Valuable Read -> Multi-Service Client -- Accuracy Pressure Testing
I frequently observe agencies encountering significant challenges with multi-booking functionality in GHL, particularly when external CRMs are integrated into the workflow. Recent discussions have highlighted recurring issues: - AI systems scheduling incorrect dates or times - System inconsistencies causing booking failures - Integration conflicts between platforms Given these concerns, I conducted a comprehensive QA test with a client to stress-test our booking infrastructure. The rationale is straightforward: While a failed email automation represents a minor setback, deploying AI into customer-facing booking operations demands absolute reliability. When your AI handles direct customer interactions, any failure becomes a critical revenue risk that inevitably leads to client churn. --- Client Context: Nail Salon with over 35 services, their main system is Vagaro, they have 5 technicians and a varying number of chairs based on pedicure and manicures. Booking Context: It's always important to understand how people are booking with a service. In this case, you have three types of booking; - Single person - single service: When someone simply wants to book a single service without add-ons. - Single person- multiple service: When someone wants to book more than one service back-to-back. - Multi-person (group booking): When someone wants to bring a girlfriend, daughter, the list goes on... As part of the QA, we ran around 30 tests, 10 different conversations styles to pressure test the system. The results are below: Group booking: Bot identifies multi-person requests and directs callers to self-book via SMS link (not handled over the call). - Worked each time. Single person – single service: Works smoothly, tested successfully 10/10 times. Correct slot and calendar every time, straight through GHL, Google and Vagaro (as Vagaro only has read only API). Single person – multiple services: - Tested ~26 times in total. - 22 successful bookings, 4 failed (lag/delay issues). - Works well for up to 3 services. - Beyond 3 services, bot becomes sluggish and response delays increase. At 7 services, lag was significant. --- Note - It's unlikely 7 would ever occur, but 2-3 services is typical when considering add-ons.