Conceived in 1959, machine learning entered the mainstream in recent years in combination with predictive analytics and artificial intelligence. Its developer, IBM's Arthur Samuel, had no mobile apps with which to use his concept, but today's programmers do.
Increasingly, they leverage machine learning to provide their apps an edge. That edge - the ability to adapt, learn, and improve - lets the application continually develop without needing constant updates from the developer. A mobile app's ability to learn frees its programmers from the requirement of constant development of new releases. It also helps keep mobile apps small. It is impractical to expect a programmer to create code to address every possible scenario since any app that code-heavy would no longer fit on a cell phone or tablet.
Machine Learning Defined
The technology machine learning lets electronic devices process, analyze and self-actualize data. This learning extends to trend identification, pattern analysis and action implementation to fulfill an objective. One of its key benefits is increased efficiency resulting in updated programming without increased development costs or timeframes.
Businesses investing in the use of machine learning are expected to double in the next three years, reaching an uptake of about 64 percent of businesses. Allied Market Research predicts the service market of machine learning will reach $5.537 million and grow at a CAGR of 39 percent during the next 7 years.
Machine Learning and Artificial Intelligence
Developers use machine learning in conjunction with artificial intelligence to create a "brain" for an electronic device capable of the human action of learning. This occurs in mobile apps much more frequently than you think. It is one thing to say mobile apps leverage machine learning with artificial intelligence to analyze streams of data and alter internal algorithms to some distinct end.
It is another thing to explain that Netflix provides an example of this. You download and install Netflix's mobile app. It prompts you to create an account and teach it what you like. Netflix immediately has you rate at least 20 movies you have watched. Having you rate them, rather than simply indicate you watched them lets its machine brain learn 20 movies you watched and what you liked or disliked.
From those 20 data inputs, it draws 40 pieces of information - 20 movies you viewed and 20 indicators of what you liked and did not like. It combines this with data drawn from other customers who liked the same movies to suggest what they liked that you have not seen unless it is a movie you hated. The next time you log in, it asks you to rate more movies. It also suggests a plethora of films it thinks you would like. Perhaps you would like some pizza with your movie night. Machine learning helps there, too.
View a 2-minute demonstration
of InetSoft's easy, agile, and robust BI software.
Machine Learning and Geo-Location
Many corporations have created an on-demand delivery app, leveraging machine learning with varying levels of success. The success Uber has experienced with Uber, Uber Eats and the failure of UberRUSH both used machine learning, but RUSH did not fail due to the app's ability to learn, but because of the pricing Uber used. RUSH could experience a comeback if Uber hones its use of machine learning to lower the vital key performance indicator (KPI): transportation and delivery costs.
Its combination of machine learning and geo-location remembers where you hailed an Uber, the location to which you traveled and maps your pick-up points to predict your destination. Its algorithm combines historical information, the time of day and your current location to make its prediction. It suggests a couple of common results from the past and it usually pegs it. The same service in the Uber Eats app gets your movie night pizza to you quickly.
If you decided to pick up your pizza on the way home from work though, Google Maps can help you find a parking place using machine learning. It combines user geo-data in 50 cities to predict how tough it will be to find parking there and where the closest spot is.
Read what InetSoft customers and partners have said about their selection of Style Scope
for their solution for dashboard reporting.
Machine Learning and Filtering & Security
Mobile app developers also use machine learning to enhance security by filtering suspicious activity. It does this even in apps that constantly evolve such as e-mail and forums. This saves programmers from needing to manually identify a spammer by e-mail or IP address, then blacklist or block them. The automation works effectively and saves time and payroll hours.
Machine Learning and Predictive Analytics
In conjunction with a predictive analytics engine, machine learning processes big data on the fly to generate recommendations. On a simple level, e-commerce apps offer shopping suggestions using predictive analytics. On a higher level, combining with machine learning lets it adapt and improve accuracy. One example of this is when the Macy's app suggests a dress that just went on sale similar to those you purchased or viewed previously in the app.
You Can Integrate Machine Learning in Your Mobile App
You can integrate machine learning into a mobile app in small or large ways. It can help you better serve ads to customers or detect credit card fraud. You can also use it via technologies to test your mobile apps and analyze their code.
It is not infallible. The technology does have humps to get over.
Currently, the technology needs near-real-time analytics to map normal behavior. It has a tough time singling out anomalies and comparing anomalous samples to historical data. While it can summarize with ease empirical regularities, it can meet with issues when the exception to the rule presents itself.
As long as the data remains predictable, in other words, machine learning helps artificial intelligence, geo-location, security mechanisms and predictive analytics to forecast appropriate suggestions with no human intervention. That's why Netflix suggests comedy horror movies when you only like total scream fests. You probably never watched the comedy horrors, so you did not rate them. Since other people who like horror movies probably did like them, it suggests them. It tried, but Netflix did not know you well enough yet.
Ideas for Integration
Integrating machine learning into mobile applications does not have to mean that comprises the core of the app. You can dip your toe in the water of using the technology by integrating it in numerous small ways.
- Develop an advanced search that better provides contextual results and remembers prior queries from the user to provide results that they favor.
- Create a more personalized experience for your customers that remembers their favorite things, delivery options, payment preference, etc.
- Improve your customer database by using machine learning to learn from your existing customers to develop your target audiences. You can learn who your potential customers are, what they want, prices they can afford, what they search for when they purchase, their personal preferences, pain points and even their hobbies. Applied to your marketing and sales funnel, this can significantly grow your bottom line.
- Show relevant ads to your customers in your mobile app like 38 percent of executives already do. You'll sell more.
- Increase user engagement in your app itself. Machine learning can enhance chatbots, voice assistants and other customer service options. Provide options like real-time voice translation for users on WiFi.
- Improve your app's security by leveraging the ability to user volunteered biometric data to access the application or recognize and grant access to levels of data or features.
How you integrate machine learning into your mobile apps is up to you. Those already doing it recommend that individual approach. Use this technology to create simple, convenient applications that serve the needs of those who choose to install it.