Grooming Salon

Booking Pipeline Targets

6 min read

Booking Pipeline Targets

Promoted from 10-Scratch/scratch-booking-vs-grooming-targets.md on 2026-07-07, and rebuilt against the current 60-month per-groomer financial model instead of the original 24-month placeholder trajectory. financial-model.xlsx’s Booking Pipeline tab is now the live, authoritative source (260 weeks, fully editable) - this file covers the method, the assumptions, and the key findings.

Every grooms target set elsewhere in this vault (8/day at Day 60, 18/day at Year 1, 32/day at the team ceiling…) is stated as grooms completed. But the salon doesn’t directly control grooms completed - it controls appointments booked. This file answers: how many appointments (new clients + returning clients rebooking) need to be on the calendar each week to hit a given grooms target?

Two Distinct Gaps (only one is modeled with numbers)

  1. Same-period leak - a slot booked for today that doesn’t turn into a finished groom today (no-shows, late cancellations not backfilled by the waitlist). Short horizon, mostly a policy/waitlist problem. Not numerically modeled - there’s no real no-show/late-cancel rate to anchor it to yet (Kendra’s experience at All Dogs Gym and Inn might give a real anchor point once asked). Treat any stated grooms target as slightly understating the true booking requirement until this is measured.
  2. Forward pipeline - how many new bookings need to be acquired each period to hit a future grooms target, once existing clients rebooking on their own cadence are accounted for. Long horizon, a marketing/admin-capacity problem. This is the part that’s fully modeled below.

The Algorithm

Inputs (all editable on financial-model.xlsx’s Assumptions tab):

  • G(t) - grooms target for week t, pulled directly from the Monthly Model’s Dogs/Month column, spread evenly across ~4.33 weeks/month. This resolves an old mismatch in the original scratch model, which assumed a flat 5-day week instead of tying back to the real 22-operating-day monthly figure.
  • r - rebook rate: probability a groomed dog books a future appointment at/near their visit. 80%, a deliberately conservative planning number, not a measured rate.
  • p_c - share of rebooking dogs on cadence c ∈ {6, 12, 18} weeks. 25% / 50% / 25%, favoring 12 weeks per Kendra’s expected norm - a placeholder pending her real breed-by-breed rebooking minimums.

The recurrence:

Returning(t)  = Σ over c of  [ p_c × r × Completed(t − c) ]
New_Needed(t) = G(t) − Returning(t)
Completed(t)  = G(t)     ← assumes the target is actually being hit

Returning(t) looks back 6, 12, and 18 weeks, takes how many grooms were completed each of those weeks, applies the rebook rate and that cadence’s share, and sums. New_Needed(t) is the actual KPI - the number of new client bookings that must be actively generated through marketing, referrals, or outreach every week, on top of whatever rebooks on its own.

Structural property: at steady state (once the pipeline has matured against a flat target), returning clients cover exactly r × target, so new bookings needed floors at exactly (1 − r) × target - 20% of target, forever, since r < 100%. New-client acquisition never goes to zero.

Booking Workload (time, not just headcount)

Per the 7-step checklist in scheduling-procedures:

  • New-client call: ~14 minutes. Full client + dog profile, vaccine record intake, pricing lookup, groomer assignment, payment info collection.
  • Returning-client rebooking: ~3 minutes. Meant to happen in-visit at checkout, not as a separate call - kept nonzero as a buffer, not a measured call length.

Both are placeholders pending real call-time data once the salon is operating.

BookingMinutes(t) = New_Needed(t) × 14 + Returning(t) × 3

Key Findings (from the current 60-month model)

MonthNew Bookings Needed/wkNew as % of TargetBooking Admin Hrs/wk
120.3100%4.7
421.346.7%6.2
925.433.3%8.5
12 (Year-1 close)27.430.0%9.6
20 (Staff 5 hired)40.629.6%14.3
22 (Kendra reaches 0)37.626.4%14.0
24 (Year-2 close)39.626.0%14.9
36+ (steady state)32.520.0% (the structural floor)14.1

Two admin-time spikes, and the bigger one isn’t where the original analysis found it:

  1. The launch spike (Weeks 5-6, ~9.5 hrs/wk) - the target jumps to the 8/day solo ceiling with zero returning-client cushion yet. This lands in the same weeks Kendra is also hitting her personal grooming ceiling (~40 hrs/week of chair time) - stacking a full extra workday of phone/admin work onto an already-maxed grooming week is a real burnout risk, not just a scheduling curiosity.

  2. The real peak - ~16.5 hrs/wk around Month 26, not at launch. The original scratch analysis only modeled a flat target after the solo phase, so it never found this. In the real 60-month trajectory, the team’s total target keeps climbing all the way to the 32-dogs/day ceiling around Month 26 - and the last step up before that ceiling is the worst moment, because the absolute target is now large (162.5/wk) even though the percentage covered by returning clients is still catching up. This is a materially bigger and later admin-capacity risk than “the launch phase,” and it’s worth planning front-desk/booking capacity around Month 20-26, not just Month 2.

After Month 26, the target goes flat (all 4 staff at their own 8/day ceiling, Kendra fully at zero per revenue-projections’s headline finding), and booking admin hours settle to their steady-state floor of ~14.1 hrs/wk - over a third of a full-time role, indefinitely, just for booking calls.

Who answers these calls is not yet decided. Early on it’s unambiguously Kendra’s own time. By Month 20-26, with Kendra at zero and 4 staff groomers on the floor, it’s an open question whether this falls to Kendra (now with more time as executive chef), gets split across staff, or justifies a dedicated front-desk/booking hire - worth modeling as its own staffing line the same way ongoing-expenses models grooming payroll.

Open Questions

  1. Real no-show/late-cancellation rate (Part 1’s same-period leak) - unmeasured, would make every target above a slight underestimate of true booking volume needed.
  2. Confirm or adjust the 25/50/25 cadence split and 80% rebook rate - both are placeholders, not measurements.
  3. Validate the 14-min/3-min call-time estimates once real bookings exist.
  4. Who absorbs the ~14-16 hrs/wk of booking admin time once the team is fully staffed - Kendra, a staff groomer, or a dedicated hire?
  5. Does each hiring transition (Months 4, 9, 15, 20) cause its own smaller booking-admin spike the same way the Month-2 launch jump does? (Visible in the full 260-week table on the spreadsheet’s Booking Pipeline tab - not yet separately called out here.)

Cross-References

  • scheduling-procedures - the 7-step booking checklist and Waitlist Management (the same-period-leak mitigation)
  • revenue-projections - the grooms-target trajectory this model consumes
  • cancellation-and-no-show-policy - relevant to the unmeasured same-period leak
  • financial-model.xlsx’s Booking Pipeline tab - the live weekly calculation (260 weeks)
  • financial-model.xlsx’s Dashboard tab - month-end snapshots and the peak-hours finding

Graph View

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