How to analyze user interviews: Step-by-step guide

The data you gather through user interviews can be priceless. But sorting and analyzing it can be a big undertaking, especially when you have limited resources or a small team.

User interviews are a qualitative research process that provides rich information. However, gleaning value from user interviews is a critical challenge for many UX and product professionals. Traditional data analytics processes that can help you quickly make sense of quantitative data aren’t helpful here. So, how can you go from interviews to analytics and final reports?

Fortunately, by establishing a process for analyzing interviews and harnessing the combined power of AI software and human oversight, you can wade through the data faster, even as you uncover new paths to growth and innovation.

User interviews are among the most valuable research methods for any product manager, UX professional, team leader, or other business stakeholder. They gather insights directly from people who interact with your designs. Depending on your niche, you might interview your clients’ employees—those who log into the portal every day to complete tasks—or individuals who routinely browse your website for shopping, news, or entertainment.

By zeroing in on “users” (especially when they differ from B2B buyers or procurement audiences), you gain new perspectives that customer interviews alone may not unlock. When analyzed effectively, user interviews are a valuable UX research method that helps you understand customer needs, behaviors, and pain points.

Every business counts on data to power decisions and inform marketing and development strategies. However, data alone doesn’t reveal anything unless you study it.

Analyzing interview data in qualitative research uncovers patterns, themes, and insights that provide depth and context to user behaviors and needs. This process transforms raw information into actionable findings that you can use to design user-centered solutions that resonate with your target audience.

UX researchers typically conduct this analysis. Stakeholders, such as designers, product managers, and marketers who also rely on its results to guide decisions and strategies, usually provide input.

Turning raw data into business insights

Interviews are immensely valuable as a standalone method, but they need careful refinement to unlock that value.

By analyzing video recordings, transcripts, and notes, researchers can distill raw data into insights—clear, actionable explanations that uncover the cognitive, motivational, or technical factors influencing user behavior. These insights shed light on opportunities and challenges and empower every department in the business to strategize effectively for growth and innovation.

Getting buy-in from stakeholders

Stakeholders should be involved every step of the way. You’ll need to be sure that they will acknowledge the new insights.

Stakeholders can also offer assistance early on in the process when you are outlining the steps for data collection and eventual analysis.

You might experience some pushback from certain stakeholders. Understand how to demonstrate value to them, whether that’s data or tangible evidence. Consider showing them some case studies or previous projects that used user interviews to effect positive change.

To maintain stakeholder buy-in, be conscious of restrictions and limitations such as budget or overall timeline and be prepared to be flexible.

Your process for transcribing interviews should make it easy to analyze and interpret data at every project stage. While you can and should adapt the process according to what makes sense for your company and resources, here’s a basic outline of how to transcribe an interview:

Collect feedback for qualitative research

Gathering qualitative data isn’t an exact science. You can use dozens of methods, from handwritten notes to cutting-edge transcription software.

Transcription software is the most accurate way to collect interview responses. This approach ensures nothing important is lost in translation.

Organize all research recordings

Organizing the recordings will make it easier to review and analyze them.

If you conduct multiple interviews across several days, consider organizing them by date, time, and the participant’s identification number.

Ensure that the software you use to store and organize the data is accessible to everyone in your company. You might find it helpful to provide a primer before sharing that explains how to download and use the software.

Transcribe focus group recordings and interviews

Transcripts are valuable tools for capturing nuance from your interview participants.

Transcription software can be extremely valuable at this stage, but human oversight is still necessary. You’ll need to review the end result, removing filler words, misspellings, and anything that doesn’t contribute to the project. Do your best not to rush through this step—it can directly impact your final analysis and results.

Used properly, transcription software can complement your team’s skills and allow you to perform better, more accurate analysis.

After your data has been gathered in an accurate, structured way, you can move on to the analysis part of the project. For many team leaders and analysts, this is the most interesting and relevant stage of the research.

Most traditional research methods involve seven steps for analysis. You may need to adjust or add more depending on your available resources. Use these steps as a framework to guide your research.

Step 1: Prepare the data

Data preparation could involve:

• Transcribing the interviews (if you haven’t already done so)

• Reviewing the transcription results for errors or filler words

• Organizing any notes you might have made during the interviews themselves

Data should be organized in a way that makes it easy to display, review, and, if necessary, explain. You’ll want all involved parties to be able to understand the way the data is sorted so they can be confident in your analysis.

Step 2: Get familiar with the content

It can take a while to thoroughly understand what your results point to and how they will impact the overall study. However, by spending time reviewing the content after transcription and organization, you can develop an initial impression of what you’re working with.

During the first few read-throughs, make some notes on any patterns or overall themes you notice and save them to reference later in the project.

Step 3: Develop a coding framework

A coding framework can help you categorize information in the transcripts. A data analyst or software programmer can assist, applying anything from descriptive codes to interpretive codes, which can help you identify themes across the gathered interviews.

Step 4: Code the data from the interviews

The data analyst should assign codes to relevant sections as you work through the transcripts.

This process can be subjective and left to the expertise of the software engineer or analyst doing the coding. However, consistency should be a priority. Codes should be applied in the same way across all interviews.

Creating a matrix can also help you compare responses across interviews so you can easily spot patterns or themes.

Step 5: Identify themes

Whether you’re performing research before launching a new product or getting customer feedback on a new feature, you’ll want to be able to identify major themes and customer pain points. This is an extremely important part of the process.

Once data has been organized and coded, review it carefully for recurring patterns, comments, and suggestions.

After you have identified themes, consider their relationships and how you’ll group them moving forward. You don’t need to spend too much time organizing data at this stage, but grouping some of the broader themes can make it easier to study and interpret findings. It can also enable you to share them with other stakeholders.

Step 6: Review and refine the themes

It’s easy to be overwhelmed by the sum of the data you’re dealing with. Take a step back and review your themes to be sure they truly represent the data.

Also consider whether the data answers the questions you had at the start of the project. While many research projects grow and evolve, you’ll want to be sure that your data is a natural progression of the intent you outlined before you conducted interviews.

It could make sense to adjust some of the themes during the review and refinement stage. Establish a hierarchy of themes if you haven’t done so already. This can help anyone reviewing the data to better understand the story behind it, revealing what deserves the most visibility.

Step 7: Interpret, validate, and share your findings

As you start to analyze the relationships between themes and group them accordingly, you’ll likely arrive at some conclusions. However, some conclusions and takeaways won’t become clear until you have organized and grouped everything and undertaken a careful analysis.Depending on your project’s size, scope, and nature, it could take days or weeks to finish this.

Here are some tips for this final step:

• Validating your research is important. Don’t generalize any conclusions.

• Consider combining multiple research techniques, especially if you’re dealing with a large amount of data.

• Don’t hesitate to go back and revisit any of the previous steps if something doesn’t seem right or isn’t adding up.

• It isn’t always possible to accurately measure each piece of information. Qualitative data is all about subjective experiences, attitudes, and perceptions.

• Report your findings in clear, concise language and include any additional sources you turned to throughout the process.

Having the right tools to analyze the collected data makes all the difference in user interview projects.

AI-driven analysis methods enhance efficiency and make it easier to get a holistic view of customer or participant groups. They may also offer other features that can save your team time and energy.

AI-driven tools like Dovetail’s research repository make it possible to sync everyone in your organization by providing a central hub for research data and insights.

Step 1: Data import and preparation

The benefits of AI software are especially evident early in the process before the data has been sorted. A robust platform will allow you to upload all raw transcripts, and many will sort them to your specifications.

If you prefer more oversight during this stage of the process, simply bypass the automatic sort features and manipulate the data as usual.

Step 2: Automated theme identification

It can take a human many hours or even days to identify and group themes in the interview data. AI software algorithms, on the other hand, can analyze text and identify patterns and themes in minutes. It can also group these themes for you based on an outlined hierarchy. You or a data specialist can then manipulate the presented data differently if that makes more sense.

Step 3: Sentiment analysis

AI will never be as nuanced as a human when it comes to detecting the subtleties in human speech and mannerisms. However, AI software, such as Dovetail’s AI analysis features, can detect emotional undertones, enhancing the analysis process. This can be especially useful for interviews that deal with particularly emotional topics.

Sentiment analysis goes beyond analyzing whether a statement is positive or negative. Rather, it looks at underlying emotions that could affect the participant’s overall statements.

Step 4: Data visualization

If you’re presenting your findings to a large group or board, incorporating imagery (such as graphs, tables, videos, and other visualizations) can make your research more impactful. Pictures can make complex concepts easier to understand and highlight important points.

Step 5: Human review and refinement

There will always be a place for human involvement when using AI software. Review the results before you present your findings to anyone else and well before these findings are announced as fact. It’s a good idea to have others review the findings as well.

Step 6: Iterative analysis

AI is very useful for testing new hypotheses and ideas. It’s not always possible to do this with a human team, especially one with limited resources.

AI software allows you to think on your feet, test new creative thoughts, and respond to market changes with dynamic precision.

Both traditional and AI-driven user interview analysis have benefits and drawbacks.

Traditional analysis allows for a nuanced understanding of data that isn’t always possible with AI software. When humans with a vested interest in the process and results analyze human data, you generate real compassion.

Additionally, traditional analysis tends to be slightly more flexible when changing the project direction than AI software. You can change the project goalposts as new insights emerge, even if the overall mission remains the same.

AI software is more efficient for many organizations, especially those working with large amounts of data. Most software applications provide real-time analysis and the ability to uncover patterns across multiple interviews, making them handy tools that you can apply to great effect in projects of almost any size.

Ultimately, there’s a place for both traditional and AI-driven analysis in reviewing user interviews. The best tool for you is one that makes the most sense for your company’s goals and budget.

A hybrid approach could also work well. You might decide to use AI in some areas and a traditional approach in others. For example, you might use AI for theme analysis and large-scale data migration and your team for reviewing responses and validating how the AI opted to group certain themes.

Certain best practices should be applied in any interview research project, regardless of how you choose to analyze the data.

• Context matters: be aware of contextual clues and understand the situation and environment in which the collected statements were made. 

• Pay attention to non-verbal cues: non-verbal communication is as important as the spoken word in many interviews. Be aware of body language and non-verbal signals while collecting the data. Take plenty of notes so you don’t forget these important moments later on.

• Address contradictions thoughtfully: contradictions might emerge over the course of your research. Make a note of these and pay close attention to any inconsistencies between interviews. Even outliers should be inspected.

• Incorporate peer review: always include time in the project for peers to review the research and results. When you are fully immersed in a project, it’s easy to overlook potentially valuable details or key patterns. Getting insight and input from other team members can ensure reliability and help you feel more confident when it’s time to present the results to a larger group.

The business landscape is constantly changing, with new innovations and approaches making headlines every day. Whether your company has fully embraced the benefits AI software has to offer or is slowly integrating products and features on a case-by-case basis, there’s a place for AI in user interview analysis. When combined with the nuanced insights and oversight provided by humans, you can design and build a process for analyzing interviews that works for you.

Drive positive change and take your organization into new territories with the power of transformative data.