The Time Value of Downloads

There’s a concept in finance that blew my mind when I first came upon it— it’s called the time value of money.

Time value of money basically states that money in the present is worth more than the same sum of money will be worth in the future.

This is true because money that you have right now can be invested to earn a return, thus creating a larger amount of money in the future. With future money, there is the additional risk that the money may never actually be received, for one reason or another.

Then one also needs to contend with inflation.

So basically a million dollars today is worth more than a million dollars tomorrow.

The time value of money basically explains why we pay interest when we take a loan from the bank — we’re basically compensating the lender for the time value of money. There’s even a formula that lets you calculate the present time value of money.

Similarly, when we look at downloads for an app and cost per download from a particular marketing channel, we should factor in a time component to the cost per download.

Just like how a million dollars today is worth more than a million dollars tomorrow, a million downloads today is worth more than a million tomorrow.

This is especially useful for consumer startups, when you need to scale acquisition and getting say, a 100,000 downloads over 2 days would be way more valuable than getting the same number spread over a couple of weeks.

Getting those many users in a crunched, much shorter period of time could be the difference between unlocking liquidity in the system, initial network effects, kicking the core user loop into gear and slow death.

Someone should just come up with a formula for the time value of cost per download — this would help measure channels and their effectiveness much more effectively. Essentially, the channel that gets me a 100 users* in 1 day should effectively get a discount to the channel which gets me the same a couple of days later and should hence be the better channel cost-wise.

*assuming the quality of users are the same across channels.