After you conducted interviews you are ready to analyze the data. For a thorough analysis, it is usually not enough to write down some observations on post-it notes. Often it makes sense to dig deeper into the data and create a complete transcript of the interview for a profound analysis.
Transcripts have the reputation of either being expensive or taking a long time to create. There are nevertheless good reasons for transcribing your interviews anyway:
- Discover hidden topics and gain insights through word analysis
- Enrich your findings with data and statistics
- Streamline your research through analysis processes and collaborative analysis
- Improve your own research skills
Record your interviews to concentrate better on the contents
An interview recording (as audio or video) is the basis for creating a transcript. Besides transcription, there are good reasons for recording your interviews in any case.
When you are leading an interview you have to take on many tasks at once. You need to listen carefully to what the respondent is saying and have an eye for their gestures and expressions. At the same time, you have to think about your hypotheses and moderate the dialogue based on the interview guide. Additionally – as if that wasn’t enough – you have to take unbiased in-depth notes.
A recording of the interview will help you concentrate on the task at hand. With a recording, you don’t need to fear missing or forgetting something that was said and can later recall the exact wording and underlying tone.
Human-generated transcript vs. AI-based transcript
Transcribing your interview recordings by yourself will take its time. If you are proficient in transcribing, have mastered the ten-finger system, and maybe even employ special hardware (like a foot pedal), you might be able to transcribe 1 hour of recording in 4 hours.
Alternatively, there are professional transcription services, that start at around $1 per minute (for the English language, your mileage may vary).
With the rise of AI, there are several speech-to-text services that can take on this task in a time- and cost-efficient way. The quality of the transcript strongly depends on the audio quality and still might not be perfect. However, the time- and cost-savings make this a powerful addition to a user researcher’s set of tools.
Benefits of a transcript
1. Save time on the evaluation
Even if you record your interview (which you should!) it takes a long time to listen to all those videos or audio files again. You have to jump back and forth to find certain parts to get the right quotes and highlights. A transcript with timestamps helps you to scan through the interview contents more efficiently. You can search the text by keywords, locate the timestamp, and directly jump to the corresponding position in the video. Furthermore, a transcript simplifies including key quotes in your reports, since you can easily copy and paste what was said. When it comes to sharing research findings, a popular deliverable is a highlight video. Here the most important parts are distilled without filtering the wording or tone of voice through the researcher. Marked sentences in a transcript serve as you as input for that.
2. Streamline your evaluation by tagging
Tagging (or coding) enriches your research in that it allows you to standardize your research approach. A well-crafted taxonomy of tags supports you in spotting new patterns and undiscovered topics. Ultimately, tagged data can be searched in future research questions and adds up to a repository of user research knowledge.
Why do you benefit from a transcript here? With the available software, tagging parts of a text is usually easier than tagging parts of videos. Besides, reading speed is usually faster than listening speed and we’re used to scanning text documents, so analyzing a textual representation of the interview content is presumably faster.
3. Objectify your research
If a certain pattern emerges after a few interviews, you risk overlooking further important findings. Even when listening to the interview again you might overhear certain statements due to biased attention.
The analysis of a transcript and the written word – as another mode of communication – has the potential to make your research more objective. For example, the systematic evaluation of user statements shows if your hypotheses about a cool new feature hold, or if you subconsciously overweighted the positive statements.
4. Discover new topics of interest
During tagging and categorizing statements to specific topics, it is likely that you discover new themes that you did not think of before.
By tagging, you unify the different wording of your test persons on a certain topic. Thereby, every statement can contribute to multiple topics. To find new themes, you consider all statements within a specific topic and look for commonalities or mentions in other topics.
Imagine you are testing your new fitness app. You tag all statements carefully and notice afterward that the category “listening to music” contains a lot of statements about the category “training preparation”. You dig deeper into your data and discover that many users create their own playlists for certain types of training. You’ve just laid the groundwork for deciding to add a new feature that users can assign a playlist to individual workouts.
5. Facilitate collaboration
To avoid interviewer bias, it makes sense to let multiple researchers analyze the raw material. Analyzing a video together with colleagues however is an immense effort, especially if you are not in the same location. A transcript normally is smaller in file size, can quickly be shared as well as edited and highlighted with any text processor, like Microsoft Word or Google Docs.
A textual representation allows you to easily (and without the need for special software) categorize statements and link them to insights collaboratively in a text document. In consequence, your research findings are directly documented and can be shared and presented easily.
6. Quantify your data
Qualitative interviews can also be evaluated quantitatively thanks to a transcript and some tagging work. If you want the tested product to evoke a certain emotion or feeling, you might check how often words matching this emotion have been used by your test persons.
Let’s say you’ve tested a new online banking app. Your primary goal with the app is to give users a sense of security. After you have interviewed your respondents and created the transcripts, you create a list of words related to the feeling of “security”, like “secure”, “protected”, “credible”, “strong”, “real”, and “certain”.
In which interviews did these words occur particularly frequently? Take a closer look at these interviews to learn what made these users feel safe. Again, the textual representation is key to efficiently find the important sections.
7. Improve your skills as a researcher
Working through transcripts of interviews you conducted allows you to assess your own interviewing style. For example, did you influence testers by using words that appear on the tested interface? How big was your share of the dialogue? Did you always formulate open questions?
Seeing this data in black and white lets you to take an objective look at your own performance and will help you to improve your skills and develop as a researcher.
8. Craft better reports
Due to the additional options for evaluation, there are also new options for reporting. Make your insights more tangible by providing them with additional data.
Have you come to the conclusion that your prices are too expensive? Include a chart in your reporting that shows exactly how often users were dissatisfied with the price. Your colleagues don’t realize how critical a usability error you found is? Show them that in interviews where the error occurred, significantly more negative sentences were said than in the other interviews.
Mixing qualitative data like verbatims and video snippets, with quantitative analysis will give your reports more weight. The analysis of a transcript opens up completely new possibilities for you to prove your insights with data.
9. Store your research data in accordance with the protection of data privacy
Interview recordings can be judged as personal data (and this has not only been the case since the EU proclaimed the GDPR). This means that respondents can be identified from it, may it be through matching the face or the voice.
ESOMAR, the European market research association, recommends deleting this type of data three months after collection. If you plan to utilize previous research data, a transcript might be a viable alternative. Transcripts are generally easier to search for person-specific data than audio or video files. These data can then be removed and the research data remains without personally-identifying information.
Call to action
Transcripts offer you many opportunities to take your research to the next level, report more objectively, and also develop your own skills as a researcher.
That’s why we at consider.ly have a built-in AI-based transcription service for several languages and amount of speakers. You can try it out for free and check out for yourself if a transcript benefits your user research.