The model looked clean. Two costly mistakes reset pricing and slowed hiring.
Success Story · Financial Modeling · SaaS · Growth-Stage
A US growth-stage SaaS business asked EasePro to review the financial model it used for board reporting and an upcoming fundraise. The model looked clean on the surface, but two costly mistakes were hiding underneath. First, it assumed cloud hosting costs would rise in a straight line with revenue — in reality those costs climb in steps and accelerate as usage grows, so the forecast understated them badly the further out it ran. Second, real sales and marketing costs — sales salaries, advertising, and content — had been parked under general expenses instead of counted as the cost of winning a customer, which made customer acquisition look far cheaper than it was. Once we corrected both, the model showed the true economics of the business. The company reset pricing on two products, slowed sales hiring, and shifted its focus toward higher-margin customers. EasePro continues to maintain the model on retainer.
The ChallengeA clean-looking model hiding two errors — the kind that grow worse the longer they go unnoticed.
The model in front of the board worked. It matched the historical numbers and produced forward-looking projections. But four problems were hidden underneath, and the upcoming fundraise was going to expose them.
- Cloud costs forecast to rise in a straight line. The past AWS hosting bills had been recorded correctly. But the forecast assumed hosting costs would grow in direct proportion to revenue — when in reality they climb in steps and speed up as usage grows, and they lag when extra capacity is added ahead of demand. The further out the forecast went, the more it understated cloud costs.
- The true cost of winning a customer was hidden. Sales salaries, advertising spend, and content marketing had been recorded under general expenses instead of being counted as the cost of acquiring customers. That made customer acquisition look far cheaper than it really was — a gap any investor would flag in the first hour of due diligence.
- The per-customer economics were built on the wrong numbers. With both cloud costs and acquisition costs wrong, the profit the model showed on each customer was too optimistic across every part of the business. Decisions about which customers to chase, which products to push, and where to hire were all being made on numbers that didn’t reflect reality.
- The same model was driving both board reporting and the fundraise. One model was doing double duty, and two sophisticated audiences were about to lean on it hard. Catching the mistakes before the raise began was the difference between a controlled reset and a credibility problem.
The OutcomeWhat changed once the model showed the real numbers.
The corrected model now sits at the centre of monthly board reporting. The new pricing has been in the market for two months; slowing sales hiring has improved how quickly the company earns back what it spends to win a customer; and the business is shifting toward its higher-margin enterprise customers. The materials investors will want are kept up to date as a living document, rather than thrown together in a panic the week before the fundraise. The company’s own controller keeps the data current and matched to the actual books, while EasePro keeps the model accurate and runs the what-if scenarios.
“The model running before EasePro produced numbers we could put in a board pack. The model running after EasePro produces numbers we can put in front of a Series B lead and defend line by line. That’s the entire difference.”
Figures shown are illustrative — based on a real engagement, anonymised and rounded for client confidentiality.