An e-commerce business generates data all day long: every order in Shopify, every email or SMS sent from Klaviyo, every customer who comes back or leaves. The problem is that this data lives on separate platforms and rarely turns into decisions in time.

With Parallel Analytics, we went beyond connecting tools. We served as active business data analyst and data architect, modeled the company's sales system in Snowflake and productized the data: we turned it into an advanced reporting service available to the market, so decisions happen right away, not weeks later.

Parallel Analytics faced a common e-commerce challenge:

• Sales in Shopify, marketing in Klaviyo: orders, products and customers on one side, and campaigns, flows and engagement on the other, with no combined view.
• Business questions without fast answers: Which campaigns drive real sales? Which customers buy again? Which products drive growth?
• Manual reporting: consolidating information from several platforms took time and produced inconsistent numbers.
• Data with no market value: the information existed, but it wasn't packaged as a product others could use to make decisions.

This project required three things at once: data modeling, deep business understanding and close client management. That's why we took on two complementary roles:

• Business data analyst: we learned how the company sells, what decisions each team makes and which metrics really matter, and translated those questions into data requirements.
• Data architect: we designed the sales system's data model in Snowflake, from source integration to reporting-ready tables.

1. Source integration

We brought Shopify transactional data (orders, products, customers and returns) and Klaviyo marketing data (campaigns, automated flows, sends, opens and clicks) into Snowflake, with automated, consistent loads.

2. Sales system modeling

We designed a sales data model that joins both worlds around the customer and the order. It is a single source of truth where each metric is calculated once, the same way, for everyone.

3. Advanced reporting

On top of that model we built advanced reporting to answer the questions that drive an e-commerce business:

• Sales by channel, product and period.
• Revenue attributed to email and SMS campaigns and flows.
• Repeat purchases, customer cohorts and customer lifetime value (LTV).
• Average order value and customer segmentation by purchase behavior.

4. Data productization

The step that made the difference: we turned the data into a product. Reporting stopped being an internal deliverable and became a data service (Data as a Service) available to the market, ready for users to make immediate decisions.

1. Joining two different logics: Shopify thinks in orders, while Klaviyo thinks in contacts and events. Connecting them required clear customer identity rules.
2. Business definitions: agreeing on what each metric means (sale, repeat customer, attributed revenue) before calculating it.
3. From project to product: designing the model from the start with the idea that others would use it as a service.
4. Client relationship: keeping the business involved in every decision so the solution met its real needs.

• Shopify and Klaviyo in one view: sales and marketing finally connected in Snowflake.
• A single source of truth for the sales system, with consistent, auditable metrics.
• Advanced reporting that answers in minutes questions that used to take days.
• Data turned into a product: a data service available to the market for immediate decisions.

Productizing data means treating it as a product, not as a byproduct of operations:

1. Start from the decisions: define which questions the data must answer before modeling it.
2. Model a single truth: unify sources into a consistent, governed data model.
3. Package it for the user: deliver data in a format anyone can use without relying on an analyst.
4. Run it as a service: keep it up to date, reliable and available to whoever needs it.

Our work with Parallel Analytics is the foundation for several of our services:

• Agentic BI: on a sales model like this one, an AI agent answers business questions in natural language, such as "Which campaign drove the most repeat purchases this month?", with exact figures.
• Data for AI: data architecture, modeling and integration in Snowflake and other platforms, so analytics and AI run on reliable information.
• Agentic Commerce: with Shopify and Klaviyo data unified, an agent can recommend products, recover carts and personalize campaigns with real customer information.
• AI Consulting and ROI: identify which business decisions gain the most from data and AI, and turn them into a measurable plan.

Do your sales and marketing data live on separate platforms? Let's talk and turn them into decisions.