Drive your Digital Business with Machine Learning
June 9, 2017
In the digital age, we are exposed to the early stage of machine learning on a daily basis. Things like Tokopedia product recommendation or Google now that give you traffic report of your daily work route show how this technology could predict user behavior from their personal record such as email, purchase transaction or even your GPS records. Today’s business should take advantage of this burgeoning tech to build an even better business and also improve the customer experience.
TechinAsia said that machine learning is one of the artificial intelligence techniques associated with data learning. Thanks to its algorithms or models that can learn patterns in data, you can use machine learning to predict information about many things. It is the machine learning that allows your gadget to differentiate between photos of cats and tables.
But, truth be told, it’s not as simple as it may sound. According to Business Insider, the goal of machine learning is to make everything programmatic, which allows companies to build better applications that interact with things people create, such as picture, speech, and text. This enables companies to create software that understands you. By utilizing machine learning, a business can revolutionize the way you engage with their store or use their service. It turns out to be true because according to some studies, the use of programmatic techniques enables campaign performance improvements of between 30 percent and 50 percent.
However, it’s not just about the ability of machine learning to automatically process vast quantities of data in order to understand customer behavior and identify. The machine learning is also getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, has been using machine learning technology for 10 years for fraud detection. The deep-learning technology can command thousands of data points, which enables PayPal to analyze a lot more information and identify patterns that are way more sophisticated. PayPal’s machine learning system can look at each situation more closely and see, If traditional analytics software sees a pattern of the same account being accessed by five internet protocol addresses within five days, it will flag that as suspicious. Yet PayPal’s machine learning can look at each situation more closely and see, for instance, that the user is a pilot buying gifts for his family while on the job. As a result, PayPal has successfully cut its false-alarm rate in half.
You don’t have to wait until you’re faced with some serious issues just to use machine learning, though. Actually, machine learning is already a part of our everyday’s life. It’s on your phone, in forms of virtual personal assistants (such as Siri or Google Assistant) that are able to follow instructions by voice recognition. Machine learning algorithm is also helping you to filter spams in your email, give some content recommendations, detect frauds in your credit card transactions, and many other things.
In a recent development, here are a few widely publicized examples of machine learning applications you may be or not be familiar with:
- Sales and Marketing. Dynamic pricing machine learning models predict the demand for a product at a given price, taking into account internal and external factors such as historical sales, weather and competition.
- Smart Cities. Traffic optimization can be achieved through an understanding of traffic patterns using sensor data, accidents and roadworks — where a machine learning model predicts delays or road obstructions and recommends a faster route for public buses and consumer and commercial vehicles.
- Warehouse Automation. For warehouse automation, machine learning models will take input conditions such as workload, product location and capacity utilization; also customer demand, and then contribute to optimizing warehouse to improve productivity and minimize cost.
- In Asset Performance Management, machine learning models take the operating conditions of assets such as wind turbines, solar panels and nuclear reactors as input and predict when failures will occur. The objective is to decrease maintenance costs and minimize downtime.
In Indonesia, machine learning is also used to reduce user data processing load that keeps getting larger from time to time. According to Ridzki Kramadibrata, Managing Director of Grab Indonesia, the use of machine learning in Grab can optimize their service model, distance estimation, and give more effective tariff for both drivers and consumers.
The Rise of Machine Learning
Machine learning technology has already existed since many years ago. In 1950, Alan Turing creates the ‘Turing Test” to determine if a computer has real intelligence. Two years later, Arthur Samuel, an IBM scientist, created the first learning program of checker game. The program observes which moves are the winning strategies and then adapt to it. A few decades later in 2000’s, adaptive programming started to gain popularity, which automatically dragged machine learning into a more advanced development. Now, thanks to internet technology, machine learning is everywhere, at the core of many applied technologies.
The presence of machine learning is crucially needed to analyze big data and make predictions. About 2.5 quintillions (two levels above trillion) of digital data are being created every single day. Businesses are no longer able to process these data traditionally to get important insight. According to IDC, 75% of enterprises and software developers will apply machine learning functions with complete business analytic tools, in at least one enterprise application.
So, buckle up, the machines are learning and we are about to see many unseen things.