A Detailed Guide on how to use Predictive Analytics for Mobile Apps

Predictions is a crystal ball for mobile marketers that lets them investigate the future and see which users will love the product and which users will covert. With the help of predictions, you can shift from being reactive to proactive about how you engage with your mobile app customers. Predictive analytics solution gives the authority to you to influence users’ future actions by sending the right message, to the right user, at exactly the right time.

The power to know bad things-when your users would abandon the app, what would drive them to leave your mobile app for some other app and power to know the chance that is in queue to be explored and knowing that which device and operating system version will they visit your app from and even how many times in a day would they visit your app.

Using predictions is easy and simple. You start your analysis by defining what you view as conversion. A conversion may be any action that a user takes in your app.

Predictive analytics takes typical mobile apps analytics to the next level. They pull from many areas of modern technology, for example, traditional statistics and user data, data mining and artificial intelligence. You can leverage predictive analytics to offer user recommendations and forecast the future of your business in order to increase engagement levels, drive sale and make more informed business decisions.

So, having proper knowledge would get you-lower churn rate, skyrocketing user engagement and revenue scale flying off the roof. The superpower that will help to get you all these and so many other benefits-the one we are going to looking into much detail today-is predictive analytics.

Analytics mainly has four parts:

  1. Descriptive Analytics
  2. Diagnostic Analytics
  3. Predictive Analytics
  4. Prescriptive Analysis

And predictive analytics is one that gets you the valuable information on how users are going to act within the app.
Now take us look at what predictive analytics is before we move on the mobile app development stages in which it can be incorporated, the benefits it would bring to the mobile app centered business and some use cases on how the analytics can be added to various industries.

Now start with mobile app development first.

How does predictive analytics expedite mobile application development?

Mobile developers create a huge amount of data specific to mobile app testing and quality check, running of a build and several other daily tasks. Mobile app developers who have used predictive analytics in their development process gather data and then create a predictive analytics framework to find out patterns that are hidden in the many unstructured and structured data sets.

Predictive analytics is a relatively new definition in software development though it’s a buzzword currently. So, how can it assist to improve your mobile app? Let’s explore below.

Now we will move to practical insight and try to understand how we use predictive analytics in our mobile app development cycle to make the whole process a lot faster and quality ensured.

Steps of predictive analytics for mobile app development

  • Predictive Planning

App developers and project managers very often underestimate the time, resources and money it would require delivering code. They might run into same delivery issue time after time. Especially when they work on similar projects. We use this technique to identify the repetitive mistakes that result in buggy codes. We also factor the number of code lines delivered by the developers and time that it took time to write them earlier.

  • Predictive Analytics DevOps

DevOps is known to influence the mobile app delivery time. When the productions environment data flows back to the developers, predictive analytics aca help identify which coding approach is causing bad users to experience in the market.

We first analyze the data specific to the usage and failure pattern of the mobile app to then predict which features or user movement are going to make the app crash, then we try to solve the issue in future releases.

So how can we use this technique for bettering your mobile app experience?

There are so many ways businesses can leverage predictive analytics for bettering the overall experience of their mobile apps.

Here are some benefits of predictive analytics for the future centered mobile app businesses.
1. For larger user retention

It helps in improving the user retention number to a huge extent. By providing the app admin a clear statistical based picture of the problem areas of the mobile app, giving them the time to get it corrected before it becomes a continuous issue making app users abandon the app.

2. For customize and personalized marketing

Personalized marketing is the key sign of how companies use analytics to lure customers to use their app. Have you ever thought how Spotify gives you recommended song playlist or how Amazon shows you customers who bought this also bought list? It is all a result of predictive analytics. By implementing in your mobile app, you will be able to give your users a more personalized listing and messages, thus making the whole experience a lot more customized for the end users.

3. For identifying the time to make a device switch

When employed right, predictive analytics in mobile apps gives entrepreneur insight into which device and fact which operating system their users are getting active on using the app.
Predictive Analytics use cases in the real world
Let us figure out those areas that are more prone to offer instant high returns when integrated with Predictive Analytics.

Predictive analytics in healthcare industry

The reason predictive analytics is one of the popular healthcare trends in 2019 in beyond is that it has expanded itself from its once prominent role of being a personalized healthcare enabler. Initially used only to help send a personalized recommendation to the patients in terms of health and care considerations that they would have to make, it is now being integrated into the healthcare industry for three crucial requirements.

Some Predictive Analytics Tools
Some tools like Flurry, Amplitude, Urban Airship, and Localtytics can be very helpful for predictive analytics.

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