Page 155 - Building Big Data Applications

Chapter 8 Travel and tourism industry applications and usage 153

Descriptive analyticsdThis method is one of the most straightforward and efficient
ways of generating actionable data. Did a recent renovation increase sales, or was
it ultimately a waste of capital? It is easy to answer questions like this via descrip-
tive analyticsdit is a decades-old method that has assumed many different forms
over the years.
Predictive analyticsdBasic examples of predictive analytics include preparing a
hotel for a seasonal rushdlike spring breakdand reducing the hours of staff
members to accommodate the fewer number of reservations in the offseason.
Prescriptive analyticsdInstead of letting the human workforce interpret and act on
this information without any guidance, some of today’s systems provide recom-
mendations and advice to improve service and increase profits. Online reservation
systems that track a guest’s past stays can automatically generate discount codes
for future reservations, assemble personalized services for each guest, and even
deliver their favorite drinks or food.
Big data analytics has the potential to completely transform the customer experience
within the hotel and hospitality industry. It is not something that will happen overnight,
but the industry is already making huge strides toward a full-on embrace of big data and
all the advantages it has to offer.
Some of the most tech-savvy hotel chains are already adopting long-term strategies
and policies for big data management. Those who are unwilling or hesitant might find it
hard to compete in the coming years. The niche of big data is still in its infancy, but it is
already sparked storms of creativity and innovation in any industry it has touched,
including hotels and hospitality. Even the most sophisticated of predictive analytics
cannot tell us exactly where big data is headed, but customers are sure to be pleased with
the results.

Examples of the use of predictive analytics

Recommender systems for travel products (e.g., hotels, flights, ancillary services)dThere
are thousands of possible combinations of flights connecting Los Angeles and New York
for example, and this figure breaks the roof when combining possible services. But which
travel solutions and services are relevant for a given passenger? Which hotel is the most
pertinent for a young couple who just booked their flights for next summer holidays?
Recommender systems provide winewin value for both users and travel providers by
proposing the most valuable and relevant options to users while maximizing revenues of
travel providers. Predictive analytics help to better understand user needs and match this
knowledge to possible products and services.
Video, image, and voice recognition systems for travel purposesdOur human
brains respond to stimulus coming from different senses. They are better adapted to
understand natural forms of communication-like images of sounds rather than textual
written information. With the development of deep learning and other AI algorithms, the