Top 10 Ways to Gain Profit from a Mobile App

An entrepreneur’s dream is to have the ability to do everything you need, all in one place. What’s better than that one place being a mobile app directly connected to your phone; allowing you to work anywhere, anytime.

Apps are definitely a growing market, especially amidst the pandemic. We have relied on apps for nearly everything. From connecting to friends, business meetings, and starting up businesses to find alternative ways to make money without the need to go anywhere. 

However, as this is a fast-paced growing market, there is a significant amount of money that goes into apps – billions of dollars for a rough estimate, and that number is always continuing to grow. 

Based on the stats from Statista – global mobile app revenues were calculated to be $69.7 billion US dollars in 2015, and in a short 5 years it is estimated to basically triple to be $188.9 billion by 2020. 

You may have come up with a savvy idea for an app, but like most, are unsure how to make it a reality. We’ll be discussing and going into detail on just how to do so.

How to Generate a Profit from Mobile Apps

In order to make money, we need to understand the HOW.

The 3 Basic Methods on how to get monetization include;

• App downloads

• In-app purchases

• In-app advertising

As you must know, if you want to maximize the number of downloads your app should be free. Although, it is recommended to create both a paid and a free version and analyze which one will make you the most money.

Deciding which monetization method to use for your app, all trickles down to WHAT kind of app you want to launch, as well as your target audience!

Conclusively, the best strategy to monetize your app, is to integrate it and make it feel like an ordinary part of the experience; you want to grow a large, active user platform to increase chances for revenue.

In-App Purchases

• An option that makes initially downloading the app free, but then provides consumers who would like to, pay for “deluxe” features within your app.

• This is most common in games or gaming apps; for new levels or accessories for characters

In-App Advertising

• Creates revenue from clicks and impressions

• In-app advertisements can vary for different sizes, positions, and placements

10 Steps to Creating a Profitable App

1. Create a vision

• Find your purpose and come up with rough ideas

• Questions you can ask yourself:

o   What do you want your app to do?

o   What problem is it going to solve?

o   Who is your target audience?

o   How are you going to make money with your app?

2. Research the Market

• Take your idea, and investigate what other apps are out there like it

• Plan accordingly and design your app so it stands out from the others

3. Create a Sketch

• Put your idea to paper, and draw out what each individual page and screen would look like

• It can be done on pen and paper or you can use a wireframing structure such as;

o   Pidoco

o   Axure

o   Microsoft Visio

o   Evernote Penultimate

4. Design the Appearance

• Take your wireframe ideas, and add colour and design

5. Back End Coding

• The largest phase of the app development

• While your code is being created by software developers, it is a good opportunity to register for a developer account with Google Play or the Apple App Store

6. Test your App

• This is a crucial stage, since you want to make sure your app is fully functioning

• At this point every screen should work and appear well

7. Modify and Adjust

• From the testing stage; adjust your prototype and make the tweaks necessary to polish up the app

8. Beta Testing

• This allows you to recruit testers from your target audience

• You can recruit the ideal beta testers from Twitter, Reddit, etc.

• Make sure the testers get engaged, giving you the most feedback

9. Launch your App

• Wrap up the final changes and then release!

• For Android: you are able to simply add your app and start selling it in Google Play

• For Apple: your app will have to be reviewed before it goes live, this takes time, anywhere between 3-10 days

10.  Market your App

• Promoting and marketing your app is important to ensure consumers know about it and start downloading the app; so, it can begin making money right away

• A good way to market your app is to promote it through your social media platforms, for example on Instagram through both stories and posts.

Creating a profitable app is no easy task, reach out to us today and learn more about how we can help you launch your next great business idea!

3 Indications to Update Your Software

Our lives are busy, and so is our technology; but sometimes our software and technology will slow down, and not perform as it used to when it was brand new, which can sometimes be aggravating for a business owner.

However, choosing to update your software to the newest version can often be difficult, as it could be pricey, and also exhausting trying to figure out a new version. BUT, keeping older application software can also take time and money the longer you put off updating.

Not upgrading your software will also lag the proficiency on how much work you can get done. It can also change the security settings, which could cause your software exposure to be exposed to unwanted information.

With that, here are 3 indications on when you know it’s time to upgrade:

1. Dysfunctional Language

Having the ability to recognize that your e-commerce business is no longer keeping up with the market and your competitors are surpassing you with their system, is a good indicator that it’s time for you to look into upgrading your software.

Warnings:

• Software Updates – that are slow or not evening happening, when your software isn’t being upgraded regularly, your security system can be affected. Not having an upgraded software won’t allow you to see the new techniques and performance will begin to lag.

• Getting support for your software will take longer, which will be a sign that your server won’t be accessible to programmers who know your specific platform or language.

• You’re starting to have problems meeting basic business requirements, which could mean maximizing your platform is limited and the server doesn’t have professionals to assist your needs.

2. Losing Control

One company is hired by a small business to create a software solution to keep tabs on their sales and production processes.

However, the application software is compacted with bugs and glitches not meeting the needs of the company, because the software is hosted by an outsourced server, you then have no control or access to it.

Warnings:

• Not having immediate access to your own software data or code – it could be on your premise in the cloud. A good server should NEVER keep your business data captive.

• Even if it’s not old, your software isn’t meeting the business needs – if you try to find a solution, but it’s not working out because your server isn’t helping, it’s an indication it’s time to find a new server.

• When your solution continues to have bugs and glitches – that are not being corrected, SERVERS should be working quickly to solve the issues, not neglecting it.

• The server is SLOW and not responding or neglecting your requests – which can often show limited professionalism, and expertise. Whatever the case, a company should be actively trying to find someone who is capable. 

3. Administration Slow Downs

Having a new company launch a cloud-based solution more than 10 years ago – that was once an industry leader, but is now slacking because of lagging software, even when they’ve done minor upgrades here and there. This is not what you want in an industry leader. 

Warnings:

• When you have daily customer complaints – about your system appearing dated and not responding to multiple devices.

• When you begin to compete in the industry – despite having irresistible features. If users think it doesn’t look good then they won’t promote your software to find clients, they will go for another more appealing company.

• Your overall software just slows down – when you add new features to try and make it better it ultimately makes it worse because of the older internal system.

Moral of the story is to ensure that you’re not only keeping up with the aesthetics of your software and applications, but also staying on top of upgrading the internal system. 

If you need assistance with your internal servers, feel free to reach out!

Thorough Analysis of Big Data Frameworks: Spark vs. Hadoop MapReduce

Choosing the right big data framework is a challenge, especially since there are many available on the market. Examining each framework from the perspective of certain needs is probably the best bet for your business, rather than comparing the pros and cons of each platform.

Our big data consulting practitioners compare two leading frameworks to address a lingering question that many people have wondered. Between Hadoop MapReduce or Spark, which framework should you choose? Let’s dive in!

Checking Out Market Situations

Taking a quick glance at the market situation, we know that both Hadoop and Spark are the flagship products in big data analytics, with Hadoop leading the market for more than 5 years. Both frameworks are open-source projects by the Apache Software Foundation. 

The user base of Hadoop amounts to 50,000+ clients according to our market research, while Spark possesses 10,000+ installations. In 2013, Spark’s popularity surpassed Hadoop in only a year. A recent growth rate for installations indicates that the trend is still ongoing. Spark correspondingly outperforms Hadoop with 47% vs. 14% (2016/2017).

Main Distinction Between Frameworks

The main difference between Hadoop MapReduce and Spark, in fact, resides in the processing approach. While Hadoop MapReduce needs to read from and write to a disk, Spark can simply do it in-memory. There is a significant difference in the speed of processing as a result from this. 

Spark potentially could be up to 100 times faster. You also have to consider the volume of data processed since that differs between the two frameworks. Hadoop MapReduce is able to operate with much larger data sets as opposed to Spark.

What tasks are each framework good at? Let’s take a closer look.

The Good About Hadoop MapReduce

Huge Data Sets – Linear Processing: 

Hadoop MapReduce permits massive amounts of data to be processed in a way where two or more processors (CPUs) handle separate parts of an overall task. This is known as parallel processing. 

Large chunks of data are broken down into smaller pieces that are processed separately on different data nodes. The results from multiple nodes automatically get gathered to return a single result. Hadoop MapReduce may outperform Spark if the resulting dataset is larger than the RAM available.

If Speed Doesn’t Matter, This Is For You: 

If the pace of processing isn’t crucial for your business, then Hadoop MapReduce is considered to be a good solution. It makes sense to suggest using Hadoop MapReduce if data processing can be done during the night hours.

The Good About Spark

Speedy Data Processing: 

Spark is faster than Hadoop MapReduce as a result from in-memory processing. Up to 100x faster for data in RAM and up to 10x faster for data in storage.

Iterative processing: 

Spark defeats Hadoop MapReduce if the assignment is to process data repeatedly. Resilient Distributed Datasets (RDDs) by Spark authorize multiple memory mapping operations. As compared to Hadoop MapReduce, it must write interim results to a disk.

Processing on-the-fly: 

Businesses should opt for Spark and its in-memory processing if it needs immediate insights.

Processing Graphs: 

For iterative computations that are common in graph processing, Spark's computational model is great! Plus, Apache Spark has GraphX, an API for computing graphs.

Machine Learning: 

Spark has a built-in library for machine learning that has out-of-the-box algorithms which run in memory. This function is called MLib. Hadoop needs a third-party to provide a machine learning library.

Combining Datasets: 

Spark can generate all combinations faster because of its speed. However, Hadoop may be better if it’s necessary to join very large data sets that require a lot of shuffling and sorting.

Practical Application Cases

Thanks to near real-time processing, Spark is likely to outperform MapReduce after examining several examples of practical applications. Let’s look at the examples.

Customer Dissection: 

To create a distinctive customer experience, businesses need to have an understanding of customer preferences. To help with this, customer behavior should be analyzed while identifying segments of customers that demonstrate similar behavior patterns.

Risk management: 

By selecting non-risky options, predicting various future scenarios can help managers make right decisions.

Fraud detection in Real-Time: 

By using machine-learning algorithms, the system would be trained on historical data where then these findings can be used to detect or predict an anomaly in real time that may indicate a potential fraud.

Industrial Big Data Analysis: 

It's all about identifying anomalies and predicting them, but these anomalies are connected to machinery breakdowns in this case. To detect pre-failure conditions, a correctly designed system collects the data from sensors.

Hmmm, What To Choose?

To determine which framework you should choose, the needs of your business will help guide you to make a final decision. Hadoop MapReduce has an advantage when it comes to linear processing huge datasets. Spark is fast, efficient and provides real-time analytics, graph processing, machine learning, and much more! One last thing that might change your mind, Spark is fully compatible with the Hadoop ecosystem.

To gain more insight on making your decision, get in contact with us.