“Turning fluff to tough.” That’s how Andrew Goward, Head of Insights and Optimisation at Awaze, summarized the importance of qualitative data visualization and analysis during Relative Insight’s latest Spotlight Series webinar.
He said: “Historically qualitative data has been viewed as fluffy — it’s very anecdotal, it’s very hard to pull any meaningful feedback or results and make any decisions based upon it. Using Relative Insight means you can put qualitative data in quantitative terms: turning fluff to tough.”
Andrew took part in a panel discussion with Ching-Wen Hung, Research Analyst at Cibyl, and Sam Valentine, Employee Experience, Listening & Culture Lead at Miro. In a conversation hosted by Relative Insight’s Emily Brooke, the trio outlined why and how they analyze text data, how to present qualitative data visually and what difference-making findings they uncovered within text data.
Emily began the session by demonstrating Relative Insight’s forthcoming Data Discovery tool. Offering you qualitative data visualization within four clicks, Data Discovery not only provides a variety of text data presentation methods, it also streamlines speed to insight.
Harnessing unparalleled insights by utilizing text
Each of the speakers talked about why they analyze qualitative data within their insights processes. All three highlighted that text analysis provides insights that help to explain the reasons for trends which surface in numerical data.
“It gives you the ‘so what?’, said Andrew. “You’ll have companies saying that their NPS is negative, but that doesn’t mean anything unless you know why it’s negative. It gives you the ‘why’ behind the data and meaningful insights into what you can change to improve things.”
He added: “Quantitative data is just numbers, it doesn’t mean anything without context — qualitative data provides that context.”
Sam noted that the scaleability and objectivity offered by text analytics tools helped to overcome historic challenges associated with analyzing qualitative data. He argued that, initially, insights professionals need to take stakeholders on a journey to embrace findings from text data, however, the quality of insights from text will lead to quicker, better decision making.
Speakers outline how to present qualitative data visually
The trio emphasized that visualization of text data was dependent on stakeholders. Each speaker talked about their data presentation, particularly how they married up visuals with verbatim responses.
Ching-Wen highlighted that it’s important to keep data presentation simple and easy to read. She stated that the sheer amount of data Cibyl communicates back to its clients means that it’s imperative to ensure feedback is clear and concise. Ching-Wen’s advice on how to present qualitative data visually was to blend text data visualizations with verbatim quotes.
“We use very simple visualizations, such as bar charts. Often we’ll display bar charts on the left and quotes on the right as a combination,” she said.
Sam emphasized that including verbatims alongside visuals helped to bring the insights to life and gave feedback extra power. However, he argued that this should be done in an unbiased way:
“One thing that I’m careful to do is make sure they’re balanced and that the meta data is visible. This ensures they are representative, the risk of decontextualized comments is that they add a bit of bias. Making sure they represent a range of sentiments is really powerful because people still want the human voice — they still want to know what people are saying.”
Fascinating insights that get stakeholders to act
Both Ching-Wen and Andrew shared anecdotes of surprising insights that resonated with them.
Ching-Wen talked about a student survey Cibyl ran for clients that found 48% wanted to engage with future employers on social media. Therefore, clients’ next question was: What should we post? Cibyl analyzed the open-ended survey responses to find out.
“With the text analysis, we found firstly that job openings and descriptions are still preferred by students. Furthermore, we were able to provide information on other employers’ posts or video content that they’re doing on social media to address this question.”
Andrew cited analysis of Awaze’s property reviews around value for money scores. Intuitively, it expected its lower-price properties to score highly and its premium properties to have lower scores. However, the scores surprisingly rated its most expensive properties to be the best value for money. Awaze used text analysis to uncover why this was.
“People were spending a lot of money and had high expectations,’ Andrew noted. “But, because these properties are such high quality, the owners leave welcome baskets, wine, champagne, food etc so they were able to meet those high expectations which is why they were getting such high value-for-money scores. What we were able to do with that was change the way we market and talk about these higher-value properties.”
Need to analyze, quantify and visualize text data but don’t know where to begin? Find out more about how Relative Insight can help you.