Sales & Setting
How to measure AI setter results beyond booked calls
Track qualification, attendance, sales, refunds, and collected cash to understand whether your inbox workflow is improving.
Measure AI setter results with qualified bookings, attended qualified calls, closed sales, and collected revenue—not bookings alone. Compare a documented baseline, keep lead sources visible, and review conversation accuracy. More calendar entries do not automatically mean better customers or incremental revenue.
Define a qualified call before counting it
A booking only proves that a person selected a time. A qualified booking also satisfies documented fit criteria for the offer. Write those criteria before launch and record exceptions separately so the definition does not drift when a dashboard looks good.
For a coaching offer, useful criteria may include the audience you serve, a relevant goal, genuine interest in the program, and readiness for the conversation. The rules should reflect the offer rather than an arbitrary lead score.
Use a consistent funnel
Track unique incoming sales inquiries, qualified prospects, bookings, attended calls, sales, and cash collected after refunds. Use the same counting rules across periods. Distinguish rescheduled calls from new bookings and returning prospects from new inquiries.
Show rate = attended calls ÷ booked calls
Close rate = sales ÷ attended qualified calls
Choose and document the denominator for every metric. A close rate based on all bookings means something different from a close rate based only on attended qualified calls.
Interpret the revenue calculator carefully
An illustrative model can estimate potential collections from inquiry volume, booking rate, attendance, close rate, and average collected cash per sale. It is useful for identifying which assumptions matter most, not for forecasting guaranteed revenue.
For example, 200 inquiries × 20% booking × 70% attendance × 20% close × $3,000 collected per sale equals $16,800 expected collections in a simplified model. Fractional expected sales describe an average, not an actual partial sale. These are hypothetical inputs, not Aylā client results. See the website calculator and confirm its assumptions before using it.
Separate attribution from improvement
A prospect who used the setter might have bought anyway. Attributing that sale to a handled conversation does not prove the entire sale was caused by automation. Record the baseline and note changes in ads, traffic quality, pricing, seasonality, and sales staffing.
Where practical, compare similar periods or groups. Where that is not possible, report uncertainty clearly. The attribution guide explains how to make the commercial definition explicit.
Review the quality behind the numbers
Read a sample of booked and unbooked conversations. Check whether the system answered the question, understood the context, qualified appropriately, respected opt-outs, and handed off unusual requests. Also ask the closer whether booked prospects understood the offer.
A useful review ends with a specific decision: improve the knowledge document, simplify a question, adjust fit criteria, fix a handoff, or change the booking explanation. Message volume on its own rarely tells you which decision to make.
Frequently asked questions
Which metric matters most?
There is no single universal metric. Qualified attended calls and collected revenue connect the booking workflow to business outcomes; accuracy, opt-outs, and handoff quality show whether the process is trustworthy.
Can I use the calculator as a revenue guarantee?
No. It is an illustrative scenario based on assumptions. Actual results depend on demand, offer fit, attendance, sales performance, refunds, and delivery capacity.
See the workflow for your offer.
Discuss your inbound demand, approved voice, qualification and booking process with founder Kedar Bhat.
Book a Free Demo Now →