{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/5afc793a028014b853c89db4/69d92fb1fdeddc4b1271bf14?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"AI Built My Revenue Projections in 10 Minutes","description":"<p>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.</p><p><br></p><p>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.</p><p><br></p><p><strong>You will hear:</strong></p><p>- 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)</p><p>- 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)</p><p>- What the seasonality math reveals about underperformance (if January extrapolates to $300K annually but March extrapolates to $260K, you underperformed market patterns in March)</p><p>- How to build a weekly-updating projection system using Claude AI that remembers your portfolio context across conversations</p><p>- When to manually adjust projections (owner stays, maintenance downtime, or market events skew individual months and need human judgment)</p><p><br></p><p><strong>We also talk about:</strong></p><p>- The three data sources you need from PriceLabs to feed Claude (historical trends, listing performance, market seasonality)</p><p>- How Freewyld Foundry now uses AI to connect directly to the PriceLabs API and generate revenue reports in minutes instead of hours</p><p>- Why voice-to-text tools like Whisper can cut your AI prompting time in half</p><p><br></p><p><strong>Mentioned in the Episode:</strong></p><p>- Claude AI: https://claude.ai</p><p>- PriceLabs (revenue data and market analysis): https://pricelabs.co</p><p>- Whisper (voice-to-text tool): https://whisper.ai</p><p>- Freewyld Foundry Revenue Report: https://freewyldfoundry.com/get-started</p><p><br></p><p><strong>Favorite Takeaway:</strong></p><p>\"You can now, once you have this, you could literally upload your bookings every week, new bookings, and tell Claude, 'Hey, here's some new bookings. How does this affect our projections?' And it will create an updated version for you.\"</p><p><br></p><p>Want us to audit your pricing strategy?</p><p>Get your free, personalized revenue report at FreewyldFoundry.com/get-started</p>","author_name":"Freewyld Foundry"}