F&B and retail AI: from enquiry to booking with fewer misses
By Larry Lo
Published
Direct answerHong Kong F&B and retail shops (about 3–12 people) take enquiries on IG, WhatsApp and phone, then book from memory. Composite: in 30 days, classify enquiry → available/unavailable templates → write confirmations to a shared sheet, with human review of discounts and changes. Illustrative missed/double bookings fell from about 3–8/week to about 0–2; peak reply median from about 30–60 minutes to about 10–20. Next: bookme/Snapshot.
Three key takeaways
- Classify enquiry types before full chatbots.
- Humans confirm slots and prices.
- Measure missed bookings and reply time—not revenue guarantees.
Problem: many inboxes, no shared state
Composite, not a named client. This is a composite workflow example, not a named-client testimonial; actual figures vary with staffing, season and tools, so verify against your own baseline.
30-day method
Types: price, booking, change, complaint. Daily slot sheet; AI drafts; manager approves discounts.
Boundary and next step
No card numbers or full customer dumps in public AI. SmartSales later if needed; start bookme or /ai-consulting.
Questions
- Can restaurants auto-reply on IG with AI?
- Draft yes; confirm slots, party size and offers before sending.
- Do we need a CRM?
- When many channels and people reply in parallel; a shared sheet can pilot first.
- How to start?
- Bring one week of anonymised enquiries to bookme.
Limitations
Composite; seasonality and staffing affect peaks—no identical-number guarantee.
Sources & references
Related services
Related cases
Bring one anonymised workflow example to a 30-minute diagnosis on bookme. Confirm the problem, owner and next step first. Use Snapshot for written diagnosis; Discovery (/ai-consulting) for a 30/60/90-day plan.
Book a 30-minute diagnosis