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Considering the Self-Selection Effect
Transcript of Considering the Self-Selection Effect
Effect of Online-Banking Usage on CLVConsidering the Self-Selection Effect
E-Finance Lab Jour Fixe, 03. May 2004
Dr. Sonja Gensler
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Introduction• Value-Based Customer Management• Multi-Channel Management in Retailbanking
Evaluation of Multi-Channel PerformanceResults of Empirical StudyConclusions and Future Research
Outline of Presentation
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Financial institutions should derive benefits from operationalizing customer orientationCustomer focused performance measures are crucial for value-based customer managementCustomer Lifetime Value (CLV) measures the discounted profit streams of a customer across the entire customer life cycle
Value-based Customer ManagementTodays Challenges in Retailbanking
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mi,t: margin of the i-th customer in the t-th period,ri,t: retention rate of the i-th customer in the t-th period,wi,t: growth rate of the i-th customer in the t-th period,d: discount rate,a0: acquisition cost.
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Multi-Channel Management constitutes the integrated management of channels with the aim to maximize a firm’s customer equityContrasting to a multi-channel strategy is a multiple channel strategy, where several sales channels exist independently from each otherSpecifics of Multi-Channel Management in retailbanking: retail banks own all sales channelsChallenges of Multi-Channel Management in retailbanking: value-based integrated management of all sales channels to enhance customer equity
Multi-Channel Management in RetailbankingProliferation of Multiple Sales Channels
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Value-Based Multi-Channel ManagementMission Statement of Cluster III
Customer Orientation
How to keep customers happy and loyal?It is crucial today for financial institutions to understand what the customers want and to provide it.
Satisfying Customer Needs
Value-BasedCustomer Management
Financial institutions are very interested in beingcustomer-centric, but the shackles of product-centricityare very difficult to break.
Financial institutions should derive benefits from operationalizing customer orientation.Customer oriented performance measures are crucial for value-based management.
Customer Management in a Multi-Channel Environment
"Optimizing Customer Equity through Multi-Channel Management." [Mission Statement of Clusters III of the EFL]
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Introduction• Value-Based Customer Management• Multi-Channel Management in Retailbanking
Evaluation of Multi-Channel PerformanceResults of Empirical StudyConclusions and Future Research
Outline of Presentation
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Evaluation of Multi-Channel PerformanceFindings from Practice
“Die Online-Kunden weisen um 40% höhere Zahlungseingänge und eine um 13% höhere Cross-Selling Quote auf.” [Postbank, 2003]“Multi-Channel Bankkunden sind um 25-50% profitabler als reine Filialkunden.”[Bachem, 2003]“Der Ertrag eines Online-Kunden pro Jahr ist um 50€ höher als der eines Offline-Kunden.” [LBBW, 2002]“… banks report that online-banking customers are more profitable, retain higher bank balances and are the highest household income bracket.” [Bernstel, 2003]
However, just comparing the mean is not sufficient to evaluatemulti-channel performance.
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Evaluation of Multi-Channel PerformanceCustomer-oriented Performance Measures
Customer Satisfaction
Revenue Contribution
Cost Contribution
Customer‘s Retention
Rate
Customer Lifetime Value
Customer‘s Profit Contribution
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Self-Selection Effect Online-banking usage identifies the
profitable customersOnline-banking customers have always been the more profitable customers
Channel effect and self-selection effect determine the profitability difference of online-banking and offline-banking customers.
Multi-channel usage changes customer behavior and customers become more profitable (e.g. increased cross-sellingrate)
Channel EffectOnline-banking usage introduces
behavioural change
Evaluation of Multi-Channel PerformanceDifferentiation between Self-Selection Effect and Channel Effect
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Evaluation of Multi-Channel PerformanceConsideration of Self-Selection Effect is necessary
Self-selection effect is present if online-banking customers have always been the more profitable customersChannel effect arises if online-banking customers are more profitable because they started to use the online channelDistinction between those two effects allows to evaluate the impact of the online channel on customer profitability
• If self-selection effect exists a simple comparison between the profitability of online and offline customers is misleading
• No adequate implications can be derived for value-based multi-channel management
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Evaluation of Multi-Channel PerformanceBasic Idea of Matching Method
One way to account for selection biases is the use of matching algorithmsMatching is a well-known method derived from the economics fieldMatching approach aims to build matched pairs of comparable individuals (twin building) from the group of online-banking customers and offline-banking customersBuilding comparable twins is ought to reduce any observable difference between online-banking and offline-banking customers Selection biases considered in the marketing literature
• Degeratu/Rangaswamy/Wu (2000), „Consumer choice behavior in online and traditionalsupermarkets:The effects of brand name, price, and other search attributes“, IJRM, 17, 55-78.
• Hitt, L./Frei, F. (2002), „Do Better Customers Utilize Electronic Distribution Channels? The Case of PC Banking“, Management Science, 48, 6, 732-748.
• Danaher/Wilson/Davis (2003), „A Comparison of Online and Offline Consumer Brand Loyalty“, Marketing Science, 22, 4 (Fall), 461-476.
• Busse/Silva-Risso/Zettelmeyer (2004), „$1000 Cash Back: Asymmetric Information in Auto Manufacturer Promotions“, Working Paper University of California, Berkeley.
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Evaluation of Multi-Channel PerformanceSelected Literature: Matching Method
Dehejia, R./Wahba, S. (2002), „Propensity Score-Matching Methods for Nonexperimental Causal Studies“, The Review of Economics and Statistics, 84, 1, 151-161.Dehejia, R./Wahba, S. (1999), „Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs“, JASA, 94, 1053-1062. Hujer, J./Caliendo, M./Radic, D. (2003), „Methods and Limitations of Evaluation and Impact Research“,Working Paper, Faculty of Economics and Business Administration, Frankfurt University.Humphreys, K./Phibbs, C./Moos, R. (1996), „Addressing Self-Selection Effects in Evaluation of Mutual Help Groups and Professional Mental Health Services: An Introduction to Two-Stage Sample Selection Models“, Evaluation and Program Planning, 19, 4, 301-308.Lee, L. (2000), „Self-Selection“, in: Baltagi, B. (Ed.), „A Companion to Theoretical Econometrics“, Blackwell Publishers. Roy, A. (1951), „Some Thoughts on the Distribution of Earnings“, Oxford Economic Papers, 3, 2, 135-146. Rubin, D. (1974), „Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies“, Journal of Educational Psychology, 66, 688-701 Singer, B. (1986), „Self-Selection and Performance Based Ratings: A Case Study in Program Evaluation“, in: Wainer, H. (Ed.), „Drawing Inferences from Self-Selected Samples“, New York.
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Observable and relevant customer characteristics: age, ownership of stocks, incomeComparing the mean: 14 – 8,67 = 5,33Result: online-banking customers have a higher CLV than offline-banking customers
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Evaluation of Multi-Channel PerformanceMatching Method for Considering the Self-Selection Effect
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Only online-banking customer „2“ and offline-customer „5“ can be matchedWhen not considering the variable „income“ the number of matched pairsincreases from one to two (loss of information)Trade-Off: The more of the relevant variables are considered, the better thecontrol of observable selection bias, but the harder to find matching partners
Evaluation of Multi-Channel PerformanceMatching Method for Considering the Self-Selection Effect
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Basic idea of matching by Balancing Scores• To overcome the problem of not finding identical twins Balancing Scores are
introduced• Compute a balancing score for every customer based on the relevant characteristics• A multi-dimensional problem is reduced to a one-dimensional problem
Propensity Score as Balancing Score• Propensity Score represents the probability of online-banking usage given the
observed customer characteristics (multinomial Logit or Probit model)• Propensity Score ranges from 0 to 1• Under ideal conditions corresponds the distribution of the propensity score in both
groups (online-banking and offline-banking customers)
Evaluation of Multi-Channel PerformanceSolving the Problem of Having Limited Number of Matched Twins
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Matching without replacement: every offline-customer can be used for matching just once
Matching with replacement: every offline-customer can be used several times for matching purposes
Trade-off: bias versus variance• Matching with replacement reduces the bias, but increases the variance of
estimates
Matching methods• Nearest-Neighbour Matching• Caliper and Radius Matching• Stratification/Interval Matching• Kernel Matching
Evaluation of Multi-Channel PerformanceAlternative Procedures for Matching
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Introduction• Value-Based Customer Management• Multi-Channel Management in Retailbanking
Evaluation of Multi-Channel PerformanceResults of Empirical StudyConclusions and Future Research
Outline of Presentation
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ResultsSimple mean comparison of online-banking and offline-banking customers
For confidentiality reasons offline-banking customers have been set to 100%.All differences are significant at the 5% level.
103.80%
518.48%
69.66%
76.87%
66.60%
139.02%
75.50%Duration of relationship
Number of products
Number of transactions
Value of current fondportfolio
Value of current stockportfolio
Balance of saving account
Balance of current account
Offline-banking customers Online-banking customers
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Matching by propensity score (logit model)• Dependent variable: Usage of online-banking• Independent variables: (duration of relationship), number of saving accounts, number
of current account, usage of call center, ownership of a credit, ownership of a credit card, lifecycle segment, sex
Matching without replacement
Imposition of common support: online-banking customers whose propensity score is higher than the maximum or less than the minimum propensity score of the offline-banking customers are dropped
5% trim level: imposes common support by dropping 5 percent of the online-banking customers at which the propensity score density of the offline-banking customers is the lowest
Approach results in about 90% per cent reduction in bias for every consideredindependent variable
ResultsDifferences between online-banking and offline-banking customers
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ResultsComparison of online-banking and offline-banking customers (matched)
For confidentiality reasons offline-banking customers have been set to 100%.
100.00%
94.84%
87.78%
93.30%
125.16%
89.19%Duration of relationship
Number of products
Number of transactions
Value of current fondportfolio
Value of current stockportfolio
Balance of saving account
Balance of current account
Offline-banking customer Online-banking customer
1183.77%
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ResultsSummary of preliminary results
For confidentiality reasons offline-banking customers have been set to 100%.
518.48%
139.02%
103.80%
69.66%
76.87%
66.60%
75.50%
125.16%
100.00%
94.84%
87.78%
93.30%
89.19%Duration of relationship
Number of products
Number of transactions
Value of current fondportfolio
Value of current stockportfolio
Balance of saving account
Balance of current account
Online-banking customer (mean) Offline-banking customer Online-banking customer (matched)
1183.77%
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ResultsEffect of Online-Banking Usage on Customer Lifetime Value
Hypothesis that online-banking customers have a lower balance of accounts can be approved
Hypothesis that online-banking customers have a higher value of current stockportfolio can be approved
Hypothesis that online-banking customers have a higher value of current fondportfolio can not be approved
Hypothesis that online-banking customers have a higher number of transactions can be approved
Hypothesis that online-banking customers own more products can not be approved
Effect of online-banking usage on Customer Lifetime Value is twofold.
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Introduction• Value-Based Customer Management• Multi-Channel Management in Retailbanking
Evaluation of Multi-Channel PerformanceResults of Empirical StudyConclusions and Future Research
Outline of Presentation
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Evaluating the channel effect enables value-based Multi-Channel Management
ConclusionsImplications for Multi-Channel Management
Offline-banking
customers
Online-banking
customers Channel Effect Balance of current account 100.00% 94.84% -5.16% Balance of saving account 100.00% 87.78% -12.22% Value of current stock portfolio 100.00% 1183.77% +1083.77% Value of current fond portfolio 100.00% 93.30% -6.70% Number of transactions 100.00% 125.16% +25.16% Number of products 100.00% 100.00% 0.00% Duration of relationship 100.00% 89.19% -10.81%
Customer channel migration seems to some extent reasonabledue to the fact that channel effect exists.
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Effect of online-banking usage on customer retention and customer loyalty
Differences between online-banking and offline-banking customers over timeCompute average change in Customer Lifetime Value (CLVi,online vs. CLVi,offline)
• Different retention rates for online-banking and offline-banking customers result from previous analyses
• Multiplication over all customers allows for analyzing changes in revenues• Separate analysis of cost allows for evaluation of multi-channel investments
Future ResearchA more detailed analysis and evaluating multi-channel investments
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Thank you for your attention.Any questions?
Dr. Sonja GenslerTel.: +49 69 4272 [email protected]
Department of Electronic Commerce
www.ecommerce.wiwi.uni-frankfurt.dewww.efinancelab.de