1. Welcome B2C eCommerce Trends in Pricing Jonathan Wareham j.wareham@esade.edu

2. Assumption that electronic markets have less friction than comparable markets. Search costs lower Competition increases Average prices should fall, converging on market level Study of prices of books and CDs and software sold on internet: Higher prices & greater variance in electronic channel !!!!! Price Levels

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5. 1.Superior disc. pricing techniques: lower registration and menu costs 2.Heterogeneity: wine in store or restaurant Versioning 3.Temporal preference: consumer behavior and types 4.Imperfect information: bait and switch 5.Neural real estate: 5% sites/75% traffic 6.Market immaturity: eMarkets too young Possible Causes

6. P Q $1.00 1 Coke Fixed Prices

7. Consumers Surplus Dead Weight Loss MC

8. Get a little more revenue

9. 2nd Degree Price Discrimination “product line pricing”, “market segmentation”, “versioning” Gold Club, Platinum Club, Titanium Club, Synthetic Polymer Club First Class, Business Class, World Traveler Class Professional Version, Home Office

10. 3rd Degree Price Discrimination  The practice of charging different groups of consumers different prices for the same product  Examples include student discounts, senior citizen’s discounts, regional & international pricing, coupons

11. Maximize the Revenue ! Perfect (1 st degree) Price Disc.

12. Perfect Price Discrimination Price $ Quantity D 10 8 6 4 2 1 2 3 4 5 Profits:.5(4-0)(10 - 2) = $16 Total Cost MC

13. Prefect Price Discrimination  Practice of charging each consumer the maximum amount he or she will pay for each incremental unit  Permits a firm to extract all surplus from consumers  Difficult: airlines, professionals and car dealers come closest

14. Caveats:  In practice, transactions costs and information constraints make this is difficult to implement perfectly (but car dealers and some professionals come close).  Price discrimination won’t work if you cannot control three things: Preference profiles Personalized billing; (anonymous transactions lesson seller’s discriminatory power over consumers) Consumer arbitrage

15. What is different about this site?

16. 1.Internet double edged sword: Consumers enjoy lower search costs, but… eMarketers have superior tools to register your consumption patterns and price sensitivity 2.The end of fixed pricing??? Fixed pricing as an institution only 100 years old!! Developed in response to large scale economies/production models….with standard products !!!! Conclusions

17. Horizontal Differentiation  The game of location (proximity to customer’s tastes) Alice Bob 1/2 Alice Bob

18. Vertical Differentiation Price Quality High Low

19. 1.Versions 2.Timing and delays 3.Ease of use 4.Pathways into site 5.Segregation of markets and users 6.Analysis of click stream and previous purchasing history How???

20. Making Self-Selection Work  May need to cut price of high end  May need to cut quality at low end  Value-subtracted versions May cost more to produce the low- quality version.  In design, make sure you can turn features off!

21. How Many Versions?  One is too few  Ten is (probably) too many  Two things to do Analyze market Analyze product

22. Analyze Your Market  Does it naturally subdivide into different categories? AND  Are their behaviors sufficiently different?  Example: Airlines Tourists v. Business travelers  “This created visible differentiation in customer service. It was essential for our customers to see the perks that the others were getting.”

23. Analyze Your Product  Dimensions to version  High and low end for each dimension  Design for high end, reduce quality for low end  Low end advertises for high end in service industries – Cheap rates  High end – Flagship products - advertises for low end in many products.

24. Goldilocks Pricing  Mass market software (word, spreadsheets) Network effects User confusion  Default choice: 3 versions  Extremeness aversion  Small/large v. small/large/jumbo

25. Extremes Aversion  Bargain basement at $109, midrange at $179 Midrange chosen 45% of time  High-end at $199 added Mid-range chosen 60% of time  Wines Second-lowest price  “Framing effects”-example

26. Cross-Subsidies  Prices charged for one product are subsidized by the sale of another product  May be profitable when there are significant demand complementarities effects  Examples Browser and server software Drinks and meals at restaurants Long distance and local access Auto spare parts Razor & Blades Burger, fries, drinks Auto financing

27. Lessons  Version your product  Delay, interface, resolution, speed, etc.  Add value to online information  Use natural segments  Otherwise use 3  Control the browser, access, comparisons, etc.  Bundling & cross subsidies may reduce dispersion

28. Down & Dirty  First degree (perfect) price discrimination “market of one”  Second degree price discrimination “product line pricing”, “market segmentation”, “versioning”  Third degree price discrimination “different prices to different groups” Other definitions in literature…

32. RM coming of age t Airline deregulation in the U.S. t People Express vs. American Airlines t Edelman Award: RM for AA $1.4 billion in 3 years t virtually every airline has implemented RM t National Car Rental (vs. GM) t Edelman Award: RM for SNCF t AA: $1 billion incremental revenues from RM t Marriott Int’l RM: 4.7% increase in room revenue t Deregulation Europe: telecom, media, energy … t e-distribution supports dynamic pricing & profiling t Dell, Amazon & Coca Cola experiment dynamic pricing t RM spans wide range of industries … 1985: 1978: 1992: 2000-01: 1997: 1999: 2003:

33. RM Evolution 1980 Airlines 1985 Rail Transp. 1990 Hotels Car rental 2000 Media Energy Cruise lines Telco/ISP 1995 Tour Operators Freight, Cargo Sports Parks Entertainment HealthCare/ Hospitals Insurance/ banking Manufact. Retailers

34. Revenue Management Strategies & tactics for OPTIMIZING PROFITS based on DYNAMIC PRICE INVENTORY SETTING CONTROL under real-time, disaggregate updating of DEMAND FORECASTS

35. The RM Challenge Arrivals of high paying customers… Closer to departure! Arrivals of low paying customers …Earlier!

37. Overbooking metrics  Service level based:  P(denial) =0.05  E[#denials]=2  Etc.  Cost based: assign a cost to each and optimize Overbooking cost (airlines): Direct compensation cost Provision cost of hotel/meal Reaccom cost (another flight/airline) Ill-will cost (~ “lifetime customer value”)

38. Industries Overbooking  Airlines  Hotels  Car rentals  Education  Manufacturing  Media No Overbooking  Restos  Movies, shows  Events  Resort hotels  Cruise lines

39. ...Decisions Are Not Always “Rational”

40. Price Perception Issues are Complex...  More Acceptable Pricing Product-Based Open Discretionary Discounts and Promotions Rewards  Less Acceptable Pricing Customer-Based Hidden Imposed Surcharges Penalties

42. CRM  “Attract & retain customers”  maximize profit from each customer  Segment by customer LTV  Price/availability= fct. of forecasted customer LTV to the organization  Ignores capacity issues and opportunity costs (displacement)  Wealth of data DPRM  “generate revenue”  maximize profit from available assets  Segment by customer WTP  Price/availability = fct. of forecasted demand & available supply  Ignores customer value issues and long term revenues  Quantifiable value Maximize long-term profits

43. CRM & RM

44. Variables to track  Actual win or loss  Number of days played  Credit history  Length of stay at hotel  Individual spending preferences  Demographics  Psychographic profiles

45. Theoretical Revenue  Theoretical = (total amount wagered) X (house advantage) 100$ hand x 10 hours x 100 Hands/hour x.01 ( house adv. 49/51 ) = $1,000

46. Can you track every single person???  Not always  Difficult in table games  Theoretical = (total amount wagered) X (house advantage) Where.. Total amount wagered = estimated average bet x estimated time played

47. Future estimates…  ADT = Average Daily Theoretical Revenue  Assumes that this level is constant  Multiply by estimated # of days of future trip to gain value  Combined with CRM data on consumption of food and beverage, entertainment, pshychographics, etc

48. Rooms, a scarce resource  Heads in beds: make money on gaming  Comp. Rooms: traditionally a fixed number of rooms given to big gamblers  Used averages to cost out, did not dynamically look at “opportunity cost”

49. ReInvestment amount  % of the ADT  ADT $1,000  Reinvestment amount = 30%  = $300  Total value of the room, F&B, Entertainment, etc. must be less than the  Room 200, F&B 100, Ent. 80..more than ADT x reinvest.  Ergo…try and sell room..  Sophisticated applications use dynamic pricing to asses opportunity costs..

50. Requirements  RM – Yield management like the airlines..  Player tracking systems..Use cards like Harras, to register all activity and psychographic profiles  POS resturants, theaters, spas, retail stores, entertainment, etc…  CRM integrates all of the above!!  Statistical analysis and optimization applications.