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Retention rate, churn and duration

By on November 2, 2011

Retention rate, churn and duration all measure the same thing: how effective are you at getting users to come back to your game.

Regular readers will know that I view retention as the most important of the three ARM constituents (Acquisition, Retention, Monetisation).

The relationship between retention rate, churn and duration is as follows:

  • Retention rate is defined as what percentage of the people who played your game in month 1 are still playing in month 2
  • Churn is one minus the retention rate as a percentage (i.e. 1-R%). If 80% of your users returned from month 1 to month 2, you would have a churn of 20%. The reason to calculate churn this way is that it ignores the distorting effect of new users
  • Duration is the reciprocal of churn (i.e. 1/churn). If your churn is 20%, your duration – the average number of months a player stays with you – is 5 months.

Note that retention rates, churn and duration are more nebulous concepts in the free-to-play world than in a subscription businesses. A subscriber who cancels his subscription is generally gone. The effort of cancelling is, intentionally, sufficiently high that it takes a real effort to leave, and consequently it is unlikely that that subscriber is will return.

Free to play games aim to minimise friction. They make it easy for users to keep spending. A user might spend a little in month one, a lot in month two (counting as a retained customer), nothing in month three (counting as a churned customer) and then a little again in month four (potentially counting as a new customer for the purposes of calculating retention.

I emphasise, therefore, that this spreadsheet is a tool. It is an approximation designed to help you tweak your funnel to drive revenues. Its simplicity is its strength, and its weakness. (Having said that, I am always keen to get feedback on ways I could improve it).


An appropriate benchmark for duration is somewhere between two months and six months. That implies a churn of 15%-50% and a retention rate of 50-85%. According to flurry, for iOS and Android apps, 24% of customers continue using after three months. After 6 months, this percent shrinks to 14%, and, by 12 months, only 4% are left.

Raf Keustermanns of Plumbee also stated at the Social Games Summit 2011 that any dev should expect to lose 96% of their user base within 12 months. Playnomics have found that approximately 85% of players do not return after the first day.

Your game design can have an enormous influence on the retention rate. According to Playnomics;

  • users that are brought back into the game within two days of their first session play 334% more overall
  • players who do not churn play for twice as long on their first day
  • players who carry out more actions in a game session are retained for longer.

Facebook games

Reports suggest that Zynga has a retention rate of only 50%. Given its success, I’m inclined to believe that this is the retention rate for an individual game, not across the whole of Zynga. Zynga gives you two months on any given game to see if you will spend. If you don’t, Zynga tries to move you out to a different game to see if you are more inclined to spend there. It is a sensible strategy if you have a portfolio of online games.

Kixeye has an average duration of 7 months for daily active users, which suggests a retention rate of 85%.


Giordano Bruno Contestabile of PopCap said during his GDC talk in March 2012 that 7-day retention on iOS for Bejeweled was double that of Facebook.

Individual titles

  • Subway Surfers has a 30-day retention rate of 70%. (source: Forbes, 06/05/2013)

About Nicholas Lovell

Nicholas is the founder of Gamesbrief, a blog dedicated to the business of games. It aims to be informative, authoritative and above all helpful to developers grappling with business strategy. He is the author of a growing list of books about making money in the games industry and other digital media, including How to Publish a Game and Design Rules for Free-to-Play Games, and Penguin-published title The Curve:
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  • Andrea Keil

    Hi there, I have a question, you wrote:
    “Retention rate is defined as what percentage of the people who played your game in month 1 are still playing in month 2″

    Okay, so if 1000 people played in month 1 and 800 in month 2, then you have a retention rate of 80%.
    But what about user gain/inflow? What if 1000 people played in month 1 and 1500 in month 2? Then, you had a retention of 150%…? And thus a churn of -50%…?

    I think that the opposite of churn is not retention, but user gain/inflow, which is quite a difference… or am I wrong?

  • Nicholas Lovell

    Hello Andrea,
    User gain/inflow is separate from retention/churn. You have some users this month. Next month you keep some (retention), you lose some (churn) and you gain some new ones (new users).
    If your churn rate is 20%, your retention rate is 80% (1- churn%, or “how many users who came last month stuck around to this month). It’s not an opposite.
    Hope that helps.

  • Andrea Keil

    Yay, fast answer!
    But calculating “1-x%” IS the opposite of x, isn’t it?
    Okay, so next challenge: What do you think about this formula?
    The sections above this “answer” deal with easier formulas that have dangerous flaws that sound really really dangerous, and I’m “scared” to fall into that trap.
    What do you think about that?

  • Andrea Keil

    How does this churn rate / duration formula work? You have an example of 20% churn rate, and duration = 1/churn = 5 months.

    If your churn rate is 20% (monthly), then each month 20% of your users churn.
    If that meant that after 5 months (5*20% = 100%) all the user will be gone, then that wouldn’t mean “average duration = 5 months”, but “maximum duration = 5 months”, right?

    Moreover: If you say “each month, 20% of the users churn”, that wouldn’t mean:
    Month 1: 100 users
    Month 2: 80 users
    Month 3: 60 users
    Month 4: 40 users
    Month 5: 20 users (linear decrease)


    Month 1: 100 users
    Month 2: 80 users
    Month 3: 64 users
    Month 4: 51.2 users
    Month 5: 40.96 users (exponential decrease)
    …and so on.

    So why should this formula work? I don’t get it.

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  • Kevin

    Can you include more math evaluation on churn? How should one be optimizing to minimize churn? Hoping for something more like