The Value of Personal Information in the E-Commerce Market

Toshiya Jitsuzumi1 and Teppei Koguchi2

1 Faculty of Economics, Kyushu University, Japan2 Faculty of Informatics, Shizuoka University, Japan

The purpose of this analysis is to clarify the effect of personal information on switching costs in the Internet shopping market.

We empirically demonstrate the extent to which personal information drive up switching costs.

We revealed that when users change Internet shopping sites, personal information registered on the site represent switching costs of the same magnitude as traditional switching costs.

Abstract

1. Background

2. Analysis framework

3. Estimation

4. Conclusion

Table of contents

2,706 3,867 4,375

5,253 5,892

2,838

2,222 2,321

2,535 2,567

5,544 6,089

6,696

7,788 8,459

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2007 2008 2009 2010 2011

Background: Internet shopping in Japan

Growth of the Japanese B2C e-commerce market

Source: Ministry of Economy, Trade and Industry

8.6%

Retail and service industries

other

Background: Internet shopping in Japan

Shares of Internet shopping sites in 2012 in Japan

Source: Rakuten, Inc. fiscal 2012 Financial Results

28.8%

12.4%

6.2%

52.6%

Rakuten

Amazon.co.jp

Yahoo! Japan

other

In order to shop on Internet shopping sites, users must register and provide information.◦ names, e-mail addresses, postal addresses, credit card numbers, etc.

In addition, many Internet shopping sites provide the user’s viewing and buying histories on the site to make shopping more convenient.

If a user changes to another Internet shopping site,◦ The personal information that has been registered and stored on previously used site is not

transferred to the new site. The user must re-register his or her personal information. The personal information on previously used site may be continuously used for the business of

previously site.

◦ Viewing and buying histories on previously used site can’t watch in the new site. The user can’t use wish list and recommendation function based on buying history.

This point may represent a switching cost for users.

Background: Personal information on Internet shopping

Switching Cost;◦ The psychological or economic costs incurred when customers switch from one good or

service to another. Traditional switching cost; familiarity, attachment, etc. to the service.

◦ Switching costs make consumer hard to switch different service. If high switching cost exists, it is possible to prevent price competition.

Klemperer (1987) ◦ Analysis for competition between new entry brand and existing brand.

Shy(2002)◦ A model analysis of switching costs in the financial industry.

Valletti and Cave (1998)◦ Analysis the mobile phone market in the UK.

Brynjolfsson and Smith (2000)◦ In the e-commerce market, consumer confidence in the service provider becomes the factor

of switching costs and justifies price differences.

Background: Early studies

Analysis framework: Scenario

Site Merger

OR

Personal information will be transferred to the merged site, but there are variations in the treatment of the information stored in the closed site.

Cash compensation

Hypothesis of scenario “Rakuten and Amazon will be integrated, and one of them will close.”

Respondents recognize as switching costs for;◦ Traditional switching cost

familiarity and attachment to the site that will close.

◦ Switching cost associated with personal information the management of registered

information (names, e-mail addresses, credit card numbers, etc.) on the site that will close.

the migration of the viewing histories at the site.

the migration of the buying histories at the site.

Analysis framework: Attributes and levels

Attributes Levels

Which site exists? Rakuten Amazon.co.jp

What will become of information registeredin the shuted down site ?

Used in other business ofshut down site operator

Completely deleted

What will become of buying history in theshuted down site ?

Carry over to the survivingsite

Completely deleted

What will become of browsing record in theshuted down site ?

Carry over to the survivingsite

Completely deleted

Compensation for the situation above 1,000 yen 5,000 yen 10,000 yen 20,000 yen

Analysis framework: Conjoint analysis

ijioncompensati

viewiviewbuyibuyi

RakuteniRakutenAmazoniAmazonij

oncompensati

DDD

DDU

,

,,infinf,

,,

ijioncompensati

viewiviewiviewpurchasei

viewfreqi

viewage

buyibuyibuypurchasei

buyfreqi

buyage

iipurchaseifreqiage

RakuteniRakuteniRakutenpurchasei

Rakutenfreqi

Rakutenage

AmazoniAmazoniAmazonpurchasei

Amazonfreqi

Amazonageij

oncompensati

Dpurchasefreqage

Dpurchasefreqage

Dpurchasefreqage

Dpurchasefreqage

DpurchasefreqageU

,

,

,

infinf,infinfinf

,

,

)(

)(

)(

)(

)(

(Without shift parameter) (With shift parameter)

Variables;D means dummy variableIf DAmazon = 1, merging into AmazonIf DRakuten = 1, merging into RakutenIf Dinf =1, deleting registered informationIf Dbuy =1, carrying over buying historyIf Dview =1, carrying over viewing historyIf Dcompensation =1, compensation for each situation (yen)

Shift parameter; age = agefreq = purchase frequency during last yearpurchase = average purchase price

dg

X

XP

j i

iik

'

'

exp

exp

Probability function

Utility function

Estimation: Results

Without shift parameter With shift parameterVariable Shift Parameter Coefficient Standard Error p-value

Amazon -2.431 1.101 0.027age 0.016 0.008 0.048purchase frequency 0.566 0.105 0.000average purchase price 0.060 0.112 0.589

Standard deviations 0.163 0.489 0.738Rakuten -1.015 1.082 0.348

age 0.018 0.008 0.027purchase frequency 0.367 0.103 0.000average purchase price -0.056 0.112 0.620

Standard deviations 0.393 0.601 0.513Registered information 0.272 0.949 0.774

age 0.010 0.007 0.156purchase frequency -0.082 0.894 0.360average purchase price -0.027 0.097 0.779

Standard deviations 0.014 0.342 0.968Buying history 1.416 0.987 0.151

age -0.020 0.007 0.006purchase frequency -0.013 0.093 0.888average purchase price -0.007 0.991 0.942

Standard deviations 0.290 0.460 0.529Viewing history 1.023 0.985 0.299

age 0.005 0.007 0.500purchase frequency -0.053 0.094 0.575average purchase price -0.117 0.100 0.246

Standard deviations 0.533 0.401 0.184Compensation 0.0000460 0.0000069 0.000

Variable Coefficient Standard Error p-value

Amazon 0.316 0.110 0.004Rakuten 0.306 0.109 0.005Registered information 0.289 0.820 0.004Buying history 0.289 0.838 0.006Viewing history 0.103 0.846 0.222Compensation 0.0000421 0.0000059 0.000

Estimation: Results; WTA

Variable WTA (Yen)

Amazon ¥7,506Rakuten ¥7,268Registered information ¥6,865

Buying history ¥6,865Viewing history (Insignificant)

Purchase histories or registered personal information represent switching costs of the same magnitude as traditional switching costs such as brand attachment or familiarity with the site.

Viewing histories are not regarded as the factor of switching costs.

From the managerial perspective;◦ It is effective to construct a system in which registered or stored

personal information cannot be used at different sites.◦ Especially, for young people, it is important to apply the services, for

example reduced prices, to prevent from changing to different service providers.

Conclusion

From the perspective of government policies;◦ It is necessary to analyze what types of personal information are

registered or stored on these sites. While some personal information become switching costs, others do not.

◦ If switching costs impede competition, we have to consider the policy that makes possible transportation of personal information.

The “midata” project (BIS in 2011). The goal of the “midata” project is for consumers to be able to access and use

their personal and company data. This project would be able to solve the problem of switching costs associated

with personal information and therefore promote more competition.

Thank you for your attention