Welcome B 2 C e Commerce Trends in

Welcome B 2 C e. Commerce Trends in Pricing Jonathan Wareham j. wareham@esade. edu

Price Levels 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 !!!!!

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Possible Causes 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: e. Markets too young

Fixed Prices P $1. 00 1 Coke Q

Fixed Prices Consumers Surplus Dead Weight Loss MC

Get a little more revenue

2 nd Degree Price Discrimination n n “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

3 rd Degree Price Discrimination o o 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

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

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

Prefect Price Discrimination o o o 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

Caveats: o o 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: n Preference profiles n Personalized billing; (anonymous transactions lesson seller’s discriminatory power over consumers) n Consumer arbitrage

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Conclusions 1. Internet double edged sword: • Consumers enjoy lower search costs, but… • e. Marketers 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 !!!!

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

Vertical Differentiation Price High Low Quality

How? ? ? 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

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

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

Analyze Your Market o o o Does it naturally subdivide into different categories? AND Are their behaviors sufficiently different? Example: Airlines n o 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. ”

Analyze Your Product o o o 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.

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

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

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

Lessons o o o o 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

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

RM coming of age 1978: t Airline deregulation in the U. S. 1985: t People Express vs. American Airlines 1992: t 1997: 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) Edelman Award: RM for SNCF AA: $1 billion incremental revenues from RM t Marriott Int’l RM: 4. 7% increase in room revenue t 1999: Deregulation Europe: telecom, media, energy … t e-distribution supports dynamic pricing & profiling t 2000 -01: t Dell, Amazon & Coca Cola experiment dynamic pricing 2003: t RM spans wide range of industries …

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

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

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

Overbooking metrics o Service level based: o o P(denial) =0. 05 E[#denials]=2 Etc. Cost based: assign a cost to each and optimize Overbooking cost (airlines): n n Direct compensation cost Provision cost of hotel/meal Reaccom cost (another flight/airline) Ill-will cost (~ “lifetime customer value”)

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

. . . Decisions Are Not Always “Rational” Tickets; $7. 95 Tickets; $6. 95 $1. 00 Discount for Children & Seniors $1. 00 Extra for Middle Aged People

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

CRM o o o DPRM “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 o o o “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

CRM & RM

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

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

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

Future estimates… o o 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

Rooms, a scarce resource o o o 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”

Re. Investment amount o o o o % of the 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. .

Requirements o o o 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.