Antonio Evans
The danger in interview analysis is rarely that the data is thin. It is that the reader has already decided what the data means before opening the file. Confirmation bias, the habit of seeking only information that confirms existing beliefs, is one of the standard bias categories in customer discovery work, alongside selection bias and response bias, and it distorts conclusions before anyone writes a summary (Innovation Within).
Practitioners are blunt about the scale of the problem. One discovery guide argues that the goal of customer discovery is not to validate your idea at all, because confusing validation with understanding produces research that confirms what you already believed rather than challenging it (Skimle). Another notes that founders routinely listen only for what fits their hypothesis, and that asking about solutions rather than problems produces false validation (Startups.com lexicon).
Blind-coding interrupts that loop by changing the order of operations. Instead of reading a transcript, forming an impression, then hunting for support, you:
- Remove the details that tell you whose interview it is and what business question is at stake.
- Fix a codebook before the model sees anything, so categories cannot drift toward the hypothesis.
- Let a model apply the codebook first.
- Audit a sample of its tags against the redacted source before you read anything in full.
The output you want is not a finished analysis. It is a set of tagged excerpts with a measurable error rate, which you then correct by hand.
Why the model is the first coder, not the analyst
Qualitative coding is the act of attaching labels to extracts so patterns can be derived later, and it exists to make analysis systematic and reviewable by someone else (Grad Coach). That reviewability is what makes a two-pass process work: if the model's tags are written down in a fixed format, a second person can check them.
The evidence for caution is concrete. A study of semi-structured patient interviews (n = 34) compared two open-source models against human inductive coding and found that roughly 45% of model-generated codes carried meaningful context, while 22% to 39% were duplicative (International Journal of Qualitative Methods). A model can tag fast, but between one in five and two in five of its tags may be noise. Any process that skips the audit trusts a tool whose published error profile does not support that trust.
There is a second reason to treat the model as a coder rather than an analyst. Coding is a systematic pass through the data, and the interpretation of themes comes after it, drawn from the research aims and questions (Grad Coach). If you let the model jump straight to themes, you lose the layer that makes the work auditable.
