Everything You Wanted to Ask About Using Big Data for Mobile Apps

Use of mobile devices has reached fever pitch already. Pocket devices like phablets are predicted to multiply in usage in the coming years. So much has become peoples’ intimacy with their mobile devices, that individuals carry their cell phones with themselves, each minute of their waking hours, and often, through their sleeping hours too! Talking figuratively, mobile phones are honest witnesses to peoples’ activities throughout the day.

While such a deep-placed use of mobile devices is stirring streaks of concern with respect to deteriorating inter-human bonds, it is a potential space where businesses can thrive.

And that’s where big data comes to play its role. We all know what Big Data is. While it began its protruding roots in 2005, Big Data has come a long way, and worn a lot of functional layers ever since. It has definitely evolved a lot from Hadoop.

Hadoop v/s Spark — What should businesses go for?


Read more about the difference between Hadoop and Spark here

Combine the amazing capacity of Big Data to pocket in petabytes of information, with the amazing data points about user behaviour, that mobile devices can collect, and you will get the magic potion of a successful business.

Well, we think so…..but there are a few things here and there, that you must know to ensure, you are making a balanced investment.

Big Data Statistics You Need to Know


How is Big Data big for the app market?

No matter what your app is meant to do (that might be something as private as a period tracker, or something as generic as an investment planner), you sure need a thing — information about your users. This information would necessarily contain intricate details about their behaviour related to app usage ( what are the triggers that motivate them to download the app, what are the scenarios that drive app usage and what are the turn off points that lead them not to rate the app or uninstall it).

How do you get all this data?

During the onset of internet, collection of such data was scanty. Slowly, web innovators like Facebook and Google begun to have such a huge inflow of user data (user behaviour was the very base structure of the foundation of these giants…they would simply go extinct without a deep knowledge about how their users perceive them). There was soon the need to create something that could efficiently source all the data, filter it, analyse it and present it in a format, that would give a pure insight that was actionable and implementable.

Legacy infrastructure was built and that was how Big Data came into being.

Enterprises, now a days, use this very thread and have evolved ot to weave a fabric of deliverable user experience. Big Data offers a complete pool of data to app makers and marketers (coupled with deep learning technologies, which we shall discuss in later part of this article). They are exposed to the whereabouts of their users — when did a user wake up, what was the last time he calls a friend for hanging out, which restaurant they went to, what did they order, which can service they used, which music they swayed at, how many selfies they uploaded on their Facebook accounts, who were the friends he went out with, where those friends went to after hanging out….whoa!!!! This is dangerous.

With all that information at hand, app marketers get an exact idea of which kind of messaging or push notification would influence what action.

That is what makes Big Data, a pretty cool thing, when it comes to mobile apps.

What is it with location specifically?

For marketers of a large number of mobile applications (retail being the largest candidate under this genre), to know the ‘when’ part of their communication, they need to know where their users are, specifically at this moment.

Heard of Beacons? Awesome technology we must say! Such techniques source in the data and put it in the Big Data pool.

Location is an important parameter of user behaviour and it is crucial to track their whereabouts to promise them the best kind of service experience.

Why are mobile applications of prime importance here? Here is the answer:

“The big difference is that desktops are stationary and laptops do not in general have GPS sensors, so there is a location awareness in the data that can be captured and mined from a mobile device,” says Andrew Purtell, principal architect at Intel.

Mobile devices are the only way to track app users (they literally dance in pockets all day long)

That is another reason, why Big Data just strikes more comfortable with mobile applications (the opposite deems truer).

Is Big Data indispensable for every industry?

Yes, ideally speaking. More so, if you are dealing directly in the consumer market. The reason is obvious — If you are B2B player, your product or service will have a more structured, disciplined and defined utility case. So no matter ho much you customise, the core still remains the same. However, if you are catering directly to end customers, each individual is a potential business to you. The way you customise your communication, the cannels you use to reach them and the time during which you pitch your message, play deciding roles in whether you are tapping into the potential business or are just doing some random guess work.

Here are the top 5 industries that use Big Data well to their benefits.

big data mobile apps

Retail — Use of big data in retail is ubiquitous. A deep data analytics will help you find out more about your customers, your business sentiments, the channels that they use to read you and a lot of other such stuff. BestMart has done it absolutely right. There are other major retail brand that have utilised big data really well to increase their business out put.

Macy’s says big data helped boost store sales by 10 percent, and Sterling Jewelers said it increased holiday sales by 49 percent with help from big data.

Target has used Big Data and Data analytics so well for its product range for pregnant women, it made to the best success stories of Big Data utility in retail sector.

Consumer utility — This would include banks, telecom, conveyance and other utility services that impact people in their day to day lives. We all know about Uber. This brand has made a really fabulous utilization of Bid Data to know about when and how, it’s customers use its services. They have also used predictive analysis to know which channels, existing users might be using in the future, based on their social engagements. And based on that, they have created the facility to book an Uber, right when you are planning your hangout on Facebook messenger. (use screenshot here).

Healthcare — Wearables are already using a thread of Big Data under general health and fitness criterion. But delving deeper, giants like Google and Apple are making interesting pictures in mainstream healthcare.

While Apple’s healthkit is helping connect hospitals, doctors and patients together through real time data analytics and report generation, Google’s parent company in healthcare, Alphabet too, is taking proud strides.

iCarbonX is doing some really interesting work in this area. It proposes to personalise health management to micro levels, thanks to the wonders of Big Data.

Home automation — We are sure this is future, and this future is completely set up in Big Data frameworks. When IOT and automation are completely implemented (they are still in their insignificant nascence), they would be entirely dependent on data.

In fact, it is practically impossible to talk about one, without the other. 

Manufacturing — The manufacturing industry can utilise the powers of big data in two very distinct ways — to track and improve on-floor operations and, to track usage patterns of users to improve ergonomic and functional utility of products that are being manufactured.

While the first use case has been used largely by different players across the industry, the second use case is still, largely overlooked.

Big Data is being currently used mainly for tracking purposes, across manufacturing units. It can however be well used to improve supply chain as well.

Banking — Personetics has built a packaged application that leverages Big Data and applies this to a bank’s digital channels. The predictive banking solution immediately presents customers with what they need to know each time they log into a banking application, always offering a personalized list of prioritized items they should be aware of and the right paths to resolve issues and answer questions within the digital self-service channels. Personetics promotes financial awareness of what’s going on in a customer’s account at any given time. Right now, banks can leverage Big Data and predictive customer interactivity technology to improve CX, reduce service costs, and increase revenue with timely, personalized offers.

Big Data or Cloud?

This is a question which a lot of Big Data adopters have. Worth noting here is, Big Data is not a technology or a tool….it is a pool of data that businesses source from various devices that their consumers use. As such, it is an ever expanding pool. Scalability is the crux here.

Same is the case with cloud.

Building an entire business in a framework functioning as its core backend is costly affair. Thing is, even start ups now-a-days, need ample information about their customers, to plan proper service quality. These are businesses that cant invest a lot initially, and have a lot more to lose than they have to gain. Investing phenomenal amount of cash in a framework that is so huge, right from its birth, is not a really advisable risk.

Cloud is a better solution. Two main reasons — You share it, so it costs you less. Second, larger conglomerates take care of the security of the cloud framework. So you have less security risks.

Amazon and Google are two major names in this area. New players are joining in. Apple has still kept its cloud a little guarded out, yet liberation is on the rise, bit by bit.

Machine learning, AI and Big Data

When we talk about Big Data, Machine learning and AI come unasked for.

Watch this and stay as awed as we were.

Machine learning is so damn important, because mobile devices and wearables are the only sources through which user behaviour is conveyed to businesses.

The more machines get intuitive, the more complex and interesting Big Data will get. And the more insightful will be decisions that lead to business success or failures.

The scenario in 2020

With app revenue expected to double from existing $51 billion, big data is crucial for the app market to touch $100 billion in 2020. Data analytics will play a huge role in achieving this.

Business Insider reports a study conducted by Pyze, an app analytics startup, found that companies using big-data to optimize customer insights, showed that leveraging their data helped the generate a 35% increase in app engagement. This in turn drove revenue up 20%.

Big data and analytics can also be instrumental in helping large enterprises improve user engagement (even if the enterprises are not going for app) but wish to improve relationships with customers over mobile.

In Summary

Using big data will be inevitable for brands and businesses if they want to get the best out of their mobile apps. Big data provides useful insights into customer behaviour and can help brands/businesses make better decisions that revolve around important stakeholders besides end-customers. We are already seeing around 73% of organizations investing in big data to get more sense out of of numbers to bring positive impact on their business. And, this percentage will only increase in the coming years.

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