Your Customer is Trying to Tell You Something, But You Need to Read Between the Lines

We live in an era of customer-centricity.  No one knows this better than customer support professionals. Even before customer service was the new marketing, support professionals had been saying for years that customer support was your secret weapon to uncovering product insight and innovation.

While the business world is shifting emphasis to the insights that can be gained from a deeper understanding of the customer feedback that lives in the service center, the resources haven’t always followed. This is primarily due to the fact that there are three main challenges in support:

1. Volume of data - the thousands and hundreds of thousands of support requests that are generated

2. Manual processes - typically a lack of integration of support tools leads to manual processes

3. Siloed departments and individuals - the lack of integration can lead to lack of communication between both departments and from one support individual to another

Support always seems to be playing catch up and due to the real-time requirements of supporting customers, companies don’t get the opportunity to shut down support in order to catch their breath. If ever there was a use case for machine learning, analyzing the avalanche of data that comes forth directly from customers, this is it.

Equally important is the fact that internal feedback such as support requests and external data such as product reviews are rich with emotion. Yet support teams rarely track this most human form of feedback. We tag top issues or note the level of severity but nuanced emotion goes completely unnoticed by support teams other than to note occasionally that a customer was angry.

Big Data is a game changer for customer support

Not only can we finally catch up (whew!) simply tracking not just the top 10 but the top 100 or the top 1000 issues in support, but we can also finally do a true evaluation of what is most important to our customer base.

Breakthroughs in natural language processing now allow us to bring data science and computational psychology together to expose the nuance in what the customer is telling us. Customers have much stronger feelings about certain parts of their experience than other parts. Their feedback likewise gives us insight into which parts they actually care more about. With these breakthroughs, we now have the opportunity to uncover emotional insights behind the words that a customer uses to explain their problem, or ask their question or request a refund.

Extract quantitative insights from qualitative data

We all know that it’s not only what you say but how you say it. Computational psychology goes far further than simply noting sentiment (sad, happy, mad). Breakthroughs in NLP now allow computational psychologists to identify implied feedback and truly prioritize and identify customer emotions. No longer will the customer who shouts on the phone or types in all caps be given all the attention. We also care about the customer who whispers, for these customers may have far more important things to tell us.

For more information about gaining deeper customer insights, read our Whitepaper.

[Blog by Kate Hobbie]