AI-Powered Pricing Adds $500M in Revenue for a Global Hotel Leader
AI Demand Forecasting & Dynamic Pricing:
Intercontinental Hotel Group (IHG)
A major hotel brand needed to modernize its revenue management approach to keep pace with volatile demand, rising distribution complexity, and growing competition from both brands and alternative accommodations.
By deploying AI/ML engines for demand forecasting and dynamic pricing, the group significantly improved forecast accuracy, boosted tool adoption by revenue managers, and unlocked a 2.7% uplift in total revenue—equating to more than $500 million in additional revenue.
2.7%
4x
3
Revenue increase, delivering $500M+ in incremental revenue
Increase in pricing tool adoption, from 20% to 85%
Month deployment
The Challenge
For this hotel group, the challenges were significant:
Traditional revenue management systems relied heavily on historical data and static or semi-static pricing rules, with limited ability to understand price sensitivity or react quickly to changing market conditions.
The systems did not fully incorporate how different price points influence booking behavior across segments, channels, and stay dates.
As a result, rates were often set conservatively, leading to missed revenue opportunities in high-demand periods and overpricing in soft demand windows. Revenue teams spent substantial time manually adjusting rates and still lacked confidence that they were capturing optimal value.
The organization needed a solution that could quantify price elasticity, improve forecast accuracy, and provide recommendations that revenue managers would actually trust and use.
PrimeAI’s Solution
PrimeAI implemented AI/ML algorithms that integrated advanced demand forecasting with dynamic pricing optimization into the hotel group’s revenue management stack. These models consumed rich data feeds, including historical bookings, on-the-books data, competitor pricing, events, seasonality, and channel mix, to generate more accurate demand forecasts at the property, segment, and date level.
On top of these forecasts, a pricing optimization layer modeled price sensitivity and revenue trade-offs across different scenarios. This engine recommended optimal room rates, inventory controls, and restrictions that maximized revenue while protecting occupancy and guest mix targets.
The solution was designed to embed directly into revenue managers’ workflows, with clear explanations of recommendations, guardrails aligned to brand strategy, and the ability for users to accept, modify, or override AI-suggested actions. This transparency and control helped build trust and accelerate adoption.
AI/ML Engines, Deployment & ROI
The AI-powered revenue management capabilities focused on two core engines:
Demand Forecasting: Machine learning models that improved short- and medium-term demand predictions, capturing non-linear patterns that traditional models missed.
Dynamic Pricing Optimization: Real-time rate recommendations that incorporated price sensitivity, competitor actions, booking pace, and remaining capacity to optimize ADR and occupancy.
The deployment was completed in approximately 3 months across the target portfolio, with the hotel group achieving ROI within roughly 2–3 months as the new pricing recommendations rolled out and tool adoption increased.
Adoption of pricing tools jumped from 20% to 85%, as revenue managers came to rely on AI-driven recommendations as their default starting point. This shift not only improved financial performance but also reduced manual workload, enabling teams to focus more on strategy and less on routine rate changes.
The Results
With AI-enhanced forecasting and dynamic pricing in place, the hotel group achieved significantly higher demand forecasting accuracy, allowing teams to plan inventory, promotions, and distribution strategies with greater confidence.
Total revenue increased by 2.7%, translating into more than $500 million in incremental revenue, as the system captured upsides during peak periods and mitigated revenue leakage during slower demand windows.
Customer Success Stories
-
![Man working at a desktop computer in a busy office, wearing a headset, with multiple monitors and laptops around him.]()
Marmic Technician Scheduling & Routing
Marmic struggled with inefficient manual technician scheduling & routing caused lost technician availability, overtime, and excessive travel costs. By using our workforce scheduling & routing optimizer, they increased revenue 10%, cut overtime by 91%, and enabled 90 days of forward visibility instead of just 48 hours.
-
![Street cleaning vehicle with orange and gray exterior, parked on city street with buildings in the background.]()
CheckSammy Pricing Optimization & Workforce Routing
CheckSammy’s manual routing, scheduling, and non-optimized pricing limited capacity and profitability. With our scheduling, routing, and pricing optimization, they doubled operational and revenue capacity without adding headcount or infrastructure, supporting a $150M capital raise as revenue rose and costs fell.
-
![Laptop screen displaying a flight search website, with options to enter departure and return dates, and number of adults and children, under a sky background with an airplane.]()
Aeroméxico Dynamic Pricing
Aeroméxico’s rigid fare classes failed to reflect real customer price sensitivity across routes and seasons. Our dynamic pricing optimization algorithm improved offer relevance, demonstrated higher conversion, and lifted passenger revenue by approximately 1–3% on targeted flows.
-
![A woman with curly hair is sitting at a desk in front of six large monitors displaying spreadsheets. She is smiling and wearing a white shirt, working in a bright, modern office space.]()
LexisNexis De-Identification & Risk Mitigation
LexisNexis wanted to grow revenue by offering HIPAA-compliant enriched healthcare data products and risk insights. Using our de-identification and risk-scoring platform, they scaled compliant data products, reduced privacy risk, and accelerated regulatory certification and customer onboarding cycles.
-
![Group of friends with luggage at airport, smiling and talking.]()
Hotel Demand Forecasting & Price Optimization
A Major Hotel Group struggled with static pricing and weak demand forecasts that left rooms underpriced or empty. After deploying our forecasting and dynamic pricing optimizers, this hotel group grew revenue by 2.7% and significantly increased adoption of advanced pricing tools among global hotel managers.
-
![A worker wearing a hard hat looks at a tablet on a highway with moving trucks, with digital location markers and network lines overlaying, representing smart logistics and connected technology.]()
Forto Demand Planning & Pricing Optimization
Freight forwarder Forto faced inaccurate container demand forecasts and suboptimal pricing that constrained margins and growth. By using our demand forecasting and dynamic pricing optimizers, they added over $1M in monthly revenue, enabling a profitable, scalable growth trajectory.
