Armed with the results, Gallup used the quantitative responses to statistically explain the drivers of customer engagement. It tapped the qualitative data to describe how these drivers looked and felt to customers. From there, "we gleaned how strategies to improve engagement differed by customer type," she said.
Working with qualitative data in software is often a matter of giving it numerical form, such as by assigning a numerical rank or score to specific qualitative responses or comments. In sentiment analysis, for example, the researcher typically assigns a positive or negative value to the sentiment being expressed in the qualitative data, and then assigns a numerical value to the strength of that sentiment, Borne noted.
Text analytics involves summarizing the content of textual information in quantitative ways, such as via topic models and heat maps, while natural language and semantic processing techniques extract meaning from the language, whether written or verbal.
Transforming qualitative data into quantitative form can involve some subjective decisions. "That is a challenge, but it is also a rich opportunity," Borne noted. "There are far more subtleties and intricacies in language that we can use to extract deeper understanding and finer shades of meaning."
A variety of algorithms are at work in QSR's NVivo, analyzing data for commonly used words or sentences, for example. Numerous visualization tools are on hand to make interpretation easier, meanwhile, including word clouds and tree maps.
"This gives you a strong visualization of viewpoints and underlying reasons," said John Owen, the company's CEO.
Qualitative data collection tends to be time-consuming and requires a high level of skill by the researcher to establish rapport with respondents and mitigate bias, Gallup's Ron-Levey pointed out.
"One of the most underestimated and oft-overlooked skills that the qualitative researcher needs to have is a sense of empathy," Forrester's Lai agreed. "Often times today, qualitative researchers rely solely on coding without thinking critically about the subtext or context of the data."
Working with the data and validating the research model can also be more complicated. Where it might be sufficient to normalize a set of quantitative data on a zero-to-one scale, for instance, it's trickier with qualitative data, Borne noted.
"Standard statistical tests that might be testing hypothesis A vs. hypothesis B won’t work when there are many shades of meaning and many degrees of understanding embedded within qualitative data," he explained.
On the analysis side, it can be tempting to generalize findings beyond the sample set being studied, Ron-Levey warned.
Done well, however, qualitative data can be well worth the effort.
"In an era of big data, there is a continual search for human meaning behind the numbers," Ron-Levey said. "Understanding feelings, motivations and perceptions often leads to innovation and new strategies that more authentically appeal to customers."
Said Forrester's Lai, "qual and quant insights are the 'yin and yang' of customer understanding, because both are needed to tell a complete story about consumer behavior."
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