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Intersection between rm dm analytics alpha 1
1.
2. Hoteliers are under increasing pressure to extract knowledge and insights from vast amounts of
data in order to enhance business strategies and optimize the customer experience. It’s not news
that the better the collaboration between marketing and revenue management, the more
profitable and successful the enterprise.
But what does this really mean when it comes to data and analytics? What data should be shared
and incorporated across disciplines? How can/should systems seamlessly share and utilize data in
real time? How can predictive analytics drive profits at every step of the guest’s journey?
When it comes to the strategic use of data and analytics across digital marketing and revenue
management, there are often more questions than answers – a trend this session aims to
reverse.
3. The Intersection of Revenue Management
&
Digital Marketing Analytics
Where Did We Come From?
7. • General statistics – Visitors / Page Views / Duration / Bounce Rates
• Mainly SEO driven efforts – ‘Silver Bullet’ tactics
• Impact driven strategies – no real ‘source’ metrics, lots of ‘implied’
conversion data based on promo-code tracking
• ‘Challenging’ ability to track ad service accuracy
• Fad or trend - for a channel representing 3-5% total revenue
• OTA’s are the ‘enemy’ – we’ll never give them the same inventory we
did in ‘01 …errr ‘08
Digital Marketing Silo: First 20 Years
9. Reporting is “the process of organizing data into
informational summaries in order to monitor how
different areas of a business are performing.”
...Analytics is “the process of exploring data and
reports in order to extract meaningful insights, which
can be used to better understand and improve
business performance.”
The Differentiation Between Reporting & Analytics
10. An Ever Changing Revenue / Profit Optimization Team
Where Did We Come From? Current State
Revenue Management
Finance
Sales
Operations
Profit
Optimization
Revenue
Management
Finance
SalesOperations
Marketing
11. Framework For Successful Analytics: Unlocking Capability
Collaboration
Common
Vernacular
Integrity in Data
TransparencyBuild Trust
Enabling Business
Decisions
Continuous
Improvement
12. Collaboration
• Working together as one unified
team across all departments
involved with profit optimization
• Establishing a committee to
govern the next steps of the
framework
13. Common Vernacular
• Common Language
• Does customer segmentation really mean
the same thing as marketing
segmentation?
• Common KPI’s
• Are stakeholders using the right KPI’s that
generate holistic views of performance at
the highest level?
• Common Goals
• Lowering the media budget to optimize
profit might not be what marketing wants
to hear for next year’s budget, but do our
holistic KPI’s show that we’ve reached the
point of diminishing returns?
14. Integrity In Data
• Identify what data we have from
each respective area that we can
combine into our data model
• Make sure the data is accurate,
consistent & correctly represents
the common vernacular
established
• Do not practice ‘statistical
bending’
15. Transparency
• Be open to all team members
about what the data “is” and
what it “isn’t”
• Build visual context around what
the team are looking at
• Understand what the team has
now and what the team is trying
to build for the future
16. Build Trust
• Buy-in from stakeholders
• Trusting that the process works
towards hotel or enterprise
profit optimization and not your
department as a silo
• Acknowledge successes and
share failures
• Own what is yours
17. Enable Business Decisions
• See the big picture across all
data sets being sourced; both
internal & 3rd party
• Holistic approach to enhanced
enterprise / property decision
making
• Grow your business profitably,
not necessarily your department
18. Continuous Improvement
• What did we learn?
• How can we improve next time
around?
• Fail fast
• Team share failures and debrief
them, not hide them
• Define performance threshold
metrics with concrete decision
models
• Keep an activity log
19. The Result: A New Visual Way To Monitor Your Holistic KPI’s
20. Now What? Optimizing Mix Based on Channel Economics
Through which channels can a guest X
book a room?
Given variation in guest economics by channel and property, which channels should we incentivize guest X
to book through?
PER ROOM NIGHT
How much does it cost to acquire guest
X for that channel? $XX $XX $XX $XX
How much does it cost to process guest
X’s room reservation? $XX $XX $XX $XX
How much does guest X spend on a
room?* $XX $XX $XX $XX
Once guest X arrives, how much is spent
on-property? $XX $XX $XX $XX
Tracked Contribution Margins $XX $XX $XX $XX
Brand.com
21. Understanding Your Channel Behaviors
Booking Channel
How do guests book rooms?
What is the average profitability of a guest
booking
through each channel?
“Products” to Acquire Customers
What do we use to attract different guests?
• Direct vs. Indirect (external & partners)
• Online (e.g., websites) vs. offline (e.g., Call
Center, Front Desk, Travel Agents)
• Rates (e.g., Wholesale, Group, BAR, etc.)
• Promotion (% off, value add, etc.)
• Media (online, offline, etc.)
Customer Type
What type of customer is booking rooms in each
channel? How do his / her
behaviors change?
Identify customer segments within each
channel based on variations in their spending
behaviors
• Loyalty vs. non-loyalty customers
(known vs. unknown)
• Ancillary spending habits (dining, spa,
entertainment, etc.)
Known
Unknown
22. WHAT ASSUMPTIONS DO
Person 1
• Born in 1948
• Grew up in England
• Married twice
• 2 children
• Successful in Business
• Wealthy
• Spends winter holidays in the Alps
• Loves dogs
Person 2
• Born in 1948
• Grew up in England
• Married twice
• 2 children
• Successful in Business
• Wealthy
• Spends winter holidays in the Alps
• Loves dogs
Profiling: What Assumptions Do
23. Future State: The Path To Conversion Optimization & True Personalization
Profit
Optimization
Revenue
Management
Finance
Sales
Operations
Marketing
IT
25. Why Technology & Automation Are Essential to Conversion Optimization
Booking Engine
PMS
CRS
RMS
CRM
3rd Party Data
Services
Campaign
Management
Conversion Optimization
&
True Customer Personalization
• PMS delivers historical data on known
guest behaviors (spending history, room
preferences, etc.)
• CRS delivers availability for dates of
interest
• RMS delivers demand forecast & rate
recommendations by room type
• CRM delivers customer worth & detailed
customer data for known customers
• 3rd party data services deliver detailed
information about the known or unknown
guest based on cookies which include flight
information, searches, preferences & more
• Booking engine will be personalized at the
customer level to take advantage of all
source systems & data with an offer
tailored to that particular guest for that
particular trip in real-time
26. WHAT ASSUMPTIONS DO
Future State: Brand.com Guest Value Proposition
Brand.com 3rd Parties
Personalized
loyalty pricing
based on
customer value
Personalized
room sort order
based on
preferences
Personalized
packages & add-
ons based on
CRM & 3rd party
data
Personalized
promotional
offers based on
CRM & 3rd party
data
Additional
trigger-based
pre-arrival
opportunities
Seamless
experience from
booking to
mobile check-in
27. WHAT ASSUMPTIONS DO
Future State: Brand.com Operator Value Proposition
Brand.com 3rd Parties
Personalization
enhances the
customer
experience
Operators finally
have a viable
opportunity to
shift share from
3rd parties
Investments in
technology &
automation will
lead to higher
margins
Operators can
better understand
their customers’
wants and needs
Team can test,
measure and
learn what really
drives conversion
Have this slide up while people are walking into the room.
Funny montage of some screen shots of early revenue / marketing things.
Revenue management and digital marketing are both relatively new to the hotel landscape in the grand scheme of things. They were developed and existed in silos for quite some time, operating independent of each other with separate goals and KPI’s.
Pricing used as the main lever for optimizing hotel demand to meet room revenue forecasts
Reports were the main foundation for analysis; analytics not yet a major focus
Demand data not readily accessible or reliable
Room forecasts based solely on pace and % growth
Pricing not based on elasticity of demand but rather the comp set
Customer segmentation buckets too wide to differentiate customer value
Optimal mix based solely on top-line room revenue potential
Hotel distribution landscape fragmented
No integration with Marketing to align on strategies or tactics
Basic traffic monitoring
No real building block strategies, but rather a pursuit of ‘what is the next trick to fool Google’ conversion based on ‘implied metrics
All data interpretations are based on engagement metrics with the website of ad traffic, no real origin tracking in place, or validation of exposure of ad’s, and no real metrics with conversion data that is accurate.
Lots of ghost reporting on ad service platforms
In our current state, revenue management and digital marketing are beginning to work together; especially from a data perspective. KPI’s have begun to encompass a more holistic view of the operation and the conversation between the two disciplines are getting more sophisticated.
Reporting – Informational summaries, monitoring
Analytics – Visual data exploration, meaningful insights
_______________________________________
What data should be a part of both? KPI’s
In the past few years, the revenue strategy meeting and its members have changed to better align with the growing sophistication of our data. No longer are we solely focused on top-line revenue generation; the focus is on profit. Marketing is also now a part of the profit optimization team and can bring key insights into the conversation.
Now that the right players are in the room together, how can we unlock capabilities and move from a culture of reporting to a culture of analytics?
In order to bring analytics to the forefront of data-driven decision-making, we need to build a framework for success. Let’s go over each of these steps in detail.
Collaboration is the first step in unlocking capabilities for your analytics.
Working together by establishing a committee to oversee the next steps is crucial. The Profit Optimization Team in it’s current state is a great starting point. Stakeholders from either IT & Information Management might be a good add as well.
Common Vernacular is the single most important step to unlocking capability. Why?
Metrics that span across divisions not specific to one. Example ROAS
Ex- 5:1 ROAS is not making EBITA / 10:1 on a special offer rate below forecasted ADR is not yielding to successful revenue levels
4 out of 5 dentists agree – 20% disapproval rating
Before any strategic development, a success goal must be defined, not a moving target on the end of a ‘stick’, or only ‘better than what we have now’
Extra slide potential if we have to much content to share
So far we have only approached the booking process in future state. What about the entire customer journey? What do we do with all of the data at our fingertips? How can we ensure that this information is available in real-time throughout all departments pre, during & post stay? How can we automate where possible and capitalize on human interactions where automation is not?
Searching & Booking
Ann is looking for a weekend getaway at a hotel with a spa. She begins her search using metasearch, online travel agencies & review sites. After compiling her shortlist, she decides on a property. One of her decision points was based on having stayed with this brand before on previous occasions in other cities; she’s already a known guest. Before choosing where to book, she heads to brand.com to see if their rates are in parity with other offers around the web.
The booking engine begins an API call to gather information to personalize the booking engine. From previous stays on her profile, the room sort is optimized to show Deluxe rooms first. A spa package is displayed first on main availability given her search criteria from 3rd party cookie data. She ultimately decides to book the “Members Only” rate on brand.com in the Deluxe room type, giving her the best possible offer that she was able to find. The reservation add-ons highlight spa treatments, but she needs to do more research on options before committing to a treatment at this time.
Mobile App
In the lead up to the weekend she is booked for her getaway, Ann visits the hotel’s mobile App and searches through spa treatments. She spends time looking at a few options and reads through them vigorously. They all sound so good! After spa, she heads over to view photos of her Deluxe room type, but also checks out some photos of a few Suite options.
This mobile activity information is appended to her profile in real-time.
Trigger Based Marketing
7 days prior to arrival, campaign management deploys a trigger based marketing alert on her app with customized offerings based on her mobile activity. The spa treatments she had looked at were all there, as well as a suite upgrade offer. She decides to book her spa appointment through the app, which displayed real-time spa availability over her stay dates and was showing only a small number of appointments available.
The success of the TBM campaign in converting a spa booking, but not a suite upgrade is recorded to her profile in real-time.
Arrival
During lineup, the Front Desk Manager goes over the upsell strategies for the day based on personalized increment suggestions from the RMS as well as previous offers made for this stay through TBM. Once Ann arrives at the hotel; she is offered an upgrade to the suite she was interested in at $10 less than what she was offered 7 days ago, which she accepts.
We have successfully driven conversion on the Deluxe room type via brand.com and now an upsell into a Suite via the Front Desk.
Play
Ann relaxes in the spa on Saturday. As she exits the spa, Ann drops by the concierge to talk about dinner plans. The concierge, with access to real-time profile information, sees little information on Ann with respect to dining preferences and uses this as a great opportunity to build on her profile for future stays. Their conversation about her food preferences for the evening and in general are cordial, and she decides to dine at the award-winning Italian restaurant at the resort. After the conversation and booking, the concierge adds her general food preferences to her profile.
Attend
On the way to dinner, Ann tweets “Feeling so relaxed after a great spa treatment at “X” hotel”.
The social marketing team likes her tweet and direct messages back to Ann to invite her to a morning yoga class. This team uses predictive analytics provided by the Profit Optimization committee to match cross-sell opportunities based on activities. Ann joins the class and has a fantastic start to her last day in the resort. She is also really impressed with the personal touch there.
Depart
Ann checks out after a great experience at the hotel & spa and immediately writes a positive review upon returning home. All of the touch points from her stay, folio & human interaction, are appended to her profile in real-time.
A few months later she receives an offer for a week-long getaway, which she is planning to redeem.
Searching & Booking
Ann is looking for a weekend getaway at a hotel with a spa. She begins her search using metasearch, online travel agencies & review sites. After compiling her shortlist, she decides on a property. One of her decision points was based on having stayed with this brand before on previous occasions in other cities; she’s already a known guest. Before choosing where to book, she heads to brand.com to see if their rates are in parity with other offers around the web.
The booking engine begins an API call to gather information to personalize the booking engine. From previous stays on her profile, the room sort is optimized to show Deluxe rooms first. A spa package is displayed first on main availability given her search criteria from 3rd party cookie data. She ultimately decides to book the “Members Only” rate on brand.com in the Deluxe room type, giving her the best possible offer that she was able to find. The reservation add-ons highlight spa treatments, but she needs to do more research on options before committing to a treatment at this time.
Mobile App
In the lead up to the weekend she is booked for her getaway, Ann visits the hotel’s mobile App and searches through spa treatments. She spends time looking at a few options and reads through them vigorously. They all sound so good! After spa, she heads over to view photos of her Deluxe room type, but also checks out some photos of a few Suite options.
This mobile activity information is appended to her profile in real-time.
Trigger Based Marketing
7 days prior to arrival, campaign management deploys a trigger based marketing alert on her app with customized offerings based on her mobile activity. The spa treatments she had looked at were all there, as well as a suite upgrade offer. She decides to book her spa appointment through the app, which displayed real-time spa availability over her stay dates and was showing only a small number of appointments available.
The success of the TBM campaign in converting a spa booking, but not a suite upgrade is recorded to her profile in real-time.
Arrival
During lineup, the Front Desk Manager goes over the upsell strategies for the day based on personalized increment suggestions from the RMS as well as previous offers made for this stay through TBM. Once Ann arrives at the hotel; she is offered an upgrade to the suite she was interested in at $10 less than what she was offered 7 days ago, which she accepts.
We have successfully driven conversion on the Deluxe room type via brand.com and now an upsell into a Suite via the Front Desk.
Play
Ann relaxes in the spa on Saturday. As she exits the spa, Ann drops by the concierge to talk about dinner plans. The concierge, with access to real-time profile information, sees little information on Ann with respect to dining preferences and uses this as a great opportunity to build on her profile for future stays. Their conversation about her food preferences for the evening and in general are cordial, and she decides to dine at the award-winning Italian restaurant at the resort. After the conversation and booking, the concierge adds her general food preferences to her profile.
Attend
On the way to dinner, Ann tweets “Feeling so relaxed after a great spa treatment at “X” hotel”.
The social media team likes her tweet and direct messages back to Ann to invite her to a morning yoga class. This social media team uses predictive analytics provided by the Profit Optimization committee to match cross-sell opportunities based on activities. Ann joins the class and has a fantastic start to her last day in the resort. She is also really impressed with the personal touch here.
Depart
Ann checks out after a great experience at the hotel & spa and immediately writes a positive review upon returning home. All of the touch points from her stay, folio & human interaction, are appended to her profile in real-time.
A few months later she receives an offer for a week-long getaway, which she is planning to redeem.
The full customer journey as an ecosystem of analytics formed by the partnership of the Profit Optimization Team. The intersection of analytics among disciplines in our future state can be automated in real-time or can be throughout a guest’s journey stay as a human interaction. Each area of excellence benefits from conversion optimization throughout all areas of the enterprise.