Revenue Operations at Scale: How Lopesan Hotels Manages 7,000 Rooms Across Gran Canaria

Fabrice Stahl

Digital & Revenue Operations · Lopesan Hotel Group

Fabrice Stahl oversees digital and revenue operations at Lopesan Hotel Group, one of Gran Canaria's largest resort operators with over 7,000 rooms across multiple properties. He has spent the past four years building Lopesan's data infrastructure for revenue management, distribution channel control, and guest communication.

May 27, 202655 min

Key Takeaways

  • At 7,000 rooms, a 2% improvement in channel mix is worth more than a 10% lift on any single rate. Distribution discipline is the highest-ROI lever most large resort operators are not pulling hard enough.
  • Most revenue management problems are data problems. The RMS tool is only as good as the demand signal you feed it. If your booking data is clean, the decisions are easy.
  • We ran three properties on a new reporting layer before touching the main hotel. A pricing mistake at that property volume is worth six figures. Caution was the right call.
  • AI-assisted demand forecasting did not replace our revenue manager. That system replaced the three hours a day she spent pulling reports, freeing those hours for actual decisions.
  • Channel parity is not a one-time fix. We check it automatically every Monday. Every violation gets flagged before rate strategy meetings, not after.

Most revenue management conversations focus on tools. Fabrice Stahl's conversation is about the data layer that makes those tools work, or fail. At 7,000 rooms, a pricing error or channel conflict does not affect one booking; it affects hundreds. Building the infrastructure to surface the right information at the right moment, across seven distinct properties, dozens of distribution channels, and a dynamic demand curve, took three years of architectural decisions that Fabrice walks through in detail. He covers the channel discipline that recovered margin Lopesan had been leaking for years, the reporting layer that made real-time pricing possible, and the AI-assisted demand forecasting model his team now runs alongside the traditional RMS.

We were not leaving money on the table because our rates were wrong. We were leaving it because our channel mix was wrong. Fixing the distribution layer recovered margin we had been leaking for three years.

Fabrice Stahl, Lopesan Hotel Group

The question I get asked is whether AI will replace revenue managers. The right question is whether a revenue manager without AI data infrastructure can compete with one who has it.

Fabrice Stahl, Lopesan Hotel Group

Transcript

Full transcript coming soon. In the meantime, watch the episode or download the white paper for the key frameworks from this conversation.

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Questions from this episode

Distribution channel discipline. At 7,000 rooms, a 2% improvement in channel mix is worth more than a 10% rate lift on any single segment. Most large resort operators are not monitoring channel parity weekly. Fabrice Stahl at Lopesan runs automated channel parity checks every Monday and flags violations before rate strategy meetings.
By removing the data-gathering work, not the decision-making. AI-assisted demand forecasting replaces the hours spent pulling reports from multiple systems, giving the revenue manager clean, current data in one view so pricing decisions can happen faster and on better information. At Lopesan, this freed approximately three hours per day for the revenue management team.
A single reporting layer that consolidates booking data, channel data, and demand signals in real time. The challenge is that most properties run several disconnected systems. Lopesan spent three years building that infrastructure across seven properties and dozens of distribution channels before their revenue management decisions became truly data-driven.

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