AI & Data: The Silver Lining for the Travel Industry

Everyone loves a well-planned trip where almost everything goes right and becomes memorable, but we all know, behind most of such trips lies a scapegoat who has been tasked with the research, planning, and booking – leaving the door wide open for the inevitable human error. The buck doesn’t stop there.

Tourists of today want holistic options that lets them travel without burning a hole in their pocket. On the other hand, travel operators have suffered enough with the pandemic related slowdown and want to make up for it quickly by creating attractive traveler experiences.

To help overcome these hurdles on the road to Instagram-worthy stories, the travel industry has turned to technology. AI and data are being leveraged to deliver optimal solutions that translate into better experience for travelers and cost savings for operators, here’s how.

1. Chatbots

Every day, travel websites get hundreds of queries regarding several travel options. Some tourists would like a customized travel itinerary, while some want to check the best month to travel to a certain destination. Answering these questions quickly and accurately with real-time information is something chatbots accomplish with ease.

There are different kinds of tourists based on the experience they would like to have – adventurous, off-beat, lavish, budget-friendly etc. It is tricky to come up with an itinerary of places and experiences at a destination that can address each tourist’s custom needs. Chatbots backed with a logical travel algorithm can prepare such customized lists that consider factors like time, cost, destination type, interests etc. based on traveler data.

2. Analytics

Data is a powerful entity and with the right insights drawn from it, destinations can flourish with a good influx of tourists. Data driven decisions on offerings, cross selling opportunities and discounts can help travel operators make the most of the current travel scenario. Travelers also benefit from insights that can get them better deals in line with their preferences. Here are some sample use cases on how analytics can be used by travel operators:

  • Predictive Analytics: Predictive analytics help in analyzing predictions like foreseeable flight offers, stay offers, probable disruptions, etc. It helps in better planning for both tourists and travel operators.
  • Real-time Analytics: While predictive analytics share insights into the future, real-time analytics helps to deep dive into the present. Weather conditions, flight delays, price comparisons are some examples of variables that can change travel plans. They influence traveler behaviour and give operators a heads up if things go south, enabling them to swiftly take measures to tackle the situation.
  • Customer Sentiment Analytics: Customer sentiment analytics gathers traveler reviews across different platforms and help travel operators know which parts of the experience delivers the best value and understand the gaps in traveler experience to improve their services.
  • Trend Analytics: Tourist decisions are highly susceptive to trends and influencers. If travel operators want to capitalize on this, they need to have the right insights to understand current and developing trends like staycations, workcations, glamping etc.

3. Targeted Marketing

Travelers are fed up with generic one-size-fits-all monotonous marketing that is sold to the masses. People need something relatable that connects with them as individuals with specific priorities and needs, and that’s the right way to promote services.

  • Customer Segmentation: Customer segmentation analytics considers several data points and segregates customers based on demographics geography, age, profession, etc. to unearth insights that help operators customize offerings based on their target personas.
  • Loyalty Programs: Certain tourists travel often and with the help of targeted marketing, it is possible to provide them with special offers or complimentary add-ons, that ensure they turn into regulars.
  • Preference Tracking: Insights on the decisions that travelers make at each touchpoint, their likes, and dislikes, can be a treasure trove of data to mine insights for future cross selling, reselling, and recommendation customization.

4. Personalization

A lot of manual work that goes into planning for travel can be reduced with help of Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML) etc.

Here are some examples of personalization using Data Science:

  • Travel Planner: From selecting the destination to returning home, a travel planner using data science is a seamless option. Based on user interests, budget, time, etc., one can generate customized itineraries in a few minutes with different options that the tourist can choose from which, manually make take days.
  • Travel Fare Prediction: If there is one thing that tourists want, it’s the travel experience under their budget. Budget traveling has become a trend after the pandemic as people have become accustomed to hybrid work and tend to travel more frequently. Hence, based on user budgets, AI can generate distinct travel experiences that make both travel operators and tourists happy.
  • Recommendation Engines: Based on customer interests, recommendation engines always suggest places, experiences, etc. This intrigues even customers who have no travel plans to make an impromptu one.

The travel industry has always kept pace with evolution in technology when it comes to creating unforgettable traveler experiences – right from traversing the seven seas on ships to AI built itineraries for everyone. Hence, the only way to stay one step ahead in an industry where wanderlust is the only constant is by re-imagining travel with technology, which is the driving force of Tech4TH. Our services will help optimize your facilities and create solutions customized to your travel customers, colleagues, and community. Talk to us to find out how.

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