How Businesses Can Use Machine Learning to Improve the Customer Experience

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Despite being in widespread use throughout countless industries, machine learning remains commonly misunderstood (much like AI in general), even though it’s a fairly straightforward concept. To clarify, it involves programming computer systems with the capability to adapt their operations based on the data they receive and the conclusions they can reach from it.

Because of this general confusion, it’s underestimated and overestimated in comparable measure, with some writing it off as a convoluted gimmick and others expecting it to see human workers consigned to obsolescence in the coming years. The truth is somewhere in the middle; it isn’t a replacement for human analysis, but it’s hugely powerful when used correctly.

One task that’s perfectly viable for machine learning is the optimization of customer experience (CX), something that can make or break a modern business. But does this work? How can you (or any business) use machine learning to keep customers happier? Here’s how:

By providing 24/7 automated support

Few things can sour a prospective customer in the digital age more than being unable to find what they’re looking for online. We’ve all become accustomed to an astonishing level of convenience, after all, with all the answers at our fingertips. If you can’t rapidly give someone the information they want, they won’t wait around for it. They’ll just go elsewhere.

That means you need a strong and robust level of customer support if you’re going to keep people from souring on your brand – but it’s challenging to provide such a thing when you’re handling it all manually. It might be workable on a slow day during standard office hours, but what about busy periods when you’re getting orders in the middle of the night? Even with live chat making multitasking support viable, it’s too much work.

A chatbot, though, can answer questions at any time of the day, and at scale – and with machine learning, it can become better at dealing with variable phrasing. This can provide an experience not overwhelmingly dissimilar from dealing with a support assistant, all without requiring any manual intervention.

By using dynamic pricing

Prices change rapidly throughout the ecommerce world. Retailers are always in search of an edge, looking around for trends and trying to maximize their sales and profit margins, and the never-ending cycle of deals and discounts is effectively partially because it confuses people and leads them to think that certain offers are better than they really are.

But price alterations don’t need to be bad for customers. They can actually be extremely useful when done correctly. For instance, it’s possible to use machine learning to keep your prices in line with those of your competitors. Think about how traditional retailers will provide price-matching schemes – if they’re provided with evidence of better offers elsewhere, they’ll immediately trump those offers.

Machine learning could ensure that the prices on your high-margin products stay in line with those of other sellers. As a result, your customers would soon learn of your absolute commitment to giving them good retail experiences.

By making email marketing contextual

As customers, we all understand the importance of marketing, and we don’t begrudge companies for sending it forth – particularly given that it’s often very useful to us. As much as we may hate the spam that clogs our inboxes from overzealous retail suitors, each of us is likely to have a handful of regular ecommerce marketing emails we actually welcome. It’s rather more convenient than having to visit sites just to find out if there’s anything new that interests us.

The problem with bad email marketing is often that it’s generic. Everyone on the mailing list gets the same content – the same intro, the same recommendations, and the same calls to action. Over time, this lack of contextual content becomes increasingly damaging to the customer experience. Don’t we all want to feel that we’re being given unique treatment?

Applied to email marketing, machine learning can ensure useful segmentation. Let’s say that you want to send out emails with certain discounts to the most valuable customers: you can leave it to a machine learning algorithm to determine which customers are worth the most, and distribute the emails accordingly. This type of segmentation is particularly useful for drip email campaigns, because the responses over time provide a lot of valuable data – it’s also a perfect fit for the unified communications field and its rich cross-platform analytics.

By advancing search functionality

How shoppers search for products and services has changed a great deal in recent years. What used to be a simple matter of entering search terms until something relevant came up has become more nuanced. For instance, Google’s algorithm uses machine learning heavily, factoring in context to figure out what you mean when you start typing.

There are other ways to search, too. There’s voice search now, as popularized by Amazon’s Alexa, and increasingly powerful image search (Google Now on Tap can do it with live camera feed). This matters because it’s getting closer to the kind of customer experience you can have in a physical store – of describing the product you’re looking for in whatever way possible, and getting useful suggestions in return.

Imagine a prospective customer reaching your website and having nothing but a set of categories. Now imagine them having a feature-packed search bar allowing them to throw in an audio clip, a video file, an image, or just a fragment of text, and come away with some context-relevant results. The latter provides a much better experience.

Machine learning for general business is still in its adolescence, but you should already be thinking about how you might add it to your customer experience strategy. Keep an eye on these uses, and be on the lookout for cheap and simple ways to implement them.