A Comprehensive Guide on Using Predictive Analytics for Mobile Apps

Predictive analytics is aimed at making predictions about future outcomes based on current or historical data, statistical algorithms and analytics techniques such as machine learning. With the help of predictive analytics tools and models, any organization can accurately forecast trends and behaviors milliseconds, days, or years into the future.

Predictive analytics has gained huge support from a wide range of organizations, with the global market projected to reach USD 28.1 billion by 2026, growing at a CAGR of 21.7% during the forecast period of 2021 to 2026, according to a report by Markets and Markets.

Various factors such as acquisitions, product launches, and increasing use of artificial intelligence and machine learning are expected to drive the adoption of predictive analytics software in the market.

How Does Predictive Analytics Expedite Mobile Application Development?

Mobile app developers generate a huge amount of data specific to mobile app testing, quality check, and a number of other daily tasks; Integrating predictive analytics in the app development process helps gather data and create a predictive analytics framework to find 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 the 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

At Appinventiv, our team of professionals uses predictive analytics models to help businesses attract, retain, and grow their customers. It is a smart way to add more insight and clarity into your business decisions.

Here are the ways in which we make use of predictive analytics to make our mobile app development process more efficient.

1. Predictive Planning

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

We use predictive analytics for mobile apps to identify the repetitive mistakes that result in buggy codes. We also factor in the number of code lines delivered by the developers and the 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.

2. 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 for mobile applications can help identify which coding approach is causing a bad user experience in the market.

We analyze the data specific to the usage and failure pattern of the mobile app to predict which features or user movements are going to make the app crash. Then we fix the issue in future releases.

3. 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 for mobile applications works, it is time to look at the use cases of this analytic framework.

How Predictive Analytics Enhances Mobile App Experience – Use Cases

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

From giving them better insights on the research front, in terms of which geographical region they should promote their app more 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. Greater user retention 

Predictive Analytics helps in bettering the user retention number to a huge extent. 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 helps entrepreneurs correct issues and amplify the features that are attracting the users. 

2. Personalized marketing 

Personalized marketing is the biggest sign of how companies use analytics to lure customers to use their predictive apps.

Ever wonder how Spotify gives you recommended song playlists? It is 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.

3. Identifying which screen’s content needs to be changed 

Predictive analytics helps identify which elements of the app are 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. Identifying the time to make device switch

When employed right, predictive analytics in mobile apps gives entrepreneurs insight into which device and 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. Making their notification game better 

Predictive analytics helps businesses identify which notification message is causing what reaction. 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.

Industry-wise Application of Predictive Analytics

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

1. Predictive Analytics in Healthcare

Predictive analytics is being incorporated in the healthcare industry for three main crucial reasons – geo-mapping, risk estimation, and planning out the what-if scenarios in terms of surgery and patient inflow in the hospital. 

Using analytics leads to more effective treatments, better patient outcomes, and cost savings across multiple departments. 

For instance, a device for asthma patients that uses predictive analytics can record and analyze the breathing sounds of patients and provide real-time feedback using a smartphone app to help patients be prepared for an attack and better manage their symptoms.

2. Predictive Analytics in eCommerce

When we talk about Predictive Analytics applications, it is important to have a discussion without the mention of the eCommerce industry. The analytics not just helps 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.

[Also Read: 15 outstanding features of your e-commerce application]

3. 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 canceling the ride, etc.

Apart from this, predictive analytics also helps in estimating the accident scenario in terms of drivers who are most likely to rashly drive, 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 also helping a lot in real estate industry and beneficial for real estate app developers.

[Also Read: On-Demand Services Mobile App: Beneficial for Both Buyers and Sellers]

4. Predictive Analytics in Enterprises

The futuristic 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.

[Also Read: Predictive Analytics in HR – Benefits, Applications, Challenges and Real Examples]

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.

5. Predictive Analytics in Supply Chain Management

Another important area where the application of predictive analytics is essential is supply chain management. A poorly optimized supply chain can have bad implications on every aspect of your business. Thus, it becomes vital for enterprises to use advanced technologies like predictive analytics. 

The information you gather using predictive analytics will be as up-to-date as possible as it can incorporate real-time data. You can also become more agile in your decision-making process since the predictive model will indicate the impacts of different variables on your supply chain’s efficiency. 

Now that we have seen all – impact of predictive analytics in the mobile app economy (an impact that both mobile app development companies and the mobile app businesses face) along with the applications of predictive analytics, 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 and enterprises get a better hang on where their app business is headed –

Final Note

Predictive analytics is an advanced analytics approach to peek into your app’s future, allowing you to make better decisions and outperform your competitors. Organizations can make use of predictive analytics to take pre-emptive action in a wide range of areas. 

It can be used for greater user retention, personalization, targeted marketing campaigns, and more, which is why it will be a tangible asset in the future. 

In case you want to successfully build a mobile app using predictive analytics, you should consult and hire an experienced software company like Appinventiv.

You should also look out for new data analytics solutions in the market to make continual enhancements and gradually expand your application into a newer, better product with the latest features.

THE AUTHOR

Sudeep Srivastava

Co-Founder and Director