Global Hotel Alliance (GHA)

AI Machine Learning: Revenue Protection - Anomaly Detection in Stay Data

Situation

GHA struggled to detect subtle anomalies in their stay data, such as unexpected drops in the number of departures or missed checkouts, relying solely on daily reports that often failed to identify these discrepancies. This made it difficult to react in real-time to potential revenue impacts.
Solution

Exquitech deployed Machine Learning models specifically designed to detect unusual patterns in revenue and departure data. These models flagged abnormal trends such as sudden drops in checkouts or revenue, also highlighted specific dates where data entry issues occurred, prompting immediate action.
Impact

“By addressing data anomalies early, GHA was able to maintain data accuracy and prevent significant revenue losses. The system’s real-time alerts allowed for faster response and correction at both the property and brand levels.”

– Gary Zavaleta, Senior Director of Analytics & Data Science

Situation

Fraudulent booking activities, such as multiple reservations by the same customer within a short period or frequent cancellations, were going undetected due to the manual, time-consuming nature of fraud detection processes. This exposed GHA to potential revenue loss and resource strain.
Solution

Exquitech’s AI models were designed to analyze booking patterns and identify high-risk behaviors. These included flagging bookings where members frequently canceled or booked an unusual number of stays in a short timeframe, signaling potential abuse of membership discounts.
Impact

“With automated fraud detection, GHA could now identify and mitigate fraudulent activity quickly, reducing revenue leakage. The system not only saved valuable time for staff but also enhanced the overall security of the booking process.”

– Gary Zavaleta, Senior Director of Analytics & Data Science

Situation

GHA’s ability to provide tailored marketing and loyalty incentives was limited by a lack of deep customer segmentation. Without insights into customer behavior, it was difficult to target high-value customers or those likely to churn, leading to missed opportunities for personalisation.
Solution

Exquitech applied RFM analysis, breaking down customers into six distinct segments based on their recency of stays, frequency of bookings, and overall monetary value. This segmentation enabled GHA to understand customer behaviours better and create personalised offers and interventions for each group.
Impact

“By understanding the unique needs and behaviours of each customer segment, GHA increased engagement with high-value customers and reduced churn. The personalised marketing campaigns led to higher customer satisfaction, improved retention, and increased lifetime value.”

– Gary Zavaleta, Senior Director of Analytics & Data Science

Situation

GHA needed a reliable way to forecast future bookings, stay revenues, and income fees to guide business planning. Without accurate predictions, it was challenging to make data-driven decisions that would optimise operations and ensure consistent revenue growth.
Solution

Exquitech developed advanced machine learning forecasting models that provided accurate predictions for key performance metrics such as booking counts, stay revenues, and income fees. The models forecasted up to one month in advance, allowing GHA to anticipate demand and adjust strategies accordingly.
Impact

“These accurate forecasts empowered GHA’s management team to make informed decisions, reducing operational risks. The predictive insights helped the team optimise staffing, marketing efforts, and pricing strategies, leading to improved financial outcomes and operational efficiency.”

– Gary Zavaleta, Senior Director of Analytics & Data Science

Situation

GHA aimed to enhance customer engagement and satisfaction by offering more personalised property recommendations. The challenge was to tailor recommendations based on customer preferences and browsing behavior without overwhelming the user with irrelevant options.
Solution

Exquitech implemented a multi-tiered recommender system. It included a general recommender for new users, a contextual recommender based on recent searches, and a personalised recommender using the customer’s booking history. This allowed GHA to deliver relevant suggestions at every stage of the user’s journey.
Impact

“The personalised recommendations significantly boosted customer satisfaction and loyalty by offering more relevant property suggestions. This tailored approach increased user engagement, driving more bookings and enhancing the overall customer experience.”

– Gary Zavaleta, Senior Director of Analytics & Data Science