Qualitative is Better Than Quantitative: Prove Me Wrong
Rich data exists everywhere in our digital world, but how can we possibly mine the meaningful insights in the data that isn’t quantitative?
Why Does Qualitative Data Matter?
Qualitative research methods produce insights that quantitative data can’t; like nuanced, rich descriptions of your target customers and their experiences with your brand, product or service. These qualitative data sources help your team identify unspoken or latent needs, and understand why people make the choices they do. When combined with quantitative data, qualitative information provides a more complete, complex, and dynamic picture of your target market. This dual approach makes it easier to answer critical business questions, like which aspects of your brand or product need improvement, and where you should invest resources to achieve greater impact.
Qualitative data is increasingly present in digital marketplaces, and the ability to analyze it quickly and effectively is becoming more important than ever before. With data itself as one of this century’s hot commodities, your company needs to know how to interpret it. Online reviews, for example, provide a vast cache of leverageable information, if they can be converted into insights efficiently. A recent study by Forbes revealed that 93% of consumers say that online reviews influenced their purchase decisions, which is to say, this material is rich, and it’s in your best interest to leverage online reviews with programs that can digest, convert, and convey patterns. In this post, we’ll walk you through the challenges of qualitative data analysis, as well as introduce you to a new tool for unlocking insights from qualitative research in areas like online reviews.
The Problem with Qualitative Data
Qualitative data is often difficult for companies and data scientists to analyze because it is multifactorial and slippery, used to answer complex questions, like “why are certain people buying my product?” as opposed to the hard and straightforward quantitative data used to answer questions like “how many people are buying my product?”. Numbers and stats can be used to make comparisons and provide a sense of the size or scale of something, whereas qualitative data is more about understanding what’s behind those numbers and statistics. One challenge is that qualitative data, and methods of gathering and interpreting it, are constantly evolving — so much so that new insights are being discovered daily. This can lead to what’s known as “analysis paralysis”, where there’s simply too much information to determine the right course of action. It could seem easiest at that point to stick with the familiar and apply traditional quantitative data analysis techniques like NPS surveys, but there is so much left on the table when companies don’t analyze the rich, contextual, qualitative data that customers/users are generating online. The latter is often put aside because companies don’t have the resources, tools, bandwidth, or knowhow to sift through and make sense of all of this complex information.
Limitations of Current Approaches
Qualitative data is the most important type of data for marketers. Former Chairman of the Board and CEO of Procter & Gamble (P&G), AG Lafley, accredits qualitative data for being the deciding factor in one of his most important decisions in his pre-CEO career at P&G. When quantitative data failed to provide clear evidence in support of a $250 million investment to create compact detergent, Lafley turned to qualitative data. After spending days analyzing the verbatims that respondents wrote, he argued in favor of the investment — which turned out to be a big win for P&G and for Lafley. Lafley’s experience not only highlights the importance of qualitative data, it also reveals a significant limitation to qualitative data: it is difficult to analyze.
Qualitative research requires a labor-intensive analysis process that often involves multiple experienced researchers, extensive categorization, and coding. It’s difficult for researchers to visualize trends from large volumes of diverse data which are not easily charted on graphs. Information that answers questions like “Who? Why? In what context?” take the form of language-based content, filled with subtle indicators of habits, states, needs, personalities, moods. Social data, human data, qualitative data, is itself difficult to analyze — in huge volumes? Forget about it! It would take an analyst hours just to scroll through one page of results from an Amazon review info-mining project. With more than 1.5 billion reviews on Amazon alone, it’s extremely difficult for humans to make sense, and use, of this vast volume of available data!
A New Approach to Qualitative Data Analysis
Humans who want to understand humans may call on AI-powered tools to help. Marketers who want to understand markets can’t do it alone. Resultid’s solution addresses the analysis paralysis problem in the face of exponentially growing data caches.
Let’s talk about Mark, a Director of Customer Service & Sales, who aims to improve the customer experience of his product by understanding the overarching themes in customer feedback, and resolving negative experiences in a timely manner. Mark is responsible for managing 5 brands, and is tasked with analyzing the reviews for each. He is often overwhelmed by the hundreds of reviews that pour in through various channels, including Yelp, Google Reviews, Facebook Reviews, and Trustpilot. Previously, his data analysis approach was entirely manual, and so time-consuming that he could only complete it on a weekly basis. Given the fact that the information being reviewed online was constantly evolving, he often missed out on new insights, and could not resolve negative comments until it was too late.
The Resultid app accelerates the data analysis of professionals like Mark. Instead of manually searching across his data to isolate trends, Mark can simply upload a CSV file of his data and Resultid will intelligently analyze it for him. In doing so, Mark can more easily make sense of what his customers are saying, and respond to customer reviews in a timely manner. With the time Resultid saves him, Mark can more efficiently build rapport with customers by resolving negative experiences quickly and crafting unique responses to feedback.
Summing up
Qualitative data, like customer feedback and online reviews, are invaluable, yet often not leveraged to their full potential. The analysis process can seem like a daunting task when you don’t have the right tools. Resultid is constantly iterating to efficiently produce this type of analysis, and other insights that users are looking for in their data. Within a matter of seconds, the Resultid app can help visualize trends in qualitative data to unlock the insights that improve business strategy, understanding of customer feedback, and accelerate research without requiring users to read every word. Want to turn a month of work into a three day project? Click here to learn more.
