Revenue Variance

When doing any price estimation, the best or the “most likely” model is not enough to get a reliable benchmark of what can be expected. This section will explore how changes in some of our assumptions might affect the final token price.

Revenue estimations are just that - estimations. We need to take into account that those numbers are likely to vary a lot when the real business commences. Using a beta distribution (a fairly standard approach for modeling of uncertainty) we will model the expected range of achieved revenue and, by extension, the expected range of the token price.

We will operate under the assumption that the company will achieve 200% of its projected revenue in a best-case scenario. In contrast, it will achieve only 50% in the worst-case scenario while still keeping the most likely scenario at 100% revenue target achieved.

A cumulative probability function, modeling the expected range of variation for the company's revenue

A discrete probability function, modeling the expected range of variation for Favrr's revenue

Using the above discrete probability, we can estimate the range in which we expect the token price to fall each year.

The projected token price range by year, based on the projected token price (revenue-based) and the revenue sensitivity estimations