Andrew Chen

The multiplicative nature of retention
Retention metrics, like viral marketing, have a powerful multiplicative approach that makes them important to optimize. This is an often-overlooked metric that is easily sacrificed to the Great God of Viral Traffic I wanted to dig into this issue a bit more, and give my thoughts on how your retention numbers can be an incredibly powerful method of driving up total traffic numbers to your site, not just the user acquisition piece.

Matt Humphrey from Bumba Labs on user retention curves
A frequent collaborator of mine, Matt Humphrey, has often referenced some of these ideas in a public posting below, which I’ll excerpt. I’ve worked with Matt closely for over a year and a half now, and know a lot about his skillsets, background, and interests. His background is influenced by his time as a Carnegie Mellon alum, YCombinator entrepreneur, and working on quantitative marketing projects with me.

In my conversations with Matt, we often discussed the metric of “user retention rate” which tells you what % of users are revisiting after some period of time. So you might define 50% user retention as, 50% of the users on your site come back after a month, or what % come back after an initial signup, or whatever suits your particular business.

What’s amazing is the multiplicative aspect of this number:

[...] Having month-to-month user retention of 92%, 96%, and 97.3% will get you on average 1, 2, and 3 user-years respectively per user that ever signs up on the site.

Okay, in English? If each month you lose 8% of your existing users (92% retention) from the previous month, the average use will stay for 12 months. If you can hold just 4% more of your users (96% retention), then they will stick around for 2 years. If you can hold only 1.3% more than that (97.3% retention), they will be in for 3 years.

It’s easy to think of retention percentages in the 90’s as good. It just feels good. But over the course of time, products in the low-to-mid 90’s will fade super-fast, and ones only slightly more sticky will do much, much better. Single percentage points here are mission critical, that’s why attention to detail and rigorous analytics become so important on the web. [...]

To restate this, as you approach high levels of user retention, you begin to see powerful multiplicative effects.

Let me state, for reality-checking purposes, that retention rates over 90% are unrealistic, but are useful for discussion purposes because they bring out the extreme cases. More realistically, the numbers I’ve seen are generally much closer to 30-60% revisit rates after the initial registration. Similarly, the typically retention rates are not linear – you see the most churn initially, but then the cohort usually settles and becomes much more loyal.

But the core issues still hold, and the reason, of course, is simple – it’s simple arithmetic, which we’ll examine below.

An example of two subscriber cohorts
Let’s say you’re running a subscription site, and you compare two sets of subscribers, both starting in the same month, both numbering 1000. Let’s say that one cohort has 80% monthly retention, and the other is 90%.

In the short run, the numbers are close to the same:

• 80% monthly retention, after 1 month = 800
• 90% monthly retention, after 1 month = 900

Although obviously you’d want to have the 90% retention, the differences are not huge – you end up with 12.5% more users, after one period.

But let’s look at these numbers once you get to 12 months:

• 80% monthly retention, after 12 months = 1000*(0.8)^12 = 68
• 90% monthly retention, after 12 months = 1000*(0.9)^12 = 282

Of course, this ends up being a staggering 3X difference. Wow! And when you compare aggregate lifetime value, the numbers are even bigger.

Retention-focused features are very powerful
The point of all of the above is that retention-focused features are very powerful because they let you create dramatic improvements in all the important metrics, across the board – be it pageviews, total time usage, revenue, etc.

This means that you can, just as people do with addressbook importers and the like, put a tremendous amount of time into a whole host of retention-driven features like:

• A great product and value proposition
• Targeted notifications
• Fresh news and content on every return
• Desktop app-integration (which has a much lower rate of uninstall)
• The number of friends on the site (the more that are there, the more notifications can be generated)

All of the above contribute meaningfully to this user retention number.

As weird as it is to imagine that something as pedestrian as how you deliver your notifications can cause success or failure to your web business, in fact it can. The reason is that how you deliver notifications can have a huge impact on whether or not your users come to your site, and every percentage point of improvement may lead to many times the revenue, pageviews, and content. This is certainly an area worth focusing on, as much as Bay Area companies have focused on just the viral metrics.

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UPDATE: Thanks for Bhanu Sharma for correcting some dumb typos on the post – I changed the numbers halfway through and forgot to update all of it.