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Home > Analytics > Financial Forecasting When Modeling with Missing Data

Financial Forecasting When Modeling with Missing Data

February 10, 2020

The partner at my previous venture capital firm used to tell me that financial forecasting for startups should start at a granular level. Coming from a macro hedge fund background, I initially did not understand what that meant, as I was used to forecasting based on trends, recurring revenue, and benchmarks. But these are mostly unavailable for startups. Often, it is even unclear what the target market is, let alone the revenue that it can capture.

It is difficult to convince someone of something that isn’t there. Founders usually sleep on an idea for six or more months before approaching their first investor. By this point, they would have simulated more than a thousand different scenarios and models in their minds with a grand vision of what they will achieve in 10 years’ time—only to get frustrated that investors do not conclude on the same vision. They often forget that they need to handhold investors through the same journey that they have been through themselves, albeit via a shortcut, to reach the same grand vision. The only way to achieve that is through facts and logic.

It is true that investors do not have the time to look through too much detail in the first meeting. That’s why techniques are created to capture their attention – from an elevator pitch, a 15-minute teaser, to an hour-long presentation. Eventually, investors still need to scrutinize the plan to ensure that the founder’s vision is real and the underlying assumptions are plausible.

A financial forecast is like a map that leads investors to the end goal. Most forecasts fail because they automatically assume the ability to capture one or more percent of the market or a 100-percent monthly growth rate without detailing the strategies and assumptions that will get them there. A financial model for startups needs to be logical and plausible; it needs to align with strategy and it needs to be granular with no missing steps from points A to Z.

To continue reading the full article, click here: https://www.toptal.com/finance/financial-forecasting/granular-financial-forecasting

Posted with permission from Toptal.

Filed Under: Analytics, Blog, Finance, Forecasting Tagged With: analytics, finance, forecasting, missing data

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