Intersection between rm dm analytics alpha 1
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Hoteliers are under pressure to extract insights from vast amounts of customer data to optimize strategies and experiences. While marketing and revenue management collaborate better when sharing data, questions remain around what data to share, how to share it in real-time, and how predictive analytics can drive profits. This session aims to address these questions and advance the intersection of revenue management and digital marketing analytics.
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Intersection between rm dm analytics alpha 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?
- 4. Where Did We Come From?
- 6. Revenue Management Silo: First 20 Years
- 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
- 24. WHAT ASSUMPTIONS DO The Hurdle From Current State to Future State From To
- 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
- 28. WHAT ASSUMPTIONS DO Conversion Does Not End After Booking! We Have Only Just Begun…
- 29. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Yoga Ann Roberts, 45 VP, Marketing Frequent Flyer & Loyalty Program Member Offer Hotel App Theatre The Future of the Customer Journey
- 30. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Booking engine is personalized to Ann’s search and preferences based on conversion algorithm. Ann’s profile is updated with search and booking info.
- 31. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Mobile activity appended to profile for use in trigger-based marketing & upsell algorithm
- 32. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Ann’s profile information flagged her for a suite upgrade offer upon arrival at a more competitive price than TBM.
- 33. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Real-time decisioning assists concierge with appropriate recommendation based on flow of business, guest preferences & behavior
- 34. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Real-time decisioning delivers appropriate offer for team to tweet. Social media monitoring flags tweet from Ann for response.
- 35. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Ann’s customer profile is updated with latest stay information, which drives the return email offer.
- 36. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Reception Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Marketing Operations Guest Profile / CRM RM RM RM RM Marketing RM RM
- 37. ARRIVE RELAX ATTEND DEPART SEARCH Spa Upgrade Dine Shop Yoga Ann Roberts, 42 VP, Marketing Frequent Flyer & Loyalty program member Offer Hotel App Theatre Data Results Results Data Data Results Data Results ….Enabled By Big Data & Predictive Analytics
Editor's Notes
- 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.