Sticky Smartphone

Posts tagged active users

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How to forecast active user growth in your app (Shareable spreadsheet)

One of the most important things when developing your mobile app (or web app) is determining how many people are returning to use it and how to measure if the changes you are making is really improving it for the users. The number of registered users is not a great measure of how your app is engaging users. You could have 1 million users signing up but none of them may return to use the app after the first time.

A better measure is to see how many people are actively using your app. However, this number can get confused with the number of people who are new users, so you need to work out how many people stop using your app for each group of new users and when they stop using it.  This makes it complicated to forecast the growth (or non-growth) of your app, making it hard to measure if your changes are having the desired  effect of making your app sticky. 

When is it time to get more users?

If your losing lots of users, you may not want to focus on acquiring lots of new users because they will likely only use your app once, so you need a good indication of when to start applying resources to get more users.

Spreadsheet

I’ve created a basic google spreadsheet that lets you to set up some active user goals and makes some forecasts for you. You can setup some targets for a future number of active users for a certain point in the future and understand how many people you need to acquire to hit your goal. 

This lets you decide if it is an appropriate time to turn on the taps for user acquisition. e.g. if achieving 100,000 active users in 50 weeks means acquiring 10,000,000 new users, it’s clearly not the time to plough financial resources into acquiring new users.

The Output


It will produce a chart like the above.

  • The Redline is the forecast of active users. 
  • The orange line is your real data. 
  • The blue bars are the number of new users you are acquiring for each data point (day, week, month.. it’s up to you). 
  • Target Active Users is the number of active users you hope to achieve
  • Target Period is the time by which you are looking to reach that number of active users
  • User Acqusition Growth rate is the linear % increase of the number of users you need to acquire for each time period. 
  • Total Users is the number of overall users you need to have acquired by the final period to hit your target number of active users

What do you need to Input?

You need to type in the Target number of active users and the target period you hope to achieve it by. To generate the graph you need to input into the fields described below

The first 2 fields are your baseline. It’s the number of new users you acquired in the last period and the number of active users you had in the last period.

Churn rates

Churn rate is the % of users you lose every period. e.g If you acquired 100 users in a period and in the next period, 30 of them are still active, your churn rate is 70%. However, usually the churn rate is high for the first period and subsequent periods are lower, this is because in the first period people are trying out your app and quite a few of them will decide it is not for them. The subsequent periods tend to have a more ‘natural’ churn.

So in this chart you need to apply a first period churn rate and a second churn rate for all other periods. You should be able to extrapolate these figures form your analytics tool.

New Users and Active Users

The last things to fill out is the number of new users you get every period. You can start by plugging in old data to see if your churn rate estimates are accurate. Fill in the actual number of active users and you can see how well the prediction is based on your inputted churn rates.

Fill in your new user forecasts and it should give you a forecast on the number of active users. 

Testing your performance

When you get new data for a period, you can input the number of new users and the actual active user number. Look at the numbers and the chart and if it starts diverging upwards, then the changes you have made to your app are having a positive effect on your churn rate.

You can reuse the chart with your new churn rates to re-estimate the targets you are trying to achieve.

If you find this useful, or know someone who may find this useful, spread it around.

The example spreadsheet can be found here

Image from Cillian Storm

Filed under active users mobile analytics churn rates user acquistion forecast predictive analytics

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How to use analytics and segmentation to find value from users with no account

Engaged Mobile User

I was reading this blog post by Fred Wilson and it occured to me that engaged users are not just about people who create accounts, regularly log in and contribute. But also about the different cross sections of people who are engaging with your app on their first visit, or returning visits on a passive level.  The gist of the linked blog post is about twitter and how many people visit it in a month.

  • 400M active users per month
  • 100M users who log in
  • 60M users who tweet

Usually, the 300M people who didn’t create an account are not measured as active users or engaged users. If we ignore it, we are missing out on the potential insights of those 300M users if we are not measuring correctly. If we look only at the drop-out points along the account creation funnels and see them only as failed conversions we are ignoring them as active users and failing to utilise the value they are gaining from just observing. These 300M should be segmented further (e.g. to returning and non-returning visitors) and we should analyse what they are doing to give us insights into their behaviour.

How About Mobile Apps?

One key difference between websites and mobile apps is re-discoverability. For many people, if they don’t like an app, they will delete it. For website, a new blog post, a link from a trusted source, a search engine can bring people back to the website, but for apps, once you have deleted it, the hurdles to re-find the app and re-install it are much higher than for websites. This poll shows that 26% of people uninstall an app after only using it once.

For mobile apps that require an account before being able to use the app, this can be a problem. The app could be redesigned to provide value to a user before they created an account. Then we can segment the groups and do some deeper analysis, and potentially reduce the number of app uninstalls. This opens up the chance to retain these “unconverted” users.

What are we measuring?

We are segmenting the users into different levels of engagement, in a similar way to how games can segment people into different ‘levels’ based on their progress in a game. This way we can discover different types of behaviours and insights to potentially convert to higher value actions.

The segments we are effectively using here are

  • Used the app once only
  • Used the app more than once (or on multiple days) but never created an account
  • Created an account
  • Contributed to system 

These segments gives us insights into the behaviour of each group, and allows us to optimize the app for each group to increase retention. Engagement is the key, changing the engagement into a revenue event or some other high value event can come later. The more people you keep engaged in these channels the more possible high value events that can occur. An example is referral, people who may not have created an account but are regular passive users may go on to contribute to its organic growth.

Secondarily, high value events for each of these segments may be very different. They are different user groups with different behaviours, what they find high value may be different for each group.

How do I track it and what actionable insights can I get?

Check the number of people who return to your app. Some analytics tools, such as Flurry can provide you with the number of users who are one-session users. Compare this with each iteration of your app and see if you can reduce the % of people who are one-session users. Segment this group so you can drill down into their behaviour. What events are they doing?  What events are they not doing when compared to returning users who have not created an account?  Can you improve the app to make one-session users less likely to leave? Some examples of things you may want to optimize based on what you discover -

  • Improve ways for non-account owners to refer your app or content.
  • Increase visibility of non-account owners to account owners. 
  • Increase accessibility of public contributions by account holders to non account holders

Just by providing more value at the beginning of your app, can help you to retain users and refer users. By identifying, segmenting and drilling down into their behaviours and comparing their behaviours to ‘active users’, it should give you insights on what to focus on to improve your app (or website). By engaging ‘non-active’ users, it’s possible to increase value to ‘active’ users as well.

Image courtesy of Flickr, Ed Yourdon

Filed under analytics mobile analytics engaged users active users inactive users