Lighthouse

As a revenue manager, every so often I'd get handed a new hotel I'd never worked on... with the next revenue call only a few days out. 😱 Blake Reiter and I ran through that exact scenario recently with Ernest, Lighthouse's AI teammate for hotels. Within a few minutes, I had the midweek and weekend business mix, the key events, and our rate position against the comp set. Every answer came from that hotel's own data, with sources I could click through and check. Watch the full discussion from The Spotlight podcast here: https://mylh.co/4yB7CwS

A wedding asks for 10 rooms a night for two nights at your best and lowest rate. The hotel is already forecasting a sellout, so taking the block means giving up 20 room nights you'd likely sell anyway. That's about $8,240 in room revenue. Divide by 20 and you have your floor: $412. Quote in the $439 to $449 range, walk away below $412. If they counter just under the floor, F&B might break the tie. Especially the Sunday brunch, because you can't send wedding guests home without a bagel. 🥯 Daniel Foreman and I worked through this displacement analysis on Strategy Lab, using forecast and pace data from Lighthouse Performance. Watch the full episode here: https://mylh.co/4AYFDco

Revenue managers spend years trying to get guests to stay longer. The MLOS restriction is a household name. 🙅 But would you ever want guests to stay FEWER NIGHTS? Picture this: a quiet Sunday through Thursday at 40% occupancy, priced around $99. Then a big sports championship on Saturday night, forecasting 100% with a $500 BAR. The last thing you want is a $99 guest staying through and eating into your most valuable night. So you cap it. Max LOS of five nights for Monday arrivals, stepping down to one night MAXLOS for Friday arrivals. Saturday remains protected. Fair warning: MaxLOS is one of those “in case of fire, break glass” stay restrictions, revenue managers don’t typically put one on at the house-level very often. It's usually paired with minimum LOS to tightly control stay patterns on specific rate buckets, or hard coded into very deep individual discounts (so an opaque rate doesn't book 30 nights). It’s rare, but when you need it, it does the job. Full breakdown in this week's Revenue Management Term of the Week 👇 Revenue managers: When was your last MaxLOS restriction that you put on the house-level??

🗓️ How far out are your guests searching... and how long are they planning to stay? For one October weekend, searches made back in June were for 7 to 9-night stays. By early August? Down to 2 and 3 nights. 📉 For this market, the further out travelers were searching, the longer they planned to stay. That won’t always be true for every hotel or every date, which is why it's so important to watch your forward-looking demand data. When the long-LOS crowd does show up in the data, that’s your window to put a “stay 4 nights, save 20%” offer in front of them (hello, length-of-stay 'fence' 👋) and target the markets those searches are coming from. Blake and I dug into this on The Spotlight podcast, using forward-looking demand data from Lighthouse Pricing. Watch the podcast episode here 👉 https://mylh.co/4ygpCM2

If your booking system, channel manager, and OTA dashboard all show different numbers for the same week, you don’t have a reporting problem — you have an integration problem. And during peak season, that disconnect can quietly impact every pricing decision you make. Revenue strategy only works when teams trust the data behind it. But when systems aren’t connected, hotel teams end up spending more time reconciling numbers than acting on insights. The real solution isn’t adding another dashboard. It’s building a connected commercial system where data flows automatically between platforms, giving teams a single source of truth for pricing, performance, and demand. Because better decisions start with better data.

One person managing revenue, distribution, pricing, and sales this peak season? That’s the reality for many independent hotels. Large hotel groups often have entire commercial teams dedicated to specialized roles: revenue managers, sales managers, distribution experts - all working together to optimize performance. Independent operators are still doing those same jobs. They’re just doing them with far fewer people. That’s where AI is starting to change the game. AI-powered agents can help hospitality teams automate repetitive analysis, surface smarter recommendations, and improve decision-making across revenue management, distribution, and commercial strategy — without the need to significantly expand headcount. For independent hotels especially, AI isn’t about replacing people. It’s about giving lean teams the operational support and commercial intelligence they’ve never had access to before. Peak season is demanding enough. Technology should help teams operate smarter, not just work harder.