No-Show Analysis · Treetop + Discovery ABA
No-show Analysis
326 interviews booked since Maigrate went live (May 1 – Jun 26, 2026).
Maigrate · prod + Salesforce · 2026-06-23
The baseline · May 1 – Jun 26, 2026 (~8 weeks)
The numbers since Maigrate went live
326
total booked
290 people
16
still upcoming
16 people
63
cancelled ahead of time
55 people
130
no-showed (missed slots)
111 people
47%
calculated no-show rate
130 ÷ 277 slots held
326 bookings → 16 upcoming, 63 cancelled ahead; the rest held 277 slots: 147 attended, 130 missed = 47%. 100 of those bookings were never attended — 130 counts repeat misses on the same booking too.
Trend · no-show rate by meeting week
Not improving — 37–62% every week, target is <30%
Range 37–62% of held slots missed; no downward trend — every week is well above the <30% target. Bar = % of slots missed; n = slots held that week (attended + missed).
What the data says
Our top 2 working theories
17→57%
1 · Lead time — the longer from first contact to the meeting, the colder they go.
30→59%
2 · Day-of silence — no reply the morning of the interview.
These two separate no-shows more cleanly than anything else we tested. The next two slides dive into each — then what happens when both go wrong.
Theory 1 · lead time
No-show rate by first contact → meeting
Monotonic. ≤2 days = 17–24%; 2+ days = 43–57%. The single largest separation in the dataset. (% of held slots missed.)
Theory 2 · day-of silence
No-show rate by day-of reply & conversation depth
No reply on the morning of the meeting ≈ 6 in 10 miss (59% vs 30%). A single reply then silence is the highest-risk pattern (69%). (% of held slots missed.)
The kicker · the two theories together
Lead time × day-of reply
Replied day-of
Silent day-of
Booked ≤2d
Booked >2d
The two strongest drivers stack: long lead + silent day-of = 67% vs short lead + replied = 19%. Booking close and a day-of confirmation each help, and most misses are where both are bad. (% of held slots missed.)
A third lever · reschedules
A booked candidate who reschedules — how often they then no-show
Each reschedule (the candidate moving an already-booked slot) compounds risk: 36% → 64% → 79% of held slots missed. Timing of reschedule requests (approx.): ~72% a day or more ahead, ~24% same-day, ~4% within an hour of the meeting.
What to do
Recommendations
Short term
- Don't rebook no-shows
- Cap reschedules at 1 — no more than one
- Require a day-before confirmation when booked 3+ days out
Long term · needs scoring / product
- Be more selective on who to interview (scoring) + book within 2 days where possible
- No reschedules
- Only schedule within 2 days
Appendix · other working theories
Other working theories, against the data
| Theory | The data | Verdict |
|---|
| Reply channel | email-only 50% · SMS-engaged 33% | mild |
| Calendly self-booking | Calendly 49% · agent/Google 45% | no effect |
| Candidate quality | "recommend" 45% · "review" 47% | weak |
| Conversation tone | 98% read normal | non-factor |
| Prior no-show | 75% repeat (n=4) | directional |
None of these separate no-shows the way lead time and day-of silence do. Full per-cut numbers + confidence intervals follow in the data-breakdown tables.
Appendix · data breakdown (1 of 2)
Data breakdown
| Cut | slots | missed | rate | 95% CI |
|---|
| Lead time (first contact → meeting) |
| <1 day | 24 | 4 | 17% | 2–32% |
| 1–2 days | 37 | 9 | 24% | 10–38% |
| 2–3 days | 30 | 13 | 43% | 26–61% |
| 3–5 days | 51 | 24 | 47% | 33–61% |
| 5+ days | 129 | 74 | 57% | 49–66% |
| Day-of reply |
| Replied day-of | 121 | 36 | 30% | 22–38% |
| Silent day-of | 150 | 88 | 59% | 51–67% |
| Conversation depth (inbound messages) |
| 0 | 17 | 8 | 47% | 23–71% |
| 1 (one-and-done) | 36 | 25 | 69% | 54–84% |
| 2–3 | 93 | 37 | 40% | 30–50% |
| 4+ | 125 | 54 | 43% | 35–52% |
95% confidence interval (Wald) on each rate. Wide intervals = small samples — treat those buckets as directional.
Appendix · data breakdown (2 of 2)
Data breakdown
| Cut | slots | missed | rate | 95% CI |
|---|
| Reschedules |
| 0 | 188 | 68 | 36% | 29–43% |
| 1 | 64 | 41 | 64% | 52–76% |
| 2+ | 19 | 15 | 79% | 61–97% |
| Reply channel |
| SMS + email | 24 | 8 | 33% | 14–52% |
| SMS only | 198 | 92 | 46% | 40–53% |
| Email only | 32 | 16 | 50% | 33–67% |
| No inbound | 17 | 8 | 47% | 23–71% |
| Candidate quality (model fit) |
| Recommend | 202 | 91 | 45% | 38–52% |
| Review | 17 | 8 | 47% | 23–71% |
| Unscored | 51 | 24 | 47% | 33–61% |
| Prior no-show history |
| First-time | 267 | 121 | 45% | 39–51% |
| Prior no-show | 4 | 3 | 75% | 33–100% |
95% confidence interval (Wald) on each rate. Wide intervals (e.g. 2+ reschedules, review, prior no-show) = small samples — directional only.
Appendix · by recruiter
No-show rate by recruiter
Recruiter A handles ~60% of all slots and runs 52% — above the 47% average, so they drive the headline. Not a controlled comparison: territory, demand, seniority, and volume differ by recruiter. Lead time and day-of silence still apply within each. (n = slots held.)
Appendix · does confirming help?
Confirmation does not equal attendance
48/130
missed slots where the candidate had explicitly confirmed — then no-showed
30%
no-show rate when they replied on the meeting day
59%
no-show rate when they were silent on the meeting day
Replying day-of roughly halves the risk (30% vs 59%) but doesn't remove it — 48 of 130 missed slots were by candidates who'd confirmed. Pair confirm-or-release with light overbooking / standby.
Appendix · is chasing no-shows worth it?
After a no-show, rebooking a new meeting rarely pays
111
people who no-showed an interview
9
showed for a newly rebooked meeting
0
of those were ever hired
Recovery = booking a brand-new meeting after the candidate's initial no-show. Only 9 of 111 then showed, and none were hired.
Appendix · was it the candidate at all?
23 of 130 missed slots had an operational cause
| Operational / data-quality cause | Count |
|---|
| Meeting not held or mis-recorded | 7 |
| Duplicate record / double-booking | 6 |
| Wrong time communicated / system bug | 5 |
| Missing meeting link / already-rejected re-booked | 3 |
~18% of the 130 missed slots were operational, not the candidate (21 distinct bookings) → the true candidate no-show rate is below 47%.
Appendix · every no-show, categorized
The 130 missed slots by primary reason
| Primary reason | missed slots | Share |
|---|
| Long lead time (went cold) | 55 | 42% |
| Reschedule churn | 17 | 13% |
| Engaged then ghosted (silent day-of) | 11 | 8% |
| One-and-done (shallow) | 11 | 8% |
| Confirmed then ghosted | 11 | 8% |
| Never engaged | 8 | 6% |
| Repeat offender | 3 | 2% |
| Missed, then recovered or cancelled | 14 | 11% |
Event-weighted (a booking missed 2–3× counts each miss). The last row = misses on bookings that later completed or cancelled. Assigned by priority waterfall; ~18% also carry an operational flag. Detail in the data export.
Appendix · meeting weekday
Monday is the worst day
Monday meetings (booked before the weekend) go cold — 56% vs 42–47% the rest of the week. No time-of-day effect (morning 41% vs afternoon 40%). Of 111 who no-showed, 17 did so more than once. (% of slots missed; n = slots held.)