Travel

In a digitalised world, the ability to manage and analyse a large volume of data from different sources is a critical asset for businesses and organizations, especially in the transport, tourism and cultural Industries.
In transport and tourism, Next Data Science uses big data analytics techniques which change the fundamentals of the competition: it help operators capture and extrapolate information to enrich and diversify their travel and visit experience, develop new business opportunities, improve customer relationships as well as optimizing the management of their internal processes.
NDS uses Technologies which help businesses to map the digital footprints of passengers and tourists leave online and data analytics is crucial to identify clients’ behaviour and new business potentials. To take advantage of these opportunities team NDS integrate relevant data, identify meaningful relationships and create valuable knowledge.

Case Study 1:

Problem:
How to track the dynamic flight fares which we can’t track at manual level?
Solution:
Next Data Science is using various predictive tools to track the fares by collecting the past data. From that data, we are fetching the common trends and forecast the requirements of customer.
Outcome:
By using the predictive analytics, customer can easily book the cheapest flights. Special offers and deals are also generated with the help of this analytics.

Case Study 2:

Problem:
How can we recommend places a traveller can plan to visit?
Solution:
-> Most travellers know more-or-less what they are expecting from their next trip, but are flexible regarding the exact destination.
-> Whether it is skiing, sightseeing, history, music festivals etc., we have the ability to adapt searches to your desires and meet your constraints.
-> NDS recommendations are based on the experience of many different user profiles, so it’s actually data science that’s helping us in finding good deals and taking you to cool places.
Outcome:
Data Science is making the things simpler for a traveller to plan the trips.

Case Study 3:

Problem:
How to solve the Tatkal issues?
The process of procuring a Tatkal ticket is proving to be one of the most complicated and annoying tasks ever. The server gives up on many systems just seconds before 10 am, and comes back to life only when all the seats have gone.
Solution:
-> Next Data Science thinks from customers’ point of view. By using various big data tools it becomes possible for the system to handle maximum multiple logging at a time.
-> With the help of data analytics we can derive the choices filled by the passenger in historical transactions. Introducing the e-wallet system can also save the time for booking the Tatkal Ticket.
Outcome:
The most obvious concern i.e. booking the Tatkal tickets can be reduced to some extent.