Project funding goals are kind of arbitrary since budgeting is hard. Projects might not know what goal works best for them.
So i had the idea that the funding goal is automatically set and adjusted by the system.
It might be that there is only one best option when and how to change the funding goal. We would need research on that. And if so, it would not make sense to let projects choose a worse goal.
So let’s see the dynamic:
We need to research which difference from currently-funded to funding-goal has the maximum crowdmatching effect (visitors are encouraged to become patrons). When the difference is very high (100$ funded but goal is 10.000$), contributing seems pointless. When the difference is very low (9.900$ funded from 10.000$), it’s very encouraging, but it will be 100% funded before the payout, so there is a time without the crowdmatching effect.
Also, we want projects to get funded, so the might not let the funded % drop below 50% (?).
So, how could it look like:
- A new project is created at the start of a month. Starting funding goal is 100$.
- it slowly get’s more and more patrons. they finally reach the goal after 3 months
- the system adjusts the goal to 200$. they reach the goal in 1 month
- the system adjusts the goal to 300$. they reach the goal in 1 week
- the system adjusts the goal in a way that it is likely that they reach 100% at the end of the month (when the money it payed out), but not more than 100%. in this case, the historic data shows a growth of say 100$ per week, so the goal is set to 600$. at the end of the month 700$ of 600$ goal is funded. so 100% of the pledge is transferred. the goal is not changed when the 600$ goal is reached, but after the payout (so it don’t drop below 100%)
Now assume a stagnating project
- the funding goal is 1000$ and it is 900$ funded. it looses 50$ more funding in the month. so the system adjusts the goal down to 900$.
so while a project loses more and more funding, they still get say more than 80% of the pledges and new visitors have high motivation to become patrons, since it looks like it will reach 100% soon. so the project has a good chance the funding grows again, when they do good work.
So we need an algorithm, that sets a new funding goal after the payout which tries to get the project 100% funded at the next payout, but maximize getting more patrons in the mean time by letting the funded% not drop below 50%. It should also not get more than 110% funded.
We see that the algorithm is successful, when a growing project mostly get’s 100% of the pledges (that is the goal of the project and the patrons), but also get most of the potential new patrons (we have to track the conversion rate of visitors). A stagnating project still get’s 80% or more.
So the algorithm is optimizing funded% at payout AND new patrons. Simple min-max strategy. It’s probably simple machine-learning forecasting and not even deep learning, but maybe that is usefull too. Data is the historic projects funding, also from all projects and new patron conversion rate of all projects and from the project itself weighted more. Maybe a strategy work for one project, but not for another.
With this, we would offer very simple and efficient funding, since the project just has to create account, setup payout, create project and continue working on the project. We acquire the maximum potential funding for them.