Current: The existing plan focuses on using close rates to identify and correct underpricing, specifically targeting a 30-40% close rate as optimal pricing efficiency.
New: The new analysis shifts focus from close rate as a pricing diagnostic to optimizing for 'cost per qualified booked appointment' (a proxy for cost per sale), emphasizing lead quality over quantity.
While both relate to sales efficiency, the existing plan uses close rate for pricing, whereas the new analysis uses it as part of a broader 'cost per acquisition' optimization, suggesting a shift in primary metric.
Current: The existing plan implicitly suggests that a close rate <30% indicates an avatar or sales process problem, but doesn't detail specific process improvements.
New: The new analysis provides concrete sales process improvements such as implementing 'iceberg questions' for deep discovery, stricter qualification by setters, and specialisation of sales roles.
The new analysis offers actionable strategies for improving the sales process beyond just identifying a problem, providing specific tactics like enhanced discovery and role separation.
Current: The existing plan does not explicitly address lead qualification methods or criteria.
New: The new analysis introduces principles for aggressive lead filtering, emphasizing 'quality over quantity' for setters, and cutting underperforming lead sources to improve downstream conversion rates.
The new analysis introduces direct and actionable strategies for improving lead quality upfront, which is a critical precursor to efficient pricing and higher close rates.
Current: Increase show rates 20% by optimizing AIAS and sales scripts to prioritize tomorrow's slots over next-week bookings.
New: Alex Hormozi breaks down scaling a business from 56 units by fixing five operational leaks: targeting quality leads over cheap leads, implementing deep discovery questions (the iceberg method), and restructuring the sales team to specialize roles rather than having the CEO manage sales.
The existing plan focuses narrowly on booking window optimization for show rates, while the new analysis offers a broader strategy for operational leaks and scaling, including lead qualification.
Current: Sales operations insight on the correlation between booking lead time and call show rates.
New: Optimize for cost per sale, not leads; quality over quantity in appointment setting.
The new analysis shifts the operational focus from solely show rate optimization to a more holistic cost-per-sale perspective, emphasizing lead quality from the outset.
Current: Optimize AIAS and sales scripts to prioritize tomorrow's slots over next-week bookings.
New: AIAS should qualify harder and book fewer appointments (higher intent) rather than maximizing volume - this increases show rates and close rates downstream.
The new analysis provides a more strategic and impactful directive for AIAS, moving beyond just booking window preferences to deeper qualification for overall higher intent and conversion.
Implement deep discovery ('iceberg questions') in AIAS to surface emotional investment and qualify for intent, not just interest.
Update AIAS Claude prompts to include iceberg-style discovery: 'Why do you want a website?', 'What would change in your life if this worked?', 'Who else would this affect?' before booking
Shift TFWW ad targeting from broad 'want free website' (18-24) to 'established service professionals' (25-45 with job history) to improve show rate and close rate
Add 'Cost Per Qualified Booked Appointment' and 'Show Rate %' to AIAS dashboard to track quality metrics, not just lead volume
Include AIAS-collected 'iceberg' answers (deep motivations) in the booking confirmation/handoff to Dylan so he has emotional context before the close
We built AIAS specifically to solve the 'setter quality' problem Hormozi identifies - by automating the iceberg discovery questions upfront, we qualify harder and book fewer, higher-intent appointments. Volume is vanity, booked shows are sanity.
The iceberg discovery framework is exactly why we programmed Claude to ask 'why' three times before booking. Surface interest = surface commitment. Deep motivation = show up + close.
What it is: A diagnostic framework for leaky sales funnels specifically targeting appointment-setting businesses. Presents a shift from volume-based to efficiency-based operations.
How it helps us: Directly applicable to TFWW's AI appointment setter (AIAS). The 'iceberg' discovery questions can be programmed into Claude prompts to improve qualification. The targeting shift from 'cheap leads' to 'qualified leads' applies to TFWW's Meta ad strategy. The setter efficiency insight (fewer people, higher expectations) maps perfectly to AI automation: qualify harder, book fewer but better appointments.
Limitations: The team restructuring advice (hiring sales managers, firing closers) assumes a human sales org larger than TFWW's current size. However, the principles scale down to solo operators by 'firing' low-performing lead sources rather than people.
Who should see this: Dylan for TFWW sales process and AIAS prompt engineering; Meta ads manager for targeting adjustments
| Step | Prompt | Completion | Cost |
|---|---|---|---|
| analysis | 11,925 | 3,032 | $0.0120 |
| similarity | 898 | 198 | $0.0003 |
| plan | 9,313 | 4,983 | $0.0152 |
| Total | $0.0274 | ||