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

1. Fred Deschamps vice president, global revenue generation

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. brief introduction to

4. 9 th Largest Hotel Company in the World Lead position in Europe, India and Russia 53 341 640 # 1 # 1 36 32 41 98 52 1 # 26 2011 Total = 1,319

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. sm Country Inns & Suites By Carlson Luxury Upper Upscale Upscale Upper Midscale Midscale

7. Park Inn by Radisson Luxury Upper Upscale Upscale Upper Midscale Midscale

8. Radisson Luxury Upper Upscale Upscale Upper Midscale Midscale

9. Radisson BLU Luxury Upper Upscale Upscale Upper Midscale Midscale

10. Hotel Missoni Luxury Upper Upscale Upscale Upper Midscale Midscale

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. challenges at

13. Price Transparency Challenges Segmentation @ Travel management Business travel companies search engines Brand.com Leisure travel On-line travel agencies

14. Transient Rates Are The Most Affected Column1 Negotiated Transient 47% 53%

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. 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. Solution Approach and OR Methodology Pelin Pekgün, Ph.D. lead scientist and manager, analytical services, JDA software group

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. 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

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. 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:

24. Hurdle Rates vs. Optimal Rates “What price to charge to generate the most return on remaining inventory?”

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

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 Changes Fluctuations from the current price …with additional monotonicity and non-negativity constraints, etc.

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. 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

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

34. Data-Driven Competitor Set Identification Step I. Attribute-based classification (Score-based Benchmarking) - Location - Size - Brand - Service - Amenities MAPE: 18.73% 4.61%

35. Data-Driven Competitor Set Identification Step II. Constrained regression (Automated Weight Estimation) – Dynamic price shops analyzed over time – Competitor weights constrained to add up to 1 User Computed Competitor 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

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. Business-Driven Elasticity Estimation Distributes 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)

40. Implementation and Development Suresh Acharya vice president, analytical services, JDA software group

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. Processing by Zone 6PM Users en route to work System optimizing prices Users working recs Regular rate uploads Users sleeping System idle

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. 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. Processing by Zone 6PM Users en route to work System optimizing prices Users working recs Regular rate uploads Users sleeping System idle

47. Enterprise Architecture Internet and Users Application Database Load Balancer Servers Servers and Web Tier Grid Servers

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. TPO is an extensible tool that has been applied in several industries Carlson Rezidor, 2010 Passenger rail, 2011 Yacht rental, 2011 Golf , 2012

50. TPO is an extensible tool that has been applied in several industries Group Evaluation

52. Driving Revenue Upwards – An Ongoing Journey Product and System Rollout - Worldwide Demand Forecasting Development 2006 2007 2008 2009 2010 2011 2012 Price Optimization Prototyping Rollout - Americas Group Evaluation and Market Trial

54. Benefits and User Acceptance Kathleen Mallery director, revenue optimization, planning and development

56. gaining stakeholder trust

57. Conservatively Measuring Benefits SNAP benefits measurement timeframe

58. Test vs. Control Recommended Rates Manual Rates

59. Proved the Value – Simple Yet Effective Series 1 +5%105 Hotels using the +4% Incremental Year-over-Year 104 prototype realized a +3%103 Incremental 2-4% incremental Revenue +2%102 year-over-year Value from tool revenue improvement +1%101 Baseline 100 99 98 Control Hotels SNAP Pilot Hotels

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. ease of use

62. SNAP - Stay Night Automated Pricing • Prioritize work • Manage exceptions • Be clear and intuitive

63. Prioritize Work • High priority dates are in red • Users determine priority levels

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. Be Clear and Intuitive

66. SNAP Now Has Many Supporters • 64% of hotels use SNAP • 183 hotels auto-pilot • 35% observed maximum unit revenue growth

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

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)

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

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