Qualitative data can seem difficult to parse—and may seem like it’s better suited for marketing than product management. However, qualitative research uncovers customers’ inner motivations and feelings in a way that will benefit any product team. It asks (and answers) the holy grail of customer questions: Why?
Qualitative data analysis provides a rich, nuanced look into your customers’ ideas, preferences, and needs. Learning how to interpret it faithfully can give you the deep insight you need to create and maintain truly customer-driven products.
Qualitative research involves asking people for their responses to open-ended questions through interviews, focus groups, and case studies. It generates a rich, nuanced data set that provides context for people’s decisions and preferences. Gathering qualitative data starts on an individual level: you connect with customers one-on-one or in small groups and record their thoughts and feedback. From there, you can spot trends that show how your customers feel about your product.
Qualitative data aims to get at a deeper level of information than quantitative research does. While quantitative data is great for tracking key product metrics like user retention, it’s not as well-suited for stepping into the shoes of your customer and empathizing with their experience.
This form of research works particularly well for new feature ideas. Before you can take steps to validate whether your feature is a good idea, you need a hypothesis to test. If you’re just starting out and don’t yet have a value hypothesis, qualitative research can help you evaluate what your customers need from your product. Qualitative studies can reveal your customers’ nuanced, emotional reactions to your proposed product changes.
A famous example of this phenomenon is the story of New Coke: Coca-Cola changed its flagship flavor after encouraging taste test results, but they didn’t account for their customers’ strong, emotional connection to their original product. New Coke flopped, but the re-introduced “Coca-Cola Classic” boosted the brand’s popularity.Keep in mind that qualitative data collection isn’t a replacement for quantitative, and vice versa. They both have their uses and, for the most in-depth, complete understanding of what your customers need and desire, you should combine approaches.
Qualitative research methods are intended to find proof of an idea or concept—you could be looking for evidence explaining why your customers feel the way they do about your product, or you might be collecting information about the general sentiment in your market. Conduct your research with a set plan and parameters in place, always keep your purpose in mind, and practice excellent record-keeping.What does that methodical approach look like in practice?
Finally, watch out for your own biases while you collect data. Bias is inevitable; you’ll walk in the door of every interview or focus group with your set of preconceived ideas and personal context. While you can’t switch that off, you can reorient your thinking, so you don’t allow your own biases to affect your research process.
A good way to think of it is that, as an interviewer, you’re co-creating the data with the people in your interviews. The questions you ask, your presence, and your follow-ups will all have an impact on the information that your participants share. The observations you make during the sessions are also part of the data that you collect. For instance, if a customer says they would probably use a proposed feature, but they don’t appear particularly excited about it, that may be a good time to ask follow-up questions about what’s missing.
Qualitative data analysis is your way of turning pages and pages of interview transcripts into illustrative and actionable information. It’s difficult to turn people’s individual thoughts, opinions, and feelings into a cohesive data set, but with a replicable, thorough analysis process, you can get at the valuable insights needed to manage your product.
Content analysis is perhaps the most accessible of the qualitative methods. With this process, you collect information and generate written transcripts from your respondents, then you analyze the text to look for certain words, phrases, and patterns. Your research findings should uncover details like how common certain opinions about your product are or how different thoughts or feelings relate to one another.
To use content analysis, you’ll need to determine your research objectives: What information are you hoping to find out? What do you need to know in order to move forward with development? Informed by these goals, you’ll then write up a list of words and phrases to search for during your analysis.
For example, if your objective is to learn more about why customers use your product, you might scan for words or phrases that describe your product’s value, like “easy to use,” “fast,” “affordable,” etc. Once you’re ready to scan through your text transcripts, you have a few options:
Thematic analysis breaks up text transcripts into common themes that you can compare and contrast to get an understanding of your customers. This approach is similar to content analysis in that you’ll scan your transcript for certain words and phrases, but it differs in one key area: Under this research method, you’re looking for shared themes and patterns of meaning, not just shared words. Searching for specific keywords (as you would in content analysis) won’t necessarily uncover every shared theme since people talk about similar topics in different ways.
To find common themes, you’ll have to do some subjective interpretation of the data. Keep your respondents’ context and implications in the forefront of your mind as you analyze your transcripts. You’re looking for insights based on what they meant, not just their word choices.
Based on the meaning that you uncover, you should be able to spot some common themes among your customers. They might indicate ambivalence about a product update, for instance, or they could show loyalty to your product in the way they talk about it.
Narrative analysis is a unique approach to understanding your qualitative data. This method looks at information through the lens of the story: You examine your respondents’ answers to your questions as if you’re reading a narrative, where the content, context, and structure of the story impact its meaning.
To analyze your qualitative data using a narrative approach, deconstruct your transcripts into narrative “blocks,” where each block represents a complete story. Look for key components that any good story has, like a plot, core message, setting, and resolution. Then compare and contrast these narrative blocks across your data set to come up with a larger understanding of your customers.
For instance, if you want to find out why new customers decide to try your product, you could use narrative analysis to understand that process. Through each customers’ story, you can find insight into what product they used before, what caused a problem for them, and why they decided to give yours a try instead. That story can help you understand your competitors and your customers’ pain points.
This approach focuses on getting to the core of your customers’ experience, and you’ll benefit from collecting background information about each participant, like their job role and company size. These details help explain their context; the story a C-suite-level executive at an enterprise company tells will differ from that of a junior-level employee at a small start-up.
Grounded theory relies on research data to create an informed, overarching theory explaining what the data shows. Usually, data analysis starts with a hypothesis that you’ll either prove or disprove after your research. With grounded theory, you’ll start with the data instead, using that to develop a conclusion that’s “grounded” in the information you’ve collected.
To conduct a grounded theory analysis, begin by finding specific themes in your transcripts (as you would for a content or thematic analysis). Compare your data from each of these themes to each other and consider how they’re related. Can they be grouped into categories that build toward a larger theory?
For example, if you’re trying to learn more about your customers’ experience with a new feature, you can use common feedback like “loads quickly” or “user friendly” to generate an overall theory that you’re providing a positive experience.
Qualitative data analysis has its roots in scientific research, and these approaches reflect that complexity. In product management, you don’t need to follow each approach precisely—instead, think of them as ways to gather interesting insights and get at the meaning behind the data you’ve collected. Make these methods work with your team, budget, and time constraints.
Building a custom approach to qualitative data is just one part of creating your own product validation process. Find other ways to approach product validation in our step-by-step guide to building an internal process.