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Inconsistent revenue performance across 47 properties with RGI variance ranging from 85 to 127, making it impossible to predict portfolio-level outcomes or identify which properties needed intervention.
Implemented RevEVOLVE BI and RM Copilot to standardize revenue management practices, automate pricing recommendations, and provide real-time visibility into portfolio-wide performance patterns.
By early 2024, EMA Hospitality was experiencing a paradox that many multi-property management companies face: strong overall performance masking significant inconsistency at the property level.
While the portfolio averaged 106.5 RGI (Revenue Generation Index); solid performance by industry standards individual property performance ranged dramatically from 85 RGI to 127 RGI. This 42-point spread meant that some properties were underperforming their competitive set by 15%, while others were exceeding it by 27%.
Michael Chen, EMA’s SVP of Revenue Strategy, described the problem: We couldn’t confidently forecast portfolio-level results because property performance was all over the map. One month we beat budget by 8%, the next we miss by 5 and we wouldn’t know why until we dug into individual property data after the fact.
Each revenue manager had developed their own approach and tools different spreadsheets, different data sources, different decision frameworks. This made it nearly impossible to ensure best practices were being applied consistently across all 47 properties. A strategy that worked brilliantly at one property might never make it to similar properties managed by a different RM.
By the time leadership identified underperforming properties, valuable booking windows had already closed. We were essentially driving by looking in the rearview mirror; Chen noted. We realize a property was behind pace with 10 days out, when there was limited ability to recover.
Revenue managers spent an estimated 15-20 hours per week pulling data from multiple systems (PMS, RMS, STR, brand portals) and consolidating it into Excel reports. This left limited time for strategic analysis and proactive decision-making.
The company had identified 8 potential acquisition targets but was hesitant to pursue them. Adding more properties would require hiring additional revenue managers, and there were legitimate concerns about whether quality could be maintained at a larger scale with the current manual approach.
Michael Chen, SVP Revenue Strategy, EMA Hospitality
The team built comprehensive Excel-based reporting templates to create consistency. While this helped somewhat, it still required significant manual data entry and didn’t solve the fundamental problem of delayed insights.
Leadership instituted weekly revenue management meetings to share best practices and review performance. These were valuable but time-consuming, and insights still couldn’t be applied fast enough to impact near-term results.
The company invested in advanced training for their existing revenue management system. While this improved individual RM capabilities, it didn’t address the core issues of data aggregation, consistency, or portfolio-level visibility.
None of these initiatives delivered the step-change improvement EMA Hospitality needed. The company realized they required a technology solution that could provide real-time portfolio intelligence while systematizing best practices.
In mid-2024, EMA Hospitality began evaluating revenue intelligence platforms that could address their portfolio consistency challenge. The evaluation process took three months and included demonstrations from four vendors.
EMA’s evaluation criteria focused on five key requirements:
Real-time portfolio visibility with the ability to identify variance patterns and outliers instantly
AI-powered recommendations that could systematize best practices and reduce decision-making time
Seamless integration with existing PMS, RMS, and data sources without requiring workflow disruption
Scalabilityto support growth to 60+ properties without proportional increase in headcount
Implementation supportfrom a team that understood hotel operations, not just software
While other platforms were designed primarily for individual hotels, RevEVOLVE was built specifically for management companies operating multiple properties. The portfolio dashboard, variance detection algorithms, and cross-property comparison tools were exactly what EMA needed.
RevEVOLVE RM Copilot didn’t just provide generic pricing suggestions. It analyzed historical performance patterns across similar properties and market conditions to generate contextual recommendations. Chen explained: The system essentially learned from our best performers and helped us apply those strategies across the portfolio.
During the evaluation process, EMA was impressed by RevEVOLVE team. They spoke our language; Chen noted. They understood the challenges of managing revenue across a diverse portfolio because they have lived it. That gave us confidence we have real partnership, not just software.
EMA Hospitality began implementing RevEVOLVE in September 2024 with a phased rollout approach designed to minimize disruption while maximizing learning.
The first phase focused on technical integration and baseline establishment:
Rather than rolling out to all 47 properties immediately, EMA selected 10 pilot properties representing diverse markets and performance levels:
During this phase, revenue managers used RevEVOLVE alongside their existing workflows, allowing them to build confidence in the system’s recommendations while maintaining their established processes.
Results from the pilot properties began showing within 4-6 weeks, with the underperforming group showing the most dramatic improvement.
Based on strong pilot results, EMA expanded RevEVOLVE to the entire portfolio in late November 2024. The revenue management team transitioned to using RevEVOLVE as their primary workflow tool, with their previous systems serving as backup reference.
The rollout included weekly training sessions covering advanced features, best practices for interpreting AI recommendations, and strategies for managing exceptions or unusual market conditions.
No implementation is without challenges. EMA encountered three main hurdles:
Several veteran revenue managers were initially hesitant to trust AI-generated recommendations, preferring their gut instincts developed over years. The solution? RevEVOLVE team conducted recommendation retrospectives showing how the AI’s suggestions would have performed versus actual decisions over the past 6 months. This data-driven approach-built trust.
Two properties had older PMS systems with limited API capabilities, requiring custom integration work. RevEVOLVE implementation team worked directly with those properties to build workarounds, ensuring no properties were left behind.
Some general managers were accustomed to their local RM’s specific reporting format and were initially resistant to new dashboards. EMA addressed this by having the RevEVOLVE team create custom views for key stakeholders while maintaining standardization behind the scenes.
Sarah Martinez, Director of Revenue Management, EMA Hospitality
By March 2025, six months after full portfolio implementation EMA Hospitality had achieved measurable, transformative results across their entire portfolio.
| Metric | Before | After |
|---|---|---|
| RGI Variance (Range) | 85–127 (42 points) | 94–115 (21 points) |
| Portfolio Average RGI | 106.5 | 109.7 |
| Portfolio RevPAR | $94.20 | $97.20 (+3.2%) |
| Time Spent on Data Work | 18 hrs/week per RM | < 2 hrs/week per RM |
| Properties per RM | 9–10 | 12–14 (capacity) |
The 50% reduction in RGI variance (from 42 points to 21 points) represents the most significant achievement. This wasn’t achieved by dragging top performers down it was accomplished by systematically elevating underperformers.
Beyond the quantitative metrics, EMA Hospitality experienced several qualitative improvements:
Chen noted that we can now forecast portfolio-level results with 95% accuracy 60 days out, compared to maybe 70% accuracy previously. That’s game-changing for owner communications and financial planning.
When EMA hired a new revenue manager in February 2025, they were fully productive managing 12 properties within 6 weeks half the time it previously took.
Revenue managers reported higher job satisfaction, with one stating: I spend my time solving interesting problems instead of wrestling with Excel. I feel like a strategist again.
The standardized, professional reporting from RevEVOLVE improved owner confidence in EMA’s revenue management capabilities, leading to contract renewals ahead of schedule for 3 properties.
The system’s alert functionality enabled the team to identify and address issues days or weeks earlier than before, recovering an estimated $340K in revenue that would have been lost.
Michael Chen, SVP Revenue Strategy, EMA Hospitality
With six months of strong results behind them, EMA Hospitality is now pursuing growth opportunities that would have been impossible with their previous operational model.
EMA is actively pursuing 12 hotel acquisitions that will expand the portfolio from 47 to 59 properties by Q4 2025. The company is confident they can achieve this growth with their current 5-person revenue management team plus one additional hire rather than the 3 additional hires that would have been required previously.
RevEVOLVE has fundamentally changed our unit economics Chen explained. We are no longer limited by linear scaling constraints. Our revenue per revenue manager has increased by nearly 40%, which directly impacts our ability to invest in growth.
EMA is exploring several advanced applications of the RevEVOLVE platform:
Using RevEVOLVE data and AI capabilities to model potential revenue performance of acquisition targets based on market characteristics and competitive positioning
Implementing more sophisticated real-time pricing adjustments based on demand signals, weather patterns, and local events
Extending the platform’s capabilities to optimize group business decisions and displacement analysis
Creating customized views for individual owners showing their properties performance within the broader portfolio context
When asked if they would recommend RevEVOLVE to other management companies, response was unequivocal:
Michael Chen, SVP Revenue Strategy, EMA Hospitality
Discover how RevEVOLVE can help you:
✓ Reduce portfolio variance and achieve consistent performance
✓ Scale your portfolio without proportional headcount growth
✓ Free your revenue managers from manual data work
✓ Make data-driven decisions with real-time portfolio intelligence
Join the ranks of successful hoteliers who are leveraging AI to maximize their revenue. Get started today and see the difference our solution can make.
The Challenge: The Variance Problem