challenges and opportunities in multichannel customer management

Multichannel customer management is the design,deployment, coordination, and evaluation of channelsthrough which firms and customers interact, with the goalof enhancing customer value through effective customeracquisition, retention, and development. The authorsidentify five major challenges practitioners must addressto manage the multichannel environment more effectively:(a) data integration, (b) understanding consumer behavior,

(c) channel evaluation, (d) allocation of resources acrosschannels, and (e) coordination of channel strategies. Theauthors also propose a framework that shows the linkagesamong these challenges and provides a means to conceptu-alize the field of multichannel customer management. Areview of academic research reveals that this field has expe-rienced significant research growth, but the growth has notbeen distributed evenly across the five major challenges.

This article is the result of the authors’participation in the CRM Thought Leadership Conference, held at the University of Connecticutin September 2005.

Journal of Service Research, Volume 9, No. 2, November 2006 95-112DOI: 10.1177/1094670506293559© 2006 Sage Publications

Challenges and Opportunities inMultichannel Customer Management

Scott A. NeslinDartmouth College

Dhruv GrewalBabson College

Robert LeghornING Life Insurance and Annuity Company

Venkatesh ShankarTexas A&M University

Marije L. TeerlingUniversity of Groningen

Jacquelyn S. ThomasNorthwestern University

Peter C. VerhoefUniversity of Groningen

The authors discuss what has been learned to date andidentify emerging generalizations as appropriate. Theyconclude with a summary of where the research-generatedknowledge base stands on several issues pertaining to thefive challenges.

Keywords: customer relationship management; multi-channel management; channel data integra-tion; multichannel retailing; mutlichannelshopping

One of the most dramatic trends in the shopping envi-ronment has been the proliferation of channels throughwhich customers can interact with firms. The Internet,kiosks, ATMs, call centers, direct marketing, home shop-ping networks, and catalogs, as well as bricks-and-mortar stores, are now commonplace means by whichconsumers shop. This proliferation has created a chal-lenge for firms to manage this environment effectivelyand opportunities for academics to produce insights thatcan help address these challenges. The field of “multi-channel customer management” has emerged as a result.The purpose of this article is to (a) identify key chal-lenges practitioners must address to manage the multi-channel environment more effectively, (b) propose aframework that shows the linkages among these chal-lenges and provides a conceptual structure of the field,and (c) summarize academic research thus far about howto address the key challenges. As a result, we hope to pro-vide a blueprint for academics wishing to conductresearch in this area and a substantive summary for man-agers to enhance their decision-making abilities.

By channel, we mean a customer contact point, or amedium through which the firm and the customer inter-act. Our emphasis on the term interact reflects that we donot include one-way communications, such as televisionadvertising, though we do include home shopping televi-sion networks and direct response advertising in massmedia.

We define multichannel customer management as thedesign, deployment, coordination, and evaluation of chan-nels to enhance customer value through effective customeracquisition, retention, and development. A key point is theemphasis on the customer as a strategy for creating morevalue for the firm (see Payne and Frow 2005; and Bouldinget al. 2005). Multichannel customer management is acustomer-centric marketing function, unlike traditionalsales channel research, which focuses on the firm and dis-tributors (Rangaswamy and van Bruggen 2005). However,similar issues emerge in both research streams (e.g., chan-nel conflict), and we discuss these as appropriate.

We proceed first to elucidate our framework. Then weidentify a set of managerial challenges. Finally, we spendthe bulk of the article discussing what academic researchto date has taught us about these issues. We concludewith a summary and identify key research areas.

A FRAMEWORK FOR MULTICHANNELCUSTOMER MANAGEMENT

We present in Figure 1 a framework that joins the cus-tomer’s and the firm’s decision processes (see Blattberg,Kim, and Neslin 2006). We assume that the customer pro-gresses through need recognition, information search,purchase, and after-sales service. For example, a customermay realize he or she needs life insurance. The customerthen searches various channels for information about lifeinsurance, decides on which channel to make the pur-chase, and afterwards receives sales support (advice onincreased coverage, etc.) via a particular channel.

Additional aspects of this process are crucial. First,customer perceptions and preferences drive channelchoices (e.g., the customer may prefer the Internet forsearch because it is easy to use). Second, the customerlearns from and evaluates his or her experiences, whichfeed back into the perceptions and preferences that guidehis or her next shopping task (e.g., the customer maylearn that the Internet search did not answer all theimportant questions). Third, the customer chooses bothchannels (A or B) and firms (k), so from the customerperspective, it is a two-dimensional choice.

Typically, the management decision process startswith data generated by the customer decision process.These data are at the customer level—what channel(s)did the customer use for which purpose, and what did heor she purchase? Consistent with the emphasis on thecustomer, the firm’s decision process is driven by suchcustomer-level data. After the data have been assembled,the firm evaluates its channels (Are they profitable? Arethey serving the purposes for which they are designed?).With this knowledge in hand, the manager can specify amultichannel strategy (which channels to employ, how todesign them, how to allocate resources across channels)and a marketing plan (pricing, assortment, service levels)for implementing the strategy.

FIVE KEY CHALLENGES FOR MULTICHANNELCUSTOMER MANAGEMENT

We take the viewpoint of the firm and focus on five chal-lenges we believe are particularly crucial for managers.

96 JOURNAL OF SERVICE RESEARCH / November 2006

Data Integration Across Channels

The ideal position for a firm would be complete cus-tomer data integration (CDI), or an integrated, singleview of the customer across channels. The ideal databasewould depict which channel(s) each customer accessedduring each stage of the decision process, including com-petitors’ channels. However, the less ambitious focus ofCDI has been on the firm’s own channels and empha-sized purchase and after-sales support rather than search.In turn, CDI gives rise to questions such as the following:

• Which data need to be integrated? Is it sufficient tointegrate purchase data only, or should search dataalso be integrated?

• Which marketing activities benefit from integra-tion? Cross-selling is an obvious beneficiary, butwhat other marketing activities benefit?

• What is an acceptable level of data integration? Is100% necessary?

• Does data integration pay off? Is it worth theinvestment to derive a single view of the customer?

Understanding Customer Behaviorin a Multichannel Environment

Managers must understand how customers choosechannels and what impact that choice has on their overall

buying patterns. Therefore, key questions pertaining tocustomer choice include the following:

• What determines customer channel choices? Whatchannel attributes are important? Do marketingcommunications influence channel choice?

• Is a multichannel approach a means to segmentcustomers? That is, are there distinct segments ofconsumers who use various channels and combina-tions of channels?

• Do customers make channel decisions according tothe channel or the firm? Does the customer firstsay, “I will check out a few Web sites of retailersthat sell HDTVs,” or does he or she say, “I willcheck out Best Buy’s Web site, then go to the storeto get a better look”? Similarly, during the searchstage, do customers consider firms at all?

• What is the impact of the multichannel environ-ment on customer loyalty?

• Does a multichannel strategy grow sales for thefirm?

Channel Evaluation

When the firm has gathered data and obtained anunderstanding of the consumer decision process, it canevaluate channel performance. The key questions in thisstep include the following:

Neslin et al. / MULTICHANNEL CUSTOMER MANAGEMENT 97

Search Purchase After Sales

Channel Ak Channel Ak

Channel Bk Channel Bk Channel Bk

PostEvaluation

Data

Consumer Channel Perceptions and Preferences

ProblemRecognition

ChannelEvaluation

Channel Strategy

Channel Coordination- Price, Production, Promotion- Design, Distribution, Service

Resource Allocation- Channel selection- Investment

Channel Ak

FIGURE 1A Framework for Multichannel Customer Management

SOURCE: Adapted from Blattberg, Kim, and Neslin (2006).

• What is the contribution of an additional channel tothe firm? If the firm were to add a channel, whatimpact would it have on sales and profits?

• What is the contribution of each existing channel?This input can be difficult to assess when the con-tribution of a channel emerges during the searchphase and the company lacks an integrated data-base of search and purchase across customers.

• What channels synergize best with others? The fullimpact of the firm’s set of channels should be morethan the sum of the parts, and synergies shouldexist, but which are best?

Allocating Resources Across Channels

The firm’s channel policy is manifested in its resourceallocation. Therefore, key questions include the following:

• What is the optimal channel mix? How necessary isa Web presence? What is the impact when channelsare removed or downsized?

• How should marketing resources be allocatedacross channels? How much should be spentdesigning and developing each channel, and shouldadvertising and promotional activities be designedto drive customers to specific channels, or shouldthey be channel neutral?

• What determines the equilibrium channel structurein an industry? Should all firms offer the samechannels to customers? Will firms differentiatetheir channel strategies?

Coordinating Channel Strategies

In perhaps the most difficult task for managers is tocoordinate the objectives, design, and deployment ofchannels to create synergies. Questions pertaining to thisstage include the following:

• Should channels be independent or integrated?This basic question does not have a simple answer.

• Which aspects of channel design should be inte-grated? Should prices be the same in all channels;should channels carry the same products? In arelated issue, how should the organization be inte-grated with regard to channel management?

• How can the firm design synergies in its channelstrategy? If the argument in support of an inte-grated channel strategy relies on synergy, how canthese synergies be achieved?

• Should channels be designed around segmentsor functions? For example, one strategy mightencourage customers to use the Internet for search,the bricks-and-mortar store for purchase, and thecall center for after-sales support, which wouldrepresent a functional channel strategy. In contrast,a segmentation channel strategy would recommend

that the customers in Segment A use the Internetfor all stages of their decision process, those inSegment B use the store for all stages, and those inSegment C use the call center. Obviously, interme-diate combinations are possible.

• How can firms manage the research shopperphenomenon, whereby the customer searches onChannel A but purchases through Channel B (notnecessarily from the same firm)? Does a firm’ssuperior search capability confer a competitiveadvantage that grows sales and profits?

• How should firms manage their relationships withchannel partners when applying a multichannelstrategy?

DATA INTEGRATION

Data integration is a prerequisite for successful multi-channel customer management, but very little researchhas addressed it. A survey of customer relationship man-agement (CRM)–oriented companies with more than1,000 employees indicates that 64% had a single view ofthe customer (CDI Institute 2004). However, it is partic-ularly difficult for retailers to achieve an accurate singleview through bricks-and-mortar stores, in which manycustomers purchase without providing any identifyinginformation (e.g., they pay cash) and it is costly to matcheach store purchase to the customer database. It is there-fore not surprising that a Forrester survey (Yates 2001)found that 48% of 50 retailers had learned “nothing”about cross-channel shoppers.

The key question in this context is whether dataintegration increases earnings and, if so, in what ways.Ridgby and Leidingham (2005) suggested that successfulCRM applications do not necessitate full data integrationand its associated costs. Yet the banking industry hasinvested substantially in full data integration to constructa single view of the customer. Enhanced cross-sellingappears to be a prime beneficiary of these efforts (seeKnott, Hayes, and Neslin 2002).

Zahay and Griffin (2002) provided indirect evidencethat customer data integration (CDI) might pay off usinga survey of 208 business-to-business executives, in whichthey find that “CIS Development” (the quality and avail-ability of the Customer Information System) yieldedbetter customer-based performance (retention, sellingeffectiveness, lifetime value, and marketing return oninvestment) and ultimately company performance(growth in income and sales). However, Zahay andGriffin did not specifically measure data integration indeveloping their CIS Development scale.

To show the factors that come into play in determin-ing the appropriate level of CDI, we propose a model toexamine the potential payoff (in customer lifetime value


[LTV]) of investing (I) in a higher single view percentage(c). Presumably, firms can leverage the insights generatedfrom a higher single view percentage to increase cus-tomer value through, for example, better service or add-on selling, which would then result in either a highercontribution per customer per period (M) or a higherretention rate (r). We write the model as follows:

(1)

(2)

M(c) = M0 + M1 (1 – e–K2c), and (3)

r(c) = r0 + r1 (1 – e–K3c), (4)

where

LTV = lifetime value of the customer;c = single view percentage, or the percentage of cus-

tomers for whom the firm has an integrated view of theirbehavior across channels (0% ≤ c ≤ 100%);

I = investment per customer in achieving the singleview percentage c;

M = profit margin per customer per period (a functionof c);

R = retention rate (a function of c);d = discount rate;K1 = efficiency parameter reflecting how quickly invest-

ments in CDI result in higher single view percentages;M0 = profit contribution per customer per period if the

firm does not have a single view of any of its customers(i.e., c = 0);

M1 = parameter reflecting the additional profit contri-bution per customer per period if the firm achieves ahigher single view percentage;

K2 = efficiency parameter, reflecting how quicklyincreasing the percentage of single view customersincreases profit contribution;

r0 = retention rate per customer per period if the firmdoes not have a single view of any of its customers (i.e.,c = 0);

r1 = parameter reflecting the incremental retention rateper customer per period if the firm achieves a highersingle view percentage; and

K3 = efficiency parameter, reflecting how quicklyincreasing the percentage of single view customersincreases the retention rate.

Equation 1 is the “simple retention” formula for LTV,from which we subtract CDI investment per customer tocompute net LTV. Equation 2 translates CDI investmentinto single view percentage. We assume a fixed cost Imin,

the minimum investment required to begin increasing thesingle view percentage. Equations 3 and 4 translate sin-gle view percentage (c) into contribution and retentionrate. Note also the assumed decreasing returns to scale.1

Figure 2 illustrates the relationship between CDIinvestment (I) and LTV and single view percentage (c) forhypothetical parameter values and shows that LTVslightly decreases until the firm makes a minimum invest-ment (Imin). Profit then increases to its maximum when theinvestment equals $38 per customer. At that level, thesingle view percentage is not 100% but only around 83%.That is, with decreasing returns for contribution or reten-tion as a function of single view percentage and decreas-ing returns for single view percentage as a function ofCDI investment, the goal should not be to have a singleview of each customer, which would require, according toFigure 2, $80 per customer investment. In other words,our illustration shows that in a world of decreasing returnsto investment in CDI, the optimal level of CDI is less than100%. However, we emphasize that this example is justone illustration; the model parameters undoubtedly woulddiffer across companies, potentially yielding differentresults, and research is needed to estimate them.

In summary, some companies have achieved “com-plete” CDI, whereas others, especially retailers, have dif-ficulty achieving a 100% single view of the customer.Furthermore, some evidence suggests that investments incustomer databases pay off (Zahay and Griffin 2002), butthe specific contribution of a single view of the customerinvestment has not been investigated. Finally, a simplemodel of CDI investment suggests that a 100% singleview will not always pay off.

c ={

100 × (1 − e−K1I ) if I ≥ Imin

0 if I ≤ Imin,

LTV = M(c)1 + d

1 + d − r(c)− I,


FIGURE 2Customer Lifetime Value (LTV) and

Coverage as a Function of Investmentin Customer Data Integration

0%

20%

40%

60%

80%

100%

$0 $20 $40 $60 $80 $100Investment per Customer in Coverage

% C

ove

rag

e

$1,000

$1,100

$1,200

$1,300

$1,400

$1,500

$1,600

Coverage Level LTV

LT

V

NOTE: See Equations 1 through 4; M0 = $400, M1 = $200, K1 = 1.5, r0 =0.7, r1 = 0.2, K2 = 0.02, d = 0.1, and Imin = 5.

UNDERSTANDING CONSUMER BEHAVIOR

How Does a Multichannel EnvironmentAffect Customer Loyalty?

If a multichannel environment is, at worst, loyalty neu-tral, businesses could grow through multichannel strategiesby acquiring more customers or selling more to each cus-tomer. However, a multichannel environment might erodeloyalty, whether to firms or to the brands offered by thesefirms, because it encourages extensive search, which maylead to purchases from different firms. In addition, manymodern channels (Internet, ATM, call centers) entail littlehuman contact, which can erode loyalty itself (see Ariely,Lynch, and Moon 2002). However, more channels also sug-gest better service, which often leads to greater loyalty.

Research results are mixed though they generally leantoward the finding that a multichannel environmentenhances loyalty. Wallace, Giese, and Johnson (2004)found that multichannel usage is associated with higherperceptions of the firm’s channel offerings, which in turnare associated with higher customer satisfaction andgreater loyalty. Shankar, Smith, and Rangaswamy (2003)revealed that Internet usage is associated with greaterloyalty; and Danaher, Wilson, and Davis (2003) foundthat Internet usage benefits the loyalty enjoyed by high-share brands offered by an e-tailer. However, with regardto the banking industry, Wright (2002, p. 90) stated thatthe addition of new channel technologies has “loosenedthe banker–customer relationship”; and Ansari, Mela,and Neslin (2005) found a negative association betweenInternet usage and loyalty.

In summary, the interesting initial research on multi-channel approaches and customer loyalty point to threemain impacts: (a) the multichannel environment as awhole on customer loyalty, (b) adding channels on cus-tomer loyalty toward the firm, and (c) individual channelson customer loyalty.

Does a Multichannel Strategy Grow Sales?

Research on whether a multichannel strategy growssales has generated the provocative generalization that mul-tichannel customers have higher expenditure levels than dosingle-channel customers. The key question is why.

Evidence for the generalization comes from a varietyof sources. According to Table 1, which shows purchasevolumes at a major U.S. retailer (DoubleClick 2004b),single-channel customers purchase less than dual-channelcustomers, who in turn purchase less than triple-channelcustomers. Similarly, Kumar and Venkatesan (2005)found that multichannel customers buy more; and Myers,Van Metre, and Pickersgill (2004, p. 1) reported that

“multichannel customers spend 20 to 30 percent moremoney, on average, than single-channel ones do.”Kushwaha and Shankar (2005) reported that multichannelshoppers buy more often, more items, and spend morethan single channel shoppers. Thomas and Sullivan(2005b) reported that multichannel customers spend moreon average but also noted two subtleties. First, single-channel customers buy more items but do not spend asmuch in total. Second, not all multichannel combinationsare associated with higher sales than all single channels.For example, catalog users spend more than customerswho shop at a bricks-and-mortar store and on the Internet.However, for a given firm’s Channel A, customers whopurchase on A plus any Channel B yield more sales thanthose who purchase on A alone. Kushwaha and Shankar(2006) found that customers who shop through any two ofthree channels (direct mail, store, and Internet) spendmore than twice as much as customers who shop at anyone channel alone and that customers who shop throughall the three channels spend more than three times asmuch as customers who shop at any one channel.

Although the above shows an intriguing associationbetween multichannel purchasing and total sales levels,the key question is what are the underlying causes of thisrelationship. Multichannel customers may spend morebecause of their higher loyalty, self-selection, or as aresult of marketing. Higher loyalty does not provide adefinitive explanation because it is not yet a generaliza-tion that multichannel purchasing breeds higher loyalty.However, the evidence we discussed previously, thatmultichannel shopping may breed higher loyalty makesthis explanation plausible.

The self-selection explanation is that heavier usersdecide to use multiple channels. Kumar and Venkatesan(2005) found that multichannel business-to-business(B2B) customers are likely to be larger companies.However, Ansari, Mela, and Neslin (2005) ruled out thisexplanation and found instead through a longitudinal


TABLE 1Purchase Volume of the Multichannel Shopper

Channels Used for ShoppingAverage Annual Expenditure

Internet Retail Catalog Per Customer

√ $157√ $195

√ $201√ √ $446√ √ $485

√ √ $608√ √ √ $887

SOURCE: DoubleClick (2004b).

comparison that catalog-loyal and multichannel/Internet-loyal customers started with equal sales levels at thebeginning of the period of observation but the latter groupdemonstrated higher sales levels at the end.

Marketing provides another potential explanation forthe sales levels of multichannel customers. Ansari, Mela,and Neslin (2005) reported that the multichannel/Internet-loyal customer received more marketing andtended to respond to it more strongly in terms of purchaseincidence. Kumar and Venkatesan (2005) indicated thatmultichannel customers received more contact from thecompany through a variety of channels. A simple viewmight argue that multiple channels are a type of extendeddistribution; in the same way that more soda machinesincrease soda sales, more channels increase firm sales,following a pure availability effect.

In summary, we know that multichannel customers buymore, but we are not sure why. Existing support, thoughneither unequivocal nor generalizable, indicates it may bedue to higher loyalty, customer self-selection, or increasedmarketing exposure. Additional insight can be foundin profiles of the “multichannel-prone” customer. Forexample, Kumar and Venkatesan (2005) found that multi-channel customers have innately higher sales levels, comefrom particular industries, are more likely to partake incross-buying, make a medium number of product returns,receive more marketing contacts, have been customers fora longer period of time, and purchase more frequently.This multichannel customer might also be profiled interms of his or her attitudes toward various channels forsearch, purchase, and after-sales support (Burke 2002).

What Determines CustomerChannel Selection?

Perhaps the most heavily researched area of multi-channel customer management is what determines cus-tomer channel choice. Table 2 summarizes six basicdeterminants: firm marketing efforts, channel attributes,channel integration, social influence, situational variables,and individual differences.

Marketing efforts. Knox (2005) and Ansari, Mela, andNeslin (2005) have found that e-mails and catalogs bothinfluence channel choice; e-mails seem especially effec-tive at channeling customers to the Internet. Various pro-motions can also encourage customers to use a certainchannel (Burke 2002; Myers, Van Metre, and Pickersgill2004; Teerling et al. 2005).

Channel attributes. Table 2 provides an extensive list ofchannel attributes found to correlate with channel selec-tion. Importantly, attributes play different roles dependingon the channel and stage of the customer decision process.

For example, Verhoef, Neslin, and Vroomen (2005) foundthat privacy concerns have a stronger impact on using theInternet to purchase than on using a store. They also foundthat enjoyment is an important determinant of searching acatalog (i.e., customers like to browse through catalogs)but does not influence their propensity to purchase from it.

Channel integration. Montoya-Weiss, Voss, andGrewal (2003) as well as Bendoly and colleagues (2005)have found that well-integrated channels encouragedesirable customer behaviors. For example, if the firmallows products ordered on the Internet to be picked up atthe store, it encourages Internet users to use the store aswell. Burke (2002) pointed out that if the Internet pro-motes the store by providing store location information,it prompts customers to use the store.

Social influence. Verhoef, Neslin, and Vroomen(2005) found that customers’ selection of channels isinfluenced by the belief that people similar to them usethe channel. Keen and colleagues (2004) applied the“social norm” construct from attitude theory. Nicholson,Clarke, and Blakemore (2002), in field research, foundthat a mother bought an outfit for her child at a bricks-and-mortar store rather than from the Internet simplybecause the higher effort required to use the store wascommensurate with the mother’s care for her child.

Situational factors. Nicholson, Clarke, and Blakemore(2002) also identified five “situational factors” that candetermine channel selection: physical setting (weather,crowding), social setting (shopping with friends), tempo-ral issues (time of day, urgency of the purchase), task def-inition (type of product; see also Burke 2002; Thomasand Sullivan 2005a), and antecedent state (mood).

A few studies have focused on task definition.Mathwick, Malhotra, and Rigdon (2002) hypothesizedthat certain channels will be amenable to goal-directedshopping tasks, whereas others are suited for experien-tial tasks. A few papers have emphasized the role of thetype of product being purchased. Gupta, Su, and Walter(2004) argued that search goods are more likely to bebought on the Internet, whereas experience goods aremore likely to be purchased at a store. Mahajan,Srinivasan, and Wind (2002) posited that digital, exist-ing (not new-to-the-world), search, and customizableproducts are more likely to be purchased on the Internet.Inman, Shankar, and Ferraro (2004) posited that cus-tomers develop category/channel associations based onprevious experience with buying category j on channelk and the presumed assortment of category j in channelk. They did not measure category/channel associationsdirectly but found that previous experience and assort-ment factors determine channel usage. Kushwaha and


Shankar (2005) found that channel category associationsdrive the choice of multichannel versus single channelshopping.

Note that situational variables are distinct from chan-nel attributes. Belk (1974, p. 157) defined situationalvariables as “all those factors particular to a time andplace of observation which do not follow from a knowl-edge of personal (intraindividual) or stimulus (choicealternative) attributes.” Situational and nonsituationalvariables can be related, as demonstrated by Inman,Shankar, and Ferraro (2004), because the purchasedproduct is a situational variable (particular to a time andplace), but their theory is based on personal experienceand channel attributes (e.g., assortment).

Individual differences. Internet experience, which dif-fers substantially across customers, is clearly a determi-nant of Internet usage (Montoya-Weiss, Voss, and Grewal2003), though demographics such as gender, age, educa-tion, income, family size, and region also influencechoice (Ansari, Mela, and Neslin 2005; Gupta, Su, andWalter 2004; Inman, Shankar, and Ferraro 2004;Kushwaha and Shankar 2005; Verhoef, Neslin, andVroomen 2005), as does the stage in the customer lifecycle (Thomas and Sullivan 2005a).

In summary, there is ample evidence for six basicdeterminants of channel selection, which is good news tomanagers, because many of them are actionable. Forexample, one situational variable is the purchase task. If


TABLE 2Determinants of Channel Selection

Determinant Variable References

Marketing E-mail Ansari et al. (2005); Knox (2005)Catalog Ansari et al. (2005); Knox (2005); Kushwaha and Shankar (2005, 2006)Incentives Myers et al. (2004); Burke (2002); Teerling et al. (2005), Kushwaha and Shankar (2006)

Channel determinants Ease of use Keen et al. (2004); Nicholson et al. (2002); Burke (2002); Montoya-Weiss et al. (2003); Teerling andHuizingh (2005)

Price Keen et al. (2004); Jiang and Rosenbloom (2004); Verhoef et al. (2005); Burke (2002); Thomas andSullivan (2005a); Morton et al. (2001); Ancarani and Shankar (2004); Pan et al. (2002); Tang andXing (2001); Teerling and Huizingh (2005)

After-sales Jiang and Rosenbloom (2004); Verhoef et al. (2005)Search convenience Verhoef et al. (2005)Search effort Verhoef et al. (2005); Burke (2002); Gupta et al. (2004)Information quality Montoya-Weiss et al. (2003); Teerling and Huizingh (2005)Aesthetic appeal Montoya-Weiss et al. (2003); Teerling and Huizingh (2005)Info. comparability Verhoef et al. (2005); Gupta et al. (2004)Service Verhoef et al. (2005); Burke (2002); Montoya-Weiss et al. (2003); Teerling and Huizingh (2005)Risk Verhoef et al. (2005); Montoya-Weiss et al. (2003); Gupta et al. (2004)Purchase effort Verhoef et al. (2005); Keen et al. (2004)Negotiability Verhoef et al. (2005)Speed of purchase Verhoef et al. (2005); Burke (2002); Gupta et al. (2004)Privacy Verhoef et al. (2005); Burke (2002)Assortment Verhoef et al. (2005); Burke (2002); Bendoly et al. (2005); Teerling and Huizingh (2005);

Inman et al. (2004)Enjoyment Verhoef et al. (2005); Nicholson et al. (2002); Teerling and Huizingh (2005);Security Burke (2002); Montoya-Weiss et al (2003)Channel category Inman et al. (2004); Kushwaha and Shankar (2005)

associationsChannel integration Ease moving from Montoya-Weiss et al. (2003)

Channel A to BSocial influence Subjective norm Keen et al. (2004); Verhoef et al. (2005); Nicholson et al. (2002)Situational factors Physical setting Nicholson et al. (2002)

Social setting Nicholson et al. (2002)Temporal issues Nicholson et al. (2002)Shopping task Nicholson et al. (2002); Burke (2002); Mathwick et al. (2002); Gupta et al. (2004); Inman et al.

(2004); Thomas and Sullivan (2005a); Kushwaha and Shankar (2005)Antecedent state Nicholson et al. (2002)

Individual differences Demographics Gupta et al. (2004); Ansari et al. (2005); Verhoef et al. (2005); Inman et al. (2004); Kushwaha andShankar (2005); Pauwels and Dans (2001)

Previous experience Keen et al. (2004); Meuter et al. (2000); Inman et al. (2004)Stage in life cycle Thomas and Sullivan (2005a)

the Internet tends to be used for gift purchasing, the firmcan offer features like free gift wrapping or gift cards.

Are There Clearly DefinedChannel Segments?

Because individual differences influence channelchoice, it is natural to suppose that there are clearlydefined channel segments. Keen and colleagues (2004)posited four segments: “generalists” who care about allissues, “formatters” who have particular channel prefer-ences, “price sensitives” who care primarily about priceand select channels accordingly, and “experiencers” whoinertially tend to use the same channel they used the pre-vious time. Thomas and Sullivan (2005a) identified fivesuch segments according to the impact of product type,customer lifestyle, and price sensitivity on the con-sumers’ channel choice. Knox (2005) found that cus-tomers develop preferences for various channels overtime and that, in equilibrium, there are clearly definedmultichannel versus single-channel segments. Kushwahaand Shankar (2006) found that distinct customer seg-ments based on preferred channel(s) of shopping exist.

The preceding suggests that multichannel customersegmentation exists, but researchers have not settled on asingle segmentation scheme. If managers want to pursuea segmentation-based multichannel strategy, they shoulduse segmentation variables relevant to their product andthe actions they can take at the segment level. Thomasand Sullivan (2005b) also indicated how, given a seg-mentation of the market, better communication strategiescan be developed.

CHANNEL EVALUATION

The key questions revolve around the economic con-tribution of each channel to the firm. Three studies inves-tigate whether the addition of the Internet cannibalizessales from existing channels and find generally it doesnot. If this finding turns out to be a generalization, it isextremely important, because it suggests that multichan-nel strategies are a vehicle for growth.

Deleersnyder and colleagues (2002) offered a time-series analysis of whether the addition of an Internet ver-sion of a newspaper took away from its circulation oradvertising growth rate or level. For most newspapers,the introduction of an Internet channel had no impact onany of the dependent variables. For example, of 67 news-papers for which circulation data were available, theparameter representing the impact of the Internet ongrowth rate was negative 35 times and positive 32 times.Of the 35 negative signs, 5 were statistically significant;

of the 32 positive signs, 10 were significant. The numberof significant effects is too high (22%) for all to be TypeI errors. The authors elaborated that the newspapers thatsuffered declines had high content overlap between thehard copy and online versions. That is, the Internet willnot cannibalize sales, and might even enhance sales, if itscontent is different than the incumbent channel, but if thecontent is similar, there could be cannibalization.

Biyalogorsky and Naik (2003) examined the experi-ence of Tower Records, which added an Internet channelto accompany its bricks-and-mortar store channel. Theauthors constructed a latent variables time-series model,which modeled offline sales as a function of lagged offlinesales and current and lagged visits to the new Internetchannel. The coefficient for the impact of online visits onoffline sales was negative but not statistically significant.

Coelho, Easingwood, and Coelho (2003) investigated62 U.K. financial services companies by creating a “sales”measure consisting of customer acquisition, market share,and sales growth and a “profit” measure consisting of cus-tomer retention, profit, customer service, and cost control.They found that multichannel companies enjoyed highersales levels but lower profits. Upon further examination,though significant only at the 15% level, the authors alsofound that the multichannel companies suffered especiallyin terms of customer service and customer retention. Inother words, providing good, coordinated service is a chal-lenge for multichannel companies, and a multichanneloffering may erode loyalty rather than enhance it.

The preceding suggests that (a) the addition of theInternet may enhance total sales; but (b) if the Internetcompletely duplicates the current channel, cannibaliza-tion may occur, and though sales may increase, long-termprofits may decline due to decreases in service levels andretention.

Five studies provide additional perspectives. Geyskens,Gielens, and Dekimpe (2002) examined the stock marketresponse to a firm’s announcement of the addition of anInternet channel for 98 European newspaper companiesand found that the Internet addition has a positive effecton stock price. Lee and Grewal (2004) considered 83retailers that adopted the Internet through the impact ofthe speed of adoption on Tobin’s Q, a stock market–basedmeasure of firm performance. They found that fasteradoption of the Internet as a communication mediumenhances performance, but adoption for purchase has noeffect, except for firms with preexisting catalog opera-tions. Evidently, the market was concerned that the addi-tion of an Internet purchase channel might duplicate thefirms’ sales efforts. Ward (2001) studied the human capi-tal consumers invest in learning to use a channel by con-sidering the purchases of 10,000 consumers in a broadvariety of categories. He found relatively high correlations


in human capital investments between the Internet anddirect (catalog) channels but lower correlations betweenthese channels and retail. That is, the Internet and catalogsmay be more directly substitutable and hence more opento cannibalization. Chu, Chintagunta, and Vilcassim(2005) analyzed the PC industry using an economicmodel of pricing competition coupled with a consumerdemand model that includes choice of firm and channel.The authors found for example that the retail channel isespecially valuable to Compaq and Hewlett-Packard andthat Dell’s exit from the retail channel was economicallyjustified. Srinivasan and Moorman (2005) found that theability of the effectiveness of CRM efforts is best enhancedwhen the firm has moderate experience (not very high orvery low) in either bricks-and-mortar or online channels.They argued for example that moderate bricks-and-mortarexperience provides a critical level of expertise withoutthe problems that can occur due to inertia or concernsabout channel cannibalization.

The above work provides an excellent start to evaluat-ing the profit contribution of various channels. Forexample, there is promising evidence that adding anInternet channel increases company sales. However,more research is needed. For example, none of the abovestudies considers the role of marketing in determiningchannel value and the nature of the contribution of eachchannel in terms of acquisition versus retention.

ALLOCATING RESOURCESACROSS CHANNELS

For allocation decisions, the key issues are not onlyhow to invest marketing and other resources across chan-nels but also which channels to employ. This area hasreceived little research attention, with two exceptions:First, Verhoef and Donkers (2005) and Villanueva, Yoo,and Hanssens (2003) both found the important result thatthe long-term value of a customer differs depending onthe acquisition channel. This means that firms must bal-ance this long-term value with the cost of acquisition in aparticular channel to allocate their acquisition fundsproperly. Second, Kushwaha and Shankar (2006) haveinvestigated the allocation of resources across channel-customer segments.

Verhoef and Donkers (2005) studied customersacquired by an insurance company and found that thechannel by which the customer was acquired is associ-ated with his or her retention and receptivity to futurecross-selling efforts. For example, the direct mail channelled to customers who offered low retention rates andwere not receptive to cross-selling, Internet-acquired cus-tomers yielded mixed results, and outbound telephone

and magazine acquisitions tended to have high retentionrates and were receptive to cross-selling. Thus, they pro-vided clear evidence of a relationship between the acqui-sition channel and subsequent customer behavior that canbe correlated with customer value.

Villanueva, Yoo, and Hanssens (2003) supportedVerhoef and Donkers’s (2005) finding that the quality ofan acquired customer differs by channel. They showedhow this finding, along with their finding that acquisitioncosts differ substantially by channel, could be incorpo-rated into an optimization model for allocating acquisi-tion dollars across channels to maximize profit for agiven budget. The key equations are the profit functionand the customer acquisition function, as follows:

(5)

ck = αk + (sk – αk)(1 – e–βkxk) (6)

(7)

where

mk = contribution per customer acquired throughchannel k,

ck = number of customers acquired through channel k,sk = ceiling level—maximum number of customers

that could be acquired through channel k,αk = number of customers acquired through channel k

even if the firm spends no money on it,βk = efficiency parameter for the acquisition function,xk = amount of money spent acquiring customers

through channel k, andB = budget.

To maximize profits (Equation 5), acquisition expendi-tures (xk) must be allocated correctly across channels.Profit reflects the contribution per customer (mk) timesthe number of customers acquired (ck) through eachchannel. The acquisition function (Equation 6) translatesacquisition expenditures into the number of customersacquired. Equation 7 provides the budget constraint.Villanueva, Yoo, and Hanssens’s (2006) analysis providesmeasures of the mks but not the acquisition function,which would have to be estimated using previous data(Reinartz, Thomas, and Kumar 2005) or judgmentally.The authors illustrated their model using assumed valuesfor the parameters of the acquisition channel. Theyshowed, for example, that budget allocations differ sig-nificantly according to differences in customer contribu-tion and acquisition costs across channels.

Kushwaha and Shankar (2006) developed an approachfor optimal allocation of marketing efforts across multiplechannel-customer segments (based on different combina-tions of channels of purchase). Their approach comprises

�kxk = B,

Maxxk

� = �kmkck(xk) − B,


three customer response models, the first for the probabil-ity or timing of purchase, the second for frequency orquantity of purchase, and the third for the monetary valueof purchase, and an optimization model. Their modelincludes customer returns. They illustrated their model byanalyzing customer level purchase, cost, and marketingpromotional data on about 800,000 customers over a2-year period for a large marketer of shoes and apparelaccessories across multiple channels, namely, the directmail, the store, and the Web. They estimated the market-ing response models using a Markov Chain Monte Carlo(MCMC) approach and solve the optimization modelusing simulations. Their optimization model results indi-cate that profits can be increased by realigning the currentmarketing efforts across the different channel-customersegments. In particular, they showed that more productand promotional catalogs should be mailed to customerswho shop at all the three channels and to those who shopat the store and direct mail channels and to those whoshop at the store and Web channels.

The preceding descriptions illustrate how optimal allo-cation can work in a multichannel context. The necessaryinformation managers must have pertains to (a) the rela-tionship between the investment and some desired out-come (e.g., customer acquisition, customer retention) and(b) the value of that outcome for the firm (e.g., the valueof a customer acquired through Channel X).

Another basic issue of resource allocation is whatchannels the firms should employ. This depends not onlyon the marginal contribution of a firm adding a channelbut on competitive response as well. For example, theprovision of multiple channels in the name of better cus-tomer management may simply mask the harsh reality ofa prisoner’s dilemma, as illustrated in Figure 3. The stud-ies reviewed in the Channel Evaluation section suggestadding a channel increases a firm’s sales, thus supportingthe off-diagonal cells in Figure 3. The question is whathappens in the lower right cell. Firms might competemore intensively and decrease prices while incurring

higher channel costs. Alternatively, the proliferation ofchannels may increase industry sales, possibly benefitingall firms, or enhance opportunities for firm differentia-tion. For example, Jindal and colleagues (2005) foundthat when one firm uses a differentiation strategy andanother uses a low-cost strategy, they are more likely touse multiple channels. See also our discussion ofZettelmeyer (2000) below.

Competition in a multichannel environment has beenanalyzed using game theoretic models. These modelsoften employ a Hotelling framework, where the con-sumer utility function can be expressed as a function ofthe “distance” of customer i from channel j, where dis-tance represents the accessibility or availability of thechannel to the customer.

Based on this framework, Balasubramanian (1998)analyzed competition between a direct marketing firmand several bricks-and-mortar retailers and found thatwhen a direct marketing firm enters the market, eachbricks-and-mortar firm competes directly against thatchannel rather than against its neighboring retailer. Pan,Shankar, and Ratchford (2006) examined the case of abricks-and-mortar retailer competing against a pure-playe-tailer when firms decide on price and level of service.Their results show that if customer heterogeneity in will-ingness to pay for service is sufficiently large comparedto heterogeneity in channel preference, the bricks-and-mortar retailer provides better service at higher price andearns greater profits than the pure-play e-tailer. Pan,Shankar, and Ratchford (2002) began with a bricks-and-mortar firm competing against a pure-play e-tailer. Theyfound that if the bricks-and-mortar firm can launch anInternet channel perceived as superior to the pure-playe-tailer (e.g., because of synergies between the Internetchannel and the bricks-and-mortar channel), the bricks-and-mortar firm will launch the Internet channel, offermore services, and charge higher prices than the pure-play retailer. The authors found empirical support for thepricing part of their hypothesis. Ancarani and Shankar(2004) found that multichannel retailers have higheraverage prices than pure e-tailers, regardless of whetherthe price refers to the posted price or includes shippingcosts; similarly, for DVDs, Tang and Xing (2001) indi-cated that multichannel retailers had significantly higherprices than did pure e-tailers (14% on average).

COORDINATING CHANNEL STRATEGY

Our framework in Figure 1 suggests channel coordina-tion can take two forms: (a) coordinating across channelsat a given stage in the customer decision process (e.g.,should purchase prices be the same across channels?) or


FIGURE 3The Potential Prisoner’s Dilemma

in Adding New Channels

The Potential Prisoner’s Dilemma in Adding New Channels

Firm BDo Not Add Add

Do Not Add Status Quo Firm B gains customers

Firm A Add Firm A gainscustomers

Firms A and B competefor customers on abroader front andpossibly grow market.

(b) coordinating across channels and across stages of thecustomer decision process (e.g., profitably managing theresearch shopper phenomenon).

The degree of coordination can range from the com-plete separation of channels to full coordination. Forexample, some firms may treat their Internet operationsessentially as a separate company, whereas others mightcoordinate their Internet and bricks-and-mortar storeoperations fully by using the Internet as a search serviceto funnel customers into the store (see Gulati and Garino2000). There are several benefits and costs to coordina-tion; we start with the potential benefits:

• economies of scale (e.g., order fulfillment),• ability to differentiate offerings by channel

(Zettelmeyer 2000),• higher margins by avoiding channel conflict and

possibly charging higher prices (Tang and Xing2001; Zettelmeyer 2000),

• better information about customers (Stone, Hobbs,and Khaleeli 2002),

• improved intraorganizational communication,• reinforced relationship between the customer and

the firm,• prevention of channel partners from becoming

competitors,• higher entry barriers (new entrants must have sev-

eral coordinated channels),• better service levels (Sousa and Voss 2004; Stone,

Hobbs, and Khaleeli 2002),• decreased potential for channel conflict because

efforts are coordinated and agreed upon acrosschannels (Berger, Lee, and Weinberg in press), and

• ability to compensate one channel’s weakness withanother channel’s strength (Achabal et al. 2005).

The costs of channel coordination include the following:

• loss of strategic flexibility (especially with regardto newly emerging channels),

• large capital investments (e.g., information tech-nology to obtain a single view of the customer),

• increased fixed costs to provide coordinated activi-ties such as service (e.g., management coordinationtime; Sousa and Voss 2004),

• decreased incentives for non-owned intermediariesand partners,

• increased expertise needed to manage the differentchannels, and

• inability to move quickly in the marketplace.

Coordination at One Stage ofthe Customer Decision Process

Should the firm charge the same price in each chan-nel? Managers often refer to this in terms of “channel

price integrity.” A compelling reason to offer differentprices is price discrimination. If an Internet shopper isless price sensitive than a traditional store shopper, thefirm should charge lower prices in the traditional store;research already suggests price sensitivity is lower onlinethan offline (e.g., Lynch and Ariely 2000; Shankar,Rangaswamy, and Pusateri 2001).

Shoppers may find a multiprice strategy policy con-fusing and unfair, and competitors can subvert the policyby positioning themselves as price consistent acrosschannels and offering lower prices than those availableat the firm’s store. However, there are at least three waysa firm can charge different prices across channels. First,a manufacturer may charge the same price across chan-nels, but a bricks-and-mortar retailer may choose to dis-count the product, effectively lowering prices in thatchannel. Second, firms can add surcharges for the use ofcertain channels. For example, an airline can sell ticketsthrough its Web site, call center, bricks-and-mortartravel agents, and online travel agents. Although it main-tains the same ticket price across channels, the net priceis higher if the customer uses a value-added channel, forexample, a $10 fee might be added for the service pro-vided by a live agent on the telephone. The airline mightcommunicate this clearly when customers contact itscall center, both in the interest of perceived fairness aswell as the desire to save costs by encouraging cus-tomers to use the Internet.2 Third, a manufacturer cansell different products in different channels, so prices arenot directly comparable.

In conclusion, there are good reasons to suspect firmsmay want to charge different prices across channels, butresearch is needed to examine the extent to which thisoccurs, why it occurs, and what mechanisms firms usemost effectively to achieve price differences.

Berger, Lee, and Weinberg (in press) offered an opti-mization model to analyze how a firm should coordinateits communication expenditures between the Internet andother channels. They considered three strategies: (a)“separation,” in which the Internet is completely sepa-rate, such as with an independent distributor; (b) “partialintegration,” in which the firm considers the Internet aseparate entity but is willing to pay some of its advertis-ing cost; and (c) “full integration,” in which the firm con-siders the Internet completely a part of its own operation.The authors developed separate optimization formula-tions for each scenario. Although they did not estimatethe response functions that drive this analysis, using plau-sible values for the parameters, they found that full inte-gration is more profitable than partial integration, whichin turn is more profitable than separation. This finding isintriguing, but additional work needs to determine if itgeneralizes, both theoretically and empirically.


Coordination Across Channels andStages of the Decision Process

Zettelmeyer (2000) considered channel coordinationbetween the Internet and an offline channel when firmscan decide on the pricing (relevant for purchase) and thelevel of information provided (relevant for search). Histheme is that information provision is a means for firmsto differentiate, which leads to different levels of infor-mation provided across firms and channels. For example,if a medium number of customers have access to theInternet (e.g., many but not all customers prefer to use theInternet for shopping), firms adopt the same informationand pricing strategy in both channels but differentiatethemselves in terms of the information they provide, aswell as price.

Zettelmeyer’s (2000) analysis shows that the existenceof two channels, heterogeneous customer preferences,and two stages in the consumer decision process (searchand purchase) provides firms with the flexibility to dif-ferentiate themselves. This very encouraging result relieson a sensible basic intuition, but empirical testing of thetheory would be a welcome addition to the literature.

Another issue that has attracted significant attention isthe research shopper phenomenon. In Table 3, we providedata on the frequency with which research shoppers usevarious channels for their search and purchase(DoubleClick 2004a). The table suggests that the mostcommon link pertains to using the Internet for search andthen buying at the retailer store—43% of all researchshoppers follow this route. In fact, a survey conducted bythe Dieringer Group estimated that “83.4 million U.S.consumers made offline purchases influenced by onlineinformation in 2004” (CrossMedia Services 2006). VanBaal and Dach (2005) found that 20.4% of offline pur-chases took place after the customer had consulted theWeb site of a different retailer but that the reverse processwas a bit more common—24.6% of online purchasestook place after the consumer had consulted an offlinechannel of a different retailer.

Research shopping presents both an opportunity and aconcern. The opportunity relates to the firm’s ability toencourage customers to search on its Web site and thenuse that site to direct the customer to the retail store. Oncein the store, the customer has lower service demands andcan more readily be exposed to cross-selling. However,the concern is that the customer will use Firm A’s Web sitefor search but purchase from Firm B’s store. For single-channel Internet retailers, the concern is particularly acutebecause they offer no store to which they can route thecustomer.

Verhoef, Neslin, and Vroomen (2005) delineated threefactors that contribute to research shopping: (a) channelattributes, (b) lack of lock-in, and (c) cross-channel syn-ergy. Channel attributes may cause research shoppingbecause the Internet may be perceived as superior interms of search-related attributes, such as ease of com-paring prices, whereas the store might be perceived asbetter in its purchase-related attributes, such as speed ofobtaining the product. Lock-in refers to the ability of achannel, separate from its attributes, to hold on to theconsumer for both search and purchase and might be con-sidered an inertial or one-stop shopping effect. Verhoef,Neslin, and Vroomen found that the Internet has a verylow lock-in, whereas the store channel has very highlock-in. Finally, cross-channel synergy means that thecustomer has higher utility from searching on Channel Aand buying on Channel B, apart from the obvious chan-nel attributes. For example, searching on the Internetbefore going to the store may enable the customer tolearn about trading off product attributes and hence makehim or her better able to select the right product when inthe store. Verhoef, Neslin, and Vroomen found cross-channel synergy between the Internet and store, but itwas not highly significant.

One approach to managing research shopping is toemploy an information-only Web site that is highly inte-grated with the store (Bendoly et al. 2005). Teerling et al.(2005) uncovered some important findings in reference tothis strategy; more Web site visits initially are associatedwith fewer store visits (possibly due to the consumersearching at various Web sites), but eventually, more Website visits translate into more store visits. Teerling andHuizingh (2005) also found that online and store satisfac-tion reinforce each other and lead to more store and Webloyalty. In turn, these loyalties reinforce each other, suchthat the more loyal the customer is to the Web site, themore loyal he or she becomes to the store. These loyaltiesthen translate into higher sales levels. The search-onlyInternet/purchase store strategy can work as exhibited byTeerling’s findings, but it would be risky in an environ-ment in which most competitors’ Web sites provide bothinformation and the ability to make purchases.


TABLE 3Research Shopping

Percentage of CustomersBrowsing Channel Purchase Channel Who Utilize Each Pattern

Catalog Internet 11Catalog Retail 19Internet Catalog 6Internet Retail 43Retail Catalog 5Retail Internet 16

SOURCE: DoubleClick (2004a).

Managing Channel ConflictWith Channel Partners

When coordinating channels, firms also must considercoordinating with channel partners, especially firms thatsell through intermediaries. One important coordinationissue in this case is the collection and integration of cus-tomer data. Adding direct channels can have significantimpact on intermediaries, such as in the air travel market,in which the advent of the Internet channel led to directsales by airlines, and decreased sales by travel agents.A firm’s use of both its own and independent channelsis also referred to as “concurrent” channel use. Concurrentchannels can benefit customers, but they also might cre-ate conflict through intrabrand competition betweenindependent and direct channels. Vinhas and Anderson(2005) studied the use of concurrent channels in a B2Bsetting and showed that when the products are highlystandardized, customer behavior is variable, and productlines tend to be purchased as a group, firms are less likelyto use concurrent channels. The effect of multichannelcustomer systems on multichannel conflict, and its resul-tant impact on channel and firm performance, hasreceived no attention (Rangaswamy and van Bruggen2005), though it is important to know how firms can mit-igate such potential channel conflict problems, such asthrough incentive structures or offering different productlines in different channels (e.g., Vinhas and Anderson2005).

SUMMARY

In summary, we have (a) identified five key challengespractitioners face in multichannel customer management,(b) proposed a framework that links the challengestogether and provides a conceptual structure of the field,and (c) summarized the knowledge base that academicresearch has generated to date about the five challenges.The five challenges we identify are as follows:

• data integration,• understanding customer behavior,• channel evaluation,• allocating resources across channels, and• coordinating channel strategies.

The framework in Figure 1 shows how the challenges areinterrelated: Data integration enables managers to under-stand consumer behavior and evaluate channel perfor-mance. This in turn provides the means to formulatestrategy, particularly as it pertains to channel coordinationand resource allocation. An important contribution of the

framework is that it marries consumer and firm decisionprocesses, which means that multichannel customer man-agement entails managing customers as they progressthrough their decision process and using channels toenhance each stage of that process.

For each of the five challenges, we have generated sev-eral key research questions. In Table 4, we summarize theextent of research progress in each and provide brief sum-mary comments. Note that most of the research to datehas focused on three main channels: catalogs, bricks-and-mortar stores, and the Internet. Clearly there is a need inall the areas listed in Table 4 to consider more channels,ranging from bank ATMs to telemarketing to direct sell-ing, and in fact to develop a typology for organizing themany types of channels.

As we suggest with Table 4, most research has beenconducted to understand customer behavior. We have agood idea of what determines channel choice and a gen-eralization that multichannel customers buy more. We justdo not know why—is it enhanced loyalty, self-selection ofdesirable customers, or increased marketing exposure?There is evidence to support each hypothesis, but nodefinitive conclusions can be drawn at this time.

Table 4 also suggests that data integration is the leastresearched area. Ironically, integration is where compa-nies are probably spending the most money and may bethe most concerned. One study suggests IT investmentsfor CRM can pay off (Zahay and Griffin 2002), but thatwork does not consider CDI specifically. Our model, ifparameterized, could help managers decide about theoptimal level of data integration; we also highlight that asingle view of 100% of the firm’s customers may not beoptimal. Another issue is measuring customer activitywith competitors’ channels. See Du, Kamakura, andMela (2005) for promising work on measuring cus-tomers’ total purchase activity with competitors across allchannels. It may be possible to adopt their method tosearch, after-sales, as well as purchase, and to specificchannels.

Allocating resources across channels also has notreceived much attention, though good work has beenconducted that could form the basis for optimal alloca-tion across channels for acquisition.

Channel evaluation and coordinating channel strategieshave received more attention than data integration andresource allocation but less than understanding consumerbehavior. Adding an Internet channel may not completelycannibalize other channel sales, but the related studiesneed further development and should include variablessuch as the impact of marketing. Channel coordination hasreceived its attention particularly in the area of researchshopping. We know, for example, that this phenomenon


exists (Table 3), and some evidence suggests that thesearch-Internet → buy-store route is the most commonresearch shopper consumer strategy. We also have someidea about the determinants of research shopping (per-ceived attributes, lack of channel lock-in especially for theInternet, and perceived consumer synergies betweensearching on one channel and buying on another).

In conclusion, there are plenty of challenges andissues to be investigated by academics. The academicworld has begun to make good progress toward answer-ing some of these questions, but much work remains.Researchers need to investigate the issues identified inthis article and generalize findings across a variety ofindustries, including retail, B2B, and services. This arti-cle, we hope, will serve to stimulate that research.

NOTES

1. These functions are adapted from Blattberg and Deighton (1996).We believe the assumption of decreasing returns is reasonable, but it isan assumption. For evidence of decreasing returns of customer acquisi-tion rate, customer duration, and profitability with respect to marketingexpenditures, see Reinartz, Thomas, and Kumar (2005).

2. We thank an anonymous reviewer for this insight.

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TABLE 4Summary

Challenge

Data integration

Understand consumerbehavior

Channel evaluation

Allocating resourcesacross channels

Coordinating channelstrategies

Topic

Does integration pay off?

What is an acceptable amount ofintegration?

What data should be integrated?What activities benefit from integration?Impact on brand loyalty

Does multichannel grow sales?

What determines channel choice?

Are there channel segments?Do consumers decide by channel or firm?What is the contribution of an additional

channel?What is the contribution of each channel?

What channels synergize best withothers?

Which channels should the firm employ?

How do we allocate marketing acrosschannels?

What determines equilibrium channelstructure

Should channels be independent orintegrated?

Which aspects should be integrated?Should prices be consistent across

channels?How do we develop channel synergy?

Use channels to segment or for differentfunctions?

How do we manage research shopping?

Research Progressa

**

*

******

***

****

***

***

*

**

*

**

*

**

***

**

*

***

Comments

Some indication that customer relationship management(CRM) IT pays off, but no study of customer dataintegration (CDI).

No formal research; see Equations 1-4 for framework.

No formal research.Some evidence that cross-selling benefits.Mixed results regarding impact of multichannel.

Clear generalization that multichannel customerbuys more.

Much research on attributes, situational factors, etc.See Table 2.

Segmentation definitely exists, but no universal scheme.No formal research.Appears Internet usually does not cannibalize other

channels.Initial work suggestions contribution varies by channel

and firm.Some findings that Internet and store can synergize.

No formal research

Some methodological studies; no substantivegeneralizations.

Little formal research—is it a prisoner's dilemma?

Some work suggests integration is better, but notdefinitive.

Some related work but no clear conclusions.May be opportunity for price discrimination. More

research needed.Promotions and information available in Channel A →

purchase in Channel B may work.No formal research

Can change channel attributes, achieve lock-in, usepromotions.

a. Amount of research progress made regarding each topic, ranging from one to potentially five stars.

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Scott A. Neslin is the Albert Wesley Frey Professor ofMarketing at the Tuck School of Business, Dartmouth College.His research interests include database marketing, sales promo-tion, and market response modeling. In the database marketingarea, he has worked on topics related to multichannel customerbehavior, customer churn, and cross-selling. He is an area edi-tor at Marketing Science and a member of the editorial boardsof Journal of Marketing Research, Journal of Marketing, andMarketing Letters.

Dhruv Grewal (PhD, Virginia Tech) is the Toyota Chair inCommerce and Electronic Business and a professor of market-ing at Babson College. He is currently coeditor of the Journalof Retailing (oldest marketing journal, 2001 to the present). Hisresearch and teaching interests focus on e-business, retailing,global marketing, pricing, and value-based marketing strate-gies. He was awarded the 2005 Lifetime Achievement inBehavioral Pricing Award (Fordham University, November2005). He is a distinguished fellow of the Academy of MarketingScience. He has coauthored Marketing Research (HoughtonMifflin, 2004, 2007). He is currently working on writingMarketing (McGraw-Hill).

Robert Leghorn is the vice president of market research for INGLife Insurance and Annuity Company in the United States. Hehas more than 20 years of experience in the financial servicesindustry. He graduated from Trinity College in Hartford,Connecticut, and was instrumental in establishing the INGCenter for Financial Services at the University of Connecticut.

Venkatesh Shankar is a professor of marketing and ColemanChair in Marketing at Mays Business School, Texas A&MUniversity. His research areas include e-business, competitivestrategy, international marketing, branding, pricing, innovationmanagement, and supply chain management. His research hasbeen published in Journal of Marketing Research, MarketingScience, Management Science, Strategic Management Journal,Journal of Marketing, and Journal of Retailing. He is coeditorof Journal of Interactive Marketing; an associate editor ofManagement Science; and is on the editorial boards of MarketingScience, International Journal of Research in Marketing, Journalof Retailing and Journal of the Academy of Marketing Science.


Marije L. Teerling is a PhD student at the Department ofMarketing, Faculty of Economics, University of Groningen.Her research focuses on determining the effectiveness of Websites on an individual customer level.

Jacquelyn S. Thomas is an associate professor in the IntegratedMarketing Communications (IMC) Department at NorthwesternUniversity. She earned a PhD in marketing in 1997, an MS inmarketing in 1996, and a BA in mathematics in 1991, all fromNorthwestern University. Her research interests are in the areasof customer relationship management, database and direct mar-keting, and statistical modeling of the customer life cycle. Shehas written a book titled Customer Equity and articles thatappeared in the Journal of Marketing Research, Journal of

Marketing, Harvard Business Review, and the Journal ofService Research.

Peter C. Verhoef is a professor of marketing at the Departmentof Marketing, Faculty of Economics, University of Groningen,the Netherlands His research interests concern customer man-agement, customer loyalty, multichannel issues, category man-agement, and buying behavior of organic products. Hispublications have appeared in journals such as Journal ofMarketing, Journal of Marketing Research, Marketing Science,and Journal of Retailing. He is an editorial board memberof the Journal of Marketing, Journal of Service Research,International Journal of Electronic Commerce and the Journalof Retailing.


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