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April 30, 2014
Addressing high priority business issues in
Hospitality through Analytics
A wide range of examples show that the ROI on these efforts
is high if done properly

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Agenda
Success Stories
Analytics in Hospitality
Marketing Mix Optimization
New Hotel Decisions
CRM Strategy
Guest Satisfaction

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CXOs are looking for Answers to Critical Business Questions
Which new
properties and
locations should we
invest in?
How do we maximize
REVPAR?
How do we enhance
flow through to the
bottom line?
How do we maximize
customer value?
How can we
optimize Marketing
ROI?

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How do we maximize
REVPAR?
Which new properties and
locations should we
invest in?
These questions can be answered better through analytics
Concept Testing & Product Design
 Understand central delivery capacity to markets
 Assess impact of new branded and un-branded supplies
Concept Testing & Product Design
 Understand price sensitivity to demand
 Forecast demand at various price points
 Identify opportunities for higher opaque pricing
Concept Testing & Product Design
How do we enhance flow
through to the
bottom line?
 Offer discounts only when there is a clear incremental impact
 Move customers to profitable booking channels
 Identify acquisition sources that bring in loyal, high value customers
Concept Testing & Product Design
How can we maximize
customer value?
 Measure Wallet Size & identify high value customers
 Identify opportunities to engage, up sell, cross sell, prevent attrition and re-engage
 Develop customer centric programs that drive revenue and engagement
Concept Testing & Product Design
How can we optimize
Marketing ROI?
 Assess revenue that is truly attributable to channels and marketing investments
 Understand synergies, optimize spend and timing
Business Questions How Analytics can help

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ROI on these efforts is high - if done properly
FIVE PRE-REQUISITES FOR SUCCESS
 Senior management buy-in
 Integration of disparate data sources with enterprise wide access
 Test and learn culture
 Collaboration across departments
 Scalable insights delivery model

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Market Mix Optimization
Case Study
Business Concern Complications
 To find the True Value of reported impact in a multi channel,
multi value ecosystem.
 Same numbers were being reported by multiple platforms. The
question was how to increase the effectiveness of the platforms
 how do we optimize marketing ROI across online and off line
channels?
30% 45% 60% 20% 100%
???
Resolutions Business Impact
Phase 1- Holistic Base Model, Market Mix
 Holistic approach incorporates all drivers with Appropriate level
of modelling sophistication - OLS, HB
Phase 2- Refinements, e.g. Structural Equation Models
 Assess Synergies, Refine Attribution
Phase 3 – Triangulation e.g. Cookie Data
 Cookie data captures Unique ID activity and measure recency
and frequency
 Attribution’s % impact of each media channel drives daily
proportions
53.1%
20.5%
14.1%
6.0%
4.1% 2.9% 0%
23%
45.6%
12.3%
8.8%
5.5% 4.5%
0.1%
0%
10%
20%
30%
40%
50%
60%
Search TV Affiliates Display PR E-mail Print
Primary Attribution
After Secondary
Attribution (Actual
Contribution from
Model)
Impact of Secondary
Relationship
on Search= - 30%
Impact of Secondary
Relationship
on TV= +25%

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New Hotel Decisions
Case Study
Business Concern Complications
 They also wanted to predict the wallets for stays at client’s
hotels versus all competitors, with lower error rate (higher on
accuracy)
 The Client needed to take the decision of where to locate its
new hotels, and their estimated ROI
Resolutions Business Impact
 Identified destinations which a customer is likely to visit based
on his/her geographic and demographic profile – requires
external data e.g. Visa to assess historical size of wallet
 Overlaid the untapped opportunity (1-share of wallet) to
identify opportunity spaces – using past stay behaviour from
C360
 Identified customers that are most likely to stay incrementally at
the proposed new property – using behavioural and
demographic information from C360.
Measurement in solos gives wrong answers
30% 45% 60% 20%
100%
???
 50% improvement in the accuracy rate in classifying guests into
the High/Medium/Low Categories
 The actual size of the wallet (point estimate) was predicted with
a reduction in the error rate by 20%
 The predicted point estimates target customers with the highest
potential

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CRM Strategy
Case Study
Business Concern Complications
 In an ever increasing loyalty customer base, the client needed
to scientifically decide which promotion should be sent to
which customer, at what time, and using which channel
Resolutions Business Impact
 The loyal customers get blasted with promotions, while the non-
loyal ones do not receive them
 In the email world, the promotional & communication calendar
is managed by moving the offer schedules, a process that does
not truly address underlying conflict or optimize value.
 Email is reaching Saturation
Unified Metrics
Financial Metrics
Engagement Indices
Quantifying Value
Response
Short Term Value
Incremental Value
Long Term Engagement Impact
Optimize Algorithms
Prioritized Contacts
Constraints
Go / No Go Decisions
Test & Learn
Enhance Offers
Improve Targeting
Enhance Business Rules
 Synergies with other direct marketing initiatives to develop new
vehicles
 Combined Impact expected to exceed $100 MN over12 months

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Guest Satisfaction
Case Study
Business Concern Complications
 Satisfaction Scores were Dropping for a lot of client branches.
They wanted to know why, and ways to mitigate it
Resolutions Business Impact
 51% guests retained by implementing the findings
 40% improvement in customer satisfaction
PROPERTY
LEVEL
ANALYSIS
DATABASE
Internal Database
(Customer mix, Staffing
Levels)
Survey Information
(Satisfaction and
Loyalty scores)
Trade Audit Data
( Competitor syndicated data,
relative satisfaction scores)
Data sources disparate from each other
 Specific focus areas were identified and shared with branch
managers to
– Help them manage their assets better
– Prioritize key action points to improve customer satisfaction
Best
Better
Good
Average
Low
 Each property was moved from its current
performance band to next higher band
 Multiply the target movements with pre-
determined %increments

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