Customer feedback with the evidence intact.

Define what the sample represents

Start with the collection window and where the comments came from. The supplied data contains eight messages from seven fictional accounts across email, support, and interviews. It is a convenience sample for practicing analysis, not a representative survey or evidence about a real product.

Keep stable feedback IDs and an account identifier that does not reveal unnecessary personal information. If identifiers are missing, count messages and report that distinct-account counts cannot be established. Never assume two similar comments belong to the same person just because the wording overlaps.

Build a small, inspectable coding scheme

Begin with concrete themes describing what people reported: difficulty finding export, a request for scheduled export, a reported export defect, and positive onboarding feedback. Attach each theme to its source IDs. Keep the original text available so a reviewer can disagree with a classification.

A message can discuss more than one topic, but the sample uses a single primary theme for each substantive record. State that rule before counting. If you choose multiple labels in real work, explain that theme totals can exceed the number of messages; do not present overlapping percentages as a partition of customers.

Count repeated messages and distinct accounts separately

F01 and F03 are two messages from A11 about the same difficulty. The discovery theme therefore has three messages from two distinct accounts when F02 is included. Scheduled export has two messages from two accounts. These are observations in the supplied sample, not estimates of how common either issue is among all users.

Retain follow-ups because they can show unresolved work, but do not let repetition silently inflate the apparent breadth of a request. Record your denominator next to any percentage. A safer first report uses raw message and account counts and explains what was excluded rather than adding misleading precision.

Separate frequency from investigation priority

A single report that exported CSV rows are missing deserves investigation even though it is less frequent than the discoverability comments. The record does not establish the cause, reproducibility, severity, or number of affected users. Label it as a reported defect and propose a reproduction check.

Feature requests need a different next step. Ask which workflow a weekly export supports and what delivery format would be useful. Do not convert two requests directly into a roadmap promise. Product impact, reach, effort, and strategic fit require evidence this small feedback file does not contain.

Check the quotes and the negative space

Review the theme table against all eight rows. F08 says the account has not tried export; it should not be counted as positive or negative export experience. F07 is useful positive feedback about onboarding and should not disappear because most other comments concern export.

Use short exact quotes or faithful paraphrases and preserve record IDs. An Agent may generalize “hard to find” into “broken”; that changes a discovery problem into a functional failure. Check that the summary does not strengthen customer wording or invent a sentiment score that the method never defined.

Turn the analysis into a repeatable review

Save the source CSV, theme definitions, and results together in a Project. For a later batch, retain the previous definitions or document any changes before comparing counts. A renamed theme or a new collection channel can create an apparent trend even when customer experience is unchanged.

If the dataset becomes large, ask for a complete row-to-theme mapping before trusting the executive summary. Review unclassified rows, mixed themes, and missing identifiers. A reusable Agent can apply your approved rubric, but its test set should include duplicates, neutral comments, and a serious one-off defect such as the examples here.

Try it with a small example.

An illustrative result.

Fictional sample, written to show the expected structure. Your result depends on your inputs and needs review.

Fictional expected theme table

Find export: 3 messages / 2 accounts | F01, F02, F03
Scheduled export request: 2 messages / 2 accounts | F04, F05
Reported missing CSV rows: 1 message / 1 account | F06
Positive onboarding: 1 message / 1 account | F07
No export experience: F08; retained, excluded from substantive export themes.

Investigation: reproduce the owner-filter export reported in F06; cause and severity are unknown.
Scope: 8 messages, 7 accounts. These counts describe this sample only. F03 adds follow-up evidence, not a new account.

Before you use the result.

• All eight records appear in the evidence mapping.
• The discovery theme has three messages and two distinct accounts.
• The possible export defect remains a reported issue, not a verified diagnosis.
• F08 is not treated as satisfied or dissatisfied with export.
• No population-level percentages or product commitments were invented.