Reading Your Study's Recruitment Data Correctly
Starts, screen-outs, and completions tell three different stories. Reading them together — not separately — is what actually diagnoses a study.
The three numbers on a Survey Sphere study — starts, screen-outs, and completions — are each individually ambiguous. A low number of completions could mean low visibility, a broken screener, or a study that's simply unappealing once opened. The numbers only become diagnostic when you read the *ratios between them*, not any one of them alone.
Starts-to-completions ratio: is the study itself the problem?
If a study gets a healthy number of starts but a low completion rate relative to those starts, and screen-outs aren't the explanation, the issue usually sits inside the study — length, question clarity, or a drop-off point partway through. This is a content problem, not a recruitment problem, and no amount of retargeting fixes it.
Screen-out rate relative to your category norm
A screen-out rate has no universal "good" number — it depends entirely on how narrow your targeting is. What matters is whether it's consistent with what your criteria should produce. A criterion designed to exclude roughly 30% of a general pool that's instead excluding 80% suggests either the targeting reaching the study doesn't match the criteria, or a criterion is stricter in practice than intended (a common cause: an eligibility question worded ambiguously enough that borderline-eligible people answer it "wrong").
Time-to-fill as a leading indicator
A study that fills its target sample quickly relative to similar studies in your account has healthy reach for its criteria. One that stalls well below target after an initial burst of activity has exhausted the pool that matches its targeting — more time won't fix that; the targeting or criteria needs to change. Watching the *shape* of fill-rate over time (a steady trickle vs. an early spike that flattens) tells you which of those two situations you're in before the study has fully stalled.
Reading these together, not in isolation
A study with high starts, low screen-outs, and low completions has a content problem. A study with low starts and healthy screen-out and completion rates has a visibility problem. A study with high starts and high screen-outs has a targeting-mismatch problem. Same low completion count in all three cases — three completely different fixes. The [Study Detail page](/sphere/studies) surfaces all three numbers side by side specifically so this comparison is a glance, not a spreadsheet exercise.