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
147
completed
143 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 — 37–67% every week

Wk May 4
43%
n=14
Wk May 11
42%
n=26
Wk May 18
56%
n=41
Wk May 25
37%
n=52
Wk Jun 1
47%
n=43
Wk Jun 8
62%
n=42
Wk Jun 15
38%
n=45
Wk Jun 22
67%
n=12*
Range 37–67% of held slots missed; no downward trend. * Wk Jun 22 is a partial week (small n). Bar = % of slots missed; n = slots held that week (attended + missed).
What we tested

Every working theory, against the data

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

< 1 day
17%
n=24
1–2 days
24%
n=37
2–3 days
43%
n=30
3–5 days
47%
n=51
5+ days
57%
n=129
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

Replied day-of
30%
n=121
Silent day-of
59%
n=150
4+ replies total
43%
n=125
2–3 replies
40%
n=93
1 reply only
69%
n=36
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.)
Hypothesis · reschedules

No-show rate by reschedule count

0 reschedules
36%
n=188
1 reschedule
64%
n=64
2+ reschedules
79%
n=19
Each reschedule compounds risk: 36% → 64% → 79% of held slots missed.
Hypothesis · reply channel

No-show rate by where they engaged

SMS + email
33%
n=24
SMS only
46%
n=198
Email only
50%
n=32
Email-only candidates miss more than SMS-engaged ones (50% vs 33%). (% of held slots missed.)
Hypothesis · was it the candidate at all?

21 of 100 no-shows had an operational cause

Operational / data-quality causeCount
Recruiter no-show / candidate actually attended7
Duplicate record / double-booking6
Wrong time communicated / system bug5
Missing meeting link / already-rejected re-booked3
RBT-certified candidate confirmed both times — "I'll be there" — joined the Meet both times: "No one was on the call." System then flagged her a repeat no-show and recommended rejection.
Verified from the message thread · name withheld
~21% of no-show bookings (≈23 of the 130 missed slots) carry an operational flag → the true candidate no-show rate is below 47%.
Hypothesis · does confirming help?

Confirmation does not equal attendance

41/100
no-shows explicitly confirmed they'd attend — then missed
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 — 41 confirmed and still missed. Pair confirm-or-release with light overbooking / standby.
Hypothesis · is chasing no-shows worth it?

Rebooking recovers meetings, not hires

130
interview slots missed (no-shows)
9
rebooked and showed up
0
of those were ever hired
Across 111 people who missed, rebooking recovered only 9 completed interviews and 0 hires.
What the data points to

The levers, ranked by the numbers

The takeaway

~47% of interview slots missed — flat for 8 weeks.

Lead time and day-of silence move it most.

1 in 5 was never the candidate.

Maigrate · reproducible from treetop_funnel.sqlite · 2026-06-23
Appendix · evidence (1 of 2)

Data breakdown

Cutslotsmissedrate95% CI
Lead time (first contact → meeting)
<1 day24417%2–32%
1–2 days37924%10–38%
2–3 days301343%26–61%
3–5 days512447%33–61%
5+ days1297457%49–66%
Day-of reply
Replied day-of1213630%22–38%
Silent day-of1508859%51–67%
Conversation depth (inbound messages)
017847%23–71%
1 (one-and-done)362569%54–84%
2–3933740%30–50%
4+1255443%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

Cutslotsmissedrate95% CI
Reschedules
01886836%29–43%
1644164%52–76%
2+191579%61–97%
Reply channel
SMS + email24833%14–52%
SMS only1989246%40–53%
Email only321650%33–67%
No inbound17847%23–71%
Candidate quality (model fit)
Recommend2029145%38–52%
Review17847%23–71%
Unscored512447%33–61%
Prior no-show history
First-time26712145%39–51%
Prior no-show4375%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
52%
n=163
Recruiter B
43%
n=72
Recruiter C
11%
n=18
Others (3)
33%
n=18
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

HypothesisThe dataVerdict
Calendly self-booking drives no-showsCalendly 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)
Wacky / unpleasant / non-human convos98 of 100 conversations read normalnon-factor
Screen-rejected → no-show18 of 23 rejected because of the no-showexcluded (leakage)
Appendix · every no-show, categorized

The 100 no-shows by primary reason

Primary reasonnShare
Long lead time (went cold)4545%
Reschedule churn1212%
Engaged then ghosted (silent day-of)1111%
One-and-done (shallow)1111%
Confirmed then ghosted1111%
Never engaged88%
Repeat offender22%
The 100 bookings that ended in a no-show (= 116 of the 130 missed slots; the other 14 misses were on bookings that later completed or cancelled). Assigned by priority waterfall; ~21% also carry an operational flag. Per-candidate detail in the data export.
Appendix · operational no-shows (1 of 2)

Not the candidate — cases 1–14

#What happenedOutcome
① Recruiter no-show / candidate actually attended (7)
1Said she attended; was told she lacked RBT — likely mis-recordedlost
2Confirmed; actually showed up — mis-recorded, agent kept reschedulinglost
3"I'll be there," joined the link — no one else did (recruiter missed)lost
4RBT-certified, confirmed twice, joined both times to an empty Meetrejected
5Joined to an empty Meet; cert-rejected but interview never cancelledrejected
6Date mix-up; joined ~15 min late after it was marked no-showcompleted
7Recruiter missed the 1st meeting; rebooked, then missed the 2ndrejected*
② Duplicate record / double-booking (7)
8Duplicate application; meeting link never deliveredlost
9Duplicate records; conflicting May 18/19 dates, missing invitelost
10Duplicate record (2nd); same conflicting-date issuelost
11Recruiter double-booked; reschedule never settledlost
12Double-booked; only daytime slots offered (didn't fit her)lost
13Recruiter double-booked; original slot never replacedlost
14Recruiter rescheduled off-system; conflicting/unverified bookingsrejected*
Appendix · operational no-shows (2 of 2)

Not the candidate — cases 15–21

#What happenedOutcome
③ Wrong time / system bug (5)
15"This was a bug," wrong 7pm time sent in error (also repeat misses)rejected*
16Operator read the booking as 2:30 but confirmed 10:00lost
17Contradictory booking (10:30 confirmed → "taken" → re-confirmed)lost
18Availability-lookup failures + recruiter-reassignment churnrejected*
19First/last name swapped on reminders; holiday-slot blockslost
④ Other (2)
20Already interviewed & rejected, yet re-bookedrejected
21Interview appeared ~1 mo after "no slots," with no booking steprejected*
* 5 rejected as "prior_no_shows" for misses that were operational or the recruiter's. 3 (cases 1–3) likely attended — so the no-show count is overstated. (Cases 15, 18, 21 are mixed: a real bug plus genuine repeat misses.)
Cross-section · the drivers compound

Lead time × day-of reply

Replied day-of
Silent day-of
Booked ≤2d
19%
n=32
24%
n=29
Booked >2d
34%
n=89
67%
n=121
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.)
Cross-section · meeting weekday

Monday is the worst day

Monday
56%
n=57
Tuesday
47%
n=68
Wednesday
42%
n=52
Thursday
44%
n=52
Friday
45%
n=47
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.)
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