalized product recommendations become achievable goals by segmenting customers based on insights derived from purchase history, demographics, and online behavior. Cloud-based data lakes and Snowflake further enhance scalability and cost-effectiveness, making data accessible across various departments within the organization.

According to McKinsey2, personalization marketing has real advantages for companies: it can reduce customer acquisition costs by as much as 50%, lift revenues by 5 to 15%, and increase marketing ROI by 10 to 30%.

For instance, Nike uses historical and current data to segment customers based on their purchase history and browsing behavior, allowing them to create campaigns relevant to each customer’s interests. A former Nike executive described three of the customer segments namely “The Weekend Runner, Style Shopper, and Dedicated Sneakerhead” that Nike has broken out, and the different lifestyle-targeted offerings it makes available to them. To date, Nike stands out as one of the most successful companies because of its unique customer approach and strategies. Here’s the classic example of segmentation:

Enable Personalization through Data Segmentation

Strategy 2: Optimize Inventory Management and Demand Forecasting using AI and ML

Accurate demand forecasting is crucial for retailers to avoid stockouts and overstocking. Machine learning and AI hold immense potential for retailers seeking to extract insights from data. These advanced algorithms can analyze vast datasets to identify patterns, predict future trends, and drive informed decision-making. Here are the basics of demand forecasting for retail businesses:

  • Data collection and forecasting are iterative processes: Start small and improve over time.
  • Action is key: Don’t wait for perfect data, any data is better than none.
  • Data forecasting requires a specific mindset: Actively seek data points and understand their context.

Key Points:

  • Start with basic data: Customer count, order value, etc.
  • Gradually add complexity: Seasonal trends, competitor data, and historical trends.
  • Focus on the 3 C’s: Ensure data is Contextualized, Clean, and Connected.

Thomas3, North America’s leading industrial sourcing platform shared: “It turns out many of today’s leading fast-fashion companies, like H&M and Zara, are turning to artificial intelligence to help them predict tomorrow’s trends to stay ahead of the curve, track inventory levels, analyze historical data, and understand what customer demand will look like in the future.”

Snowflake’s Data Cloud Solution delivers granular and timely insights to predict demand and optimize inventory, supply chains, and fulfillment. Once retailers have mastered optimized inventory management and demand forecasting using AI and ML, they can seamlessly transition to streamlining operations for improved efficiency. Let’s explore how retailers can harness the power of data to enhance their operational efficiency and drive business success.

Strategy 3: Elevate Omnichannel Experiences via 360-degree Customer View

To cultivate seamless omnichannel experiences, retailers must enrich their understanding of the 360-degree customer view by:

  • Gathering comprehensive customer data from online & offline sources: Online transactions, social media interactions, forums, physical interactions, third-party engagements, customer support, and service.
  • Aggregating the customer 360 data points: Data Lake and warehousing platforms like Snowflake play a pivotal role in combining various data—whether structured, semi-structured, or unstructured—into an SQL data warehouse.
  • Ensuring seamless integration: Integrate data with top-tier marketing, customer experience, business intelligence, machine learning, and data management platforms.

By prioritizing a frictionless customer journey across all channels, from online browsing to in-store interactions, you can foster brand loyalty, increase conversion rate, and ultimately drive significant return on investment. To achieve this, a Customer Data Platform (CDP) acts as a central hub, an all-in-one platform for marketing teams as a unified customer database that is accessible to other tools and systems across the organizations. However, as customer data needs evolve, traditional CDPs might become rigid. This is where composable data platforms come in. These platforms offer modular solutions for data collection, storage, and activation, empowering scalable strategies aligned with business growth and changing customer expectations.

For example, Marks and Spencer, a major British multinational retailer shared4 its omnichannel vision and its strategy to leverage data better to connect customers to M&S. Katie Bickerstaffe, Co-CEO at M&S, reset her leadership team to bring together the capabilities and skills required for M&S to become a world-class omnichannel retailer with data at the heart. She later added “While our customer data platform enables us to leverage data to create an omni-channel experience, it’s also critical to personalizing our marketing to deliver increased frequency and spend. This will help us bridge the gap in our profitability with our key competitors. We’re starting to generate substantial value from the customer data engine through personalizing offers and product recommendations, repeat purchase recommendations, and using personalized language.”

Setting competitive prices while maximizing revenue requires retailers to adapt to real-time market dynamics. Retailers can implement dynamic pricing strategies by leveraging data on competitor pricing, market trends, and customer behavior. Amazon’s use of data-driven dynamic pricing is a prominent example, showcasing the effectiveness of this approach in maintaining competitiveness and maximizing profit margins. Here’s how it works:

  • Imagine you’re browsing for running shoes online. One retailer might use historical sales data to adjust prices based on seasonality, so a popular model might be slightly more expensive during peak training months.
  • Additionally, real-time competitor pricing data can be factored in. If a competitor has a similar shoe on sale, the first retailer might dynamically lower their price to stay competitive.
  • This same strategy can be applied in-store. Imagine a carton of milk nearing its expiry date. A dynamic pricing system can automatically reduce the price to clear stock and avoid waste, while still generating revenue.

These are just a few examples of how data empowers retailers to personalize pricing for each customer and product. Retailers equipped with robust data initiatives thrive, while those burdened by outdated infrastructures struggle to keep up. Snowflake Retail Data Cloud platforms unlock your data insights into customer needs, enhancing your brand’s relevance and delivering seamless experiences. Its data cloud empowers retailers and manufacturers to effortlessly access, govern, and share data for personalized customer experiences, streamlined supply chains, and informed merchandising decisions.


Retailers that embrace a data-driven approach can unlock valuable insights, personalize the customer experience, optimize operations, and ultimately achieve significant business success. Snowflake’s advanced technology, coupled with Bridgenext’s team of skilled Snowflake engineers, provides a powerful solution for retailers seeking to harness the full potential of their data.

With Bridgenext’s expertise, retailers can maximize the benefits of Snowflake Retail Data Cloud, ensuring seamless integration, efficient data management, and informed decision-making. Together, retailers can navigate the complexities of the modern retail landscape and drive significant business results through data-driven strategies.







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Topics: AI and ML, Brand Strategy, Branding, Customer Experience (CX), Data & Analytics, Digital Realization, Digital Strategy, Digital Transformation, Gen AI, Marketing Strategy, Personalization

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