The Impact of the Opaque Channel on Online and Offline Sales: Empirical Evidence from the Airline Industry

Transcription

1 The Impact of the Opaque Channel on Online and Offline Sales: Empirical Evidence from the Airline Industry Nelson F. Granados Kunsoo Han Dan Zhang 1

2 Background and Motivation Opaque intermediaries (hotwire, priceline, etc.) have become an established distribution channel in the travel industry Trade-off: Market expansion vs. sales cannibalization What s the net effect? 2

3 Opaque Selling Literature Literature to date focuses primarily on analytical models Opaque selling: Jiang (26), Fay (28), Fay and Xie (28), Jerath et al. (29), Huang and Sosic (29) Related concepts: Last-minute selling: Jerath et al. (29), Koenigsberg et al. (28) Flexible products: Gallego and Phillips (24), Gallego et. al. (24) 3

4 Research Questions What are the demand generation and cannibalization effects of the opaque channel? What factors moderate these effects? What is the impact of opaque offering on revenue? Implications for revenue management and channel strategy? 4

5 Data 5 months of booking data for an airline 8, records By channel Offline, online, opaque Booking details Booking class, booking date, departure date, origin-destination Price Aggregated at the weekly level 5

6 6 Empirical Model Market Expansion (adapted from Carpenter & Hanssens, 1994) Impact on total volume (contraction vs. expansion) Q exp( A A p ), j { offline, transparent, opaque} t j j jt price elasticity A j p j Estimation equation ln Q p p p 1 off 2 tran 3 opa Control variables: Departure week, origin city, leisure/business, market concentration, low cost carrier market share, airline share

7 7 Empirical Model Cannibalization (adapted from Carpenter & Hanssens, 1994) Impact on channel share m it i exp( B B p ), i, j { offline, transparent, opaque} j ij jt Estimation equations ln m off 1 11 p off 12 p tra 13 p opa ln m tra 2 21p off 22 p tra 23 p opa ln m opa 3 31p off 32 p tra 33 p opa

8 Estimation Method Feasible Generalized Least Squares (FGLS) Accounts for heteroskedasticity and autocorrelation present in the data As efficient as seemingly unrelated regression (SURE) because the regressors are the same in all equations 8

9 Overall Results By Channel (n=737) The opaque channel cannibalizes the online transparent channel, and it does not expand the market. Offline Transparent Opaque Total Channel Share Volume Offline Transparent -.39*** (.4) *** (.4) (.4) -.12*** (.1) *** (.1).19.1 (.1).12*** (.2) *** (.2) ** (.2).3 Opaque.48*** (.8).64.32*** (.8) *** (.7)

10 1 Overall Results By Segment (n=737) Cannibalization by opaque is in the discounted fare segment. Total Volume Full Fare Discounted Full Fare -.7*** -.11***.2 (.1) (.3) (.2) Discounted -.3***.22*** -.46*** (.3) (.6) (.4) Super- -.78***.61***.45*** Discounted (.7) (.12) (.1) Opaque.2.7.7* (.2) (.5) (.4) Segment Share Super- Discounted.1 (.1).15*** (.2) -.47*** (.3) -.1 (.1) Opaque.18*** (.3).19*** (.5).67*** (.11) -.87*** (.5)

11 11 Channel Results by Season Opaque channel cannibalizes online transparent channel in high demand season. High Season (n = 51) Low Season (n = 242) Expansion/Cannibalization of the Opaque Channel: Split Sample by Season Total Channel Share Volume Offline Transparent.3 (.7).12 (.8).1 (.1).1 (.1).7*** (.2).5.4 (.3) Opaque -.93*** (.9) *** (.13) -.46

12 Channel Results by Degree of Competition (Herfindahl Index) Opaque channel cannibalizes online transparent channel in markets with low competition. The opaque channel expands markets with high competition (low Herfindahl). High Competition (n = 51) Low Competition (n = 242) Expansion/Cannibalization of the Opaque Channel: Split Sample by Degree of Competition Total Channel Share Volume -.19*** (.7).3 (.6) -.13 Offline.3 (.2) -.1 (.1) Transparent.3 (.4).1*** (.3).6 Opaque -.66*** (.6) -.16*** (.11)

13 13 Insights for Channel Pricing Revenue elasticity of channel k a function of: R k Rev p k / / Rev p k Price Elasticity D k Channel k revenue share Rev k Rev Marginal revenue share impact of channel k Rev Rev i i R opa (transp.) -.2 (opaque), so a 1% increase in opaque price leads to a.2% revenue increase

14 Conclusions The opaque channel cannibalizes the online transparent channel in high demand season Focus opaque offerings in low season The opaque channel expands markets with high competition, and cannibalizes the online transparent channel in markets with low competition Focus opaque pricing in competitive markets 14