Data Analytics in the Travel Industry

Analytics solutions can forecast customer demand by identifying patterns in historical data (e.g., seasonality and peak times of past sales and bookings) and monitoring real-time trends and events such as destinations’ popularity and competitor activity. It is also possible to use what-if modeling and forecasting capabilities to predict the popularity of a new offering and make informed decisions on its development.

Segmenting customers by demographics (for B2C clients) and firmographics (for B2B clients), as well as booking patterns, preferred destinations, and transportation modes, helps create highly personalized offers to increase customer loyalty and retention. Customer analytics solutions can also give insights into customer sentiment via NLP-powered analysis of data from social media, online review and rating platforms, and feedback surveys.

Segmenting prospects by preferences and analyzing data on their behavior (e.g., visited pages, hotel amenities used) helps create detailed personas and boost customer acquisition rates through targeted content. You can also monitor sales KPIs (e.g., average deal size, sales growth rate) across regions, customer segments, and distribution channels, benefit from automated lead scoring, identify drop-off points throughout the sales pipelines, and get cross- and upselling recommendations.

You can balance service and product prices on the go with the help of real-time big data analytics across multiple sources, including data on customer-related factors (e.g., booking history, search behavior), pricing history and competitor pricing data, weather conditions, and more. Using this data, ML/AI-powered algorithms automatically adjust prices in a way that balances competitiveness and profitability.

Continuous monitoring of inventory availability (e.g., airline seats, rental vehicles, hotel rooms) and inventory turnover helps to avoid over- and underbooking and make sure that your offering is timely adjusted and replenished. SCM analytics also helps conduct lead time analysis (i.e., time from booking to service delivery) and provides insights into supplier performance and compliance with contract terms and SLAs.

E.g., for airlines, airports, bus tour operators

The solution enables real-time vehicle monitoring, including fleet location and status, fuel consumption, and maintenance schedules. This can help drive insights into vehicle utilization rates and enable predictive fleet maintenance. Travel companies can also benefit from route analytics, including real-time route optimization, multi-stop route planning, and what-if route simulations.

ML/AI-powered algorithms can detect suspicious patterns in payment, booking, and other customer-related data and provide alerts on possible fraudulent activity. For example, multiple bookings from the same IP address for the same dates and accommodations may indicate ghost booking. It is also possible to spot employee fraud, e.g., agents frequently providing credits and discounts to the same customer.

The solution enables monitoring of business-specific metrics (e.g., room occupancy rate for hospitality companies or load factor and flight performance for airlines) and common operational metrics like first call resolution rate for customer support service. Real-time monitoring capabilities make it possible to react to events as they occur, for example, to address facility maintenance needs or to notify passengers of flight updates.

Continuous monitoring of weather, economic, geopolitical, and other external factors allows businesses to forecast risks and take preventive measures. It is also possible to use what-if modeling to build risk mitigation strategies under various conditions, including natural disasters, economic slowdowns, currency rate fluctuations, and workforce shortages.

Tracking metrics like revenue and cost per available seat mile, operating margin, average trip value, operating cash flow. You can segment services or service bundles (e.g., itineraries, packages, flights) by profitability. With more advanced features like financial modeling and forecasting, you can make informed decisions about budget planning and perform sensitivity analysis under different variables (e.g., seasonality trends, fuel prices, economic changes).