Quantifying 'growth friction' when projecting target goals
As part of my DS work I spend some fraction of my time helping the team make growth projections, either for setting growth targets or when forecasting actual data.
There is obviously a range of ways to go about doing this but the one thing I don't have a good solution for at the moment is being able to fold in or at least quantify how much harder it is to grow in a market this year as opposed to historical years. It is easy to anchor growth on historical rates but in markets at which you've got a solid penetration already and are perhaps heading toward saturation it is likely a harder task to maintain a historical level of growth than it was to achieve that level the first time around.
I've thought about a few approaches that basically involve taking the derivative of a growth curve to look at the rate of change of growth, but it is hard to know if that is really probing the truth of how much harder it is to grow, or if its just that we're doing a worse job and growing more slowly!
Does anyone have a good approach to either measuring some sort of 'growth friction' for a current year vs historical years, or perhaps some other forecasting technique that allows information about this to be folded in somehow?
Topic forecast
Category Data Science