Retail and consumer goods organizations are operating in an increasingly complex environment. Customer expectations are higher than ever, channels continue to multiply, and margins remain under constant pressure. Decisions around marketing, merchandising, pricing, supply chain and customer experience must be made faster — and with greater confidence — than in the past.
To support these decisions, retailers collect massive volumes of data across physical stores, ecommerce platforms, marketing systems, customer service tools and third-party partners. Data now informs nearly every part of the business, raising expectations for speed, accuracy and alignment across teams.
Yet despite this abundance of data, many retail organizations still struggle to turn information into shared, actionable intelligence.
While these challenges exist across retail broadly, ecommerce teams often feel them most acutely. Digital channels generate high-velocity data, require constant experimentation and demand near real-time decision-making across marketing, merchandising and customer experience.
Campaigns launch and iterate quickly. Customer behavior shifts in days, not quarters. Performance signals arrive from dozens of platforms at once. In this environment, the limitations of static dashboards, delayed analysis and siloed insights become immediately visible.
Ecommerce has become the proving ground for new approaches to intelligence — exposing the gap between data collection and decision-making faster than any other part of the business.
Most ecommerce organizations operate with highly fragmented data environments. Audience and transaction data is spread across ad platforms, ecommerce systems, CRM tools, customer support applications and analytics stacks. Performance insights are often siloed by function, with marketing, customer service, product and leadership teams working from different metrics and definitions.
At the same time, critical business context lives outside traditional analytics systems. Campaign postmortems, customer feedback, product launch retrospectives and operational insights are buried in spreadsheets, decks, emails and Slack threads. While this institutional knowledge shapes decisions every day, it remains difficult to analyze or connect to structured data.
The result is an intelligence gap: Teams spend more time reconciling numbers and searching for context than acting on insights. Decisions slow down, trust in data erodes, and opportunities to improve customer experiences or drive growth are missed.
Leading retail organizations are beginning to close this gap by enabling teams to work with data and context together, rather than separately. Instead of relying on static dashboards or one-off analyses, business users can explore data dynamically, ask complex questions in natural language and understand not just what happened — but why.
Decile provides a concrete example of how this shift is playing out in ecommerce. As an AI-powered analytics platform serving ecommerce brands, Decile brings advanced analytical capabilities directly to business users who need to move fast. By unifying customer, campaign and commerce data within its platform, Decile enables teams to uncover insights and take action without waiting on analysts or navigating increasingly complex dashboards.
This approach allows ecommerce organizations to act while moments still matter, but making it work requires more than access to data alone.
Some of the most valuable insights in retail and ecommerce don’t live in databases at all. They live in the experience of teams and the artifacts they create, such as:
Campaign postmortems explaining why initiatives succeeded or failed
Customer service notes revealing recurring product or usability issues
Merchandising and pricing decisions shaped by historical context
Analyst commentary and internal discussions that influence strategy
Without a way to connect this knowledge to data, organizations repeatedly relearn the same lessons. Bringing structured data and institutional knowledge together allows teams to act with greater confidence, consistency and speed.
As retail teams move faster, they need a more intuitive way to work with data. Instead of navigating dozens of dashboards or submitting requests to analytics teams, business users increasingly want to ask direct questions and receive trusted answers immediately.
Snowflake Intelligence is designed to help teams explore both structured and unstructured data using natural language, while preserving governance, consistency and transparency. Teams can reason across data and context together — without bypassing the controls required to maintain trust.
This helps make intelligence accessible to the people making decisions every day, not just the teams building reports.
Decile illustrates how Snowflake Intelligence capabilities can be delivered to ecommerce teams through a retail analytics platform.
Decile is an AI-powered analytics solution designed to democratize access to insights across ecommerce organizations. Built on Snowflake Cortex AI, Decile enables brands to move beyond predefined dashboards and static segments, giving marketers, executives and analysts the ability to explore data and take action more quickly.
Before adopting this approach, Decile’s solution relied on standardized dashboards, predefined segmentation and activation workflows. While effective for common use cases, this model struggled to scale. Custom requests increased, analytics teams became overloaded, and customer success teams spent significant time translating reports into insights.
Decile needed a way to empower users to move faster without creating additional complexity. That meant finding a solution that could understand user intent, operate on the correct data model, generate transparent SQL and deliver explainable results grounded in trusted data.
By building on Cortex AI, Decile embedded agent-driven intelligence directly into its platform. With data already governed in Snowflake, Decile leveraged existing semantic descriptions, security policies and metadata to stand up agents quickly and safely.
Decile began by piloting agents with a small group of customers, using semantic views seeded with existing documentation and lightweight instructions. Over time, it scaled this approach by automating semantic resource generation and embedding agents directly into its application.
Customers could ask questions such as:
“How is my new product performing?”
“What percentage of orders came from this email campaign?”
“Which customer segments are driving the most revenue this week?”
In response, agents delivered not only results but context — explaining why patterns emerged and how conclusions were reached. This transparency was key to building trust among nontechnical users.
The impact was immediate. According to Decile, within the first week of launching its agent to the pilot customer group, more than half of brands opted in to use it. For those customers, approximately 75% of previous support requests were handled by the agent, freeing teams to focus on higher-value work.
“Snowflake Intelligence is giving our clients access to insights they didn’t have the resources to pull previously,” says Hailey Sims, Senior Product Manager at Decile. “Seeing the agent’s thought process and building trust in it was key during the testing.”
The Decile example highlights more than a successful platform implementation. It points to a broader shift in how retail organizations are approaching intelligence.
What’s emerging is the Snowflake Intelligence model: enabling teams to bring together structured data, institutional knowledge and agent-driven reasoning to support faster, more confident decisions without sacrificing governance or trust.
Across retail and consumer goods, this shift signals that:
Intelligence is moving closer to the business user
Trusted, explainable answers can help reduce reliance on analytics bottlenecks
Context matters as much as metrics
Governance and transparency remain nonnegotiable
Decile shows how these capabilities can be delivered through a retail platform. More broadly, it reflects how retail organizations are rethinking how intelligence supports decision-making.
Snowflake Intelligence is Snowflake’s enterprise intelligence experience designed to help business and technical teams turn data and institutional knowledge into confident decisions. It brings together structured and unstructured data, applies enterprise semantics and uses AI agents to reason across that information in a governed, transparent way.
Snowflake Intelligence is built on Snowflake Cortex AI, which provides the underlying AI, agent and semantic capabilities. While Cortex AI enables organizations and partners to build custom agent-driven experiences, Snowflake Intelligence delivers these capabilities out of the box as a trusted, decision-centric experience within the Snowflake platform.
Decile demonstrates one way Snowflake Intelligence capabilities can be delivered to ecommerce teams. Snowflake Intelligence itself offers these same capabilities directly to retail and consumer goods organizations looking to operationalize insight across marketing, customer experience, merchandising and leadership workflows.
Beyond ecommerce, Snowflake Intelligence enables a range of high-value use cases across retail and consumer goods:
Marketing performance and personalization: Optimize spend, targeting and messaging by analyzing campaign performance and audience behavior while campaigns are still active.
Customer experience and retention: Combine transaction data, customer service interactions and feedback to identify recurring issues and reduce churn.
Merchandising and assortment planning: Understand product performance across stores, channels, regions and segments, and connect quantitative metrics with qualitative insights from reviews and returns.
Supply chain and inventory decisions: Bring together demand signals, inventory levels and operational context to support more responsive planning and fulfillment.
Executive and cross-functional decision-making: Provide leaders with trusted, explainable answers that reflect a shared understanding of data and context across the organization.
In each of these scenarios, Snowflake Intelligence enables teams to do what was historically difficult or impossible at scale: reason across data and context in real time and act with confidence.
When retail organizations can move faster from data to decisions, they’re better equipped to act while moments still matter. Faster insight leads to better customer experiences, stronger alignment across teams and more durable relationships built on trust.
By enabling teams to reason across governed data and institutional knowledge, Snowflake Intelligence helps retail and consumer goods organizations scale insight securely and confidently, turning data into decisions, and decisions into long-term advantage.
If you’re ready to learn how you can use Snowflake Intelligence to empower every user in your business to get data-driven answers to complex questions in natural language, watch our on-demand virtual event, Snowflake Connect: AI.
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