Data Analytics for Hotels

1. Descriptive Hotel Analytics

Descriptive analytics is a traditional form of analysis. It analyzes past trends based on large groups of data. Descriptive analytics is used in everyday hotel operations and is most commonly used in performance reports.
2. Predictive Hotel Analytics

Predictive analytics is a bit more complex than a descriptive analysis. It not only determines past trends but also predicts the future. Since predictive analytics takes an educated guess, it can never be 100% certain, but it does give some valuable insights for your hotel management strategy. An example is analyzing past booking trends to predict next weeks’ occupancy.

1. Improved Inventory management:

Effectively managed inventory helps improve ordering and decrease loss. But the most common inventory management problem is the lack of balance – you are either running out of things or having too much of things in store. Aiosell’s real-time data analytics allows you to not only keep track of your supply but also determine the trends in the usage.
2. Demand Forecasting

Data analytics in hotel industry predicts customer behavior patterns and forecasts demand more accurately. Using these insights about hotels and their competitors, hotels can develop and deploy effective pricing models. Aiosell’s revenue management software uses machine learning to define the optimal room rate in real-time. The revenue management system consolidates and analyzes large amounts of data gathered from internal and external sources to identify anomalies and patterns. One such solution is the OTA insight platform, which consists of 3 modules, each designed to solve a specific task.
Parity Insights identifies parity issues by comparing room rates on the main metasearch engines and OTAs with the ones on the hotel website.
Revenue Insight provides smarter hotel analytics by combining past and future performance to provide quick consolidated reports on hotel KPIs with year-over-year performance comparison.
Rate Insight Forecasts room demand in the area to allow hotel managers to fix reasonable room prices using real-time data based on past, current, and future competitor rates.
3. Effective Targeted Marketing

Real-time analytics helps in developing targeted and personalized offers to customers based on their unique interests, buying indicators, past behaviors, and other factors. The hotels can use data-driven marketing to develop tailored marketing strategies that can influence the buying decisions of guests. By dynamically optimizing pricing packages and deals and offering them to the right customers at the right time, hotels can increase booking and boost their revenue.
4. Competitor Rate Analysis

Real-time data analytics helps determine competitor rates. The reports generated compare the hotel’s current pricing strategy with that of the competitors. This helps in setting the best price for each room with 24X7 dynamic pricing, thus increasing hotel bookings.
5. Reviews and Reputation

Data in hotel industry helps in online review management and reputation management. This is how hotels can use data for review and reputation management:

• To figure out the difference in services with the help of guests’ feedback
• To analyze hotel reputation against the competitors
• To invest all this information in planning forthcoming strategies
• To assign real-time goals to the teams

Better Customer Satisfaction

Hotels can use data analytics to create an excellent customer experience. By being aware of behavioral motivators such as how guests make their booking decisions, how they make their choices, and why they return, hoteliers can use these insights for long-term planning. Data analytics in hotel industry will undoubtedly drive to new insights, and as technology evolves, hoteliers will find new and improved ways to use big data to delight customers and boost revenue.