Clanker CloudCustomer feedback arrives through emails, support conversations, and interviews. An Agent can help organize those records into themes, but the counts and evidence need to survive the summary. Three messages from one account are different from three independent accounts reporting a problem.
Keep the original records in a Clanker Workspace Project, ask an Agent for a sourced analysis, and review the themes before using them to set priorities.
Preserve the records behind the themes
Start with feedback IDs, dates, channels, and an account identifier appropriate for the task. Avoid including personal details the analysis does not need. Write down the collection window and how the comments were selected.
Use a small fictional sample for the first trial:
| ID | Account | Comment |
|---|---|---|
| F1 | A1 | Cannot find the export button on mobile. |
| F2 | A2 | Export is hard to find, but it works once found. |
| F3 | A1 | Following up: still cannot find mobile export. |
| F4 | A3 | Please add a weekly scheduled export. |
| F5 | A4 | I have not tried export yet. |
This sample contains five messages from four accounts. It provides a checkable exercise, not evidence about an actual customer population.
Ask for themes that retain evidence
Give the Agent a goal such as:
Analyze the feedback records in this Project.
Preserve the original IDs and account identifiers.
Group supported themes and cite the IDs behind each one.
Count messages and distinct accounts separately for each theme.
Identify follow-ups, reported defects, and feature requests.
Keep comments with no applicable experience separate.
Save a findings file and questions for the product owner.
Do not infer population percentages or commit roadmap changes.
The feedback analysis starter includes a larger fictional dataset and example checks. Use a custom Agent if this becomes a repeated review, with the classification method in its instructions or a reusable Skill.
Check the conclusions against the sample
The export-discovery theme has three messages from two accounts: F1, F2, and F3. Scheduled export has one message from one account: F4. F5 does not establish satisfaction or a problem with export because that account has not tried it.
The comments describe difficulty finding a control. They do not establish that export itself fails. Keep that distinction visible so a navigation issue does not become a claimed data-loss defect in the summary.
If account identifiers are missing, the Agent can count messages but should report that distinct-account counts are unavailable. Similar wording alone does not prove that two records came from the same person.
Turn findings into questions for the business
A useful findings file can contain a theme table, evidence IDs, count definitions, and questions for the next research step. For this sample, the product owner might investigate mobile discoverability and ask what cadence the scheduled-export requester needs.
Keep severity, breadth, and proposed remedies separate. One serious report can warrant investigation without being described as widespread. A frequent request can still need a business decision about cost and scope.
For a regular review, identify how new records reach the Project and configure a tested hosted Schedule. For email intake, use the appropriate mailbox workflow; don't assume connecting a mailbox imports every historical conversation or creates an analysis dataset automatically.
Open the saved findings and sample-check several theme assignments before sharing. Retain the source IDs so the next reviewer can follow the reasoning instead of relying on the Agent's confidence.
Sources
Give your assistant one useful job
Use fictional feedback records to test theme grouping, source links, and counts before relying on the analysis for a product decision.