Tarun Kushwaha & Venkatesh Shankar Are Multichannel Customers Really More Valuable? The Moderating Role of Product Category Characteristics How does the monetary value of customer purchases vary by customer preference for purchase channels (e.g., traditional, electronic, multichannel) and product category? The authors develop a conceptual model and hypotheses on the moderating effects of two key product category characteristics—the utilitarian versus hedonic nature of the product category and perceived risk—on the channel preference-monetary value relationship. They test the hypotheses on a unique large-scale, empirically generalizable data set in the retailing context. Contrary to conventional wisdom that all multichannel customers are more valuable than single-channel customers, the results show that multichannel customers are the most valuable segment only for hedonic product categories. The findings reveal that traditional channel customers of low-risk categories provide higher monetary value than other customers. Moreover, for utilitarian product categories perceived as high (low) risk, web-only (catalog- or store- only) shoppers constitute the most valuable segment. The findings offer managers guidelines for targeting and migrating different types of customers for different product categories through different channels.

Keywords: customer relationship management, channels, multichannel marketing, retailing

anaging customers according to their channel pref- regardless of the product category. For example, the U.S.- erence—that is, whether they purchase from a tradi- based multichannel retailer Nordstrom finds that across M tional channel (e.g., catalog, store), an electronic/ categories, customers who use more than one channel spend digital channel (e.g., web, mobile), or multiple channels— four times as much as those who shop only through one has become a cornerstone of marketing strategy (Neslin et channel (Clifford 2010). The limited relevant scholarly arti- al. 2006). Multichannel marketing refers to the practice of cles that typically analyze a single category demonstrate simultaneously offering customers information, goods, ser- that multichannel customers purchase more often and spend vices, and support through two or more synchronized chan- a larger share of wallet than single-channel customers nels (Rangaswamy and Van Bruggen 2005). The high (Kumar and Venkatesan 2005; Venkatesan, Kumar, and growth in retail sales through electronic and multiple chan- Ravishanker 2007). nels indicates a need for marketing managers and scholars It is unclear, however, whether these results generalize to develop a deeper understanding of this important topic in to all product categories, which are typically classified a retailing context that includes direct marketing (catalog, along two key characteristics: utilitarian (e.g., office sup- web, mobile) retailers and brick-and-mortar stores. plies, garden supplies) versus hedonic (e.g., apparel, cos- Conventional wisdom, shaped by anecdotal evidence metics) and low perceived risk (e.g., books, home furnish- and initial research studies, suggests that multichannel cus- ings) versus high perceived risk (e.g., computers, jewelry). tomers constitute the most valuable segment for marketers Are multichannel customers the most valuable for utilitar- ian, hedonic, high-risk, or low-risk categories? This issue is important because customer behavior fundamentally varies Tarun Kushwaha is Assistant Professor of Marketing, Kenan-Flagler by these product category types (Ailawadi et al. 2006; School of Business, University of North Carolina at Chapei Hiii (e-maii: [email protected]). Venkatesh Shankar is Professor of Marketing Ailawadi, Lehmann, and Neslin 2003; Inman, Winer, and and Coleman Chair in Marketing, Mays Business Schooi, Texas A&M Uni- Ferraro 2009; Kamakura and Du 2012; Narasimhan, Neslin, versity (e-mail: [email protected]). The authors thank i-Behavior and Sen 1996). For example, customers of a hedonic prod- for data and the Direct Marketing Education Foundation for doctoral sup- uct category may seek variety and spend more on items in port. This article is based on an essay from the first author's dissertation. that category across different channels. In contrast, cus- The authors thank three anonymous reviewers; seminar participants at the Marketing Science Conference, Haring Symposium, University of tomers of utilitarian categories may want to shop efficiently Houston Doctoral Symposium, University of North Carolina at Chapel Hill, in one channel and spend more in that channel. Similarly, Syracuse University, Georgia Institute of Technology, University of Illinois, low- (high-) risk product categories may attract customers and McGill University; Roger Kerin; Bill Perreault; Alina Sorescu; Rajan of traditional (electronic) channels and induce them to Varadarajan; and Manjit Yadav for helpful comments. Address all corre- spend more in those channels. However, because much spondence to Venkatesh Shankar. Aric Rindfleisch served as area editor research on multichannel customer behavior is based on for this article. data from a single product category or firm, it precludes the

© 2013, American Marketing Association Journal of Marketing ISSN: 0022-2429 (print), 1547-7185 (electronic) 67 Voiume 77 (July 2013), 67-85 study of the product category's role in the monetary value domly drawn from 96 million customers of 750 direct mar- of shoppers' purchases by channel preference. keting retailers, spanning 22 product categories across the We define a multichannel customer of a broad product catalog and web channels over a four-year period. We gen- category as a customer who buys items in that category eralize the results to the store channel with a longitudinal from more than one channel.' B> viewing multichannel analysis of transaction data from a large multiproduct shopping from the customer angle, our approach provides a retailer with the store channel in addition to the catalog and holistic view of a customer's behavior. We address two web channels. important research questions in the .retailing context: Our results show that contrary to conventional wisdom, multichannel customers form the most valuable segment 1. How does the monetary value of purchases by multichannel only for hedonic product categories. We also find that tradi- customers differ from that of single-channel customers? tional channel customers of low-risk product categories 2. How does the relationship between a customer's channel preference and monetary value -'ary by key product cate- provide higher monetary value than other customers. More- gory characteristics (utilitarian v;. hedonic nature and per- over, web-only (store- or catalog-only) customers of high- ceived risk)? (low-) risk/utilitarian categories offer higher monetary value than other single-channel or multichannel customers. The answers to these questions are critical from both Our findings provide valuable managerial guidelines for theoretical and managerial standpoints. For example, if the shopping in different channels. monetary value of multichannel customers is higher than This article contributes to the literature in at least two that of single-channel customers across all categories, mar- key ways. First, it offers a theoretical understanding of the keters should reach customers of all categories through dif- moderating effects of category characteristics on the chan- ferent channels. Similarly, if web-only shoppers are the nel preference-monetary value relationship. Second, it pro- most valuable channel segment for high-risk/utilitarian vides empirically generalizable counterintuitive findings categories (e.g., computers, electronics), marketers should about this relationship. Furthermore, as Table 1 shows, target these shoppers. Finally, if traditional channel shop- unlike related research (e.g., Ansari, Mela, and Neslin 2008; pers of low-risk categories (e.g., office supplies, garden Kumar and Venkatesan 2005), the current research exam- supplies) provide higher monetary value than multichannel ines the moderating effects of the product category and ana- or web-only shoppers, marketers should focus on these lyzes data from multiple categories and firms in an inte- shoppers. grated framework. To address these research questions, we develop a con- ceptual framework and important hypotheses related to the moderating role of the two key product category character- Conceptual Development istics—utilitarian versus hedonic nature and perceived We first develop a conceptual model of the relationships risk—on the link between channel preference and customer between channel preference, product category characteris- monetary value. We test our hypotheses and obtain empiri- tics, and monetary value (see Figure 1). We focus on both cally generalizable insights by andyzing a unique large- traditional and electronic channels. We classify the store scale, cross-sectional data set of 1 million customers ran- and catalog channels under the unifying banner of a tradi- tional channel because they have a much longer history 'In our subsequent empirical analysis, we test for alternative than electronic channels and are perceived as close substi- defmitions of multichannel customers and show that our results tutes (Avery et al. 2012). Any combination of these chan- are robust to alternative definitions. nels constitutes a multichannel. Among the outcomes, we

FIGURE 1 A Conceptual Model of Relationships Between Channel Preference, Monetary Value, and Product Category Characteristics

Product Category Characteristic Product Category Characteristic Utilitarian Versus Hedonic Nature Low Versus High Perceived Risk

Interactions: H2, Hg Interactions: H4, H5

Channel Preference t •Traditional (catalog, store) Monetary Value •Electronic (web) Main Effect: H, •Mult channel — Hypothesized effects — Controlled effects

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Are Multichannel Customers Really More Valuable? / 69 focus on a key managerial variable—that is, the monetary effects. Our overarching argument is that different shoppers value of customer purchases—measured as the dollar value have different foci based on RFT, and if shoppers' focus fits of customer transactions. with their channel preference based on the product category Prior research has examined the role of the product characteristics, they will experience greater regulatory fit. category in influencing different facets of customer behav- In tum, stronger regulatory fit will lead to higher spending ior in traditional and electronic channels (Yadav and in their preferred channel on the product categories that Varadarajan 2005b). Consistent with this research, we exhibit those characteristics. examine the moderating role of two key product category characteristics (i.e., utilitarian vs. hedonic nature and per- Main Effect ceived risk) in shaping the relationship between channel We first develop our hypothesis about the main effect of preference and customer monetary \alue. customer channel preference on the monetary value of pur- We define a utilitarian category as a category dominant chases across categories. An entity (e.g., customer) evalu- on attributes such as functionality, practicality, cognition, ates the outcome of an exchange process with another entity and instrumental orientation, consistent with Dhar and (e.g., firm) by comparing the perceived benefits with the Wertenbroch (2000). Computing equipment, consumer perceived costs related to the exchange, consistent with the electronics, office supplies, home appliances, and garden quid pro quo notion (Bagozzi 1975). In addition to eco- equipment are examples of utilitarian categories. We define nomic aspects, social and psychological aspects (e.g., a hedonic category as a category dominant on attributes mutual respect, commitment, trust) play an important role such as experiential benefits, affect, enjoyment, enduring in determining the entities' perceived overall benefits, costs, involvement, intrinsic motivation, and aesthetics (Dhar and and value in an exchange (Frazier 1999). Wertenbroch 2000). Examples cf hedonic categories Depending on their perceived value of an exchange include CDs, DVDs, antiques, and ipparel. Unlike hedonic through a channel, customers prefer to use different chan- products, utilitarian products can be easily compared and nels and spend different amounts in different channels. Cus- evaluated along different attributes. tomers who perceive exchanges in a channel as being of We refer to perceived risk of a product category as the high value become frequent customers with a high degree "customers' (overall) perceptions of uncertainty and of trust and commitment to purchase through that channel. adverse consequences of buying a good (or service)" Customers with a stronger commitment spend more on their (Dowling and Staelin 1994, p. 119; see also Bart et al. purchases than other customers (Venkatesan, Kumar, and 2005). Perceived risk of a product category is evaluated on Ravishanker2007). five dimensions of uncertainty: functional (not performing The use of multiple channels is associated with a high to expectation), financial (losing money), safety (causing level of monetary value for customers across all product physical harm), psychological (tarnishing self-image), and categories for several reasons. First, additional channels social (lowering others' perceptions of the user) (Jacoby provide greater convenience value for customers, increasing and Kaplan 1972). Office supplies and books are examples their purchase frequency and accelerating purchases across of low-risk categories, while jewelry and computers are multiple items and categories. Second, the wide assortment examples of high-risk categories. of products across different channels offers multiple oppor- We chose these two product category characteristics as tunities for customers to buy and increase their spending. potential moderators from two maia theoretical considera- Third, customers can combine the benefits from different tions. First, both category characteristics are grounded in channels, realize greater value, and increase their spending regulatory focus theory (RFT), the basis for our hypotheses (Frazier 1999). The web and traditional channels are comple- development. Prior research in marketing (see Chemev mentary rather than cannibalistic with regard to the money 2004; Yeo and Park 2006) treats hedonic (utilitarian) and spent on shopping (Deleersnyder et al. 2002). Thus, channel high (low) perceived risk attributes as consistent with a pro- segment membership (single-channel or multichannel) is a motion (prevention) focus in goal orientation, the key ingre- proxy for customers' perceived value of and commitment to dients of RFT. that channel. This commitment is positively related to cus- Second, utilitarian versus hedonic nature and perceived tomers' spending in that channel across products. Therefore: risk constitute fundamental bases for consumer purchase and consumption. Batra and Ahotla (1990, p. 159) state that H]! Across all product categories, multichannel customers "consumers purchase goods and ser/ices and perform con- have a higher monetary value of purchases than single- sumption behaviors for two reasons: (1) consummatory channel customers. affective (hedonic) gratification (from sensory attributes) and (2) instrumental (utilitarian) reasons." Likewise, the Moderating impact of Product Category study of perceived risk as an inherent product category Characteristics characteristic behind purchase and consumption behavior We now develop hypotheses on how the hedonic versus has a long tradition in the marketing literature (e.g.. Cox utilitarian nature and perceived risk moderate the strength and Rich 1964; Sheth and Venkatesai 1968). of the relationship between channel preference and mone- Our conceptual model includes t!ie direct/main effect of tary value. We present a summary of the hypotheses channel preference and the interaction effects of channel together with the associated rationale in Table 2. preference and product category characteristics on mone- We adopt RFT to motivate our hypotheses. According to tary value. We develop hypotheses pertaining to these RFT, people can be classified into two types on the basis of

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Are iVluitichannei Customers Reaiiy More Vaiuabie? / 71 their regulatory orientation in pursuing a goal: prevention and adventure through the use of different channels. There- focused and promotion focused (Avnet and Higgins 2006). fore, multichannel customers are also likely to have a A prevention focus stresses safety, security, and responsibil- greater promotion focus. ity, whereas a promotion focus emphasizes hope, advance- In summary, customers patronizing traditional modes of ment, and achievement. Thus, a promotion focus involves transaction (catalog and store) are likely to have a greater maximizing positive outcomes, whereas a prevention focus prevention focus. In contrast, customers adopting nontradi- means minimizing negative outcomes. As RFT postulates, tional modes (web and multiple channels) are likely to have people make choices that are consistent with their regula- a greater promotion focus. However, product category char- tory orientation (promotion or prevention focus) in goal acteristics moderate the monetary values of customers by pursuit (Avnet and Higgins 2006). When such choices sus- channel preference. tain their regulatory orientation, people experience a regula- tory fit, leading them to continue their pursuits (Aaker and Utilitarian versus hedonic nature and channel preference Lee 2006). Thus, a customer is likely to engage repeatedly interaction. Because utilitarian products (e.g., computers, in his or her preferred buying process (channel) if doing so garden equipment, sports equipment) have clear and well- is consistent with his or her regulatory orientation (Avnet defined attributes, they are relatively easy to compare and and Higgins 2006). Regulatory fit leads to greater customer evaluate. Thus, for utilitarian product categories, shopping engagement through two well-documented processes tasks involve planned purchases, goal-directed choice, and (Cesario, Higgins, and Scholer 2008): (I) feeling right cognitive involvement (Novak, Hoffman, and Duhacheck about the task and (2) increased information processing. 2003). Goal-directed behavior can lead to habit formation These engaged customers tend to value and pay more for and automatic behavior (Aarts and Dijksterhuis 2000). In products than customers lacking in regulatory fit (Avnet and addition, utilitarian categories are typically high on search- Higgins 2006). Therefore, promotion- and prevention- dominant attributes. People allocate time, a scarce resource, focused customers will tend to spend more on their pur- to different activities, including search (Becker 1965). chases in their preferred channel. Scarcity of time is negatively linked to search efforts (Beatty and Smith 1987). Therefore, consumers of utilitar- Some channels are closely associated with a prevention ian products value efficiency in shopping (Mathwick, Mal- focus, whereas others are aligned with a promotion focus. hotra, and Rigdon 2002). In general, efficiency attributes Because of their long history, traditional channels (i.e., cata- are associated with a greater prevention focus (Chernev log and store) offer high levels of familiarity, safety, confi- 2004). Goal-oriented shopping behavior associated with dence, and trust. In the case of physical stores, customers utilitarian product categories is efficient in time utilization can browse, touch, and feel products before purchase. Fur- when both search and purchase are done habitually and thermore, many catalog companies and physical stores have repeatedly in a single channel. Because efficiency is para- had a long-standing practice of accepting returns from cus- mount, these customers prefer using a single channel to tomers without asking questions. Therefore, traditional multiple channels. channels offer customers high confidence and trust in their Customers of traditional channels have a greater preven- purchases. Thus, catalog- and store-only customers are tion focus, which maps with the prevention-focus attributes likely to have a high prevention focus as they repeatedly of utilitarian product categories, providing a strong channel- patronize traditional channels that offer high levels of category fit. In contrast, because multichannel customers safety, minimizing negative outcomes. are promotion focused on utilitarian products, they experi- In contrast, the relatively newer electronic channels ence a relatively weak regulatory fit. A stronger regulatory (e.g., the web) evoke high behavioral and environmental fit is associated with greater engagement and higher spend- uncertainty (Schlosser et al. 2006; Van Noort, Kerkhof, and ing, and thus we expect traditional channel customers of Fennis 2008). A large-scale survey by the Pew Internet Project utilitarian products to spend more than their multichannel (2008) shows that the web is perceived as highly uncertain counterparts. such that approximately three-quarters of participants were Although web-only and multichannel customers are unwilling to provide personal and credit card information likely to have a greater promotion focus, which provides a over the Internet. Furthermore, the web entails the risk of weak regulatory fit with the utilitarian attributes, the greater identity theft, a significant deterrent to online channel adop- efficiency of the web maps well with the shopping goals tion (Garver 2012). Despite more than a decade since the associated with utilitarian categories. Thus, web-only cus- advent of electronic commerce, the adoption of the web as a tomers of utilitarian categories are also likely to have better transaction channel is still limited because of a high level of goal-attribute fit than their multichannel counterparts. perceived risk. Therefore, web-only customers who repeat- Taken together, we expect that single-channel customers of edly patronize the electronic channel are likely to be driven utilitarian product categories have higher monetary values by adventure and the need to signal advancement—that is, than multichannel customers. Thus: by focusing on positive outcomes. Moreover, web-only shoppers tend to be younger, better educated, and more H2: The monetary value of purchases by single-channel cus- prone to search on the web than other shoppers. Thus, these tomers of utilitarian product categories is higher than that customers are likely to have a greater promotion focus. by multichannel customers of these categories. Customers who adopt multiple channels seek greater Hedonic categories, such as apparel, cosmetics, and convenience and display boldness associated with the adop- DVDs, are conducive to unplanned or impulse buying and tion of the electronic channel. They seek greater enjoyment variety seeking (Novak, Hoffman, and Duhacek 2003).

72 / Journal of Marketing, July 2013 Impulse purchases are characterized by spontaneity, com- ditional channels, whereas high-risk categories may draw pulsion, excitement, and disregard for consequence (Kou- promotion-focused customers, such as those shopping on the faris 2002). Variety-seeking behavior is, in part, driven by web or through multiple channels. Consistent with the notion factors such as product category-specific differences (Van that the perceived risk of the web is due to the relative new- Trijp, Hoyer, and Inman 1996). Customers of hedonic prod- ness and impersonal nature of the channel (Montoya-Weiss, uct categories likely have high goal ambiguity because of Voss, and Grewal 2003), we expect that web-only cus- affect-dominant attributes and the salience of the experien- tomers have a greater promotion focus than a prevention tial value of hedonic products. Such goal ambiguity leads focus. Similarly, because multichannel customers may seek customers of hedonic categories to include disparate prod- greater enjoyment and adventure through different chan- ucts in their consideration set (Ratneshwar, Pechmann, and nels, they are likely to have a greater promotion focus. In Shocker 1996) and seek variety. In addition, perceived contrast, because of the low-risk profiles of the store and uncertainty about future preferences is likely to be higher catalog channels, single-channel customers are likely to for hedonic products, leading to variety seeking as a choice have a greater prevention focus. heuristic (Simonson 1990). In general, hedonic attributes are With the high degree of fit among prevention focus, associated with a greater promotion focus (Chemev 2004). low-risk categories, and traditional channels, traditional Thus, customers are more likely to engage in variety-seeking channel customers tend to spend more than web-only or behavior for hedonic categories and spend more (Garg, multichannel customers in low-risk categories. In contrast, Inman, and Mittal 2005; Kurt, Inman, and Argo 2011; Ratner web-only and multichannel customers of high-risk categories and Kahn 2002; Stilley, Inman, and Wakefield 2010a, b). likely spend more than customers of traditional channels Multichannel customers are likely to have a greater pro- because of the high degree of fit between these categories motion focus, which provides a strong regulatory fit with and channels. These arguments lead to following hypotheses: hedonic attributes. Multiple channels provide a greater H4: The monetary value of purchases by traditional channel assortment of products than a single channel. More hedonic customers of low-risk product categories is higher than products across multiple channels offer customers more that by electronic channel and multichannel customers of opportunities to engage in impulse purchases, enhance cus- these categories. tomers' consideration set, and promote greater variety seek- H5: The monetary value of purchases by electronic channel ing. In contrast, the prevention focus of traditional channel and multichannel customers of high-risk product cate- customers has a weak regulatory fit with hedonic attributes. gories is higher than that by traditional channel customers Thus, multichannel customers are likely to be more strongly of these categories. engaged and have a higher spending level than their coun- terparts from traditional channels. Web-only customers are also likely to have a greater Empirical Analyses promotion focus, which provides a strong regulatory fit We examine an empirical context comprising a carefully with hedonic attributes. However, the use of multiple chan- compiled, unique, and large cross-sectional database of nels offers greater convenience and variety, which are more approximately 1 million U.S. customers who were ran- satisfying for variety-seeking and impulse purchase behav- domly selected from a cooperative database of 96 million iors commonly involved in hedonic categories. Thus, multi- customers of 750 direct marketers covering 22 product cate- channel customers of hedonic categories are likely to have a gories and several subcategories during a four-year period better goal-attribute fit than their web-only counterparts. (2001-2004). We obtained data from i-Behavior, a syndi- In summary, we expect that multichannel customers of cated data aggregator firm. Firms in the cooperative database hedonic product categories have higher monetary value have only the web and catalog channels (no physical stores than their single-channel counterparts. These arguments exist), so the catalog is their offiine channel. The data contain lead to the following hypothesis: customers' demographic characteristics, shopping experi- ences, preferred purchase channels, order details, and product H3: The monetary value of purchases by multichannel cus- tomers of hedonic product categories is higher than that categories purchased. This period adequately captures the by single-channel customers of these categories. growth phase of the web as a distribution channel. Details on these 22 product categories appear in the Web Appendix Perceived risk and channel preference interaction. For (WAI; www.marketingpower.com/jm_webappendix). product categories with high perceived risk, such as elec- Our data set is neither firm nor industry specific, and it tronics, telecommunications equipment, and musical instru- captures customer purchases across a comprehensive set of ments, customers face considerable uncertainty. Therefore, product categories and competing firms. Such data are highly product categories with high perceived risk fit the goal ori- representative of the population and allow for empirical entation of promotion-focused customers (Yeo and Park generalizability. Data sets from prior research are primarily 2006). A promotion focus is consistent with risk-seeking from a single firm across one or a few product categories. behavior (Avnet and Higgins 2006). Conversely, a preven- Our database covers a wide range of product categories, tion focus is synonymous with risk-averse behavior and fits such as apparel, accessories, gifts, hobby items, electronics, with low-risk product categories, such as office supplies, and musical instruments, for 750 multichannel direct mar- books, and toys. keters, which enables us to develop a richer understanding Because of the channel-category fit, low-risk categories of a customer's channel preference and behavior than when likely attract prevention-focused customers who shop in tra- analyzing data from a single firm or a few categories.

Are Multichannel Customers Really More Valuable? / 73 Operationalization of Variabies only and riiultichannel shoppers, web-only retail sales grew The operationalization of the variab.es in our data appears by approximately 12% during the 2006-2010 period in Table 3. The exogenous classes of variables, such as (Jupiter Research 2011). The summary snapshot suggests demographic characteristics, shopping experience, high-end that multichannel customers spend approximately one and a catalog usage, and the number of unique mailing lists that half times more than catalog-only customers and approxi- contain the customer's name, are based on the customers' mately five and a half times more than web-only customers. past transaction history. Similarly, multichannel customers buy more often (higher frequency) than single-channel customers. However, are Table, 4 provides summary s:atistics for the key these initial summary observations true for all product cate- variables in our model. Of the usable sample (customers gories? We address this question in our empirical analysis. with data on every variable in the database), 71.8% pur- chased only through the catalog channel, 5.3% purchased Measurement of Product Category Ciiaracteristics only through the web, and the remaining 22.9% purchased through both channels.2 Although the number of purchases We use data from exogenous sources to measure the two of web-only shoppers is much smaller than that of catalog- key category characteristics of the 22 product categories. We classify product categories with a higher utilitarian 2We compared the usable sample (n = 412,424) with the unus- score than hedonic score as utilitarian, and vice versa for able sample on each of the five dependent variables analyzed in the purchased product category basket. We classify the cate- the subsequent sections. The means of the key dependent variables gories as high or low risk on the basis of a median split of the usable and unusable samples are similar. along these dimensions for the purchased product category

TABLE 3 Operationalization of Variables in the Data

Variable Operationalization Focal Dependent Variable Monetary value (DLLR) Total dollars spent by the customer in the four-year data window. Other Dependent Variables Channel preference (WEB, CTLG) Dummy variables representing web-only and catalog-only with multichannel (both web and catalog) as the base. Based on the customer's purchase channel over the data window. Mailers (MAIL) Number of marketing mailers sent to the customer in the past four years transformed to a near normal distribution using Anscombe's (1948) transformation. Frequency (ORDR) Number of orders by the customer in the four-year window transformed to near normal distribution using an Anscombe's (1948) transformation. Product Category Characteristics Dummy variables representing utilitarian and hedonic categories with all categories Utilitarian versus hedonic (UTL, HED) as the base. Dummy variables representing low- and high-risk categories with all categories as Perceived risk (LR, HR) the base.

Control Variables/Instruments The midpoint of the age range to which the customer belongs (seven intervals). For Age (LAGE) the last age range, which is open-ended (75+ years), the lower bound of the range is taken as the measure. Family size (FSI2E) Number of adults and children in the customer's household. Education (EDU) Number of years of education of the customer. Number of high-end catalogs (HIGH)a Number of high-end catalogs from which the customer ordered. Largest past spending (HISPEND) Dollar value of the customer's largest order in the data window. Relative credit card use (RCCU)^ Percentage of occasions during which the customer used a major credit card. Returns (RET) Number of items the customer returned. Shopping experience (EXP) Number of weeks since the customer placed the first order before start of the data period. Number of categories bought (CAT} Number of different product categories the customer bought. Unique mailing lists on (UNQML) Number of unique mailing lists on which customer is listed. Target customer net worth (NTWTH) The net worth score of a target customer as reported by Claritas on a ten-point scale. Lists responded to (UNQRS) Percentage of unique mailing lists to which a customer responded. aData aggregators in the direct marketing industry classify catalogs into five categories on a continuum from "low-scale" to "high-scale" cata- logs. The number of times a customer orders from the highest category of the "high-scale" catalogs is the operationalization of the variable. tMCd and Visa issued by major banks, American Express, and Discover are classified as major credit cards.

74 / Journal of Marketing, July 2013 TABLE 4 Summary Statistics of Key Variables in the Data

Variable/Item Catalog Only Web Only Multichannel Sample size (n) 296,079 21,776 94,569 Channel preference (%) 71.79 5.28 22.93 Monetary value ($) 1,123.92 477.69 1,542.03 Frequencya 6.89 3.54 7.41 Mailersa 5.12 2.73 5.53 Age 57.22 45.99 48.99 Family size 2.42 2.63 2.67 Education (years) 13.31 13.96 13.78 Number of high-end catalogs .68 .55 1.07 Largest past spend in an order ($) 124.54 201.56 158.74 Relative credit card use .32 .44 .45 Returns .03 .01 .03 Shopping experience (weeks) 157.67 80.63 166.96 Number of categories bought 4.92 2.16 5.41 Unique mailing lists on 5.77 1.39 7.06 Target customer net worth 6.41 6.56 6.61 Unique lists responded to (%) .27 .02 .27 »The reported summary statistics are before performing an Anscombe transformation. basket. We dummy-code consumer shopping baskets on Risk scale load on one factor, measuring the underlying four variables: hedonic dummy, utilitarian dummy, high- concept of perceived risk. Details on the scale properties risk dummy, and low-risk dummy. The mixed shopping and computation of thé composite measures of HEDUT and basket, containing both hedonic and utilitarian and high- Perceived Risk appear in the Web Appendix (WA2; www. risk and low-risk products, serves as the base case scenario. marketingpower.com/jm_webappendix). Table 5 reports the mean composite scores for each product category on each Measurement scale. Consistent with prior research, we of these three underlying dimensions across our sample. operationalize the hedonic versus utilitarian nature of a product category using the hedonic utility (HEDUT) scale Measurement validation. The measures have consider- (Voss, Spangenberg, and Grohmann 2003). The scale mea- able face validity. The categories with higher technological sures the strength of a product category on utilitarian and complexity and higher prices or those used more in social hedonic aspects using an equal number of items. The details settings scored higher on perceived risk (e.g., electronics, of the scale items and anchors used for measuring each of photography and video equipment, jewelry, apparel and these two aspects appear in the Web Appendix (WA2; www. accessories). Similarly, the respondents perceived the marketingpower.com/jm_webappendix). We operationalize beauty and cosmetics, wines, and home furnishing cate- perceived risk using the scale developed by Jacoby and gories as largely hedonic and the computing, telecommuni- Kaplan (1972) and used by others (e.g., Chaudhuri 1998). cations, and office supplies categories as mainly utilitarian. Product category risk has five components: functional (not We cross-validated these findings through the ratings of performing to expectation), financial (losing money), safety five experts who classified all 22 categories into this 2x2 (causing physical harm), psychological (tarnishing self- matrix. The interrater reliability was .93, suggesting high image), and social (lowering others' perceptions of the external validity of our survey results. user). Figure 2 depicts the relative positions of categories Model Formulation on these two dimensions. We calibrate the axes in the map on deviations from the median score. Details of the scale Monetary value. We model the key dependent variable items appear in WA2. of interest, monetary value (DLLR¡) of a customer i, as a function of his or her channel preference, purchase fre- Data collection. We collected data on these measures quency (ORDRj), and number of marketing mailers from students in a nationally ranked business program of a received (MAILj), as follows: large well-known university in the eastern United States. To reduce cognitive fatigue associated with long question- (1) DLLRi = ßo + ßiCTLGi + ßaWEBj + ßjUTLi + naires, we used a split sample approach in which each + ßjLRi + ßeHR; + ß7-26PCIi + respondent evaluated only 11 product categories. We ran- domly assigned the product categories to the two types of questionnaires and randomly distributed the questionnaires where CTLG (catalog only) and WEB (web only) are to the respondents. Of the 78 questionnaires, we received dummy variables for the use of the catalog and web chan- 67 usable responses. nel, respectively, with multichannel as the base channel. In Scale properties. Both the HEDUT and Perceived Risk addition, UTL (utilitarian categories only), HED (hedonic scales possess excellent convergent validity, divergent categories only), LR (low-risk categories only), and HR validity, and reliability. All five items from the Perceived (high-risk categories only) are dummy variables represent-

Are Multichannei Customers Realiy iVIore Valuable? / 75 FIGURE 2 Relative Positions of Product Categories Along Key Category Characteristics

2- IComputingl

<3 [jeveliy (O OJTelecomniunication Equipmej £ 1 - |MugicnlIn8trument8| [Beauty &Cosnietic»[

[Photography & Video]

[Automotive Acceigorie» [Gifts &Holiday8|c Booki |P et Items [ [Craft Supplieg] £ -1 - o Home&GardenI

[office Suppliei|

-2-

-3 -2 -1 Utilitarian Hedonic

Notes: X- and y-axes are calibrated as deviations from the median {0, 0).

ing category characteristics with all categories as the base. probit model for the probability of channel preference (P) Finally, PCI is a vector of 12 two-way and 8 three-way on the basis of the following equation: interaction variables of category characteristics and chan- nels, ID is a vector of instruments for the monetary value equation, ßs are the response parameters, and \|/ is a nor- (3) p,,=j.,,j.( mally distributed random error component. To isolate the impact of channel preference on monetary value through Equation 1, we account for the endogeneity or simultaneity where O is the probability density function of normal distri- of channel preference, purchase frequency, and number of bution and V is the deterministic component of utility. mailers .3 Purchase frequency. The purchase frequency of a cus- Channel preference. A customer is likely to prefer a tomer is given by channel (or combination of channels) that provides the (4) ORDRj = j + Y2WEBÍ + + Y4MA1L¡ highest utility. Let the utility U of customer i's preference of channel j be given by

(2) Uij = ij + e¡j, where Y is a parameter vector, IO is a vector of instruments for the purchase frequency equation, Ç is a normally distrib- where IC is a vector of instruments for channel preference; uted random error component, and the other terms are as j G 1,2,3 such that 1 = catalog, 2 = web, and 3 = multi- defined previously. channel; OjS are channel-specific response parameters, e is a normally distributed random error component, and the other Mailers. The number of marketing mailers a customer terms are as defined previously. We specify a multinomial receives is given by

(5) MAIL; = Ô0 + Ô,CTLGi + ôsWEBj + 03DLLR¡ + O4ORDRÍ example, a customer may prefer to use multiple channels because he or she has a larger shopping basket or purchases more expensive items. Similarly, a customer may have a high monetary value because he or she receives many marketing mailers or has a where IM is a vector of instruments for the mailer equation, 5 high purchase frequency. We model and account for such simul- is a parameter vector, ö is a normally distributed error com- taneity and endogeneity. ponent, and the rest of the terms are as defined previously.

76 / Journal of Marketing, July 2013 Identification and Instrumentai Variables behavior. Different socioeconomic classes may have differ- ent predispositions to buy from different types of channels, To identify the four equations, each with three endogenous and customer demographics play an important role in the variables, we require at least three excluded exogenous choice of the information channel and the resulting share of variables or instruments for each one. Theoretically, a good instrument should be correlated with the left-hand-side volume for a channel (Inman, Shankar, and Ferraro 2004). endogenous variable but uncorrelated with the independent Age, family size, and education are key demographic variables.^ We propose nine excluded exogenous variables variables influencing channel preference .5 The literature on for each equation that constitute appropriate instruments store choice behavior (e.g., Popkowski Leszczyc, Sinha, according to theoretical considerations examined in the and Timmermans 2000) and channel-category associations marketing literature. (Inman, Shankar, and Ferraro 2004) suggests that three cus- tomer demographic variables —age (LAGE), family size Customer-ordering characteristics (ID). Three customer- (FSIZE), and education (EDU) —most likely influence ordering characteristics—namely, the number of high-end channel preference. catalogs used, the value of the highest basket, and the rela- tive use of a credit card—may influence the monetary value Customer shopping experience (10). We expect that of purchases for the following reasons. Customers who customer shopping experience, which includes years of browse high-end products (HIGH) are likely to spend more shopping (EXP), number of product categories purchased because the average unit price of such items is higher than (CAT), and number of items returned (RET), influences that for other items. Similarly, the dollar value of the cus- purchase frequency for the following reasons. Typically, tomer's largest order (HISPEND) will be strongly corre- customers who have shopped longer are more knowledge- lated with the monetary value of customers but only weakly able about selling practices and channels, have greater related to the other endogenous variables such as purchase shopping involvement, and order more frequently (Bolton frequency. Finally, consistent with previous research that 1998). The number of categories and purchase frequency shows that customers who purchase with a credit card are are positively related because a customer tends to buy asso- likely to spend more than those who use other payment ciated categories on a given occasion (Kumar and Venkate- modes (Soman and Cheema 2002), we use relative credit san 2005).6 Customers with higher returns also likely have card usage (RCCU) as an instrument for monetary value. higher purchase frequencies (Kumar and Venkatesan 2005). Customer demographics (IC). A customer's demograph- Customer marketing profile (IM). Firms send marketing ics may significantly influence his or her channel preference mailers to prospects according to their marketing profiles. Three key variables that constitute marketing profile are (1) subsequently test for the quality of instruments in the the number of unique mailing lists containing the cus- "Robustness Checks" section. tomer's name (UNQML), (2) the net worth of the target customer (NTWTH), and (3) the number of unique mailing TABLE 5 lists to which the customer has responded (UNQRS). Direct Summary Scores of Product Categories on marketers typically use these factors when selecting new HEDUT and Perceived Risk Scaies customers to target in their direct mail campaigns (Direct Utilitarian Hedonic Risl< Marketing Association 2005). Category Score Score Score Estimation Apparel and accessories 5.13 5.84 3.91 Arts and antiques 2.96 4.38 3.54 The proposed simultaneous system comprises an observed Automotive accessories 6.19 2.57 3.50 endogenous discrete choice variable (channel preference), Beauty and cosmetics 4.30 5.92 4.85 endogenous count variables (frequency and mailers), and an Books and magazines 5.04 5.67 3.18 endogenous continuous variable (monetary value). Because CDs and DVDs 4.65 5.64 3.47 Collectibles and memorabilia 3.08 5.03 3.99 we have a combination of discrete and continuous variables Computing equipment 6.66 5.60 5.22 Craft supplies 4.77 4.18 3.03 5We do not include gender in our analysis because there are no Electronics 6.16 5.07 4.79 strong theoretical reasons to expect differences in channel prefer- Gifts and holidays 4.24 5.23 3.45 ence due to gender differences and because a subsequent empirical Home and garden equipment 5.65 3.19 2.77 analysis involving gender showed that gender has a nonsignificant Home furnishing 3.56 5.80 3.48 effect on channel preference (p > .10). This finding is consistent Jewelry 4.12 5.03 4.87 with the result that there are no significant differences between Musical instruments 5.14 4.74 4.55 male and female web-only shoppers (Jupiter Research 2011). We Office supplies 6.77 2.84 2.23 also exclude income because it is highly correlated with education Pet supplies and items 6.14 3.42 3.07 in the data we subsequently analyze. Photography and video 6.14 5.27 4.53 ^Although the number of categories may seem positively corre- Sports equipment 5.70 5.11 4.08 lated with monetary value, there is no theoretical reason to believe Telecommunication equipment 6.63 5.13 4.98 this is so. A customer buying one low-value item each from sev- Toys and games 3.95 4.93 3.44 eral categories may have a lower monetary value than another cus- Wines 4.56 5.53 4.09 tomer buying several high-value items from a single category. Notes: The scores are based on a seven-point scale in which higher Indeed, the correlation between these two variables is not high in numbers indicate greater strength of the measured attribute. our data (.39).

Are Multichannel Customers Really More Valuable? / 77 in the system, traditional two-stage or three-stage least TABLE 6 squares estimation will lead to biased estimates. To estimate Results of Monetary Value Model this system, we extend the generalized probit framework (Amemiya 1978), which assumes that the random error Coefficient Estimate SE component in each equation is normally distributed. To Main Effects make the joint estimation tractable, we transform the nega- Intercept ßo 436.76 20.77 tive binomially distributed count variables (frequency and Catalog-only dummya ßi -60.13 15.98 marketing mailers) into near-normal distributed variables, Web-only dummya ß2 -108.92 16.99 Utilitarian using Anscombe transformation (Anscombe 1948). This ßs -1.71 10.68 Hedonic -105.23 15.51 procedure ensures that our system has equations with only ß4 Low-risk ßs -24.89 18.66 two types of dependent variables with normally distributed High-risk ße 59.34 15.78 errors. We follow the two-step estimation approach detailed Two-Way Interactions in the Web Appendix (WA3; www.marketingpower.com/ Catalog only x utilitarian ß7 63.79 15.06 jm_webappendix). In Step 1, we regress each endogenous Web only x utilitarian ßs 105.53 18.37 variable on all the exogenous instruments. In Step 2, we Catalog only x hedonic ßg -83.97 9.92 regress monetary value on the included exogenous variables Web only x hedonic ßio -109.84 16.31 and predicted values of endogenous variables (from Step 1). Catalog only x low risk ßii 107.54 13.17 Web only x low-risk 47.75 27.25 We use the ordinary least squares estimation for the mone- ßl2 Catalog only x high-risk ßi3 49.27 13.78 tary value, purchase frequency, and marketing mailers mod- Web only x high-risk ßi4 114.12 31.59 els and the multinomial probit estimation for the channel Utilitarian x low-risk ßi5 -5.64 12.61 preference model. Utilitarian x high-risk ßi6 -98.50 17.82 Hedonic x low-risk ßl7 112.49 24.21 Hedonic x high-risk ßi8 -37.68 26.35 Results and Discussion Three-Way Interactions Catalog only x utilitarian x ßi9 69.86 17.86 i\/lain Effect low-risk Web only x utilitarian x ß20 -84.36 21.77 We present the results of the monetar>' value model in Table low-risk 6.'^ Consistent with Hi, across all product categories, multi- Catalog only x utilitarian x ß21 6.06 10.88 channel customers have a significantly higher monetary high-risk value than single-channel customers (p < .01). The average Web only x utilitarian x ß22 47.55 11.46 multichannel customer outspends the average catalog- and high-risk web-only customers by $60.13 (ß,) and $108.92 (ßj), Catalog only x hedonic x ß23 -150.86 23.72 respectively. In addition, the intercept is positive and signifi- low-risk Web only x hedonic x ß24 -67.67 12.49 cantly high ($436.76; ßo), highlighting the expected high low-risk spending level of multicategory, multichannel shoppers. Catalog only x hedonic x ß25 -65.64 22.85 high-risk Hypothesized interaction Effects Web only x hedonic x ß26 -103.91 18.19 H2 suggests that single-channel customers of utilitarian high-risk product categories spend more than other customers. After Other Controls we control for the effects of other variables, the average Purchase frequency ß27 149.83 9.30 catalog-only, web-only, and multichannel customers of Mailers ß28 15.15 6.69 High-end catalog 23.27 3.63 utilitarian product categories spend $438.70 (ßo + ßi + ß3 + ß29 Largest past spend ß30 2.01 .53 ß7), $431.65 (ßo + ß2 + ß3 + ßs), and $435.05 (ßo + ßs), Relative use of credit card ßsi 20.49 7.05 respectively.8 However, the difference in spending among Model fit (R-square) 62.45% the average catalog-only, web-only» and multichannel cus- tomers is not significant (p > .10). Thus, we fmd that the ^Multichannel is the base case. Notes: All boldfaced coefficients have ps < .05. monetary values of utilitarian category purchases do not significantly differ among traditional, electronic, and multi- ßio), and $331.53 (ßo + ß4), respectively. The difference in ple channel customers. spending between the average multichannel and catalog- H3 proposes that multichannel customers of hedonic only customers ($144.09) and between the average multi- product categories will outspend their single-channel coun- channel and web-only customers ($218.76) is positive and terparts. We fmd that the average catalog-only, web-only, significant (p < .01). These findings suggest that multichan- and multichannel customers of hedonic product categories spend $187.43 (ßo + ßi + ß4 + ßg), $112.77 (ßo + ß2 + ß4 + nel customers of hedonic product categories significantly outspend their single-channel counterparts, in support of H3. model results for the other endogenous variables not used H4 proposes that traditional channel customers of low- for hypotheses testing appear in the Web Appendix (WA4; www.markelingpower.com/jm_webappendix). risk categories have higher monetary value than that of ^We tested the differences between the effects by accounting for other customers. The average catalog-only, web-only, and the standard errors and covariances of the parameter estimates. multichannel customers of low-risk product categories

78 / Journal of Marketing, July 2013 spend $459.27 (ßo + ßi + ßs + ßii)- $350.70 (ßo + ßa + ßs + the highest monetary value, but the monetary values of ß,2), and $411.87 (ßo + ßs), respectively. The difference in catalog-only and multichannel customers do not differ. spending between the average catalog- and web-only cus- This result is consistent with the arguments used for tomers ($108.57,p < .01) and between the average catalog- theorizing two-way interaction effects. Promotion-focused only and multichannel customers ($47.41,/? < .10) is posi- web-only customers are often comfortable buying high- tive and signiflcant, suggesting that traditional customers of value items from high-risk/utilitarian categories (e.g., con- low-risk categories offer higher monetary value than other sumer electronics, computing equipment) (Van Noort, customers. These results are consistent with H4. Kerkhof, and Fennis 2008). Furthermore, utilitarian cate- According to H5, multichannel and web-only customers gories typically require a high degree of information search. of high-risk categories have higher monetary value than that For such categories, the web is conducive for information of other customers. The average catalog-only, web-only, gathering and offers a high level of convenience for shop- and multichannel customers of high-risk product categories ping and ordering items (Yadav and Varadarajan 2005a). spend $485.24 (ßo + ßi + ße + ßis). $501.30 (ßo + ßa + ße + Moreover, as we suggested previously, web-only shoppers ßi4), and $496.10 (ßo + ßo), respectively. The difference in tend to be younger, better educated, more risk taking, and spending among the average multichannel, catalog-only, more prone to obtaining information on utilitarian products and web-only customers is not significant (p > .10). Thus, on the web than other shoppers. Satisfaction and enjoyable these results do not support H5. experience with the information search through an online channel can lead to positive outcomes (Mathwick and Rig- Other interaction Effects don 2004). Consequently, web-only customers of high- We did not have formal hypotheses for the effects of three- risk/utilitarian categories buy more often and spend more way interactions among channel preference, utilitarian ver- than other single-channel customers. Because the utilitarian sus hedonic nature, and perceived risk because we treat nature of the categories leads to efficient shopping through these effects as empirical issues. We now discuss the results a single channel, web-only customers also outspend multi- of these interaction effects. channel shoppers. Low-risk/utilitarian. We find that the average catalog- Low-risk/hedonic. We now turn to the effects of the only, web-only, and multichannel customers of low-risk/ interactions of perceived risk with the hedonic nature of the utilitarian product categories spend $585.57 (ßo + ßj + ß3 + product category. For low-risk/hedonic product categories, ß5 + ß? + ßii + ßi5 + ßi9). $322.78 (ßo + ß2 + ß3 + ßs + ßs + the average catalog-only, web-only, and multichannel cus- ßi2 + ßi5 + ß2o)> and $404.51 (ßo -n ßj + ßs + ßu), respec- tomers spend $231.72 (ßo -t- ßi + ß4 + ßs + ßg + ß,, H- ßn + tively. The average catalog-only customers of low-risk/ ß23), $180.46 (ßo + ß2 + ß4 + ßs + ßlO + ßl2 + ßl7 + ß24), utilitarian product categories spend $262.79 {p < .01) and and $419.13 (ßo + ß4 + ßs + ßn), respectively. The average $181.05 (p < .01) more than their web-only and multichannel multichannel customers of low-risk/hedonic product cate- counterparts, respectively. Therefore, for low-risk/utilitarian gories spend $187.41 (p < .01) and $238.68 (p < .01) more categories, traditional channel customers outspend non- than their catalog- and web-only counterparts, respectively. traditional channel customers. Although the interaction The result of the test of H4 shows that traditional channel effects of channel preference and a utilitarian nature suggest customers of low-risk product categories outspend other no significant difference between the monetary values of customers of these categories. The result of the test of H3 single-channel and multichannel customers (lack of support shows that multichannel customers of hedonic categories for H2), the interaction effects of channel preference and the spend more than other customers. However, for both the low-risk nature of the category indicate a higher spending low-risk and the hedonic nature categories, multichannel level by customers of traditional channels (H4). Because customers have higher monetary value than other cus- traditional channel customers are a subset of single-channel tomers. This finding suggests that the hedonic nature has a customers, for low-risk/utilitarian categories, traditional stronger effect than low risk on monetary value. channel customers (who tend to have a prevention focus) High-risk/hedonic. The average catalog-only, web-only, experience a stronger regulatory fit than their web-only and and multichannel customers of high-risk/hedonic product multichannel counterparts. categories spend $192.73 (ßo -i- ß, + ß4 + ße + ßg + ßn + High-risk/utilitarian. We find that the average catalog- ß,8 + ß25), $144.64 (ßo + ß2 + ß4 + ße + ßio + ßi4 + ßi8 + only, web-only, and multichannel customers of high-risk/ ß2e), and $353.19 (ßo + ß4 -H ße + ßig), respectively. The utilitarian product categories spend $454.88 (ßo + ßi + ß3 + average multichannel customers of high-risk/hedonic prod- ßö + ß, + ß,3 + ß,6 + ß2,), $554.16 (ßo + ß2 + ß3 + ße + ß8 + uct categories spend $160.47 (jj < .01) and $208.55 (p < ßi4 + ßi6 + ß22). and $395.88 (ßo + ß3 + ße + ßie). respec- .01) more than their catalog- and web-only counterparts, tively. The average web-only customers of high-risk/utili- respectively. The two-way interaction effects of channel tarian product categories spend $99.28 (p < .05) and preference and hedonic nature (H3) and of channel prefer- $158.27 (p < .01) more than their catalog-only and multi- ence and high-risk nature (Hg) indicate a higher spending channel counterparts, respectively. In addition, we find that level by multichannel customers than other customers. Con- the difference between the monetary values of the average sequently, the interaction of the hedonic nature with high multichannel and catalog-only customers is not statistically risk has a more positive effect on the spending of multi- significant (p > .10). These results suggest that for high-risk/ channel customers than that of single-channel customers. utilitarian product categories, web-only customers provide Taken together, the results show that for hedonic categories.

Are Multichannel Customers Really More Valuable? / 79 multichannel customers provide the highest monetary value The adjusted R-square for these regression equations is also regardless of the risk level of the category. healthy (at least 60%), and the instrumental variables used in each equation are significant (p < .001), suggesting that Extension and Generaiization of Resuits to the the instruments are strong. Store Channei We also perform two formal tests to evaluate the validity The large data set in our study helps uncover empirically of our instruments. First, consistent with Bound, Jaeger, and generalizable findings across multiple product categories Baker (1995), we examine the validity of our instruments and direct marketers with catalog, web, and multiple chan- by using the correlation test. The correlation matrix reported nels. To generalize these findings to the store channel, we in the Web Appendix (WA6; www.marketingpower.com/ extend this study with an analysis of a U.S. multiproduct jm_webappendix) suggests that the correlations of instru- retailer's data set that includes (1) lime-series data and (2) mental variables with the associated endogenous variables data from physical stores. Time-series data facilitate the are high, whereas those with other endogenous variables are study of the potentially causal relationship between channel moderate to low. Second, to ensure that our choice of preference and monetary value. Because store purchases instruments does not drive the directions of our results, we constitute a majority of transactions for many multichannel compare the observed average values of monetary value retailers, analysis of physical store data enables us to gener- across the different baskets. The directions of these alize our results. observed differences are similar to those from the results The details of this analysis appear in the Web Appendix estimated through our simultaneous system. However, by (WA5; www.marketingpower.com/jm_webappendix). The accounting for endogeneity and simultaneity in our model, fmdings from this analysis are consistent with those from we can determine the correct magnitudes of the differences the larger cross-sectional data set and reinforce our conclu- in monetary values across the baskets. Together with the sions. In addition, the fmdings bolster the temporal links theoretical arguments, these tests support the appropriate- among the variables in our model and extend the generaliz- ness of our instruments. ability of our results for the catalog-only channel segment The instruments are exogenous or predetermined with to all traditional channel segments, including the store-only respect to the variables studied. Nevertheless, to ensure channel segment. complete independence from the variables, we estimated the model with values of the instruments from a matched Robustness Checi(S sample of customers in the database. We used the propen- Out-of-sample predictions. We validate our findings sity score matching method to select the matched sample, with an out-of-sample prediction on a randomly selected consistent with Rosenbaum and Rubin (1983). The results holdout sample of 50,000 customers. Using the parameter of this analysis, reported in the Web Appendix (WA7; estimates from our estimated model on a sample of the www.marketingpower.com/jm_webappendix), are consis- remaining 362,424 customers, we predict the monetary tent with those reported in Table 6. value of customers in the holdout sample. The mean Operationalization of category characteristics as con- absolute deviation (MAD) is 250.14, the mean monetary tinuous variables. We test the robustness of our findings to value of the holdout sample is $1,254, and the MAD is altemative (continuous) measures of category characteris- approximately 19.95% of the sample mean. These values are tics. We generate continuous measures for each of the utili- reasonable for cross-sectional out-of-sample validation .9 tarian, hedonic, and perceived risk characteristics for a cus- Quality of instruments. We test the validity and strength tomer's basket by averaging the scores of each of our instruments in multiple ways. In the "Identification characteristic across the product categories bought. For and Instrumental Variables" subsection, we argue that our example, if a customer purchased only apparel and acces- choice of instrumental variables is based on theory. We test sories and beauty and cosmetics, his or her hedonic, utilitar- for the strength of the instrumental variables using Staiger ian, and perceived risk scores wouid be 4.72, 5.88, and and Stock's (1997) approach. In this approach, we test the 4.38, respectively. The results of the monetary value model first-stage F-statistic for each equation with the instrumental from this analysis appear in the Web Appendix (WA8; variables. The bias introduced by the v/eak instruments is of www.marketingpower.com/jm_webappendix) and are con- the order of the inverse of the F-statistic. We follow Stock sistent with our main model results. and Watson's (2003) rule of thumb; that is, an F-statistic Alternative definitions of multichannel customer. We greater than 10 is acceptable because it corresponds to a perform robustness checks for altemative definitions of a bias of less than 10% in the estimates. Staiger and Stock's multichannel shopper. First, we define a multichannel shop- (1997) test for the first-stage regression in our data does not per as someone who shops across channels but within one indicate the presence of poor instruments. The F-statistics specific category (e.g., shoes) and across firms. The results of the monetary value, frequency, and mailers equations are are largely consistent with those reported in Table 6. Second, 26,747, 28,033, and 31,058, respectively. Thus, any weak we define a multichannel shopper as someone who shops instrument introduces, at worst, a less than .0001% bias. across channels and across categories but within a firm. We discuss this analysis in detail in the previous section on MAD percentage values are comparable to those Jen, extension and generalization of results to the store channel Chou, and Allenby (2009) report in a similar direct marketing con- (see WA5 in the Web Appendix; www.marketingpower. text for predicting monetary value using linear regression. com/jm_webappendix). Finally, we define multichannel

80 / Journal of Marketing, July 2013 shoppers as those who shop across channels within a cate- We also find that the perceived risk of a product cate- gory and within a firm. We use the same data as those in the gory moderates the relationship between channel preference previous robustness check. However, we classify a customer and monetary value for utilitarian product categories. A as a multichannel shopper if he or she purchased across plausible rationale follows. According to RFT, for high- channels within a given product category of the multiproduct risk/utilitarian product categories, promotion-focused cus- retailer. The results are largely consistent with those tomers spend more in the web channel, whereas for low- reported in Table 6. risk/utilitarian product categories, prevention-focused customers spend more in the catalog or store channel. Because of the regulatory fit of their orientation with the Implications product category and the channel, these customers spend We summarize our findings in Table 7. Our main effect more in the respective channels than their other single- finding is that across product categories, an average multi- channel or multichannel counterparts. channel customer provides higher monetary value than an Theoretical Implications average single-channel customer. As discussed in the "Con- ceptual Development" section, customers who prefer multiple We contribute to the literature in several ways. First, we channels may become more engaged in the purchase process extend prior research on the value of multichannel shoppers as they shop across channels. Greater engagement may lead (e.g., Kumar and Venkatesan 2005) and offer new insights to more frequent purchases, a greater order quantity, greater iiito the moderating effects of the product category on the spending, or a combination of all these outcomes. channel preference-customer monetary value relationship. Contrary to conventional wisdom and prior research, we Importantly, our results show that product category show that multichannel customers are not the most valuable characteristics moderate the relationship between channel segment for all product categories. Our results demonstrate preference and monetary value. The results of the two-way that traditional channel customers of low-risk/utilitarian interactions show that the positive relationship between the categories outspend multichannel customers and that web- preference for multiple channels and monetary value is only customers who buy only high-risk/utilitarian categories stronger for hedonic product categories than for utilitarian offer higher monetary value than multichannel customers. categories. A plausible explanation is that hedonic product Second, we extend prior research on the importance of categories are likely to evoke impulse purchase and variety- product category characteristics on outcomes of managerial seeking behaviors, and multiple channels provide greater relevance. Prior research has examined the importance of opportunity and convenience to engage in those behaviors. category characteristics on variables such as unplanned pur- A key finding is that for low-risk categories, traditional chases (Inman, Winer, and Ferraro 2009), category manage- channel customers have higher monetary value than other ment (Dhar, Hoch, and Kumar 2001), sales promotion channel customers. This may be because low-risk product (Ailawadi et al. 2006; Narasimhan, Neslin, and Sen 1996), categories attract prevention-focused shoppers, who pur- revenue premium (Ailawadi, Lehmann, and Neslin 2003), chase mainly from traditional channels and spend more and spending during economically difficult times than their electronic and multichannel counterparts. (Kamakura and Du 2012). We extend this research by

TABLE 7 Summary of Results Hypothesis and Finding Retailers and Their Target Channel Segment Strategie Question: Which Customer-Channel Segment to Target for High Monetary Value? IE Large mass-merchandise retailers (e.g.. Target, Sears) -^ multichannel customers Hg: TU = EU = MU Specialty retailers of utilitarian products (e.g.. Best Buy, Staples) -> all channel segments Specialty retailers of hedonic products (e.g.. Pottery Barn, Ulta, GameStop) -^ multichannel customers H4: TLR > MLR, ELF Specialty retailers of low-risk products (e.g.. Office Depot, Tractor Supply Co.) -> traditional channel customers Hgi MHR = EHR = Specialty retailers of high-risk products (e.g.. Pier 1 Imports, Kay Jewelers) -^ all channel segments JU-LR > Specialty retailers of low-risk/utilitarian products (e.g., PetSmart, Office Depot) -^ store-only or catalog-only customers Specialty retailers of high-risk/utilitarian products (e.g.. Wolf Camera, Crutchfield) -> web-only customers Specialty retailers of low-risk/hedonic products (e.g.. Toys "R" Us, Ulta) -> multichannel customers Specialty retailers of high-risk/hedonic products (e.g., J.C. Penney, Pier 1 Imports) -> multichannel customers Notes: M = multichannel, T = traditional channel (store/catalog), and E = electronic; superscripts: H = hedonic, U = utilitarian, HR = high-risk, and LR = low-risk.

Are Multichannel Customers Really More Valuable? / 81 showing the effects of product category characteristics on and Sears) should induce multichannel customers to buy the relationship between channel preference and monetary more by investing in all the channels. value. Second, specialty retailers of hedonic products, such as Third, we illustrate the importance of the utilitarian ver- Pottery Bam, Ulta, GameStop, and J.C. Penney could sus hedonic nature of a product category in determining the incentivize their single-channel customers to shop in other value of shopper channel segments. The finding that web- channels, because our findings show that multichannel only (catalog- or store-only) customers spend more than shoppers provide the highest monetary value for such prod- multichannel customers on high-risk/(low-risk)/utilitarian ucts. Shopping in multiple channels provides shoppers with categories suggests that the value of shopper channel seg- more opportunities to indulge in favorable experiences ments depends on whether the category is utilitarian or offered by hedonic products, increasing their spending on hedonic. For utilitarian categories, ii is highly efficient to those products. For example, when a web-only shopper pur- shop in a single channel and realize the best value. How- chases a fashion clothing item on the web, a retailer such as ever, for hedonic categories, customers shopping in multi- J.C. Penney could invite that shopper to visit its brick-and- ple channels have multiple opportunities to spend, seek mortar store by offering a gift or a preferred item at a dis- variety, or purchase on impulse. Our fmdings add to the lit- count that can be collected only at the store. When the erature on the importance of the utilitarian versus hedonic shopper visits the store to pick up the item, he or she might nature of product categories (Chitturi, Raghunathan, and try more hedonic products, perhaps prompting the purchase Mahajan 2008; Dhar and Wertenbroch 2000; Inman, Winer, of more items. and Ferraro 2009). Third, our results show that traditional channel cus- Fourth, our findings highlight the role of perceived risk tomers of low-risk categories provide high monetary value of a product category in determining the value of shoppers due to a strong channel-category fit in prevention focus. by their channel preference. The amount of money shoppers Specialty retailers of low-risk products (e.g.. Office Depot, spend on a product category in th^ir preferred channel Tractor Supply Co.) could induce traditional channel cus- depends on their perceptions of the risks associated with the tomers to spend more at their physical stores or through category. These findings supplement prior conceptual and their catalogs by emphasizing items that are consistent with empirical research on consumer behavior in different chan- prevention focus. They could group similar products (e.g., surge protectors with cables and batteries, livestock feed nels (Balasubramanian, Raghunathan, and Mahajan 2005; with dog food and dog collar) through displays at the physi- Van Noort, Kerkhof, and Fennis 2008; Yadav and Varadara- cal stores or in catalogs to remind prevention-focused cus- jan 2005b). tomers to buy more items on each purchase occasion. Fifth, our results suggest important implications for the interaction of perceived risk with the DtiIJtarian nature of the Fourth, our findings demonstrate that traditional channel customers of low-risk/utilitarian products spend more than category. The finding that a single-channel segment offers other customers. Specialty retailers of low-risk/utilitarian higher monetary value than the multichannel segment and products (e.g.. Office Depot, PetSmart) could help tradi- the result that different single-channel segments provide tional channel customers routinize their shopping and pur- higher monetary values of utilitarian categories for different chase more efficiently and repeatedly at their stores or levels of risk suggest that there are some commonalities but through their catalogs. They could track the purchase histo- important differences in the underlying mechanism that ries of these customers and prompt them to buy more of the may induce high spending. Because utilitarian categories same items on a periodic basis. are typically associated with a prevention focus, consumers Fifth, our findings reveal that electronic channel cus- may be emphasizing purchasing efficiency, which is better tomers of high-risk/utilitarian products tend to outspend other realized in a single channel than in multiple channels. Con- customers. Specialty retailers of high-risk/utilitarian products sequently, single-channel customers of utilitarian categories (e.g.. Wolf Camera, Crutchfield) could make their websites may be buying more items at higher spending levels. How- "sticky" through features such as single-click ordering, prod- ever, at the same time, if the risk levels are high, promotion- uct reviews, and new item recommendations. In this way, focused consumers using the web can obtain more informa- these retailers could make it convenient for promotion- tion and buy utilitarian items more often with higher focused customers who typically prefer the electronic chan- spending levels than those using other channels. In contrast, nel to continue shopping and spend more in their preferred if the risk levels are low, prevention-focused consumers can channel. routinize their shopping and spend more on traditional Sixth, specialty retailers of high-risk/utilitarian products channels (e.g., catalog, store) than on other channels. could also educate their prevention-focused customers who Managerial Implications prefer to shop through catalogs or at physical stores about the high trust levels at their websites. In this way, marketers The results offer several actionable managerial implica- can help such customers reduce their risk perceptions and tions. First, managers can use the finding about the direct buy more from the web channel. For example, the staff at a effect of channel preference on monetary value to make Best Buy store could provide reassurance to prevention- channel-specific investments. Our finding reveals that in focused store customers by demonstrating the ease and general, multichannel customers who buy in multiple cate- trustworthiness of ordering online through computers at the gories are most valuable, so retail firms that sell multiple store and by enabling them to purchase online. Customers product categories (e.g., mass merchandisers such as Target who become accustomed to the online channel might shop

82 / Journal of Marketing, July 2013 for more high-risk/utilitarian items online, leading them to fruitful research avenue. Such an investigation would pro- provide a higher monetary value in the future. vide a deeper understanding of the role of discounts in cre- Seventh, retailers could use the insights from our ating differences in monetary values by channel preference. research to make more effective targeting decisions. Our Fourth, if longitudinal customer purchase data on a findings imply that retailers of hedonic product categories broad array of categories across firms are available, a (e.g., J.C. Penney, Pottery Bam, Pier 1 Imports) should tar- deeper analysis of channel switching across product cate- get multichannel customers. The results also suggest that gories could be undertaken to obtain greater insights into retailers of low-risk/utilitarian products (e.g.. Office Depot, multichannel shopping. Such an analysis would offer a Tractor Supply Co., PetSmart) should target customers who nuanced understanding of changes in monetary values due prefer traditional channels. Similarly, retailers of high- to channel switching. risk/utilitarian products (e.g.. Best Buy, Wolf Camera, Fifth, although our conceptual arguments are rooted in Crutchfield) should target competitors' web-only customers individual motivation, we use behavioral outcome data for switching and offer incentives to their own web-only (spending)—not data at the decision process level. Supple- customers to enhance retention. menting our study with behavioral experiments at the indi- vidual level would bolster the validity of the findings. Finally, with the surge in the sales of mobile devices, Limitations, Further Research, and such as smartphones and tablets, customer use of the mobile Conclusion channel is growing rapidly. As data on mobile channel This study has limitations that further research could become available, it would be useful to extend our study to address. First, we examined observed purchase behavior. the mobile channel. We do not have data on how customers use the channels for In conclusion, contrary to conventional wisdom that all information search. Although such data are difficult to col- multichannel customers are valuable, our results show that lect, analyzing them together with transaction data could multichannel customers are the most valuable segment only shed additional light on single- versus multiple-channel for hedonic product categories; single-channel customers of shopping, extending the work of Verhoef, Neslin, and utilitarian categories and traditional channel customers of Vroomen (2007). low-risk categories provide higher monetary value than Second, if data on customer referrals are available, our other customers. The results reveal that for utilitarian prod- model of customer value could be expanded to include uct categories involving high (low) risk, electronic (tradi- referral value, extending Kumar, Petersen, and Leone's tional) channel shoppers constitute the most valuable seg- (2010) study to the multichannel context. Such an analysis ment. Our findings offer managers guidelines for targeting could provide a richer understanding of customer value. and migrating different types of customers for different Third, if data on price promotions are available, an product categories through different channels. They also investigation of the differences in the effectiveness of price serve as an impetus for further research on the growing phe- promotions across different channel shoppers would be a nomenon of multichannel marketing.

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