How do you make sense of your customers’ words?
On that topic, I’m delighted to share a second guest blog post by our first guest blogger, Annette Franz:
There are not only a ton of different customer listening posts these days, but the types of customer data are equally as varied and voluminous.
Data comes 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 or is not organized in a pre-defined manner. 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 programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.
Techopedia puts it into simpler terms: Unstructured data represents any data that does not have a recognizable structure. It is unorganized and raw and can be non-textual or textual.
We know this much: unstructured data comes from a variety of sources, i.e., customer feedback (surveys, etc.), employee feedback about their own experience or about the customer experience, call center interactions, account manager conversations, blogs, tweets, shares, online reviews, medical records, books, and more.
You have a ton 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 unstructured data that you’ve amassed?
The answer: get yourself a great text mining or text analytics tool. In its simplest form, text analytics tools turn 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 tools are not a manual approach to making sense of the data; they take a machine approach to categorizing comments and identifying sentiment of customer comments and other unstructured textual data.
I think it’s pretty fair to say that I’ve simplified the definition and that there’s much more to it than that.
Other than the obvious “making sense of something that doesn’t make sense” reason, why else use text analysis tools?
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.
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.
Once open-ended data is categorized, it can then be used for deeper analysis with your existing quantitative data.
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.
Most importantly, on a survey, asking follow-up, open-ended questions is necessary to understanding why something happened and to understand in the customer’s voice what would make the experience better for him. We need to continue to ask these open-ended questions, but we need a more simplistic and automated way to analyze those responses.
There’s a caveat and a balance with all of these. There really is nothing like reading verbatims to get the tone, the pain, the delight, the rich detail of the experience. I would strongly advise continuing to do that. But I also know that when there are thousands of data points, that’s difficult to do.
So, 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.
The words. Why did they have to exist? Without them, there wouldn’t be any of this. -Markus Zusak, The Book Thief
Have you had success analysing unstructured data? Have you seen insights from text analysis? Do let us know.