25 February 2026

Restaurants Call Handling Benchmark

Peak concurrency and lost covers for Australian restaurants and hospitality.

Restaurants call handling benchmark: peak concurrency and lost covers

This micro-report focuses on restaurants and hospitality. For the full benchmark methodology, see the Australian SME Call Handling and Automation Benchmark Report.

Unique data angle

Peak concurrency and lost covers (missed reservation enquiries).

Lunch and dinner rush create the lowest answer rate. Frustrated callers book elsewhere.

Revenue model

missed_calls × reservation_conversion × avg_cover_value × party_size_multiplier

By daypart: answer rate and lost bookings (modelled)

DaypartTypical answer rateLost bookings estimate
Lunch rushLowHigh
Dinner rushLowHigh
After-hoursVery lowMedium (voicemail)
Off-peakHigherLower

See AI receptionist for restaurants for implementation guidance.

Methodology

Scope

  • Country: Australia
  • Verticals: Service SMEs (clinics, gyms, restaurants, trades, professional services)
  • Date range: As specified in each report section

Data sources (hierarchy)

  1. Government and statutory sources: Australian Bureau of Statistics (ABS), Fair Work Ombudsman (FWO), Australian Taxation Office (ATO), Office of the Australian Information Commissioner (OAIC)
  2. Published vendor pricing (timestamped, linkable)
  3. Valory anonymised aggregates (if used): sample, timeframe, and exclusions defined per report

Definitions

  • Missed call: Inbound call that was not answered by the business (voicemail, ring-out, or overflow)
  • Answered call: Call that reached a human or automated system and received a response
  • Qualified lead: Caller who expressed intent to book, enquire, or purchase and provided contact details
  • Booking captured: Confirmed appointment, reservation, or callback scheduled

Modelling formula estimated_loss = missed_calls × lead_to_book_rate × average_value × LTV_multiplier

  • missed_calls: Monthly count of unanswered calls
  • lead_to_book_rate: Proportion of missed callers who would have converted if answered (modelled)
  • average_value: Average transaction or booking value (AUD)
  • LTV_multiplier: Repeat/referral factor (1.0 = single transaction; higher for recurring)

Limitations

Model sensitivity

  • Results are sensitive to lead-to-book rate and speed-to-contact assumptions
  • Conservative, base, and aggressive scenarios are modelling ranges, not industry benchmarks
  • Actual outcomes depend on business-specific factors (vertical, location, call volume, staff capacity)

Data availability

  • Wage and cost data sourced from government publications; rates change periodically
  • Vendor comparison uses publicly documented attributes only; "Unknown" where not verifiable

Legal and compliance

  • Privacy, consent, and retention rules vary by jurisdiction and business context
  • For Australian businesses, refer to OAIC Australian Privacy Principles (APP 5 notice, APP 11 security/retention)
  • Implement business-specific legal review before deployment

References

Government and statutory

Valory