Predictive Analytics for Mobile Apps-Detailed Guide
A few years ago, Gartner estimated for 2018 that less than 0.01 percent of consumers’ mobile apps would be considered a financial success.
The shocking fact has shaken up the businessmen from top to bottom who remain stuck in the optimistic system of thought and forbid the term ‘failure.’ If you want your app to stand out in the noisy app market, knowing the worst things that can cause your app to fail is the key to app success. What can you do to help users abandon an app, attract user attention, or convert users (revenue)?
This is a mystery that is difficult to solve and if not solved, it will make your app history unknowable. Do not worry! 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 responds to the application before the actual experiment.
What is Predictive Analytics?
Predictive Analytics is a crystal ball that tells you everything that happens to the app and the actions that prevent or enhance it to engineer an app that will delight future users. It takes essentials from every activity related to mobile app development, which speeds up the development process.
Once the venture is on the floor, a stream of data is generated during sprint development, testing, source code compilation, program management, regular meetings, and many other tasks that can be transformed into important insights when patterns and future results can be pre-identified. Here, predictive analysis is coming into the scene.
Predictive Analytics analyzes historical and current data sets using statistical data methods, algorithms, and machine learning and predicts outcomes such as constraints, hidden opportunities, and pop-up quality problems in the development cycle. It’s not just about development, but rather, it says a lot about post-consumer behavior.
Few examples of predictive analytics applications:
Amazon, eBay, and hundreds of other shopping platforms use predictive analytics to provide product recommendations based on your browsing history and past purchases.
Google and other search engines use predictive analytics to provide suggested search terms based on your search history, location, and regional search trends.
Predictive analytics can be used to recommend another member of the online dating website that is a good match.
Now let’s understand what ways to use Predictive Analytics for Mobile Apps?
1. Using Predictive Analytics to Drive Sales and Profits
If you are developing a mobile e-commerce platform, Predictive Analytics can give customers good recommendations for products or services they haven’t purchased. For example, the Predictive Analytics Engine may indicate the items that a shopper adds to their shopping cart, as well as the items that are frequently purchased. It is ideal for any product with accessories or refills. The mobile device has the added advantage of determining the exact geographical location of the user, which helps in making recommendations for brick and mortar shops, restaurants, attractions, and other businesses.
Similarly, apps can collect shoppers’ browsing data. It is used to find out which featured items appeal most to a person who has seen or purchased a particular product or service. Apps that make money using ads also benefit from predictive analytics. This technology is widely used to serve ads related to past purchases, past product/service page views, location, and other user data.
2.Predictive Analytics to Promote Engagement and Interaction
Predictive Analytics is effective in making recommendations based on user behavior, which is best suited for mobile applications that rely on engagement or interactions, such as a social media platform or dating app. A predictive app provides recommendations on who to follow, which groups to join, and which pages to like. Your users can also get recommendations for friends (or matches). Social platforms such as Facebook and Twitter are already using this technology to recommend a friend and a follower, so users expect some of this.
What’s more, predictive analytics applications can be built to collect user data such as user’s location, friends, likes, hashtags, and keywords in posts. Based on this data, the social platform increases the attractiveness of the content in the user’s feed.
3. Predictive analytics to reduce risk and prevent fraud
It can help to assess risk levels for security breaches, theft incidents, fraud, and other damages. Therefore, many financial institutions, identity theft surveillance agencies, and cybersecurity groups are leveraging PA technology to detect trends that indicate higher than average risk.
The Predictive Analytics Engine collects data over time, identifying trends around high-risk areas. Then, the PA engine can monitor those trends, ranging from monitoring the customer’s account activity to monitoring the login efforts of a company application that contains sensitive information. Developers can also configure your system so that it freezes account functionality or prevents additional app login attempts from pending identity verification.
Now Understand in detail why predictive analytics is gaining traction
1. Increase personalized marketing
When you browse a product or service on mobile apps, you may see recommendations like “things you like.” This is the result of Predictive Analytics integration on Spotify, Amazon, or eBay.
Predictive Analytics based on consumer data provides custom recommendations tailored to users’ browsing patterns, purchase history, and demographics. Personalized referrals make marketing campaigns relevant and, as a result, generate more sales and profits.
2. Improve user engagement
The Predictive Analytics Engine helps in identifying which content or design element of the app rejects or is of interest to users so that the changes are reflected accordingly.
The engine also offers a range of insights into which computer and operating system the users are still most interested in using the app. The useful knowledge helps to design the particular app as per the specifications of the computer. Through tailoring the app to the user’s preferences and needs, we can maximize the User Engagement
3. Improve retention
Predictive analytics can help build credibility, along with greater user acquisition, through a larger picture of pain points that need to be addressed and improve features.
Minimizing guesswork, the engine precisely defines the touchpoints that influence consumer interactions and conversion rates, and by resolving the problems, users will be pleased to browse or purchase repeatedly. Growing patronage gives the app a competitive edge.
4. Simplify the game to adapt to the trends
Major operating systems, such as Android and iOS, update OS after a year to improve features and fix bugs. Many versions of the OS are of concern to enterprising entrepreneurs because the app supports any version of the OS because not all users have the latest OS on their phone.
Predictive Analytics compares the application for different OS versions, and then users can generate information about the version of the OS they are most likely to adopt so that developers can build an app that supports at least the older version. This helps the app become user-centered, leading to higher adoption.
5. Reduce the risk
Analytics assesses the risk of fraud, security breaches, or theft incidents by continuously collecting data and monitoring application usage trends. By keeping track of every activity, the engine can detect the unauthorized attempt and freeze the account or block the unauthorized activity or notify users about it for the next few hours without allowing the app to use it.
Impressive benefits push every entrepreneur to integrate predictive analytics tools into the development process. There is no rocket science to start using tools, but before we get into that, let’s take a quick look at two predictive analytics tools that can help you.
Some Predictive Analytics Tools that facilitate attendance analysis
This platform is a boon for marketers by looking at the future and seeing which customers are converting, returning, or pranking. Previous predictions enable developers to be proactive rather than reactive, which helps engage users and improve the user experience.
Firebase is a mobile platform (supported by Google) that is used to develop applications that help your user base grow on iOS and Android. From analytics, databases, messaging, and crash reporting, you can analyze everything in one central location. Firebase is also integrated with other Google products such as Google Ads and AdMob.
Analytics tool provides reports such as Acquisition Report, Onboarding Report, Conversion Report, Activity Report, Retention Reports and Re-engagement Report at every stage of the customer lifecycle to increase exponential growth with marketing success.
SAS offers a range of analytics products such as data mining, statistical analysis, optimization and simulation, text analytics, forecasting, and others to embrace the latest technologies and leverage the power from raw data.
Any predictive analytics software leverages existing data to provide great insight into growth opportunities and potential risks by identifying trends and best practices for any industry. At present, every industry is deeply embedded in software, operations, processes, and workflow to improve the services they offer and enhance the customer experience.
In a Nutshell
The predictive analytics market size is expected to increase to USD 12.41 billion by 2022, with a CAGR of 22.1 percent.
Analytics Key factors driving the market are the prospects and projections that predict the user’s interest in the app, which can reduce churn rate, reduce application abandonment, increase conversion rate, and increase revenue.
Businesses prefer to integrate smart hidden cameras (predictive analytics) into the app, which does not monitor every user activity when predicting the outcome in the near future through reports based on data analysis. It enables businesses to make critical decisions painlessly and makes the app more important to consumers than ever before.
