We started with a metrics workshop to understand exactly what insights would drive their business forward. From there, we built a complete data foundation designed to serve Expandi's current and future needs.

At the core, we implemented a medallion architecture that progressively refines data through bronze, silver, and gold-standard datasets. This ensures data quality and reliability at every level of transformation.

A key component was our approach to modeling B2B relationships. Since companies pay for users in Expandi's business model, we needed to capture both company-level actions (like invoice payments) and individual user behavior. Our activity schema handles this complexity, enabling insights at both account and user levels.

With this foundation in place, we:

• Set up automated quality checks with Slack notifications
• Enhanced their customer data with enrichment sources (boosting coverage from 30% to 90%)
• Built specific data marts for different teams (Revenue, Customer Success, Product)
• Connected everything through Looker Studio Pro for reliable reporting

The entire system is organized using a star schema, making it both powerful and accessible. Teams can access the data they need without requiring deep technical knowledge. This structure serves as the foundation for all analytics at Expandi, supporting both daily operational decisions and strategic analyses.

The transformation gave Expandi:

1. One source of truth for all their metrics (no more debating about numbers)
2. Clear insights into customer health and revenue patterns
3. Rich customer data that helps them understand their market better
4. Custom views for different teams (Customer Success owners got their own insights)

Expandi went from drowning in data to surfing on insights. What's particularly exciting is how this has transformed their daily operations - teams now have the information they need at their fingertips, enriched with meaningful context about their customers and market. If you're sitting on a mountain of data but can't seem to extract valuable insights from it, we should talk. Sometimes you don't need more data - you just need to make better sense of what you already have.