Returns on Business-To-Business Relationship Marketing Investments: Strategies for Leveraging Proﬁts

Returns on Business-to-Business Relationship Marketing Investments: Strategies for Leveraging Proﬁts

Robert W. Palmatier University of Cincinnati, 422 Carl Linder Hall, P.O. Box 210145, Cincinnati, Ohio 45221, [email protected] Srinath Gopalakrishna University of Missouri–Columbia, 434 Cornell Hall, Columbia, Missouri 65211, [email protected] Mark B. Houston University of Missouri–Columbia, 339 Cornell Hall, Columbia, Missouri 65211, [email protected] irms invest heavily in different types of business-to-business relationship marketing activities in the belief Fthat such programs bolster their bottom line. In this study, we develop and test a conceptual model that links customer-specific relationship marketing investments to short-term, customer-specific financial outcomes. Data from a matched set of 313 business customers covered by 143 salespeople of 34 selling firms indicate that investments in social relationship marketing pay off handsomely, financial relationship marketing investments do not, and structural relationship marketing investments are economically viable for customers serviced frequently. We conceptualize relationship marketing in a context involving nested participants (customers, salespeople, selling firms) and employ a hierarchical linear modeling approach to account for observations that are not independent. Across the three hierarchical levels, the impact of the financial, social, and structural components of relationship marketing investments and the potential moderating factors offer valuable insights into contextual factors and managerial strategies for leveraging relationship marketing investments. In an attempt to suggest normative guidelines to managers, we extend our analysis to a simple resource allocation model that describes the optimal mix of relationship marketing resources based on firm strategies. Key words: relationship marketing; customer relationship management; marketing strategy; financial outcomes; allocation; hierarchical linear modeling History: This paper was received July 29, 2005, and was with the authors 2 months for 2 revisions; processed by Gary Lilien.

Introduction 2004). Furthermore, two aspects complicate the inves- Relationship marketing has undergone explosive tigation of customer-specific payoffs of relationship growth during the past decade (Sheth and Parvatiyar marketing: 2000) due to widespread beliefs that it leads to 1. Different relationship marketing programs (fi- improved financial performance. However, empiri- nancial, social, and structural) may build different cal evidence on this topic is mixed (Dowling and types of relational bonds and norms that generate Uncles 1997, Reinartz and Kumar 2000), and more varying levels of return (Berry 1995, Bolton et al. 2003, Cannon et al. 2000). research is needed to isolate the conditions where 2. The returns from relationship marketing pro- relationship marketing is effective (Shugan 2005). grams may vary according to factors associated with Although some studies address relationship market- any of the relational participants (customer, sales- ing issues in business-to-business (B2B) interactions person, selling firm), but the factors for each par- (e.g., Ryals 2005), no research has documented the ticipant influence a different set of relational bonds returns from specific B2B investments in relationship (Reinartz and Kumar 2000, Sirdeshmukh et al. 2002). marketing programs, or has explained how to lever- Customer factors affect returns from relationship mar- age these investments for specific customers. This is keting investments only for that customer, whereas especially surprising given the academic and man- salesperson factors influence the efficacy of relation- agerial interest in measuring marketing productivity ship investments for all customers handled by that and customer-level effects (Bolton 1998, Bolton et al. salesperson, and selling-firm factors leverage invest- 2004, Gupta and Lehmann 2005, Johnson and Selnes ments across all the customers of a selling firm.

The first observation implies that investment re- third section, we isolate the drivers and potential cus- turns may vary by relationship marketing program tomer, salesperson, and selling firm factors that may and must be isolated to unravel the distinct effects leverage the impact of relationship marketing on CSR. that are masked within an aggregate measure. The sec- We describe our research method and model in the ond observation suggests that each relational partici- fourth section, and our data analysis and results in pant’s perspective should be considered when inves- the fifth section. Finally, in the sixth section, we offer tigating the factors and strategies that may leverage discussion and managerial implications. the effect of relationship marketing investment on returns. In effect, these points imply that customer, Influence of Relationship Marketing salesperson, and selling firm variables may interact with different relationship marketing programs and Investments on CSRs alter their return on investment. For example, Berry Dwyer et al. (1987) initiated research on the key role (1995) argues that financial relationship marketing of relationships in B2B exchanges, describing how programs may be ineffective for segments of deal- relationship marketing activities can generate cus- prone customers because of the lack of switching bar- tomer-seller bonds and exchange norms. Subsequent riers for financial-based relational bonds. Therefore, research in B2B, in applying insights from trans- the return on financial programs should be greater for action cost economics (Anderson and Weitz 1992) customers who are not deal prone but who are inher- and relational norms (Heide and John 1992), has ently committed to the relationship. We should not found that, in certain conditions, strong buyer-seller expect the same effects for other relationship market- relationships enhance exchange performance, though ing programs based on relational bonds, which are not these linkages may be multidimensional (Cannon easy to replicate (e.g., interpersonal bonds take time et al. 2000). Researchers in service and consumer and effort to produce). Because customer, salesperson, markets have linked relationship marketing activities and selling firm factors affect the payoff of relation- to intermediate outcomes (e.g., sales growth, higher ship marketing but involve different sets of customers, customer share, lower price sensitivity) that should any related research framework must align a poten- enhance a firm’s profit (Gwinner et al. 1998, Reynolds tial moderator with its corresponding set of relevant and Beatty 1999). Similar to B2B researchers, these customers. Some mixed findings in the extant relation- researchers propose that various relationship market- ship marketing literature may be due to a failure to ing activities lead to different forms of relational ties. incorporate these distinctions. Overall, findings from studies in B2B and con- In this study, we examine the customer-specific sumer markets are consistent: Relationship marketing return (CSR)—a marginal return on investment—of efforts affect a customer’s value to the firm by increas- relationship marketing efforts in a B2B context within ing the length, breadth, and depth of the buying three nested levels of data: 313 customers served by relationship and generating positive word of mouth 143 salespeople from 34 selling firms. Based on extant (Bolton et al. 2004, Verhoef 2003). Different relation- studies, we categorize relationship marketing efforts ship marketing activities may also generate distinc- into three components: financial, social, and struc- tive customer bonds and relational norms, affect the tural. We examine how each component can generate relationships unevenly, and thereby vary in terms distinctive customer bonds and norms, and whether of economic returns. Therefore, relationship market- the program will eventually pay off. Furthermore, we ing efforts must be broken down into components identify and empirically test customer, salesperson, prior to any evaluations of customer-specific eco- and selling firm factors that may leverage those pay- nomic returns. offs. Finally, through a resource allocation model, we provide guidance on spending levels for each type of Relationship Marketing Investments program, contingent on salesperson and selling firm The extant literature uses several criteria to describe factors. Our primary theory contribution is a frame- and disaggregate relationship marketing efforts, such work of the varied and complex paths through which as customer bonds formed (Berry 1995), exchange con- relationship marketing investments affect customer- trol mechanisms utilized (Cannon et al. 2000), ben- specific profitability that specifies theoretical drivers efits offered (Gwinner et al. 1998), functions served and key moderators from each relational perspective. (Hakansson and Snehota 2000), and content area We also contribute to practice by examining specific supported (Morgan 2000). These diverse typologies strategies that can alter the payoffs of relationship use different perspectives and criteria to identify the marketing and isolating the differing payoffs of three salient categories for grouping relationship-building relationship marketing programs. activities, but the outcomes remain consistent. Most We organize this article as follows: In the second typologies include financial (economic), social, and section, we explicate the impact of relationship mar- structural components and suggest that customer- keting investments and other drivers on CSR. In the seller linkages are similar within each category, but Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 479 that they vary with regard to their effectiveness dedicated personnel, and tailored packaging. These across categories. We adopt Berry’s (1995) labels of programs typically require considerable setup efforts financial, social, and structural relationship marketing and offer unique benefits; thus, customers may be programs. reluctant to switch or fragment their business among Financial Relationship Marketing Programs. suppliers. Such idiosyncratic investments bind the These programs include discounts, free products, or buyer and seller. Structural bonds also may gener- other financial benefits that reward customer loyalty. ate strong competitive advantages because customers However, unless enabled by unique sources (e.g., increase their business with the seller to take full low-cost structure), any advantage accruing from advantage of these value-enhancing linkages (Berry financial relationship marketing is unsustainable 1995). Overall, although the nature and magnitude because competitors can easily match the programs of effects may vary, we expect all three relationship (cf. Day and Wensley 1988). Moreover, customers marketing programs to have a positive impact on attracted by such incentives may be deal prone and customer-specific return (see Figure 1). less profitable to serve (Cao and Gruca 2005). How- ever, Bolton et al. (2000) find that financial programs Customer, Salesperson, and Selling Firm Factors can provide sufficient returns in certain situations. In addition to relationship marketing, other factors may influence CSR. Typical B2B customers inter- Social Relationship Marketing Programs. These act with salespeople and the selling firm; thus, cus- include meals, special treatment, entertainment, and tomer, salesperson, and selling firm factors all could personalized information. The resultant social bonds affect exchange performance. The literature suggests are difficult to duplicate and may lead customers two types of customer factors: relational (affective to reciprocate via repeat sales and recommendations, or behavioral) and customer characteristics (Dwyer and by ignoring competitive offers (Blau 1964, De et al. 1987). Rust and Verhoef (2005) conclude that Wulf et al. 2001). These programs are believed to have these types of factors are also key in consumer con- a strong impact on relationships (Gwinner et al. 1998). texts, finding that heterogeneity of response to CRM Structural Relationship Marketing Programs. interventions was explained in part by customers’ These programs increase productivity or efficiency (or past interactions with the company and by unique both) for customers through investments that cus- customer characteristics. Customer commitment to tomers would probably not make themselves. Exam- the selling firm (desire to maintain a valued relation- ples include customized order processing systems, ship) captures the customer’s affective state toward

Figure 1 Effects of Relationship Marketing Investments on Customer Speciﬁc Return

Leveraging relationship marketing investments

Interaction Commitment to Ownership CRM Experience frequency selling firm interest system Relationship marketing (S) investments (C) (C) (SM) (SM)

Financial (S)

Social (S) CSR (S, SM) Structural (S)

• Experience (S) Control • Compensation (SM) • Average tenure of variables • Ownership interest (SM) salespeople (SM) (C, S, SM) • Relationship-focused • CRM system (SM) objectives (SM) Salesperson-level variables Selling firm-level variables Notes. C: Reported by customer; S: Reported by salesperson; SM: Reported by sales manager. Customer relationship management (CRM), customer-specific return (CSR). Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 480 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS the selling firm, whereas interaction frequency with operate through different mechanisms, we evaluate the firm and relationship duration are key behavioral some proposed moderation effects across programs. factors. Turning to customer characteristics, a cus- Table 1 summarizes our theoretical rationale, possible tomer’s sales growth can lead to increased selling firm leveraging variables, and the variables included in the sales; other customer characteristics that may affect model. profit are captured within a salesperson’s perception of the customer’s potential or attractiveness (Johnson Customer-Level Moderators and Selnes 2004). From the customer perspective, commitment-trust Research on salesperson performance suggests that theory and related relationship marketing literature both ability and motivation are critical to sales and (De Wulf et al. 2001, Morgan and Hunt 1994) suggest profit outcomes (Churchill et al. 1985). We use expe- two theoretical drivers that may leverage the impact rience as a general proxy for ability, because experi- of relationship marketing investments: the customer’s enced salespeople are more proficient in uncovering motivation to have a relationship, and willingness to and closing sales opportunities and adapt more eas- reciprocate the seller’s investments. Higher returns ily to different situations (Grewal et al. 2001). For may ensue from customers who desire a relation- motivation, we recognize from sales and agency the- ship and reward sellers for their relationship-building ory literature that compensation (base salary plus efforts. Cost savings and benefits from a relationship commission) affects job satisfaction and motivation, also affect a customer’s loyalty. If relationship mar- which in turn affect selling effort. As more effort keting efforts create tangible benefits, the customer is exerted, sales performance and profits should will be motivated to respond positively. However, if increase (Fang et al. 2004). Aligning firm and sales- those efforts introduce inefficiencies (e.g., added costs, person goals through ownership interest (profit shar- unwanted social interactions), resentment may result ing, stock ownership plans) makes the profit impact (Bagozzi 1995, De Wulf et al. 2001). Thus, factors that of actions more salient to salespeople (Bergen et al. increase a customer’s motivation to engage in a strong 1992). Finally, relationship-focused objectives should relationship with the seller should increase the return enhance customer satisfaction, leading to greater on investments. retention and superior financial outcomes. Many factors can increase a customer’s need or Next, we identify factors that reflect a selling motivation for stronger relational linkages, including firm’s indirect and direct efforts to build and main- customer dependence, interaction frequency, prod- tain profitable customer relationships. For indirect ef- uct involvement, and environmental uncertainty (e.g., forts, we consider the average tenure of salespeople Cannon et al. 2000, De Wulf et al. 2001). Because at the firm because tenure results in stronger cus- relationship marketing programs operate through dif- tomer relationships, fewer customer defections, and ferent relational mechanisms, each program must more customer-specific knowledge (Bendapudi and be evaluated separately to determine if a proposed Leone 2002), which can minimize customer churn moderator would alter a customer’s relational moti- and enhance profits. To capture overall direct efforts, vation or perceived value. For example, interaction we assess the use of customer relationship manage- frequency has been noted as a way to increase ment (CRM), a strategic approach to create share- the value of structural relationship marketing for a holder value by developing relationships with key customer (Berry 1995). Because structural programs customers and customer segments through the use can increase customer productivity or efficiency (or of data and information technology (Payne and Frow both) through a customized interface, more frequent 2005). In addition, CRM indicates support for relation- interactions lead to increases in perceived value as ship marketing because access to integrated customer customers gain greater productivity during more data should enable firms to target their efforts more interactions, and there is little reason for any dimin- effectively, thereby increasing customer-specific prof- ishing effects at higher interaction levels. The cost its (Mithas et al. 2005, Reinartz et al. 2004). Consistent (to seller and buyer) to implement a structural pro- with prior research, we control for advertising expen- gram is typically fixed; after the interface is set up, ditures and selling firm size. the additional costs to maintain the program are minimal. Thus, customer value increases with interaction frequency, resulting in stronger bonds, enhanced loy- Leveraging the Effects of Relationship alty, and more business to the selling firm. We do not Marketing Investments expect the same effect, however, for social or financial In this section, we identify drivers and variables programs. For social programs, when a strong rela- that may leverage relationship marketing investments tionship has been built, there is little additional value across the three exchange participants (customer, for the customer from more interactions, and the costs salesperson, and selling firm). Next, because the three for the seller and buyer to build and maintain a types of programs (financial, social, and structural) social bond are more variable. Thus, we do not expect Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 481

Table 1 Customer, Salesperson, and Selling Firm Variables for Leveraging the Inﬂuence of Relationship Marketing Investments on Customer-Speciﬁc Return

Theoretical driver(s) for leveraging Perspectives relationship marketing investments Potential leveraging variables Variables tested

Customer Factors influencing a customer’s motivation to Interaction frequency, customer dependence, product Interaction frequency have a strong customer-seller relationship involvement, environmental uncertainty, relationship (Dwyer et al. 1987, Morgan and Hunt 1994) proneness (individual difference variable), and customer’s processes for rewarding strong supplier relationships Factors influencing customer’s willingness to Customer commitment, possibility of future interaction, Customer commitment reciprocate for benefits received (Cialdini 2001, customer stake (i.e., investment) in the relationship, De Wulf et al. 2001) individual difference for reciprocity, and customer firm’s norms Salesperson Factors influencing a salesperson’s ability to Experience, adaptive selling skills, and interpersonal skills Experience allocate relationship marketing investments efficiently (Weitz et al. 1986) Factors influencing a salesperson’s motivation Ownership interest, sales management attention, and Ownership interest to allocate relationship marketing investments supervision of relationship marketing expenditures efficiently (Bergen et al. 1992) Selling firm Factors influencing a selling firm’s employees’ Selling firm’s CRM; customer segmentation processes; CRM ability to allocate relationship marketing management and tracking processes for relationship investments efficiently (Mithas et al. 2005, marketing investments; and employee recruiting, training, Reinartz et al. 2004) and incentive programs Factors influencing a selling firm’s employees’ Selling firm’s CRM, market orientation, or customer-centric CRM motivation to allocate relationship marketing culture, and organizational climate investments efficiently (Boulding et al. 2005; Deshpande et al. 1993)

Note. Customer relationship management (CRM). customers to perceive higher value from social pro- and effort to develop than financial programs. More- grams as the interaction frequency increases. Simi- over, as Berry (1995, p. 239) makes clear, the posi- larly, interaction frequency will not affect the value of tive effect of financial investments on profit may be a financial program, because its value depends chiefly undermined if benefits are provided to deal-prone on economic savings. We test these claims by assess- customers who are open to (and may even seek out) ing whether interaction frequency leverages the influ- competitive offers. Thus, we expect that a customer’s ence of structural investments on CSR. commitment to the selling firm will positively mod- The second theoretical driver—customer’s willing- erate the impact of financial relationship marketing ness to reciprocate—indicates that relationship mar- investments on profit, but will not moderate social keting will have a greater effect on profit when or structural investments, which involve more effort invested in customers who are willing to recipro- and are difficult to switch among sellers. We test this cate the value they receive. Thus, factors that increase conjecture by including customer commitment as a the likelihood of reciprocation should leverage the potential moderator of the influence of financial rela- economic payoff. For example, if a buyer expects tionship marketing on CSR. to interact with a seller in the future or has a stake in maintaining the exchange, the buyer should Salesperson-Level and Selling Firm-Level behave less opportunistically (Anderson and Weitz Moderators 1992). Those efforts toward customers committed to At the salesperson and selling firm-levels, we con- maintaining the relationship should generate higher sider variables that influence the ability and motiva- returns, because these customers are likely to recip- tion of decision makers to allocate relationship mar- rocate (e.g., increase sales, pay a price premium) to keting investments efficiently. For example, experi- maintain the relationship. For which type of program enced salespeople should be effective at selecting, would willingness to reciprocate be most meaning- aligning, and delivering targeted programs to select ful? The proposed moderating effect is most likely to customers (Weitz et al. 1986). Thus, relationship mar- occur with programs that require little investment by keting should have a greater impact on performance the customer (cost, time, or effort) to extract value, for experienced salespeople. Agency theory also sug- because such programs inherently offer little protec- gests that ownership interest in the selling firm moti- tion from opportunism (Cao and Gruca 2005). Recall vates salespeople to act in the best interests of the firm that social and structural programs require more time (Bergen et al. 1992), but if earnings are linked to sales Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 482 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS revenue and salespeople have no ownership inter- range of relationship marketing programs to solidify est, a misalignment may be created. Such salespeople, their relational assets. who have some discretion in allocating their expenditures, may spend their discretionary dollars aggres- Sample and Data Collection sively on their customers without worrying about the We drew a random stratified sample of 3,000 indus- direct costs of these programs. With ownership inter- trial customers from 13,850 contacts provided by est, salespeople would be more discerning in targeting 41 rep firms, with support from the Manufacturers’ their relationship building resources, and thus would Representatives Educational Research Foundation. A minimize inefficient spending. four-wave mailing (presurvey card, survey, follow- At the selling firm-level, variables that influence the up card, and a second survey) to these customers ability or motivation of employees to spend resources generated 511 completed surveys, with 220 returned wisely across customers should have a greater impact as undeliverable—an effective response rate of 18%. on performance (Deshpande et al. 1993). In general, We then mailed surveys to the 195 salespeople who CRM processes and systems motivate and enable handle these customers. In cases in which the same employees to allocate marketing resources efficiently, salesperson covered multiple customers, the salesper- and in a systematic and proactive manner (Mithas son completed all measures for each customer. We et al. 2005, Reinartz et al. 2004), by identifying those received 165 salesperson surveys (85% response rate). customers who meet criteria for specific programs To the sales manager at each rep firm, we sent a cus- (Chen and Iyer 2002), evaluating and improving tomized survey, listing each customer and salesperson the effectiveness of programs, or reducing the time by name and requesting two years of sales data for needed to implement a program (or both). Thus, all 511 customers, compensation data for all 195 sales- firms that employ CRM should be able to generate people, and other data regarding the sales manager’s higher levels of profits for a given relationship build- rep firm. We contacted each sales manager to review ing investment than other firms (Boulding et al. 2005). the survey items for clarity. After a follow-up contact, 34 of 41 sales managers provided the requested Research Method and Model data (83% response rate). After we removed those Our data come from industrial customers, salespeo- cases with missing data and outliers, the final data set ple, and sales managers of each selling firm. These included 313 triads across 143 salespeople from 34 rep multiple sources reduce concerns about same-source firms, for an effective response rate of 11.3%. bias, and enable us to collect data from the most We used multiple tests to assess response bias. First, knowledgeable sources. The selling firms were manu- following Armstrong and Overton (1977), we com- facturers’ representative firms (rep firms), which typ- pared early and late responses (mean comparisons ically represent several manufacturers as exclusive repeated for the first 25%, 33%, and 50% versus last sales agents in specific territories. This context is well 25%, 33%, and 50% of respondents) in the buyer and suited to assessing the economic impact of relation- salesperson data for all variables and found no sig- ship marketing, because a rep firm does not man- nificant comparisons p > 005. Next, we compared ufacture or inventory the products it sells, and its those respondents not included in the final sample costs do not vary with small changes in sales vol- due to missing triadic data with those whom we umes except for the salesperson’s variable pay. Thus, included. For example, on buyer-rated constructs, we the rep firm’s return on any incremental sales equals compared buyers not included in the study with those the manufacturer’s commission less the salesperson’s who participated. Again, no comparisons were signif- variable pay. For example, if the manufacturer pays a icant p > 005, so buyer characteristics do not appear 5% commission on sales and the salesperson receives to affect whether salespeople or managers responded 30% of these commission dollars, the rep firm receives systematically. Finally, for salespersons and sales man- a 3.5% return on any incremental sales. This relation- agers, we compared those included in the analysis ship holds until the revenue increases to the point that with those who were not included due to deficien- the rep firm needs additional resources to support its cies in corresponding data, and again, no comparisons increased business. were significant p > 005. Based on these analyses, This rep firm context offers two additional advan- we believe that response bias is not a major concern. tages for evaluating the return on relationship mar- Our sample includes firms that sell in a wide keting investments. First, rep firms sell a range of range of end markets, including electronics, electri- products from multiple manufacturers; thus, the influ- cal, plumbing, telecommunications, and maintenance ence of any one product or brand is minimal. Second, supplies. Most of the rep firms’ sales pertained to rep firms have few tangible assets, and manufacturers products rather than services (on average, 93% of normally can terminate an agreement simply with a sales came from products). Furthermore, the major- 30-day notice, which makes their customer relation- ity (69%) of sales resulted from products or services ships their primary asset. Thus, rep firms institute a for which customers had an alternative supplier. The Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 483 average customer bought 3.8 different supplier lines Model from the rep firm. In specifying our model to relate relationship marketing expenditures to CSR, we incorporate several Measurement important aspects in terms of the phenomena that We used existing measures whenever possible and must be reflected. First, we note that the effects of tested and refined all items through interviews with relationship marketing expenditures are likely to play buyers, salespeople, and sales managers (see Ap- out over time, especially because we are dealing with pendix A). All measures used a seven-point Likert customer relationships that develop and evolve. To scale, unless noted otherwise. In Table 2, we summa- capture the effect of prior expenditures, we include, rize the descriptive statistics and correlations for all from the standpoint of parsimony, the CSR of the pre- continuous variables. vious period, similar to a Koyck-type formulation that Customers reported their commitment to the sell- represents lagged effects (Hanssens et al. 2001). Sec- ing firm through three items (De Wulf et al. 2001) ond, in addition to relationship marketing expendi- = 095. They also provided single-item measures tures, we include variables at the three different levels for interaction frequency (#/week), growth rate of the and note the relevant interaction effects. We specify our model as follows: customer firm (%), and relationship duration (years). The salespeople reported (for each customer) their CSRit = CSRit−1 + RMit + Xt (1) financial, social, and structural relationship marketing investments. Each salesperson received a list of where activities for each program, followed by a question CSRit = return generated from customer i in time regarding the average monthly spending for this cus- period t tomer over the past year for each activity. We repeated CSRit−1 = return generated from customer i in this process for each of the three relationship market- time period t − 1 ing programs. The list of activities for each program RMit = relationship marketing expenditures on cus- is based on previous studies (Berry 1995, Gwinner tomer i in time period t et al. 1998) and was refined during in-depth inter- Xt = vector of control variables. views. A final item asked about any relationship mar- The vector Xt involves variables at three levels: cus- keting efforts not captured by our list. A low mean tomer, salesperson, and selling firm. We depict the entire model using to represent the fixed param- (1.4) suggests that our items provided adequate cov- 0-21 eters; , u , and e to represent the selling firm-, erage of the domain. Finally, salespeople also reported 0k 0jk 0ijk salesperson-, and customer-level random deviations the overall sales potential and average commission from the intercept ; and , and u to rate (%) for each customer, as well as their experience 0 1k2k3k 1jk2jk3jk represent the selling firm- and salesperson-level ran- (years). dom deviations from the financial, social, and struc- Sales managers provided selling firm and sales- tural relationship marketing investment coefficients person information, as well as customer sales data. , respectively. For ease of exposition, we For selling firms, sales managers reported the aver- 1 2 3 drop the subscript t and note that CSR0ijk is the return age tenure of salespeople (years), CRM (0 = use CRM, in the previous year for the ith customer served by 1 = do not use CRM; 21 of 34 firms use CRM), adver- the jth salesperson from the kth selling firm: tising ($), and selling firm size (million $). For each salesperson, the sales managers reported compensa- CSRijk = CSR0ijk + 0ijk + 1jkFINijk + 2jkSOCijk tion ($), ownership interest (0 = no ownership, 1 = + STR + INT + COM ownership; 73 of 143 salespeople had ownership inter- 3jk ijk 4 ijk 5 ijk est), and relationship-focused objective (presence = 1, + 6GRWijk + 7DURijk + 8POTijk absence = 0; 55 of 143 salespeople received compen- + FIN ∗ COM + STR ∗ INT sation tied to relationship-focused objectives). Sales 9 ijk 10 ijk managers also provided two years of archival sales + 11 EXPjk + 12COPjk + 13OWNjk data for each customer, which we used to calculate the + REL + ADV + SIZE returns. We calculated CSR for each customer by mul- 14 jk 15 k 16 k tiplying the sales revenue by the effective commission + 17TENk + 18CRMk rate for that customer. Effective commission reflects + 19FIN ∗ EXPijk + 20FIN ∗ OWNijk the average commission from a customer reduced by (1−variable salesperson pay) (see Appendix A). Thus, + 21FIN ∗ CRMijk (2) CSR represents the contribution margin a rep firm where earns on sales, which remains valid until incremental sales require additional selling costs. 0ijk = 0 + 0k + u0jk + e0ijk (3) Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 484 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS ∗ 000 408 1 ∗∗ 446 303 0 000 000 085 0 105 0 052 1 1 0 0 − 143 for evaluating pairwise = ∗∗ ∗∗ N 000 269 164 286 054 0 1 0 0 ∗ ∗ ∗∗ 000 163 184 071 0 144 011 0 1 0 0 0 − ∗∗ ∗ ∗ 000 975 075 0 094 0 137 140 063 0 − 015010 1 0 048 0 102 0 066001 0 0 000 056 0 − 071 067 0 015 0 014 0 011075 0 0 009 1 081 0 000 0 0 0 − ∗ ∗ 115 118 089 0 102 0 043094 0 0 071 023 0 000 065 1 0 0 0 − ∗∗ ∗∗ 000 224 054 0 171 032 0 038010 0 0 087 0 034 054 1 011 0 0 0 − − 025 1 030 0 030 0 011 0 015 0 001 013 0 033 0 011 007 0 000 010 0 0 0 0 0 0 0 0 0 0 − − − − − − 34 for evaluating pairwise correlations between firm-level variables. = ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗ ∗∗ N 000 145 031 0 267 234 171 149 012 183 028 0 002 1 068 141 1 0 0 0 0 0 0 0 − − ∗∗ ∗∗ ∗ ∗ ∗∗ ∗ 000 208 312 064 0 065 0 128 061 0 127 178 086 0 034 034 060 0 112 1 0 0 0 0 0 0 − − − ∗ ∗∗ ∗ ∗ 122 068 021 0 062 0 086 0 020 0 020 0 065 0 183 000 136 018 125 009 101 0 0 0 − − 472 0 001 885 0 387 0 535 0 195 181 0 611 0 898 0 355 0 085 1 447 0 803 0 467 0 639 0 263 528 709 033 994 3 105 14 125 0 261 14 102 14 228 9 924 1 764 19 946 936 096 292 582 1 898 1 345 1 243 21 481 1 313 for evaluating pairwise correlations between customer-level variables and customer-, salesperson-, and firm-level variables; 2 = 559 275 077 N 1 05. 0 p< ∗ ($) 8 1 01; ($) 9 − t t 0 . Customer-specific return (CSR). CSR marketing investments ($) CSR marketing investments ($) marketing investments ($) customer firm (%) salespeople (years) p< Square root transformation. 1 ∗∗ 6. Relationship duration 4. Growth rate of 6 9. 3. Structural relationship 748 2. Social relationship 330 1. Financial relationship 766 7. Interaction frequency 1 5. Potential of customer 4 8. Commitment to the selling firm 4 15. Average tenure of 8 10. 13. Advertising ($) 21 correlations between salesperson-level variables and salesperson-level and firm-level variables; 12. Compensation 3 11. Experience (years) 12 Constructs Mean Std. dev. 1. 2. 3.Notes 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. Table 2 Descriptive Statistics and Correlations 14. Selling firm size (million $) 39 Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 485

1jk = 1 + 1k + u1jk (4) (+deviance1 = 14110; p<001). These results suggest that salesperson-level and selling firm-level variables = + + u and (5) 2jk 2 2k 2jk have direct effects on returns, in support of the notion that group membership matters. The total variation in 3jk = 3 + v3k + u3jk% (6) CSR can be split among these levels: The customer- CSRijk ∼ NXB'; (k) ∼ N0'v; (ujk) ∼ N0 level explains 61.9%, the salesperson-level explains 'u; (eijk ) ∼ N0'e; and 9.5%, and the selling firm-level explains 28.5% of the FINijk = financial relationship marketing invest- variance. ments Model 2 adds customer-level main effects to SOCijk = social relationship marketing investments Model 1, explains 29.7% of the variance in CSR, and STR = structural relationship marketing invest- ijk represents a significant improvement (+deviance8 = ments 96086; p<001). Model 3 adds the products of INTijk = interaction frequency the mean-centered variables to test the incremental COMijk = commitment to the selling firm effect of the customer-level interactions and demon- GRWijk = growth rate of customer firm strates that customer-level direct effects and interac- DURijk = relationship duration tions explain 35.1% of the variance in CSR and that POTijk = potential of customer customer-level interactions significantly improve the EXP = experience jk model (+deviance2 = 24745; p<001). With Model 4, COPjk = compensation we include the main effects of salesperson-level fac- OWNjk = ownership interest tors, which explains an additional 5% of the vari- REL = relationship-focused objectives jk ance in CSR (+deviance4 = 17656; p<001. Then, ADVk = advertising Model 5 adds the main effects of selling firm-level SIZEk = selling firm size factors to Model 4 and explains an additional 9.6% of TEN = average tenure of salespeople k the variance in CSR (+deviance4 = 19949; p<001). CRMk = use of a CRM system. In Table 3, we provide a summary of the HLM esti- mations and variance extracted by each model. Analysis and Results Models 1–5 include random effects only for the We employ hierarchical linear modeling (HLM), which intercept; they assume that the coefficients of cus- accounts for the lack of independence across differ- tomer-level variables are constant across different ent cases for some variables, and thus overcomes salespeople and selling firms (i.e., no random-slope the limitations of traditional methods of analyzing effects). We test this assumption through a series of nested data (Raudenbush and Bryk 2002). There is nested models. For example, modifying Model 5 by recent precedence in marketing for the use of HLM allowing the coefficient of financial relationship mar- (e.g., Mittal et al. 2005) and other multilevel mod- keting investments to vary based on salesperson-level eling methods (e.g., Elsner et al. 2004). Specifically, and selling firm-level factors adds four parameters: we estimate the models using HLM full maximum random variance coefficients for financial investments likelihood, in an empirical Bayes procedure, through at the (1) salesperson and (2) selling firm-levels, the iterative generalized least squares algorithm in (3) salesperson-level intercept-slope covariance, and MLwiN 2.1d (Rasbash et al. 2000). We evaluate (4) selling firm-level intercept-slope covariance. Thus, the CSR determinants using an incremental model- to determine whether the fit significantly improves, building approach (Table 3, Models 1–6), as suggested we can compare this modified model with Model 5 by Kreft and de Leeuw (1998). This technique allows using a deviance difference test with four degrees sequential model testing, which balances theory and of freedom, a procedure we repeat for social and model parsimony. Random or fixed effects can be structural investments. In this sample, slopes do not tested by comparing the deviance (−2 log likelihood vary significantly for social or structural investments, criterion) between two nested models with a *2 dis- which suggests that the parameter estimates for social tribution, such that the degrees of freedom equal the and structural programs are constant across salespeo- difference in the number of parameters between the ple and selling firms when we control for the main two models (Ang et al. 2002). Multicollinearity is not effects of salesperson-level and selling firm-level fac- an issue; the final model has a variance inflation factors. Thus, the random deviations for social (2k and tor of less than 10. u2jk and structural (3k and u3jk) relationship market- We compared a series of nested empty models with ing investment coefficients are both 0 in Equations only CSR0. Adding a random intercept effect at the (5) and (6) (Snijders and Bosker 1999). However, the salesperson-level significantly improved the model model with random-slope effects for financial pro-

fit (+deviance1 = 26777; p<001), as did adding grams at the salesperson and selling firm-levels has a a random intercept effect at the selling firm-level significantly better fit (+deviance4 = 29290; p<001) Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 486 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS

Table 3 Results: Hierarchical Linear Model Estimation for Customer-Speciﬁc Return1

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept 1,008.294∗∗ −2307497∗∗ −2371608∗∗ −1205234 −2959734∗∗ −1781380∗∗ 405855 707743 678390 862517 996755 852729 CSR (prior period) 0989∗∗ 0971∗∗ 0965∗∗ 0967∗∗ 0964∗∗ 0971∗∗ 0012 0010 0010 0010 0010 0009 Financial RM investments 0128 0021 0052 0022 −0015 0147 0143 0140 0138 0289 Social RM investments 1675∗∗ 1765∗∗ 1588∗∗ 1766∗∗ 1775∗∗ 0543 0524 0517 0510 0467 Structural RM investments 1140∗∗ 1142∗∗ 1113∗∗ 1072∗∗ 0998∗∗ 0163 0156 0154 0154 0142 Interaction frequency 10698 64180 55262 47261 8601 99551 96835 94921 93750 88936 Commitment to the selling firm 78384 84800 61692 57328 54903 88715 85604 84087 83077 80331 Growth rate of customer firm 27370∗∗ 30043∗∗ 30297∗∗ 31184∗∗ 30660∗∗ 7201 6914 6766 6688 6205 Relationship duration 69438 98774 91765 51600 1557 129706 124999 122492 119613 107755 Potential of customer 269032∗∗ 247627∗∗ 248329∗∗ 236739∗∗ 227574∗∗ 86695 83482 81748 81136 74975 Structural RM investments 0451∗∗ 0439∗∗ 0447∗∗ 0435∗∗ × interaction frequency 0130 0127 0127 0117 Financial RM investments 0321∗∗ 0314∗∗ 0304∗∗ 0181∗ × commitment to the selling firm 0093 0092 0091 0104 Experience 74508∗∗ 65290∗∗ 63625∗∗ 20692 19743 19583 Compensation −672083∗∗ −587439∗∗ −647088∗∗ 215964 205546 188846 Ownership interest 47033 96678 −364274 396530 355724 332786 Relationship-focused objectives 409418 289155 321397 432072 386361 333447 Advertising 0062∗∗ 0064∗∗ 0015 0011 Selling firm size −10578 −10955 11002 8819 Average tenure of salespeople 124110∗ 44502 59964 45559 CRM system −711272 29454 464087 398669 Financial RM investments × experience 0006 0021 Financial RM investments × ownership interest 0637∗ 0366 Financial RM investments × CRM system −1122∗∗ 0434 Deviance (−2 log likelihood) 5,908.021 5,811.935 5,787.190 5,769.534 5,749.585 5,711.204 Deviance difference 96086∗∗ 24745∗∗ 17656∗∗ 19949∗∗ 38381∗∗ Degrees of freedom for evaluating 8 2 4 4 7 deviance differences Proportion of variance explained (%) 29.73 35.06 40.08 49.67 51.57

Notes. Relationship marketing (RM), customer relationship marketing (CRM), customer-specific return (CSR). N = 313 for customer-level; N = 143 for salesperson-level; N = 34 for selling firm-level. Unstandardized coefficients are reported with standard errors in parentheses. 1Degrees of freedom for t-test to evaluate the significance of the estimated coefficients are 298 for Level 1 variables and cross-level interactions, 138 for Level 2 variables, and 29 for Level 3 variables. ∗∗p<001; ∗p<005. Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 487

Figure 2 Results: Direct and Indirect Effects of Relationship Marketing Investments on Customer Speciﬁc Return

Effects of RM investments 1.78 (0.47)** Social RM investments

1.00 (0.14)** Structural RM investments

0.44 (0.12)** Structural RM investments × interaction frequency CSR (time 2) 0.18 (0.10)* Financial RM investments × commitment to selling firm

Financial RM investments × Other variables ownership interest 0.64 (0.37)* ** • CSRt–1 0.97 (0.01) • Growth rate of customer firm 30.66 (6.21)** Financial RM investments × • Potential of customer 227.57 (74.98)** 1 CRM system –1.12 (0.43)** • Experience 63.63 (19.58)** • Compensation –647.09 (188.45)** • Advertising 0.06 (0.02)** Notes. Unstandardized parameter estimate (standard error) are shown for each significant effect. Relationship marketing (RM), customer relationship management (CRM), customer-specific return (CSR). 1Negative coefficient represents the effect of not having a CRM system. ∗∗p<001, ∗p<005.

than Model 5, which suggests we must investigate with high interaction frequency (B = 0435; p<001).1 cross-level interactions between salesperson-level fac- For example, at two interactions per week, structural tors and financial investments and between selling programs appear to break even, but when customers firm-level factors and financial investments to explain engage in four interactions per week, a $1,000 invest- this significant random-slope variance. Model 6, ment in structural relationship marketing generates which adds the random-slope effects for financial pro- $1,231 of profit (23% return; interactions are mean grams and the proposed cross-level interactions, pro- centered). The story of financial programs differs. As we vides a significantly better fit (+deviance = 38381; 7 show in Table 3, financial relationship marketing has p<001) than Model 5, and explains 51.6% of the vari- no significant direct relationship with CSR, although ance in CSR. variables at each hierarchical level demonstrate sig- Model 6 (Table 3) and Figure 2, in which we sum- nificant interactions with financial relationship mar- marize the significant results, thus offer insight into keting, namely, commitment to the selling firm the complex pattern of effects for the influence of (B = 0181; p<005, customer-level), ownership inter- relationship marketing on CSR. The model’s specifica- est (B = 0637; p<005, salesperson-level), and the tion has an advantage in that the parameter estimates absence of a CRM system (B =−1122; p<001, firm- for relationship marketing investments can be inter- level). For example, even with committed customers preted as the marginal return for each type of pro- (commitment = six on a seven-point scale), sales- gram. For example, in our sample specifically, a $1,000 people who have ownership interest and a selling additional investment in social relationship market- firm that employs CRM, investing $1,000 in financial ing generates $1,775 of incremental profit (78% return) relationship marketing produces only a $686 return when we control for other variables in the model (B = (31% loss). 1775; p<001). Because both financial and structural programs have significant interactions, we also must 1 Although we only expected interaction frequency to moderate account for the level of the moderators when we inter- structural relationship marketing, for completeness, we also tested pret the results. Investments in structural programs its moderating effect on financial and social relationship marketing programs, a procedure we duplicated for the interaction of cus- have a positive direct effect on CSR (B = 0998; p< tomer commitment with social and structural relationship market- 001) but generate higher returns for those customers ing programs. None of these additional four tests was significant. Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 488 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS

In addition to the relationship marketing variables, three types of programs differently is consistent with we include other covariates at the three levels and the first two implications and underscores that rela- find most results consistent with our expectations. At tionship marketing effectiveness truly can be under- the customer-level, previous period CSR0, customer stood only when the unit of analysis stays at the growth rate, and customer potential have positive and program-customer-level. significant effects on profit. At the salesperson-level, Our findings also suggest that social expenditures experience has a strong positive effect, but salesper- have a direct and significant impact on profit and son compensation has a significant negative impact thereby reaffirm the notion that such investments are on CSR (contrary to our expectations). Post hoc dis- worthwhile and can translate to goodwill among B2B cussions with sales managers indicate that the nega- customers. Social investments appear to deliver the tive impact of total compensation on CSR may be due highest short-term return, which may be due to the to those highly compensated salespeople who reach immediacy of social relationship marketing, in that a plateau and stop selling aggressively. At the firm- sellers can implement social programs in response level, advertising dollars have a positive impact on to current events with little prior planning. Social profit. In summary, the final model (Model 6) captures programs also may create a feeling of interpersonal more than half (51.6%) of the overall variance debt for customers that results in a pressing need to in CSR.2 reciprocate, which then generates immediate returns (Cialdini 2001). Furthermore, social programs are not significantly moderated in our sample, which may be Discussion because of the interpersonal nature of delivery, where We model the customer-specific payoff for financial, salespeople reallocate resources in real time to mini- social, and structural relationship marketing invest- mize misallocations. ments; provide a theoretical framework of customer, Structural relationship marketing investments gen- salesperson, and selling firm factors that may enhance erate positive short-term economic returns for those relationship marketing productivity; and empirically customers with above-average interaction frequencies support the framework by identifying four variables (>two interactions per week), which makes these that leverage the impact of relationship marketing on programs attractive for some customers. Sellers can CSR. These contributions align well with two recent leverage their structural relationship marketing dol- trends in marketing: determining the return on mar- lars by targeting customers with relatively more fre- keting expenditures, and moving toward one-to-one quent interactions, for whom the customized struc- customer marketing. Furthermore, the results support tural solutions offer the most value. Structural link- our premise that evaluations of the economic returns ages also should have an ongoing impact on prof- on these investments are more complex than they its into the future; although customer response in the may first appear, which thus suggests implications for short term may be based on reciprocation for a per- research and practice. ceived investment (De Wulf et al. 2001), customers First, different relationship marketing programs should continue to take advantage of the value pro- appear to differ in their effectiveness; therefore, fur- vided by these structural interfaces in the long run. ther research should disaggregate relationship mar- The return on financial relationship marketing ex- keting activities to isolate their actual effects. Sec- penditures demonstrates a high degree of hetero- ond, the influence of relationship marketing on CSR geneity across customer, salesperson, and selling firm is leveraged by factors associated with each of the factors, even though the main effect is not signif- three exchange participants (customer, salesperson, icant and fails to generate positive returns in any and selling firm). This finding indicates that program context evaluated. The lack of positive short-term returns may be improved through diverse strategies, returns probably is linked to the ease with which com- including customer segmentation, salesperson selec- petitors can match incentives (Berry 1995) and with tion, training, incentives, and selling firm initiatives. which financial relationship marketing dollars can Third, our empirical finding that moderators affect the be misallocated. However, financial relationship marketing, though not economically viable in the short run, may have an important strategic role. First, such 2 Model robustness and sensitivity to data were evaluated multiple ways. We compared parameter estimates (e.g., for relationship investments may be necessary to respond to com- marketing investments) generated from the final model with new petitive threats and protect existing business, rather estimates after varying the input data by ±20%, sequentially omit- than as means to generate new business. This rea- ting five cases (until 10% dropped) on the basis of Mahalanobis dis- soning implies that financial relationship marketing tance, sequentially omitting independent variables and interaction may be more defensive, whereas social and struc- terms, and using alternative model specification. In all these cases, directionality, significance level, and the relative value of parameter tural relationship marketing dollars represent offen- estimates remain consistent. Results from these additional analyses sive relational weapons. Second, an important com- are available on request. ponent of customer portfolio management pertains to Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 489 attracting less-valuable customers and building rela- support of relationship marketing. Second, our results tionships that may grow in the long run (Johnson and identify those circumstances in which relationship Selnes 2004). The usefulness of financial relationship marketing programs can be employed beneficially. For marketing in long-term relationship building remains example, many firms may be underspending on social an open empirical question that is worthy of future programs, and additional investments could generate investigation, but it is interesting to note that many greater profits for them. However, because the rela- factors moderate its effectiveness. Again, this finding tional tie may reside with the salesperson, the firm may indicate the relative ease, compared with social also risks greater damages if the salesperson leaves. and structural programs, with which financial rela- Because structural programs offer the greatest returns tionship marketing can be misallocated. For example, when directed toward those customers with whom it is relatively easy for a customer service employee the firm interacts frequently, managers could target or salesperson to provide a financial incentive (e.g., their structural investments toward these specific cus- free sample, special discount), whereas building an tomers. The recommendations for financial relation- interpersonal relationship or implementing a struc- ship marketing expenditures, however, grow more tural program requires much greater investments of complex. The returns from financial programs are time and effort. Third, our findings are consistent with improved when the selling firm has CRM in place, the the premise that the advantage of CRM may not be salesperson has ownership interest, and customers are to influence profit directly, but rather to improve the committed to the selling firm; however, as a stand- allocation and targeting of marketing efforts (Mithas alone investment, financial relationship marketing is et al. 2005). not viable in the short run. Thus, financial programs The empirical support found for the moderators should be used strategically, such as to respond to that we test increases our confidence in the theoret- competitors or attract new customers, not with the ical framework (Table 1) and provides insight into expectation of a short-term increase in profit. Man- other potential leveraging variables. This framework agers may want to institute formal policies to govern may enable relationship marketing research to move the conditions under which financial incentives are beyond testing Morgan and Hunt’s (1994) key medi- justified to limit misallocations. In this sense, financial ator model by identifying boundary conditions and incentives may resemble a pricing policy more than contextual moderators. a relationship marketing program. Overall, managers Another important aspect of this article is our in- should develop a profile of customers or customer seg- vestigation of the effects of relationship marketing ments that can become the focus of targeted relation- on profit across three levels of nested exchange par- ship marketing efforts and vary the mix of relationship ticipants (customers, salespeople, selling firms), for marketing programs according to the segment charac- which we employed a mixed-effects model to account teristics. for the nested structure. Our use of data from three Because financial, social, and structural relationship sources and the modeling of complex interactions marketing dollars provide different returns, allocating combine to reduce concerns that our results may be resources across programs is a complex challenge that explained by same-source biases. Furthermore, 61.9% must consider interactions with customer, salesperson, of the variance in CSR comes from the customer-level, and selling firm factors (Gopalakrishna and Chatterjee which reinforces the importance of customer-level 1992, Tellis and Zufryden 1995). How should a man- variables and suggests that research operationalized ager optimally allocate a given budget across relation- at higher levels of analysis may be unable to isolate ship marketing programs? To address this question, key drivers of profitability. The small percentage of we develop a post hoc resource allocation model that salesperson-level variance (9.5%) in CSR is somewhat provides directional insights into the optimal mix of surprising for this industrial context, in which sales- relationship marketing programs (for a given budget) people are viewed as critical. The remaining 28.5% of for different salesperson and selling firm strategies CSR variance comes from the firm-level, so firm-level (see Appendix B for the model details and Table 4 for strategies clearly are vital to performance. specific results). The optimization model indicates that, for our sam- Managerial Implications ple, sellers should allocate about two-thirds (69%) of Our analysis offers several perspectives that can help their spending to social programs and the rest (31%) guide managerial activities. First, managers should to structural programs, but nothing to financial pro- have greater confidence knowing that relationship grams. If we then evaluate the optimal relationship marketing programs work and that their impact on marketing mix after splitting the sample according to bottom-line results is measurable. In addition, the abil- salesperson ownership interest and the use of a CRM ity to document these economic returns provides man- system, we find that it makes sense to expend some agers with a stronger basis to request resources in dollars on financial relationship marketing activities Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 490 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS

Table 4 Optimal Relationship Marketing Allocation These findings offer insight into the conditions in which the deployment of a specific type of rela- Financial Social Structural relationship relationship relationship tionship marketing activity might be most beneficial. marketing marketing marketing Social programs manifest the highest payoff, proba- investments investments investments bly because salespeople quickly adapt and channel Description of scenario (%) (%) (%) these investments into specific social activities that Overall sample 0 69 31 incite returns. Additional development and empiri- Ownership interest 8 71 21 cal research might examine the role of salesperson No ownership interest 0 64 36 adaptability in the allocation of social relationship No CRM system 0 74 26 marketing investments. We also challenge researchers CRM system 7 66 27 to account for the nested structure of relationship Note. Customer relationship marketing (CRM). marketing effects (customers, salespeople, and firms) in research design and analysis. Although we recognize that this issue can be managed through careful (8%) if salespeople have an ownership interest; oth- operationalizations (e.g., single-level effects) and data erwise, financial relationship marketing investments collection (e.g., collecting truly independent observa- do not pay off. This finding reinforces the need for tions), our analytical approach accounts for fixed and managers to control the use of financial relationship random effects and enables testing of the interactions marketing when the interests of salespeople are not across nested levels. Future researchers may want to investigate these multilevel effects from a Bayesian well aligned with those of the selling firm (Joseph perspective. 2001). Similarly, when the selling firm has a CRM sys- Our study also has limitations that suggest avenues tem in place, shifting some relationship marketing dol- for further research. First, given our objectives, we lars (7%) to financial programs is optimal. Across the looked for a business context (rep firms) in which five scenarios reported in Table 4, social investments relationship marketing was critical to the sustainabil- remain within a range of 64%–74%, which implies ity of the business. However, it would be useful to social relationship marketing should be the key focus replicate our approach in contexts in which relation- of any relationship marketing portfolio. Structural ship marketing activities may not have such a cen- relationship marketing allocation ranges from 21% to tral role and examine returns in those arenas. The 36%. The recommended allocations are highest for same note of caution on generalizability would apply structural and lowest for social in the “no owner- to our results from the resource allocation model, ship” group, which suggests that when salespeople as well. Second, we evaluate the returns from rela- have little stake in selling firm profitability, building tionship marketing deployment in a specific year, strong interpersonal customer-salesperson bonds may so future efforts should consider longer-term effects. be suboptimal because salespeople can defect to com- Idiosyncratic factors across periods (e.g., economy, petitors or may be more likely to allocate their social industry-specific issues) may influence our reported expenses poorly. effects. Thus, a study that examines the dynamics of the impact of relationship marketing expenditures, Conclusion alternative relationship marketing typologies, and dif- Determining the return on marketing activities re- ferent measurement methods would be valuable. Fur- mains a hot topic for academics and practitioners. In thermore, although short-term economic returns from this research, we investigate the impact of a selling investment decisions are critical to managers, relation- firm’s relationship marketing expenditures on profit at ship marketing programs should generate other long- the level of a specific customer. Although we note pos- term outcomes not captured in our data, such as cross itive economic returns, we also identify 25 potential selling and up selling. Further research could attempt variables that can leverage these expenditures. Due to to explore the long-term payoff of relationship mar- research design and measurement limitations, we only keting investments by including such variables. empirically test a subset of these variables, but we find significant moderation across all three exchange con- Acknowledgments stituents (customer, salesperson, and selling firm) and The authors thank the Marketing Science Institute and across the three different theoretical drivers (motiva- the Manufacturers’ Representatives Educational Research Foundation for their financial support of this research. tion to build a relationship, customer’s willingness to Thanks also to David Curry, Murali Mantrala, Rebecca Slote- reciprocate, and seller’s ability to efficiently allocate graaf, Leigh M. McAlister, Jean L. Johnson, Lisa Scheer, resources), suggesting future research should explore and Kenneth Evans for their input into this project. The other potential factors that may leverage the return on authors thank the editors and reviewers for their construc- investments. tive comments. Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 491

Appendix A. Construct Measures

Measures (units) Source

Interaction frequency (interactions per week) Customer How many times do you interact with this rep firm in a typical week? Commitment to the selling firm (average of three seven-point Likert scale items, = 095) Customer I am willing “to go the extra mile” to work with this rep firm. I feel committed to my relationship with this rep firm. I view the relationship with this rep firm as a long-term partnership. Growth rate of customer firm (%) Customer What is your estimate of your company’s growth over the past year? Relationship duration (years) Customer How long have you had business dealings with this rep firm in your career? Financial relationship marketing investments (annualized $) Salesperson This customer often gets free product and services. This customer frequently gets special pricing or discounts. This customer receives special financial benefits and incentives. The average monthly cost to provide the financial benefits listed above is Social relationship marketing investments (annualized $) Salesperson This customer is often provided meals, entertainment, or gifts by me or my rep firm. This customer often receives special treatment or status. This customer often receives special reports or information. The average monthly cost to provide the social benefits listed above is Structural relationship marketing investments (annualized $) Salesperson This customer often receives special value–added benefits (inventory control, expediting, etc.). Special structural changes (EDI, packaging, etc.) have been instituted for this customer. Our policies and procedures are often adapted for this customer. Dedicated personnel are assigned to this customer beyond what is typical for our rep firm. The average monthly cost to provide the structural benefits listed above is Potential of customer (seven-point Likert scale) Salesperson The customer represents a large potential opportunity for me. Experience (years) Salesperson How many years have you worked for any rep firm, including this one? CSR ($) Sales manager CSR = (sales to customer) ∗ (average commission at customer) ∗ 1 − salesperson and salesperson variable pay); sales to customer ($), and salesperson variable pay (%) reported by sales manager; average commission reported by salesperson for each customer (%). The next three questions regarding salesperson compensation were prefaced by “Please answer the following questions for each salesperson listed.” Compensation (1 <30k$, 2 30k$ to 60k$, 3 60k$ to 90k$, 4 90k$ to 120k$, 5 >120k$) Sales manager Total 2002 compensation Ownership interest (0: 0% ownership interest in selling firm, 1: >0% ownership interest in selling firm) Sales manager % of salesperson’s ownership in the rep firm Relationship-focused objectives (0: 0% of compensation based on relationship-focused objectives, Sales manager 1 >0% of compensation based on relationship-focused objectives) % of total compensation which was based on customer satisfaction or relationship objectives Advertising (annual spending in dollars) Sales manager How much did your rep firm spend in 2002 on all types of marketing programs including tradeshows, advertising, brochures, etc.? Selling firm size (annual sales in million of dollars) Sales manager What was your rep firm’s approximate annual sales for 2002? Average tenure of salespeople (years) Sales manager How many years does an outside salesperson typically stay at your rep firm? CRM system (0: employ CRM system, 1: no CRM system) Sales manager Did your rep firm utilize a CRM system in 2002?

Notes. All Likert items are seven-point scales anchored at 1 = strongly disagree and 7 = strongly agree. Customer-speciﬁc return (CSR), customer relationship management (CRM). Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments 492 Marketing Science 25(5), pp. 477–493, ©2006 INFORMS

Appendix B.Resource Allocation Model Let . = net profit = gross profit − relationship marketing One important consideration in specifying a model is parsi- expenditures, and mony. A second is that the model reflects the basic features . = X 1 X 2 X 3 X 4 X 5 X 6 − X + X + X (B2) of the phenomenon. For example, interaction frequency 1 2 3 4 5 6 1 2 3 and customer commitment are important variables that can To optimize, set the first derivative to 0: leverage relationship marketing dollars. Therefore, we state /.//X = X 1−1X 2 X 3 X 4 X 5 − 1 = 0 the resource allocation model at the individual customer- 1 1 1 2 3 4 5 level in the following manner: 1 2 3 4 5 6 Simplifying, 1X1 X2 X3 X4 X5 X6 /X1 = 1, which 1 2 3 4 5 6 leads to Y = X1 X2 X3 X4 X5 X6 % 0 < 1 2 3 < 1 (B1) X∗ = YX∗ = optimal financial expenditure) (B3) where 1 1 1 Y = profit generated from customer i in 2003 In a similar way, we obtain X1 = financial relationship marketing dollars spent on X∗ = Y and X∗ = Y (B4) customer i 2 2 3 3 where X∗ = optimal social expenditure, and X∗ = optimal X2 = social relationship marketing dollars spent on 2 3 customer i structural expenditure. X = structural relationship marketing dollars spent on Given a budget K for which we are trying to find the 3 3 customer i optimal split, we have

X4 =profit generated from customer i in 2002 (previous X∗ + X∗ + X∗ = K (B5) period) 1 2 3 Thus, from Equations (B3) and (B4), we obtain, after substi- X5 = interaction frequency with customer i tution into (B5), X6 = commitment level of customer i. The and all s are parameters to be estimated. 1 + 2 + 3Y = K (B6) Equation (B1) is a common functional form to represent a variety of marketing phenomena (Hanssens et al. which leads to 2001). The model specification is appealing on two levels. Y = K/ + + = K/ (B7) First, the multiplicative form (log linear) implicitly consid- 1 2 3 i ers interactions between variables. Second, by specifying When we substitute the expression for Y in Equation (B7) that < 1, the model features diminishing returns into Equations (B3) and (B4), we get 1 2 3 on spending. To maintain parsimony, interactions among ∗ X1 = 1 i K (B8) customer-level variables are not explicitly specified, but are ∗ implicitly captured through the parameter (Lilien et al. X2 = 2 i K and (B9) 1993). The model then can be solved to determine the X∗ = K (B10) optimal expenditures to maximize profits (see subsequent 3 3 i derivation). The optimal allocation of relationship market- Thus, the optimal split is equal to the relative weighting of ing dollars is equal to the relative weighting of the coef- 1, 2, and 3. ficients (an intuitive result driven by the functional form).

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3 ∗ ∗ ∗ A more general representation is X1 + X2 + X3 ≤ K, which leaves Analytical Derivation of Optimal Allocation the total budget unconstrained and helps indicate the optimal total The original expression for gross proﬁt is budget. Our goal, however, is to understand broad allocations across relationship marketing components rather than solve for the 1 2 3 4 5 6 Y = X1 X2 X3 X4 X5 X6 % 0 < 1 2 3 < 1 (B1) optimal budget. Palmatier, Gopalakrishna, and Houston: Returns on Business-to-Business Relationship Marketing Investments Marketing Science 25(5), pp. 477–493, ©2006 INFORMS 493

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