DATABERG
There has been massive growth in digital travel sales over the past few years, partly due to advances in big data machine learning.
Over 500 billion dollars was made in this sector in the year 2016 alone. What can we thank for this growth? Mostly advances in technology, including the ever-popular data science. Below we outline some of the top examples of data science projects that help explain these trends.
The top 10 examples of big data machine learning in travel
1. Recommendation engines
This one is pretty obvious, but the sophistication of recommendation engines is a huge factor driving up sales. From Amazon and Netflix to online travel agents like Expedia, automated recommendations based on customer data works well to increase sales, upsell, and keep loyal customers coming back for more.
2. Flight fare and hotel price forecasting
Many customers are getting savvy, using data tools such as price forecasting applications to get the best deal of flights and hotels. Many of these tools automatically monitor the market and send users alerts with the hottest details. Sites like Hopper are great examples of a service like this, helping its users to book cheap flights using analytics. Adding a tool like this to an online travel agency portal is a smart way to hook customers in and entice them to book more trips.
3. Intelligent travel assistants
Users are increasingly looking for convenience and frictionless service. Data analytics can assist through virtual travel assistants. These digital concierge applications use artificial intelligence to automate certain tasks. The user interfaces with the bot through a chat conversation. This makes the booking process feel more like a conversation with a personal assistant. There are many users who love this kind of easy, turn-key booking experience. As AI becomes increasingly sophisticated, we should expect this feature to become very popular.
4. Optimized disruption management
What is automated disruption management? It basically means resolving roadblocks that a traveler may face on their way to the destination. As the name suggests, it’s a way to automatically handle disruptions to the plan. Interestingly, advances in AI and predictive analytics now provide companies a way to prevent disruptions before they occur. This real-time disruption management can take the form of a new route to avoid bad weather or significant delays. Because such things are a major source of dissatisfaction travelers experience on trips, finding new ways to manage and even prevent disruptions is a significant opportunity.
5. Customer support
AI and chat-bots are a great way to streamline certain aspects of customer service and support. Basic informational and transactional services can offered through a custom programmed chatbot.
6. Tailored offers for MVCs (most valuable customers)
Loyalty programs are nothing new the travel business, but AI promises to improve these classic plans through tailored offers for your most valuable customers. Using legacy data and customer purchases, you can develop a solid model for offering special deals to your most loyal clients.
8. Sentiment analysis in social media
This one is a bit out of the box, but it is very important. Big data and AI can help you track what customers are saying about your company on social media. This can help you identify issues and resolve them to improve customer goodwill. Using supervised learning and natural language recognition, data tools can tap into the great wilderness of social media conversation to identify opportunities for intervention.
9. Dynamic pricing in the hospitality industry
Adjust your prices to adapt to changing market dynamics is called dynamic pricing. This is a common practice that has taken place for years in the hospitality industry. However, new practices have made it even more effective. Using machine learning for dynamic pricing can enhance the effectiveness and profitability of such schemes.
10. In-stay experience
AI can assist even after the booking is done. Virtual assistants can be deployed inside hotel rooms, for example. These tools can be used to control room lights and electronics or validate through facial recognition for check-in.
As data science, big data, machine learning, and artificial intelligence takes off, the travel industry will have to adapt, improving and streamlining the way our customers travel.
