[ig1]

The underlying data might be different but the process is the same, you need to figure out what are the contributing factors, how they relate and establish an upper and lower bounds for the values you're assuming.

Once you have data you can make corrections to those bounds, but other than that the process is the same.

It's a skill that a lot of first time startup founders lack. They have no-idea how to estimate the market size for their startup, you need to understand the process of how to build an estimation model.

[Ziomislaw]

Process is not the same, in one case you have real data, in the other one you pull the data out of your arse.

[ig1]

It sounds like you build estimation models by looking at the data you have and combining it together to try and figure out your goal.

The disadvantage with that approach you often end up missing factors (because you don't have the data to hand) and end up with a suboptimal model.

In the same way that a lot of startups end up analyzing user behaviour by page analytics rather than user analytics simply because Google gives them page analytics.

It's a good idea to know how to do both top-down and bottom-up estimation models, as best practice is to make estimations using several different models and compare the results.