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MANAGEMENT SCIENCE

Articles in Advance, pp. 1–21

issn 0025-1909  eissn 1526-5501

informs ®

doi 10.1287/mnsc.1090.1066

© 2009 INFORMS

Cost Structure, Customer Profitability, and Retention

Implications of Self-Service Distribution Channels:

Evidence from Customer Behavior in an Online

Banking Channel

Dennis Campbell, Frances Frei

Harvard Business School, Harvard University, Boston, Massachusetts 02163

{dcampbell@hbs.edu, ffrei@hbs.edu}

This paper uses the context of online banking to investigate the consequences of using self-service distribution

channels to alter customer interactions with the firm. Using a sample of retail banking customers observed

over a 30-month period at a large U.S. bank, we test whether changes in service consumption, cost to serve, and

customer profitability areassociated with theadoption of onlinebanking. Wefind that customer adoption of

onlinebanking is associated with (1) substitution, primarily from incrementally more costly self-service delivery

channels (automated teller machine and voice response unit); (2) augmentation of service consumption in more

costly service delivery channels (branch and call center); (3) a substantial increase in total transaction volume;

(4) an increase in estimated average cost to serve resulting from the combination of points (1)–(3); and (5)

a reduction in short-term customer profitability. However, we find that use of the online banking channel is

associated with higher customer retention rates over one-, two-, and three-year horizons. The documented

relationship between the use of online banking and customer retention remains positive even after controlling

for self-selection into the online channel. We also find evidence that future market shares for our sample firm are

systematically higher in markets with high contemporaneous utilization rates for the online banking channel.

This finding holds even after controlling for contemporaneous market share, suggesting it is not simply the

result of increased market power leading to the acquisition of online banking customers.

Key words: service operations; self service; online banking; customer profitability; cost to serve

History: Received March 8, 2006; accepted June 24, 2009, by Paul H. Zipkin, operations and supply chain

management. Published online in Articles in Advance.

1. Introduction

This paper investigates the consequences of using

self-service distribution channels to alter customer

interactions with the firm and considers the implications of these consequences for performance measurement and management in service firms. Firms

are increasingly implementing self-service technologies with the goals of reducing cost and increasing

service quality, revenue, and customer retention rates

(Meuter et al. 2000, Hitt and Frei 2002). However,

the success of strategies for deploying these technologies depends on how customers use them (Dabholkar

2000). For example, although technologies such as

the Internet and automated call centers reduce the

marginal cost of customer interaction from the firm’s

perspective, they may also reduce the cost of interaction from the customer’s perspective. This could

lead to a significant expansion in overall service consumption and an increase in total distribution costs.

In this paper, weexaminetheimpact of oneselfservice channel, online banking, on customer-level

service demand, cost, profitability, and retention. We

also investigate whether increased customer acceptanceof theonlinebanking channel is associated with

broader market outcomes in the form of increased

market share.

Our paper makes four specific contributions. First,

prior research in the area of self-service technologies

has largely focused on identifying attitudinal, behavioral, and demographic factors associated with the

consumer self-service adoption decision as well as

the consumer self-service experience evaluation process (e.g., Froehle and Roth 2004, Zeithaml et al. 2002,

Iqbal et al. 2003, Tsikriktsis 2004, Parasuraman and

Zinkhan 2002, Bitner et al. 2000, Meuter et al. 2000,

Curran et al. 2003). There has been limited attention to how such technologies alter actual customer

demand for service and/or the financial performance

of individual customer relationships. In one of the

few studies that link utilization of self-service channels to performance, Xue et al. (2007) document that

“efficient” customers who make more extensive use

1

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of self-servicechannels aremoreprofitablethan customers who are less efficient in their use of these

channels. In this paper, we build on this literature to

investigatethebehavioral changearound theadoption of online banking and attempt to identify a causal

link between self-service adoption and performance.

Second, existing research in the operations management literature has specifically examined the online

banking channel and found that online customers

tend to bemoreprofitablethan offlinecustomers (Hitt

and Frei 2002, Xue et al. 2007).1 However, because this

past research leaves the issue of postadoption behavioral change as an open empirical issue, it remains

unclear whether these differences are the result of

more profitable customers selecting into the technology rather than being caused by its adoption (Hitt and

Frei 2002). This is an important distinction, because

distribution strategies in many retail banks involve

allocating resources to actively migrating customers

to online banking under the assumption that cost, revenue, and retention benefits will follow. We build on

this literature to examine the extent to which performance differences among online and offline customers result from postadoption individual behavioral

change as opposed to selection of moreprofitablecustomers into theonlinechannel.

Third, prior literature has documented that oneyear customer retention rates are increasing in the utilization of self-service channels (Hitt and Frei 2002,

Xue et al. 2007). There are two potential alternative explanations for the increases associated with

theuseof self-servicechannels: (1) theuseof selfservice channels such as online banking may increase

retention rates through increased switching costs,

enhanced service quality, or both (Dabholkar 1991,

Bitner et al. 2000, Buell et al. 2009); or (2) increased

retention rates may simply reflect a particularly loyal

segment of customers self-selecting into the online

channel. In this paper, we attempt to isolate the effect

of online banking on customer retention through the

use of instrumental variable techniques designed to

control for the self-selection. We also contribute to

the literature by examining the relationship between

online banking and customer retention over a multiyear horizon. Extending our analysis beyond the oneyear horizon common in previous studies allows us to

1 In one of their tests, Hitt and Frei (2002) do exploit a “pseudo

time series” based on variation in product adoption dates to test

whether the use of online banking is associated with increased

product adoption rates. However, their primary analyses of customer profitability, retention, and cross-sell rates are cross-sectional

in nature. Empirical results reported in Xue et al. (2007) add to the

evidence of Hitt and Frei (2002) by demonstrating that customers

with a longer tenurein theonlinechannel tend to bemoreprofitablethan both offlinecustomers and onlinecustomers with less

experience in the channel.

identify potentially changing patterns in the strength

of the self-service retention link further into the duration of the customer relationship.

Finally, prior studies have not examined the link

between market outcomes and the use of self-service

channels by the firm’s customers. Broad acceptance

of self-service channels among its customer base may

help a firm maintain or increase market shares to the

extent that such channels attract relatively more profitable customers or increase retention rates. We provide initial evidence on the extent to which market

shares areassociated with theutilization of theonline

banking channel among a firm’s customer base.

Although self-service technologies vary significantly across service industries and individual service

firms, banking represents an ideal setting to investigate the consequences of using self-service channels

to alter customer-firm interactions for two reasons.

First, in service firms such as retail banks, variation in the demand for organizational resources

is tied directly to customer behavior (Chase 1978,

1981; Cooper and Kaplan 1999; Fitzsimmons and

Fitzsimmons 2001). As a result, customer interaction is widely regarded as a key driver of cost and

profitability in thebanking industry. Second, banks

have a relatively long history of introducing technologies aimed at lowering the costs of customer

interaction (e.g., automated teller machines (ATMs),

touch-tone banking, centralized telephone call centers, and online banking; Clemons et al. 2002, Frei and

Harker 2000, Roth and van der Velde 1989). Anecdotal

evidence, however, suggests that the introduction of

these supposedly less-expensive means of interaction

has increased the total cost of service distribution

(Frei and Harker 2000). Among these technologies,

online banking is a particularly interesting innovation

to study because it represents an area where many

firms have pursued strategies aimed at simultaneously reducing costs, increasing revenue, and increasing customer retention with little or no recognition

that tradeoffs might exist (Hitt et al. 1999).

Using a variety of panel data methods on a large

sample of retail banking customers observed over a

30-month period at a large U.S. bank, we test whether

changes in service consumption, cost, and customer

profitability at the individual level are associated with

theadoption of onlinebanking. Wefind that customer adoption of online banking is associated with

(1) substitution, primarily from incrementally more

costly self-service delivery channels (ATM and voice

response unit (VRU)); (2) augmentation of service consumption in more costly service delivery channels

(branch and call center); (3) a substantial increase in

total transaction volume; (4) an increase in estimated

average cost to serve resulting from the combination of points (1)–(3); and (5) a reduction in shortterm customer profitability. However, we find that
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useof onlinebanking is associated with higher customer retention rates over one-, two-, and three-year

horizons. The documented relationship between the

use of online banking and customer retention remains

positive even after controlling for self-selection into

theonlinechannel. Wealso find evidencethat future

market shares for our sample firm are systematically

higher in markets with high contemporaneous utilization rates for theonlinebanking channel. This finding holds even after controlling for contemporaneous

market-share, suggesting it is not simply the result of

increased market power leading to the acquisition of

onlinebanking customers.

The focus of this paper on a specific self-service

technology and theuseof data from a singlefirm

limit the generalizability of our results. However, the

detailed data weareableto obtain from our field site

allows a uniqueopportunity to examinetheperformance implications of self-service distribution channels in the context of online banking. Future research

can makea considerablecontribution by examining

the performance implications of firm investments in

self-service distribution channels for other technologies in other industries. There also remains a significant opportunity to examine how firm investments

in capability enhancement and customer management

strategies (e.g., pricing, penalties, service tiers, etc.) in

existing self-service channels affect the performance

of customer relationships in these channels.

The remainder of the paper proceeds as follows.

Section 2 reviews related literature from marketing

and operations management and develops hypotheses based on this literature. We discuss our research

site and data in §3. Section 4 presents the research

design and methodology used in this study. Results

are presented in §5. We end with a discussion and

conclusions in §6.

2. Literature Review and Hypothesis

Development

In this section, wedraw on findings in theliterature

on consumer self-service adoption decisions as well

as the conceptual literature on customer involvement

in services (e.g., Chase 1978, 1981) to argue that the

adoption of self-service distribution channels fundamentally changes the economics of service interaction

from both the firm’s and the customer’s perspective.

Weorganizeour hypotheses around theimplications

of self-service adoption for cost structure, customer

profitability, and customer retention.

2.1. Cost Structure

The implications of customer involvement in the

“production” of services have been discussed in the

operations management literature for more than two

decades, starting with the work of Chase (1978, 1981),

Sasser (1976), and Lovelock and Young (1979). Customer involvement in the service production process

implies that adoption of self-service technologies can

affect the economics of service production from both

thefirm’s and the customer’s perspective. The overall

cost-structure implications for firms depend not only

on how such technologies affect the marginal cost of

providing service but also on how such technologies

affect customers’ consumption of service resources.

The use of self-service technologies may result in a

reduction in the consumption of service resources per

service interaction, but they may also affect the customers’ demand for service in ways that increase the

overall demand for service resources.

Self-service channels are widely regarded as having

the potential to lower the marginal cost of service

interaction from the firm’s perspective by substituting

variable cost labor for relatively fixed-cost technology

assets (Sasser 1976). Self-service technologies may also

lower the marginal cost of service interaction from

the customer’s perspective through increased convenience, accessibility, and/or reductions in wait times

(Curran et al. 2003, Bitner et al. 2000, Marshall et al.

1988, Zeithaml and Gilly 1987). A lower marginal

interaction cost from the customer’s perspective can

affect a firm’s cost structure by changing both the

overall customer demand for service interactions and

the relative rates of substitution from alternative service delivery channels.

In the context of the specific technology of online

banking, banks hopeto achievelower costs through

a “substitution effect” in which customers migrate

transactions from relatively more costly offline channels to the online channel. Cost reductions would

then follow in two specific ways. First, steps performed by decentralized labor in the branches could

be performed by centralized labor or automated with

technology. Second, customers could perform process steps that the firm had previously performed.

Both of these changes would result in a lower use

of service resources and, hence, cost per interaction,

thereby lowering overall distribution costs. However,

this channel can significantly lower the cost of interaction from the customer’s perspective, leading to a

higher demand for service transactions. Customers

interacting through theonlinechannel may not incur

the opportunity costs (e.g., time) that stem from, for

instance, traveling to the bank branch or ATM and

waiting in queues. This raises the possibility of a

“volume effect,” in which total transaction volume

increases because of increased demand for transactions in theonlinechannel morethan offset any associated reductions in transaction demand in offline

channels.
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Additionally, even the degree to which substitution

of service demand from offline channels will occur is

unclear for at least two reasons. First, substitution of

service demand between online and offline channels

may be limited to the extent that the mix of services

available across a firm’s portfolio of self-service and

employee-assisted service channels is not constant.

For example, not all banking transactions can be performed in all channels (e.g., not all transactions are

substitutable). Paper-based transactions such as withdrawals and deposits cannot be performed via online

banking. In thebanking setting, substitution is most

likely to occur from offline self-service delivery channels, where the mix of available services is most similar to that offered by online banking (e.g., automated

call centers).

Second, self-service technologies allow customers to

control service delivery in a manner that more closely

meets their needs (Dabholkar 1991). In a multichannel setting, increased customer control of the service

production process may allow the customer to customize her portfolio of interactions with the service

firm across channels (Bitner et al. 2000). This is consistent with survey-based research, which has found that

theimportanceattached to face-to-facecontact actually increased significantly as the importance attached

to remote interactions increased. This result suggests

that banking customers want increasing access to all

available delivery channels and do not necessarily

regard them as mutually exclusive or substitutable

(Durkin et al. 2003). Moreover, additional surveybased research suggests that electronic channels may

allow banking customers to become more efficient

“money managers” (Barczak et al. 1997). Because a

primary function of onlinebanking is to allow customers continuous access to detailed information on

their accounts, adoption of this self-service technology may lead to increased information monitoring

and, in turn, moreactiveaccount management in

offline assisted-service channels such as branches and

call centers, where employees are available to field

inquiries and a broader mix of services is available.

This raises the possibility that online banking could

havean augmentation effect rather than a substitution

effect in assisted-service channels.

Thus, self-service technologies such as online banking—which lower the cost per transaction from the

customer’s perspective—can lead to either a “substitution” effect (customers shift transactions from

offlinechannels to thelower-cost onlinechannel) or

an “augmentation” effect (customers increase transaction consumption in offline channels). Based on the

arguments above, to the extent that these effects are

present, the substitution effect is most likely to be

found in offline self-service channels, whereas either

substitution or augmentation effects are distinct possibilities in offline assisted-service channels. Moreover,

either of these potential effects from adoption of the

online channel can coincide with a “volume” effect

(customers facing a lower implicit cost of transacting

via the online channel increase overall service consumption). If substitution effects dominate in offline

channels, then a volume effect could arise if increased

demand for transactions in theonlinechannel more

than offsets any associated reductions in transaction

demand in offline channels. If augmentation effects

dominate in offline channels, then a volume effect

would arise naturally, because of increased demand

for transactions in both offlineand onlinechannels.

To examine these potential service demand effects,

we test the following hypotheses. Because of the difficulty in making directional predictions on changes in

transaction demand in some channels, and to clarify

when wedo and do not makesuch predictions, we

state all hypotheses in both null and alternative form.

Hypothesis 10 (H10). Transactions in offline selfservice channels do not change following the adoption of

the online channel.

Hypothesis 1A (H1A). Transactions in offline selfservice channels decrease following the adoption of the

online channel (“Substitution Effect”).

Hypothesis 20 (H20). Transactions in offline assistedservice channels do not change following the adoption of

the online channel.

Hypothesis 2A (H2A). Transactions in offline assistedservice channels change following the adoption of the online

channel (“Substitution Effect” or “Augmentation Effect”).

Hypothesis 30 (H30). Total transaction volume does

not change following the adoption of the online channel.

Hypothesis 3A (H3A). Total transaction volume

increases following the adoption of the online channel

(“Volume Effect”).

Any realized cost reduction from the customer

adoption of self-service channels depends on the

extent to which customers substitute transactions

from (or augment transactions in) traditional offline

channels, the degree of any associated increases in

overall transaction volume, and the estimated cost

of providing service through different channels. We

investigatehow thesubstitution, augmentation, and

volume effects combine to affect overall cost service under reasonable assumptions regarding the cost

per transaction for offlineand onlinechannels. In

particular, weestimatethefull cost implications of

changes in individual customer behavior surrounding theadoption of onlinebanking by combining our

estimates of changes in transaction consumption by

channel with reasonable estimates of the cost of performing transactions in each channel.
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2.2. Customer Profitability

Customer involvement in services also implies

that self-service channels may alter the customer’s

marginal benefits from service interaction through

enhanced control and perceived service quality

(Dabholkar 1991, 1996; Bitner et al. 2000). A greater

ability for thecustomer to control and customizethe

service experience may lead to higher customer satisfaction (Meuter et al. 2000) and hence higher rates of

repurchases and revenues for the firm. Alternatively,

greater control over the service experience may allow

customers who adopt self-service channels to more

closely manage their relationships with service firms

and to gain similar levels of service consumption at a

lower price. Both of these potential effects are possibilities in thespecific caseof theonlinebanking selfservice channel.

Banks have increasingly turned to profitability

enhancement as a rationale for ongoing investments

in their online banking capabilities. They believe that

the added convenience of the online channel will

encouragecustomers to consolidatemoreof their

activity in onebank through increasing both thenumber of products held (cross-selling) and the average

balance held per product (Hitt et al. 1999, Hitt and

Frei 2002, Shevlin et al. 2002, Hoffman 2002). Customer adoption of online banking may lead to either

or both of these benefits to the extent that greater control over the service experience from multiple points

of access to the same services leads to an increase in

perceived service quality.

However, Hitt and Frei (2002) found in their sample of banks that increased product adoption is not

a strong driver of the difference in estimated value

between electronic and traditional banking customers.

In addition, many standard checking and savings

accounts earn little to no interest for consumers.

Researchers have puzzled over why consumers keep

assets in such low-return accounts while simultaneously holding high levels of debt, for instance, on

credit cards (Gross and Souleles 2002). One potential

explanation is the existence of some form of transaction costs, such as the inconvenience associated

with closely managing balances in bank accounts. The

increased convenience and control associated with

onlinebanking may reducesuch transaction costs,

allowing customers to more efficiently manage their

money (Barczak et al. 1997) by transferring excess balances more frequently to higher-yield accounts within

thesameinstitution or among multipleinstitutions.

Either case could yield a reduction in net-interest

revenue. Alternatively, customers holding accounts

with minimum balance requirements could use the

added convenience and control to manage balances

just above the minimum, thereby potentially avoiding

fees.

Because a range of effects are possible, we examine

thecustomer-profitability implications of onlinebanking by testing the following hypothesis (stated in both

null and alternative form):

Hypothesis 40 (H40). There is no change in customer

profitability following adoption of the online channel.

Hypothesis 4A (H4A). There is a change in customer

profitability following adoption of the online channel.

2.3. Customer Retention

Customers incur implicit fixed costs from adopting

self-service technologies. These implicit costs include

the costs of learning to use a new technology as well

as the costs of establishing a relationship through a

new channel (Klemperer 1987). Although these costs

are sunk once the investment in a self-service relationship by the customer is made, they are relevant to the

customer when considering the choice of remaining

with a serviceprovider or incurring thesamecosts to

establish a similar relationship with another provider.

Thus, customer adoption of self-service technologies

may result in long-term benefits to the firm through

higher customer retention rates.

Because acquisition expenses associated with new

accounts are so high, financial services firms are

increasingly looking to electronic channels to increase

customer retention rates. Online channels may createadditional customer switching costs and improve

retention either because of increased product utilization or because of implicit switching costs such as

those created by learning to use a new technology

(Chen and Hitt 2002). However, customer use of

onlinebanking may reducetheimportanceof a bank’s

physical presence in any given local market, making customers more willing to switch to alternative

providers with more favorable fees and interest rates.

To investigate these effects, we test the following

hypothesis (stated in both null and alternative form):

Hypothesis 50 (H50). There is no association between

customer retention and the use of online banking.

Hypothesis 5A (H5A). There is an association between

customer retention and the use of online banking.

Someevidenceexists in theliteraturethat theuseof

online banking is associated with customer retention

rates (Hitt and Frei 2002, Xue et al. 2007). However,

prior research is silent on whether this result is due

to a particularly loyal segment of customers selecting

into the channel or is a result of increased switching

costs, enhanced service quality, or both (Dabholkar

1991, Bitner et al. 2000). We attempt to estimate a

causal link between online banking and customer

retention by testing H4 after controlling for customer

self-selection into the online banking channel.
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2.4. Market Outcomes

A direct corollary of our customer-level hypotheses

is that useof onlinebanking among a firm’s customer base may be associated with market-level performance. If theonlinebanking channel attracts more

profitable customers and/or decreases the likelihood

that they defect to competitors, then a firm should

have higher market shares in markets where it has

achieved higher rates of use of online banking among

its customer base (higher “online banking penetration rates”). To investigate the extent to which online

banking is associated with market-level performance

outcomes, we test the following hypothesis:

Hypothesis 60 (H60). There is no association between

market share and the degree of online banking penetration

among a firm’s customer base.

Hypothesis 6A (H6A). Market share is increasing in

the degree of online banking penetration among a firm’s

customer base.

3. Research Site and Data Collection

3.1. Research Site

Our research site (hereafter referred to as “National

Bank”) is one of the largest diversified financial service firms in the United States It serves millions

of customers through more than 3,000 branches in

more than 20 states and also services customers

through electronic delivery channels such as telephonebanking, ATMs, and theInternet. Thebank

provides a variety of traditional financial products

and services, in linewith thosethat could befound

at other banks of similar size and scope, and considers its retail deposit customers its core customer

base. Because of its size and operation across multiple

states, National Bank serves customers across a broad

range of demographic profiles, making it unlikely that

its customers would differ substantially from those

of most largetraditional banks. National Bank was

a top performer among its industry peer group during thetimeframeof this study (2003–2007), earning annual stock returns that averaged approximately

6.5% higher than its competitors over this period.2

Throughout the past decade, the bank has pursued a variety of alternative distribution strategies to

lower costs. These strategies have ranged from colocation of branches (e.g., supermarket branches) to

activeprograms for migrating branch traffic to ATMs.

The impetus for most of these strategies came from

either the bank’s own internal data or industry data

that estimated huge cost differentials for servicing a

transaction through traditional branches versus nontraditional branch formats and, in particular, electronic distribution channels.

2 Source. MorningStar. Industry peer group used for comparison:

Super Regional Banks.

3.1.1. Online Banking at Our Research Site. Our

research site offers a variety of services through its

onlinechannel, including theability to query account

history, open new accounts, perform balance transfers, and pay bills electronically. The company was an

early innovator in electronic banking, being among

thefirst group of financial institutions to introduce

onlinebanking through theInternet. Thebank’s strategy for the online channel largely mirrors the threepart valueproposition discussed in theprevious

section: cost reduction, revenue enhancement, and

customer retention. Consistent with this strategy, the

bank has aggressively encouraged customer migration to theonlinechannel to reducecosts and increase

the profitability of its customer base. Fees are not

charged for the basic online banking service, reflecting

thebank’s desireto encourageadoption in thehopes

of realizing cost, revenue, and retention benefits.

3.2. Data Collection

Theprimary data for this study consist of a random sample of 100,000 customers who enrolled in

theonlinebanking channel during 2006. Wehave

constructed an unbalanced monthly panel data set

on these customers for the 30-month period ranging from December 2004 to May 2007 consisting of

monthly transactions disaggregated by channel, number of accounts by type of product (checking, loan,

or investment), balances by product type, customer

profitability, tenurewith thebank, and age.

To examine the customer retention effects of online

banking, weusea second data set consisting of a

random sampleof 100,000 customers drawn from

thepopulation of all National Bank customers (both

online and offline) as of the end of December 2003.

This second data set consists of these customers’

online channel enrollment status, number of online

transactions performed during 2003, tenure, age,

number of accounts, and balances by product type. In

this second data set, all these variables are defined as

of year-end 2003. However, we also observe each customer’s relationship status (e.g., retained or defected)

as of year-end 2004, 2005, and 2006. This allows us

to investigate customer retention over a multiyear

horizon.

3.2.1. Transactions by Channel. For each customer, weobservethetotal number of monthly transactions through theonlinebanking, branch, ATM,

call center, and VRU channels.3 Access to transaction data by channel allows us to separately test

hypotheses about the effect of online banking on serviceconsumption in offlineself-serviceand assistedservicechannels (H1, H2). Table1 shows thedistribution of the types of transactions performed through

3 The VRU is an automated call center.
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Table 1 Distribution of Transactions by Channel During June 2006

Transactions (%) Cumulative (%)

Branch

Deposit 5880 5880

Cash check 2725 8605

Verifyfunds 671 9276

Payment 424 9701

Purchase 285 9985

Withdrawal 012 9997

Miscellaneous 003 10000

ATM

Withdrawal 6521 6521

Inquiry1804 8325

Deposit 1480 9804

Transfer 130 9934

Purchase 058 9992

Payment 008 10000

Online

Queryhistory 5103 5103

View basic account information 3883 8985

Payment 548 9533

Transfer 406 9940

Download financial information 060 10000

Notes. This table shows the distribution of transaction types for each of the

branch, ATM, and online channels. The percentages reported represent all

transactions performed in each channel at National Bank during June 2006.

Because of data limitations, similar breakdowns cannot be provided for either

of the phone channels (VRU or call center).

thebranch, ATM, and onlinechannels for National

Bank’s entirecustomer baseduring June2006.4 The

table shows that the branch is used heavily for monetary transactions (86% of branch transactions involve

deposits or cashed checks), the ATM is used for

both monetary and information-based transactions

(98% of ATM transactions aredeposits, withdrawals,

or account inquiries), and theonlinebanking channel appears to be used primarily to monitor account

information (approximately 90% of online transactions areaccount inquiries).

Table1 suggests that themix of transactions performed is fundamentally different across these channels. Not all transactions aresubstitutableacross

channels. However, inquiry-based transactions such

as balance inquiries at ATMs or funds verifications in

branches aredirectly substitutablevia onlinebanking.

In addition, transfers of funds between accounts performed in thebranch will becounted as a withdrawal

from one account and a deposit into another. Thus,

onlinetransfers can substitutefor somedeposit- and

withdrawal-based transactions in the branch.

3.2.2. Customer Profitability. Theprimary performance measure investigated in this paper is National

Bank’s measure of customer profitability (PROFIT).

4 Becauseof data limitations, weareunableto perform a similar

breakdown for call center and VRU transactions.

PROFIT is defined as net interest income plus fees

and service charges less product unit costs and provisions for loan losses. The unit costs for each product

type in each channel are determined by the bank’s

activity-based costing system and consist of allocated

overhead related to items such as personnel, supplies, telephone, equipment, occupancy, and processing. Unit costs, interest, and fee revenues in PROFIT

are calculated across all deposit, loan, and investment

products a customer holds and thus capture a measureof performanceof thefull customer relationship

with thefirm.

It is important to notethat thecosts in this profitability calculation are allocated at the product level,

with each product having an associated unit cost that

can bethought of as theaveragecost of supporting a customer in a given product. As a result, estimated costs are not driven by customer behavior, but

rather, customer-level costs are determined solely by

thecustomer’s choiceof product portfolio. This is

a limitation of our study, as changes in this measureof customer profitability around theadoption of

online banking will only partly reflect true underlying

changes in customer behavior through the revenue

component. However, it is the best measure of the full

profitability of the customer relationship at the firm

we study. Moreover, this measure of customer profitability will capturetheextent to which theadoption

of online banking is associated with changes in the

profitability of the average customer in a given product portfolio. Later in thepaper, weestimatethefull

cost implications of changes in individual customer

behavior surrounding theadoption of onlinebanking

by combining our estimates of changes in transaction

consumption by channel with reasonable estimates of

the cost of performing transactions in each channel.

3.2.3. Customer and Account Characteristics. For

each customer, we observe the number of retail

deposit, loan, and investment accounts they hold

(NDEP, NLOAN, NINVEST) as well as end-of-month

balances in those accounts (BALDEP, BALLOAN,

BALINVEST). We also observe length of the relationship with the bank, measured in years (TENURE), as

well as the age for each customer.

4. Research Methodology

Our basic approach to testing H1–H4 is to measure

how service consumption and customer profitability

change, relative to a control group, after customers

adopt theonlinebanking channel. As a starting point

for constructing our control group, weexploit therelatively large group of customers at our research site

who enroll in, but do not subsequently use, the online

banking channel. These “inactive enrollers” likely

result from the bank’s marketing practices, whereby
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current customers are actively encouraged to sign up

for theonlinechannel if they havenot. Thelargenumber of such customers at our research site is consistent

with prior research, which suggests that a substantial

portion of customers who adopt online services discontinueor do not usetheserviceafter they adopt

it (Parthasarathy and Bhattacherjee 1998). Survey evidence specific to online banking customers also suggests that there is, in general, a substantial segment of

customers who have activated online accounts but use

the service infrequently (Sarel and Marmorstein 2003).

An alternative starting point for constructing a control group would be the set of customers who never

enroll for access to the online banking channel during

our sample period. However, in untabulated analyses,

when compared with “inactive enrollers,” this group

demonstrated larger differences with active adopters

in levels and trends in transaction demand across

channels as well as customer profitability. Given the

bank’s active marketing efforts to enroll customers

in theonlinechannel, much of which happen at

thepoint of customer contact, it is not surprising

that customers who never enroll in online banking would have relatively low product and transaction demand. These customers interact with the bank

less frequently, giving the bank less opportunity to

actively encourage enrollment in the online channel.

Thus, using “inactive enrollers” as the starting point

for constructing our control group yields a set of customers who are never truly exposed to the event of

interest (activeadoption of theonlinechannel), but

naturally have similar levels of product and transaction demand as well as customer profitability.

We further refine this control group by selecting a subset of propensity score-matched “inactive

enrollers.” We used a logistic propensity score model

to estimatetheprobability of a customer using5 online

banking within six months subsequent to a given

enrollment month as a function of tenure; the average number of products and balances held over the

six months prior to enrollment; the average number

of transactions by channel in the six months prior to

enrollment; changes in the number of transactions by

channel over the six months prior to enrollment; and

changes in the number of products and balances held

over the six months prior to enrollment. By modeling thepropensity to becomean activeadopter in this

way, weareattempting to identify a control group

that is similar to active adopters in both levels and

changes in preadoption characteristics.

5 We define a customer as using the channel if they perform one

or moretransactions in theonlinechannel within six months of

enrollment for access to online banking. Later in the paper, we further distinguish among online adopters in terms of those who use

the channel “passively” versus “actively.”

For our tests of H1–H4, weusethefollowing strategy to select the final sample for analysis: Starting with

the random sample of 100,000 customers who enrolled

in online banking between January and December

2006, weselect thesampleof all customers for whom

wehaveat least six months preadoption and six

month postadoption data. This yields a sample of

80,658 customers. Of these customers, 40,631 are classified as “inactive enrollers” (no recorded transaction

activity in the online channel postenrollment) with

the remaining 40,027 classified as “adopters” (at least

one recorded transaction in the online channel postenrollment). To facilitate a matched control group for

adopters, we also randomly sampled an additional

50,000 inactive enrollers from National Bank’s customer database, yielding a sample of 90,631 inactive

enrollers as candidates for our matched control sample. For each adopter, we then identified the matched

inactive enroller with the closest estimated propensity to actively usetheonlinebanking channel subsequent to enrollment. This procedure yields a sample

of 40,027 adopters and a control group of 40,027 inactiveenrollers. Thesetwo groups constitutethesample

used for all tests of H1–H4.

As will be demonstrated in the sections that follow,

our propensity-matched set of “inactive enrollers”

has several properties that make them a natural candidateas a control group for studying thebehavior of “adopters” including demonstrating broadly

similar patterns in behavior prior to enrollment in

theonlinebanking channel. Our analysis approach

includes standard “difference-in-difference” estimation as well as econometric specifications that readily account for unobserved fixed differences in levels

of transaction behavior and profitability between customers and time periods (months and years). The

major threat to our strategy for identifying the effect

of online banking on customer behavior is differential trends in behavior across customers. In particular,

our approach would beinvalid if transaction behavior and customer profitability were trending differently between adopters and our control group before

enrollment in the online banking channel. Untabulated results reveal no difference in preenrollment

trends in behavior for any of the performance measures used in this study.

4.1. Substitution/Augmentation (H1 and H2) and

Volume (H3)

We test H1 and H2 in two different but complementary ways. First, we measure differences in the change

in transaction consumption before and after the adoption of online banking between adopters and the

control group. These simple difference-in-difference

estimates provide a useful starting point for measuring behavioral changearound theadoption of online
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banking, but may not adequately control for differential postenrollment trends between adopters and the

control group caused by changing product portfolios,

balances, and other factors over time.

Our second approach supplements these estimates

with a more detailed econometric specification that

controls for these potentially confounding factors. We

face two key specification issues in this approach:

(1) the number of transactions per month for a

given customer is discrete and is frequently zero,

particularly when measured for each channel separately; and (2) there are likely to be persistent (fixed)

differences across customers in monthly transaction

volume related to unobservables such as access to

technology as well as income and other demographic

characteristics, which need to be controlled for. We

deal with both issues via the conditional fixed-effects

Poisson model of Hausman et al. (1984). In the basic

Poisson model, the probability of observing yit transactions in a given offline channel for customer i at

time t is

P yit = e−ityit

it

yit! 	

where it, the Poisson parameter, is the expected

valueof yit. We test H1 by modeling this parameter as

Eyit = exp0 + 1POSTit + 2POSTit × PASSIVEi

+ 3POSTit × ACTIVEi

+ EnrollMonthit + Xit + i	 (1)

where i is the individual customer fixed effect and

Xit represents a vector of time-varying control variables.6 EnrollMonthit takes on a value of 1 if customer i enrolls in theonlinechannel in month t and

equals 0 for all nonenrollment months. The inclusion of this indicator is intended to capture transient

effects occurring during the enrollment month. For

example, customers tend to visit the branch or phone

the call center to set up access to the online channel,

which yields a spike in transaction activity during the

month of enrollment.

To test whether different levels of postadoption

activity in theonlinechannel giveriseto different

effects on transaction patterns in offline channels,

we further segment adopters into two groups: those

6 The exponential form of the Poisson model ensures nonnegativity

for  and allows estimation of the parameters through maximum

likelihood in the standard way. In estimation the fixed effect, , is

not estimated directly, but rather is conditioned out by modeling

the event in the likelihood function as the sequence of transactions

for a customer conditional on total transactions for that customer

over time. This yields a likelihood function that is globally concave

and readily maximized (see Hausman et al. 1984 or Becker and

Henderson 2000 for details). We also ran all analyses using the negative binomial fixed-effects count data model without substantive

changes in the results.

who perform fewer than the median number of

onlinetransactions in thesix months subsequent to

enrollment (passive adopters) and those who perform morethan themedian number of onlinetransactions in the six months subsequent to enrollment

(activeadopters).7 PASSIVE and ACTIVE areindicators for these two groups, respectively. Intuitively,

segmentation of our sample into these groups allows

our estimates of any substitution or augmentation

effects around the adoption of online banking to vary

with how extensively the customer has used this

technology.8

For each customer in the sample, POST is an indicator taking on a valueof 1 for all post-onlineenrollment months and 0 otherwise. This variable is

our primary focus for testing H1 and H2. The coefficient 1 captures any changes in average transaction consumption in offlinechannels for thecontrol

group of inactive enrollers. If adoption of the online

banking channel leads to substitution from (augmentation in) offline channels, then we expect 2 and 3

to be negative (positive), as these coefficients captureaveragechanges in transaction consumption in

offlinechannels relative to the control group for passive and active adopters, respectively. Furthermore,

we expect 2 < 3, as any substitution or augmentation effects of the online channel should be more

prevalent among adopters who are most active in the

channel.

To fully examine substitution and augmentation

among channels, we estimate Equation (1) separately

for transactions through each offline channel (branch,

ATM, VRU, and call center), as well as for the total

7 Among adopters, the mean (median) number of online transactions performed in the six months subsequent to enrollment in the

online banking channel is 28.1 (18), with a standard deviation of

35.8. Adopters in the bottom and top deciles have mean levels of

online transactions of 4.5 and 64.5, respectively, in the six months

subsequent to enrollment in the online banking channel.

8 Because the PASSIVE and ACTIVE variables are simply indicators

for which particular sample a customer belongs to, main effects of

these variables are not estimated, because they would represent the

average difference over time among the passive, active, and control samples. Such main effects cannot be estimated in fixed-effects

panel data models because these econometric methods eliminate

individual fixed effects, which would subsume any such average

main effects. Our panel data methods essentially “wash out” these

fixed average differences between the different samples so that our

estimates of the online adoption effects are not confounded by

unobserved heterogeneity across customers. Our approach of interacting time-constant and time-varying variables in a fixed-effects

framework is consistent with prior literature in a wide variety of

contexts (Ashenfelter and Rouse 1998, Becker and Henderson 2000,

Gross and Souleles 2002, Wooldridge 2002, Lapré and Tsikriktsis

2006, Campbell 2008). However, the estimates of the postadoption

effects reported in Tables 3, 4, and 6 are directionally and substantively similar when alternative specifications are used that directly

include PASSIVE and ACTIVE as control variables without accounting for customer fixed effects.
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number of transactions in all offline channels. We test

the volume effect (H3) by replacing yit in Equation (1)

with thetotal volumeof transactions across all channels (including online banking transactions).

Weincludein Xit thenumber of accounts by product type(NDEP, NLOAN, and NINVEST), as well as

balances by product type (BALDEP, BALLOAN, and

BALINVEST), in all specifications to avoid attributing any changes in transaction activity to postadoption useof onlinebanking that areactually dueto

theadoption of additional products.9 Wealso include

in Xit year and month indicators to control for seasonality and general time trends. Standard errors are

adjusted for heteroskedacticity across customers as

well as serial correlation within customers in all specifications prior to inference (Petersen 2009).

4.2. Customer Profitability (H4)

We test H4 using a similar approach to our tests for

H1–H3, including the use of both standard differencein-difference and econometric estimation. Because the

particular estimation issues that arise from the discrete nature of the dependent variables used in Equations (1) and (2) do not arisefor themeasures of

financial performance used in this study, we test

theeffect of adoption of theonlinechannel on customer profitability (H4) using the following linear

specification:

Pit = Pit−1 + 1POSTit + 2POSTit × PASSIVEi

+ 3POSTit × ACTIVEi + EnrollMonthit

+ i + it	 (2)

where Pit denotes PROFIT, i is the customer-specific

fixed effect, and it is a random error term. We

includepast performanceto control for trends in

performance unrelated to the adoption of onlinebanking. We estimate Equation (2) in first-differences

to eliminate the customer-specific fixed effect. The

use of differences controls for individual effects in

the levels of these performance metrics. Equation (2)

is estimated using two-stage least squares, with Pit−2

as an instrument for the lagged first-difference in

PROFIT, Pit−1, because of the endogeneity of the

lagged dependent variable in the first-differenced

9 Untabulated analyses suggest that the number of accounts

increases on average for adopters prior to going online. This is consistent with the bank’s marketing practices, whereby when existing

customers open new accounts, they are actively encouraged to sign

up for the online channel. Results are very similar when the number of accounts and account balances are not included. We also

ran the analyses on a subsample of customers who experienced no

changein their product portfolio over theentiresampleperiod and

obtained similar results. This suggests that the effects we attribute

to useof onlinebanking arenot driven by changes in product holdings coincident with theadoption of onlinebanking.

model (Anderson and Hsiao 1982, Nickell 1981).10 We

do not includecontrols for number of products or

balances, as these are components of the customer

profitability measure, and either or both of these may

be the actual sources of any observed changes in

profitability around theadoption of onlinebanking.11

Standard errors are adjusted for heteroskedacticity

across customers as well as serial correlation within

customers in all specifications prior to inference.

4.3. Retention (H5)

We test H5 by using probit regression to estimate the

following cross-sectional model on our random sampleof 100,000 customers (both onlineand offline) as

of year-end 2003:

PrRETAINi = 1  ·

= f 0 + 1ONLINEUSEi + 2TENUREi

+ 3AGEi + Xi (3)

RETAIN is an indicator variablethat takes a value

of 1 if a customer as of December 31, 2003, remains

with thebank oneyear later. Wealso investigate retention over two- and three-year horizons by

redefining RETAIN to takeon a valueof 1 if a customer remains as of year-end 2005 and 2006, respectively. ONLINEUSE is theaveragenumber of monthly

transactions performed by the customer in the online

channel during 2003 and will be 0 for all customers

who arenot enrolled in theonlinechannel. Thesign

and significanceof 1 is thebasis for testing H5.

The probability of retention is expected to increase

over the tenure of relationships as customers consolidatemorebusiness with oneprovider or simply

because of switching costs inherent in an established

relationship. AGE is included to control for differences among age groups in the propensity to remain

with thebank.12 Xi is a vector of further control

variables, including the number of accounts held by

type(NDEP, NLOAN, NINVEST), balances in those

accounts (BALDEP, BALLOAN, BALINVEST), and the

10 The error term in the first-differenced version of Equation (2) is

a first-differenced error term from the corresponding levels model

and will be correlated with the lagged dependent variable by construction. This leads to biased coefficient estimates (Nickell 1981).

Instrumenting with the twice-lagged level of the dependent variable is a valid approach for overcoming this problem under the

assumption of no second-order serial correlation.

11 Wealso ran theanalyses on thesubsampleof customers who

experienced no change in their product portfolio over the entire

sample period and obtained similar results, suggesting that the

profitability effects weattributeto theuseof onlinebanking are

not driven by changes in product holdings that coincide with the

adoption of onlinebanking.

12 Results are robust to the inclusion of higher-order terms of

TENURE and AGE.
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level of competition faced by National Bank in a customer’s local market (measured as the percentage of

deposits in the market controlled by National Bank’s

competitors). All control variables are measured as of

December 31, 2003.13

Any documented increase in retention associated

with theonlinebanking channel may betheresult

of a particularly loyal segment of customers selfselecting into the online channel rather than indicating that online banking increases retention rates

through increased switching costs or enhanced service quality. As a consequence of this self-selection,

theonlineindicator and usevariables in Equation (3)

are endogenous. We also consider a linear version of

Equation (3)

RETAINi = 0 + 1ONLINEUSEi + 2TENUREi

+ 3AGEi + Xi + i (4)

and allow for potential correlation between

ONLINEUSEi and i.

To address this potential endogeneity, we estimate

Equation (4) using two-stage least squares with variables measuring the availability and prevalence of

high-speed Internet access in a customer’s zip code

serving as instruments for ONLINEUSE. Thedata

on high-speed Internet access comes from the Federal Communications Commission (FCC).14 TheFCC

data report (1) whether a particular zip code has

any companies providing high-speed broadband services such as DSL or cable modem access; (2) whether

a zip code has between one and four providers of

these services; and (3) for those zip codes with more

than four providers, the total number of providers of

theseservices. Becauseof thenatureof theFCC data,

weinstrument theonlinedummy with a dummy

indicating between one and four high-speed Internet

access providers in a customer’s zip code, a dummy

indicating four or moreproviders, and thenumber

of providers for customers in zip codes with four

or more high-speed Internet access providers. These

variables are expected to be determinants of the decision to useonlinebanking, becausetheavailability

of high-speed Internet access should increase the convenience of performing online banking transactions.

Moreover, availability of high-speed Internet access

13 In untabulated results, we also included a full set of market area

dummies to control for geographic characteristics that may be systematically related to retention, such as branch and ATM density

within a customer’s market area, without any substantive changes

in theresults.

14 FCC form 477 data areavailableat http://www.fcc.gov/wcb/

iatd/comp.html. The FCC collects these data to determine the

extent of local telecommunications competition and deployment of

broadband services.

within a customer’s zip code is unlikely to be a determinant of a customer’s decision to remain with the

bank independent of the decision to utilize online

banking (i.e., these instruments are unlikely to be correlated with the error term i. In first-stage regressions, each of these variables is significant p < 001,

with theprobability of onlineuseincreasing in the

availability and number of high-speed Internet access

providers. The first-stage R2 is approximately 0.09,

which is relatively large in the context of related

consumer-level studies in the banking industry (Hitt

and Frei 2002, Gross and Souleles 2002).

4.4. Market Outcomes (H6)

We test H6 by estimating the following model:

MarketSharemt+1 = 0 + MarketSharemt

+ 1OnlineRatemt + mt+1	 (5)

where “m” denotes markets and “t” denotes years,

respectively. National Bank operates branches across

369 internally defined banking markets that consist

of contiguous zip codes. MarketShare is theshareof

all bank account deposits in a banking market that

are controlled by National Bank. Data on market-level

deposits come from the Federal Deposit Insurance

Corporation’s Summary of Deposits database. OnlineRate denotes online penetration among National

Bank’s customer base in a given market and is defined

as theproportion of all of National Bank’s consumer

accounts in that market that are linked to the online

banking channel.15 There may be a simultaneous relationship between market share and online penetration

rates if increased market power enhances the firm’s

ability to acquiremoreaccounts from therelatively

profitable segment of online banking customers.

To eliminate this alternative explanation, we control for current market share. H6 would be supported

if online penetration rates among the firm’s customer

base are associated with future market shares after

controlling for current market share 1 > 0. Wealso

estimate a version of (5), where all variables are firstdifferenced to further control for unobserved heterogeneity across markets. We have access to National

Bank’s online banking penetration rates across markets only for year-end 2002 and 2003, limiting our

analysis to these two years. In the first-difference

specification, weusetwo-stageleast squares, with

MarketSharem	 t−1 as an instrument for MarketSharemt,

because of the endogeneity of the lagged dependent

variable in the first-differenced model (Anderson and

Hsiao 1982, Nickell 1981).

15 An account is linked to theonlinebanking channel if thecustomer has adopted the channel and set up the account to be

accessed online.
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5. Results

5.1. Substitution and Augmentation

Effects (H1 and H2)

Before discussing tests of H1 and H2 in detail, we

begin with a few observations from Figures 1(a)

and 1(b). These figures plot the mean number of transactions by channel for all customers in our sample

with at least 12 months of preadoption and postadoption data. In these figures, channels are grouped

into offline “self-service” (ATM and VRU) and offline

“assisted-service” (branch and call center) channels.

Figure1(a) shows that, relativeto all groups, there

appears to be substitution away from other selfservicechannels on adoption of theonlinechannel by

activeadopters. Figure1(b) shows no apparent substantial changes in transaction behavior in “assisted

service” channels. Although there appears to be a

peak at the enrollment month, this is likely the result

of transient behavior associated with setting up the

online channel. Thus, a baseline observation is that

substitution appears to occur only from channels that

are estimated to be just incrementally more costly

than theonlinechannel.

Figures 1(a) and 1(b) also demonstrate some persistent differences in the average level of transaction

behavior over time between each group of customers.

However, differences in levels of transaction activity

between groups are relatively fixed prior to adoption

of the online banking channel. These differences in

levels can be readily accounted for through the fixedeffects models proposed in §4 or through the use of

standard difference-in-difference techniques. That differences in levels of preadoption activity are relatively

fixed, and trends in preadoption behavior are similar,

between adopters and the control group lends credence to our choice of propensity-matched inactive

enrollers as an appropriate control group for passive

and activeadopters.

Panel A of Table 2 contains estimates of differences in mean transaction activity before and after

enrollment in the online banking channel for the control group, passiveadopters, and activeadopters.

The patterns revealed in Table 2 are broadly consistent with thoseof Figure1. Transaction volumein

offline self-service channels (ATM and VRU) shows

a significant declineonly for thegroup of active

adopters. Active adopters appear to reduce transactions in offline self-service channels by approximately

1.3 transactions per month on average, with this

decline coming exclusively from substitution away

from the VRU channel. However, the results in Table 2

point to thepotential of an augmentation effect of

onlinebanking on transaction consumption in offline

assisted-service channels (branch and call center).

On average, transactions in assisted-service channels

Figure 1(a) Transactions in Offline Self-Service Channels:

ATM and VRU

1

3

4

5

6

7

2

–12 –10 –8 –6 –4 –2 0 2 4 6 8 10 12 Number of transactions

Event time

Inactive

Passive

Active

Figure 1(b) Transactions in Offline Assisted-Service Channels:

Branch and Call Center

Number of transactions

–12 –10 –8 –6 –4 –2 0 2 4 6 8 10 12

Event time

Inactive

Passive

Active

0

3

2

1

Figure 1(c) Total Transaction Volume: Online and Offline Channels

–12 –10 –8 –6 –4 –2 0 2 4 6 8 10 12

Number of transactions

 Event time

Inactive

Passive

Active

2

4

6

8

10

16

14

12

Notes. Each figure shows the mean number of transactions through each set

of channels for customers who adopted online banking during 2006. Time is

recorded in event time, with “0” in each graph corresponding to the month of

adoption. Passive (active) users are defined as adopters with average postadoption online transactions per month below (above) the median number of

monthlyonline transactions for all adopters, with at least one postadoption

transaction through the online channel.

increase for inactive enrollers as well as both passive

and active adopters. Moreover, the change in transaction volumein thesechannels for activeadopters

is larger than thechangefor both inactiveenrollers
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Table 2 Descriptive Statistics for Online Adopters

Means with standard deviations in parentheses

Control group (propensity-matched

inactive enrollers) Passive adopters Active adopters

Before After Change Before After Change Before After Change

Panel A: Transactions

Branch 124 134 010∗ 151 173 023∗+ 175 228 053∗+++

214 223 124 496 525 153 282 385 249

Call center 018 022 004∗ 019 024 004∗ 031 035 004∗

064 074 055 053 057 048 325 146 194

ATMs 212 230 018∗ 248 270 023∗+ 341 354 014∗++

471 502 270 431 442 284 526 442 374

VRU 141 157 01∗ 151 140 −011∗+ 290 145 −145∗+++

442 478 237 419 425 253 628 405 452

Total assisted-service 142 156 014∗ 170 197 027∗+ 206 263 058∗+++

(branch and call center) 236 249 141 505 533 166 438 419 324

Total self-service (ATM and VRU) 353 387 034∗ 398 410 012∗+ 631 499 −132∗+++

705 753 376 655 664 397 885 641 619

Total offline (assisted service 495 543 048∗ 569 607 039∗+ 836 762 −074∗+++

and self-service) 817 874 433 869 889 462 1047 792 745

Total transaction volume 495 543 048∗ 569 746 177∗+ 836 1753 917∗+++

(offline and online) 817 874 433 869 896 499 1047 1157 1067

Panel B: Customer profitability

Customer profitability2289 3085 796 2332 2817 485 2034 2807 773

7022 7413 7402 6827 9178 7475 6266 6681 5267

Notes. This table shows mean levels and changes of selected variables before and after enrollment in online banking for inactive enrollers (no transactions in

the online channel subsequent to enrollment in the channel), passive adopters (fewer than the median number of transactions in the online channel subsequent

to enrollment in the channel), and active adopters (greater than the median number of transactions in the online channel subsequent to enrollment in the

channel) respectively. ∗Denotes significantlydifferent than 0 at least at the 10% level.

+Denotes that the change in the selected variable is significantlydifferent from the change for inactive enrollers at least at the 10% level.

++Denotes that the change in the selected variable is significantlydifferent from the change for passive adopters at least at the 10% level.

and passive adopters (0.58 compared to 0.27 and

0.14, respectively). These differences-in-differences for

activeadopters relativeto both groups arestatistically significant at the 1% level. Thus, active users

of the online channel appear to increase activity in

costly offline assisted-service channels relative to passiveand nonusers of thechannel.

Turning to our formal tests of H1 and H2,

Table 3 shows results from estimation of Equation (1).

Results are largely consistent with those shown in Figure 1 and Table 2. The coefficient estimates on POST×

PASSIVE and POST × ACTIVE in columns 2 and 3

demonstratethat after adoption of theonlinechannel,

transactions in the branch channel increase for passive

adopters, relative to the control group (coefficient =

0035; p < 0001), and that transactions in thebranch

and call center channels increase for active adopters,

relativeto both passiveadopters and thecontrol

group (coefficients = 0083, 0.034 for branch and call

center, respectively; p < 0001, p < 005, respectively).

These results point to an augmentation effect of selfservice adoption in assisted-service channels.

In contrast, the coefficient estimates on the postadoption indicators in columns 4–5 of Table3 demonstratesubstitution away from theATM and VRU

channels, respectively. The channel demonstrating the

largest postadoption substitution effect is the VRU.

The estimates of the substitution effect for active

adopters in the VRU channel suggest that, on average, after adoption, transactions in this channel will

declineby 41% and 59% relativeto passiveadopters

and the control group, respectively.16 There appear to

be smaller rates of substitution from the ATM channel. For active adopters, the coefficient estimates for

this channel suggest a reduction in ATM transactions

16 The postadoption substitution effect for active adopters in any

postadoption period, relativeto thecontrol group, can becomputed

as 100 ∗ exp1 + 3 − exp1. The first expression is simply the

percentage of change in VRU transactions after adoption of online

banking for the average active adopter, whereas the second expression represents the implied percentage of change for inactive enrollers

if all else is equal. Similarly, the same effect for active adopters relativeto passiveadopters can becomputed as 100 ∗ exp1 + 3 −

exp1 + 2.
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Table 3 Substitution Between Offline and Online Service Channels

Total transaction

Total offline Branch Call center agent ATM VRU volume

POST 0037∗∗∗ 0020 0104∗∗∗ 0021∗ 0063∗∗∗ 0017

424 127 515 186 338 007

POST × PASSIVE −0050∗∗∗ 0035∗∗ 0007 −0025∗∗ −0185∗∗∗ 0056∗∗∗

662 205 001 212 1019 626

POST × ACTIVE −0236∗∗∗a 0083∗∗∗a 0034∗∗a −0105∗∗∗a −0816∗∗∗a 0245∗∗∗a

2188 574 203 753 2987 3265

Number of deposit accounts 0233∗∗∗ 0253∗∗∗ 0366∗∗∗ 0246∗∗∗ 0153∗∗∗ 0252∗∗∗

2334 3166 1968 2754 1529 3276

Number of loan accounts 0047∗∗∗ 0066∗∗∗ 0121∗∗∗ 0046∗∗∗ −0016 0028

476 645 324 325 012 017

Number of investment accounts −0012 −0010 0028 −0014∗∗ −0043 −0010∗∗∗

135 100 127 201 159 262

Deposit account balances ($000s) 000001 000002∗∗∗ 000006∗∗∗ −000001∗∗∗ −000005∗∗∗ 000001

149 313 283 296 347 077

Loan account balances ($000s) −000002∗∗∗ −000001 −000004 −000002∗∗ −000001 −000002∗

283 098 156 255 144 187

Investment account balances ($000s) 000001 000001 000003 −000004 −000003 000001

119 119 086 060 112 113

EnrollMonth 0123∗∗∗ 0240∗∗∗ 0793∗∗∗ −0007 0077∗∗∗ 0218∗∗∗

2172 3449 3789 143 601 4170

Year indicators +++ +++ +++ +++ +++ +++

Month indicators +++ +++ +++ +++ +++ +++

R2 within (%) 13.38 17.14 1.42 4.30 7.69 25.86

Number of observations 1,370,661 1,342,716 1,031,823 1,108,415 796,420 1,378,215

Notes. Absolute values of z-statistics based on standard errors adjusted for correlation within customers over time are in parentheses. EnrollMonth = 1 for

month of adoption, 0 otherwise. POST = 1 for postadoption months and 0 otherwise. PASSIVE = 1 for adopters with fewer than the median number of

postadoption online transactions and 0 otherwise. ACTIVE = 1 for adopters with more than the median number of postadoption online transactions and 0

otherwise. +++ denotes jointlysignificant at the 1% level using Chi-squared test. R2 within denotes the R2 from a traditional fixed-effects regression and

captures the proportion of within-customer variation in each dependent variable over time that is explained by within-customer variation over time in the

independent variables. ∗p < 0.10; ∗∗p < 0.05; ∗∗∗p < 0.01. a

Denotes that the coefficient for active adopters is significantlydifferent than that for inactive adopters at least at the 10% level.

of 8% and 10% on average, relative to both inactive adopters and the control group, respectively. The

net effects of these results are 17% and 22% reductions in total offlinetransactions for activeadopters

relativeto passiveadopters and thecontrol group,

respectively. Overall, these results provide support

for H1A and H2A, demonstrating substitution from

offline self-service channels (ATM and VRU) and augmentation in offline assisted-service channels (branch

and call center). These two effects yield a net substitution effect in total offline transactions, because

therateof substitution in offlineself-servicechannels is greater than the rate of augmentation in offline

assisted-service channels.

5.2. Volume Effect (H3)

Figure1(c) is similar to Figures 1(a) and 1(b), but

plots total transaction volumein all channels (including the online channel) for each subsample. This figure suggests a substantial volume effect for active

adopters: the mean number of total transactions

appears to climb from less than 8 to more than 14 for

active adopters. The differences in preadoption versus postadoption means in total transaction volume

reported in panel A of Table 2 confirm the evidence in

Figure1(c), with total transactions increasing by 9.17,

1.77, and 0.48 for activeadopters, passiveadopters,

and inactive enrollers, respectively. The increase in

transaction volumefor activeadopters is significantly

different than the increases for both passive adopters

and inactive enrollers at the 1% level.

Column 6 of Table3 shows theresults from estimation of Equation (1) with total transaction volume

(including online transactions) as the dependent variable. These estimates suggest that on average after

adoption, transaction volumes increase by about 22%

and 28% all else being equal for active adopters relativeto both passiveadopters and thecontrol group,

respectively. This substantial increase in total transaction volumefollowing theadoption of onlinebanking

channel provides support for H3A.
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Table 4 Postadoption Trend Analysis of Substitution Between Offline and Online Service Channels

Total transaction

Total offline Branch Call center agent ATM VRU volume

POST1 0055 0037 0082∗∗∗ 0026 0103 0003

013 043 332 039 009 352

POST1 × PASSIVE 0007 0062∗∗∗ 0125∗∗∗ 0025 −0096∗∗∗ 0082∗∗∗

138 494 304 038 626 829

POST1 × ACTIVE −0098∗∗∗a 0147∗∗∗a 0217∗∗∗a −0005∗∗∗a −0541∗∗∗a 0343∗∗∗a

1365 829 544 263 2526 3826

POST2 0041 −0005 0109 0011 0119 0044

087 157 033 167 069 143

POST2 × PASSIVE −0011∗∗∗ 0056∗∗∗ −0003 0011∗∗∗ −0121∗∗∗ 0067∗∗∗

402 1076 023 281 2256 497

POST2 × ACTIVE −0177∗∗∗a 0126∗∗∗a 0092∗∗∗a −0049∗∗∗a −0763∗∗∗a 0328∗∗∗a

1882 649 274 514 2797 3200

POST3 0047∗∗∗ 0001∗∗∗ 0111 0012 0137 0045∗∗∗

304 327 015 168 145 522

POST3 × PASSIVE −0062∗∗∗ 0024∗∗∗ −0044 −0021∗∗∗ −0217∗∗∗ 0060∗∗∗

855 325 101 321 1028 353

POST3 × ACTIVE −0234∗∗∗a 0079∗∗∗a 0049∗∗∗a −0091∗∗∗a −0852∗∗∗a 0259∗∗∗a

2276 329 528 898 2850 2415

POST4 0034∗∗∗ −0015∗∗∗ 010 −0001∗∗∗ 0128∗∗∗ 0031∗∗∗

466 401 065 213 314 548

POST4 × PASSIVE −0073∗∗∗ 0030∗∗∗ −0039 −0038∗∗∗ −0244∗∗∗ 0062∗∗∗

887 247 088 380 1052 459

POST4 × ACTIVE −0254∗∗∗a 0054∗∗∗a 0036∗∗∗a −0112∗∗∗a −0864∗∗∗a 0208∗∗∗a

2275 243 664 879 2767 4108

Number of deposit accounts 0238∗∗∗ 0256∗∗∗ 0372∗∗∗ 0259∗∗∗ 0152∗∗∗ 0261∗∗∗

3266 3155 1962 2748 1522 3271

Number of loan accounts 0049∗∗∗ 0070∗∗∗ 0120∗∗∗ 0043∗∗∗ −0003 0032∗∗∗

491 652 334 334 001 433

Number of investment accounts −0002 −0001 0016 −0029 −0017 −0002

133 100 131 148 155 086

Deposit account balances ($000s) 000002 000002∗∗∗ 00004∗∗∗ −000007∗∗∗ −000003∗∗∗ 000005∗∗∗

161 310 280 300 350 165

Loan account balances ($000s) −00002∗∗∗ −00002 −00003 −00003∗∗∗ −00001 −00002∗∗∗

295 104 163 262 154 218

Investment account balances ($000s) −000004 −000003 000001 −000006 −00005 −000004

125 120 093 054 126 118

EnrollMonth 0159∗∗∗ 0268∗∗∗ 0822∗∗∗ 0013∗∗∗ 0143∗∗∗ 0210∗∗∗

2172 3352 3743 245 471 3271

Year indicators +++ +++ +++ +++ +++ +++

Month indicators +++ +++ +++ +++ +++ +++

R2 within (%) 1490 1980 150 440 810 2810

Number of observations 1,370,661 1,342,716 1,031,823 1,108,415 796,420 1,378,215

Notes. Absolute values of z-statistics based on standard errors adjusted for correlation within customers over time are in parentheses. EnrollMonth = 1 for

month of adoption, 0 otherwise. POST1, POST2, POST3, and POST4 = 1 for post adoption months 1–3, 4–6, 7–9, and 10–12, respectively, and 0 otherwise.

PASSIVE = 1 for adopters with fewer than the median number of postadoption online transactions and 0 otherwise. ACTIVE = 1 for adopters with more than

the median number of postadoption online transactions and 0 otherwise. +++ denotes jointlysignificant at the 1% level using Chi-squared test. R2 within

denotes the R2 from a traditional fixed-effects regression and captures the proportion of within-customer variation in each dependent variable over time that

is explained bywithin-customer variation over time in the independent variables. ∗∗p < 0.05; ∗∗∗p < 0.01. a

Denotes that the coefficient for active adopters is significantlydifferent than that for inactive adopters at least at the 10% level.

5.3. Trends in Substitution, Augmentation, and

Volume Effects

The substitution, augmentation, and volume effects

documented in Tables 2 and 3 capture average

changes in transaction behavior around enrollment in

theonlinechannel for passiveand activeadopters

relative to the control group. However, they do

not provide evidence on the extent to which these

effects persist or change over time. To provide evidence on any potential trends in these various effects,
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we report results in Table 4 from estimation of a

version of Equation (1) in which we replace the

single POST indicator variablewith four separate

indicators, POSTk k = 1	 2	 3	 4, representing postonline-enrollment months 1–3, 4–6, 7–9, and 10–12,

respectively.17 The pattern of the coefficient estimates

on the POSTk × ACTIVE coefficients k = 1	 2	 3	 4 in

Table4 suggests that therelativeincreasein branch

and call center transactions (augmentation effects), the

relative decrease in ATM and VRU transactions (substitution effects), and the relative increase in overall

transaction volume(volumeeffect) for activeadopters

persist throughout the postadoption period.18

5.4. Cost Structure Implications of Substitution,

Augmentation, and Volume Effects

Thenet cost implications of thesubstitution, augmentation, and volume effects documented above depend

on the extent of upward or downward adjustment

of resources to accommodate changes in transaction

demand across channels. Based on internal activitybased costing studies of the level and cost of resources

required to support transactions in different channels, National Bank estimates that when the cost of

a transaction in thebranch is normalized to be1,

thecost of supporting similar transactions in thecall

center, ATM, or online channel is 0.94, 0.31, 0.18,

and 0.09, respectively.19 Table5 shows that taking the

preadoption mean number of transactions by channel for active adopters as our benchmark, National

Bank’s transaction cost estimates, combined with the

documented substitution, augmentation, and volume

effects shown in Table 3, suggest that estimated

17 Formally, this specification takes the following form:

Eyit = exp

0 +4

k=1

kPOSTkit + Passive

k POSTkit × PASSIVEi

+ Active

k POSTkit × ACTIVEi + EnrollMonthit + Xit + i





18 These coefficient estimates suggest that differences in branch

and call center transactions for active adopters relative to passiveadopters and thecontrol group aredeclining over thesample

period. To check this, we repeated the analyses in Table 4 using

only thecohort of customers who signed up for theonlinechannel during January 2006 and remained customers to the end of our

sampleperiod (May 2007). This is thegroup of customers for which

wehavethemaximum postadoption observations (16 months). For

this cohort, branch and call center transactions remain higher for

activeadopters relativeto both passiveadopters and thecontrol

group 13–16 months after adoption. Moreover, this relative difference in transaction activity in postadoption months 13–16 is not

significantly different than the relative difference for postadoption

months 10–12. Thus, we find no evidence that postadoption transaction levels in the branch channel for active adopters converge to

the levels of either passive adopters or the control group.

19 Wenormalizetheestimated cost of a branch transaction to be1 to

maintain confidentiality of National Bank’s cost-to-serve estimates.

Table 5 Estimate of Change in Normalized Cost to Serve for

Active Adopters

Preadoption Preadoption Postadoption

Normalized mean normalized normalized

Channel cost ($) transactions cost ($) Change (%) cost ($)

Branch 100 175 175 9 190

Call center 094 031 029 4 030

ATM 031 341 106 −10 095

VRU 018 29 052 −59 021

Online 009 0 000 N/A 090

Total 362 427

Notes. This table shows the estimated change in normalized cost to serve

for the sample of active adopters. The unit cost of performing a transaction in each channel is normalized bythe unit cost of a branch transaction

to preserve confidentialityin National Bank’s cost estimates. Preadoption

mean transactions bychannel are taken from Table 2. The estimates of percentage change in transactions attributable to adoption of online banking

for each channel are computed using the coefficient estimates from Table

3. The postadoption normalized cost estimate in the final column assumes

the average level of monthlypostadoption transactions for active adopters of

10 per month.

monthly cost to serve will increase by approximately

18% for theaverageactiveadopter after adopting the

onlinechannel.20

Although many of thecosts associated with automated channels such as the ATM, VRU, and online

banking are largely fixed in the short term, the levels of some costly resources required for these channels do adjust with customer transaction activity over

the short to medium term. An increase in transactions at ATMs, for instance, requires more frequent

cash replenishment activity. Also, deposits at ATMs

consume central processing resources. As more customers sign up for theonlinechannel, National Bank

must increase call center staff to handle inquiries

about thechannel. In addition, National Bank’s online

channel stores up to 60 days of account activity for

each account. Increases in the number of users and

their associated transactions drive the need for more

disk space to efficiently store all their information and

to maintain service levels. As transaction demand in

the online channel has grown over the last several

years, National Bank has steadily invested in increasing storageand processing capacity in theonline

banking channel.

However, other costs, such as equipment and occupancy, are fixed in the short term. Even if transactions

shift from offlineto onlinebanking channels, National

Bank will only realizethecost savings from this shift

20 Note that these cost estimates are not confounded with capacity

utilization. Our research site allocates costs based on estimates of

transaction capacity rather than actual volume. Therefore, unit cost

estimates remain constant regardless of capacity utilization. Proponents of this approach argue that it provides a better picture of the

costs of resources used versus the cost of unused capacity—with

the latter including any costs from actual transaction volumes being

lower than transaction capacity (Cooper and Kaplan 1999).
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if it can eliminate unused resources in the offline

channels. Likewise, costs will only increase if capacity is adjusted upward in channels that experience

an increase in demand for transactions. By including short-term fixed costs in the unit transaction cost

estimates, executives at National Bank are implicitly

assuming that resources will be adjusted upward or

downward over the medium to long term in response

to changes in transaction activity. These unit costs

may overstate any cost savings from shifting transactions to theonlinebanking channel if thecorresponding resources are not shifted. Similarly, they may overstate any cost increases caused by increased demand

for transactions in the online channel if resources

used to support this channel remain fixed as transaction demand grows. However, this latter case seems

unlikely, given the patterns of increasing investment

in storageand processing capacity in theonlinebanking channel commensurate with increasing demand

for transactions in this channel.21 Overall, our estimates suggest that increased transaction activity in

the online channel, coupled with small rates of substitution from other self-service channels (ATM and

VRU) and small rates of augmentation in assistedservice channels (branch and call center), combine to

yield a substantial increase in overall demand for service transactions (“volume effect”) and a net increase

in customer-level cost of service around the adoption

of theonlinebanking channel.

5.5. Customer Profitability (H4)

Panel B of Table 2 contains estimates of differences in mean customer profitability before and after

enrollment in the online banking channel for inactive enrollers, passive adopters, and active adopters.

These estimates provide no evidence of any change in

profitability corresponding to the adoption of online

banking. All groups show an increase in profitability around enrollment in the online banking channel.

However, in no case is this change significantly different than 0 at conventional levels. Moreover, although

thechangein profitability for activeand passive

adopters is smaller than the corresponding change for

inactive enrollers, none of these changes is significantly different from another at the 10% level.

Turning to our formal tests of H4, column 1 of

Table6 shows results from theestimation of Equation (2). The coefficient estimates on the POST ×

PASSIVE and POST×ACTIVE indicators demonstrate

that after adoption of the online channel, customer

21 For the two years preceding this study, the total number of transactions performed in the online banking channel grew by 150%,

while total costs attributed to the channel increased by 50%. This is

consistent with the presence of some short-run fixed costs coupled

with a nontrivial upward adjustment of resources commensurate

with increased demand for transactions in the channel.

Table 6 Effects of Online Adoption on Customer Profitability

(1) (2)

PROFITt−1 0026∗∗∗ 0026∗∗∗

9194 9344

POST 0681

039

POST × PASSIVE −141∗∗

219

POST × ACTIVE −121∗∗

212

POST1 0618

022

POST1 × PASSIVE 0319

031

POST1 × ACTIVE 1067

147

POST2 0290

035

POST2 × PASSIVE −1349

075

POST2 × ACTIVE −0447

024

POST3 0421

008

POST3 × PASSIVE −2598∗∗∗

258

POST3 × ACTIVE −2112∗∗∗

280

POST4 1232

004

POST4 × PASSIVE −2567∗∗∗

262

POST4 × ACTIVE −2297∗

174

EnrollMonth −299∗∗∗ −2952∗∗∗

821 695

Constant 1596∗∗∗ 1993∗∗∗

2023 970

Year indicators +++ +++

Month indicators +++ +++

R2 within (%) 81 83

Number of observations 1,199,206 1,199,206

Notes. Absolute values of t-statistics based on standard errors adjusted for

correlation within customers over time are in parentheses. EnrollMonth = 1

for month of adoption, 0 otherwise. POST = 1 for postadoption months and 0

otherwise. POST1, POST2, POST3, and POST4 = 1 for postadoption months

1–3, 4–6, 7–9, and 10–12, respectively. PASSIVE = 1 for adopters with fewer

than the median number of postadoption online transactions. ACTIVE = 1 for

adopters with more than the median number of postadoption online transactions. +++ denotes jointlysignificant at the 1% level using Chi-squared

test. R2 within denotes the R2 from a traditional fixed-effects regression and

captures the proportion of within-customer variation in each dependent variable over time that is explained bywithin-customer variation over time in the

independent variables. ∗p < 0.10; ∗∗p < 0.05; ∗∗∗p < 0.01.
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profitability decreases for both passive and active

adopters relative to the control group (coefficients =

−141, −121; p < 005). Taking the preadoption mean

levels of profitability for passive and active adopters

reported in Table 2 as our benchmark, these negative

coefficient estimates suggest that monthly customer

profitability will decline on average by approximately

6% for both groups relativeto thecontrol group after

adoption of the online channel. Results from estimation of an alternative version of Equation (2) in which

wereplacethesingle POST indicator variablewith

four separate indicators, POSTk k = 1	 2	 3	 4, representing post-online-enrollment months 1–3, 4–6, 7–9,

and 10–12, respectively, are reported in column 2. Taking the preadoption mean levels of profitability for

passive and active adopters reported in Table 2 as our

benchmark, the pattern of the coefficient estimates on

the POSTk × ACTIVE coefficients k = 1	 2	 3	 4 suggests that the relative decline in profitability for passive and active adopters takes at least six months

to materialize and that profitability will decline by

approximately 11% for both groups relative to the

control group within 10–12 months of adoption of the

online channel. Overall, these results raise the possibility of theonlinechannel allowing moreefficient

money management by customers, with the net effect

of reducing fees paid to the bank and/or requiring

higher (lower) rates of interest to be paid on deposit

(loan) accounts.22

5.6. Retention (H5)

Table 7 contains summary statistics for the December

2003 cross-sectional sampleof onlineand offlinecustomers for use in our retention analyses. Before

turning to our formal tests of H5, several observations about thedata in Table7 areworth noting. Simple comparisons of means between online

and offline customers reveal that online customers

tend to be younger, have less tenure with the bank,

hold more deposit and loan accounts and fewer

investment accounts, and—most interestingly—tend

to havehigher profitability than offlinecustomers on

average. All differences in means between online and

offlinecustomers in Table7 aresignificant (p < 010

in all cases using two-tailed t-tests). Consistent with

prior studies (Hitt and Frei 2002, Xue et al. 2007), the

statistics reported in Table 7 provide evidence that

online customers are different than offline customers

along a variety of dimensions. However, our tests of

22 Wereran theanalysis of customer profitability on theset of customers who experienced no change in their product portfolio over

the sample period and found substantively similar results. This

suggests that our finding of a reduction in customer profitability

associated with increased postadoption use of the online channel is

not due to customers simply shifting balances into products with

higher unit costs, but is more likely due to such revenue effects.

Table 7 Descriptive Statistics by Online Status for a Random Sample

of Customers as of January 2004

Offline customers Online customers

N = 69871 N = 30129

Mean Std. dev. Mean Std. dev.

Retention—1 year 086 035 090 030

Retention—2 years 078 042 084 037

Retention—3 years 071 045 079 041

Tenure 1033 1225 829 754

Age 4534 2077 3890 1379

Number of deposit products 102 097 179 123

Number of loan products 032 058 053 075

Number of investment products 011 194 004 030

Deposit account balances 8,781 53,778 10,530 58,718

Loan account balances 3,240 17,911 4,127 19,487

Investment account balances 1,935 29,204 483 6,587

Annual customer profitability1968 1,056 2179 1,037

Notes. “Offline” customers = customers who have not adopted online banking as of December 2003; “online” customers = customers who have

adopted the online channel as of December 2003. All differences in means

between these groups are significant at least at the 10% level using t-tests.

H1–H4 suggest that these performance-related differences are the result of preexisting differences in the

onlineand offlinecustomer populations rather than

theresult of behavioral changedueto theadoption of

online banking. Our tests of H1–H4 provide evidence

that customers adopting and using theonlinechannel

becomemorecostly to servein thelongrun and less

profitable in the shortrun, even before considering the

allocation of transaction costs. Whether these effects

are in any way compensated by increased retention of

a relatively profitable segment of customers depends

on the relationship between customer retention and

theuseof theonlinechannel.

Table 7 demonstrates that one-, two-, and three-year

retention rates for online customers are significantly

higher than those for offline customers (all differences

significant at the 1% level). Within three years, 79% of

online customers remain with the bank, whereas only

71% of offline customers do so. However, as noted

above, online customers are systematically different

than offlinecustomers in ways that may beassociated

with these differential retention rates independent of

theuseof onlinebanking (e.g., holding moreproducts). We control for these observed differences in our

estimation of Equation (3).

Thefirst columns in Table8 show theresults from

estimating Equation (3) using one-, two-, and threeyear retention rates, respectively. After controlling for

tenure, age, number/type of products, balances, and

competition in the local market area, use of the online

channel is positively and significantly associated with

one-, two-, and three-year retention rates at the 1%

level. To benchmark the estimated coefficients, the
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Table 8 Marginal Effects Probit Estimates of Retention with Online

Status Using Cross-Sectional Sample of 100,000 Customers

Observed at Year-End 2003, 2004, 2005, and 2006

Marginal effects probit estimates

Self-selection test Retention Retention Retention

1 year 2 years 3 years 2SLS

ONLINEUSE 0001∗∗∗ 0001∗∗ 0002∗∗∗ 0002∗∗

276 232 362 204

TENURE 0003∗∗∗ 0005∗∗∗ 0005∗∗∗ 0005∗∗∗

1798 2386 2391 1689

AGE −0001∗∗∗ −0001∗∗∗ −0001∗∗∗ −0001∗∗∗

466 647 928 332

Number of deposit 0052∗∗∗ 0070∗∗∗ 0076∗∗∗ 0061∗∗∗

products 3622 4064 4098 900

Number of loan 0039∗∗∗ 0048∗∗∗ 0053∗∗∗ 0041∗∗∗

products 1837 1861 1874 779

Number of investment −0001 0023∗∗∗ 0014∗∗∗ 0008∗∗

products 028 525 364 256

Deposit account 00001∗∗∗ 00001∗∗∗ 00001∗∗∗ 00001

balances ($000s) 283 288 269 023

Loan account −00002∗∗∗ −00004∗∗∗ −00006∗∗∗ −00007∗∗∗

balances ($000s) 468 652 719 520

Investment account 000007 000008 000006 −000006

balances ($000s) 148 041 074 047

COMPETITION −0047∗∗∗ −0076∗∗∗ −0067∗∗∗ −0075∗∗

454 579 453 221

Psuedo R2 (%) 61 61 52

Adjusted R2 (%) 49

Notes. Absolute values of t-statistics are in parentheses. Estimated coefficients reported as marginal effects at the mean value of all variables.

ONLINEUSE = average number of online transactions performed bythe customer per month during 2003. TENURE = length of time (in years) since

customer established first account relationship with the bank, measured as

of year end 2003. AGE = age of customer (in years) measured as of year-end

2003. COMPETITION = percentage of deposits controlled byother banks in

the market in which the customer resides. ∗∗p < 0.05; ∗∗∗p < 0.01.

unconditional probability of retention in three years

for offlinecustomers in thesampleis 71%, representing a three-year customer attrition rate of 29%.

Our estimates suggest that an active online customer,

averaging approximately 10 transactions per month,

would have a 2% higher three-year retention rate than

an offlinecustomer, all else being equal. This represents

a 7% (0.02/0.29) decrease in the attrition rate.

The results from estimation of (4) via two-stage

least squares areshown in thelast column of Table8.

For brevity, we only report results using three-year

retention rates, because results are qualitatively similar for one- and two-year retention rates. The twostageestimateof the ONLINEUSE coefficient remains

positive, significant, and similar in magnitude to the

marginal effects probit estimates even after controlling for self-selection into the online channel. This

result suggests a potential causal link between online

banking and customer retention and points to customer retention as an important value driver for the

self-service channel of online banking.

Table 9 Market Share and Online Banking Penetration

Market-Level Regressions on Cross-Sectional

Sample of 369 Banking Markets

Dependent variable = MarketSharet+1

Levels Changes

MarketSharet 0939∗∗∗ 0291∗

10537 188

OnlineRatet 0025∗∗ 0021∗∗

225 212

Constant −0003 −0002

089 107

Adjusted R2 097 002

Notes. Absolute values of t-statistics are in parentheses.

MarketShare = share of deposits in banking market controlled by

National Bank. OnlineRate = proportion of National Bank accounts

that are linked to the online banking channel in a market. “t” = 2004.

Column 2 reports estimates of a changes specification where the

change in MarketShare from 2003 to 2004 is regressed on the

changes in MarketShare and OnlineRate from 2002 to 2003. ∗∗p < 0.05; ∗∗∗p < 0.01.

5.7. Market-Level Outcomes (H6)

Table 9 provides the results from estimation of Equation (5). Column 1 shows that increases in marketlevel online banking penetration rates among the

firm’s customer base (OnlineRate) arepositively and

significantly p < 005 associated with future market

share even after controlling for current market share.

Column 2 demonstrates that these findings hold when

Equation (5) is estimated in first-differences, suggesting that the results are not caused by time-constantomitted correlated variables across markets.

The mean and standard deviation for OnlineRate

in our sample are 33.8% and 14.3%, respectively. The

mean change in OnlineRate in our samplefrom yearend 2003 to year-end 2004 is also approximately 14%.

The coefficient estimates in columns 1 and 2 show that

a changeof 14% in OnlineRate in the current year is

associated with an approximate 0.4% increase in market share the subsequent year. Though the magnitude

of the relationship between market shares and online

penetration rates appears small, it is worth noting that

this increase compares relatively favorably against the

average change in market shares for National Bank

(−03% over the same period). Overall, the evidence

in Table9 provides support for thenotion that online

banking penetration is associated with market-level

outcomes (H6).23

23 Market shares and online penetration rates may share common

unobserved trends over time, raising the possibility that our results

are due to spurious correlation. However, our analysis has several

features that substantially mitigate against this possibility. First, our

analysis uses panel data rather than a single time series. The use

of panel data over several markets lowers the possibility of spurious correlation, as trends vary over markets and therefore common
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Campbell and Frei: Customer Profitability Implications of Self-Service Distribution Channels

20 Management Science, Articles in Advance, pp. 1–21, © 2009 INFORMS

6. Discussion and Conclusions

The performance consequences of self-service delivery channels has been a relatively unexplored area

in the prior literature in service operations and services marketing. In this paper, we investigate one

self-service technology—online banking—that many

firms aredeploying with theaim of simultaneously

achieving benefits in the form of lower service costs,

increased revenues, and higher customer loyalty.

Wefind that customer adoption and useof online

banking is associated with (1) substitution primarily

from incrementally more costly self-service delivery

channels (ATM and VRU); (2) augmentation of service

consumption in more costly assisted-service delivery

channels (branch and call center); (3) a substantial

increase in total transaction volume; (4) an increase

in estimated average cost to serve resulting from the

combination of points (1)–(3); and (5) a reduction in

short-term customer profitability. However, we find

that theuseof onlinebanking is associated with

higher customer retention rates over one-, two-, and

three-year horizons, with the association increasing in

thelength of thehorizon. Theselatter findings hold

even after controlling for self-selection into the online

channel, suggesting the potential for a causal relationship between online banking and customer retention. We also find evidence that future market shares

for our samplefirm aresystematically higher in markets with high contemporaneous utilization rates for

online banking. This finding holds even after controlling for contemporaneous market share, suggesting it is not simply the result of increased market

power leading to the acquisition of online banking

customers.

Thesefindings havea number of implications for

service operations management research and practice.

First, technologies that lower the firm’s cost of service

delivery also potentially alter the economics of service

consumption from the customer’s perspective. Our

results suggest that this is an important consideration

in evaluating the likely benefits of technology investments directed at service delivery. In our setting, lowering the customer’s costs of interaction appears to

have the unintended consequence of increasing service consumption and thus reducing estimated shortterm customer profitability.

trends in the data are less likely to occur. Second, our results hold

when transforming the data to first differences prior to estimation.

First-differencing the data reduces the risk of spurious correlation

that can arise in nonstationary time series. Finally, and perhaps

most important, our analysis relates future market share to current

online penetration rates after controlling for the current level of

market share. That the relationship between current online penetration and future market share holds even after controlling for the

contemporaneous correlation between current online penetration

rates and current market share makes it unlikely that our results

are driven by spurious correlation between these two series.

Second, and moreimportant, theresult that the

estimated long-run average cost to serve increases

around theadoption and useof onlinebanking suggests that traditional costing methods alone may not

be appropriate for decision making in settings where

customer interaction is important in determining

cost. Accounting and operations management texts

(Cooper and Kaplan 1999, Fitzsimmons and Fitzsimmons 2001) recognizetheimportanceof considering

customer interaction in the design of performance

measurement systems in service firms. The results in

this paper demonstrate that the overall cost impact of

new service delivery technologies depends not only

on the estimated unit cost of a service transaction

using that technology but also on the effect of the

technology on overall service consumption. Thus, the

design of performance measurement systems for evaluating distribution strategies should explicitly consider how the use of one service delivery technology

affects service consumption and costs across all delivery channels.

Finally, our results suggest that important tradeoffs may exist among multiple performance measures,

such as short-term customer profitability, customer

retention, and market share, highlighting the potential importance of a long-term “customer asset” view

for evaluating investments in service delivery technologies (Hogan et al. 2002). In the context of online

banking, our results suggest that customers may capturethegains from this technology in theshort term

but that thesegains to thecustomer may translate

into higher customer satisfaction and, in turn, higher

customer retention rates. This could lead to potential long-term gains for firms. The provision of online

banking services may be a competitive necessity, but

many banks, including our research site, are allocating resources toward actively migrating customers to

theonlinebanking channel under theassumption that

cost, revenue, and retention benefits will follow. When

coupled with the finding in prior literature that more

profitable customers tend to select into onlinebanking

(Hitt and Frei 2002), our results suggest that the primary benefit of the online banking channel may be

in attracting and retaining moreprofitablecustomers

rather than in increasing the profitability of existing

customers.

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