NBS-5003Y Information Systems for Management 2016-17
UNIVERSITY OF EAST ANGLIA Norwich Business School
Contents Pace
Covcr page — 1
Contents — 2
Appendix — 3
Machine teaming — 4
Benefits — 4
Chang—
e 6
Actual costs — 6
Intangible costs — 7
Time — 7
Conclusion — 7
References — 8
Appendix: Brooklands hotel Surrev
4 he Brooklands Hotel in Surrey lists itself as a luxury hotel, with an iconic, art deco inspired
interior and a famous racing track running through the reception. Guests have access to
conference room facilities, chefs, spa facilities and a fitness centre. The hotel is located 30
minutes train ride from London and caters to families, couples, hen nights and weddings.
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Machine learning
Machine learning is a typc of artificial intelligence that allows computers to learn without being
specifically programmed. These programmes are developed so that can change when cxposed
to new data. Computers will search through data to look tor pattcrns and adjust program actions
accordingly (Whatls.com, 2017). Torn Mitchell gave the definition, ‘a computer program is
said to learn from experience ‘E’ with respect to some class of tasks ‘T’ and performance
measure ‘P’, if its performance at tas1‹s in ‘T’ as measured by ‘P’, improves with experience
‘E”(Mitchell, Carbonell and Michalski, 1986). Machine learning also allows computers to
carry out a process of classiflcation and predication. Classification is a process whereby a
computer can categorise elements of a data set. Prediction is when the machine will
guess/predict the value of something based on previous values (Android Authority, 2015). An
example of machine learning is Faceboo1‹’s newsfeed machine, as it uses machine learning to
personalise each members feed. If a user was to stop scrolling a like a friend’s post, the
newsfeed will in future show that particular friend’s posts earlier on.
Benefits
Nlachine learning allows hotel companies to automatically recalibrate the price of its rooms
using hundreds of different variables. Thesc variables include past and present reservation data,
booking pattern data, cancellation data, occupancy data and room type. Furthermore, external
variables are also taken into account, these include competitive pricing, weather/climate data
and other sites booking patterns. Combining this data with a machine learning system allows the
hotel to raise room prices in anticipation of high demand, and lower prices in anticipation of
cancellations. The hotei chain Starwood has implemented a machine teaming systems, meaning
since 2015 they have seen a 20% improvement in demand forecasting (Boulton, 2017). Because
rooms are priced more efficiently, revenues and profitability have been boosted. Machine
learning in the hotel industry was described by Starwood’s vice president of global revenue,
David Flueli, as ‘a man and machine’ approach to revenue management (Boulton, 2017). Each
director of revenue manager is able to have a snapshot of the business through bar graphs. These
graphs will slio w the total number of rooms available in the hotel, transient and group rates,
projected net occupancy and other variables such as the lowcst rate given on a room based on
demand (Boulton, 2017).
Thc ROS machine learning system for Starwood lfas also aided the hotel chain in counter-acting
a challenging trend to the hotel industry. The windows for hotel bookings are shrinking as
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consumers expect fast results in malting a purchase. This is a culture which has been cultivated
by Amazon’s next day delivery, Uber and more on-demand companies that have accelerated the
pace at which customers expect to have their orders fulfilled (Boulton, C. 2017). . An article
published by Timothy Webb stressed how travel boo1‹ing behaviour has changed drastically over
the past twenty years. The traditional method of boolcing months in advance with an estatc agent
has dccreased in popularity as booking ahead has evolved into a ‘fast-paced, last-minute booking
environment’. (Webb, 2016, p276). Machine learning technology would therefore aid
Drooklands Hotel iii tracking and predicting when best to raise or lower hotel room prices in
order to lengthen the time in which customers expect their orders to be fulfilled.
Furtherinore, the introduction of machine leaming to Brooklands hotel would allow the
cornpany to enhance the effectiveness of its marketing campaigns. With access to detailed data
on consuiner behaviours Brooklands would easily be able to segment markets and adjust its
marketing techniques in order to gain greater profits and increase brand awareness. Nowadays,
a targe amount of information on customers is stored in databases. Data inining, which is an
integration of machine learning, has already been used by several cornpanies in direct
inar1‹eting to target customers. Patterns and regularities can be spotted by the machine leaming
software, allowing managers to target the right customers for promotion. A study conducted
by Pål Sundsay et al, found that machine learning technology was effective in identifying and
segmenting customers in order to discover those most likely to be converted to purchasing from
their company. The marketing team at MNO was impressed by the power of machine learning
and is now looking in to iinplementing the algorithm in their operations (Sundsay, P et al,
2014). fn regards to Booklands Hotel, it currently sees a midweek occupancy of 70-80% and a
weekend occupancy of 50-60%. With the use of data mining and machine learning Brooklands
hotel could potentially target customers who would be more inclined to stay simply for a
wee1‹end, boosting their overall profits (pan-leisure consulting limited, 2004).
In addition, machine learning within a hotel can be used to compute dynamic clusters of guests
to create segmentation in real-time. Fluid segmentation would ensure that as consumer booking
patterns and buying habits evolve, the hotel can reach the correct gucsts at the right timc, with
the right price and through the right channels. If Brooklands Hotel werc to segment their market
efficiently they would be able to effectively focus on different groups of customers within their
mar1‹eting campaigns, thereby malting them more effective. (lnfonration Age, 2017). For
example, deep neural networks and image classifiers would allow Brooklands hotel to analyse
and monitor images that will provide the optimum boo1‹ing conversion rate through several
different channels. (Information Age, 2017).
Change
1°urtliermore, befoTe the machine learning system was implemented at Starwood, revenue
managers would simply compile spreadsheets manually, and then recommend prices to set lor
each room. Naturally, this system takes a significantly longer amount of time, and is therefore
less efficient and profitable. The machine learning system also frees up time for revenue
mangers to focus on short term opportunities and long term strategies (Boulton, C. 2017). As
LodglQ wrote ‘In the hotel of the future, tedious tasks like pricing and demand forecasting will
run in the background, allowing revenue teams to be unshacliled from their spreadsheets and
given the time to focus on things that really matter’(Shift, 2017).
Actual cost
When the hotel chain Starwood implemented their version of a machine learning system, ROS,
as part of their revenue optimisation scheme, it cost a total of $50 million over a period of 3
years. It is unlikely that a machine learning system for Brooklands hotel would cost as much
as $50 million or take as long as three years to implement, as Starwood Hotels and Resorts
Worldwide, Inc. is a global enterprise, whereas Brooklands is a small business with less data
to process.
However, there are a number of emerging algoTtthm market places that can offer a middle
ground in machine learning costs. Algorithmia for example offers a pay as you go service which
is ideal for growing businesses. Packages start from $20 per month where Brooklands would
be able to develop and test their algorithm using the tools available on the Algothrimia
platform. Hotel managers would then havc access to the Algorithmia inarliet where they can
view other algorithms and update their own (Algorithms, 2017). This platform would also
allow Brooklands to implement a machine learning algorithm at a faster rate than if they were
to start from scratch.
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Intaneible costs
The hospitality industry itself has never been at the forefront of technological innovation. Many
hotels, such as Brooklands hotel in Surrcy, still rely on managers to manually collect, review
and analyse data, in order to propose the optimum price for profit on each room. It is most
1il‹cly with the implementation of machine learning technology a handful of revenue managers
would be made redundant. A study conductcd by Armstrong-Stassen found that after
redundancies were conducted, survivors felt insecure and company morale decreased
(Armstrong-Stassen, 2009). Furthermore, it is unlikely that employees at Brooklands Hotel will
have experienced redundancy in the past. It was found that any redundancies made that were
outside the organisational norm were more timely to create negative responses from surviving
employees (Armstrong-Stassen, 2009). This would be an intangible cost of implementing a
machine learning system for Brooklands Hotel in Surrey.
Time
Despite the use of a non-customised machine learning algorithm being the cheaper alternative
for a company, it cannot be recommended due to several faults. Whilst, there is an abundance
of algorithms available to companies online that are quick and easy to implement, many have
bugs or are simply incorrect. These hidden bugs may in fact cost the company in time and
money more than it they had simply decided to pay for a customised algorithm. Furthermore,
the translations services agency, Lingo24, found that even of-the-shelf machine learning
algorithms require significant customisation and data science world to be an effective enough
tool within a business (Boyd, Mok and Currie, 2017). Therefore, it must be recommended that
Brooklands Hotel invest in a fully customised machine learning algorithm in order to see
greater returns in the long run. This is because, whilst a fully customised machine learning
algorithm may co St itiore and take several years to implement, it can be seen as the best option.
Conclusion
To suminarise, there is a large arnount of evidence demonstrating that Broo1‹lands Hotel would
benefit from the use of a machine leaming algorithm. It is most likely that in order to create an
effective algorithm the company will have to invest significant time and money. While also
potentially creating resentment from einployees, such as revenue managers, in regards to
redundancies. However, in the long run studies conducted by Armstrong-Stassen and Pål
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Sundsiry et at have proven that Machine learning technology can boost profits and narrow the
window for hotel bookings.
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References
Algorithms, A. (2017). Alg‹›rithn1ia - Open Marketplace %r Algoriihms. [online] Algorithmia.
Available at: https://a1gorithinia.com/ [Accessed 9 Mar. 2017].
Android Authority . (2015). What is Machine Learning. [Online Video]. 7 July 2015.
Available from: httjas://www.voutube.com/watch?v=WHM i-fgGo. [Accessed: 3 February
2017].
Armstrong-Stassen, M., 1993. Survivors Reactions to a Workforce Reduction: A Comparison
of B1ue-co1Iar Workers and their Supervisors. Canadian Journal aj AJnlinistralive
Sciences/Revue Canadienne dev Sciences de l'Administration, 10(4), pp.334-343.
Boulton, C. (2017). Starwood taps machine learning lo dynamically price hotel rooms.
[online] CIO. Available at: http://www.cio.coin/article/3070384/ana1ytics/starwood-tapsmachine-learning-to-dynamically-price-hotel-rooms.html [Accessed 16 Mar. 2017].
Infomaation Age. (2017). Machine learning transforming the hospitality industry - Information
Age. [online] Available at: http://www.information-age.com/machine-1earning-hospita1ity123462758/ [Accessed 16 Mar. 2017].
Mitchell, T., Carbonell, J. and Michalski, R. (1986). Machine Learning. 1st ed. Boston, MA:
Springer US, pp.10-27.
pan-leisure consulting limited, (2004). Barnsle y Hotel/Serviced Accommodation Sludy.
[online] Barnsley: pan-leisure consulting limited, p.27. Available at:
https.//www.barns1ey.gov.uk/media/5149/hotel-study-2004.pdf [Accessed 16 Mar. 2017].
Skift. (2017). How Machine Learning i.s Making Hoteliers Smarter. [online] Available at:
https://skift.coin/2016/04/11/how-machine-learning-is-making-hoteliers-smarter/[Accessed 18
Mar. 2017].
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Sundsay, P., Bjelland, J., Iqbal, A.M. and de Montjoye, Y.A., 2014, April. Big Data-Driven
Marketing. How machine learning outperforms marketers’ gut-feeling. In International
Conference on Social Computing, Behavioral-Cultural Modeling, and Pre‹liction (pp. 367374). Springer International Publishing.
Webb, T. (2016). From travel agents to 04 As: How the evolution of consumer booking
behaviour has affected revenue management. Journal of Revenue and Pricing Management,
I 5(3-4), pp.276-282.
Whatls.com. (2017). What is machine learning? - Definition from Whatl.s. cont. [online]
Available at: http://whatis.techtarget.com/definition/machine-learning [Accessed 3 Feb.
2017].
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