essay 1 airline search and purchase

Essay 1Modeling Online Browsing

and Purchase of Airline Tickets

Ciju Nair Washington University in St.

Louis

2

Online Purchase Process

START BROWSE BUY

Prior experienc

e

3

Motivation

Extensive search and comparison before buying

No search or comparison, buy at first store

Online browsing

and purchase

Cont

inuu

m Feasible optionsOpportunity cost for time

Consideration set

4

Motivation Consumers visit a limited set of websites

where they can buy airline tickets

Airline expenditure is a significant portion of overall online purchases

1832 households (Jul 02 - Dec 02) Mean Number of unique websites visited for a purchase 1.68 Expenditure in airline category in 6 months (US$) 570.09 Expenditure in all categories in 6 months (US$) 1018.22

5

Choice Set

Expedia

Other travel portals (8 websites) All airline websites (20

websites) Normalized to “all airlines”

6

Is there a benefit to modeling browsing and purchase jointly? What is the impact of first site visited and browsing duration?

Do websites differ in their attractiveness and conversion effectiveness?

What is the impact of browsing experience and prior purchase on current purchase and search?

What we don’t know

Yes. Improves in sample hit rate from 56% to 96% First site visited dynamically impacts browsing duration

and hence choice of purchase site

Airline websites are better than travel portals in attractiveness and conversion effectiveness

Experienced consumers first visit big brand travel portals There is an inverted U relationship between experience

and browsing duration

7

Research Objective Investigate impact of early stages on later

stages

Understand factors that affect each stage

Investigate the dynamic impact of past purchase on current decisions

8

Determinants of Browsing Limited information processing and

consideration set (Shocker et al. 1991, Roberts and Lattin 1997)

Prior Experience reduces search Prior product & category experience (Srinivasan

and Ratchford 1991, Brucks 1985) Inverted U relationship (Bettman and Park 1980,

Johnson and Russo 1984)

9

Determinants of Browsing Cost and benefits of search

Perceived financial risk / benefit increases search (Punj and Staelin 1983)

Demographics Younger consumers search more in general

(Ward and Lee 2000) Broadband users search more (Yonish, Delhagen

& Gordon 2002)

10

Determinants of Purchase First site visited and browsing duration

Primacy effect, consideration set and search costs (Anderson 1965)

Prior Experience Product experience (Bellinger et al. 1978, Janiszewski

1998, Roy 1994) Frequent visits (Moe and Fader 2004, Brynjolfsson and

Smith 2001) Category knowledge (Brucks 1985)

11

Determinants of Purchase Prior Purchase

State dependence and inertia (Seetharaman, Ainslie and Chintaguta 1999 )

Expected Level of Expenditure Price and brand of online merchant are

important determinants of website choice (Brynjolfsson and Smith 2001)

Demographics Income dampens price sensitivities in online

markets (Degaratu, Rangaswamy and Wu 2000)

12

Clickstream Prior Research

Browsing

Purchase

Browsing and Purchase on

multiple websites

Bucklin and Sismeiro 2003Park & Fader 2004Johnson et.al. 2004

Moe and Fader 2004aMontgomery et.al. 2006Brynjolfsson and Smith

2001Moe and Fader 2004b

Sismeiro and Bucklin 2004This paper

13

Modeling Approach

STAGE 1 START

STAGE 2 BROWSE STAGE 3 BUY

Prior experienc

e

Jointly model the three stages of consumer decision process

14

6 months of browsing & purchases: July 2002 to December 2002 100,000 households; 9183 surfed travel websites 3648 households made airline purchases

Selection criteria

1832 households satisfy selection criteria

Comscore Data

July December

OctoberEstimation

(last 3 months) Prior Experience(first 3 months)

16

A Discrete–Continuous–Discrete Model

STAGE 1 START

Discrete

STAGE 2 BROWSE

Continuous

STAGE 3 BUY

Discrete

Prior experienc

e

18

* *

*

1 if and only if , j

Travel portals j=1 to 4, Airlines is normalized to 0

ijt ijt ikt

f f f fj

f fijt it i ij i it ijt ijt

F F F k

F Z H S P I

Stage 1 : Discrete Choice of First Website

Categoryexperience

Latent Variabl

e

Expected levelof expenditure

DemographicsSite specificexperience

PriorPurchase

Randomshocks

20

21 1 1 2

first website visited indicator

where is log(pages viewed) by individual i at time t for current purcha

'

seijt

d d d d d d dit it i it i i itp p

d

it

j ijt

F

D Z P I I H H

D

F

Stage 2 : Continuous Browsing Duration

DemographicsExpected level of expenditure

BrowsingDuration

Priorpurchase

Categoryexperience

Randomshocks

22

* *

*

, j

first website visited indicatorbrowsin

1 if and only if U

g duration

p pj

ijt

it

ijt ijt ikt

pj

p p p pijtijt it ijtit

pit i ij i ijt

kU

F

U

D

F DU PZ H S I

Stage 3 : Discrete Choice of Purchase Website

Categoryexperience

Latent Variable Expected level of expenditure

DemographicsSite specificexperience

PriorPurchase

Randomshocks

23

Correlation of with ( ) the endogenous decision variables in stage 2 & 3

Random effects model as opposed to IV approach as its not well behaved in a non-linear equation system

Dimensionality problem

Estimate nonlinear simultaneous 3 equation system with random effects

Large covariance matrix

Estimation Issues and d p

it ijt

( , , ; , , )f d p f d pi ij i ij i i i j

,it ijtD F

26

Random Effects ; (stage1)

; (stage2) ; (stage3)

, , iid errors

, ,

f f f f fj ij ijt i

d d d d di it i

p p p p pj ij ijt i

f d pijt it ijt

f dij i ij

f fijt id dit ip pijt i

, , , individual specific and time invariant random effectp f d pi i i

The random effects are allowed to be correlated

( , , ; , , 1 to 4)f d p f d pi ij i ij ij i ij j

27

Joint Estimation The likelihood function

As the likelihood is highly non-linear simulated maximum likelihood is used for estimation

i

stage (1)

i

stage (2)

i

stage (3)

Pr( 's history of first visits| )

Pr( 's history of browsing durations| )

Pr( 's history of purchases| )

i

i

L i

i

( ; )

: individual specific parameters

i

i

dF

29

Model Estimation : Constrained Simulated Maximum Likelihood

Impose structure and exploit symmetry on to reduce dimensionality of parameters

Constrain to be positive definite during estimation

(15x15 matrix)

30

Attractiveness Hotwire least likely to be visited first

Conversion Effectiveness As likely to buy from Hotwire as Orbitz

Stage 1 Stage 3 Parameters First site visited Purchase site Expedia -1.437 (0.020)* -3.806 (0.019)* Orbitz -1.624 (0.030)* -4.239 (0.167)* Hotwire -2.673 (0.021)* -4.168 (0.056)* Other travel portals -2.033 (0.006)* -4.283 (0.064)*

Results: Brand Strength

* indicates p < .001. Standard errors are in parentheses

32

First visited site has a large impact on browsing duration and choice of the purchase site

Browsing duration increases purchase likelihood from portals

Joint model improves purchase hit rate from 56% to 96%

Stage 2 Stage 3 Parameters Browsing duration Purchase site

Expedia 0.686 (0.001)* Orbitz 0.577 (0.001)* Hotwire 0.771 (0.001)*

Stage 1 first visited site

Other travel portals 0.374 (0.001)* 5.223 (0.113)*

Expedia 0.281 (0.005)* Orbitz 0.444 (0.009)* Hotwire 0.293 (0.002)*

Stage 2 browsing duration Other travel portals

N/A 0.559 (0.007)*


Results : Importance of First Site and Browsing Duration

33

Change in choice probability Policy experiment

(effect of last purchase)

No prior purchase (choice

probability)

Last purchase

on Expedia

Last purchase on Orbitz

Last purchase

on Hotwire expedia.com 0.16 0.33 -0.05 -0.02 orbitz.com 0.13 -0.05 0.29 -0.02 hotwire.com 0.04 -0.02 -0.01 0.14

First site visited shares

All airlines 0.65 -0.26 -0.22 -0.09 expedia.com 0.15 0.36 -0.06 -0.03 orbitz.com 0.12 -0.06 0.34 -0.03 hotwire.com 0.04 -0.02 -0.02 0.18

Purchase shares

All airlines 0.67 -0.28 -0.25 -0.11

Prior purchase dramatically increases likelihood of first visiting as well as purchasing from a site for all brands

Simulation Experiment

34

Prior category browsing experience increases likelihood of first visiting big brand travel portals

However it positively impacts purchase propensity only on Expedia and Hotwire

Results: Category Experience

Stage 1 Stage 3 Parameters First site visited Purchase site Expedia 0.008 (0.001)* 0.008 (0.001)* Orbitz 0.007 (0.001)* -0.001 (0.001)* Hotwire 0.007 (0.001)* 0.008 (0.001)* Other travel portals -0.072 (0.002)* -0.121 (0.002)*


35

Results: Browsing Experience and current Browsing Duration

Category experience effects on browsing duration is non-linear and has an inverted U shape

Stage 2 Parameters Browsing duration Category prior experience 0.426 (0.002)* Squared category prior experience -0.095 (0.001)*


36

Results: Prior Purchase and Browsing Duration

One prior purchase increases browsing duration compared to consumers who have no prior purchase

However it decreases browsing duration for heavy users

Both prior purchase and browsing experience increases likelihood of purchase on travel portal

Stage 2 Parameters Browsing duration Prior purchase (one) 0.017 (0.001)* Prior purchase (more than one) -0.244 (0.003)*


38

Results: Expected Level of Expenditure

Consumers with higher level of expenditure first visit travel portals and buy from them as opposed to airline websites

Stage 1 Stage 2 Stage 3 Parameters First site visited Browsing duration Purchase site Medium level 0.708 (0.014)* 0.465 (0.002)* 2.345 (0.043)* High level 0.689 (0.016)* 0.794 (0.001)* 0.457 (0.008)*


41

Results : w/ No-purchase option

Most results from no-purchase model are consistent with that of the conditional model

Inferences from significant interactions are consistent with our findings from the conditional model. However a lot more of the interactions are not significant in the no purchase model.

Higher site heterogeneity estimates also make us believe that we could be adding more noise to the data by incorporating data from no purchase transactions.

A more detailed comparison is included in the manuscript

Effect Inference Similar Dissimilar

First Site Visited and Browsing Duration X

Prior Browsing Experience X Prior Purchase X Consumer Demographics X Brand Strength X Interactions, Heterogeneitiesand Model Fit X

42

Methodology Three stage model of browsing and purchase mapping

consumer decision processes

Findings Highlights impact of first site visited and search duration

on propensity to purchase Explores factors affecting consumer decisions in each

stage

Managerial implications Provides insight into consumer browsing and purchase

process Can be used as a benchmark model to investigate

effect of exogenous changes e.g. marketing mix or policy changes

effects of covariates (e.g. dynamic impact of last purchase) using simulation

Contribution

43

START BROWSE BUY

Prior experienc

e START BROWSE BUY

Prior expeien

ce

Previous Cycle Next Purchase Cycle

Dynamic effect

BROWSE

BUY

START

Prior experienc

e

Virtual Cycle

44

Next Steps and Future Research

Investigate basket or simultaneous purchases of airline, hotel and car rental products

Thank YouA recent version of this paper is available online at:http://students.olin.wustl.edu/~NAIRC/Online%20Search%20MS%20draft.zip

essay 1 airline search and purchase

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