Traveling has changed over the years. Tourism boards and hotels used to analyze last year’s travel history and make educated guesses about the upcoming year. For example, last year, skiing holidays in Europe were on the rise, so the resort was fully booked last February, and they assumed it would be fully booked again the following February. However, people are complex, and the world changes so quickly that spreadsheets can’t keep up.
The integration of AI and big data has enabled the industry to adapt and evolve to new data in real time, rather than relying solely on historical data and assumptions.
From Historical Data to Live Signals
The most significant advancement in travel intelligence has been the shift to the use of live signals as opposed to historical data. Most of the time, businesses relied on booking data that was months old to influence their decisions.
Now, to inform their decisions, they use real-time data such as current search volumes, last-minute price changes, and even local travel data. Because of this, airlines and hotels are able to see when interest spikes and take immediate action.
AI’s Role in Predicting Consumer Behavior
Machine learning individuals understand and analyze digital “body language” in a business context. For example, if an individual is on a travel page and clicks, scrolls, and stops, those actions provide information on what the individual is looking for. From his actions, AI learns about this individual and differentiates between dreamers and bookers. It is about quantifying search specificity.
Big Data Sources Powering The Future
Building today’s demand forecasting has been a long and complex journey. It is no longer just ticket sales. It has become an endless array of inputs. In order to achieve optimum accuracy, different streams must be integrated and processed to achieve a complete view of the market.
- Search engine trends – What are users searching for on Google or Bing?
- Airline APIs – What are the current prices, and how many seats are still available on the flights?
- OTA platforms – What is the booking speed across the different platforms, like Expedia or Booking.com?
- Weather data – Is there snow or sunshine to help predict changes in demand?
- Social media sentiment – Are there any trending locations on TikTok or Instagram?
- Mobile location data – Where are people moving, and how has their data tracked?
Personalization at Scale
In the past, travel marketing was done with a single brochure and was customized for a single person. It is no longer done this way. With the help of the power of AI, it has far exceeded those ceilings and now speaks of a world where, from just one market, there can be ‘millions of journeys’. Every single offer can be customized based on the individual’s profile and previous behavior. For example, if a user searches for luxury spas, they will not receive any advertisements for budget hostels.
All Segments Involved
This kind of hyper-personalization works across all segments at the same time. A site could change what is displayed on its homepage based on which customer is looking at it. A family could be shown offers that read ‘kids-free’, while someone traveling solo is offered ‘nightlife highlights’. This is why users feel all their digital interactions are relevant and useful.
Seasonal and Event-Based Demand Modelling
As a one-off, be it a holiday or any event, demand is never flat. Analyzing and probabilistic forecasting using machine learning models reveal these spikes. The models consider school holiday calendars and other holidays across the globe. Even a concert or a sports tournament can trigger a massive surge in a specific city.
Managing Spikes
Managing these spikes is important in order to ensure that prices are right and the services are in operation. Hotels may lose business if they do not manage their booking properly, and they could end up with double or triple-booked rooms. Knowing exactly when a peak is coming is a must today.
Dynamic Pricing and Revenue Optimisation
Looking at a flight and suddenly seeing a different price? This is what the Demand-Aware Dynamic Pricing model does and how it works. Prices are predetermined based on how popular the seats or rooms are, relative to a specific time in previous years. If a flight is filling up faster than expected, the price automatically ticks up.
AI Flexibility
It’s not only about raising prices but also about filling voids. When there is little interest, AI can activate flash sales to make sure a plane doesn’t fly with empty seats. It manages rivalry with the speed of bookings to determine the ‘best’ price. This offers flexibility for travelers and helps keep the industry sustainable.
Better Destination Management
Famous cities and natural wonders suffer from overtourism. AI assists in visitor flow so regional managers do not have to deal with overcrowding. They identify demand and analyze where a site is nearing its breaking point. They can then indicate nearby “hidden gems” via digital signage or apps.
Data also assists in spreading visitors to different times of the year. If a Saturday is predicted to be overcrowded, the city can implement incentives to visit on a Tuesday.
AI in Marketing and Media Spending
Travel brands no longer spend marketing budgets based on gut feelings. Using AI, if there is a demand for winter sports in a particular city, ads are placed for that particular city. It helps eliminate ad wastage by targeting potential customers.
Travelers receive ads that assist them in their journey at the right time. For example, if a customer just booked a flight, the brand can target them with an ad for a rental car, but only after the flight is booked. This gives brands a better idea of how to spend their marketing dollars.
Risk Management and Demand Volatility
External shocks are a frequent occurrence within the travel industry, and the industry is very fragile. Things like severe weather, political changes, or diseases can quickly reduce demand. AI helps these companies by preparing for these outcomes through predictive modeling. It allows companies to develop a playbook for these scenarios in advance.
If there’s a storm and an airport gets shut down, thousands of people can find new flights with the help of AI. It looks at alternate routes and analyzes which hotels can accommodate the displaced travelers.
Ethical Use of Data and Privacy
With the flow of data comes the responsibility of keeping it secure. Travelers have grown increasingly vigilant in monitoring the use of their data. Therefore, the need for transparency and compliance with privacy requests has grown. The loss of a customer also correlates to the loss of brand trust, which can happen instantaneously.
Using data to make AI smart is the easy part. Ethical AI is about making things better for users rather than for the organization.
Conclusion: The Competitive Advantage
Big airlines don’t consider travel demand intelligence a luxury anymore. It is a necessity for survival. In a rapidly changing and complicated world, the reliance on guesswork is no longer an option. Artificial intelligence can help provide the needed clarity to ensure decisions are informed and profitable.
