A few years back, Gartner predicted for the year 2018, that less than 0.01 percent of consumer mobile apps will be considered a financial success.
The alarming fact has shaken up the entrepreneurs from top to bottom who remain trapped in the positive thinking web and forbidden the word failure’. If you want your app to stand in the noisy app marketplace, then the secret to the app success is to know the worst things that can bring failure to your app, in advance. How can you predict which things will make the users abandon the app, draw the user’s attention, or convert the users (Revenue)?
It’s a mystery that’s difficult to solve and if not solved, it can make your app remain unknown in history. Don’t fret! There is a solution to every problem.
The name of this magic bullet is predictive analytics which gives you the power to know how the target user will react to the app in detail before the actual launch.
What is predictive analytics?
Predictive analytics is a crystal ball that let you know everything that happens to the app and the actions to prevent or boost to engineer the app that delights the users in the future. It takes the guesswork out from each and every activity related to mobile app development which expedites the development process.
Once the project goes on the floor, the torrent of data is generated during sprint development, testing, source code compiling, project management, daily meetings, and several other tasks that can be turned into meaningful information when the patterns and the future outcomes can be pre-identified. Here, predictive analytics comes in the scene.
The predictive analytics analyze the historical and current data sets using statistical data techniques, algorithms, and machine learning, and predict the results like- the bottlenecks in the development cycle, hidden opportunities, and quality issues that may pop-up. It’s not just about the development, instead, it tells a lot about the user behavior post-launch. The enormous advantages are gearing up the entrepreneurs to integrate predictive analytics and enjoy a great feat.
Let’s discuss in detail why predictive analytics is gaining traction
Level up the personalized marketing
When you are browsing the product or service on the mobile applications, you might have seen the recommendations like- The things you may like. It’s the result of predictive analytics integration in Spotify, Amazon, or eBay.
The predictive analytics based on the users’ data provide custom recommendations tailored to the users’ browsing pattern, purchase history, and demographics. The personalized suggestions make the marketing campaign relevant and consequently, drive more sales and profit.
Enhance user engagement
The predictive analytics engine helps in identifying which content or design element of the app is turning down the users or making them interested so that the changes can be reflected accordingly.
Also, the engine provides rich insights into which device and operating system the users remain most active to use the app. The valuable information helps in engineering the particular app according to the device specifications. By designing the app to the user’s expectations and needs maximizes the user engagement.
Improve the retention
The predictive analytics helps in building loyalty, in addition to higher user acquisition by providing a bigger picture of the pain points that need to be addressed and the features to enhance.
Eliminating the guesswork, the engine precisely determines the touchpoints that are impacting user interactions and conversion rate, and by fixing the issues, the users will get delighted and browse or make the purchase repetitively. The growing patronage gives an edge to the app in the competition.
Simplify the trends adoption game
A year after another the major operating systems like- Android and iOS update the OSs to improve the features and fix the bugs. The several versions of the OS raise a concern for the entrepreneurs in the sense which versions of the OS will be supported by the app because all the users won’t have the latest OS on their phone.
The predictive analytics compare the app for different OS version and then unveil the information regarding the OS version that’s mostly adopted by the users so that the developers can build the app that supports least older version to whichever is higher. This, in turn, helps in making the app user-centered which leads to higher adoption.
Minimize the risk
The predictive analytics forecast the risk level in the events of fraud, security breaches, or theft incidences by continuously gathering the data and monitoring the app usage trends. By keeping an eye on every activity, the engine can identify the unauthorized attempt and prevent the unauthorized activity by freezing the account or not allowing to use the app for next few hours alongside informing the users about the same.
The impressive benefits push every entrepreneur to integrate predictive analytics tools in the development process. There is no rocket science involved in getting started using the tools, but before we dive in, take a quick glance at a couple of the predictive analytics tools that can help you.
The predictive analytics tools that make predictive analysis easier
Localytics
The platform is a boon for the marketers by enabling them to look into the future and see which users will convert, come back, or churn. The earlier predictions enable the developers to stay proactive instead of being reactive to the user’s response that helps in engaging the users and enhancing the user experience.
Amplitude
The software that works for both mobile and web apps track the user’s activity, understand the user interactions to create user behavioral report, and instantly provide the insights to accelerate the purchase.
Urbanairship
The analytics tool provides the slew of reports such as acquisition report, onboarding report, conversion report, activation report, retention reports, and re-engagement report at every step of the customer lifecycle to drive exponential growth with marketing success.
SAS advanced analytics
Embracing the advanced technologies, SAS provides an array of analytics products such as data mining, statistical analysis, optimization and simulation, text analytics, forecasting, and others to carry out the power from the raw data and meet the purpose.
All the predictive analytics software leverage existing data to provide great insights into growth possibilities and potential risks by identifying the trends and best practices for any industry. Presently, every industry is making the software deeply ingrained into the operations, processes, and workflow to enhance the services they offer and improve the user experience.
The industries implementing predictive analytics to enjoy its perks
M-commerce
Becoming a fortune-teller is essential for the retail space to bring true delight to the customers. With predictive analytics, the brands will have more information about the customers at their disposal, and they can leverage the data to send personalized recommendations to the customers and personalize the in-app journey in an automated fashion.
This approach helps brands to look at the customer experience in context and never let the customers lose interest in the brand. Conclusively, the customer churn will stop, the conversion will increase, and the acquisition will turn into retention.
Healthcare
Learning the merits of predictive analytics, the healthcare space has jumpstarted to adopt the software to improve the patient care, increase diagnosis accuracy, enhance hospital administration, make the chronic disease management better, satisfy staff and workforce needs, and reduce waste. The clinical and financial benefits are enticing the healthcare leaders to implement the new generation prediction tools, despite having some barriers.
Banking
Satisfying the growing customer’s needs, ensuring long-term loyalty, fraud detection, application screening, and cross-selling the products in fierce competition is difficult. Taking a step further, the banking industry has found many use cases of the predictive analytics that are addressing the customer’s concerns, augmenting the product sales, and increasing the revenue alongside keeping the customers happier.
Entertainment
With increasing multimedia content consumption, the number of players offering the games, music streaming services, and entertainment-related content has also increased. To win the customers forever and get an edge over peers, it’s essential to maintain a pace with the customers, market and latest trends and fill the gap proactively, which can be achieved through predictive analytics.
The entertainment giants have turned the number of users, sales, and ROI northwards by tapping on the opportunities at the right time. For instance, the music streaming services offer tailored recommendations to the users to listen to the music that’s trending or they mostly listen to, which eliminate the user’s hassle to browse through thousands of the albums, genres, and songs.
The industries on the list are endless and give a signal that predictive context-aware app is the way forward in the years to come, but only if it’s done right, else it will become a roadblock.
Here’s how to efficiently use predictive analytics in mobile app development
Planning
Writing the buggy code and repairing them wastes hell a lot of time, efforts and resources. Such repetitive errors are not healthy for app development and even sometimes make the deadlines uncertain.
With predictive analytics, it becomes easier to identify the time it takes to write the number of lines of code by the developer and the extra time requires in fixing the mistakes in the code that helps in earlier prediction whether the app can be developed in the defined time or not.
Analysis
Predictive analytics is worth an ounce of treatment as they help in dealing with the issues before they occur in DevOps workflow rather than clearing out the rubble in the storm’s wake.
When the data from the real-world use cases send back to the developers, then using predictive analytics, identifying the mistakes in the code that are deprecating the app’s functionality and resulting in bad user experience is viable. Additionally, predictive modeling reduces the waste in app development all the way and minimize the time it takes to recover from significant failures.
Testing
Tracking the user interaction with every element of the app that’s causing crashes is difficult. With predictive analytics, it’s easier to continuously monitor which user interactions through different paths is when leading to app crash and save a lot of time and efforts in finding the mistakes.
In a nutshell
The predictive analytics market size is expected to grow to USD 12.41 Billion by 2022, at a CAGR of 22.1%.
The key factors that are driving the predictive analytics market are the future estimations and predictions for the user’s interest in the app that will lower the churn rate, minimize the app abandonment, increase the conversion rate, and uplift the revenue.
The businesses are preferring to integrate the smart hidden cameras (Predictive analytics) in the app that will not just monitor every user activity while forecasting what will be the result in the near future through reports based on data analysis. It enables the businesses to make critical decisions painlessly and make the app most significant for users like never before.
Want to take the next step towards getting the most out of the app? If so, make predictive analytics an integral part of your mobile app development strategy.
