2 minute read
By Jen Snell
Posted in Customer Engagement
Based on recent market polling through digital channels, I hosted a two-part webinar series, “Ask the Experts,” alongside Joe Dumoulin, Chief Technology and Innovation Officer of Verint Intelligent Self-Service, to address some of the hottest questions around Intelligent Self-Service. Specifically, we explore the basics of machine learning and how businesses should evaluate and measure it—with particular focus on providing the kind of practical, straightforward information that businesses need to achieve their goals.
Machine learning is an application of artificial intelligence (AI) that allows machines to automatically learn new things. Data mining uses algorithms to look for patterns in a given set of information. Machine learning goes a step further: It changes a program's behavior based on what it learns.
There are all kinds of machine-learning techniques that have been developed over the years, but regardless of which algorithms are running under the hood, the true value of any machine-learning tool or platform is measured in terms of business results.
No technology can define your business goals for you, so before you put machine learning to work, you need to define in specific terms what the “right” outcomes are—based on your vision.
What machine learning can provide is analysis at scale. Machine learning tools help businesses to analyze vast quantities of unstructured data, including call notes, web-based self-service notes, chat logs, emails, unstructured texts, and conversations. Rather than assuming you know what your customers want or need, this kind of analysis gives you an unbiased look at not just what your customers say but at how they say it.
Good machine-learning tools help you design the user experience customers really want, instead of the one you think they want.
Automating analytics at scale gives you the dexterity you need to enable change in your business. The important thing to keep in mind, however, is that automation only goes so far. Businesses that successfully leverage machine learning are continuously validating the quality of their machine “intelligence.”
Business intelligence is anything but artificial, and that’s why human input remains vital. Humans help ensure the integrity of the “intelligence” machine learning generates. And, humans leverage the insights machine learning gathers to critically evaluate how a business is succeeding or how it can better deliver on its promises.
Listen to the two-part webinar series on-demand to learn more about how machine learning can help you understand the user experience, uncover new opportunities, and adapt to internal and external trends here:
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