The document discusses how artificial intelligence (AI) will impact revenue management in the hospitality industry by arming consumers. It makes three key points: 1) AI will filter information for consumers, compare offers more efficiently, make purchase decisions and payments within set limits, potentially acting as the customer instead of just assisting humans. 2) This will empower consumers in relationships with hospitality companies and require companies to communicate with consumer AI as much as humans. 3) For revenue managers, it means more properties will be considered, discounts more utilized through rebooking, and booking fluidity will increase, requiring new approaches targeting consumer AI alongside humans.

1. The AI-armed consumer in
hospitality and its impact
on revenue management
Stanislav Ivanov
Email: stanislav.ivanov@vumk.eu
Web: http://stanislavivanov.com
AIHR Innovation in Hospitality Conference, 11-12 April 2017,
Stenden University, The Netherlands

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Stanislav Ivanov
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• Professor and Vice Rector (Research),
Varna University of Management,
Bulgaria (http://www.vum.bg)
• Editor-in-chief of the European Journal of
Tourism Research (http://ejtr.vumk.eu)
• CEO of Zangador Ltd.
(http://www.zangador.eu)

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Content
• Revenue management assumptions
• The AI-armed consumer in hospitality
• Revenue management implications
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What is Revenue Management?
• A set of tools and actions dedicated towards the
achievement of an optimal level of the hotel’s net
revenues by offering the right product to the right
customers via the right distribution channel at the
right time at the right price.
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Economic fundamentals of RM
• Product perishability
• Limited capacity
• High fixed and low variable costs
• Unequal distribution of demand during the
day/week/month/year
• Possibilities to forecast the demand
• Possibilities to segment the market demand
• Different willingness-to-pay of market segments
• Different price elasticity of market segments
• Possibilities for advance bookings
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Revenue management
assumptions might be already
outdated and probably do not
reflect the new realities
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From Revenue management to
Total profit management
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Room revenue management
Total revenue management
Total profit management
Room
revenue
Room
revenue
Room
revenue
Ancillary
revenue
Ancillary
revenue
Costs

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From Revenue management to
Total profit management
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Revenues Costs
Performance metrics
Demand Hotel
Channel
Rules

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Price elasticity of demand
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P
Q
D1
D2
D1 – linear demand
D2 – constant elasticity
Explicit
assumption

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Price elasticity of demand
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P
Q
D3
P1
Q1
D3 – kinked demand
curve
Reality

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The “fixed” nature of fixed costs
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FC
VC=VC1*Q
TC=FC+VC
TR=P*Q
QE Q
TR; FC; VC; TC
0
E
FC
Explicit
assumption

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The “fixed” nature of fixed costs
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FC
VC=VC1*Q
TC=ƩFCj+VC
TR=P*Q
Q1
Q
TR; FC; VC; TC
0
E2
FC
Q2
Qmax
E3
QE2 QE3
QE1
E1
Reality

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The decreasing returns of sales
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FC
VC=VC1*Q
TC=ƩFCj+VC
TR=Ʃ(Pi*Qi)
Q1
Q
TR; FC; VC; TC
0
E1
FC
Q2
Qmax
QE1
Reality

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The power lies with the customer
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Explicit
assumption

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The power lies with the company
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Implicit
assumption

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Customers in the future will be
armed with artificial intelligence
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Source: http://vignette4.wikia.nocookie.net/starwars/images/c/c8/Counterparts.jpg

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Source: http://vignette2.wikia.nocookie.net/avp/images/4/49/Terminator.jpg

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Source: http://www.kurzweilai.net/images/robot-thinking-one.png

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Robots and AI application:
• Manufacturing
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http://www.businesskorea.co.kr/sites/default/files/field/image/Industrial-Robots-Hyundai-Heavy.jpg

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Robots and AI application:
• Warehousing, supply and logistics
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http://spectrum.ieee.org/automaton/robotics/industrial-robots/german-warehouse-robots-tackle-picking-tasks

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Robots and AI application:
• Agriculture
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http://sparc-robotics.eu/wp-content/uploads/2014/05/polni-robot-amazone-bonirob-44e031.jpg

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Robots and AI application:
• Transportation / Self-driving cars
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http://www.google.com/selfdrivingcar/images/home-where.jpg

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Robots and AI application:
• Medicine
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http://vignette3.wikia.nocookie.net/robotics/images/2/21/Medical_robot.jpg/revision/latest?cb=20140602041221

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Robots and AI application:
• Hotels
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http://www.mirror.co.uk/news/uk-news/futuristic-hotel-thats-like-robotic-6449905

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Robots and AI application:
• Warfare
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http://spectrum.ieee.org/automaton/robotics/military-robots/why-should-we-ban-autonomous-weapons-to-survive

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Robots and AI application:
• Legal services
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http://www.kurzweilai.net/will-ai-replace-judges-and-lawyers

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Robots and AI application:
• Households
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http://www.roboticstrends.com/images/photos/xiaomi-mi-robot-vacuum.jpg

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Robots and AI application:
• Search engines
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Robots and AI application:
• Social media chatbots
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http://chatbotsmagazine.com

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Robots and AI application:
• Finance
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http://static3.businessinsider.com/image/581cd80d46e27a2e008b4cf3-1920/fintech-ecosystem-diagram.png

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Robots and AI application:
• Online trade
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Robots and AI application:
• Sex services
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https://www.minds.com/blog/view/638819052533329937

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The AI-armed consumer in
hospitality
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The AI-armed consumer in hospitality
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The Future

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The AI-armed consumer in hospitality
• AI will filter the information that reaches the
consumer > not every offer will reach the consumer
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The AI-armed consumer in hospitality
• AI will compare offers more effectively and
efficiently than a human customer can do > an
alternative to meta search engine (or meta search
engine 2.0?)
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The AI-armed consumer in hospitality
• AI will make purchase decisions within specific
limits
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The AI-armed consumer in hospitality
• AI can make / amend bookings at most convenient
time > no need the customer to be present or even
awake!
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The AI-armed consumer in hospitality
• AI will make payments within specific limits >
access to credit card details
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If AI can perform all these
activities who is the customer
then:
the AI or the human being?
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Revenue management implications
• The consumer will not be helpless in his/her
relationships with tourist companies
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Revenue management implications
• Companies’ AI will communicate with customers’
AI
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Revenue management implications
• Increased number of properties checked (by the
AI!) before the purchase decision is made
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Revenue management implications
• Greater utilization of discounts through rebooking
/ booking amendments
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Revenue management implications
• Greater booking fluidity – increased multiple
and/or last minute booking amendments
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Revenue management implications
• Companies will need to invest heavily in AI and
expand their engineering / software development
teams.
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Revenue management implications
• Companies need to overcome the filter imposed by
customer’s AI > the ‘right’ distribution channel is
the one that could pass the filters
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Revenue management implications
• The distribution channels landscape will change –
division of distribution channels into ‘preferred’
(‘trusted’) and ‘regular’ depending on their
relationships with the PDA producers >
concentration of bookings in the ‘preferred’
distribution channels > larger distributors.
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Revenue managers need to
target not only the customers,
but their personal digital
assistants (and their producers!)
as well
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Concluding thoughts
• The future is here and revenue managers need to
face it
• The AI will change not only the travel, tourism and
hospitality industry but every business sector – no
one is safe
• We actually do not know what the complete effects
of AI adoption would be
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THANK YOU FOR THE
ATTENTION!
QUESTIONS?
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