How can yield management leverage big data and analytics to gain insights and competitive advantage? (original) (raw)

Last updated on Jul 2, 2024

Powered by AI and the LinkedIn community

Yield management is a strategic approach to optimize revenue and profitability by adjusting prices and inventory based on demand and supply. It is widely used in industries such as hospitality, airlines, and car rentals, where the products are perishable and the demand is variable. However, yield management is not a static or simple process. It requires constant monitoring, analysis, and decision-making to adapt to changing market conditions and customer preferences. In this article, you will learn how yield management can leverage big data and analytics to gain insights and competitive advantage.

Top experts in this article

Selected by the community from 4 contributions. Learn more

• Hotels should definitely embrace big data and analytics for better yield management. These tools help us understand customer behaviours, market trends, and competitor actions, allowing us to adjust our strategies dynamically and most importantly shift from gut feeling decisions to data driven decisions

• Analyze vast amounts of historical data i.e. booking patterns, seasonality, competitor pricing, external factors like events or weather. Understand real-time demand, customer behavior & employ dynamic pricing strategies. Maximize revenue by charging more during peak periods, attract customers with lower prices during slower times. Segment customer basis demographics, booking habits, preferences to tailor pricing & promotions, maximize revenue potential. Predict cancellation, no-show, for overbooking strategies with minimal risk, maximum utilization of available inventory, minimize lost revenue. Predict demand accurately, optimize pricing dynamically, personalize offering to increase revenue, higher occupancy, stronger market position.

Challenges of big data and analytics

Big data and analytics offer many benefits for yield management, but also present some challenges. Data quality and integration must be ensured, with reliable sources and consistent compatibility. Security and privacy must be maintained, meeting relevant laws and respecting customer rights. Analyzing and interpreting data effectively requires the right skills, tools, and methods, while avoiding bias or oversimplification. Lastly, data-driven decisions should be balanced with human judgment and intuition, considering ethical implications. It is also important to communicate these decisions clearly to stakeholders.

• Big data and analytics are game-changers for yield management, offering unparalleled insights and competitive advantages. 📊 However, ensuring data quality and integration is crucial to avoid costly errors. 🔍 Security and privacy must be top priorities to maintain customer trust and comply with laws. 🛡️ The right skills and tools are essential to interpret data effectively without falling into the trap of bias. 🧠 Finally, balancing data-driven decisions with human intuition can lead to more ethical and well-rounded outcomes. 🤝 Communicating these decisions clearly to stakeholders ensures alignment and transparency. 🌟

Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

• Yield management can greatly improve by using big data and analytics to better understand data, handle large amounts of information and automate basic tasks. Automation helps ensure the data is accurate, and predictive models make forecasting more reliable. This means yield analysts can spend less time on routine data work and more time on making important decisions, helping the business stay competitive and efficient.

More relevant reading

``