Vanity vs Sanity

I just read a great post by Dan Wolchonok at HubSpot about the impact of retention on user growth and revenue. At face value, this sounds pretty darn obvious, but really it isn’t until you see the models that you realize just how much of an impact this one metric can have on your product.

Retention-and-LTV-as-Core-Metrics

There are many different ways to calculate retention, the most common being what percentage of users returned within a predefined period of time since they installed the app. Most mobile apps push for an increase in users with an inorganic app download campaign, however users acquired this way are less likely to retain, meaning that the number of installs ends up being just a vanity metric. You would onboard users month on month, but the number of users would remain more or less the same because of people churning out of the app.

The interesting thing here though is that for all intents and purposes, you can tell people that your user base is growing! The true story though would be that in Month 1, your growth rate was a cushy 30% but in Month 2, your growth rate has shrunk to 5%. I liken it to a leaky bucket. Unless your inflow is greater than the outflow, user growth is impossible and therein lies the importance of retention.

Like Dan says, the impact of this can be seen on retention as well. If the app was a freemium app with in-app purchases, it is likely that only users loyal to the product or users that can’t function without your product will begin to pay for it. That means you need to retain users for a certain period of time before they become actual payers. If your retention numbers are crap, then you’re not going to have enough revenue. You could try to increase the revenue you get from the payers that you do have do even things up, but that rarely ever works.

But yeah, there’s a nifty Excel sheet in the blog post which I haven’t had the chance to play around with, but I’m definitely going to! Check it out. As a side note, I really appreciate when writers model this kind of stuff with real world examples and data because otherwise it ends up being this totally abstract concept that I’d never think to even apply in the first place.

Image credit: http://measureofluck.com/

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