Operate the ongoing loop where each cycle of feedback is read on arrival and coded against the last, on the same record. Compare cycle-over-cycle on one ID and route every flag to a named owner.
In short: A continuous stakeholder feedback loop in Sopact Sense reads each cycle of responses the week it arrives and codes it against the same stakeholder's prior cycle on one persistent Contact ID, so movement — improvement, disengagement, new risk — is surfaced with the source sentence and routed to a named owner. Clean-at-source collection and the same method every cycle keep results comparable; a flag nobody owns is a finding that sat there.
The loop starts before the responses arrive. Design collection so open text maps to your indicators at intake — clean-at-source — rather than being cleaned up months later at analysis time. In Sopact Sense the form is built so each open answer is structured against your framework the moment it lands.
Design clean-at-source collection so open text maps to our indicators/framework at intake, not after export.
A feedback loop only closes if you read responses when they come in, not at annual review. Process this cycle now so a disengaging stakeholder is caught this week — while you can still act — instead of surfacing in a year-end summary when it is too late to respond.
Read this cycle of responses [BATCH] for [COHORT] now — code each response against our indicators as it lands.
Because every response sits on one persistent Contact ID, you can code this cycle against the same stakeholder's last cycle on the same record. That comparison is what surfaces movement — someone improving, disengaging, or showing a new risk — and Sopact Sense attaches the source sentence so the flag is evidence, not a vibe.
For each stakeholder, compare this response to their prior cycle on the same record, and flag movement with the source sentence.
Surfacing a flag is not closing the loop. Route each flag to a named owner with a deadline so the feedback drives an action. A flag nobody owns is a finding that sat there until the next review.
Route every flag to a named owner with a deadline so nothing surfaced is left unactioned.
Run the loop in a single pass each cycle, using the same method as last time so results are comparable and you never re-ask what the record already holds.
Read this cycle of responses [BATCH] for [COHORT]. For each stakeholder, code the response against our indicators, compare it to their prior cycle on the same record, and flag movement with the source sentence. Use the same method as last cycle so results are comparable.
Expected output. Each response coded against your indicators on its persistent Contact ID; a cycle-over-cycle comparison per stakeholder with movement flagged and the source sentence attached; and a routed action list assigning every flag to a named owner with a deadline. Input: this cycle's batch of responses for the cohort. Output: a comparable, coded read of the cycle on one record, with owned actions — not a pile of raw responses waiting for annual review.
GRADE: green | read + compare | coded on IDs, movement flagged, owners set; amber | read | coded but not compared to prior cycle; red | pile | raw responses held for annual review
Green is a cycle read on arrival, coded and compared to the prior cycle on the same ID, with every flag owned. Amber is a cycle that's coded but not yet compared to the last one, so movement is invisible. Red is the default failure: raw responses sitting in a pile until the annual review.
A flag nobody owns is a finding that sat there. Surfacing risk without routing it changes nothing. Assign every flag to a named person with a deadline so the loop produces action, not just a report.
Never re-ask what the record already holds. If the persistent record already has a stakeholder's baseline, don't re-collect it — ask only what changed. Re-asking known facts fatigues stakeholders and buries the new signal.
Use the same method every cycle. Comparability comes from consistency. Reuse the same indicators, codebook, and wording each cycle so cycle-over-cycle movement is real change, not a change in how you coded.
Improve accuracy by pointing at your own program page. Give the Assistant your program page URL or codebook so coding uses your indicators and language, which sharpens the movement flags.
You run a continuous stakeholder feedback loop by collecting clean-at-source so open text maps to your indicators at intake, reading each cycle of responses the week it arrives, coding each response against the same stakeholder's prior cycle on one persistent Contact ID, and routing every flag to a named owner. In Sopact Sense one prompt codes, compares, and flags movement with the source sentence, so the loop closes on action instead of sitting until annual review.
An annual survey reads everything once, long after most of it was collected, so problems surface too late to act on. A continuous feedback loop reads each cycle the week it arrives and compares it to the same stakeholder's prior cycle on one record, surfacing movement — disengagement or new risk — while you can still respond. The loop is longitudinal on one ID; the annual survey is a late snapshot.
A feedback loop needs one persistent Contact ID so each cycle can be coded against the same stakeholder's prior responses, which is the only way to see movement rather than a fresh score each time. Without one record, every cycle is a disconnected survey and improvement or disengagement is invisible. On one ID in Sopact Sense, the loop compares like with like and flags real change with its source sentence.
ChatGPT, Claude, and Gemini are fine for a quick test — but not for an answer you'll put in front of a funder or board. When it has to hold up, run it in Sopact Sense.
Try it in Sopact →
