Customer Focus Feito

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50Journal of Marketing

Vol. 72 (September 2008), 5068

2008, American Marketing Association

ISSN: 0022-2429 (print), 1547-7185 (electronic)

V. Kumar, Rajkumar Venkatesan, & Werner Reinartz

Performance Implications ofAdopting a Customer-Focused Sales

CampaignThrough field experiments conducted in two business-to-business firms, the authors evaluate the financial andrelational consequences of adopting a customer focus in sales campaigns. In both the experiments, salespeopleadopting the customer-focused sales campaign coordinated their sales calls with the objective of selling all theproducts that a customer was predicted to purchase only at the time the customer was expected to purchase. Theauthors compare this strategy with the current practice in the organization in which salespeople for each productcategory independently contacted the customers who were expected to purchase in that category without anyguidance on the expected timing of customer purchase. The experiments show that adopting a customer-focusedsales campaign can significantly increase firm profits and return on investment. The total incremental profitsobtained from implementing the customer-focused sales campaign was more than $1 million. High-revenuecustomers were the source of improvements in the efficiency of marketing contacts, whereas low-revenuecustomers were the source of improvements in the effectiveness of the marketing contacts. A customer-focusedsales campaign also improved the relationship quality between the customer and the firm. This research provides

empirical evidence for theoretical expectations of the benefits provided by a customer-focused sales campaign.Organizations can use the field experiments illustrated in this study as a template for implementing the first step inmigrating to a customer-centric organization.

Keywords: customer focus, field experiment, cross-selling, performance metrics, sales force coordination

V. Kumar is Richard and Susan Lenny Distinguished Chair in Marketingand Executive Director of the Center for Excellence in Brand and Cus-tomer Management, J. Mack Robinson College of Business, GeorgiaState University (e-mail: [email protected]). Rajkumar Venkatesan is AssociateProfessor of Business Administration, Darden Graduate School of Busi-ness, University of Virginia (e-mail: [email protected]).Werner Reinartz is Professor of Marketing and Director of the IFH Center

for Retail Research, University of Cologne, and Associate Professor ofMarketing, INSEAD (e-mail: [email protected]). The authorsthank the business-to-business firms not only for sharing their customerdatabase but also for providing an opportunity to conduct a field experi-ment with their sample of customers.The research has benefited from thefinancial support from the Center for Customer Relationship Managementat Duke University and the Marketing Science Institute.Special thanks areowed to Rick Staelin, Vithala Rao, Wagner Kamakura, Don Lehmann, andScott Neslin for their comments since the inception of the research pro-posal. The authors also thank the anonymous JM reviewers for their valu-able comments.

According to a 2003 Gartner Report (Shah et al. 2006,p. 1), By 2007, fewer than 20 percent of marketingorganizations among Global 1000 enterprises will

have evolved enough to successfully leverage customer cen-tric, value added processes and capabilities. The report alsostates (p. 1) that by 2007, marketers that devote at least 50percent of their time to advanced, customer centric market-

ing processes and capabilities will achieve marketing ROI[return on investment] that is at least 30 percent greater thantheir peers, who lack such emphasis. Our research revealsthat many Fortune 100 firms, such as Citigroup, GeneralElectric (GE), United Technologies Corporation, and Pep-

siCo, have organized their marketing and sales activitiesaround the products they offer rather than the customersthey serve.

Anecdotal evidence finds that customer centricity isoften misinterpreted by organizations as selling a bundle ofproducts to all customers. For example, Gulati (2007) indi-cates that GE medical systems faced major setbacks when

equipment salespeople also began selling consulting ser-vices for all GEs customers. By marketing the units con-sulting services with its product portfolio, GE generatedsolutions for customers whose problems could be solvedusing GEs equipment, but these services were less com-pelling for those whose needs were linked only loosely tothe imaging products. In another context, because ofHewlett-Packards failure to realize benefits from customer-focused sales campaigns and because of competition frommore focused competitors, Mark Hurd scaled back Hewlett-Packards customer-centric initiatives. Whereas the earlierobjective of Hewlett-Packards sales force was to sell prod-uct bundles to all customers, it was reorganized to be more

product-focused, with the belief that it would reduce sellingcosts because less central coordination would be required(Burrows 2005).

The academic literature suggests that the strategicadvantage of a customer-centric organization is to createvalue for the customer and, in the process, to create valuefor the firmthat is, a focus on dual value creation (Bould-ing et al. 2005). A successful migration from a product-centric to a customer-centric organization is expected toproceed through a multistage process of aligning the organi-zations structure, performance metrics, processes (espe-


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Adopting a Customer-Focused Sales Campaign / 51

cially customer-facing activities, such as sales calls), andculture to be externally focused with the objective of satis-fying customers needs (Shah et al. 2006). The first step inthis migration is proposed as the informal coordination ofcustomer-connecting activities, such as sales calls acrossproduct silos (Day 2006)that is, implementing acustomer-focused sales campaign. Although customer-focused sales campaigns are theoretically expected toincrease profits and improve ROI, firms adoption ofcustomer-focused sales campaigns has been low (Day

2006). Major reasons identified in the literature (Day 2006;Gulati 2007; Shah et al. 2006) for the failure of the migra-tion toward a customer focus include (1) poor implementa-tion of the coordination of customer-facing activities acrossproduct silos, (2) the failure to understand customerrequirements across product categories, and (3) the failureto customize firm offers to customer requirements.

Our goal in this study is to provide an assessment of theconsequences of implementing a customer-focused salescampaign through field experiments. Following the dualvalue creation objective of customer centricity, we assessthe performance of customer-focused sales campaignsusing both relational and financial metrics. The relational

metrics provide an evaluation of customer perceptions ofthe value provided by a customer-focused sales campaign.The financial metrics enable us to evaluate whether acustomer-focused sales campaign provides value to thefirms. In the field experiments, we control for the accuracyof customer knowledge available to salespeople and evalu-ate the consequences of aligning a sales force along cus-tomers or products. Objective evidence regarding the bene-fits obtained from adopting a customer-focused salescampaign can serve as an aid for top management to gainsupport for initiatives that would help develop a customer-centric organization (Gulati and Oldroyd 2005). The fieldexperiments can provide organizations with a template for

implementing the first step in migrating toward a customer-centric organization.Through the field experiments, we also intend to con-

tribute to the theoretical understanding of customer-focusedorganizations by generating insights into the process of ROIimprovement. In other words, if profit consequences can bedemonstrated, we attempt to understand the source of thesebenefits. Higher profits can be obtained from cost reduction(greater efficiency), revenue growth (greater effectiveness),or both. Improved efficiency of targeting implies that theorganization is able to reduce the campaign cost whilemaintaining overall revenue levels. For example, by under-standing each customers total needs, a firm can design asingle, consistent message, leading to a lower number of

sales calls required to complete a sale. An improved effec-tiveness of targeting implies a match between customerneeds and either the type of message or the timing of themessage. For example, predicting when a customer is likelyto purchase and timing the sales call to coincide with thecustomers expected purchase time would enable a firm toachieve better customer penetration, thus leading to higherrevenue. We propose that a customer-focused sales cam-paign can provide efficiency and effectiveness gains relativeto a product-focused sales campaign.

1We are unable to reveal the product category because of confi-dentiality reasons.

To summarize, in this study, we present two case studiesin which we (1) conduct a field experiment that explicitlycompares the proposed customer-focused sales campaignwith a more traditional product-focused sales campaign and(2) assess the efficiency and effectiveness of the customer-focused sales campaign by documenting relationship qual-ity, cost, revenue, and ROI implications.

The empirical context of the first field experiment is anorganization that markets a range of high-technology prod-ucts and services to other firms. In each planning period

(quarters), the company allocates sales campaign resources(or marketing investments) to contact its customers for threeprincipal product categories: A1, A2, and A3.1 The firstfield experiment shows that the firm can obtain impressivefinancial returns from adopting a customer-focused salescampaign. For an average investment of $5,000, which wasrequired by both the test- and the control-group customers,the test-group customers provided $13,253 in profits,whereas the control group provided only $9,584 in profits.The test-group customers, who were exposed to a customer-focused sales campaign, provided more than $1 million intotal incremental profits compared with the control-groupcustomers, who were exposed to a product-focused sales

campaign. The total profits from the test-group customerswere more than $3.7 million.

The results from the second field experiment, whichwas conducted in another multinational organization in thetelecommunications industry, validate the results from thefirst field experiment and improve the generalizability ofour findings. This telecommunications firm provided fourdifferent services in the business-to-business (B2B) envi-ronment. We observed in the second field experiment thatfor an average investment of approximately $4,000, cus-tomers in the test group provided $10,082 in profits,whereas the control group provided only $7,938 in profits.The test-group customers provided more than $500,000 in

incremental profits compared with the control-group cus-tomers and $2.4 million in total profits.In the next section, we provide the conceptual back-

ground and develop hypotheses regarding the benefits fromadopting a customer-focused sales campaign. We then pro-vide the context of the first field experiment, illustrate themodel used to develop predictions of the customer require-ments used in the experiment, and provide the results of themodel estimation and details of the results observed. Fol-lowing this, we contrast the second field experiment withthe first to highlight the generalizability of our findings.Next, we provide a discussion of the results and highlighttheir managerial implications. Finally, we provide the limi-tations of our study and provide suggestions for further

research.

Conceptual Background andHypotheses

The concept of customer focus or customer centricity hasbeen discussed widely in the marketing literature. For


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52 / Journal of Marketing, September 2008

example, Deshpand, Farley, and Webster (1993, p. 27)define a customer orientation (which has also been referredto as customer focus) as the set of beliefs that puts the cus-tomers interest first, while not excluding those of all otherstakeholders such as owners, managers, and employees inorder to develop a long-term profitable enterprise. Further-more, Shah and colleagues (2006, p. 115) suggest that thetrue essence of the customer-centricity paradigm lies not inhow to sell products but rather on creating value for the cus-tomer and, in the process, creating value for the firm.

Although most of the early literature concentrated on theperformance benefits of an organizationwide focus on cus-tomers, there is a dearth of research on implementation ofvarious steps required in the migration to a customer-focused organization. Therefore, our experiment takes anarrower view and tests the benefits from a customer-focused sales campaign. This activity is considered the firststep in the migration toward a customer-centric organiza-tion (Day 2006). The customer-focused sales campaignscan be considered an organizational process that needs to beimplemented for an organization to be customer centric(Shah et al. 2006).

In a customer-focused sales campaign, the entire set of

product or service needs for each customer or customer seg-ment and the consumption rate (i.e., purchase frequency) ofthe customer are first identified. The firms sales callswould then focus only on each customers needs and wouldtarget the customer only when the need is expected to arise.A customer-focused sales campaign would entail coordinat-ing sales calls across product silos to address each cus-tomers expected needs. In other words, salespeople fromdifferent product specializations would coordinate theirsales calls to provide a coherent and consistent message to acustomer who has a need for multiple products.

In contrast, under a product-focused sales campaign, afirm would identify the customers who are likely to purchase

a product. The sales force of that product division wouldthen target all the customers who are likely to purchase thatproduct. Within a product-focused sales campaign, the samecustomer is likely to be targeted by different salespeople(each with a specialization in a particular product) separatelyand multiple times from the same firm. As Shah and col-leagues (2006) indicate, the goal of a product-focused salescampaign is to maximize the number of customers to whoma product can be sold. Conversely, the goal of a customer-focused sales campaign is to maximize the extent to whichthe firms products address customers needs. The sales-people in a product-focused sales campaign do not aim tocontact customers only when they expect customers to needthe product. Therefore, a customer focus in a sales campaign

calls for both coordinating sales calls across product silosand restricting the timing of sales calls to coincide with theexpected customer purchase rate.

Customer-focused sales campaigns are different fromcustomer-oriented selling. In customer-oriented selling, asalesperson assists customers in making purchase decisionsthat aim to satisfy their underlying needs (Siguaw, Brown,and Widing 1994), and it refers to the behavior of a singlesalesperson. However, we are interested in the orientationof the entire sales campaign across salespeople, product

categories, and time. Salespeople could individually prac-tice customer-oriented selling for their respective productcategory, but the resultant sales campaign is still productfocused if there is no coordination among salespeople fromdifferent product category groups. Furthermore, salespeoplewould need to know the timing of customer purchases tocoordinate their sales calls.

Impact of Customer-Focused Sales Campaigns

Consistent with theoretical expectations, previous empirical

research has found that the collection and integration ofcustomer information needs to coexist for improved perfor-mance (Jayachandran et al. 2005). In addition to sharingcustomer information, the benefits of a customer focus canbe obtained only if the acquired customer information isdeployed in customer-facing activities (i.e., sales calls) in amanner consistent with the philosophy of customer centric-ity. However, the benefits obtained from an effective andconsistent deployment (in customer-facing activities) of theinformation obtained from customer data have not beenexplored.

Recall that we defined a customer-focused sales cam-paign as one in which salespeople coordinate their contact

strategy across product categories, salespeople, and time toaddress customers underlying, dynamically changingneeds. We propose that such an approach has a positiveassociation with customer-level revenues, a negative asso-ciation with customer-level costs to serve, and a positiveassociation with relationship quality compared with aproduct-focused sales approach.

The positive association with revenue generation (i.e.,improved effectiveness of marketing actions) is likely tocome from the following factors: First, there is a greaterlikelihood of sales conversion because of a better alignmentwith customers needs as a result of possible complemen-tary cross-category effects and better incorporation of

purchase-timing information. Second, there is upside vol-ume potential because of the various possible categorycombinations now coming from the same firm (as opposedto sales potentially lost to competitors for individualproducts).

The negative association with costs of serving cus-tomers (i.e., improved efficiency of marketing actions) islikely to come from the following effect: Because of theincorporation of the purchase-timing component, there willbe a better alignment of actual sales interventions and occa-sions of high purchase propensities. For example, sales-people might consciously spend more time with establishedpersonal contacts, even though they have little additionalsales potential, rather than targeting potentially interestingbut personally unknown customers. However, a model-based approach will help the salesperson use the scarceresource time as effectively as possible (Gensch 1984).

Finally, we propose that there is a positive associationbetween a customer-focused sales campaign and customerrelationship quality, which is likely to come from theimproved ability to address true customer needs. Thisimprovement will be derived from the better matching ofsales propositions with actually needed product require-ments, the better matching of sales propositions with actual


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timing of requirements, and the possible second-order syn-ergistic effects due to better product compatibility acrosscategories and/or better internal functional coordination inthe customers organization. These effects should drive cus-tomer perceived value and satisfaction, which in turn shouldlead to improved loyalty and recommendation likelihood(Gupta and Zeithaml 2006). Formally, we hypothesize thefollowing:

H1: A customer-focused sales campaign is associated with (a)higher revenues (i.e., improved effectiveness), (b) lowercosts (i.e., improved efficiency), and (c) more improvedrelationship quality than a product-focused salescampaign.

Sources of Performance Improvement

Conceptual models propose that, all else being equal,improved productivity can come from (1) more efficientlycreating valueachieving equal response as before but withless inputand (2) efficiently creating more valueachiev-ing greater resource-produced value than before (Hunt andMorgan 1997). We further refine H1a and H1b by proposingdifferent sources of these effectiveness and efficiencyimprovements.

In particular, we argue that effectiveness and efficiencygains also depend on the historical level of sales calls or thelevel of marketing investment. Customers who receive ahigher number of sales calls are likely to be those the firmexpects to generate higher current volume (or higher poten-tial). Similarly, customers who receive fewer sales calls arelikely to be those the firm expects to generate lower currentvolume (or lower potential). This is the well-known endo-geneity phenomenon that has been well documented indirect-marketing contexts (Shugan 2004). Note that thisendogeneity issue does not create any statistical problems inour context, because we are comparing two experimentalgroups at the same point in time. It can be hypothesized that

the benefits flowing to the firm (efficiency and effectivenesscreation) are distributed unequally among these two cus-tomer groups. Figure 1 illustrates our rationale for expect-ing different gains from these groups.

Specifically, we hypothesize that the current high-sales-call customers are those who disproportionately contributeto the cost savings, whereas the low-sales-call customersdisproportionately contribute to the revenue gains. This is

because there is a ceiling effect among high-sales-call cus-tomers, who already spend a lot with the firm, and thereforethey have little upside potential. Thus, we would expect thatthe gain, if any, would come from the cost-savings side (i.e.,using fewer but more calculated sales calls). The low-sales-call customers have more of an upside potential, thoughthey have a ceiling effect as well. Here, the ceiling effect ismore likely due to an overall smaller wallet size or an over-all lower utility for the firms offering. Therefore, althoughgrowth potential is likely, low-sales-call customers would

never be expected to grow to the same size as high-sales-call customers. In addition, the possibility of savings gainsfrom low-sales-call customers is low because they alreadyreceive few sales calls. Thus, there is a floor effect withrespect to marketing touches. Formally, we hypothesize thefollowing:

H2: The improvement in the efficiency of customer-focusedsales campaigns is greater (a) for customers with a higherlevel of marketing investment and (b) for customers with alower level of marketing investment.

Field Experiment 1

MethodWe conducted the first field experiment with a multinationalfirm that provides three product categories in the informa-tion technology industry to business customers. The firmthat participated in this experiment is similar to a Fortune1000 firm in terms of annual sales, sales growth, netincome, and number of customers. Through its strategicalliances, the firm provides products and services in threerelated major categories, which is typical of several high-tech firms, such as Microsoft, Dell, IBM, Hewlett-Packard,and Cisco. External storage devices, antivirus software, net-work servers, personal computers, and workplace produc-tivity software are examples of products that are analogous

to those provided by the firm. The sales transactions forcustomers in the field experiment range from $5,000 toapproximately $25,000. In this field experiment, customersare proposed a combination of three product categories: A1,A2, and A3. The sales force of the organization is alsostructured along this categorization strategy. Therefore, ourproduct categorization allows for easy execution of the fieldexperiment.

FIGURE 1Effectiveness and Efficiency Gains Across Customer Segments

Past Behavior ExpectedBehaviorCustomer Firm Current State

Segment 1 Higher utility foroffering, thus

higher revenue

Allocation ofhigher level of

sales calls

Customer haslittle upside

potential

Gains, if any,are derived

from cost sav-ings (efficiency)

Customer Firm

Segment 2 Lower utility foroffering, thuslower revenue

Allocation oflower level of

sales calls

Customer hasmoderate

upsidepotential

Gains, if any,are derived

from revenuegrowth

(effectiveness)


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2We did not match the test and control customers on sales orsales growth (e.g., Lodish and Pekelman 1978), because all thecustomers selected in this experiment have similar sales.

We use a sample of 566 customers, who are currentlyserved by 850 salespeople. For a particular product cate-gory, more than one customer is assigned to a single sales-person. However, one customer can also be assigned manysalespeople, such that each salesperson is responsible for adifferent product category. In other words, a customer canbe contacted by a maximum of three salespeople given thatthere are three product categories. Thus, there are moresalespeople than customers involved in this experiment. Thecustomers are assigned to the test (n1 = 283) and control

(n2 = 283) groups on the basis of matched-pair compari-sons. We compare the customers across several customercharacteristics, such as establishment sales, number ofemployees, distribution of industry category, and behavioralfactors (e.g., purchase frequency, total revenue, past cus-tomer value). The customer assignments to the test and con-trol groups are carried out such that the distribution of thepreviously mentioned factors is similar in both the test andthe control groups. For example, we ensure that if the testgroup had 20% of the customers with between 100 and1000 employees, approximately 20% of the customers inthe control group also had between 100 and 1000 employ-ees. We chose the factors used for the matched-pair com-

parisons on the basis of our discussions with the organiza-tion that provided the data. The firm uses these factors tosegment customers for sales and marketing purposes.2

When we obtained the matched pairs, we randomlyassigned the customers to the test and control groups.Depending on the customer assignment, we also assignedthe corresponding salespeople to the test and controlgroups. We conducted the experiment for one year betweenthe first quarter of 2006 and the end of the last quarter of2006.

Customer-focused sales campaign. We use the predic-tions from a joint-timing and product category choicemodel to prioritize sales calls for customers in the test

group. During the experiment, the customers in the testgroup are contacted only in the quarter when they areexpected to make a purchase. The salespeople from theproduct categories that the customer is expected to purchasefrom work as a team to make coordinated sales calls. Forexample, if a customer is expected to purchase from prod-uct category A1, the salesperson responsible for A1 callsthat customer. If a customer is expected to purchase frommultiple product categories, salespeople from the respectiveproduct categories form a team to make coordinated or jointsales calls. For example, if a customer is expected to pur-chase from product categories A1 and A3, both the sales-person responsible for A1 and the salesperson responsible

for A3 call this customer together.Product-focused sales campaign. The salespeople for a

product category are proactive in proposing only theirrespective product categories on their sales calls to the

control-group customers. The joint-timing and product cate-gory choice model also provides inputs for the controlgroup. Unlike the test group, the salespeople remain alignedwith their product category in the control group. We use theoutputs from the joint-timing and product category choicemodel to identify customers who are likely to buy a particu-lar product categoryfor example, A1in the experimen-tal period. The salespeople responsible for category A1 areprovided the list of customers expected to purchase A1 andare instructed to contact these customers in the upcoming

year. A similar approach is implemented for product cate-gories A2 and A3.

The timing of sales calls within the year is typically atthe discretion of the sales force; in general, customers witha higher predicted purchase probability were contactedbefore others. Under a product-focused strategy, multiplesalespeople (corresponding to the different product cate-gories) from the firm might contact a customer at the sametime to propose their respective products. In addition, ignor-ing the timing of customer purchases could lead to sales-people from the firm proposing the right product to a cus-tomer at the wrong time, which could result in lost sales.Figure 2 illustrates the implementation of our field

experiment.Salesperson compliance with the field experiment. Fully

controlled laboratory experiments allow for random alloca-tion of participants to treatments in a between-subjectsdesign to ensure that all other relevant factors do not varysystematically across treatments. In contrast, our data aregenerated in a field experiment that does not allow for asimilar level of controlled comparison. An issue that isimportant to monitor is whether the experimental conditiongenerates a potential demand effect, which then mightaffect the participants (salespersons) behavior (Orne1962). In our case, it could be argued that participants knowthat their behavior is being observed and that this would

generate potential deviations from their usual behavior.However, in our case, we had several conditions that wouldhelp minimize any potential demand effects.

First, salespeople are not involved in the customer scor-ing process. The analysis and scoring of customers is con-ducted by a specialized market intelligence group, whichthen delivers the results to the sales function. Therefore,although participants were fully aware that a field experi-ment was being conducted, they were not aware of thekinds of models that were being employed.

Second, both groups (test and control) were involved inthe experiment. Thus, both groups were normalized insofaras they knew that an experiment was being conducted; thisshould minimize demand-effect impacts between the twogroups. Furthermore, the ability to compare pre- and post-experiment behavior of the control group enabled us todetermine that there was no significant behavior changewith respect to our dependent measures.

Third, the organization that we worked with has strongorganizational processes in place. Indeed, this organizationis well known among its peers for its thoroughly developedinternal processes. Thus, compliance with existing salesprocesses and scripts is high, which helps minimize anypotential demand effects. Another advantage of the strong


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FIGURE 2Customer-Focused Versus Product-Focused Sales Campaign

aThe best-performing model is chosen after a detailed comparison with other benchmark models presented in the Appendix.

process orientation was that we did not need to revert tousing incentives to motivate salespeople to engage in thetest group. Rather, we found that incentives would poten-tially aggravate the demand-effect problem and thereforewould be counterproductive.

Predicting Purchase Timing and CustomerCategory Choice

One aspect of a customer-focused sales campaign involvesthe ability to predict a customers category needs reliably.We accomplish this through a joint-timing and choicemodel because it addresses the main idea of a customer-focused sales campaignthat is, providing customers withall the products they need at the time they need them. Wecontrol for bias due to level of customer profitability by tar-geting only highly profitable customers as the sample forthe field experiment.

Figure 3 shows the conceptual objective of our model.Our joint approach to investigating purchase timing andcategory choice is based on the dynamic McFadden modelformulation (for an application, see Chintagunta and Prasad1998). Although it is possible to implement our experimentusing a simpler model structure, the proposed model struc-ture will enable us to minimize loss of revenue due to inac-curate predictions of customer behavior. In turn, this willenable us to assess and identify the potential gains of acustomer-focused sales strategy. Therefore, we incorporatethe recent developments in the literature to create a sophis-ticated model of purchase timing and category choice forthe experiment.

Let Pi(t) denote the probability that customer i will pur-chase from the firm at time t, and let Pi(j|t) denote theprobability that customer i will purchase in product cate-gory j, given that the purchase time is equal to t. Then, thejoint probability of customer i purchasing in product cate-gory j in time t, Pi(t, j), is given by the following:

(1) Pi(t, j) = Pi(t) Pi(j|t).

We assume that a customer has a specific interpurchasetime for each of the products purchased from the firm. Theinterpurchase times in each product category will result inan overall interpurchase time for the customer with the firm.We model this interpurchase time using a statistical distri-bution, which answers the question, When is the customerlikely to purchase next [Pi(t)]? Knowledge about a cus-tomers history of interpurchase times, the product cate-gories from which he or she purchased at each purchaseinstance, and the timing of the current purchase occasionwill enable us to deduce consumption patterns in each prod-uct category and thus satisfactorily predict the category inwhich a customer is most likely to purchase [P i(j|t)]. Wefirst develop our model formulation for each of the compo-nents, Pi(t) and Pi(j|t), and then we provide the joint likeli-hood function. As we explained previously, the joint proba-bility of purchase timing and category choice is the productof the marginal probability of purchase timing and the con-ditional probability of category choice, given purchasetiming.

Purchase timing [Pi(t)]. We use a log-logistic distribu-tion to model customer interpurchase times because the dis-


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FIGURE 3Conceptual Model Specification

Drivers

Category Choice

Purchase Timing

Customer needs

Category familiarity

Cumulative +current

effects

Past buying behavior

Marketing interventions

Cumulative +currenteffects

3On the basis of the Andersen-Darling tests, we found that alog-logistic distribution would represent the data best. In otherwords, we failed to reject the null hypothesis that the data belongto a log-logistic distribution at a significance level of = .01. Werejected the null hypothesis for other distributions, such as log-normal, exponential, and Weibull.

tribution accommodates a variety of forms and is suitable tomodel consumption patterns (Chintagunta and Prasad

1998).3 The purchase times are measured from the sametime origin to reflect the natural sequence of events; that is,the time for the first purchase, T1, is less than the time forthe second purchase, T2, and so forth. If we assume a log-logistic distribution for interpurchase time, the probabilitythat the kth purchase for customer i will occur at time t,given the timing of the customers previous purchases, isgiven by the following:

where the two parameters of the log-logistic model are 0i

and 1ik, both of which are greater than zero. The parameter1ik is expressed as 1ik = exp(Ziki), where i are a set ofresponse coefficients of customer i and Zikdenotes the vec-tor of variables for each customer i in purchase occasion k.We use a random-effects formulation to estimate customer-specific response coefficients, i.

Category choice [p(j|t)]. At each purchase occasion t,customer i makes purchase decisions (Yit) across J productcategories. We model the observed binary (buy/not buy)decision for each product category j, in terms of latent utili-ties for the categories. The latent utilities for the jth cate-gory can be represented as follows:

(3) uijt = xijt j + t*ij ij + ijt,

where Xijt represents the covariates affecting the utility (uijt)for product category j at time t for customer i, j represents

( )21

0 11

1

0 0

0 0

i k

i ik k

ik k

(t )t

t

i i

i i

=+

,,

4Because customers do not purchase in all categories in oursample, allowing the coefficient to vary across customers alsodoes not provide reliable estimates.

the response coefficient for product category j,4 and ijt is arandom error obtained from a multivariate normal distribu-

tion. We capture coincidence in purchases across productcategories by allowing the covariance terms in thevariancecovariance matrix of the multivariate normal errordistribution to be nonzero.

A customer is expected to make a purchase in a particu-lar product category if his or her latent utility in a productcategory is higher than a threshold that is set to zero in ourmodel formulation. Therefore, we can represent the linkbetween the observed behavior and the latent utility forproduct category j as follows:

This formulation of the category choice model repre-sents the multivariate probit model (Chib and Greenberg1998; Manchanda, Ansari, and Gupta 1999). We follow theprocedure that Edwards and Allenby (2003) recommend toensure that the model parameters are identified in the multi-variate probit formulation. Unlike previous applications ofthe multivariate probit model, we include only observationsin which a purchase was made because we model the condi-tional probability of category choice given expectationsabout when a purchase is expected to occur rather than theprobability of purchase in a product category in any timeinterval, such as weeks or months.

Relationship between purchase timing and categorychoice. It can reasonably be assumed that a customer has aninherent purchase pattern for each product category. There-fore, we include the time elapsed since a customer pur-chased in product category j, t*ij, in the utility function forproduct j. We measure the covariate t*ij as the difference

( ) .41

yijt =>

if u 0

0 if u 0

ijt

ijt


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between the period of the current purchase occasion and theperiod when product j was last purchased. This enables usto model explicitly the relationship between purchase tim-ing and product category choice. For example, a customermay be expected to purchase A1 every six months and A2every three months. At the current purchase time t, if thetime since the last purchase of A1 (t*ij = P1) is six months andthe time since the last purchase of A2 (t*ij = P2) is one month,the customer is more likely to purchase A1 at time t.Finally, ij is the response coefficient that measures theinfluence of t*ij on the utility for product j.

Joint likelihood of purchase timing and product cate-

gory choice. The joint likelihood function for customer i isgiven by the following:

where

ri = the number of purchase occasions (spells) forcustomer i,

ci,k= 1 if the kth spell for customer i ends in a

purchase and 0 if otherwise,di,j,k= 1 if customer i chooses product category j in

spell k and 0 if otherwise,

fi(tk,jk) = the joint probability of purchasing in productcategory j at time t (Equation 1), and

s() = the survivor function of the log-logistic distri-bution in the purchase-timing model.

When an observation is censored (i.e., cik = 0 j), thelikelihood function is not affected by the product categorychoice factor, dijk, and therefore depends only on the sur-vivor function s() of the log-logistic distribution. We esti-mate the model in Equation 5 using Markov chain Monte

Carlo methods. We simultaneously estimate both purchasetiming and category choice so that the variance in the inter-purchase time probabilities is accommodated in the estima-tion of the category choice probabilities. The Appendix pro-vides further details on the model framework; it comparesour proposed framework with other benchmark models inwhich choice and timing models are estimated either inde-pendently or jointly and determines whether these modelsaccount for customer heterogeneity.

Data

Longitudinal information on each customers purchasedates, the corresponding purchase category, and amountspent is available to the managers for decision making.

Drawing from existing literature in cross-category pur-chases and customer lifetime value (Knott, Hayes, and Nes-lin 2002; Reinartz and Kumar 2003; Rust, Zeithaml, andKumar 2004), we obtain drivers of (variables that influence)product category choice and purchase timing. The descrip-tion, the operationalization of the variables, and theexpected effects appear in Table 1. We include variables thatare specific to a particular product category in the categorychoice model and variables that are common across all cate-gories (e.g., relationship benefits) in the purchase-timing

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model. Our expectation is that customers needs for certainproduct types and their familiarity with the focal categoriesare the key drivers of product category choice. In contrast tothe choice of product categories, timing of purchases is afunction not only of the customers buying behavior andpatterns but also of managerial interventions. This specifi-cation reflects the finding that in the context of capitalgoods, it is easier to influence when a customer purchasesthan whether a customer purchases (Anderson and Narus1999).

We can classify the variables used in the models ascumulative or current effects. We calculate the current-effects variables in terms of the activities of the customer orthe supplier (in case of channel communications) betweenthe previous observed purchase (t 1) and the currentobserved purchase (t). We calculate the cumulative-effectsvariables in terms of the activities of the customer or thesupplier from the first purchase occasion until the currentobserved purchase (t).

Table 2, Panel A, provides the descriptive statistics forthe drivers of category choice, and Table 2, Panel B, pro-vides the distribution of category purchases. Table 2, PanelA, shows that the customers tend to split their purchases

across product categories evenly. On average, 32% of theirpurchases are within a single category (the mean proportionof same-category purchases is equal to .32). This impliesthat customers in our data exhibit a fair level of cross-category purchases, which provides face validity for the useof a product category choice model. The distribution ofproduct category purchases (Table 2, Panel B) indicates thatonly the purchase of A3 is the most prevalent in the sample(48%) and that A1 and A3 are purchased together moreoften (20%) than any other product category combination.Finally, the least prevalent product category combinationsare A1 and A2 (2%) and A2 and A3 (2%). On average, thereare approximately ten product types within each product

category, and Table 2, Panel A, shows that the mean level ofcross-buying within a product category is approximatelyequal to 2.

Table 3 provides the descriptive statistics for the driversof purchase timing. The average interpurchase time for thecustomers in our sample is 4.2 months. Customers in theanalysis sample make an average of at least one upgradeand have bought across two product types within each prod-uct category (A1, A2, or A3). The customers make frequentcontacts with the organization through the Web sites (onaverage, once every two months). The number of customer-initiated contacts through online channels is less frequentthan the number of standardized contacts made by the orga-nization (on average, 1.6 contacts every month) but more

frequent than the number of contacts made by the organiza-tion through rich modes (on average, once every quarter).The customers also make transactions across two channelsand seem to prefer using direct modes of transaction.Finally, we evaluate the correlation matrix of the indepen-dent variables for both the product category choice andpurchase-timing models and found that multicollinearity isnot an issue in our analyses.

To address potential endogeneity in the model covari-ates that could be caused by time-varying missing variables,


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DriverCategory Variable Operationalization Type

ExpectedEffect Rationale

Category Choice Model

Categoryfamiliarity

Proportion ofsame-

categorypurchases

The ratio of number of pastpurchases in the focal productcategory to the total number of

purchases. For example, P1purchases are purchases in the

focal product category forpredicting P1 category choice

(indicator of category dominance)

Cumulative + The more a customer purchasesin a particular product category,

the higher is the propensity of thecustomer to purchase in thesame category in the future.

Size ofsame-

categorypurchases

Total number of items boughtin the focal product category

(indicator of size of walletin focal category)

Cumulative + Customers who spend more in aproduct category have a highersize of wallet and also recurrent

needs. This leads to higherexpected propensity to purchase

again in the category.

Cross-buyingwithin acategory

Total number of unique producttypes bought in the focal product

category (indicator of within-

category knowledge)

Cumulative + If a customer purchases severaldifferent products within a

category, it increases switching

costs in the category, leading tohigher propensity to shop in the

category in the future.

Customerneeds

Recency ofsame-

categorypurchase

The time interval between themost recent focal categorypurchase and the current

purchase occasion (indicator ofbuying needs)

Current + Contrary to consumer packagedgoods, for high-tech products, the

customers typically use theproduct before repurchasing it.Therefore, the longer the time

since last purchase in a productcategory, the more likely the

customer is to purchase in thatcategory.

Purchase-Timing Model

Past buyingbehavior

Upgrading Number of upgrades until thecurrent purchase (indicator of

need to buy)

Cumulative Customers who upgrade havehigher switching costs with each

upgrade, leading to lowerpropensity to churn (Bolton,Lemon, and Verhoef 2004).

Cross-buying Number of different producttypes that a customer has

purchased (indicator of affinityto the firm)

Cumulative Customers who purchase acrossseveral product categories have

higher switching costs andrecurrent needs (Reinartz and

Kumar 2003).

Returns Total number of products

returned by the customer(indicator of satisfaction)

Cumulative U Returns provide an opportunity

for firms to satisfy customers(Reinartz and Kumar 2003).Toomany returns can be detrimental

to the relationship and canindicate that the firm has notused the return opportunities

appropriately.

Relationshipbenefit

Indicator variables of whether acustomer is a premium service

member (indicator of commitmentto the firm)

Current Acknowledgment of customerswith relationship benefits reduces

the propensity of customers toquit (Morgan and Hunt 1994).

TABLE 1Variables and Operationalization


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DriverCategory Variable Operationalization Type

ExpectedEffect Rationale

Number ofdistinct

channels oftransaction

Cumulative number of distinctchannels used for a transaction.The available channels include

salesperson, telesales, Web site,catalog, distributor, reseller, and

retail (indicator of clientsophistication and relationship

quality)

Cumulative Customers who shop in multiplechannels are expected to transactfrequently with the firm and alsohave deeper relationships with

the firm (Venkatesan, Kumar, andRavishanker 2007).

Number ofdirect

transactions

Cumulative number oftransactions through the direct

channel. The available channelsinclude salesperson, telesales,Web site, and catalog (indicator

of client size)

Cumulative Customers who use the directtransaction channels valueefficiency and trust the firm(Morgan and Hunt 1994).

Laggedinterpurchase

time

The duration between theprevious two purchase occasions

(indicator of past purchase

frequency)

Current + Control variable used to accountfor missing variables and past

customer characteristics

(Venkatesan and Kumar 2004).

MarketingInterventions

Bidirectionalcommunication

The ratio of total number ofcustomer-initiated contacts to the

total contacts between thesupplier and the customer

(indicator of relationship strengthand customer involvement)

Current Two-way communication betweenparties strengthens the

relationship and leads to frequenttransactions (Morgan and Hunt

1994).

Frequency ofWeb-based

contacts

Number of times the customercontacts the supplier through theInternet per month (indicator of

marketing intensity)

Current Customers who use onlinecommunication want transactionefficiencies, and customers whowant to create efficiencies arehighly relational and transact

frequently (Venkatesan andKumar 2004).

Frequency ofrich modes of

communication

Number of contacts made to thecustomer by the supplier firm permonth through sales personnel(indicator of marketing intensity)

Current U

Timely communication betweenparties reduces the propensity ofa customer to quit a relationship(Morgan and Hunt 1994), but too

much communication can be

detrimental to the relationship(Fournier, Dobscha, and Mick

1997).

Frequency ofstandardized

modes ofcommunication

Number of contacts made by thesupplier firm to the customer in a

month through telephone or directmail (indicator of marketing

intensity)

Current U

Intercontacttime

Average time between twocontacts made to the customer

by the supplier across allchannels of communication

(indicator of marketing intensity)

Current U

Notes: U = U-shaped relationship.

TABLE 1Continued


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TABLE 2Descriptive Statistics for Category Choice

A: Drivers of Category Choice

Variable M SD

Proportion of same-category purchases .32 .26Size of same-category purchases 2.65 2.45Cross-buying within a category 2.41 2.20

B: Distribution of Category Purchases

A3 = No Buy

A2

N Y

A1 N 0% 4%Y 21% 2%

A3 = Buy

A2

N Y

A1 N 48% 2%Y 20% 3%

TABLE 3Descriptive Statistics for Drivers of Purchase

Timing

Variable M SD

Interpurchase time 4.23 5.32Upgrading 1.15 .60Cross-buying 2.42 1.19Bidirectional communication .65 2.09Returns .96 2.58Web-based contactsa .50 3.17Relationship benefit .2 .86Rich modesa .3 .16

Standard modesa

1.56 7.87Intercontact time .25 3.3Distinct channels of transaction 2.12 .85Transactions using direct channels 1.94 1.80

aWeb-based contacts, rich modes, and standard modes representthe frequency of contacts in each channel respectively.

5Lagged covariate values have been identified as suitable instru-ments to control for any time-varying factor that is not included inthe model (i.e., missing variable) but is correlated with both theindependent variables and the dependent variable. The laggedcovariate values are expected to be correlated with the currentcovariate value but uncorrelated with the missing variable (Villas-Boas and Winer 1999).

we use lagged variables in our analysis (Venkatesan andKumar 2004; Villas-Boas and Winer 1999).5 Specifically,for observed purchase j, the cumulative-effects variablesrepresent activity of the customer since birth until observedpurchase t 1. Similarly, for observed purchase t, thecurrent-effects variables represent activity of the customer

(or supplier) between observed purchases t 2 and t 1.

6Marketing contacts refer to sales calls in the field experiment.

Model Estimation Results

We have a sample of 6350 observations that belonged to the566 customers in the test group and control group. We esti-mate the choice and purchase-timing models simultane-ously (from the likelihood function in Equation 5) in aBayesian framework using Markov chain Monte Carloalgorithms. The Appendix provides the in-sample fit capa-bility and the predictive capability of the proposed jointmodel of category choice and purchase timing along withother benchmark models. The in-sample fit (Table A1) andpredictive accuracy (Table A2) results provide support forthe full model specification outlined in Equation 5. Thecoefficient estimates of the drivers of category choice andtiming appear in Table 4. The estimation results confirmed amajority of the expected effects (Table 1). Because the esti-mated effects of the drivers of category choice and purchasetiming are similar to the previous literature, we do not dis-cuss them here. Subsequently, we discuss the findings fromthe category choice model that are unique to this research.

The estimation results indicate that recency of productpurchase is significant and positive for the A1 and A2 prod-uct categories. However, we do not find a significant influ-ence for recency of purchase in the A3 category. We specu-

late that this may be because the customers in our samplepurchase A3 regularly. The average interpurchase time forA3 purchases in our sample is approximately 1.5 months.Therefore, it is reasonable to expect a high probability forthe purchase of A3 at every purchase occasion. The signifi-cant influence of recency for A1 and A2 emphasizes theneed to model the dependence between purchase timing andcategory choice. The significance of the recency measurealso translates into better predictive accuracy for the jointmodel than for the independent model.

Field Experiment Results

Recall that in the field experiment, 283 customers were

assigned to the test group, and another 283 customers wereassigned to the control group on the basis of matched-paircomparisons. A customer-focused sales campaign wasadopted to contact customers in the test group from the firstquarter of 2006 to the end of the last quarter of 2006 (forone year). The customers in the control group were con-tacted using a product-focused sales campaign. Both groupsobtained inputs from the proposed joint-timing and productcategory choice framework. Thus, we control for modelaccuracy in the experiment. Figure 2 highlights the differ-ence in inputs for the test and control groups. The product-focused strategy used in the control group is the null modelfor comparison of the results in the test group. The results

from the field experiment appear in Table 5, Panels A andB. We evaluate the performance of the field experimentusing both financial and relational metrics. We capture thevalue the customers provide to the firm with financial met-rics, including revenue, marketing investment, number ofcontacts per purchase,6 profits, and ROI for each customer.The relational metrics capture the value the firm providesand the relationship quality the customer perceives. The


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TABLE 4Results from Model Estimation

CoefficientVariable Estimate

Product Category Choice: A1

Intercept .51**Proportion of A1 purchases .39**Size of A1 purchases .31**

Cross-buying within A1 .37**Recency of A1 purchase .12**


Intercept .31**Proportion of A2 purchases .17**Size of A2 purchases .22**Cross-buying within A2 .22**Recency of A2 purchase .09**


Intercept .61**Proportion of A3 purchases .57**

Size of A3 purchases .23**Cross-buying within A3 .48**Recency of A3 purchase .003

Purchase Timing

Intercept .22**Upgrading .11*Cross-buying .07**Bidirectional communication .52**Returns .25**Square of returns .07**Frequency of Web-based contacts 1.5**Relationship benefits .06**Frequency of rich modes of contact .82**

Square of frequency of rich modes of contact .51**Frequency of standard modes of contact .23**Square of frequency of standard modes of

contact .09**Intercontact time .37**Number of distinct channels of transaction .16**Number of transactions in direct channels .08**Log of lagged interpurchase time .45**

*Significant at = .05.**Significant at = .01.Notes: Aggregate logconditional predictive ordinate (aggregate

log-CPO) = 5,641.

various relational metrics measure (1) whether the firmunderstands the customers needs, (2) whether the firm pro-vides value to the customer, (3) whether the customer islikely to repurchase from the firm, and (4) whether the cus-tomer is likely to recommend the firm. These metrics weremeasured with a ten-point interval scale, anchored bystrongly agree (10) and strongly disagree (1). Priorresearch has indicated that these financial and relationalmeasures are critical indicators of the profitability and sus-tainability of the customer firm relationship (Kumar,

Petersen, and Leone 2007; Rust, Zeithaml, and Lemon2004; Venkatesan and Kumar 2004).

Overall benefits from a customer-focused sales cam-

paign. Table 5, Panel A, provides a comparison of both thefinancial and the relational metrics within the test and con-trol groups during the experimental period (first quarter in2006 to last quarter in 2006) and during the correspondingpreexperimental period (first quarter of 2005 through fourthquarter of 2005). The mean values for the test and controlgroups appear in Table 5, Panel A; they represent theincrease or decrease in the experimental period from thepreexperimental period.

We first evaluated whether the test and control groupswere different from each other in the five financial metricsduring the preexperimental period using Hotellings T-square test. The test revealed that customers in the test andcontrol groups did not significantly differ in any of the fivefinancial metrics in the year before the experiment was con-ducted. This indicates that there were no sources of biasbetween the test and the control groups before the experi-ment. Hotellings T-square test indicated that the means ofat least one of the metrics were significantly differentbetween the experimental and the preexperimental periods

for both the test and the control groups. We then tested thedifference in means for each metric using a T-test with Bon-ferroni adjustment (Table 5, Panel A) for both the test andthe control groups.

We find that the revenues ( < .10, $898) and, thus,profits ( < .10, $890) increased between the preexperimen-tal and experimental periods for the control group. Theimprovement in revenues and profits for the control group isattributable to the better performance of the proposed modelin identifying the customers who are expected to purchasein each product category. For the test group, we observe asignificant improvement in the performance between thepreexperimental and the experimental periods along all the

metrics: revenues ( < .01, $1,828), marketing investment( < .05, $1,906), profits ( < .01, $3,734), and ROI (

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