How can you test and validate your app hypotheses with mobile analytics?

1 Define your hypotheses

The first step is to define your hypotheses clearly and explicitly. A hypothesis is a statement that expresses a causal relationship between a problem, a solution, and a metric. For example, you might have a hypothesis like this: "Adding a social media login option will increase the sign-up rate by 10%". Your hypothesis should be specific, measurable, achievable, relevant, and time-bound (SMART).

2 Choose your analytics tools

The next step is to choose the right analytics tools for your app. There are many options available, but you should consider factors such as the platform, the features, the pricing, and the integration. Some popular mobile analytics tools are Firebase, Google Analytics, Mixpanel, and Amplitude. You should also use a tool that allows you to run experiments, such as A/B testing, feature flags, or multivariate testing.

3 Set up your tracking

The third step is to set up your tracking code and events. Tracking code is a snippet of code that you embed in your app to collect data from your users. Events are actions that your users perform in your app, such as launching, signing up, purchasing, or rating. You should identify the key events that are relevant to your hypotheses and define the parameters and properties that you want to track for each event.

4 Segment your users

The fourth step is to segment your users into different groups based on their characteristics, behaviors, or preferences. Segmentation can help you understand how different types of users interact with your app and how they respond to your experiments. You can use various criteria to segment your users, such as demographics, location, device, source, frequency, engagement, retention, or revenue.

5 Run your experiments

The fifth step is to run your experiments and compare the results of your different variations. An experiment is a controlled test that allows you to measure the impact of a change in your app on a specific metric. For example, you can test whether adding a social media login option increases the sign-up rate by creating two versions of your app: one with the option and one without. You can then randomly assign your users to either version and track their sign-up events.

6 Analyze your data

The final step is to analyze your data and draw conclusions from your experiments. You should use statistical methods and tools to evaluate whether your results are significant and reliable. You should also look for patterns, trends, and insights that can help you understand your users better and improve your app. You should then validate or invalidate your hypotheses based on your data and decide whether to implement, iterate, or discard your changes.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?