45 Edition 2020 FINAL

45th | Vol 30 | 2020 | www.hotelier-indonesia.com 57
language, recognizing objects and
sounds, learning, and problem solving.
Today, with the collection of customer
data, coupled with continued improvements
in computer technology, AI can
perform a wide range of routine tasks,
from basic customer service to personalized
job duties, more advanced
decision-making, even sales processes
and direct messaging.
Professionals from all different fields
use AI for predictive analysis and
interpretation. AI is one of the biggest
trends in tourism and related
fields, and hoteliers are also using big
data and AI to innovate their pricing
strategies.
AI combined with data analytics will
enable the automation of many daily
tasks. Therefore, when human participation
and manual work becomes
unnecessary, we should find a good
balance between the human resource
and machine, and reasonably allocate
our time on what we are good at.
Among the most repetitive tasks, AI
technology can take on the burden
of large amounts of demand and pricing
analysis. Hoteliers look forward to
and appreciate this change when they
experience explosive data growth.
Today, the production of various daily
reports takes more and more time
with the increase of data elements and
analysis dimensions.
The amount of time spent on this data
processing and reporting is bound to
affect the scheduling of more important
analyses and decisions, so it is far
more efficient to leave these repetitive
tasks to AI.
In addition, the data analysis methods
of AI nowadays are getting more accurate,
and more data analysis normally
leads to more insights. Allowing AI to
complete more accurate data analysis
leads to more rational decision-making
and can be another great benefit to
revenue managers, leaving them with
more time to monitor the automated
decisions based on the analysis, focus
on the implementation and make
proper adjustment to the decisions.
AI can also be applied to different
research tasks, such as generating
specific market segments, which can
reveal the implicit correlation between
customer information and preferences.
Traditional hotel revenue management
systems are based on a pre-set
market segmentation model for future
demand forecasting and management.
With AI, more advanced revenue management
systems would automatically
assign attributes to more detailed rate
code levels to generate its own forecast
group, based on both attributes
and historical booking patterns. For
example, two market segments may
have the same attributes, and may be
grouped together.
But meanwhile the system may place
them in two different forecast groups
after analyzing their booking patterns
and finding they differ greatly regarding
the timing of when the business
books.
The advantage of doing so is to divide
the forecast group as much as possible
according to the actual business attribute
and behavior pattern, rather than
only relying on the existing market
segmentation system, which may be
wrong, resulting in inaccurate forecast.
Also, with AI and a machine-learning
algorithm, the revenue system can
evaluate the nearest competing hotel’s
demand level, competitor pricing, destination
special events, room type, and
so on.
Demand forecasts provide critical information
for pricing decisions for each
market segment or room class and
can help revenue managers to select
appropriate distribution strategies, as
well as explore what customers want
and describe their price sensitivity.
An intelligent, data-driven revenue
management system can greatly
improve pricing efficiency. For example,
in some international hotel groups,
the machine-learning-based revenue
management system combines different
strategies and data sources to set a
best available rate for each room class
on each date.
The algorithms behind this dynamic
pricing engine take into account both
customer profiles, room types and
prices, as well as external data, such
as competitor prices, reputation score
data, and even booking patterns captured
on other websites.
In addition to pricing, another important
aspect of revenue management
is inventory control. The key point of
revenue management is optimizing
revenue and profit through proper
pricing and space controlling of hotel
rooms, meeting space, restaurants and
other entertainment areas. Revenue
managers seek to capture the opportunity
to increase prices and maximize
revenue on high-demand dates while
maximizing occupancy on lowdemand
days.
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