How to use analytics to ensure your software launch is a success

Analysis is one of the most overlooked aspects of launching software. Follow this guide for your next app launch so stakeholders can drive future development from a data-driven position.

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When your company launches a software application, analytics should be part of strategic plans from the start. You’ll need to work closely with developers, product owners, and customers to determine what your minimum viable product is, and talk to your business owners to decide which KPIs should be measurable from day one. . At a minimum, your analytics should answer two questions: Who are your users and what are they doing?

For example, if your business knows that 90% of your users use Android devices and are acquired through Facebook, you have data that shows who is using your app and can make an informed decision on markets. Should you double down and expand your Android user base, or should you seek other acquisition channels to grow the iOS base?

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A solid minimum viable analysis will give you the data you need to drive your application after launch. If you’re building a digital product or service and haven’t yet thought about what kind of analytics you need for launch, this guide can help.

Here are good starting points on what data to capture for most minimal viable analytics implementations.

Data to capture User data What are these users doing?
Geography X
Demography X
Time of the day X
Device type X
Operating system X
acquisition channel X X
Download counts X
Active daily users X
Commitment X
Unique visits X
Revenue per user X
Conversions X

What technology tools should you use with this analytics strategy?

A single analytics tool won’t give you the 360-degree view of your application in the field and the operational flexibility you’ll need post-launch; this is especially true in the mobile app space where changes must go through an in-store submission and approval process. A good approach is to combine several tools in the analysis, abstraction, and quality of service categories to ensure that you have everything you need in your application.

Analytic

An analytics package is the main component for aggregating and reporting on your user data. When trying to choose an analytics package, many factors go into choosing the right one. An important factor is to ensure that the analytics solution is suitable for your application platform. In addition, in the offers of the same supplier, there may be several solutions to be evaluated.

Two of the most popular analysis packages are Google Analytics for Firebase and Google Analytics 4.

Google Analytics for Firebase

After several rounds of confusing name changes, Google Analytics for Firebase is Google’s solution for mobile app analytics. It still uses Google Analytics at its core, but it’s exposed as an event-based model, which is better aligned with how people use a mobile app. Google Analytics for Firebase is free with unlimited use, but there is no service level agreement.

Google Analytics 4

Google Analytics 4, which was previously called Google Analytics, retains its classic approach to analytics, i.e. page views. It has a fairly robust free tier as well as paid plans for advanced use cases.

Abstraction

Once the app is published and you start collecting analytics, you might want to have collected additional data points or formatted the data differently to make more advanced correlations. This is where a tag management system comes in. A TMS allows you to quickly update measurement code and related code snippets, usually from a web console. In many cases, after initial implementation, these updates can be made without requiring updates to your live application code in the field.

If you’ve never used a TMS before, there are plenty of vendors to consider, including Google, Adobe and Mixed panel. An interesting aspect of a TMS is that most are independent of your analytics solution, allowing, for example, the use of Google’s Tag Manager with Adobe Analytics and vice versa.

Quality of service

Even with a robust TMS and analytics implementation, you’ll find that these data points aren’t the same data points your engineering team needs to diagnose issues with your users. This brings us to the third tool in your MVA toolkit: measuring service quality.

Flavors of these QOS tools exist for both mobile and web, as fundamental technical differences in these applications dictate a different approach. For mobiles, popular QOS solutions include Crashlytics, Instagram and ray gun. On the web, you’ll want to consider tools like Pneumatic brake and Uptrends.

Common data to collect include:

  • Crash dumps (stack traces).
  • Smart clustering (quantification of single crashes as opposed to multiple instances of the same crash).
  • Custom data (breadcrumb inserted by developer).
  • Device status (hardware, operating system, and environment modifiers).

What details about privacy and use of user data do you need to know?

While ultimately the goal of collecting analytics is to provide the best possible user experience, privacy and data usage are important considerations. On iOS devices, you currently need to ask users for permission to follow them on apps and websites owned by other companies. Android and the web aren’t far behind, so be sure to read and understand your platforms’ privacy requirements and how they apply to the analytics you collect.

What are the benefits of having analytics on your software?

Successful software products must adapt to an ever-changing market. A well-planned MVA approach will create a direct line between you and your customers and dramatically improve your application launch. You’ll have the User Behavior Reporting Analytics SDK, a Tag Manager to make real-time changes to what you collect, and the QOS metrics your developers will need to troubleshoot user downtime . This 360 degree view will provide the data you need to make your application a success.