Intersection between rm & dm analytics

Editor's Notes

• #3: Have this slide up while people are walking into the room.
• #4: MK
• #5: LG Funny montage of some screen shots of early revenue / marketing things.
• #6: LG 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.
• #7: MK 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
• #8: LG 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 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
• #9: LG 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.
• #10: MK The current state is where we are beginning to see a clear differentiation between reporting & analytics. Reporting – Informational summaries, monitoring Analytics – Visual data exploration, meaningful insights
• #11: MK In the past few years, the revenue strategy meeting and its members have evolved to better align with the growing sophistication of our data and our complexity of our business. 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?
• #12: MK In order to bring analytics to the forefront of data-driven decision-making, we need to build an analytical framework for success. Let’s go over each of these steps in detail.
• #13: LG Collaboration is the first step in unlocking capabilities for your analytics. Working together in establishing a committee to govern 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.
• #14: MK Common Vernacular is the single most important step to unlocking capability. Why? Because each stakeholder from each department is an expert in his or her own area. They speak and are comfortable speaking to measureable data points in their own areas. In order to build a common language with KPI’s and enterprise wide goals, common vernacular must be established. 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
• #15: LG Now that we have established our common KPI’s and goals, we can begin to plan the data modeling process. IT or information management are good partners during this step of the framework. Each area of the business has data sources which need to be combined or joinable. Some data might be available via direct query (database) and some might be flat files (Excel, Access). This step of the framework governs the process of ingesting the data into a model that’s accurate. 4 out of 5 dentists agree – 20% disapproval rating
• #16: MK Transparency cannot be practiced until we achieve data integrity. Once we have it, we know what our data capabilities are and what they are not. It’s important to remain agile during this phase. You might not have every data point you want available in version one due to data aggregation or lack of reliability, but these things can be added into the data integrity process in future versions. Do not sacrifice getting a product out to market just because you do not have everything the first time around… 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’
• #17: LG We’re at the point in our framework cycle where we begin to see the fruits of our labor. With the new enterprise-level KPI’s and access to insights across all departments, stakeholders see the opportunities ahead of us.
• #18: MK Bring your data model to life. There are many options for visual analytics software to layer your data model onto. Pick one and begin exploring your new KPI’s in ways you haven’t before. See your marketing KPI’s layered on top of your revenue management KPI’s. The benefits of seeing a holistic picture will help towards your ultimate goal of optimal mix and profit optimization across the enterprise.
• #19: MK Schedule hourly or daily refreshes of your data model as you see fit. Setup alerts to changing business conditions so that decisions can be made to align with your goals. You now have the capabilities to create strategies and drive tactics to reach your goals. See how revenue management decisions impact your digital marketing KPI’s and vice versa.
• #20: LG Run the cycle at least once or twice per year in order to unlock new capabilities. Keep notes on what you’d like to see included via the next version of the data model. A new source perhaps?
• #21: MK As we begin to monitor our business, we now have the data points needed in order to better understand our channel economics. In order to truly understand our optimal mix strategy, we need to understand the variation in our guest economics by channel. The tracked contribution margin will help us understand what channels to incentivize.
• #22: MK In addition to understanding channel profitability, we’ll need to understand the behaviors of each channel in order to identify customer segments within each channel. Does channel “X” spend twice as much in our F&B outlets than channel “Z”? Is one particular channel better represented as a drive-by channel, utilizing paid parking charges for over 90% of room nights? These types of channel behaviors are important as we begin to build towards profit optimization, so that we know what channel and what customer segments to target each day.
• #23: LG On the previous slide we discussed the need to better understand channel behaviors to identify customer segments. In our current state within the intersection of RM & DM analytics, our acquisition strategy still heavily relies on the bucketing of behaviors into segments. For known guests, we might understand spending behaviors from previous stay history. For the unknown guests, much of the bucketing today relies on profiling and assumptions. Will everyone bucketed into the same customer behavior respond and convert at the same rate on the same offer? Probably not as we see above. Some might seem similar demographically, but in reality their spending behaviors will be night and day. As we peak into the future and begin to unravel technological capabilities, we’ll be able to move from bucketing customer behaviors within channels to personalizing the direct booking path for each individual.
• #24: LG Welcome to the future state. Silos no longer exist between revenue management and digital market analytics. Finance, sales & operational data are flowing through our data model as well and we’ve been able to monitor and strategize around our holistic views of performance. As we get into a rhythm, we realize that there are still opportunities to automate some of our strategies. The future state is really about taking the learnings from our analytics and applying them throughout the guest journey.
• #25: MK In current state, we had our analytics readily available to monitor the business. For RM, analytics might have lead us to manually making rate changes for a particular day in the RMS. For DM, analytics might have to lead us to changing the room sort order on a particular page for increased conversion on brand.com The data is there for us to monitor our business, but what if we could build these “actions” that we currently take from our insights into algorithms within the booking engine? What if the booking engine was actually a clean slate with all of these sources of data from our model and more connected and learning from each other to personalize the booking path at the customer level? With investments in your technology stack, these are the types of capabilities we’ll uncover in the future state.
• #26: MK In our future state, conversion optimization and true personalization at the customer level are our main focus. Adding a service layer to the tech stack and customizing our booking engine to react to each customer individually will, for the first time, give our direct channel a better customer experience than some of our third party partners. Imagine having the power to shape what rate is offered to known (based on customer worth) & unknown guests, what room types to show first and last, what promotions or packages display & what add-ons to display based on their loyalty profile (if applicable), what activities they booked and/or searched for before their visit to your booking engine, 3rd party data services and more. This level of personalization will give us the ability to test and learn in real-time what conversion algorithms convert the highest and allow us to automatically revise what is recommended via machine learning.
• #27: LG Investments in technology is a must for hoteliers in the future. The guest value proposition as well as the operator value proposition is very strong. Hoteliers also run the risk of not having the same capabilities of our competitors, both on the hotel side and the 3rd party side. For guest value proposition, it really comes down to a personalized and seamless booking experience through Brand.com. All of his preferences as a known guest and everything we are now able to unlock as an unknown guest will, for the first time, factor into the booking experience in real-time.
• #28: LG For the operator, value proposition is a long term ROI, but a very worthwhile and necessary endeavor. Optimizing our business mix in favor of one of our higher profit channels like Brand.com will have very positive impacts on EBIDTA. Building customer loyalty and enhancing the customer journey will have huge positive implications for branding. Having the ability to measure and test in real-time will lead to optimized conversion and high margins overall. Converting these customers into known guests so that we continue using our analytical framework throughout the customer journey is also a key benefit to the operator.
• #29: LG 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?
• #30: MK Meet Ann Roberts, 45 years of age, VP of Marketing. She’s a frequent flyer & a member of our brand’s loyalty program. We’re going to take a look at the future of the customer journey through her lens. We’re going to take a look at her experience through the earliest stages of her journey (search) to the latest stages (re-engagement).
• #31: MK 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.
• #32: MK 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.
• #33: MK 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.
• #34: MK 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.
• #35: MK 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.
• #36: MK 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.
• #37: MK 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. The digital marketing team experiences rewarding conversion and ROAS growth through their departmental KPI’s, not at the expense of other areas. The operations teams saw their highest guest satisfaction scores to-date. Upsell revenues at the front desk are the highest they’ve ever been with precise targeting of offers from the intersection of our analytics. Outlet occupancy at the spa & restaurants are consistently better than the current state. Tipped employees are seeing additional earnings from their engagement with the CRM and other analytics available to them. The revenue management and finance teams see their highest level of EBITDA after optimizing business mix and driving profitability through all areas of the business.
• #38: MK All of the capabilities we can unlock begin with the intersection of analytics across all areas of our business to predict guest behavior and capitalize on this personalization through conversation optimization at all points of the customer journey.
• #39: LG