With the increasing use of mobile devices like smartphones, most people prefer accessing the web through mobile devices. The use of mobile phones is increasing tremendously. 

According to a CNBC report, by 2025, nearly 72.6 percent of internet users will access the web using mobile phones. This percentage will be equivalent to 3.7 billion people. In this data-driven era, mobile analytics plays a significant role in marketing.

Businesses can comprehend netizens’ behavior through massive data generated by users from different apps. Big data will play influential roles in analyzing sales and marketing and determining the behaviour of the users. 

This article will give a clear idea of mobile analytics and the role of big data in rendering effective mobile analytics, also known as big data mobile analytics.

What is mobile analytics?

Mobile analytics is the practice and approach to capturing data from mobile devices that run apps, websites, and other web apps. This information is visitors’ or users’ data that companies analyze to determine unique users, record their behavior, track their journeys, identify use patterns, and report on different apps’ performance. Through mobile analytics, enterprises and marketing teams can improve conversion rates, engagement, and user retention potential.

Today, numerous app development companies and marketing departments use mobile analytics. According to the Allied Market Research report, the global mobile analytics market cap was 4.72 billion USD in 2021. It will grow to 27.60 billion USD by 2031. This significant hike will project a compound annual growth rate of 19.4 percent from 2022 to 2031. 

Big data also plays a considerable role in mobile analytics. Let us understand what big data mobile analytics means and how it is essential to businesses.

What is big data mobile analytics?

Big data mobile analytics is a particular domain of app analytics that deals with analyzing apps and websites to understand the behavior & app usage pattern of users based on gender, geographic region, etc. Big data mobile analytics use the massive collection of data produced by company systems, apps, users, and customers. By leveraging big data in mobile analytics, enterprises and app development companies can extract indicative insights, such as unknown correlations, user requirements, app demand, hidden patterns, market trends, customer preferences, etc.

Big data analytics is growing exponentially, and big data mobile analytics is no different. According to Fortune Business Insight’s report, in 2021, the global Big data analytics market capital was 240.56 billion USD. The market projection says it will grow from 271.83 billion USD in 2022 to 655.53 billion USD by 2029. It will exhibit a compound annual growth rate (CAGR) of 13.4 percent from 2022 to 2029. Since big data holds a massive amount of structured and unstructured data, it becomes nearly impossible to store, analyze, process, or manage such data with traditional tools.

Big data mobile analytics feed on those terabytes of structured, semi-structured, and unstructured data to understand market trends, customer demands, and app-related insights. By fetching massive data collection from various sources, enterprises can apprehend different metrics and identify issues by analyzing them. Big data tools and techniques can collect, examine, and analyze enormous data amounts to discover actionable insights, app trends, and customer habits that can help mobile app development companies make better decisions.

Benefits of Mobile Analytics when implemented using Big Data

In this cut-throat competitive data-driven market, performing big data-based mobile analytics can be critical to business success. 

Let us closely look at the various advantages big data mobile analytics can bring to enterprises that leverage mobile analytics.

1. Analyzing massive data metrics:

Mobile analytics measures various metrics like download rate (geographically, age-wise, etc.), app engagement metrics, in-app purchase analytics, customer events, locations from where the app is getting used, session time, app crashing, bug detection, etc.

Through big data, mobile analytics can easily manage semi-structured and unstructured data in massive amounts. Using big data tools, the mobile analytics team can handle a wide variety of data having high volumes from various sources.

2. A better assertion of customer acquisition and retention:

With massive data streamlined, enterprises can see an increase in customer acquisition and retention data. These data could depend on time, geolocation, age group, gender, app development businesses, etc. 

Enterprises can better utilize mobile analytics if the company holds a massive amount of acquisition and retention data. Big data tools can help easily manage that massive data and organize them in the warehouse so that business intelligence and mobile data analysts can use these mobile app reports to generate actionable insights.

3. Improve UI/UX in software and web apps

Since big data can hold semi-structured and unstructured data in massive quantities, development teams can feed that data into mobile analytics to generate insights on which potions of the app the users are finding difficulty with while using the app. 

From such tremendous data, business executives can also determine whether the target audience or users like the particular app’s design or color.

Based on the mobile analytics reports, the product development team, together with the design team, can improve the UI/UX of the app/site. The app development team can deliver a smooth and uniform user experience by accumulating and leveraging engagement metrics.

4. Massive data visualization

Big data mobile analytics also comes with data visualization of massive data volume. Data visualization techniques make it easy for business intelligence analysts, product managers, and decision-makers to communicate with the data and make prompt decisions. Visualizing various metrics from a massive collection of data can help non-technical professionals like marketers, sales representatives, and other departmental managers to quickly gauge and use the metrics to improve performance.

5. Data risk management

Big data features various tools and cloud platforms that can keep data backup. Such practices protect analytical data from any damage or mishap. Also, big data techniques can help narrow down the data pipeline so that the mobile analytics department can use only those particular datasets they need. It again prevents the company from data leakage, fraudulent activities, and discrepancies.

Conclusion

We hope this article has given a crisp idea of mobile analytics and big data mobile analytics. It also highlighted some significant benefits of leveraging big data in rendering mobile analytics. Big data and analytics are buzzwords, not because of the fancy terms but because of their usage. All modern businesses are data-driven and customer-centric.

Therefore, enterprises should collect massive data containing different metrics to understand customer behavior and pattern on various apps and sites. That is where big data mobile analytics can best fit the purpose.