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How desktop apps beat websites at building large active userbases

Why does everyone hate on desktop applications? Homer and spiderpig love them.

Desktop apps have better retention, while websites have better user signup rates – which factor wins?
There’s a lot of conventional wisdom that it’s dumb to build a desktop app, and it’s marketing suicide to do so.

This argument usually has two parts:

  • Poor conversion rates: Maybe 1-2 out of every 100 – will download your application
  • Poor virality: Since desktop apps are often standalone utilities, they lack the social element that makes them viral

You can compare this to web products which often have 10%+ registration rates, up to 50% when they are through friend-to-friend invitations.

It seems obvious that going down the web route is a slam dunk, but in fact it’s not – desktop applications often have a better long-term retention, and this can easily offset the lower download+install rates. The way to look at this is that the number of active registered users is a function of signup rate AND retention, and you can balance one with the other. (And of course, ideally you have both). To me, this discussion opens the way for more innovation in browser extensions, downloadable apps, and other low signup % products as long as the long-term value is great enough.

Note that this blog post will focus exclusively on the signup rate versus retention rate, and leave the virality discussion for another day. Let’s dig into this further.

Comparing applications versus websites
Here’s an example of the simple differences between the two channels:

Total users Signup % Registrations
Application 100,000 1% 1,000
Website 100,000 10% 10,000

Starting with the same number of new unique users, it’s obvious that this can lead to a huge difference in account registrations.

However, because web products are so easy to get into, they are also easy to get out of – it’s hard to be sticky. The one true retention mechanic is using e-mail notifications to get the user back. Compare that to a desktop product that use techniques like:

  • Open itself whenever a file extension is clicked
  • Install itself on the system tray
  • Add itself to the desktop
  • Start up automatically when the OS loads
  • Run nicely in the background to pop up when appropriate
  • … and many other retention-happy features

(Of course, you should never use these techniques without contributing value to the user, lest you get uninstalled and reported to Symantec).

Similarly, there is also emerging a world of in-between web-triggered applications like Firefox extensions and Adobe AIR apps, which are easier to install but also take advantage of a wider set of retention hooks to stay relevant.

So when all of this has been taken into account, you can see how our 100,000 new users fare after a couple time periods below. Here’s a table that describes two retention rates period-over-period, and how many active users are left after each period, starting with the initial numbers (1k vs 10k) discussed previously:

Retention 0 1 2 3 4 5
Application 80% 1,000 800 640 512 410 328
Website 50% 10,000 5,000 2,500 1,250 625 313

As you can see, after the course of 5 months, the application now has more active users than the website, even though it started with a 1/10th the registered users.

Note that retention rates usually improve period-over-period, and are not constant as shown above – I’m just using a constant retention rate so that we can simply the math in the next section!

Looking at the math
For the readers that fall asleep when an equation is shown, please skip this section :-)

Ultimately, the function for describing the number of active users at any period is:

# of active users at time t = initial user signups * (retention rate)^t
= (new users * signup %) * (retention rate) ^ t

So if you have 100,000 new users, a 10% signup rate and 50% retention rate, then your equation looks like:

# of website actives at time t = 100,000 * 10% * (0.50)^t

If you want to calculate when a website’s active users falls below a desktop app’s active users, you can set the two equal to each other and solve for t:

100,000 * 10% * (0.50)^t = 100,000 * 1% * (0.80)^t
10% * 0.5^t = 1% * 0.8^t
10% / 0.1% = 0.8^t / 0.5^t
log 10 = log(0.8^t/0.5^t)
1 = log(0.8^t) – log(0.5^t)
1 = t * log(0.8) – t * log(0.5)
1 = t * (log(0.8)-log(0.5))
1 = t * log(0.8/0.5)
t = 1 / log(0.8/0.5) = 4.9

Thus, after 4.9 periods you’d see the higher retention product start beating the high signup product. In the cases where they never intersect, you’d get a negative number there. I will leave it as an exercise for the reader to solve this in the general case where you know that a website’s signup rate is X times more than desktop app, and Y times in retention.

Conclusions
Ultimately, I believe my calculations show that desktop apps have natural advantages (and disadvantages), but are not strictly worse than building a web property. You still need a long-term value proposition that drives great natural retention. You need expertly-done “hooks” into the OS, email, and other notification systems that encourage repeat usage. And finally, you need social hooks into viral channels (whether web or beyond) that encourage virality and user-to-user interaction

I think it’s not a surprise that there have been great success stories in desktop apps in recent years, such as Skype, Twitter clients, new browsers, and other tools that follow the design patterns of the above. And of course, nothing beats building a killer product that spreads naturally through word-of-mouth – that said, you can stack the deck using great retention and virality :-)

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