Making Sense of Your Qualitative Data with Text Analytics


Author Bio


Author Bio

There is a ton of different customer listening posts and types of customer data these days. Data come in all different shapes and sizes: structured, unstructured, solicited, unsolicited…oh my! A lot is written about survey data and analyzing structured quantitative data, but let’s take a look at unstructured data. 

 
What is unstructured data? According to Wikipedia, unstructured data is “information that either does not have a pre-defined data model and/or does not fit well into relational tables. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional computer programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.” PC Magazine puts it into simpler terms: “Data that does not reside in fixed locations. The term generally refers to free-form text, which is ubiquitous.”
 
Ubiquitous, indeed! It comes from various sources: surveys, Voice of the Customer through employees, call center interactions, account manager conversations, blogs, tweets, shares, online reviews, and the list goes on.
 
They all provide you with lots of great data from, and about, your customers, but how do you make sense of it all? How do you glean insights from all of the open-ended data that you’ve amassed? The answer: using Text Analytics. Text Analytics basically means that you’re turning your qualitative data into quantitative data, thereby allowing you to use that data for cross-tabbing, filtering, and a variety of other analytical approaches. Text Analytics is not manual but a machine approach to categorizing comments and identifying sentiment of those comments. It’s fair to say that I’ve simplified the definition and that there’s much more to it than that.
 
Why use Text Analytics?
1. You can shorten your surveys by asking open-ended questions, knowing that you’ll have some systematic (and not manual) way to transform and analyze the data.
2. The trade-off to shortening surveys is that you get more robust feedback in the respondent’s own words, rather than in words that you selected.
3. Once open-ended data is categorized, it can then be used for deeper analysis with your existing quantitative data.
4. When you’re analyzing call center or social media conversations, for example, you may identify current or emerging issues long before they would have ever been uncovered otherwise.
5. Most importantly, on a survey, asking follow-up, open-ended questions is necessary to understanding why something happened or why something is good or bad. We need to continue to ask these open-ended questions, but we need a more simplistic and automated way to analyze those responses.
 
Let me shift to surveys for the moment and say, just because you have a way of analyzing and categorizing your qualitative data doesn’t mean you can ask more open-ended questions on a survey. You still need to be conservative with your approach here, and more importantly, ask direct questions that elicit direct responses, i.e., responses that actually tell you what you need to know rather than just vagaries and ambiguous responses. The “garbage in-garbage out” rule still applies.