Metrics that matter to social platforms (Part 1/3)

By Angela, September 28, 2015

A few months ago, we wrote about the data we focus on to evaluate marketplaces and later shared a marketplace KPI dashboard that we created to guide founders on the important metrics they should track.

As we have been developing our investment thesis on social platforms, we want to provide the similar information and tools.

There are lots of great blog posts and articles out there that talk about social platform metrics.  The problem, however, is that these resources are quite fragmented because each social platform is unique in its own way.

In fact, when we use the term “social platforms” at Version One, we’re referring to messaging (e.g. WhatsApp, SnapChat, Facebook Messenger), private social networks (e.g. Facebook, LinkedIn, Shift Messenger), public social networks (e.g. Instagram, Twitter, Figure 1), and communities (e.g. Reddit). With this kind of range, it should come as little surprise that a “one-size-fits-all” analysis simply doesn’t work.

Our aim is to provide a standard framework in which we can think about social platforms… because despite the uniqueness of each individual platform, there are still some commonalities.

Just as we created a KPI dashboard for marketplaces, we built a KPI dashboard for social platforms. We hope this tool helps founders manage their business and preempts those due diligence questions that arise during a fundraise.

You can access the template via the Google doc here (make a copy of it and then you can edit away). Over the course of the next few weeks, we’ll be diving into each section in more detail in a series of blog posts:

Part 1: High-level metrics

Part 2: Measuring engagement by content

Part 3: Measuring engagement by relationships

 

SS1Our KPI dashboard for Social Platforms: access the Google spreadsheet here

 

Part I:  High-Level Metrics

First and foremost, social platforms are defined by their engagement so the key metrics to consider are:

  • Daily active users (DAU):  the number of unique users who engage with the product in the past 24 hours.
  • Monthly active users (MAU):  the number of unique users who have engaged with the product within the previous 30 days.
  • DAU / MAU:  the “stickiness” ratio that tells you what percentage of your monthly active users come back on a daily basis.  For example, a DAU/MAU of 50% would mean that the average user is engaged 15 out of 30 days of the month.  Ideally, you want this number to be above 30-40% to have a highly engaged platform.  To calculate DAU/MAU, take the number of unique users at the end of a given day and divide it by the number of unique users for the last 30 days, including the day in question.  

Note that the benchmark for “active” can vary:  some apps record this when a user simply visits a site while other apps only consider a user to be engaged when s/he logs in and performs another action.

In addition, the total number of active users on your platform at the end of a cycle (i.e. day, week, month) can be viewed as a sum of the following:

Total Users = New Users + Returning Users

  • New users:  self explanatory 🙂
  • Returning users = Retained users + Resurrected users
  • Retained users:  active users this cycle who were also active last cycle
  • Resurrected users:  active users this cycle but not in the previous cycle

From here, you can calculate % new users, % retained users, and % resurrected users relative to the total number of active users.  You can also compute growth in these categories, from which you can see how effective your growth, engagement, and re-engagement campaigns are, respectively.

And if you want to understand your retained users deeper, you can break this down in a similar manner, i.e. “new” (i.e. new users in the last cycle who are active this time) + “retained” (i.e. users who continue to be active this cycle as they were previously).

On the flip side of engagement, we can look at churn as a measure of the number of individuals moving out of a collective group over a specific period of time.  Churned users can be segmented as follows:

Total Churned Users = New Churned Users + Old Churned Users

  • New churned users = inactive users in the current cycle who were active in the previous cycle
  • Old churned users = inactive users from the previous cycle(s) who continue to be inactive in this cycle

Knowing the number of users that have churned allows you to calculate your churn rate.

Churn Rate = # of users churned at end of the time period / # of total users at the beginning of the time period

After we understand engagement and churn, we can perform cohort analysis.  Rather than looking at all users as one unit, this breaks them into related groups or cohorts which usually share common characteristics or experiences within a defined timespan.

We won’t recreate the wheel here as our friend, Christoph Janz of Point Nine Capital published a great template on Andrew Chen’s blog.  While this is catered to SaaS, the concept is still very relevant to social platforms:  substitute “paying customers” with “active users”.

ChristophJanz_CohortAnalysisNotes.0011

Also, good news!  Mixpanel and Google Analytics (and other tools) can automatically create tables for you.

Another great way to understand your data is to visualize it.  In fact, last week, my friend Jonathan Hsu wrote about how he and his team at Social + Capital perform due diligence and shared a helpful “growth accounting” graph that summarizes most of the data above.

1-x09N6OuNlqQTaFxyC6apvA

And finally, for high-level metrics, make sure you track customer acquisition cost (CAC; and how much and what percentage is organic versus paid), net promoter score (NPS), lifetime value (LTV) and average revenue per users (ARPU).  The cohort analysis spreadsheet by Christoph helps with this too.

 

We hope you’ll make a copy of the Social Platforms KPI dashboard, start playing around with your own numbers, and find the process helpful to better understand and measure engagement on the platform you’re building. If you have any questions or thoughts, please start a conversation in the Comments section below. And stay tuned for next week for more details on measuring engagement by content.

Now you can read Part 2 here!  And Part 3 here!

  • Great post. Super looking forward to the rest.

    Quick question: from what I’ve read and calculated ARPU is usually == LTV but you mention them as two seperate numbers to track. From my understanding they are pretty simillar or almost the same? I’d love to hear your thoughts/comments.

  • atkingyens

    Thanks, Kamil! And good question – the two are related: ARPU is calculated over a monthly or annual basis, while LTV is the total revenue generated by a user over the course of him/her using the product.

  • Thanks for the response Angela. That does make sense. Turns out I was doing it wrong 🙂 Or well in my particular context the ‘total lifetime’ was a year since that was the historical data available so I am guessing in certain scenario’s they would turn out to be the same.

  • ramh

    Great article. Part 3 link is broken.

  • khairop

    hi Angela

    i

    great article. is any link available for the cohort analysis spreadsheet by chtistophe?

    thanks
    Kostas

  • atkingyens

    Thanks, Kosta! Here’s the link I believe you’re looking for: http://christophjanz.blogspot.com/2013/04/a-kpi-dashboard-for-early-stage-saas.html

  • Willson Terrence Campbell Cros

    Hey – love this sheet and I have gone back to it time and time again… What does sell-through-rate mean?

  • atkingyens

    Glad to hear that you like this dashboard, Wilson. Sell-Through rate applies to marketplaces and is calculated as (# of units sold through the end of the month) / (# of items at the beginning of the month).

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