Carlson Rezidor Hotel Group and JDA: Creating Next Generation Revenue Optimization

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Innovation distinguishes a leader from a follower. Carlson Rezidor Hotel Group stands out as an innovator that’s setting new industry standards with its next-generation pricing and revenue management approach. Accordingly, Carlson was named as a finalist for the 2012 Franz Edelman Award for its exemplary achievement in leveraging both operations research and analytics to improve the real performance of its business. Carlson partnered with JDA Software to create Stay Night Automated Pricing (SNAP), a next-generation revenue management approach that maximizes revenue by recommending optimal prices based on real-time competitor rates and customers’ willingness to pay. The company’s one-two punch resulted in a consistent revenue premium of 2-4 percent for U.S. properties.

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Carlson Rezidor Hotel Group and JDA: Creating Next Generation Revenue Optimization

  1. 1. Fred Deschampsvice president, global revenue generation
  2. 2. in partnership with:Carlson Rezidor Hotel Group maximizes revenue through improved demand management and price optimization Franz Edelman Award for Achievement in Operations Research and the Management Sciences
  3. 3. briefintroduction to
  4. 4. 9 th Largest Hotel Company in the WorldLead position in Europe, India and Russia 53 341 640 # 1 # 1 36 32 41 98 52 1 # 26 2011 Total = 1,319
  5. 5. Six Brands Span The Entire Service Level Range Luxury room distribution by brand Upper 23% 0% Upscale 34% Upscale 15% 5% 23% Upper Midscale Midscale Missoni Park Plaza Radisson BLU Park Inn Economy Radisson Country Inns & Suites
  6. 6. smCountry Inns & Suites By Carlson Luxury Upper Upscale Upscale Upper Midscale Midscale
  7. 7. Park Inn by Radisson Luxury Upper Upscale Upscale Upper Midscale Midscale
  8. 8. Radisson Luxury Upper Upscale Upscale Upper Midscale Midscale
  9. 9. Radisson BLU Luxury Upper Upscale Upscale Upper Midscale Midscale
  10. 10. Hotel Missoni Luxury Upper Upscale Upscale Upper Midscale Midscale
  11. 11. Hotels Vary Greatly In Size cumulative frequency of room count by hotel 100% 80% 60% 50% 40% 20% 110 0% 20 60 80 40 100 140 160 180 220 240 260 300 320 340 380 400 420 460 480 500 120 200 280 360 440 0 >500
  12. 12. challenges at
  13. 13. Price Transparency Challenges Segmentation @Travel management Business travelcompanies search enginesBrand.com Leisure travelOn-line travel agencies
  14. 14. Transient Rates Are The Most Affected Column1 Negotiated Transient 47% 53%
  15. 15. Hotels Are Rarely Full U.S. average occupancy (STR) 64.0 63.0 63.1 62.8 62.0 60.0 60% 59.8 59.9 60.0 58.0 57.6 56.0 54.0 54.6 52.0 50.0 2005 2006 2007 2008 2009 2010 2011E 2012E
  16. 16. A Challenging Time To Price Correctly• Elementary pricing approach - Decentralized - No forecasts - No optimization• Revenue-dilutive pricing behaviors - Chasing competition - Locking on lowest price - Flat rates
  17. 17. Solution Approach and OR MethodologyPelin Pekgün, Ph.D.lead scientist and manager, analytical services, JDA software group
  18. 18. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  19. 19. Solution Overview Market Response Competitive Demand Forecasting Model Modeling • Demand Patterns • Customer Segmentation • Competitor prices and • Seasonality and Booking Pace • Price Elasticity Modeling availability • Special Events and External • Promotional Effects • Market Reference Price Factors Constraints Price • Capacity • Network effects Optimization • Business Rules Price Recommendations
  20. 20. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  21. 21. Time Series Demand Forecasting• Forecasting Horizon: 120 days• Demand Forecasting Unit (DFU): Arrival - Hotel Dates - Rate Segment - Length of Stay (LOS) 0 - Day of Week Days Left - Booking Interval• Methodology: Multiple Linear Regression - Seasonality (Fourier series) - Special Events (Concerts, Sporting events, City convention, etc.) 120 - Holidays (New Year’s Day, Christmas, Thanksgiving, etc.
  22. 22. Hierarchical Forecasting and Reconciliation Aggregate level: Parent DFU Hotel, Day of Week, Booking Interval Low level: Child (Opt.) Child (Opt.) Hotel, Day of Week, Booking Interval, DFU DFU Rate Segment, LOS• Higher number of terms for Parent DFU to capture seasonality• Lower number of terms for Child DFUs to address data sparsity• Top down reconciliation:
  23. 23. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  24. 24. Hurdle Rates vs. Optimal Rates “What price to charge to generate the most return on remaining inventory?”
  25. 25. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  26. 26. Why Stay Night Prices?• Variety of prices to optimize  difficult to review by the users: Rate Segments Arrival Dates Booking Intervals LOS Patterns Rate Menu Menu Stay Night Rate Segment Type Offset 4/14 $250 Base Rate Mult. 1.0 4/15 $220 Value Added Add. +$20 4/16 $180 10% Off Mult. 0.9 Arrival Date: 4/14, LOS=3; Rate = 250+220+180 = $650 Rates to Optimize 20% Off Mult. 0.8
  27. 27. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  28. 28. Price Optimization Formulation DFU price as a function Penalty to of the stay night price Unit Ancillary minimize price via rate menu Cost Revenue fluctuations Maximize Revenue minus Penalties State Trajectory of Bookings with Survival Price Sensitive Demand (Bookings-to-come) Function Capacity Constraint Business Rules to Bound Price ChangesFluctuations fromthe current price …with additional monotonicity and non-negativity constraints, etc.
  29. 29. Price Optimization Structure• Network optimization Demand for Stay Night 4/14• Linear price demand curve• Quadratic programming• CPLEX solver Arrival Date: 4/11 Departure Date: 4/15 4/11 4/12 4/13 4/14 4/15 4/16 4/17
  30. 30. Price Optimization Structure• Network optimization • Floating-point price recommendations• Linear price demand curve • Fixed rate segments controlled for availability• Quadratic programming • Group forecast pre-deducted from• CPLEX solver room availability• Supported by a discrete-time step simulator – Constrained Forecast Evaluation
  31. 31. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  32. 32. Market Reference Price (MRP) Computation Prepare Competitor Price Shops Construct MRP applying user weights Use MRP directly in price optimization Baseline Own Price Market Forecast Elasticity Reference Price
  33. 33. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  34. 34. Data-Driven Competitor Set IdentificationStep I. Attribute-based classification (Score-based Benchmarking) - Location - Size - Brand - Service - Amenities MAPE: 18.73% 4.61%
  35. 35. Data-Driven Competitor Set IdentificationStep II. Constrained regression (Automated Weight Estimation) – Dynamic price shops analyzed over time – Competitor weights constrained to add up to 1 User ComputedCompetitor Weights Weights A 0.2 0.27 B 0.2 0 C 0.2 0.46 D 0.2 0.27 E 0.2 0
  36. 36. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  37. 37. Statistical Price Elasticity Estimation• Regression based methods• Requires historical data - Demand history - Actual prices paid by customers - Competitive price information• Does not help with - Property initialization - Non-dynamic markets - New properties
  38. 38. Business-Driven Elasticity EstimationDistributes elasticity values across rate segments given• an ordering of segments based on expected price sensitivity• upper and lower elasticity bounds (e.g., -0.5 to -3.5)• a centering value (e.g., -1)• expected remaining demand forecasts RATE SEGMENT Median Rate Forecast Price Elasticity Weighted Forecast Value Added $145.00 100.22 -0.50 -50.11 Base Rate $127.00 944.97 -0.66 -619.55 Special Rate $122.55 102.00 -0.81 -82.75 10% Off $113.40 838.26 -0.97 -810.52 20% Off $101.60 1427.16 -1.12 -1602.04 25% Off $95.25 711.25 -1.28 -909.10 50% Off $49.50 59.72 -1.83 -109.51 Totals 4183.58 -4183.58 Total Weighted Forecast / Total Forecast = -1.00 (Centering Value)
  39. 39. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  40. 40. Implementation and DevelopmentSuresh Acharyavice president, analytical services, JDA software group
  41. 41. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  42. 42. …Must Get Results by 8am Automated Processing User Interactions Forecast Optimize Prices Manage System Demand Parameters Shop Process Competitive Inventory Data Rates Compute Process Market Booking Data Reference Prices Maintain Competitors Review Rate Update Recommendations Elasticities Upload Prices
  43. 43. Processing by Zone 6PM Users en route to work System optimizing prices Users working recs Regular rate uploads Users sleeping System idle
  44. 44. Processing by Zone 2AM Users working recs Regular rate uploads Users sleeping System optimizing prices Users en route to work System is idle
  45. 45. Processing by Zone 8AM Users sleeping System forecasting demand Users en route to work System forecasting demand Users working recs Regular rate uploads System forecasting demand
  46. 46. Processing by Zone 6PM Users en route to work System optimizing prices Users working recs Regular rate uploads Users sleeping System idle
  47. 47. Enterprise Architecture Internet and Users Application Database Load Balancer Servers Servers and Web Tier Grid Servers
  48. 48. Key Solution Components JDA Demand JDA Travel Price Optimization (TPO) Internet and Users Application Database Load Balancer Servers Servers and Web Tier Grid Servers
  49. 49. TPO is an extensible tool that has been applied in several industriesCarlson Rezidor, 2010 Passenger rail, 2011 Yacht rental, 2011 Golf , 2012
  50. 50. TPO is an extensible tool that has been applied inseveral industries Group Evaluation
  51. 51. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  52. 52. Driving Revenue Upwards – An Ongoing Journey Product and System Rollout - Worldwide Demand Forecasting Development2006 2007 2008 2009 2010 2011 2012 Price Optimization Prototyping Rollout - Americas Group Evaluation and Market Trial
  53. 53. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  54. 54. Benefits and User AcceptanceKathleen Mallerydirector, revenue optimization, planning and development
  55. 55. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  56. 56. gainingstakeholder trust
  57. 57. Conservatively Measuring Benefits SNAP benefits measurement timeframe
  58. 58. Test vs. Control Recommended Rates Manual Rates
  59. 59. Proved the Value – Simple Yet Effective Series 1 +5%105Hotels using the +4% Incremental Year-over-Year 104prototype realized a +3%103 Incremental2-4% incremental Revenue +2%102 year-over-year Value from toolrevenue improvement +1%101 Baseline 100 99 98 Control Hotels SNAP Pilot Hotels
  60. 60. Compliance Key To Capturing Revenue Opportunity 90% 89% Revenue Opportunity 88% 87% 86% 85% 84% 83% 82% 81% 80% 0% 25% 50% 75% 100% Overall SNAP Compliance Level
  61. 61. ease of use
  62. 62. SNAP - Stay Night Automated Pricing• Prioritize work• Manage exceptions• Be clear and intuitive
  63. 63. Prioritize Work• High priority dates are in red• Users determine priority levels
  64. 64. Manage Exceptions• Look only at changes• Drill down to determine reason Previous Recommended Rooms to be Sold at Rate Rate Recommended Rate Rooms to be Sold at Previous Rate
  65. 65. Be Clear and Intuitive
  66. 66. SNAP Now Has Many Supporters• 64% of hotels use SNAP• 183 hotels auto-pilot• 35% observed maximum unit revenue growth
  67. 67. User Testimonial “With SNAP we are now able to see our future rate mix and see what [rates] will be displaced. This allows for interjections before day- of-arrival, to assure we maximize every potential source of revenue.” Randy Smith Country Inn and Suites Dothan, Alabama
  68. 68. Solution Requirements Models seasonality and special events Works on high- and low demand nights Recommends stay night prices Minimizes day-to-day price fluctuations Keeps rates in line with the marketplace Identifies true competitors Understands price elasticity Makes results available by 8 am Not a custom solution for Carlson Rezidor only Progressive return on investment Gains the stakeholders’ trust Revenue Management in 30 minutes a day
  69. 69. Thank You“Through the first 39 days of 2012, ADR is 38% higher year -over-year while the average hotel in my market is up 6% - 10%. I attribute the majority of this increase to SNAP and its autopilot functionality.” – Himansu Patel, Owner of the Radisson Hotel in Akron/Fairlawn, Ohio“[SNAP] is easier than other brands have, we love it and have seen a GREAT lift in ADR due to SNAP, up 6.97 USD” – Andrew Behnke, GM & Owners Rep for Radisson Buena Park, California (manages multiple hotels brands)

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