F E AT U R E A R T I C L E
F E AT U R E A R T I C L E
What your Revenue
Management System
should do for you
Minimum analytic requirements
for an advanced RMS
We’ve seen tremendous changes in hotel technology
over the last few years. Channels are evolving at a
rapid pace, customers have the ability to compare
options like never before, and even selling systems
are finally seeing investment to keep pace. That said,
few systems hoteliers interact with are as analytically
advanced as their revenue management system (RMS)
– and these systems also need to keep up with business
changes. Is your system keeping up?
Understanding how different rates and
segments are controlled
Many RMS solutions roll up transaction data into
aggregated groupings such as market segments, or
simply classify reservations based on value alone.
Unfortunately, important information is often lost as
a result of this aggregation process – in particular the
information regarding how different rates are priced
and controlled. Individually capturing information
by transaction allows an RMS to separate these
transactions based on factors that influence their key
behaviors as well as critical aspects of the rates that
influence how they should be priced and controlled.
Once that is done, it is possible to build the most
effective groupings to support accurate forecasting
and optimization decisions.
Imagine we have two corporate rates: one
is contracted at a fixed price, but the hotel can
restrict availability for it as required on peak days.
Meanwhile another corporate rate is contracted at
a guaranteed 10 percent off the best available rate
(BAR) with last room availability – meaning this
rate cannot be restricted in its availability. These two
rates will often end up in the same market segment,
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and thus require different forecasting approaches
– and utilizing the immense variety of forecasting
methodologies offered by SAS provides for this.
Then, the system regularly reviews properties
and segments to see if these patterns are
changing, and refreshes the forecasting model
appropriately, moving the needle just a little
bit further. The result is a more accurate
and dependable forecasting process that is
not bounded by the limitations of a single
approach or forecasting methodology.
Elasticity and competitor
rate effects
simply because they are both corporate contracted
rates. However, from a revenue management, and
particularly an advanced analytics, perspective,
these two rates will behave differently and must be
controlled differently.
There are many rate attributes that should be used to
properly separate them into proper analytic groupings.
Creating analytic groupings by differentiating how rates
will be “controlled” in your RMS (along with other
factors) will result in more optimal decisions and
ultimately, better bottom-line performance. Meanwhile,
forecasts can still be made available to revenue
managers and other system users, including users from
other departments, in a manner consistent with their
current organizational reporting needs.
Demand forecasts that handle the variety
of customer demand patterns
Different customer segments have different booking
and staying patterns. For example, corporate customers
typically stay on weekdays and generally have short
booking windows – but leisure transient guests have
different patterns, as do groups, tours, etc. Channels
can also influence these patterns. If you add on the fact
that customer behaviors change over time, you begin to
see why it’s going to be problematic to try to “fit” one
demand forecasting approach to all of these segments
and channels. Still, some systems continue to do that
– use a single forecasting approach to predict the
disparate behaviors of many different segments.
At SAS and IDeaS, analytical techniques that
automatically review the distinct patterns in each
property, segment, or channel are applied, thereby
determining which of a wide variety of models will
best fit these patterns. Within a given property,
different segments may well have different patterns
As the industry has embraced the concept of
BAR – a dynamically priced rate available to
transient guests – the importance of measuring
and accounting for price sensitivity has moved
to the forefront in revenue management. It
simply isn’t possible to optimize this rate without
understanding how demand will change as a result of
increasing or decreasing it. Often lost in discussions
regarding elasticity is that we are not looking for a
single number: elasticity can vary by market segment,
time to arrival, day of week, season, and many other
factors. Accounting for these factors and differences is
essential to selecting the right rate at the right time.
Accounting for competitor rates is an important
part of understanding price sensitivity. Guests do not
view hotel rates in isolation: internet distribution
has made comparisons amongst competitor rates
so easy that most customers evaluate rates within
a marketplace of available offerings. In this
environment, it is very easy to simply follow the
market, by pegging your rate to competition. But
setting the rate based solely on competitors’ rates is a
risky thing to do and can lead to significant errors in
revenue management. Rather, competitor rates need
to be considered when estimating price sensitivity.
That is, the degree to which your property’s demand
will be impacted by a rate change needs to consider
not only how much we are changing our rate, but also
how our rate is and will compare to competitor rates:
will we be higher than or lower than our competition
and by how much?
this environment, it’s not sufficient to optimize rates
alone. The availability of these rates also needs to
be managed. The impact of rate pricing decisions on
demand makes simultaneously optimizing rates and
availability extremely complex – particularly when
accounting for length of stay – but that is the reality
of today’s marketplace.
Conclusion
The hospitality industry and travel distribution
continue to evolve, and today’s revenue
management systems need to keep pace. This needs
to be true not only at a technical level, but on an
analytical level as well.
Changes to business practices necessitate changes
to analytics, and technology changes and new data
enable new analytic methods that previously have
been unavailable. The capabilities outlined here
will help you keep up with the latest practices, and
optimize revenues in today’s dynamic market.
Managing rates and availability
Even with the prevalence of dynamic pricing and
BAR in the industry, most hotels continue to have
rates that need to be managed through availability. In
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