# Got 40 interview transcripts and need to find the patterns? This step-by-step guide walks through the complete analysis workflow: from transcript preparation to synthesised themes you can actually present.

  
You have completed the interviews. You have 40 transcripts, or 40 sets of notes, sitting in a folder. The findings are in there somewhere. The problem is getting them out.

This guide walks through the complete workflow, from preparing your raw transcripts to producing a structured, evidence-based thematic analysis. It assumes you are using Skimle, but the preparatory steps apply to any systematic analysis approach.

# Step 1: Prepare your transcripts

Before uploading anything, spend 30 minutes on preparation. The quality of your analysis depends significantly on the quality of what goes into it.

**Format your files consistently.** Each transcript should be a separate document. Accepted formats include PDF, Word (.docx), and plain text (.txt). If you have interview notes rather than full transcripts, format them as prose paragraphs rather than bullet lists where possible. Bullet notes can be processed, but connected prose produces richer thematic output.

**Make each document self-contained.** If you refer to the participant as "she" throughout the notes, consider adding a single-line header identifying who the interview was with (role, organisation, or demographic group, not name if anonymisation matters). The analysis will be more interpretable later.

**Anonymise if needed.** If your transcripts contain participant names that should not appear in analytical output, replace them before upload. Consistent labels ("Participant A," or role-based labels like "Head of Procurement") work better than random anonymisation that makes transcripts unreadable. See [anonymising qualitative data](https://skimle.com/introducing-skimle-anonymise-pseudonymisation-for-qualitative-research) for a detailed approach.

**Decide on your metadata scheme.** This is the most important preparatory decision. Metadata lets you cross-tabulate themes by participant characteristics later: which themes appear more among senior managers than junior staff, which appear in the European sample but not the US sample, which are specific to churned customers rather than active ones. Useful metadata fields for most research projects:

* Participant type or role
* Organisation type or size
* Geography
* Interview date (for longitudinal analysis)
* Any other grouping variable relevant to your research question

Write your metadata scheme down before you start uploading.

# Step 2: Create a project in Skimle

In Skimle, every corpus of documents lives in a project. Create a new project with a name that reflects the scope of analysis, not just the client or topic (e.g., "Customer exit interviews Q1 2026" rather than just "Acme").

Set the project language if your transcripts are not in English. Skimle supports over 100 languages. For multilingual projects where interviews were conducted in different languages, upload each document in the language it was recorded in rather than translating first. Translation introduces distortion; cross-lingual analysis within Skimle preserves the original meaning more reliably.

  
Read steps 3 to 7 in the full (free) guide here: [https://skimle.com/blog/how-to-find-themes-across-interviews](https://skimle.com/blog/how-to-find-themes-across-interviews)

Signal & Noise: How to find themes across a large set of interviews: a practical guide