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Voice of the customer then + now | An evolution of text analytics

Teresa Cain | Aug 23, 2018 Teresa Cain 08/23/18

Though the term text analytics is relatively new, the concept has been a part of customer experience (CX) measurement since the 1950s. It was obviously a much more manual approach then—referred to as natural language processing (NLP)—with the objective to make computers understand human language.

Today, with technology advancements and the introduction of artificial intelligence, text analytics is widening the possibilities of how brands can capture customer sentiment and use a qualitative approach to CX measurement. The advancement of text analytics has heavily evolved from singular phrase detection—depicting the entire journey of the customer experience and identifying loyalty trends. Customers are not only writing about their experiences, but also documenting their every move with videos, pictures, and location tagging.


Text analytics is essential to your CX measurement strategy

When customers and employees share open-ended feedback, you gain deeper insights into what they really think. While the quantitative survey data is invaluable, it’s often the qualitative insights from customer and employee comments that help you add context to scores and answer questions you may not have thought to ask. SMG’s text analytics technology helps brands turn open-ended feedback into next-level insights—with top-tier accuracy and powerful, multi-source reporting.


Adding context to your text analytics

In addition to using text analytics for quantitative insight, our text benchmark—consisting of hundreds of millions of customer comments—adds a critical layer of insight. By seeing what customers are mentioning, the sentiment behind their words, and how that compares to competitors, clients get a deeper, more contextualized understanding of how customers perceive their brand relative to competitors, providing insights like:

  • How often customers talk about the most important measures for your brand
  • Frequency of employee mentions + how that impacts satisfaction
  • The categories where customers think you’re better—or worse—than the rest

Understanding the emotion behind your customer datasets is critical to delivering better experiences. And as the technology continues to evolve, it’s clear that the best way to get a nuanced understanding of customers’ emotions is to pair cutting-edge text analytics technology with quantitative insights.


Does your text analytics measure up?

When it’s your job to listen to customers and act on their feedback, you absolutely have to use text analytics. It’s the best way to get quantitative data out of qualitative input. It’s important for brands to understand what’s essential, what’s not, and how the right technology makes it easier to uncover what customers really want.

Stay tuned for an upcoming post about how brands are using text analytics to gain rich insights from the words of the customer. In the meantime, learn more about best-in-class text analytics by downloading our best practice guide: The essential guide to text analytics for CX pros.


Teresa Cain | Manager, Product Strategy