Analyzing an interview means turning a spoken conversation into clear, evidence-based insights. Anyone who conducts customer interviews knows the disconnect: the conversation was rich, the notes are patchy, and in the end, you are left with a gut feeling instead of a solid conclusion. This guide shows you how to analyze an interview, the qualitative methods behind it, and how an AI agent can handle the repetitive parts of this work without you losing control over the results.
Analyzing interviews in clear steps
In practice, the process of analyzing an interview almost always follows the same three-part structure: first, capture what was said; then, organize the statements; and finally, identify and interpret the patterns. Each step builds on the previous one. If you do the first step properly, you will save half the time on the third.
Transcribing interviews as a foundation
It all starts with interview transcription. As long as a conversation exists only as audio, it cannot be searched, compared, or used as evidence. The transcript is the foundation for all subsequent steps. It is important to maintain a consistent format across all interviews so that statements remain comparable later on. Whether you transcribe verbatim or use a smoothed version depends on your goal. For content analysis, a readable, smoothed version where every statement is clearly attributed to a person is usually sufficient.
Coding and forming categories
In the second step, the qualitative data is prepared for analysis. Coding means labeling segments of text with a short tag that describes their content. Many individual tags eventually form categories—bundles of related statements. You can define these categories in advance, for example based on your interview guide, or develop them from the material itself. The key is that each category is clearly defined and that similar statements are consistently assigned to the same category. This discipline is what turns an interview summary into a verifiable analysis.
Summarizing and interpreting results
In the third step, you summarize what the interviews say for each category and support every statement with a direct quote. This keeps the basis of your insights traceable. Only then does the interpretation follow: which topics appear across many respondents, where do they contradict each other, and what is an isolated case? Analyzing an interview does not end with the description, but with the question of what follows for your next decision. If you want to analyze customer feedback, you are looking for exactly this bridge from statement to action.
An overview of qualitative analysis methods
Two methods have become established for the systematic analysis of qualitative interviews. Both organize text material using categories but place their focus differently.
Qualitative content analysis according to Mayring
Qualitative content analysis according to Philipp Mayring analyzes text material in a rule-based, step-by-step manner. The core is a fixed category system used to code the material. Categories can be derived deductively from theory and research questions or developed inductively from the material. Typical basic forms include summarizing, explicating, and structuring the text. This method is particularly suitable when many interviews are to be analyzed based on comparable questions, as the clear set of rules keeps the results comparable across interviews.
Grounded Theory
Grounded Theory pursues a different goal: the aim is to develop a theory from the data rather than test an existing one. Coding occurs in several stages—from open to axial to selective coding—with data collection and analysis closely intertwined. New interviews are included until theoretical saturation is reached, meaning no further conversations yield new insights. Unlike Mayring’s approach, there is no fixed category grid at the start; instead, the categories grow out of the material. This makes Grounded Theory more open, but also more labor-intensive.
How do I analyze a qualitative interview?
In practice, analyzing a qualitative interview means: transcribing, coding, summarizing, and interpreting. You convert the conversation into a transcript, assign categories to the statements, bundle the core points per category along with supporting quotes, and finally interpret the patterns across all interviews. Which of the two methods you choose depends on your goal: a fixed grid with Mayring’s content analysis if you want to compare, or an open process with Grounded Theory if you want to discover something new.
Are all qualitative interviews analyzed the same way?
No. The basic procedure is similar, but the depth and method depend on the research question. A single exploratory conversation requires a different approach than twenty structured guide-based interviews. For comparable questions across many interviews, a fixed category system is appropriate. If the goal is to first understand a field, an open, step-by-step coding process is more sensible. The level of detail in the interview transcription also depends on your goal.
Which software helps with the analysis?
There are established categories of tools for coding and managing qualitative data: classic qualitative data analysis programs, a UX research repository for collecting and retrieving insights, as well as broader user research platforms and UX research platforms. In addition, user research tools and software support data collection, while UX research tools and software assist with analysis, complemented by remote usability testing tools, UX survey tools, and UX research repository tools. Those who think beyond individual conversations also consider competitor analysis UX and more extensive UX data analysis. Suitable feedback tools help place results into a larger context. However, these tools do not handle the coding for you. This is exactly where an AI agent comes in.
Analyze interviews with an AI agent from Scalan
Analyzing an interview is a recurring task: it follows the same pattern for every conversation yet consumes time every single time. These are precisely the types of tasks that can be delegated to an AI agent. At Scalan, departments build their own agents in a guided interview, without any programming. The agent is given a name, a role, and its own address, to which you send the recording or transcript via email, in-app chat, or Telegram.
What the agent handles: transcription, coding, and themes with supporting quotes
The "Interview Synthesis" example skill turns customer interviews into themes with verbatim evidence. The process is intentionally transparent:
The key is how to handle what doesn't fit the mold. A rigid process breaks down when faced with an unexpected case. The agent is precise in its results but flexible in how it gets there: the criteria and categories are fixed, but it finds its own way to them, and if there is genuine ambiguity, it asks for clarification.
A quick calculation for orientation, using your own figures: Suppose manual transcription and coding of an interview takes you about two hours. If the agent takes over the transcript, coding, and the evidence-based theme summary, you are left with the actual interpretation. Plug in your real hourly rate per interview and multiply it by the number of conversations per study to estimate your concrete impact. Consumption per process and per agent is tracked, so costs are never a black box.
Transparent and controlled: operated in German cloud regions, with individually authorized actions
Because interviews contain personal statements, the framework matters. The models run in German cloud regions, no company data flows to the model providers, and your data is not used for training. The agent is granted individually authorized actions rather than blanket access: what it is allowed to do in connected systems is approved per action; unauthorized actions simply do not exist for it. Every run is auditable after the fact, and the description of the process in plain language serves as documentation. In this way, Scalan helps you fulfill your obligations under the AI Act without taking them off your hands.
Analyzing an interview remains your professional decision. You can delegate the recurring path to get there—from the transcript and coding to the evidence-based theme summary. Describe your analysis process once in the guided interview, test it with real sample data, and from then on, let it run for every new conversation.
