Artificial intelligence, or AI, iallows computer systems to automatically recognize and perform certain jobs that formerly would have required human intervention. If you’ve ever loaded a new image into the photos application on your computer and had it instantly recognize the faces of every person there, you’ve seen the power of AI on display.
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Machine learning, on the other hand, takes things one step farther and allows computer systems to essentially learn and improve from experience — without necessarily being programmed to do so. Using the same example as above, say you load an image into the photos app and tag a photo of yourself and your significant other.
When you load another photo featuring the two of you into the app a few weeks later, it will nstantly recognize you and display your names — without you doing anything manually. It will continue to recognize you in additional photos uploaded in the future. This is the power of machine learning on display.
So, if computers can perform tasks that used to require human interaction (as with AI) and get more effective at this the longer they perform those tasks (as with machine learning, you’re probably getting a picture of the broader implications of this technology for business.
AI + machine learning = automatic insights
In an enterprise environment, concepts like AI and machine learning are usually small but significant parts of the more recognizable concept of business analytics. To be successful, a brand needs to know as much as possible about the people it is trying to serve. The good news is that those customers are creating massive amounts of data at all times; in fact, more data has been created in the last two years than all the data accumulated up to that point.
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Unfortunately, that’s also the bad news — because pulling anything meaningful from this mountain of information becomes an uphill battle, to say the least. However, that’s where AI and machine learning come into play.
A business analytics system uses AI to process those huge volumes of data, learning how to instantly separate a “valuable insight” from “nothing of value.” Thanks to machine learning, the more data you feed it, the better it gets. And that benefits brands, end users and everyone in between.
The real-world impact on the digital experience
One example of a company benefiting handsomely from these technologies is Air Canada. Air Canada is currently the largest domestic and international airline in its home country,, serving more than 200 different international airports on six continents on a regular basis.
Of the 45 million customers using Air Canada each year, the clear majority book online. With that in mind, it’s an understatement to say that providing the ultimate customer experience is important to the airline.
Recently, Air Canada wanted to learn more about its mobile app users to identify new opportunities for customer experience improvements. That’s why the company entered into a partnership with technology provider Glassbox, that company’s CEO, Yaron Morgenstern, told me in an interview. Air Canada needed to know more about the customers using its mobile app — including what uses they preferred and which devices they were using. Glassbox’s Digital Behavioral and Digital Experience Performance Analytics solutions was the tool used to glean this information.
Air Canada also wanted to guarantee that all its customers could access any information they needed on a single, easy-to-use web page, regardless of the device used. Glassbox was able to comply using AI and machine-learning algorithms, With these tools, Glassbox could unlock a treasure trove of information about not just how people interacted with the Air Canada app — but why.
Specifically, Morganstern told me, Air Canada learned what types of devices are preferred and how people with certain devices interacted with the app, and the problems they encountered.
The airline further learned about customer trends and patterns that helped it identify bold opportunities for performance enhancements — all supported by the foundation of digital analytics. Air Canada then set about expanding those insights into its website, as well.
By recording, replaying and running advanced analytics combined with machine learning, the airline aims to consolidate insights across both channels, for an improved online experience and increased revenues.
As recently as five years ago, wading through all this data would have been a nightmare. Dozens of people would have spent countless hours poring over it, trying to identify patterns and uncover new opportunities for improvements. Thanks to state-of-the-art concepts like machine learning, however, these insights reveal themselves in a fraction of the time.
Air Canada is just one example of companies that have benefited from the power that AI, machine learning and other “automatic insights” bring to the table. Companies like Glassbox, Domo and others haven’t just strengthened our existing business analytics platforms: In many ways, they’ve created a brand new one.
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Thanks to the benefits that these technologies bring to digital customer channels, insights aren’t just easier than ever to uncover — they’re practically instantaneous. What could they do for your business?
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