A 10 Minute Guide on Using Predictive Analytics for Mobile Apps
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A 10 Minute Guide on Using Predictive Analytics for Mobile Apps

Sudeep Srivastav
By Sudeep Srivastav| Thursday, December 6, 2018 14:04 PM |10 min read
predictive analytics in mobile app

Imagine yourself as Stephen Strange a.k.a as Doctor Strange for a minute. Suppose it to be your alternate ego, different from your primary Mobile App entrepreneur personality.

Imagine being given the power to know what is going to happen even before it does.

The power to know the bad things – when your users would abandon the app, what would drive them to leave your mobile app for some other app and the power to know the opportunity that is waiting to be explored – 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.

Sounds like a modern day mobile app business centric scene from Marvel Franchise, doesn’t it?

But what if we tell you that you have the ability to estimate what is going to happen next in your app and how your users will react, all before it does?

Believe it or not, estimating your app users’ moves before they make them is possible.

Imagine what this knowledge would get you – Lower Churn Rate, Skyrocketing User Engagement and a Revenue Scale flying off the roof.

The superpower that will get you all these and so many other benefits – the one we are going to looking into much detail today – is Predictive Analytics.

Being one of four most insight offering Analytics forms – Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analysis – Predictive Analytics is one that gets you the information on how users are going to act within the app.

The ultimate aim of incorporating Predictive Analytics in a mobile app is simple: Know what is going to happen and prevent/boost the action.

Let us look at what Predictive Analytics is before we move on to the mobile app development stages in which it can be incorporated, the benefits it would bring to the mobile app centered businesses and some use cases on how the analytics can be added to different industries.  

Predictive Analytics Definition

The Predictive Analytics Definition goes somewhere like this – The Analytics form tells what is going to happen. The estimation form analyzes data and statistics to create a pattern which then helps in doing the guesswork on what is going to happen next. 

With predictive analytics definition now attended to, it is now time to look at the impact of the insightful analytics technique in the two phases of Mobile App Journey – Mobile App Development and Post Mobile App Launch.

Starting with mobile app development first.

How Does Predictive Analytics Expedite Mobile Application Development

Mobile app developers generate a huge amount of data specific to mobile app testing and quality check, running of a build, and a number of other daily tasks; these data mainly dictates short and long-term project success.

Mainly, mobile app developers who have integrated 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.

The end result: The mobile app developers get an algorithm using which they can forecast problems that the development cycle might face.

While this is the high level explanation of how Predictive Analytics in Mobile App Development Process works, let us now give you a practical insight by showing how we use Predictive Analytics in our mobile app development cycle to make the whole process a lot faster and quality ensured.

How Appinventiv Uses Predictive Analytics For Mobile App Development

Appinventiv uses Predictive Analytics

Predictive Planning

Mobile app developers and project managers very often underestimate the time, resources, and money it would require to deliver code. They might run into same delivery issues time after time, especially when they work on similar projects.

We use predictive analytics 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 them to write them earlier. It gives us the information to predict whether or not we would be able to meet the scheduled delivery date.

Predictive Analytics DevOps

The merger of mobile app development and operations – DevOps is known to expedite the mobile app delivery time. When the production environment data flows back to the developers, predictive analytics can help identify which coding approach is causing bad user experience in the market.

We 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 fix the issue in future releases.

Predictive Testing

Instead of testing every combination of the user actions and interfaces with other systems, we use predictive analytics to find the path that users commonly take and identify the stage where the app is crashing. We also, at times, use algorithms to measure commonalities between all user execution flow and identify and focus on overlap which indicates common execution paths.

Now that we have looked at how Predictive Analytics in Mobile App Development works, it is time to look at the benefits that the analytic framework has to offer to the mobile app centered businesses and entrepreneurs.

How to Use Predictive Analytics for Bettering Your Mobile App Experience

Predictive Analytics betters Mobile App Experience

There are a number of ways businesses can leverage predictive analytics for bettering the overall experience their mobile app leaves.

From giving them better insights on the research front, in terms of which geographical region should they promote their app more in to identifying the devices they should get the apps designed according to,  there are a number of ways Predictive Analytics come in handy for the future centered mobile app businesses.

1.For Greater User Retention

Predictive Analytics helps in bettering the user retention number to a huge extent. By giving 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 persistent issue making app users abandon the app.

By giving the businesses an exact picture of how users are interacting with their app and the ways they wish to interact with the app, Predictive Analytics help entrepreneurs correct issues and amplify the features that are attracting the users.

2.For Personalized Marketing

Personalized Marketing is the biggest sign of how companies use analytics to lure customers to use their app.

Ever wonder how Spotify gives you recommended song playlist or how Amazon shows you Customer who bought this also bought list? It is all a result of predictive analytics. By implementing it 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 which Screen’s Content Need to be Changed

Predictive Analytics help identify which element of the app is turning down the users or which screen are they using before leaving the app, this information helps mobile app entrepreneurs immensely as they get face to face with the problem area. And now, instead of changing the whole application, they are only concentrated on improving a particular segment/ section.

4.For Identifying the Time to Make Device Switch

When employed right, Predictive Analytics in mobile apps gives entrepreneurs insight into which device and in fact which operating system their users are getting active on to use the app. This information is a goldmine for the tech team as they can then get the app designed according to the specificity of that specific application.

5.For Making Their Notification Game Better

Predictive Analytics helps businesses identify which notification message is causing what reaction and if there is a difference between varying time and content. This information helps marketers plan their notification push in a way that it gets a maximum positive outcome.

By categorizing the mobile app users in segments like those who are interacting most with the app, those who are most likely to abandon the app, and those who have simply made your mobile app the case of install and forget, Predictive Analytics help mobile app marketers with a platform where they know how to segregate their push notifications and between what people.

With this, we have now looked at the contributing role that Predictive Analytics plays in the mobile app development industry, both from the end of the mobile app development agency and the mobile app centered business, it is now time to look at some use cases with respect to how you can add the analytics form in your mobile app, across industries.

Predictive Analytics Use Cases in the Real World

While there are a number of Predictive Analytics examples around us, let us look at those areas that are more prone to give instant high returns when incorporated with Predictive Analytics.

  • Predictive Analytics in Healthcare

The reason Predictive Analytics is one of the prominent Healthcare Trends in 2019 in beyond is that it has expanded itself from its once prominent role of being a personalized healthcare enabler.

Earlier 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 incorporated in the healthcare industry for three crucial requirements – For risk estimation, Geo-mapping, and for planning out the what-if scenarios in terms of both surgery and patient inflow in the hospital.

The prospect that Predictive Analytics in Healthcare comes with is one that promises mass transformation of a complete industry.

  • Predictive Analytics in eCommerce

When we talk about Predictive Analytics examples, it is important to have a discussion without the mention of the eCommerce industry. The analytics not just help users by giving them listings related to ‘Customers who bought this also bought’ but also in showing them ads of offers that have arrived on the products that they were looking to buy earlier or have in their shopping cart.

The benefit of keeping the users hooked to the website by giving them offers and discounts on the products that they actually wish to purchase and at the same time helping them decide what to buy next are the two factors that have drawn eCommerce giants like Amazon, eBay etc. integrate Predictive Analytics in their website and mobile apps.

  • Predictive Analytics in On-demand

In the on-demand economy specific to transport and commutation, predictive analytics come in very handy in terms of estimating the areas that are going to ask for maximum fleet demand, the price that users are most likely to pay for a tip, the stage at which they are cancelling the ride, etc.

Apart from this, predictive analytics also help in estimating the accident scenario in terms of drivers who are most likely to rash drives, the geographical area that is most prone to accident, etc.

The on-demand fleet economy has a lot to take advantage of from the predictive analytics algorithms. The industry-wide realization has led to brands like Uber and Didi Chuxing apply Predictive Analytics and Machine Learning in the business model.

  • Predictive Analytics in Enterprises

The what would happen next information that Predictive Analytics offers to the company’s business team comes in as a golden opportunity for enterprises who are struggling in their CRM domain and also in the HR area.

Predictive Analytics can give insight into the stage at which a customer is most likely to take their business elsewhere and the performance-based analysis of employees, giving the HRs an insight into whether or not the employee should be kept associated.

By researching on the skills that are most demanded by the industry, predictive analytics and enterprise mobility can together up the employees’ skills to a huge extent.

Now that we have seen all – Impact of Predictive Analytics in Mobile App economy (an impact that both mobile app development company and the mobile app businesses face) along with the real world use cases, it is now time to bring the guide to an end by giving you an insight into the Predictive Analytics tools that offer the most calculated inferences.

Predictive Analytics Tools

While a quick search on the internet will get you a great list of predictive analytics tools, here are the ones that we rely on to help our partnered entrepreneurs get a better hang on where their app business is headed –Predictive Analytics Tools for mobile app

This brings to an official end to our 10 Minutes guide to Predictive Analytics in Mobile Apps. If you need more information on how to integrate predictive analytics into your mobile app and reap the benefits of low churn rate and minimize the app abandonment instances, get in touch with our team of Predictive Analysis Experts, today!

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Sudeep Srivastav
About The Author

Sudeep Srivastav, the CEO of Appinventiv, is someone who has established himself as the perfect blend of optimism and calculated risks, a trait that has embossed itself in every work process of Appinventiv. Having built a brand that is known to tap the unexplored ideas in the mobile industry, he spends his time exploring ways to take Appinventiv to the point where technology blends with lives.

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