Translating Messy Data into a Strategic, Investor-Ready Tool

Our team partnered with a high-growth SaaS business preparing for a debt raise, by transforming messy data and complex business drivers into a robust, investor-ready forecast model to enable a successful debt raising and lay the foundation for long-term financial planning. The key objective was to demonstrate the strength of the company’s recurring revenue, particularly for its headline products, while maintaining credibility with potential investors through well-evidenced assumptions and a clear financial narrative. By collaborating closely with management, we delivered a tool that not only supported the immediate transaction but also empowered the business to make confident, data-driven decisions for future growth.

The Client’s Challenges

As the business prepared for a significant debt raise, several critical challenges emerged:

• Communicating a Compelling Growth Story: The company needed a detailed, bottom-up monthly P&L forecast that would clearly demonstrate the strength of its recurring revenue model and strategic priorities to potential lenders.
• Messy Historical Data: Data was scattered across multiple sources, with inconsistencies that made it difficult to present a clean, auditable financial narrative.
• Balancing Detail and Usability: The model needed to be sophisticated enough to satisfy investor scrutiny, yet practical and flexible for ongoing internal use.
• Alignment Across Teams: The model had to support scenario planning and decision-making for both management and cross-functional teams, ensuring alignment with the business’s growth ambitions.
• Tight Timelines: The transaction process required rapid turnaround, with little margin for error.

These challenges posed a risk to both the credibility of the financial story and the success of the debt raise.

Our Solution

Translating Messy Data into a Strategic, Investor-Ready Tool

We began with a comprehensive design phase, working closely with management to identify key revenue drivers, cost behaviours, and industry KPIs. Our approach included:

• Structured Data Extraction: Consolidated information from management accounts, CRM, and transaction-level data from the ERP system to create a consistent historical data cube with a clear audit trail to source.
• Bottom-Up Forecast Model: Built a detailed forecast model segmented by product, geography, and contract size, incorporating key business drivers.
• ARR Waterfall Logic: Clearly articulated ARR build up (e.g. new logo, upsell, cross sell, churn etc.), and developed a model for new sales linked to business development heads and ramp-up profiles.
• Integration of SaaS Metrics: Included key metrics such as gross retention rate, rule of 40, and sales team productivity to clearly articulate the strength of the business model and enable management to track these on an ongoing basis.
• Scenario Planning and Sensitivity Analysis: Enabled management to stress test assumptions and dynamically update the model for ongoing decision making.

Supporting Dynamic Decision-Making Throughout the Transaction

• Collaborative Iteration: Worked closely with management to update assumptions, reflect real-time trading data, and ensure the model met the expectations of both internal stakeholders and external investors.
• Usability Enhancements: Incorporated dynamic lookup tables and visual dashboards for geographical, product-level, and customer cohort analysis. This enabled both management and 3rd parties to drill into forecast assumptions with confidence, while helping management validate and communicate the growth narrative.

The Impact

• Successful Debt Raise: The model played a central role in articulating the company’s growth story and demonstrating credibility to potential lenders, resulting in a successful transaction.
• Strategic Planning Tool: Post-deal, the model was handed over to management, who now use it for investor reporting, resource allocation, and long-term growth forecasting.
• Empowered Decision-Making: Management gained a practical, flexible tool that supports ongoing scenario planning and performance analysis, driving confident, data-driven decisions.

“Transactions like these aren’t just about getting the numbers right – they’re about telling a compelling, credible growth story. By aligning our modelling approach with the client’s strategy, we helped them not only raise capital, but build a financial tool they can rely on long after the deal is done.” – Swan Partners