Pricing Policy: Time Customization

1. Pricing Policy:Time Customization • I. Economic and Behavioral • Foundations of Pricing • II. Power Pricing Concepts

2. Outline • Time customization of prices: The short term • Trial and accelerate purchase • Potential demand buildup • Peak and off-peak pricing • Demand probing and yield management • Potential negative consequences • The long-term dynamic effects

3. Examples • Campbell offered trade deals to retailers during summer (a eight-week period) • Introductory offer on a new product • Varying airfares over time • Early bird specials • Hotels’ winter specials

4. Basic Motivations 1. Trial 2. Purchase Acceleration Not Time driven Known 3. Potential Build-up 4. Peak Load Time driven 5. Peak Load withDemand Shift Information About Demand 6. Demand probing Initially Limited 7. Yield Management

5. 1. Trial and 2. Purchase Acceleration • On Saturday, 11/22, 1986, Ho Camera offered 5 rolls of Fuji film (24 exposures) at $15.98 less a $10 manufacturer’s mail-in rebate valid until 12/21, 1986. • The offer highlighted Fuji’s $5.98 “Final Cost After Rebate” or $1.20 per roll – approximately 60% less than the regular price. • The vast majority of consumers have been loyal to Kodak even though Consumer Reports citing virtually indistinguishable quality differences in their films. • Two goals: • To persuade consumers to switch and try Fuji • To accelerate purchase and “load pantry”

6. 1. Trial and 2. Purchase Acceleration • Two other mechanisms for enacting price customization: • Coupon • On-shelf price cut • These mechanisms differ in two important respects: • Reference price • Selectivity (areas, price-sensitive consumers, and Kodak consumers)

7. Coupon redemption • A panel-level study of how shoppers redeem coupon when they purchase consumer packaged goods • Regular users are more likely to redeem coupons than previous nonusers • What is the motivation behind coupon offers?

8. Purchase Acceleration vs. Forward Buying Shipments Consumption DEC MAR JUN SEP How do you resolve this problem?

9. EDLP versus HILO Stores • An examination of 3,000 common SKUs across 5 supermarkets (2 EDLPs and 3 HILO stores) (Ho, Tang, Bell, Management Science, 1998) • HILO stores have a higher price variance and a higher expected price • EDLP versus HILO stores • Number of trips • Average spending per trip

10. Mean and Standard Deviation of Basket Prices Tang, Bell, and Ho (California Management Review, 2002)

11. Shopping Behavior

12. Price $60 $40 Time 1 4 5 6 2 3 3. Potential Buildup of Low-WTP Customers • Mr. Coffee coffee maker (unit variable cost = $32) • The goal is to charge maximum WTP of a growing proportion of the market that would buy at regular price • Suppose customers for a coffee maker are of two types, one valuing the product at $60 and the other at $40. • Each group has a “birth” rate of 100 per month

13. 3. Potential Buildup of Low-WTP Customers Unit Variable Cost = $32

14. Example of Value Variation Over Different Time Periods

15. EuroDisney Hotel Pricing

16. 4. Peak and Off-PeakLoad Pricing Peak Sales Volume Sales Volume Off-Peak 100 75 50 37.5 $100 $50 $75 $50 $100 $150

17. 5. Peak Load with Demand Shifts • If we charge $600 for both day flight and redeye, we receive $120,000 (leading to zero demand for redeye) • If we charge $1000 for day flight and $400 for redeye, we receive $140,000 (shifting the students’ demand to the redeye)

18. Uncertain Demand • Consider selling a product to a single customer and three scenarios on information about a potential customer’s valuation of a product • You know she values the product at $5 • You know she values it somewhere between $4.00 and $6.00 • You know she values it somewhere between $0 and 10.00 (each value is equally likely) • Note the customer’s expected valuation is $5.00

19. 6. Demand Probing: Single Price

20. Optimal Price (Variable Cost = 0) 1.0 1.0 Prob. of a Sale Prob. of a Sale X Y $4 $5 $6 $10 $5

21. Optimal Two-day Sale Pricing Unit variable cost =0 1.0 1.0 Prob. of a Sale X Prob. of a Sale Y $10 $10 $5 $3.33 Day 2 $6.67 Day 1 Charge $6.67 in Day 1 and $3.33 in Day 2 Expected Revenue = 1/3 (6.67) + 1/3 (3.33)

22. 7. Yield Management • American Airlines pioneered the concept in the late 1970s • Leisure: Book well in advance, price oriented, and flexible on schedule • Business: Book on short notice, less price sensitive, and inflexible on schedule • Yield management system is to price and manage the availability of specific fare types over time as demand for a particular flight reveals itself • If bookings are above the norm, this is a signal to shut off availability of highly discounted fares

23. inquiry transaction data Reservation System availability display YM System - forecasting - allocation implemented allocations Point of sale forecasts recommended allocations bid price YM Analyst - limited domain (O-D pair) - revenue performance incentive Airline YM Operations

24. Basic Ideas: Chicago  SFO • Based on initial forecasts, start with initial allocations (number of seats) for the two fare classes. • Adjust the allocations based on demand realizations. • For example, if the demand for Full Coach looks promising, “close” the allocation for Discount. • If later the demand is lower than expected, move allocations to Discount again.

25. Examples: What motivations? • Campbell offered trade deals to retailers during summer (a eight-week period) • Introductory offer on a new product • Varying airfares over time • Early bird specials • Hotels’ winter specials

26. Potential Negative Consequences • Incremental or substitute sale (e.g., negligible increase in consumption) • Cost of customization (e.g., production and inventory costs) • System effect • Reference price effect • Wait for sale mentality • Fairness

27. Long Term Dynamic Effects Current Period Price Price Response Curve Competitive Situation Current Sales Volume Current Contribution Current Cost Future Price Response Curve and Price/Profit Realization Future Cost Position

28. Punch-line • Clearly understand the underlying motivation • Design the time-customization plan based on the motivation • Consider the potential negative consequences and long-term dynamic effects