We pick Richard Shapiro's brain on best practices for customer satisfaction and customer experience. As the founder and president of The Center For Client Retention (TCFCR), he is a great candidate for our guest interview.
Your survey results look awesome. The scores are high and customers seem generally happy. Most survey responses have top-box ratings, which means the customer selected one of the two highest rating options. Nothing to worry about, right? Not so fast.
There might be a hidden danger lurking in those positive surveys.
Have you wondered what customer sentiment analysis really is? Let's get to the bottom of it. But first, let's set some context. Customers are looking for positive experiences. Brands do too!
Most people believe that text analytics solutions fail because sarcasm in customer feedback is very common. Somebody writes “Great service, yeah right!” and the dumb algorithm tags it as positive. So, whenever I speak on text analytics, someone in the audience will always ask:
But how do you deal with sarcasm?
According to Bruce Temkin’s 2016 study, after a positive emotional experience, customers are 15 times more likely to recommend a company. 15 times more likely! That’s a huge difference. Not surprisingly, emotion analysis is receiving a lot of buzz. But do the current solutions deliver on the key question that companies
“Every time I see a word cloud presented as insight, I die a little inside”, J. Harris, data journalist. If you are a manager, there is a high chance that you’ve encountered word clouds in reports on key company issues, such as customer service or employee satisfaction.
The picture above depicts Paul McCartney wearing a mullet. This article criticizes word clouds, the mullets of the Internet. :-)
“Every time I see a word cloud presented as insight,
I die a little inside”
J. Harris, data journalist
If you are a manager, there is a high chance that you’ve encountered word
When it comes to making sense of data, getting actionable insights is the holy grail. But what does this even mean? When is a finding an insight? When is an insight actionable? Can data analysis deliver them? Let's get to the bottom of this by looking at some examples.
What is Natural Language Processing, or NLP in short? If you're unsure, you're not alone. Many people don’t know much about this fascinating technology, and yet we all use it daily. In fact, if you are reading this, you have used NLP today without realizing it.
Understanding customer comments, on a large scale, needs to be automated. So, it requires Natural Language Processing (NLP) or Text Analytics. Unfortunately, most open-source NLP tools were developed on text researchers have easy access to. These are typically news articles, research papers and movie reviews. I learned that the analysis