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6 Ways Companies Can Leverage Machine Learning Algorithms

6 Ways Companies Can Leverage Machine Learning Algorithms

No longer the exclusive domain of data-reliant businesses like Google, Microsoft, and Amazon, Machine learning has been making its way to 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 both on and offline.

There are three key reasons why machine learning has become one ofthe top 10 strategic technology trendsthat will shape digital business opportunities through 2020.

First, the volume of data companies now collect is so massive that many struggle to make sense of it. Machine learning allows companies to take advantage of the information they already have.

Second, the computing power required to process this exponential growth in data assets, previously exclusive to Google and other tech giants, 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.

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Retailers have long used traditional A/B (or 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 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 more accurately reflect  real time supply and demand.

Customers interact with websites in different ways. By analyzing a customer’s  past behavior, machine learning can generate a personalized form of engagement for him or her, 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 companiesthat have been using learning algorithms for two or more years have seen marketing ROI increase by up to 50%.

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Predictive Analytics Worldhas 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 other large companies. 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.

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In 2014, Macy’s, the American department store giant, implemented an analytics solution fromSAP which monitored user behavior in product categories and enabled the company to send emails fine-tuned for each customer segment.;

 



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