Discovering Revenue Opportunities through Context-driven Unit Economics

As a software company specializing in the long-term care sector, we aimed to optimize our hosting costs and uncover revenue opportunities through a context-driven unit economics approach.

Background

We initiated our journey by concentrating on our “cost per bed,” as this metric aligned perfectly with our core business model. By adopting this approach, we gained a size-independent understanding of our hosting costs, which enabled us to identify areas for cost-efficiency improvements, regardless of scale or hosting environment.

Data Sources

We used the following data sources to inform our processes and workflow:

• We harnessed data from our cloud database administrators (DBAs), providing valuable insights into the number of beds per customer and individual database sizes.
• Detailed revenue data, categorized by customer, product, market segment, and other dimensions, was sourced from our Finance team.
• Pricing and packaging data allowed us to analyze revenue in terms of product groups and associated package prices.

Audience

These personas have played a role in the evolution of our Unit Economics analysis. Each group had a specific interest in our analysis, from optimizing cloud infrastructure to helping us align pricing models with hosting costs.

• Finance analysts,
• Database analysts,
• Cloud team leadership,
• Sales, and
• Customer Success teams

Maturity Levels

Our journey began at the “Crawl” level, with manual ad-hoc cost reconciliation. However, we progressed to the “Walk” level, where key stakeholders now understand cloud costs in terms of units of business value and can pinpoint cost-revenue misalignments.

This user story encapsulates our quest to optimize costs and enhance revenue streams through a context-driven unit economics approach, ultimately benefiting our organization and customers.

Executive Sponsorship

We enjoyed support from top-level executives, including the SVP of SaaS Ops and SVP Engineering, ensuring the success of our FinOps unit economics initiative.

Interoperability and Automation

To overcome data disparities between DB hosting and revenue data, we created a mapping table to match entries. Any mismatches were reviewed and addressed as needed. We also streamlined data collection and processing, reducing manual effort through ongoing automation efforts.

Business Impact

Our cloud leadership gained invaluable insights from the cost per bed analysis and similar metrics, identifying areas for investigation where efficiency expectations were not met. Our cost-per-GB analysis for DB-oriented products uncovered opportunities to recover revenue, which is projected to total over $250,000 USD in regained annual revenue (based on a revised product pricing model), when aggregated across all customers.