If you’ve recently eaten at a restaurant, stayed at a hotel, or ordered something online, within minutes you’re being asked to provide your feedback. These surveys are so commonplace that even if you take the time to fill out the form, you are more than likely blindly checking some boxes just to get your award points. We all do it -- it’s the equivalent of running out of time on a multiple choice test so you resort to the “C, C, C…” strategy. While these lazy approaches are still technically feedback, how much value can you place on it?
However, if someone takes the time to add commentary in the feedback form -- actual textual feedback – this holds more weight. Think about the times you’ve actually filled out the commentary portion of a survey. I’m guessing your experience was exceptional or infuriating, certainly not middle of the road.
There is real value in textual feedback. Feedback exists in various forms across industries, not just consumers critiquing services or products. For example, there could be feedback on warranty or insurance claims, course evaluations by students, engineers updating maintenance logs, law enforcement professionals entering investigative notes, and HR professionals conducting employee evaluations. One very common example we see from our healthcare customers is the desire to analyze patient feedback to better understand the patients’ experience and look for opportunities to improve the quality of care they are delivering.
You more than likely have been capturing this type of textual data for years, but in many cases the analysis consisted of counting up the multiple choice answers to the individual questions, making a few pie charts in Excel, and creating an extra worksheet for the comments. If you’re lucky, someone read them.
The good news is that more advanced analytic techniques now exist to help quantify and extract additional value from all of this textual data. Here are some ideas of what you could do:
- Look at thousands of warranty claims and dynamically categorize them based on the text in the claim
- Look at all of customer survey responses and apply sentiment scoring to find the most positive and negative responses
- Find the most frequent terms mentioned in a set of patient surveys for a given time period.
Merging the insights from textual feedback data with all of the other business data you have is essential to becoming a more proactive organization.
If Lululemon had been able to monitor product return data, in combination with social sentiment and other customer feedback data, they may have been better able to get out in front of the PR nightmare their product recall created.
If you’re interested in learning more about how Information Builders enables organizations to get more value out of the various forms of feedback that they are collecting please attend tomorrow's(May 22nd) webcast - Feedback Analytics: Visualizing the Voice of the Customer. I’ll be presenting and will be taking questions.
Or, if you want to get started right away, take us up on our Feedback Analytics challenge.
No matter what, if you’ve taken the time to read this blog, please let me know your thoughts; I’m interested in your feedback.