For CX and in-house research teams: the themes behind every detractor and every promoter, with the same codebook in every wave.
The score calculates itself. The thousands of answers to “why did you give us that score?” end up in a spreadsheet an analyst categorizes by hand for days — and when the next wave arrives, it starts over.
Days of spreadsheet work every wave
Categories that change with whoever codes them, so waves can't be compared
The report lands when the next wave is already due
Upload the export from your survey tool. Survey Coder builds the codebook — or uses yours — codes every comment, and lets you review the doubtful ones before you export. The next wave inherits the same codebook.
Sentiment per mention: “great service but slow” isn't averaged into neutral
What moves promoters and what holds detractors back
The same themes compared across detractors, passives and promoters
A codebook inherited wave after wave, so the trend is real
What a team that measures every month needs — not once
Each theme carries its own sentiment. A comment that praises the service and criticizes the price isn't flattened into “neutral”.
The AI proposes themes from your responses, or applies the codebook you already use. You review and adjust before coding.
What each group mentions, so you know what to protect and what to fix.
Thousands of comments coded in under an hour — not days in a spreadsheet.
Point to the score column and themes are crossed by group. Works with 0–10 scales and with 1–5 scales too.
The next wave inherits the same codebook. If a theme rises, it's the customer who changed, not the way it was coded.
SurveyCoder codes open-ended comments into a controlled codebook, lets your team review the assignments, and exports results to Excel or SPSS. Research agencies and CX teams can use their own codebook or review one proposed by AI, then reuse it on the next wave.
The comments and labels below are synthetic, manually prepared reference decisions. They are not customer results or an AI accuracy benchmark.
Banking · score 4
“The app works well but the transfer fee is too high.”
Reference codes: 101 — Digital experience and 102 — Fees and charges. Keep both themes; a low recommendation score does not make every part of the comment negative.
Airline · score 3
“My flight was late and nobody explained the delay.”
Reference codes: 103 — Service and communication and 104 — Waiting and delays. Separate the delay from the communication issue so both can be counted and reviewed.
Theme differences between promoters and detractors describe associations; they do not establish what caused the score. SurveyCoder supports the coding workflow; use your survey system to collect the feedback.
These CSVs illustrate a handoff, rather than the exact product export. For a pilot, measure preparation time, review time and accepted codes on your own sample. Request a coding pilot.
We processed the same 75 English retail comments and original codebook used by the welcome project in an isolated QA project. The comments are synthetic; this coding run and its native Excel exports are real.
The initial run classified 72 comments and left 3 unclassified. Codex-assisted editorial review changed 15 responses and added 7 codes. The reviewed export contains all 75 classified comments. See the original output, the reviewed output and the reason for each change.
Run on 6 October 2026 in STANDARD mode, using 75 credits. This is a workflow demonstration, not independent human validation or an accuracy benchmark. The codebook contains examples from the sample, and the dataset has no NPS scores.
The NPS score is the easy part. The hard part is turning thousands of open-ended "why did you give us that score?" responses into something a CX or in-house research team can actually act on. The chain has five stages and each one breaks at predictable places:
A services company compared our coding of its NPS with its own, across every response. They agreed on around 96% — and when they reviewed the differences, they realized several came from errors in their own coding, not ours.
That is the hardest thing to see from the inside: when a person codes by hand for days, their inconsistencies don't show until something independent sits next to them.
~96% agreement
Across every response. In several of the differences, the error was in the company's own coding.
3-5 days → under 1 h
Typical coding time for a study of 5,000 to 8,000 comments.
Start coding open-ended responses with AI today. Free trial with 250 responses.