The most accurate and reliable financial projections have a secret ingredient

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Having worked with startups and corporate innovations for many years, and developed the financial projections for those efforts, I have discovered that the architecture used to developed top line revenue or unit growth can have a big impact on the reliability of said projections. Typically, if a projection is unrealistic it is because the projected growth is unrealistic or inconsistent.

For the architect of the financial projections, growth is typically estimated by calculating a unit growth percentage which is either applied consistently across all units of time (ie months) or in some chunked time block (ie quarters) or each month is given an individual input (ie 60 inputs for a 5 year projection)

Once this architecture is established, the architect will then try to massage the numbers to achieve a realistic growth curve. If the architecture is based on growth over chunked blocks, the growth curve is “chunky”, which is not realistic. If the architecture is based on one consistent growth percentage, then the growth curve is linear, which is also not realistic. If the architecture is base on monthly inputs then the curve needs to be manually constructed using a large number of inputs, and what typically emerges is a curve similar to this.


The solution to creating consistent and realistic growth projections is to leverage logistic growth curves which have been developed to plot realistic growth over time. There are a number of functions that are used based on the nature of growth, but a common on is the Gompertz function. The Gompertz function assumes a point of maturity, a point of slow early growth and a point of rapid growth, which describes many startups or corporate ventures. With a Gompertz function, the rate of growth and the timing of growth can all be manipulated by tweaking several function inputs rather than dozens of inputs.

A typical Gompertz growth curve might look like the following;


To validate the Growth curve, it is best practice to calculate other benchmark metrics to insure the scale and pace of growth is realistic. For example, if you plot final membership using the Gompertz curve, you will also want to calculate the number of new members per month that this represents.

If you want to play around with what is possible with growth functions, check out this little flash tool for playing with the Gompertz and Lognormal function.

Curves Image

If you have other favorite growth functions you use for projections I would love to hear about them!

If you are interested in getting help with developing robust financial projections or models, contact us.


About Brian

Innovation & Strategy Specialist; Focus on Innovation Roadmapping & Business Modeling; Husband, Father, Systems Thinker, Babson MBA, Triathlete. View all posts by Brian →

One Response to The most accurate and reliable financial projections have a secret ingredient

  1. Eliran Boraks says:

    Interesting perspective, the Gomperz function simulation tool seems like a great way to experiment and come-up with a reasonable estimate.

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