Of Tears and Text Analytics
An OdinText User Story – Text Analytics Tips Guest Post (AI Meets VOC)
Today on the blog we have another first in a soon to be ongoing series. We’re inviting OdinText users to participate more on the Text Analytics Tips blog. Today we have Kelsy Saulsbury guest blogging. Kelsy is a relatively new user of OdinText though she’s jumped right in and is doing some very interesting work.
In her post she ponders the apropos topic, whether automation via artificial intelligence may make some tasks too easy, and what if anything might be lost by not having to read every customer comment verbatim.
Of Tears and Text Analytics
By Kelsy Saulsbury
Manager, Consumer Insights & Analytics
“Are you ok?” the woman sitting next to me on the plane asked. “Yes, I’m fine,” I answered while wiping the tears from my eyes with my fingers. “I’m just working,” I said. She looked at me quizzically and went back to reading her book.
I had just spent the past eight hours in two airports and on two long flights, which might make anyone cry. Yet the real reason for my tears was that I had been reading hundreds of open-end comments about why customers had decided to buy less from us or stop buying from us altogether. Granted eight hours hand-coding open ends wasn’t the most accurate way to quantify the comments, but it did allow me to feel our customers’ pain from the death of a spouse to financial hardship with a lost job. Other reasons for buying less food weren’t quite as sad — children off to college or eating out more after retirement and a lifetime of cooking.
I could also hear the frustration in their voices on the occasions when we let them down. We failed to deliver when we said we would, leaving the dessert missing from a party. They took off work to meet us, and we never showed. Anger at time wasted.
Reading their stories allowed me to feel their pain and better share it with our marketing and operations teams. However, I couldn’t accurately quantify the issues or easily tie them to other questions in the attrition study. So this year when our attrition study came around, I utilized a text analytics tool (OdinText) for the text analysis of our open ends around why customers were buying less.
It took 1/10th of the time to see more accurately how many people talked about each issue. It allowed me to better see how the issues clustered together and how they differed based on levels of overall satisfaction. It was fast, relatively easy to do, and directly tied to other questions in our study.
I’ve seen the benefits of automation, yet I’m left wondering how we best take advantage of text analytics tools without losing the power of the emotion in the words behind the data. I missed hearing and internalizing the pain in their voices. I missed the tears and the urgency they created to improve our customers’ experience.
A big thanks to Kelsy for sharing her thoughts on OdinText’s Text Analytics Tips blog. We welcome your thoughts and questions in comment section below.
If you’re an OdinText user and have a story to share please reach out. In the near future we’ll be sharing more user blog posts and case studies.