How Aestuary Built a Unified Data Foundation Across Multiple Storefronts and Marketing Channels

Key Metrics

Industry	Ecommerce / Direct-to-Consumer
Company	Aestuary, Inc.
Service	Data Warehouse Consulting
Tech Stack	WooCommerce, Shopify, Klaviyo, Fivetran, AWS S3, Snowflake, dbt
Challenge	Multiple storefronts, sales channels, and marketing platforms operating in silos with no shared analytical foundation and no way to connect marketing spend to order outcomes
Outcome	A unified Snowflake data warehouse with dimensional models covering orders, product, customer, email and SMS campaign performance, and paid social, queryable from a single environment

The Business Had Outgrown Its Data Infrastructure

Aestuary, Inc. is a direct-to-consumer ecommerce business operating across multiple storefronts and online marketplaces. Order and product data was generated across several distinct sales channels, with fulfillment handled through third-party logistics providers. Marketing ran across email and SMS and paid social.

Each platform held a piece of the business. None of them talked to each other.

The Challenge: No Way to Connect Marketing to Orders

The core problem was attribution. Aestuary ran active email, SMS, and paid social campaigns, but the data those campaigns generated lived in separate marketing platforms, independently of the order data living in the commerce platforms. There was no infrastructure that connected a campaign touch to a placed order, a product view to a purchase, or ad spend to revenue.

Fulfillment data added another layer of complexity. Shipping and delivery records held no structural relationship to the order records in the commerce platforms. Reporting on the full order lifecycle, from marketing touch through fulfillment, required pulling from multiple systems and assembling the result manually.

Operating across multiple storefronts compounded the problem. Each one had its own data footprint, and there was no environment where order and customer data could be examined together across the business.

The Solution: A Multi-Source Warehouse With a Full Dimensional Layer

Data-Sleek designed and built a Snowflake data warehouse that consolidated revenue, marketing, and fulfillment data into a single analytical environment, then built a dimensional transformation layer in dbt that made the data queryable for business analysis.

Fivetran handled connector-based ingestion for the commerce and marketing platforms with native connector support, landing raw data into Snowflake staging tables. Sources without native Fivetran connectors were pulled via custom API integrations that routed data through AWS S3 before ingestion into Snowflake.

The dbt transformation layer built the analytical models on top of the raw data. The order model centered on a FACT_ORDER and FACT_ORDER_LINE_ITEM structure, with dimensions covering date, customer, product variant, discount, and revenue channel. Additive and non-additive measure logic was resolved at the model level, so aggregations across order lines produce accurate totals without double-counting at the order level. Order data from multiple storefronts lands in the same model, with channel source tracked through a dedicated revenue channel dimension.

The paid social model built a fact table against dimensions for campaigns, ad sets, ads, and accounts, with impressions, clicks, spend, and reach available at each level of the campaign hierarchy.

The email and SMS marketing model is the most analytically rich in the warehouse. It captures engagement at the person, campaign, and flow level, with revenue attribution fields available to connect campaign activity to order outcomes.

The Results: A Foundation Where Marketing, Orders, and Customer Data Can Be Analyzed Together

Before this project, answering a question about which campaigns drove orders required manually pulling data from separate marketing and commerce platforms, then reconciling independent data structures by hand. Cross-channel analysis was not possible at the level the business needed. Fulfillment data existed separately from order data with no structural connection between them.

The order model covers multiple storefronts in a single structure, and the marketing model’s revenue attribution fields connect campaign activity to order outcomes without manual reconciliation. Questions about customer behavior, product performance across channels, or discount impact across the business are now queries rather than multi-system extraction projects.

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The full order lifecycle, from first marketing touch through placed order to fulfillment, is represented in the warehouse for the first time. That foundation is what makes retention analysis, attribution modeling, and cross-channel performance reporting structurally possible.

Discover how a unified data foundation can connect your marketing spend to order outcomes. Book a free consultation with Data-Sleek today.

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