Matt's previous positions includes senior management roles at THQ and W3i along with supporting multiple successful start ups as founder and co-founder.
Utilising data in business today is critical to success, and the mobile app business is certainly no exception.
In this soup to nuts series on mobile analytics we will explore all aspects of using data to help us in our decision making processes. We will cover everything from App store analytics and pre-install analytics to post-install analytics and user behavior.
In this first post, I want to discuss post-install analytics, a tool that can be crucial to the success of an app and is helpful in creating a user experience that will keep your users engaged.
So, before you invest in the first analytics solution you cross paths with, here are a few key considerations.
What does post-install mean?
Post-install analytics is simply defined as analysis of data that is captured after the installation of an application. In other words, post-install analytics is analysis of user behaviour once your users download the application.
The value of data
More frequently than not, I hear from many mobile app analysts that data is useless by itself. It's true that data is only as valuable as the decisions that it helps you make.
However, it's important to understand that data in itself is a valuable tool because the decisions you make will come from the analysis of data.
One key consideration is to separate the idea of data and analytics. This will help when identifying which questions to ask and which tools to use. Server logs and databases merely provide the foundation for analysis.
Listen or ask?
There are two primary theories on using data to help improve business.
On one side there is the notion of 'listening to the data.' In other words, instrument a solution that will allow you to gather as much information as possible, then with careful review you can 'listen' to how users are behaving inside your application.
An alternative approach is to consider the questions you need answered and then instrument an analytics solution to help answer those specific questions. An example might be "Do users prefer X feature vs. Y feature after their 10th use?"
Neither of the theories is necessarily wrong and later on in the series, we will explore the usefulness of both concepts.
Questions before answers
Too often I hear things like "just plug in X analytics tool and you will be able to see everything." While there is great value in listening to the data, it's important to consider the questions you would like the data to answer.
In planning for your analytics solution it's important to think about key questions and information you think will be useful in your product.
What activities will you want to monitor? What are the top 10 questions you want to ask of the data? What are the top 10 questions you want to ask of your users?
You might be wondering what some of these questions might be. Here are some to start considering:
- What do I want my users to do in the application?
- Where and when are my users dropping off and why?
- What do users do most and why?
- For users who stick around, what do they do on a frequent basis?
- How frequently are users using my application?
- How are heavy active users using the application compared to light users?
- What activities lead to users converting to X (X can be any event of value)?
- What is my conversion rate after X days of use and/or X actions in app?
Before you get started, have an idea of what questions you want to answer. Take time to determine how information will help you make decisions as you seek to improve the performance of your application.
In the next post we will dig deeper into the fundamentals of post-install analytics such as events, parameters and cohorts that will help you in understanding which analytics services to choose and how to utilise them to the fullest.
To find out more about Taptica and the services it offers, take a look at the company's website.