Machine Learning Enables Efficient Multi-Channel Social Customer Support

Machine Learning Enables Efficient Multi-Channel Social Customer Support

Machine Learning Enables Efficient Multi-Channel Social Customer Support
Multi-channel support is becoming more and more common. Customers want the path of least resistance to getting the information they need, and part of that means they want to communicate in the manner that’s most comfortable for them.  

That could be a traditional channel like chat, email, or voice. It could also be a social channel like Facebook, Twitter, or even the Google Play Store, which are becoming more and more popular with customers, as well as ticketing platforms likeZendesk. 

These social channels differ in several critical ways from traditional one-on-one support.

Customers are interacting on them, whether you want them to or not.  If you don’t want your customers sending you emails, you don’t put an email address on the website. Ditto for phone calls. But you have no control over whether a customer decides to tweet at you, mention you in a Facebook post, or leave a negative review on the app store.

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The social nature of these channels also means that there is a lot of potential to engage with customers positively, opening opportunities for the support team to help drive customer value and new customer acquisition. Conversely, there’s strong potential for negative, brand damaging customer conversations that can reach a wide audience in a short amount of time.

Despite the lack of choice in whether their customers interact with the brand on these channels, and the strong benefits that come from actively supporting social, many companies are either engaging half-heartedly or not at all.

The reason for this is resources.

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