Why is business modeling such an important key factor when raising capital

Sameer Jagetia is one of grIPs senior advisors and his specialty lies in the practical integration of financial analysis with business realities. Sameer blends his finance expertise with operational experience to provide a quantitative strategic overlay to businesses. Having successfully guided businesses through their lifecycle to successful exits, he knows exactly what the real challenges companies face. His operational strategy is rooted in a quantitative, iterative approach. He will work with his clients to identify, measure, and track the critical drivers and leverage points that have an outsized impact. As more data becomes available, he iterates based on the results, investing more in what is working and refining other areas. One area, in particular, is optimizing each part of a sales funnel by identifying, measuring, and tracking key metrics between a Sales Qualified Lead (SQL) and a Closed-Won deal.

What are investors looking for when looking at business models?

Investors' criteria vary depending on the company's stage. For early-stage companies, investors value founders who clearly articulate the business problem they are solving and the economic drivers behind them. Essentially, they want to know if there is a problem worth solving and who captures the value.

Expanding on the economic drivers, investors care about identifying and concentrating on the KPIs or levers of the business that have the greatest impact. This isn’t a mere checklist of KPIs. While these KPIs are often embedded in the financial model, the actual spreadsheet for the model itself is secondary to using it as a tool to clarify and structure strategic thinking. 

For SaaS companies in particular, the key metrics will always include Customer Lifetime Value (LTV), Average Selling Price (ASP), Customer Acquisition Cost (CAC) and churn rate. Investors will look for thoughtful expectations around these KPIs, such as setting CAC expectations based on a pilot marketing test, comparison data from similar companies, and industry averages. For later-stage companies, the interplay between these metrics and growth will be important for investors to see if future customer cohorts generate the same unit economics.

What mistakes people usually make when building their financial models?

The most common mistakes in building financial models fall into two categories. First, some models are overly complex and detailed, making them cumbersome to use and update. These models often become static documents in a data room rather than an active tool for decision-making.

Second, some models fail to represent the main drivers of the business accurately. For example, hard-coding revenue figures without presenting what drives revenue is a critical oversight. To be effective, financial models should reflect fundamental drivers, such as price and volume projections, that are actionable items for business decisions – such as adjusting production schedules for physical goods or the number of salespeople for service-oriented companies. 

One revealing example of a flawed model I reviewed had a combination of both errors. The model had many assumptions and drivers, yet it was more complex than necessary for the business stage. However, that wasn’t the biggest issue. The irony was all the detail accounted for 10% of the revenue projections. The remaining 90% of revenue was modeled based on growth rates with limited thoughts about the key levers to drive growth. The disconnect between the story and the numbers highlighted the lack of strategic thought in the overall business strategy. 

The common thread among ineffective models is that they are not usable for decision-making. These modeling errors can lead to misguided business strategies and missed opportunities for growth. 

How often do companies change their financial models? 

Financial models should be regularly updated with current data, ideally monthly, to ensure they accurately reflect the latest business performance. This involves inputting actuals and verifying that the modeled assumptions and relationships remain valid, ensuring that it is still a reliable tool for decision-making. 

Structural changes to the model should be made as necessary based on significant changes in the business. Companies experiencing a change in strategy, such as entering new markets or launching new product lines, may need to revise their models more frequently. Conversely, stable businesses with consistent drivers may only need to update their models periodically. 

The key theme is ensuring the financial model represents the business strategy and remains useful for business decisions.