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 we tested
Every working theory, against the data
- Lead time — how far out the meeting was booked
- Day-of reply — did they respond the morning of
- Engagement depth — back-and-forth vs one-and-done
- Reschedules — how many times it moved
- Reply channel — SMS vs email
- Calendly self-booking — tool vs coordinated
- Candidate quality — screening / resume fit
- Conversation tone — wacky / unpleasant / non-human
- Prior no-show — repeat offenders
- Operational / data quality — was it the candidate at all?
Data: 247 resolved interviews joined to their full message threads, booking timing, screening, and history. Every signal measured before the meeting. Pipeline is 100% NC, so a by-state test isn't possible yet.
Hypothesis · 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.)
Hypothesis · engagement
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 drivers compound
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.)
Hypothesis · 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.
Hypothesis · reply channel
No-show rate by where they engaged
Email-only candidates miss more than SMS-engaged ones (50% vs 33%). (% of held slots missed.)
Hypothesis · 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%.
Hypothesis · 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.
Hypothesis · 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.
What the data points to
The levers, ranked by the numbers
- Book within 2 days. 17–24% vs 43–57% beyond 2 days.
- Treat day-of silence as the live flag. 59% vs 30%.
- Fix the operational ~21%. Recruiter no-shows, dupes, mis-records.
- Cap reschedules at 1. 2+ → 79% no-show, 0% hire yield.
- Confirm-or-release, plus overbook. 48/130 misses were by confirmed candidates.
- Not Calendly, not "quality." Both ~0 effect — don't spend there.
The takeaway
~47% of interview slots missed — flat for 8 weeks.
Lead time and day-of silence move it most.
Maigrate · reproducible from treetop_funnel.sqlite · 2026-06-23
Appendix · evidence (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 · evidence (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 · hypotheses the data did NOT support
Myth checks, by the numbers
| Hypothesis | The data | Verdict |
|---|
| Calendly self-booking drives no-shows | Calendly 49% (n=49) · agent-booked/Google 45% (n=222) | no effect |
| Underqualified candidates drive no-shows | "recommend" 45% (n=202) · "review" 47% (n=17) | weak (~2pp) |
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.
Cross-section · 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.)