Quick Brief

Restaurants lose 2--4 tables per night to no-shows and friction. This workflow uses WhatsApp + light AI to confirm, pre-seat from a waitlist, and pre-prep in the kitchen, so seats stay warm and AOV rises. Start small, instrument KPIs, keep what works, then scale.

Why this matters now

• Margins are tight; staffing is volatile. One missed four-top can erase a shift's profit.
• No-shows are rising (friction in confirmations, last-minute cancels).
• Guests expect messaging-first: instant confirmation, transparent wait times, fast choices.
• Managers need clarity: what to prep, when to staff, which promos convert.

Goal: Keep seats filled, smooth peaks, and lift AOV, without adding headcount.

The AI playbook (5 building blocks)

1) No-show prediction + auto-waitlist

Signals: booking history, day/time, party size, lead time, seasonality/weather, prior behavior (late cancels/no-shows).
Automation: flag at-risk reservations → trigger WhatsApp confirmation (one-tap confirm / modify / cancel). If no reply, use a controlled overbook margin or pre-seat from the waitlist.
Outcome: higher table utilization with controlled risk.

Risk controls (recommended):

• Cap overbook at X% during prime hours (e.g., 5--8%).
• Pause overbook if forecast error exceeds Y% (e.g., rainy-day "show-up" uncertainty).
• Escalate to staff check if model confidence < 0.55.

One-tap confirmation prompt (universal):
"Hi {firstName}, your table at {Venue} is set for {time} (party {size}).
Reply 1 to confirm, 2 to change time, 0 to cancel (no fee if >2h)."

2) WhatsApp ordering & upsell (with optional pre-order)

Before arrival: offer pre-order for best-sellers or limited specials.
While waiting: send a 1-minute mini-menu; collect choices so the kitchen can pre-prep.
Upsell rules: if basket < threshold, suggest one side/dessert; pair a drink when a main is selected.

Micro-flow:
Join waitlist → receive ETA + mini menu → choose items → ticket hits kitchen → faster table turn → AOV increases.

3) Smart staff scheduling (demand ↔ skills)

Forecast covers in 15-minute slots using historical data + live bookings.
Roster tweaks: e.g., add a runner 19:00--21:00; shift a bartender to patio.
Skill coverage: ensure barista / grill / runner capacity at predicted peaks.

4) Reviews & auto-replies (FR/AR/EN or your local mix)

Detect sentiment, reply in the guest's language, and escalate low scores with a manager draft (apology + voucher suggestion).
Auto-publish thanks for 4--5★ reviews using owner-approved templates.

5) Practical compliance (global-ready)

• Local-first by default: keep reservation/order data on systems you control; use cloud only for heavier tasks.
• Maintain a model register (model, purpose, data sources).
• Provide clear opt-in / opt-out for messaging (support "STOP" or local equivalent).
• Keep a simple audit log for waitlist/overbook decisions and consent status.

How it fits together (non-technical view)

Reservations/POS ↔ AI Orchestrator (no-show model + rules)
WhatsApp Business API ↔ confirmations, pre-orders, waitlist
Kitchen/Bar Display ↔ early prep tickets
Manager Dashboard ↔ live KPIs (fill, AOV, seat-to-serve, CSAT)

Start modular: launch messaging + waitlist first; add prediction once baseline data is clean.

KPIs that actually move (30--60 days)

• No-show rate: −20% to −40% (confirmations + waitlist)
• Prime-hour table fill: +8% to +15%
• AOV: +5% to +12% (pre-order + upsell)
• Seat-to-serve: −4--7 minutes (kitchen head start)
• CSAT (post-meal): +0.3--0.6 (5-point scale)

Ranges are typical after baseline cleanup; your mileage varies by venue size, channel mix, and compliance with messaging windows.

Instrumentation (quick formulas)

• No-show rate = No-shows ÷ (Confirmed + Walk-ins) per service
• Prime-hour table fill = Seated covers in prime window ÷ Capacity in same window
• AOV = Total food + beverage revenue ÷ Number of checks
• Seat-to-serve = First item served time − Seating time
• CSAT = Average post-meal rating (5-point scale)

Prerequisites (before Week 1)

• WhatsApp Business account with approved templates (your languages).
• Reservation export (CSV) or basic POS sync.
• Simple kitchen/bar ticket view or display.
• Staff contact list for escalation and overrides.

21-day rollout (works for single venues or groups)

Week 1 --- Connect & prepare

• Import reservations (CSV or POS sync).
• Activate WhatsApp templates (FR/AR/EN or local mix).
• Define at-risk threshold (e.g., model score ≥ 0.45 to start).
• Enable a digital waitlist (QR at the door + website link).
• Pick 5 menu items for pre-order (best-sellers).

Acceptance criteria:

• ≥90% template approvals; data import with <2% rejects.

Week 2 --- Launch & tune

• Start confirmations (T-24h, T-4h, T-90m).
• Pilot pre-order on best-sellers; route tickets to kitchen/bar displays.
• Add gentle upsell: dessert at T+25m or drink pairing once a main is chosen.

Acceptance criteria:

• ≥70% confirmation reply rate; ≤5% failed sends.

Week 3 --- Measure & scale

• Review KPIs vs. baseline; lock in the wins.
• Expand pre-order to a full menu section; refine roster by 15-minute peaks.
• Switch from rules-only to hybrid model + rules if data quality is good.

Acceptance criteria:

• No-shows ↓ ≥15% vs. baseline; AOV ↑ ≥3%.

Copy-paste templates

Reservation confirmation
"Hello {firstName}, your table at {Venue} is booked for {day} {time} (x{size}).
Reply 1 to confirm, 2 to reschedule, 0 to cancel (no fee >2h). See you soon!"

Waitlist invite (when fully booked)
"Thanks, {firstName}! Estimated wait {ETA}.
Want to pre-order and save ~5 minutes? Reply MENU."

Positive review (auto-thank you)
"Thanks for your ⭐⭐⭐⭐⭐!
Next visit, enjoy today's dessert on us, mention GUEST THANKS to your server."

Service recovery (manager draft)
"We're sorry about {issue}. We'll make it right: {voucher/gesture}.
Could you DM us so we can arrange your next visit? Management"

Implementation checklist (printable)

• Import reservations (CSV/POS).
• Enable WhatsApp Business + multilingual templates.
• Set at-risk threshold and message timings (T-24h / T-4h / T-90m).
• Turn on waitlist + QR at entrance and on the website.
• Select 5 items for pre-order; connect kitchen/bar display.
• Log decisions and message events (simple audit).
• Track weekly KPIs: fill, AOV, seat-to-serve, no-show, CSAT.
• Review opt-out ("STOP") handling and consent storage.

FAQ

Q1. Is this only for big chains?
No. It works for single venues using a simple booking sheet + WhatsApp. Integrations can come later.

Q2. Will confirmations annoy guests?
Not if messages are brief, well-timed, and offer easy reschedule/cancel.

Q3. What about privacy and regulations?
Use local-first storage, collect only what you need, provide clear opt-out, and keep an audit log. This aligns with major frameworks (e.g., GDPR-style principles).

Q4. Do we need POS integration on day one?
Nice to have, not mandatory. Start with CSV exports; move to real-time sync later.

Call to action

Ready to turn no-shows into revenue?
We'll set up confirmations, waitlist, and pre-ordering with multilingual templates, dashboards, and a before/after report.