Customer service: Get big data on the call

Data-driven customer service technology must be deeply context-sensitive to be effective

customerservice500.jpgSource: sun dazed/Flickr

Few things frustrate consumers more than interacting with an enterprise’s customer service staff. Most people hate the very idea of it. Despite companies deploying various automated systems over the years in hopes of improving the situation, nearly 70% of consumers in the UK, for example, think customer service call centers are getting worse or staying wretchedly the same.

The tech-oriented audience at Ars Technica is in the midst of venting about poor quality tech support staff as well as the "stupid" customer who calls. Although a few commentators offer anecdotes of good experiences, as the article that leads the discussion observes, "The state of technical support from most major vendors these days is so abysmal that an actual good support experience is almost shockingly noteworthy."

While those who work in customer service departments can point to many examples of dealing with clueless consumers, the reality is it’s business that is clueless if it skimps on support or simply turns it into a process to upsell a disgruntled customer. According to a global survey conducted a few years ago by Genesys Alcatel Lucent, companies worldwide lose more than $330 billion annually because of poor customer support.

To deliver good customer service you need to know everything about the caller’s history with the business; what products they own; how valuable they are as customers; and much, much more. Precise data about the product in question should be at the customer service representative’s fingertips, in case there are known flaws with it that can be remedied quickly. And that information has to be in front of the CSR in real time. The CSR also must know exactly the current state of the service call to avoid repeating steps prior CSRs might have taken with the caller.

Some companies understand that applying their vast data assets to customer support problems solve issues faster and result in happy, repeat customers. They use analytics on big data sources, such as social media, that will give CSRs an edge by knowing, for example, how the product in question is perceived in the marketplace and what responses are working best with customers.

But other businesses simply don’t get it. They view CSRs as expense items on the balance sheet, which is why they often get trained to also sell products. As one blogger at SmartBusiness observed, “Somewhere along the way, someone in a corporate office thought it would be a good idea to try to turn all the customer service agents into sales reps. In some situations, that’s not a bad idea. But in many cases, it’s a recipe for disaster.”

Data-driven customer support technology should be deeply context-sensitive. That way, if your business does use any kind of customer interaction as an opportunity for a new sale, the technology should be able to help a CSR know when selling is a good idea – and when it’s best to focus exclusively on help. That small bit of knowledge can make a world of difference in customer service.

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