The Dynamic Future of Customer Service: How Machine Learning Will (Finally) Make Business Personal

The Dynamic Future of Customer Service: How Machine Learning Will (Finally) Make Business Personal

The Dynamic Future of Customer Service: How Machine Learning Will (Finally) Make Business Personal

It’s no secret that brands have been trying to make business-to-consumer interactions more personal, and it’s also no secret that the B2C relationship is far from monogamous. In order to facilitate choice, convenience and control, brands have used data to categorize (or “bucketize”)  customers in any number of segments and act accordingly. For example, looking at a laundry list of factors and behaviors, “Joe’s characteristic make-up is A, B, C and his behavior patterns are predominantly G, P, X and Y, so we know that we’ll have pretty good luck engaging him in these ways…”

But business analytics and, specifically, machine learning are turning the proverbial “dating” game between businesses and consumers on its head. Instead of assembling interactions based on preconceived notions about how consumers want to be treated, machine learning enables companies to deliver truly personalized communication based on up-to-the-moment data: “Joe is uniquely Joe, and right now, he needs 1, 2 and 3.” Smart, fast and personal. Boom, boom, boom.

Let me explain it a different way

Data big and small has already been transforming the way brands interact with customers (or prospective customers) – whether it’s remarketing ads for the shoes we abandoned in our online carts, curated playlists based on our listening preferences, or automatic notifications to refill our prescriptions based on our typical use.

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Virtually all industries today, from retail and media to financial services and utilities, are progressing the ways they use data to engage consumers and facilitate easier self-service long before they visit a website or come in-store. Each purchase we make, website form we submit and phone call we place to a help desk generates more detail that businesses can use to predict our behavior and drive increasingly connected and perceivably personalized interactions.

Faced with an endless array of data points (location, time of day, purchase history, gender, payment type, etc.), most brands have been trying to distill that information into a finite number of customer journey trends – to put us in metaphorical boxes with other “customers like us.” But thanks to advances in and broader awareness of machine learning, we’re on the verge of a fairly radical shift. Rather than making the masses align with a predetermined set of buyer segments, companies will begin to seize more collective context and intelligence to dynamically tailor any interaction down to the individual using an ever-deepening pool of historical and real-time data.

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Though it’s been studied since the 1950s and applied by our nation’s military and intelligence agencies for decades, machine learning has only recently garnered modern appeal in the business world as a critical stepping stone toward applied artificial intelligence (AI). Corporations are now investing in the applied science to enhance consumability and service, while differentiating their brands and creating unparalleled cost-leverage. Using advanced algorithms and complex equations, machine learning technology combs through massive data sets to identify patterns and uncover answers without human decisioning or effort.

Machine learning provides the foundation for concepts as new and niche as self-driving cars, and as commonplace as online search. The U.S. Postal Service was one of the earliest mainstream adopters, using machine learning to read the handwritten addresses on our letters and packages. This type of computing demands more than static databases and linear IT infrastructure; instead, it requires “neural networks,” or interconnected processors capable of building, absorbing and experimenting with diverse data sources – not unlike a human brain. The more data that flows into these networks, the smarter they become, drawing on historical information to feed criteria for the next best actions.

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Whether we realize it or not, we are starting to see this in action on a daily basis.

 



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