Customers provide teams with feedback constantly, in the form of support tickets, survey comments, app reviews, and countless other sources. But much of this data is unstructured, making it challenging to identify patterns across hundreds or thousands of responses.

In the past, making sense of this data required customer service teams to manually comb through spreadsheets, categorizing feedback by sentiment, severity, and topic. This process was not only extremely time-consuming, but also quite subjective and prone to error. There’s even a name for the problem: “sentiment drift,” which refers to the tendency of human graders to gradually change their criteria, standards, or interpretation of sentiment after reviewing hundreds of data points.

Historically, it has been even more difficult to pull out trends from all of these disconnected comments.…