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Analysis of Japanese Loyalty Programs Considering Liquidity, - - PowerPoint PPT Presentation

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Analysis of Japanese Loyalty Programs Considering Liquidity, Security Efforts, and Actual Security Levels June 24, 2014 @WEIS 2014 Bongkot Jenjarrussakul, and Kanta Matsuura Institute of Industrial Science The University of Tokyo Outline

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Analysis of Japanese Loyalty Programs Considering Liquidity, Security Efforts, and Actual Security Levels

Bongkot Jenjarrussakul, and Kanta Matsuura

Institute of Industrial Science

The University of Tokyo June 24, 2014 @WEIS 2014

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 Introduction

 Loyalty Programs  Security Incidents

 Japanese Loyalty Programs  Security-Liquidity Implications  Conclusion

Outline

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Loyalty Program (LP)

  • Marketing activity that encourages customers’ loyalty

behaviors by rewarding them.

– The rewards usually take the form of Reward currency or Point. – Locates between online games and Bitcoin.

  • Liquidity of reward currencies is increased when LP
  • perators cooperate with their business partners.

– Allow their customer to exchange points between different LPs.

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etc.

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The Trend of Loyalty Program

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USA Canada

North America

  • 80% of European customers belong

to at least one LP.

  • One-third of customers are likely join

two or more LPs.

  • (In GB) 95% of UK customers join at

least one LP.

Japan ← has more than 200 LPs Europe ← Newbie to LP

20 40 60 80 2006 2008 2010 2012 2014

Number of household (%) Year

  • No. of

household with possession of e-money

  • No. of

household with possession of point card 0.973 1.335 1.796 2.089 2.647 0.0 1.0 2.0 3.0

Year

  • No. of membership in the U.S.

(Billion)

’00 ’06 ’08 ’10 ‘12

26.7%↑

116.22 120.72 119.97 50 100 Year

  • No. of membe

bershi ship p (Million

  • n)

’08 ’10 ‘12

Slightly decrease due to demographic factors.

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Security Incidents and Concerns

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USA

  • Announcement about phishing and

security incidents related frequent flyer program (FFP) on alert sites from

  • U.S. airways
  • Delta airlines

Canada

  • Scamming case in which the suspects

used fraudulent credit cards.

  • This scam included illegal

redemption of the credit card point for gift cards.

North America

  • Unauthorized access and illegal

redemption at many LPs such as

  • G-Point
  • T Point
  • Rakuten point
  • JAL
  • Malicious expense of Tesco’s gift

voucher .

  • Announcement about phishing and

security incident related to FFP from British airways.

Japan Europe

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Objective

  • Investigate Japanese LP systems with focuses on

their

– Liquidity – Operating firms’ security efforts – LP systems’ actual security levels

  • Consider a model to derive security-liquidity

implications

– Linear regression analysis

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Outline

 Introduction  Japanese Loyalty Programs and Their Network

 The Network of Japanese LPs  Liquidity of the Japanese LPs  Security-related Data of LP Operating Firms

 Security-Liquidity Implications  Conclusion

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Japanese LP systems

  • Refer to point exploration website, “poitan.net”

– Information of existing LPs in Japan – Estimated real-currency values of LP Points – Exchange/conversion rates between systems – Query of possible routes – Required duration for exchange process

  • More than 200 LPs are operated by Japanese
  • perators

– From 9 industries (refers to METI’s list of industries) – Industries with high interaction with customers

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METI : Ministry of Economy, Trade and Industry

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SLIDE 9

Example of Query at Poitan.net

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30 days Point: 20,000 (30US$) 21 days Point: 20,000 (20$) Matsumoto KiYoshi Point: 14,000 (14$) (Point exchange site) 7 days (Railway smart card) 1 days Point: 14,000 (14$) Point: 7,000 (7$) (Drug store) Total require Time 59 days.

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Miscellaneous manufacturing (13)

Japanese LP Network

The Connections between Industries

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Coming in only Going out only Both directions Type of flow (edge) Group 2: Having 2 types of flows between 2 nodes Group 3: Having only 1 type

  • f flow between 2 nodes

Group 1: Having all types of flows between 2 nodes

Electricity, gas, heat supply and water (16) VDO picture, sound info., broadcasting & commu. (17) Information Services (19) Transportation & Postal activities (20) Retail trade (22) Finance & Insurance (23) Miscellaneous non- manufacturing (26) Manufacturing of Electrical Machinery (09)

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Liquidity of the LPs

Ability that customer can exchange their points between different loyalty programs.

  • To calculate Liquidity score, we consider

– No. of corresponding type of edge (x) – Average no. of partners (y) – Then separate the score into 4 levels

  • 0  xy  15

: Low (L)

  • 15 < xy  23

: Medium-Low (ML)

  • 23 < xy  30

: Medium-High (MH)

  • 30 < xy

: High (H)

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Liquidity and Security in Industry Level

Would high liquidity imply… larger security effort? larger damages from security incidents at the LP? better actual security level at their system?

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Liquidity and Security-related Data

Industry (ID) Liquidity

  • f

LPs Average size of damage from security incidents Average size of expense on countermeasure Manufacturing of electrical machinery (09) L 12,740$ (0.04%) 70,970$ (0.20%) Miscellaneous manufacturing (13) L 4,696$ (0.03%) 74,118$ (0.45%) Electricity, gas, heat supply, and water (16) L 2,450$ (0.01%) 112,006$ (0.26%) VDO picture, sound information, broadcasting & communication (17) H 2,940$ (0.02%) 70,155$ (0.51%) Information services (19) MH 47,367$ (0.43%) 151,341$ (1.38%) Transportation & postal activities (20) H 7,525$ (0.05%) 47,753$ (0.31%) Retail trade (22) ML 8,003$ (0.05%) 40,286$ (0.26%) Financial & insurance (23) MH 12,658$ (0.02%) 235,716$ (0.32%) Miscellaneous non-manufacturing (26) ML 2,975$ (0.03%) 60,422$ (0.62%) The University of Tokyo

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% in () is percentage of the average size to average capital size. Data of 2012 by Ministry of Economy, Trade and Industry (METI).

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The University of Tokyo

[Pt] ] 1 [Yen] 0.67

ANA Mileage eage Club (IND 20) Yamada ada Denk nki (IND 22) 22)

[Pt] ] 0.09 [Yen] 0.9

QooPo

  • Po

(IND 13) Sony ny Point nt (IND 09) TEPCO (IND 16)

[Pt] ] 2.5 [Yen] 0.75 [Pt] ] 0.1 [Yen] 1

Softba bank nk Mobile ile (IND 17) 17)

[Pt] ] 1 [Yen] 0.67 [Pt] ] 0.25 [Yen] 3.75 [Pt] ] 0.025 [Yen] 0.375

JAL L Mileage eage Bank (IND 20) Pont nta (IND ND 26) 26)

[Pt] ] 0.5 [Yen] 0.75 [Pt] ] 1 [Yen] 0.67 [Pt] ] 0.1 [Yen] 1 [Pt] ] 0.029 [Yen] 0.43 [Pt] ] 0.1 [Yen] 1

Matsum umoto Kiyosh

  • shi

(IND 22)

[Pt] ] 1 [Yen] 0.67 [Pt] ] 0.2 [Yen] 0.3 [Pt] ] 0.7 [Yen] 0.7 [Pt] ] 1 [Yen] 0.67 [Pt] ] 0.5 [Yen] 0.75 [Pt] ] 0.85 [Yen] 0.85 [Pt] ] 0.85 [Yen] 0.85 [Pt] ] 5 [Yen] 1 [Pt] ] 3 [Yen] 0.9 [Pt] ] 0.33 [Yen] 0.5 [Pt] ] 1 [Yen] 1 [Pt] ] 4 [Yen] 0.8 [Pt] ] 5 [Yen] 1 [Pt] ] 0.2 [Yen] 1 [Pt] ] 4.95 [Yen] 4.95 [Pt] ] 1 [Yen] 1 [Pt] ] 3 [Yen] 0.6 [Pt] ] 0.33 [Yen] 0.5

PeX PeX (IND 19) 19) T Point nt (IND 19) Suic ica Point nt (IND 20) Mitsui i Sumit itom

  • mo

Card rd (IND 23) G-Poin Point (IND 19)

Selected LP systems

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Actual Security of the Selected LPs

Registration

  • Generally require basic personal information
  • Only LPs from industry 09 (MH) and 19 (L)

implement CAPTCHA. Authentication

  • Similar requirements: username & password

Back-up Authentication

  • Found no established heuristic back-up

authentication.

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The answer…

Would high liquidity imply… larger security effort? larger damages from security incidents at the LP? better actual security level at their system?

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If we want to answer such questions, we need a rigorou

  • us

s analys ysis is rather than a simple observation.

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Outline

 Introduction  Japanese Loyalty Programs  Security-Liquidity Implications

 Linear Regression Analysis  The Results and Implications

 Conclusion

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Data for the Analysis

  • METI data

– Average size of expense on security countermeasures – Average size of damage from security incidents

  • Poitan.net

– Rank of Japanese LPs (April 2014) – Number of partners belongs to each LP – Exchangeable type of flow (belongs to each LP)

  • Official site of 82 Japanese LPs

– Investigate security-related requirements in 3 processes

  • Registration
  • Authentication (Login)
  • Back-up authentication (Password recovery)

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Liquidity Liquidity Impa Impact ct fr from

  • m

secur securit ity y incidents incidents Secu Securit rity y scor score Proxy Variables

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Impact from incidents (impacti)

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where i the index of each selected LP (i = 1,2,…,82) INDi the industry ID of the industry LPi belongs to damageINDi the average amount of damage from incidents in industry INDi ranki the ranking score of LPi

  • Since illegal exchanges originate from compromised LP

accounts, we focus on the “Origin LP” ranking.

  • Origin LP is the LP which acts as source node where

points are exchanged to its partner system.

impacti = damageINDi * ranki

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Liquidity (liquidityi)

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where x the edge types between LPi and 9 industries where only the 82 selected LPs are considered y number of exchange partners of LPi

liquidityi = xy

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Security score (secscorei)

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  • Focus on the important requirements in 3 processes:

– Registration – Authentication (login) – Back-up authentication (password recovery)

  • Use normalized value of the security score by using

above equation. secscorei = # of satisfied requirements in LPi

# of requirements about which we can obtain data

regarding LPi

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Security score (Data collection)

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Registr trati ation

  • n

Login Back-up up authent enticati ation

  • n

Trusted information Physical card

  • r account

Implementation

  • f security

techniques Data which increases difficulty Trusted information Physical card

  • r account

LP1 1 1 1 1 1 LP2 1 n/a 1 LP3        LPn 1 n/a n/a n/a

Note: n/a means that data is unavailable. 1 indicates that the corresponding requirement is satisfied. 0 indicates that the corresponding requirement is not satisfied. Trusted info : certified information, security code, etc. Data which increase difficulty : mobile number, system generated ID, etc.

  • Example of the calculation of security score

LP1 → secscore1 = 5/6 = 0.83 LP2 → secscore2 = 2/5 = 0.40

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Hypotheses

Hypothesis 1 The impact from security incidents on an origin LP would be reduced if the LP operator implements stronger security requirements in registration, authentication (login), and back-up authentication processes. Hypothesis 2 The impact from security incidents on an origin LP would be increased if the LP has higher liquidity.

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Linear regression model

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impacti = 0+1expensei+2liquidityi+3secscorei impacti

impact from security incidents

expensei

average size of expense on countermeasures in the industry LPi belongs to. (industry-wise value)

liquidityi

LP-wise liquidity score

secscorei

security score of the LPi

Very y low correla lation tion coeffi fficien cients ts among expla lana natory tory variables ables

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The Result

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  • secscore with “-” sign

– Satisfying more security requirements would reduce the impact from security incidents – Support our Hypothesis 1.

  • liquidity with “+” sign

– Higher liquidity would increase the impact from security incidents. – p-value is extremely low. – Support our Hypothesis 2.

Variable Coef. p-value Intercept 4311.9120 0.6401 expense 0.1938 0.0027*** liquidity 643.6897 3.49e-09*** secscore

  • 30138.18

0.0115**

p-value tells significance of the data. ** indicates significance at 5% level *** indicates significance at 1% level

Explanatory variables are significant.

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Outline

 Introduction  Japanese Loyalty Programs  Security-Liquidity Implications  Conclusion

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Conclusion

  • Liquidity is an important factor when we

investigate implications regarding security efforts.

  • More security efforts particularly to satisfy strong

security-related requirements in the LP system is recommended to LP operators.

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Thank you for your attention Questions?

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