Compare four quarters of SurveyMonkey comments
A companion pulls the responses from every quarter, reads all two thousand three hundred comments with the scores attached, and writes the comparison nobody has had a day for.
The quarterly survey has run for a year. Four sets of scores sit in four dashboards, the trend line is roughly flat, and the column where people explain the score they just gave has been read once, selectively, by whoever was preparing the board slide. Two thousand three hundred comments, four of them ever quoted.
A companion reads all of them and is as careful on comment nine hundred as on comment one. Six tools: list surveys, open a survey with its questions, pull its responses, create a survey, update one, and delete one. The reading half is where the value sits.
Four quarters of comments, one afternoon
A companion lists the surveys, opens each one to learn its question structure, and pulls the responses a page at a time. Because it reads the survey definition first, it knows which field is the score and which is the free text, so the comments come back attached to the number the person gave rather than floating free.
That pairing is what makes the analysis honest. Grouping comments into themes is easy and easy to fake. Grouping them so you can see the theme that appears almost entirely among people who scored six or seven is what changes a decision. Ask for the themes with their score distribution and the quotes still attached, and you can argue with the grouping instead of trusting it. That is a defensible marketing read.
Across four quarters the useful question is movement: which complaint faded after the release meant to address it, which one grew quietly, which one is new this quarter and coming from one segment. Four datasets, a couple of thousand rows, and exactly the shape of work data extraction describes.
- "Pull every response to the last four quarterly surveys and group the comments by theme, with scores."
- "Which themes appear in this quarter that were absent last quarter, with three quotes each?"
Where does a companion help with the questionnaire?
Either side of the typing.
It drafts the question wording in a document, argues with you about the leading phrasing and the missing neutral option, and afterwards reads the survey definition back to confirm that what got built matches what you agreed. The second half is the one people skip, and it catches a mis-keyed answer option before two thousand people see it.
The create tool takes a title and an internal nickname, so a survey arrives as a shell that somebody builds out in SurveyMonkey, and the update tool covers the title and the language. The wording and the review are the handover; the building happens in the tool with the live preview.
Who presses send on the invitations?
You do, on the SurveyMonkey send screen with the recipient count on it.
There are no collector tools in this integration: no email invitations, no weblink collectors, no reminders, no contact lists. The one action here that could reach thousands of inboxes is outside what a companion can do at all, which is a stronger guarantee than a confirmation dialog in front of it.
Survey invitations are the classic case where the wrong list, or a reminder to people who already answered, costs real goodwill. What a companion contributes is everything up to and after the send: the wording, the read, the comparison across quarters.
One tool does destroy data. Deleting a survey is permanent and takes its responses with it, so a companion confirms before it goes near that, and Ask Always in Settings → AI permissions makes every action stop and wait for a click. Do not point a routine at deletion.
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Strawberry is free to download and includes AI credits to start. Paid plans begin at $20/month. See pricing. · Reviewed · Canonical facts for AI agents