Why mobile application developers are betting on big data
To survive in the digital world, developers and enterprises should harness the power of big data or risk being wiped out by smarter players. 
Yash Mehta Aug 24th 2017
The avalanche of the mobile application development industry is said to reach USD 100 billion by 2020. These figures are quite mind boggling but what is more surprising is that each year more than millions of entrepreneurs launch their mobile applications via Google play and App store. Out of this humongous number of applications launched each year, only a fraction survive in the market. The reason for failure most of the times is lack of sufficient data and business intelligence to predict market trends.
On one hand, the topmost and premium mobile applications generate revenue of millions every month, while on the other hand other small mobile applications struggle to even make USD 400. This model of constant uncertainty and unpredictability that leads to the debacle of most mobile applications can be changed by effective use of big data analytics. The market could be better understood by access to the real time data and its analysis subsequently.
How big data can help mobile application developers:
End Users: The most important aspect of any business to grow and sustain in the market is to understand their potential users. Big data and analytics can be used to understand market trends and can help mobile app developers to build a sticky consumer base. Big data is the data that is collected from a number of sources that include social media and other media channels. With the help of analytics and other machine learning tools, this data can be grouped and clustered together to get an insight into potential user behaviors and their expectations.
Efficiency and performance: If developers have a strong hold over the statistical data of how much traffic their application will generate, they can work accordingly to enhance the performance and efficiency of their application. The performance glitches often come into picture when suddenly the traffic accentuates at some point of time and the applications are unable to handle and perform smoothly.
Scale up the revenue: As big data is an ocean of user data you get to have all the intricate details of users. For example, big data can tell which place you last visited for dinner or how many pictures you clicked and uploaded on Instagram, which music you listened to and much more. The analysis of such data can determine which type of push notifications should be sent to which type of users that will initiate a call to action. This strategy helps to scale up revenue percentages as more and more users will be liable to use the mobile application. The revenue of major mobile app stores is expected to decrease in 2018 because of the launch of applications like TutuApp, which collaborate with large enterprises to provide paid apps for free, thus hampering the revenue model of app store giants.
Actionable analytics: Big data focuses on an array of business functions. The latest one that is gaining momentum is the “when” aspect of the users. Businesses that focus on location transparency can use actionable analytics to enhance the user presence and engagement. Usage of profile creation sites can also provide actionable insights to improve the branding of the product or application online. Big data analysis combined with location specifications can help mobile applications to serve their users better leading to more business conversion.
How big data is being used by giants
Myntra: If you are an avid online shopper then you must have received a number of emails and text messages from various sites. If you ever go through these emails or the suggestions, you realize that the products have a striking similarity with what you browsed last. This is where predictive analysis comes into the picture. It enables companies to strategize methods to generate desired engagement of the customer. Not just that, it also helps them to devise and diagnose their website design, peak hours of the day when they expect maximum traffic, products that will be in highest demand, and the regions from where the highest orders will come.
Uber: Uber is one of the leading and most successful mobile applications whose entire model is based on big data crowdsourcing. Uber connects users in need of a cab service to the respective driver in their location. The amount charged is based on the time taken to reach the destination. Uber implements the concept of surge pricing that uses big data analytics. Surge pricing means that you will be charged extra money, that can be double or more depending on the peak hours, traffic conditions, demand of cars in a particular area and others. This big data algorithm analyses real time traffic conditions, the time taken for the journey and accordingly implements surge pricing.
To survive in the digital world, developers and enterprises should harness the power of big data or risk being wiped out by smarter players. 
The author is founder of TuTuapp
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