For Research Analysts ·
What you'll accomplish
By the end of this guide, you'll have a repeatable workflow for coding open-ended survey responses using Claude Pro. A verbatim coding task that currently takes 5–8 hours of manual reading and tagging will take 45–60 minutes of pasting batches and reviewing results. You'll produce more consistent coding than manual review — and have time left over for actual analysis.
What you'll need
From Qualtrics or your survey platform, export the open-ended question responses as a CSV or Excel file. You need:
Clean it: remove any PII (names, emails) if your firm's data privacy policy requires anonymization before pasting into AI tools.
Troubleshooting: If your firm prohibits pasting client data into external AI tools, use example/synthetic responses to test the workflow, then apply the coding framework to real data manually using the AI-generated scheme.
This is the key step. Open Claude at claude.ai and start a new conversation. Paste this template (filling in your specifics):
I need to code open-ended survey responses from a study about [topic].
Coding framework — apply ONLY these codes:
1. [Code Name]: [brief description of what falls in this code]
2. [Code Name]: [brief description]
3. [Code Name]: [brief description]
[continue for all codes]
Rules:
- Apply 1-3 codes per response (never more than 3)
- If a response clearly doesn't fit any code, mark it as "Other — [brief description of what it says]"
- Flag any response that needs human judgment with [REVIEW]
- Return results in a table: Response Number | Response Text | Code(s) | Notes
Ready? I'll paste the first batch of responses now.
What you should see: Claude confirms it understands the task and is ready for the first batch.
After Claude confirms, paste 30–50 responses. Format them as:
1. [response text]
2. [response text]
3. [response text]
What you should see: Claude returns a table with response number, text (or abbreviated), codes applied, and any [REVIEW] flags.
This first batch is your calibration round. Check every coded response against your coding scheme. If Claude is making systematic errors (e.g., putting price complaints in the wrong bucket), correct it immediately:
"For the remaining batches: responses about [issue] should be coded as [Code X], not [Code Y]. Here's an example: [paste example]."
This calibration step is critical. One correction at the start saves you hours of corrections at the end.
Paste subsequent batches of 30–50. Claude maintains context within the conversation, so it applies any corrections you made in previous batches.
After every 3–4 batches (about 150 responses), do a spot-check review of 10–15 responses to make sure quality hasn't drifted.
When all batches are coded, copy the table(s) from Claude into Excel or Google Sheets. Consolidate all batches into a single worksheet. Sort by code to review each category as a group — this is when you'll catch any remaining inconsistencies.
Claude flags edge cases for human review. Go through these last — they're usually 5–10% of responses and genuinely are the ambiguous ones worth thinking about.
Initial coding prompt setup:
I need to code [N] open-ended responses about [topic]. Codes: [list with definitions]. Rules: 1-3 codes per response; flag [REVIEW] if ambiguous; return as table. Confirm you understand before I paste the first batch.
Calibration correction:
Correction for remaining batches: [describe the error and the fix]. Example of correct coding: "[response text]" should be coded as [Code X] because [reason].
Spot-check request:
Before I paste the next batch, please re-code these 5 responses and tell me your reasoning for each, so I can verify calibration: [paste 5 responses]
Synthesis after coding:
Here are the frequency counts for each code: [paste counts]. Write a 3-paragraph synthesis describing the main themes and what they suggest about [the business question].