Posts tagged predictive analytics
Posts tagged predictive analytics
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.
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.
It will produce a chart like the above.
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 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