Episode
AI Built My Revenue Projections in 10 Minutes
- Published
- Apr 13, 2026
- Duration seconds
- 2072
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Summary
Most STR operators treat revenue projections like fortune-telling - last year's numbers plus a prayer. But when you're managing $1M+ in bookings, you need a system that accounts for market shifts, booking momentum, and realistic pricing decay. In this technical deep-dive, Jasper Ribbers walks through building a three-method projection framework from scratch, then shows how Claude AI can automate the entire process in under 15 minutes. Here's the problem: historical data ignores market changes, market seasonality ignores your specific performance, and forward-looking bookings require assumptions about conversion rates you probably don't have. Most operators pick one method, cross their fingers, and wonder why they miss their targets by 20%. Jasper demonstrates why using all three methods simultaneously creates a reality-tested range rather than a single-point guess, and how AI can flag discrepancies automatically. You will hear: - How to extrapolate annual revenue potential from just three months of data using market seasonality (divide monthly revenue by that month's typical % share of annual RevPAR) - Why "unbooked potential" in your PMS is a fantasy number (assumes 100% occupancy at current rates, reality is 75-85% occupancy at 15% lower prices) - What the seasonality math reveals about underperformance (if January extrapolates to $300K annually but March extrapolates to $260K, you underperformed market patterns in March) - How to build a weekly-updating projection system using Claude AI that remembers your portfolio context across conversations - When to manually adjust projections (owner stays, maintenance downtime, or market events skew individual months and need human judgment) We also talk about: - The three data sources you need from PriceLabs to feed Claude (h…