In 1986, Tom M. Mitchell published this definition in his ground-breaking book Machine Learning:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”
Since then, the idea of Machine Learning (ML) has gone from a wordy definition to a widespread practice, with companies increasingly putting it to work to leverage data and tap into new revenue streams.
ML is the application of applying algorithms to parse and organizations data, learn, and then make predictions about possible results. This can mean anything from speech recognition to aid in customer services, to more complicated tasks like image recognition in video or predictive analytics to help businesses make forward looking decisions.
As ML has matured, so have the opportunities for businesses to increase revenue. Here are three of the biggest opportunities we see in the years to come:
1. Increased customer acquisition
Through ML, companies can gain instant access to crucial information about their clients. Information like interests, general location, and behaviors that can be used for better targeting and more successful campaigns.
Example: A retailer is launching a campaign around a specific holiday. Through ML, that retailer can tailor every aspect of the campaign to target only those customers identified as, say, major holiday gift-givers or past visitors to their store on that particular holiday.
Not only does this reduce the overall costs of the campaign, it narrows the scope of potential customers so the campaign can be better personalized for those likely to spend money around the specific holiday.
2. Better product recommendations
In e-commerce, data is king. With ML, companies can assess what features or functionalities will best retain users and increase revenue. They can also utilize smart data analytics to optimize inventory forecasting and test the waters on pricing strategies.
Example: A couple years ago, Forbes published an article on how jewelry.com was able to use ML to increase revenue by 39% through homepage recommendation personalization.
The secret, as noted in the article, was for the company to employ ML to automatically pinpoint the most effective promotional strategy for every user, whether they already had a history of interacting with the site or were new arrivals. The end result was a homepage that would look completely different for those already familiar with jewerly.com, with recommendations that were likely to pique their interest.
3. Business process automation
File this opportunity under cost-savings and efficiency. By using ML, businesses of all sizes are able to automate rote tasks in order to free up highly skilled employees to focus on product innovation and service improvement. They are also able to better use costly equipment.
Example: Tools such as AI-Driven Process Automation powered by DataRobot and UiPath, can increase worker performance, cut operational risks, and accelerate response times for customers via tools like chatbots.
This not only increases efficiencies in the company, it frees up valuable time and resources to focus on providing new and innovative products for customers.
To learn more about Machine Learning, as well as whether your company is ready to start putting it to work, download our free eBook: The Redapt Technical Maturity Framework.
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