No longer the exclusive domain of data-reliant businesses like Google, Microsoft, and Amazon, machine learning has been making its way into the masses as an essential approach to data. Today, machine learning is understood and accepted by a more mainstream audience, and has become a measurable driver for big business benefits both on and offline.
There are three key reasons why machine learning has become one of the top 10 strategic technology trends that will shape digital business opportunities through 2020.
First, the volume of data companies now collect is so massive that many companies struggle to make sense of it and fail to take advantage of it.
Second, the computing power required to process these exploding data assets, previously exclusive to the Googles of this world, is now widely available to smaller businesses.
And third, machine learning has been a buzzword across all types of media, attracting even more attention to the subject and fueling its growth.
We’re all familiar with Google’s targeted advertising. This is just one manifestation of how companies utilize machine learning algorithms and tools. Machine learning, though not exactly a new technology, has been gaining momentum across different industries. Let’s explore a few examples.
Retailers have long used traditional A/B and bandit tests to decide what product prices yield maximum profit. The problem with this approach is that prices are set by humans, and are therefore prone to human error.
With predictive analytics powered by data, statistical algorithms, and machine learning techniques, you can build a model to create real-time optimal pricing using historical product prices, customer behavior, preferences, order history, competitor prices, and other criteria.
Here’s a video of Uber’s senior data scientist and Airbnb’s product lead explaining how they use algorithms to set prices that suit the real time supply and demand in the best way possible.
Customers interact with websites in different ways. By analyzing customers' past behaviors, machine learning can generate a personalized form of engagement for each customer, be it viewing a product, signing up for a newsletter, clicking on a promotion, or something else entirely.
Forbes Insights and Lattice, a provider of predictive marketing solutions, have found that 86 percent of companies that have been using learning algorithms for two or more years have seen marketing ROI increase by up to 50%.
Predictive Analytics World has revealed that an undisclosed educational portal used by 1 in 3 high-school seniors adopted a predictive ad system to better match their promotions with website users. As a result, their response rate grew by 25%, generating approximately $1 million of ad revenue every 19 months.
Personalized user experience makes even more sense if a company has millions of active users. Websites like Amazon, Netflix, OKCupid, Pandora, and Twitter, all of which boast audiences in the millions, use machine learning algorithms to provide their customers with better recommendations, and therefore allow them to make more customized decisions.
This level of personalization has undoubtedly played an essential role in the success of Twitter, Netflix, and the like. Predictive analytics improves customer retention and reinforces brand loyalty by basically eliminating the users’ need to go to any other website.
Well-targeted promotions are key to the success of a retail business, but getting them right isn’t easy. Here, learning algorithms come into play by analyzing data from numerous sources and creating customized promotions that work for a certain customer or segment of customers.
In 2014, Macy’s, the American department store giant, implemented an analytics solution from SAP which monitored user behavior in product categories and enabled the company to send emails fine-tuned for each customer segment. This resulted in an 8 to 12 percent increase in online sales within the first three months.
Leveraging big data is by no means exclusive to companies in the world of retail. Turkcell, Turkey’s leading mobile carrier, analyzed usage patterns, locations, device information, and over 150 other kinds of customer data to offer relevant promotions in real-time, reducing churn and boosting ROI.
If you’re thinking machine learning is the bailiwick of digital companies only, you couldn’t be more wrong.
Companies that rely on machines can be highly impacted by outages and poor performance. Machine learning allows these businesses to avoid costly downtime or service interruption by providing an accurate forecast of impending equipment failures.
General Electric, for one, makes use of machine learning tools to process the data it collects from deep-sea oil wells and jet engines to predict breakdowns and optimize performance.
Fraud costs the US insurance industry alone about $80 billion a year. The total cost of identity theft and related crimes globally is all but impossible to fathom.
In order to detect and prevent fraudulent transactions more efficiently than the old statistical modeling approaches allow, many banks across the world have begun to adopt machine learning methods. These methods are capable of detecting even the most subtle patterns in financial transactions, and can learn to predict fraud before it’s even committed.
IBM has revealed that one large US bank, which used its machine learning technologies to fight fraud, enjoyed a 15 percent increase in fraud detection, a 50 percent reduction in false alarms, and a 60 percent growth in total savings.
PayPal, which together with Google Checkout and other digital payment methods accounts for up to 20 percent of fraud at retailers that accept these alternatives to credit and debit cards, has also been employing different machine learning algorithms to analyze thousands of data points in real-time, including recent activity at the retailer’s or PayPal’s website, purchase history, IP address, and cookies as part of their fraud detection mechanism.
Interestingly enough, machine learning has also found its way into the soft stuff of talent management.
McKinsey & Company reports that its HR personnel have tested four algorithms to predict which candidates for its open positions would prove most successful. After using the algorithms to analyze over 10,000 scanned resumes, the forecast they got strongly corresponded with real-world results.
Moreover, the algorithm actually accepted a slightly larger number of female candidates, which means that machine learning could be used to counter human bias and tap into a wider and more diverse range of talent.
Machine learning enables companies to automate analysis traditionally done by humans. This way, they not only eliminate the chance of human error, but also discover patterns and trends in their ever-growing datasets and reach evidence-based decisions in much shorter terms.
Just like a bank without databases will have a hard time competing with the one that has them, a business that hasn’t implemented machine learning techniques will lag behind a business that has.
The first company’s data engineers will continue writing hundreds of rules to anticipate what its clients want, while the second company’s learning tools will automatically devise billions of rules for every single customer, allowing it to take the best step forward in virtually any scenario.
One of the major challenges that companies face when adopting machine learning algorithms is building and maintaining a data science team capable of putting the system into production and maintaining it.
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