How Faster Restaurant Bookings Drove 45% Growth
Simplified restaurant reservations with an AI chatbot that handled availability, bookings, changes, and customer queries.
At a Glance
A UK-based restaurant reservation provider wanted to reduce its dependence on reservationists and improve booking capacity during busy periods. Maruti Techlabs introduced an AI chatbot to automate reservations and related customer interactions.
Industry
Hospitality
Function
Restaurant Reservations
Solution
AI Chatbot
Engagement
Chatbot Development
The Client
The client is a UK-based Central Telephone Reservations Management (CTRM) provider serving the hospitality industry. It manages more than 5 million restaurant reservations across the UK and supports restaurants through dedicated reservation teams and IVR-based call routing.
The Challenges
Reservation Demand Outgrew Manual Capacity
The existing reservation model relied on trained agents to handle bookings, answer questions, and update reservation details.
Agents Could Handle Only Limited Calls
Reservationists could manage a finite number of calls at a time, creating capacity constraints during busy periods.
Customers Faced Long Waits
Callers often waited 6 to 8 minutes to make a reservation, creating friction in the booking experience.
Peak Periods Caused Missed Bookings
High call volumes during peak hours and holidays led to missed reservation opportunities.
Manual Updates Consumed Time
Agents had to make bookings and manually update reservation details in the system.
Scaling Required More Staff
Onboarding additional restaurants meant recruiting, training, and upskilling reservationists for each new account.
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Create AI-powered chatbots that handle routine queries, automate workflows, and deliver faster customer experiences across your business.
The Solution
A Digital Booking Desk for Restaurant Reservations
Maruti Techlabs built an AI chatbot that handled key reservation tasks and connected directly with the systems used to manage restaurant bookings.
Customers could check restaurant availability for their preferred date and time. Backend integration kept available tables and time slots updated without manual intervention.
Customers could check restaurant availability for their preferred date and time. Backend integration kept available tables and time slots updated without manual intervention.
The Journey
Self-Service Reservation Experience
Business Outcomes
A Faster Route to Restaurant Bookings
45% Increase in Bookings
The number of restaurant bookings increased by 45% within the first three months.
From 6 Minutes to 90 Seconds
Reservation turnaround time dropped by 75%, reducing the time required to complete a booking.
55% Growth in Repeat Business
Personalized menu recommendations contributed to a 55% increase in repeat business.
$108,150 Saved Each Quarter
Automation reduced quarterly hiring and training costs by $108,150.
Lower Staffing Requirements
The business previously hired seven employees every quarter to support growing reservation operations.
More Capacity for Critical Work
Automation allowed some employees to shift their focus to other business-critical activities.
Conclusion
Automating routine reservation activities reduced wait times, increased booking capacity, and let reservation teams focus on other business-critical work.
Tech Stack
Turn Customer Conversations Into Automated Workflows
Build intelligent chatbots that understand customer intent, connect with your business systems, and automate high-volume interactions.
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Frequently Asked Questions
The chatbot was connected to the backend reservation system, allowing it to retrieve available dates and time slots and reflect reservation updates without manual intervention.
Customers could check availability, make reservations, modify or cancel bookings, and receive confirmation-related assistance through the chatbot.
Customers could complete bookings through the chatbot instead of waiting for a reservationist to become available. This reduced the reservation turnaround time from 6 minutes to 90 seconds.
Dialogflow and NLP enabled the chatbot to understand contextual nuances and different ways customers could phrase reservation-related requests.
The chatbot could provide information such as restaurant timings and promotional offers, reducing the need for customers to contact reservation teams for routine questions.