Retention Is King

This was originally posted as a guest post on Andrew Chen’s blog here.

There are too many companies asking, “How do we acquire more users?” that should instead be asking “How do we get better at keeping the users we already have?”.

Its easy when approaching the problem of growth to think that you just need to get more users, after all that seems to be the very definition of growth. However, if you take a step back though and think about growth as the maximization of user-weeks over time, it quickly becomes apparent that focusing on retention has a much larger effect than topline growth. This is also much more of a sustainable growth mindset. Rapid user growth followed by rapid user attrition is an indicator of unsustainable growth. Strong retention of users over time is a good indicator of product-market fit, something you’re hopefully looking to achieve anyway.

Viral Factor and Retention

At a high level, retention is more important than virality because if your users don’t stick around they are not able to invite others to your product over an extended period of time. If you have high retention and no virality you will sustainably grow your user-base over time. If you have high virality and no retention you will not. Between these two extremes it gets a bit more complicated. In order to explain in detail we first need to review a couple of terms: viral factor and retention.

It will help as you follow this post to use our in-house growth model to play with the numbers yourself, the graphs we reference later on in this post are derived from it.

Viral Factor
This describes the growth rate of a site or app based on invitations from existing users of the service. This is often called k factor.

i = number of invites sent by each customer
c = conversion rate of those invites (#signups/#invites)
k = i * c

Viral factor on a weekly basis usually looks something like the graph below. This varies for different products, but I’ve seen this shape again and again across the products we consult on. It is front-loaded like this for three reasons:

  1. The effectiveness of onboarding invitation flows.
    Onboarding is one of the few times you have a high level of user attention towards completing a specific goal (signup) and when you instruct users to invite others they often will without thinking much about it.

  2. Users’ level of excitement.
    Humans are most excited by new things, this applies to internet products as well. The excitement users get when trying a new product leads them to share more, but this sharing tapers off as product becomes normal in their everyday lives.

  3. Low invite saturation of users networks.
    When a user first starts using a product they know many more people that don’t use the product than do use it. Over time they share your product with others they know. Eventually, even a user who is very passionate about your product has nobody left to share it with that hasn’t already heard about it leading to lower virality as time goes on. This can also be an issue if your company grows very large, but that is a good problem to have.

Screenshot 2014-05-15 16.55.26

Retention
This is the number of users that stick around from one time period to another. There are two ways to express retention, overall retention and week-to-week retention:

  1. Overall Retention
    Overall retention is cumulative over time. If you have 30% overall retention in Week 3 it means that 30% of your users who started at the beginning of Week 1 are still around in Week 3. This is how companies normally express retention when discussing it internally.

  2. Week-to-Week Retention
    For growth purposes its often useful to look at retention on a week-to-week basis instead. Week-to-week retention is how many users move from one week to the next. If Week 2 has 40% overall retention and Week 3 has 30% overall retention than our week-to-week retention from Week 2 to Week 3 is 75%. If week-to-week retention is below 100% it means we’re still losing users.

Retention on a week-to-week basis usually looks something like the curve below. It is lowest from the first week to the second week and approaches 100% as time goes on.

Screenshot 2014-05-15 16.55.20

Why is retention so important?

In order for virality to be more important than retention your viral factor must be greater than your overall retention up to that point in time. We’ll prove this mathematically later in this post. The math is hard to simplify exactly, but there is a basic rule you can follow which approximates it. If you take only one thing away from this post it should be this:

Do not focus on improving virality unless your overall retention is stable, not continuing decrease after some reasonable period of time.

To help illustrate this, lets look at a few examples:

  1. Your product has a very high immediate viral factor.

    If your product is front-loaded with invites that are sent out and accepted at a high enough rate, it is possible to achieve an immediate k > 1 viral factor. In this case, if your invites have an acceptance rate that is quick enough, your monthly active user numbers will continue to grow even if you have zero retention past the first-use of your app/site. Growth of the high virality, low retention type is almost always unsustainable as the viral loops being exploited to attain quick k > 1 virality eventually expire. This has been the downfall of many companies that appeared to grow fast, raised a lot of money on that growth, then quickly died. Like Viddy…

    viddy

  2. Viddy growth model with viral channels working looks like this.
    Viddy growth model with viral channels broken looks like this.

  3. Your overall retention is high and decreases slowly as time goes on, but you have strong virality.

    In this case, products actually do see long term benefit from focusing on increased virality, but it’s often a false signal. Its only worth focusing on virality if you think you can improve virality more than overall retention. If you switch focus too early it will lead to sub-par growth metrics. This is because the compounding effects of retention improvement are much stronger than those of virality improvement.

    We can illustrate this easily with our retention/virality simulator and setting week-to-week retention and virality to be equal. We can then measure the effect of proportional changes to one of the other in terms of number of users that we have at some point in the future. In the real world virality is not likely to ever be equal to week-to-week retention, but for purposes of this example please disregard that as it helps illustrate our point in the clearest way possible.

    Base Case – Equal Virality and Retention: ~88k users total in Week 7, 44k from retention and 44k from virality as seen in the stacked bar chart.

    20% Increased Week 1 Virality: ~110k users total in Week 7, 53k from retention and 57k from virality.

    20% Increased Week 1 Retention: ~125k users total in Week 7, 65k from retention and 60k from virality.

    As you can see here, changes in retention have long term effects that have a greater effect than equivalent changes in virality.

  4. Your overall retention is high and stable.

    If you have maximized your retention to the point where you think you can increase virality more than overall retention, even accounting for the compounding effects of retention, then it makes sense to focus on virality.

Prove It!

It’s easy to model growth in Excel and there are some great models that have been shared online to help do this. The one I use is available for download at http://bit.ly/growthmodel. It’s a slightly modified version of this great one from Rahul at Rapportive. It will give you a nice overview of how you can expect to grow if you plug in some numbers. From a user accounting perspective this is great, but its a bit hard to conceptualize how growth actually works by looking at it that way. To get a bit of a different perspective, building a tree to see exactly where users that exist in a given week come from is quite helpful.

In the tree below w0 represents some set of users that start at time 0. Each level of the tree is a week in time. At each subsequent level you get users that stick around from retention or are invited via user virality. The number of users at a given node is just the product of all the nodes leading to that point. The coefficients represent the viral and retention factors over time, v2 is the viral coefficient in week 2 from the above virality graph, r3 is the retention coefficient in week 3 from the above retention graph, etc.

Screenshot 2014-05-13 17.24.01

The number of users at any given level can be simplified into a recursive equation.

Screenshot 2014-05-13 17.15.28

As you can see, this matches exactly what we saw in the tree graph above. The leading viral factor (current virality vi) matters relative to overall retention (trailing product of rn‘s).

Visualizing Retention and Virality

For the client work that we do at Quint Growth we built a tool to plug in numbers and visualize the retention/virality tree. Its the same tool we mentioned in the beginning of the post and used in Viddy example above. We’ve found it incredibly useful for visualizing how much more important retention is than virality (and the few cases in which it is not). We’ve made it available at http://quintgrowth.com/growthmodel.html and hope you find it as useful as we do!

Special thanks to Isaac Hodes for help with the d3 visualizations for the retention/virality visualizer.

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