Usingan alytics to address high impact business priorities inhospitality 130903055346-phpapp02
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Transcript of Usingan alytics to address high impact business priorities inhospitality 130903055346-phpapp02
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Using Analytics to address high impact business priorities in Hospitality
Examples Demonstrating Potential Impact
Guha Athreya
Hospitality Analytics Expert
AbsolutData
By:
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Agenda
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Analytics in Hospitality
Success Stories
Marketing Mix Optimization
New Hotel Location Decisions
CRM Strategy
Guest Satisfaction
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CXOs in the Hospitality industry are looking for
Answers to Critical Business Questions
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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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These questions can be answered through
analytics
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Concept Testing & Product DesignWhich new properties
and locations should we
invest in?
Understand central delivery capacity to markets
Assess impact of new branded and un-branded supplies
Concept Testing & Product DesignHow do we maximize
REVPAR?
Understand price sensitivity to demand
Forecast demand at various price points
Identify opportunities for higher opaque pricing
Concept Testing & Product DesignHow 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 DesignHow can we maximize
customer value?
Measure Wallet Size to identify high potential 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 DesignHow 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
FIVE PRE-REQUISITES FOR SUCCESS
Senior management buy-in
Integration of disparate data sources with enterprise wide access
Scalable insights delivery model
Test and learn culture
Collaboration across departments
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Market Mix OptimizationOptimizing Market Mix to increase ROI by 25%
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Business Concern
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?
Complications
Resolution Business Impact
30% 45% 60% 20% 100%
???
Phase 2- Refinements, e.g. Structural Equation Models
Assess Synergies, Refine Attribution
Phase 1- Holistic Base Model, Market Mix
Holistic approach incorporates all drivers with Appropriate
level of modelling sophistication - OLS, HB
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
Impact of Secondary
Relationship
on Search= - 30%
Impact of
Secondary
Relationship
on TV= +25%
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New Hotel Location DecisionsReducing error rate of predicting size of wallet by 20 %
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Business Concern
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
Complications
Resolution Business Impact
Overlaid the untapped opportunity (1-share of wallet) to
identify opportunity spaces – using past stay behaviour
from C360
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
Identified customers that are most likely to stay
incrementally at the proposed new property – using
behavioural and demographic information from C360.
The actual size of the wallet (point estimate) was predicted
with a reduction in the error rate by 20%
50% improvement in the accuracy rate in classifying guests
into the High/Medium/Low Categories
The predicted point estimates target customers with the
highest potential
No data of existing market
Reliance on competitor data which is usually inaccessible
and sometimes, inaccurate
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CRM StrategyTargeted Promotions impacts profits by over $100mn
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Business Concern
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
Complications
Resolution Business Impact
Email is reaching Saturation
The loyal customers get blasted with promotions resulting
in opt-outs, while the non-loyal ones do not receive them
leaving money on the table
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.
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 SatisfactionStreamlining focus areas and prioritizing action points
increased guest retainership by 51%
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Business Concern
Satisfaction Scores were Dropping for a lot of client
property. They wanted to know why, and ways to mitigate it
Complications
Resolution Business Impact
Specific focus areas were identified and shared with property
managers to
Help them manage their assets better
Prioritize key action points to improve 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
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
40% improvement in customer satisfaction
51% guests retained by implementing the
findings
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