Page 154 - Building Big Data Applications

152 Building Big Data Applications

customized services to cater to their most valuable customersdeither in groups or
as individuals. But hotels cannot always rely on internal data to predict a
customer’s return. Instead, data analysts have to collect data from surrounding,
external sources to help identify travel patterns, habits, and common timeframes,
to make this prediction. Nevertheless, the customer feels really appreciated from
personalized service. There can be a survey that can be done with simple yes
and no answers to receive feedback on the services and further tweak and
improvements can be offered in subsequent stays and visits.
Social mediadIn today’s Internet-centric age, online communities are just as
important as their traditional counterparts. Since many customers turn to social
media for questions, issues, and concerns, the platform provides a great opportu-
nity to connect with consumers in brand-new ways. Several airlines in the Middle
East have compiled vast datasets containing online search histories, completed
bookings, and even airport lounge activities on every one of their customers more
than 200 million of them. The data helps officials create personalized travel experi-
ences for their frequent guests. The frequent guests in-turn have brought more
fliers to enroll into activities which have driven revenue for the airlines across
different airports.
Yield managementdBig data analytics also affects yield management. By calcu-
lating the optimum value of each room and factoring in metrics like seasonal de-
mands, regular guests, and similar trends, hotels can ensure maximum profits.
There are several algorithms that can be used to run these calculations.

Regardless of whether hotels are trying to classify their patrons with better accuracy,
provide personalized services, engage their social media audience, or stretch the value of
their properties; they must use and apply all this data before it has an impact. The
information on its own is dormant until activated through the disciplines of big data
processing and analysis.

Analytics and travel industry

The analytics that we used in the industry was limited in the value it provided as the
infrastructure was limited in storage and compute. With the big data platforms and the
issue of storage and compute being resolved the analytics are becoming more relevant
and can be utilized for multiple purposes. The analytics that we will use the most include
the following:
Descriptive analytics is used to analyze data from past occurrences and activities,
used commonly by marketing and advertising. Predictive analytics uses big data to
try and forecast future outcomes or events, while prescriptive analytics takes
advantage of highly advanced algorithms to process big data and provide action-
able advice. All three of these methods are common strategies for applying big data
in the hotel and hospitality industry.