measuring and managing

8/2/2019 Measuring and Managing

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Rajkumar Venkatesan & Paul W. Farris

Measuring and ManagingReturns from Retailer-Customized

Coupon CampaignsThe authors assess how and why retailer-customized coupon campaigns affect customer purchases. The concep-tual model proposes effects on trip incidence and revenues through the mere exposure to campaigns (exposureeffect) and the redemption of coupons (redemption effect). The authors propose monetary savings of the coupons,regularity of the campaigns, and coupon fit with customer preferences as moderators. Analysis of data froma group of regional grocery chains that were part of a quasi experiment demonstrates that retailer-customizedcoupon campaigns have a positive exposure and redemption effect on customer purchases. Mere exposure tocustomized coupon campaigns contributes more than coupon redemption to campaign returns. Consistent withtheoretical expectations, customized coupon campaigns are more effective if they provide more discounts, areunexpected, and are positioned as specially selected for and customized to consumer preferences. The substantialexposure effects suggest that managers should look beyond redemption rates and also consider sales lift fromnonredeemers when measuring the effectiveness of customized coupon campaigns.

Keywords: customer relationship management, retailer customer contribution, sales promotions, metrics,customized coupons, retailer coupons, advertising exposure

Shopper card data have enabled major retailers, suchas Kroger, Safeway, Meijer, and CVS, to offercoupons for branded and private label products

through their own customized, direct-to-consumer programs(Angrisani 2003). These programs differ from similar free-standing inserts (FSI) cooperative programs in that theoffers are customized to each individual consumers pref-erences (as reflected in purchase histories),1 are availableonly to selected customers, and focus on increasing theretailers customer revenues rather than brand sales. Cus-tomized coupon programs represent major investments for

1Customer preference is a broad construct with multiple defi-

nitions. In this study, we refer only to preferences evident from

customer purchase behavior.

Rajkumar Venkatesan is Bank of America Research AssociateProfessor of Business Administration, Darden Graduate Schoolof Business, University of Virginia (e-mail: [email protected]). Paul W. Farris is Landmark Communications Profes-sor of Business Administration, Darden Graduate School of Busi-ness, University of Virginia (e-mail: [email protected]).The authors thank a national retailer consortium for sharing data andValassis Inc. for sharing FSI coupon distribution information. Themanuscript benefited from the comments of Paul Hunter, CharlesFlem, Brian Sampsel, Aaron Giust, Curtis Tingle, Kusum Ailawadi,Peter Debeare, Mir Salim, Kathryn Sharpe, S. Sriram, Joseph Pan-cras, and seminar participants at the University of Virginia and IowaState University. The authors thank the three anonymous JMreview-ers for their guidance. This article was accepted under Ajay K. Kohliseditorship. Gary Frazier served as coeditor.

retailers, and in our conversations with retailers, they con-firmed that they are concerned about the cost of theseprograms and are unclear on how to assess the potentialeffects. Our primary objective is to develop a frameworkfor retailer-customized coupon campaigns that would allowretailers to monitor, and possibly improve, the returns fromthese programs. Four major factors motivate our research.

First, in contrast with extensive academic research on thebrand effects of coupons (Neslin 2002), almost no researchexists on the effects of customized coupon campaigns onretailer performance. The main avenues through whichretailers benefit from customized coupon campaigns arethrough incremental store visits and incremental revenuesper visit. The combination of the resurgence of couponsin the current economic environment (PR Newswire 2010)and the recent initiatives of major retailers in this sectorsupports the need for additional research.

Second, we propose that in addition to redemptions ofthe customized coupons (redemption effect), the mere expo-sure to customized coupon campaigns (exposure effect) canaffect customer purchases. Redemption effects have domi-

nated practitioners and academics assessments of couponprograms. For example, trade journals often compare theredemption rates of different coupon distribution methods(e.g., FSIs, on-shelf, checkout, Internet) and imply thatretailers prefer higher redemption rates. Academic studieshave proposed that coupons may affect sales through mereexposure in addition to redemption, but empirical evidenceis scarce (Neslin 2002), probably because of the lack ofdetailed information on the distribution of coupons to indi-vidual households. It might be that the recent ability to

2012, American Marketing Association

ISSN: 0022-2429 (print), 1547-7185 (electronic) 76Journal of Marketing

Vol. 76 (January 2012), 7694
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customize coupon campaigns according to buyer prefer-ences and purchase histories will reduce consumer searchcosts and increase consumer attention to these campaigns.The exposure effects on customer responses may there-fore be stronger for customized coupon campaigns than FSIcampaigns.

Third, effective customized coupon campaigns wouldmotivate managers to identify the underlying reasons andthus enable them to replicate and improve their campaigns.By assimilating conceptual and empirical research in rela-tionship marketing and customized promotions, we arguethat in addition to the amount of discounts, customizedcoupons might be more effective when they are unexpectedand if they fit with customers general and idiosyncraticpreferences (Palmatier et al. 2009; Simonson 2005). We testthese arguments with a set of moderators of the exposureand redemption effects.

Fourth, coupons distributed as FSIs through newspapersor direct mail are not customized to individual consumerpreferences and are not designed for or intended to increaseretailers customer purchases. A comparison with FSIs pro-vides a benchmark for assessing the extent to which cus-tomization contributes to the retailers returns from couponcampaigns.

In summary, the objectives of our study are as follows:

To propose a framework for understanding how and why cus-tomized coupon campaigns affect retailers customer revenuesand quantify the returns to retailers,

To compare the magnitude of the exposure and redemptioneffects of customized coupon campaigns,

To identify and test theoretical reasons for the effectiveness ofcustomized coupon campaigns, and

To provide an initial benchmark for customized coupon effec-tiveness by comparing them with FSI coupons within twoheavily couponed categories.

Research Contribution

We employ data from a quasi experiment conducted ineight noncompeting regional grocery retail chains locatedacross the United States and owned by the same firm. Oursubstantive contribution is the development of a concep-tual framework that addresses how and why customizedcoupon campaigns affect retailers customer revenues. Weshow that a rarely researched aspect of coupons (i.e., theexposure effect) is important for assessing the effectivenessof this new form of coupon campaigns. By comparing thetwo main effects, we find that exposure accounts for a sub-stantial lift in customer contributions, net of marketing dueto customized coupon campaigns. Thus, this study revealsthat ignoring exposure effects can severely bias retailersassessments of their customized coupon campaigns.

The moderators show that the effectiveness of cus-tomized coupon campaigns improves if they provide moremonetary savings (or discounts) and are unexpected byconsumers. Customized coupons that offer discounts forniche products that customers prefer are the most effec-tive, followed by reward-focused customized coupons andfinally noncustomized coupons. The moderators suggestthat though mere exposure to customized coupon cam-paigns can improve sales (regardless of coupon redemp-tion), having discounts featured in the campaigns improvesthe exposure effect.

Next, we develop the conceptual framework and hypothe-ses. Then, we describe the data and the model frame-work used to assess the conceptual model and present theresults from model estimation. Finally, we provide manage-rial implications of the studys results and identify limita-tions that can guide further research.

Conceptual Background andFramework

Customized Coupon Campaigns

We define retailer-customized coupon campaigns as cam-paigns that, unlike sales promotions and FSI coupons,retailers provide only to their best customers and includeoffers that are customized to consumers preferences. TheFSI coupons are identified with the manufacturer as thesource of the discount, whereas the retailer is identified asthe primary source for the customized coupon campaigns.The customized coupon campaigns are not mailed to everycustomer who holds the retailers shopper card, nor are theyused as tools for customer acquisition. The retailers clas-sify customers as best if they cross a certain thresholdof revenue contribution. Manufacturers provide coupons for

the campaign and fund the coupons redemption costs. Theretailer then identifies the consumers whose preferencesmatch the offers. Unlike typical loyalty points programs,customers are not aware of customized coupon campaignsuntil they receive them, and retailers do not explicitly com-municate the type of customer behaviors that are rewarded.But they do inform the consumers that they received thecoupons because they are one of retailers best customersand that the coupons were especially selected for them.

Prior Research

Whereas catalog literature focuses mostly on exposureeffects (Ansari, Mela, and Neslin 2008), coupons litera-ture focuses on redemption effects but also suggests that

exposure effects are relevant for coupons. As Table 1shows, prior empirical studies have documented the positiveeffects of targeted sales promotions on manufacturer profits(Pancras and Sudhir 2007; Rossi, McCulloch, and Allenby1996) and consumer purchase intentions (Barone and Roy2010). However, prior research has not investigated theeffects of customized coupon campaigns on retailer perfor-mance, the primary focus of our study. We organize ourconceptual development according to the framework pro-vided in Figure 1. Similar to research on retailer sales pro-motions (e.g., Ailawadi et al. 2006; Heilman, Nakamoto,and Rao 2002), we expect that customized coupons affectpromoted brand and category sales and have a halo effecton sales of nonpromoted categories in the retail store. We

generate expectations of coupon campaign effects on threeaspects of customer behavior: trip incidence, trip revenue,and customized coupon redemptions.

Theoretical Framework

Coupons both provide a savings value and inform con-sumers about a product (Ward and Davis 1978). The multi-benefit theory posits that consumers derive value from salespromotion through both mere exposure (e.g., seeing a pro-motion for a product or brand) and usage (e.g., redeeming

Returns from Retailer-Customized Coupon Campaigns / 77


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TABLE 1Comparison of Proposed Conceptual Framework with Existing Research

Main Effect ModeratorRepresentative Customized Coupon

Research Coupons R edemption Exposure Redemption Exposure Redemptions Focus Metric

Narasimhan (1984); Neslin,

Henderson, and Quelch

(1985); Inman, McAlister,

and Hoyer (1990)

No Yes No Yes No Yes Manufacturer Brand choice

Bawa and Shoemaker

(1987); Leclerc and Little(1997); Srinivasan,

Leone, and Mulhern

(1995)

No No Yes No No Yes Manufacturer Brand choice

Rossi, McCulloch, and

Allenby (1996); Pancras

and Sudhir (2007)

Yes Yes No No No No Manufacturer Brand choice

Barone and Roy (2010) Yes Yes No No No No Retailer Purchase intent

This study Yes Yes Yes Yes Yes Yes Retailer Retailer returns

a coupon when buying the product) (Chandon, Wansink,and Laurent 2000). In other words, the same coupon couldproduce both an exposure and a redemption effect. Thus,the redemption effect is the change in customer purchases

due to coupon redemptions, and the exposure effect is thechange in customer purchases due to the mere receipt of orexposure to the coupon campaign (e.g., Srinivasan, Leone,and Mulhern 1995). Of course, exposure to customizedcoupons is a necessary condition for customized couponredemption. We posit that customers are more likely tonotice the campaigns and also redeem the coupons if thediscounts provided are higher, the coupons match the con-sumers preferences, and the campaigns are not expectedby the consumers.

Exposure Effect

On average, more than 50% of the space in customizedcoupon campaigns is devoted to marketing messages.2 As

Figure 2 illustrates, these campaigns also include brandmessages that are similar to feature advertisements or cat-alogs. According to Inman, McAlister, and Hoyer (1990),consumers with peripheral processing (or low need for cog-nition) react positively to retailers communications with-out considering price information. Studies investigating theexposure effect of coupons have found that the adver-tisement of the brand has a strong effect on redemption(Leclerc and Little 1997) and can induce brand sales regard-less of coupon redemption (Bawa and Shoemaker 1989;Srinivasan, Leone, and Mulhern 1995).

We expect a positive exposure effect of a retailerscoupon campaigns on trip incidence, beyond the intent toredeem coupons, for two reasons. First, customers may feel

gratitude toward the store for receiving coupons, whichreduces their search costs (Palmatier et al. 2009). Cus-tomers may reciprocate by increasing the retailers share oftheir shopping trips. Second, if customers have ill-formedpreferences about retailers in their neighborhood, retailer

2The campaigns did not differ significantly in the amount of

space allocated for brand messages. We therefore believe differ-

ences in the amount of space allocated for marketing messages

may not affect the exposure effect in our study.

communications, even those that do not provide a discount,can lead customers to increase the retailers share of theirshopping trips (Simonson 2005). Abstract shopping goals,such as gratitude, are associated with a higher degree of

unplanned buying (Bell, Corsten, and Knox 2011). Becauseexposure to customized coupon campaigns improves grati-tude, we expect that exposure to customized coupon cam-paigns leads to higher trip revenue.

In addition to these factors, it might be that cus-tomers increase trip incidence and trip revenue, intendingto redeem the coupon, but either forget to bring the couponor forget to redeem it during checkout. The customer mightalso clip coupons for specific products and then decide touse an on-shelf coupon or FSI coupon instead (Chandon,Wansink, and Laurent 2000). In any of these scenarios, anexposure to customized coupon campaigns should resultin a sales lift without redemption of the distributed coupons.Thus:

H1: The higher the exposure to customized coupons, the higheris the customers trip incidence and trip revenue.

Because exposure to the customized coupon campaign is anecessary (but not sufficient) condition for coupon redemp-tion, we do not posit a formal hypothesis for the effect ofexposure on redemption.

Redemption Effect

The intent to redeem customized coupons can lead to storeswitching, or purchase acceleration, thereby affecting tripincidence. Coupons literature proposes that consumers reactfavorably to coupons if they perceive the costs of redemp-tion (searching, clipping, and remembering to redeem) as

lower than the savings obtained (Neslin 2002). Customizedcoupon campaigns have lower search costs than traditionalcoupons, because the former are selected by the retailer tomatch consumers preferences. Thus, customized couponcampaigns, which are associated with a particular retailer,can influence consumers to perceive the net benefit of shop-ping with the retailer as higher and to substitute their shop-ping trips in favor of the retailer (Kumar and Leone 1998).Consumers might also accelerate their purchases becausethe customized coupon campaigns have expiration dates

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FIGURE 1Effect of Customized Coupon Campaigns

Triprevenue

Tripincidence

Regularity ofcampaigns

Couponfit

Customizedcoupon

exposure

Customizedcoupon

redemption

Monetarysavings

H2

Redemption

Effect

H1Exposure

Effect

H3c, H4c,

H5c, H6c

H3a, H4a,

H5a, H6a

H3b, H4b,

H5b, H6b

that motivate the consumers to shorten their interpurchasetimes (i.e., higher trip incidence with the retailer) (Bawa1996; Neslin, Henderson, and Quelch 1985).3 Purchaseacceleration is also higher when a feature is associated witha price promotion (see Figure 2) and when there is uncer-tainty in the availability of future discounts, as is the casewith customized coupon campaigns.

The redemption of customized coupons leads to highertrip revenue because of (1) unplanned buying and(2) income effects. First, out-of-store marketing, especiallyconsumer expectations of price savings from coupons,should influence customers to plan major shopping trips tothe sponsoring retailer (Kahn and Schmittlein 1992) andengage in a higher degree of unplanned buying, leading tohigher trip revenues (Bell, Corsten, and Knox 2011). Sec-ond, because retailers do not announce the availability ofcustomized coupons, the arrival of these coupons can bean unexpected reward for customers. Redemption of unex-pected coupons is associated with an income effect thatcan increase purchases in other product categories (i.e., ahalo effect) (Heilman, Nakamoto, and Rao 2002), leadingto higher trip revenues. Thus:

H2: The higher the redemption of customized coupons, thehigher is the customers trip incidence and trip revenues.

Customized coupon campaigns should improve retailerscustomer revenues because they provide unexpected dis-counts and are tailored to customer preferences (Bawa andShoemaker 1989; Palmatier et al. 2009; Simonson 2005).We include variables that capture these broad aspects as

moderators of the redemption and exposure effects.4

3Expiration dates in the customized coupon campaigns are

shorter (approximately 30 days) than those of FSIs (from three to

six months).4We did not include quantity restriction in coupons as a moder-

ator because less than 5% of the coupons distributed in our study

had a quantity restriction. Store-specific moderators, such as num-

ber of competing stores in the trading area, cannot easily be mod-

ified by the retailer. Because our study focuses on retailers best

Monetary Savings

Monetary savings, or higher coupon face value, can moti-

vate consumers to pay more attention to the brand mes-sages in the customized coupon campaigns and improvecustomer attitudes toward the brands, leading to a higherexposure effect (Bawa 1996; Bawa and Shoemaker 1989;Blattberg and Neslin 1990). Therefore, we propose thatthe potential for monetary savings positively moderates theexposure effect on customer behavior. Higher monetarysavings increase the net benefits from coupons and affectstore switching and purchase acceleration. Thus, redemp-tion effects on trip incidence should be higher when thecoupons provide higher monetary savings. Higher mone-tary savings also increase the income effect consumers real-ize when they redeem the customized coupons. Monetary

savings therefore can positively moderate the redemptioneffect on trip revenue. It is well known that coupons withhigher face values are associated with higher redemptionrates. Thus:

H3a: The higher the monetary savings in a campaign, thehigher is the exposure effect on trip incidence and triprevenue.

H3b: The higher the monetary savings in a campaign, thehigher is the redemption effect on trip incidence and triprevenue.

H3c: The higher the monetary savings in a campaign, the higheris the exposure effect on customized coupon redemption.

customers across all their stores, we did not expect a major store-

specific effect on customer behavior. We included store revenue

as a moderator and store-specific random effects but did not find

a significant effect. Consistent with previous research (Liu 2007),

we found that middle-tier customers were most responsive to the

customized coupon campaigns. The customer-specific moderator

inferences were not reliable, because the lowest-tier customers in

our study did not receive any coupon campaign.

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FIGURE 2Sample Coupon Campaigns

Campaign Regularity

Customers short-term feelings of gratitude lead to recipro-

cal behaviors that can increase long-term relationship qual-

ity and customer profitability (Morales 2005). Gratitude

itself is dependent on customer perceptions of the firmsfree will and benevolence in providing the reward. Bene-

fits available to every customer or those built on formalized

rules (e.g., points programs) are less likely to generate

feelings of gratitude (Palmatier et al. 2009). In contrast,

programs that retain some randomness or discretionary ele-

ments are more likely to generate gratitude and enhance

customer reciprocation. Although we are unable to measure

customer gratitude, we assess the moderating effect of an

antecedent of customer gratitudethat is, the unexpectednature of the campaigns.

We expect that customized coupon campaigns received atirregular intervals can consistently delight or surprise cus-

tomers because they are unexpected. Customers are alsomore likely to believe that the retailer recognizes the valuethey provide when the customized coupons are receivedunexpectedly. If gratitude leads customers to be in a bettermood when visiting a store, it also would be related tohigher impulse purchases and higher trip revenue (Beattyand Ferrell 1998). Irregular campaigns increase uncertaintyabout future rewards, which can increase the redemptioneffects of customized coupon campaigns. Thus, we expectthat exposure and redemption effects are higher for cus-



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tomers who receive customized coupon campaigns at irreg-ular intervals:

H4a: The less regular the customized coupon campaigns, thehigher is the exposure effect on trip incidence and triprevenues.

H4b: The less regular the customized coupon campaigns, thehigher is the redemption effect on trip incidence and triprevenues.

H4c

: The less regular the customized coupon campaigns, thehigher is the exposure effect on customized couponredemption.

Coupon Fit

Noncustomized coupons. Noncustomized coupons simplyprovide $ off the next purchase. The coupons provide abaseline for comparing the effect of reward campaigns andniche products. Because customized coupons are availableonly to the retailers best customers, they can be consid-ered offers exclusive to some of the retailers customers.Consumer evaluations of coupons reflect their exclusivity,in addition to their monetary savings (Chandon, Wansink,and Laurent 2000). Consumers tend to view exclusivitypositively (Dreze and Nunes 2009), and some empirical evi-dence suggests that exclusive offers lead to higher redemp-tion rates (Feinberg, Krishna, and Zhang 2002). However,recent research shows that consumers evaluate exclusiveoffers more positively when they identify strongly withthe group that is eligible to receive the offers or haveexerted much effort to belong to such a group (Baroneand Roy 2010). It is unclear whether a retailers bestcustomers, defined by their revenue contribution, stronglyidentify with this group, because consumer spending isa weak indicator of positive firm attitudes (Reinartz andKumar 2000). Furthermore, because customized couponcampaigns are not preannounced, consumers are not awareof the effort required to be eligible for these coupons. Weleave the effect of noncustomized coupons as an empirical

issue because of this conflicting theoretical and empiricalevidence.

Reward campaigns. In reward campaigns, the retailerprovides coupons for products that the customer purchasesoften. In contrast, cross-selling campaigns provide cus-tomers coupons for categories in which they have not yetmade a purchase with the retailer. Simonson (2005) pro-poses that customers can judge correctly if a coupon fitstheir preference in categories in which they have well-defined preferences. Thus, customers who receive rewardcampaigns are more likely to be satisfied with these retailersbecause of the effort expended by the retailers to learn theirpreferences. In contrast, cross-selling campaigns offer theopportunity to try new products at a lower risk (Chandon,Wansink, and Laurent 2000); however, consumers mightnot want to increase their risk by stockpiling new products(Narasimhan, Neslin, and Sen 1996). Reward campaignsmay have a higher exposure effect, because coupons forfamiliar brands may be more effective in generating con-sumer attention.

Redemption of coupons in reward campaigns have ahigher chance of providing customers an income effect,because they obtain savings for products they normallypurchase. This higher income effect leads to higher im-

pulse purchases in nonpromoted categories (Heilmann,Nakamoto, and Rao 2002) and therefore higher basketrevenues. Furthermore, discounts provided by a store onproducts that customers typically purchase may be moreeffective than cross-selling coupons for generating storeswitching. When the customer is in the store, the store canbenefit from the halo effect of customer purchases in non-promoted categories. Because customers are more likely toredeem coupons in reward campaigns, the exposure effecton customized coupon redemption is higher for rewardcampaigns than for cross-selling campaigns. Thus:

H5a: The higher the proportion of reward campaigns, the higheris the exposure effect on trip incidence and trip revenues.

H5b: The higher the proportion of reward campaigns, thehigher is the redemption effect on trip incidence and triprevenues.

H5c: The higher the proportion of reward campaigns, the higheris the exposure effect on customized coupon redemption.

Niche products. Niche product offers are based on theassumption that customers preferences can be divided intothose shared by many other customers and those idiosyn-cratic to the customer or a small segment. We define niche

products as categories with less-than-average penetrationamong the retailers customer base, such as ethnic foods,baby products, and pet foods. Idiosyncratic or signaturepreferences that separate consumers from others tend tobe overweighted in consumers evaluations of customizedoffers (Kivetz and Simonson 2003). A match or mismatchon an idiosyncratically important attribute can often deter-mine the offers perceived attractiveness. Thus, we expectthat consumers view the choice of niche products couponsas confirmation that the retailer understands their idiosyn-cratic preferences:

H6a: The higher the proportion of coupons for niche products,the higher is the exposure effect on trip incidence and triprevenues.

H6b: The higher the proportion of coupons for niche products,the higher is the redemption on trip incidence and triprevenues.

H6c: The higher the proportion coupons for niche products,the higher is the exposure effect on customized couponredemption.

DataWe obtained data for this study from a quasi experi-ment conducted by eight noncompeting retail chains locatedacross the United States and owned by a single firm.

We have purchase histories of 2,500 households that weremembers of these retailers shopper card programs fora two-year period. The quasi experiment was conductedbefore the economic downturn of 2008 and thereforewas not affected by the recent increase in coupon usage.For each customer,5 we observed the entire baskets pur-chases for every store trip. For each product purchased inthe basket, we observed the price paid, the brand name,

5In our study, a customer refers to a household.

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merchandising factors (e.g., feature, display), the redeemedcoupons face values, and the type of price discount. Cus-tomers could also obtain price discounts from nationalbrand coupons distributed through FSIs and other media,a price reduction provided by the retailers for the shoppercard members, and private label FSI coupons funded by theretailers.

Beginning in the 37th week of the first year, all eightretailers instituted a customized coupon program forshopper card members. A total of 40 customized coupon

campaigns were mailed during the following 66 weeks.Approximately 400 consumer packaged goods manufactur-ers provided coupons in one or more campaigns, with amaximum of three national brands per manufacturer percampaign. Approximately 63% (n = 1584) of the house-holds in our sample received at least one customizedcoupon campaign between the 37th and the 102nd week.These customers formed the test group in this quasi exper-iment, and the control group consisted of the rest of thecustomers. Because the retailer only selected its best cus-tomers to be part of the test group, targeting in our study isrestricted to selection of customers who received the cus-tomized coupon campaigns. Customers with shopper cardsfrom all eight retail chains had an equal opportunity to

receive the mailings. On average, the test group customersreceived four campaigns over the 66 weeks. Substantialvariation exists in the total number of campaigns mailed tothe customers. For example, 21% of the test group receivedfewer than 5 campaigns, and 7% of the test group receivedmore than 20 campaigns. Customers could not receive morethan 1 campaign in any week.

Each campaign provided an average of 18 coupons fromboth the manufacturers and the retailers (primarily for pri-vate labels). Of these coupons, 7% were for private labels.In general, only customers who spent more per trip andvisited the retailers stores more often took part in thecustomized coupon campaigns. None of the campaignsposed any restrictions on FSI redemptions, and the man-

ufacturer coupons were mostly redeemable at other retail-ers as well. However, the customized coupon campaignshad the retailers logo in the mailer. The cover letter con-tained a message from the retailer thanking the customerfor being the retailers Best Customer and stating thatthe enclosed discounts were available only for the retailersbest customers, chosen to match the customers preferences.The message content emphasized the benefits of the brandsrather than the savings in the coupons. For example, theThanksgiving campaign provided recipes that included theproducts that sponsored the coupons. The coupons them-selves appeared on three pages in the center of the booklet(see Figure 2).

Variable Operationalization andDescriptive Statistics

Table 2 provides the operationalization and the descriptivestatistics of the dependent variables. On average, a customerspent $33 each week and redeemed approximately 4.1 cus-tomized coupons on the trips with coupon redemptions.

Control factors. Using prior research, we classify thecontrol factors as seasonality, customers past experiencewith the retail chain, and customers response to con-ventional price promotions and feature or display. Details

on the operationalization of the control factors appear inTable 2. Recency, cross-buying, lagged trip revenue, andlagged trip incidence capture customers past experience.Table 2 also shows that, on average, each shopping basketcontains nine product categories. Approximately 7% of theproducts in the shopping basket coincided with a featureor a display. Customers used FSI coupons for .5% of theproducts in their basket, and a retailer discount or sales pro-motion was available for 21% of the products a customerbought. On average, customers redeemed 14 FSI coupons

over 102 weeks. We used a preference-weighted nonpricepromotion variable to control for retailer-sponsored cate-gory advertisements, in addition to the customized couponcampaigns.6 We measured each customers preference for abrand as its rank in the customers precampaign spendingin the category (weeks 136). The standard deviation of thepreference-weighted nonprice promotion variable indicatedthat many customers nonpreferred categories are also fea-tured each week.

Moderators. All the moderators we explored vary acrosscustomers. We operationalized monetary savings using theaverage face value of coupons in a campaign and the num-ber of coupons per campaign. The face value of the cus-tomized coupons averaged 36% of the product price, and

customers received 18 coupons per campaign. The stan-dard deviation of campaign regularity indicated substantialvariation in the frequency of the campaigns across cus-tomers. On average, 62% of the customized coupon cam-paigns were classified as reward campaigns. We classifiedcategories as niche if they fell in the 050th percentile interms of their total number of transactions across customers.Approximately 36% of customers received a majority ofthe customized coupons for niche categories, and 1.4 ofthe 72 coupons received by customers on average were notcustomized.

Exploratory Analysis

In Table 3, we compare the average weekly revenue for(1) the trip, (2) promoted brands (brands that providedat least one customized coupon), (3) nonpromoted brands(brands from the promoted categories that did not providea coupon), and (4) nonpromoted categories, for customersin both the test and control groups. The retailer selectedcustomers who were already providing more trip revenuefor inclusion in the customized coupon program. After thelaunch of the customized coupon program, weekly trip rev-enue per customer for the test and control group customersincreased by $9 and $1, respectively. An analysis of vari-ance test showed that the percentage increase in weekly triprevenue per customer in the test group (24%) was signif-icantly higher than the percentage increase in the weekly

trip revenue per customer in the control group (11%).The weekly revenue for promoted brands after launch ofthe customized coupon campaigns was significantly higherfor only the test group customers ($.60). The weekly rev-enue did not change significantly for the nonpromotedbrands in promoted categories during the same time framefor either the test or control groups. The exploratory resultsindicated that customized coupon campaigns can increase

6We thank a reviewer for suggesting this variable.

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TABLE 2Variable Operationalization and Descriptive Statistics

Variable Operationalization M SD

Trip revenue Dollar amount spent by a customer with the retailerin a week 331 578

Customized coupon Number of customized coupons redeemed by customizedredemption coupon redeemers in a week 41 46

Recency Days since a customers last purchase 145 364

Cross-buying Number of different product categories bought by the

customer per trip 92 119Feature or display Percentage of products in a customers basket on a

display or feature 75% 11

FSI redemptions Percentage of products in a customers basket boughtwith an FSI coupon 5% 03

Retailer discounts Percentage of products in a customers basket boughtwith a retailer discount 214% 24

Conventional promotionsa Cumulative number of FSI redemptions overtwo-year period 140 363

Preference-weighted Product of number of categories in a week that have anonprice promotion brand on feature or display and the customers

category preference 165 314

Coupon face value Average of face value as a percentage of product pricesfor all the customized coupons received by the customer 36% 02

Number of coupons Number of customized coupons provided in asingle campaign 178 46

Campaign regularity Ratio of the difference between the maximum and minimumintercampaign duration (weeks between two consecutivecampaigns) and the mean intercampaign duration. Lowervalues imply more regular campaigns. 11 82

Reward campaigns Percentage of campaigns that provided customizedcoupons for products the customer has alreadypurchased 62% 25

Niche products Equals 1 if majority of customized coupons were incategories with category penetration below50th percentile. 36% 48

Noncustomized coupons Number of noncustomized coupons received bya customer 14 195

a

We report the mean of cumulative promotions across customers in the 102nd week.

revenue and that the increase in sales of the promotedbrands is an important reason for the effectiveness ofthe customized coupon campaigns. The difference betweenweekly trip revenue and revenue from promoted categoriesshowed that these campaigns also have a substantial haloeffect on nonpromoted categories.

By our definition, only the exposure effect could increasesales of promoted and nonpromoted categories amongnonredeemers. However, both exposure to customizedcoupon campaigns and the redemption of customizedcoupons could affect sales of promoted and nonpromotedcategories for redeemers. Customized coupon campaigns

affected sales of promoted and nonpromoted categories forboth redeemers and nonredeemers, though the increase insales was higher for redeemers than for nonredeemers.7

These exploratory analyses provided empirical motivationfor studying both the exposure and the redemption effectsof customized coupon campaigns.

7The results for total revenue of promoted and nonpromoted

brands were similar to the weekly revenue and are available on

request.

Model DevelopmentWe developed our model with three major purposes inmind. First, we assessed the effects on three correlatedbut separate customer behaviors: trip incidence, trip rev-enue, and customized coupon redemptions. Because thesecustomer behaviors are correlated and also likely affectedby common antecedents, we modeled them simultaneouslyand directly estimated the covariance in the errors of theirrespective regression equations. Second, prior research sug-gests that marketing campaigns may affect sales for sev-eral weeks after the week of exposure, through a carryovereffect (e.g., Rao and Miller 1975). We allowed for carry-

over effects by developing a stock variable for customizedcoupon exposure and redemption of customized couponcampaigns. Third, as is clear from Table 3, the retailersprovided more customized coupons to higher-spending cus-tomers. We accounted for this endogeneity in the selec-tion of customers for the customized coupon campaigns bymodeling the joint distribution of customer behavior andthe retailers targeting decisions.

We adopted a Bayesian approach to our model spec-ification (Rossi and Allenby 2003). The objectives of

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TABLE 3Customer-Level Exploratory Analysis

Test Group (n = 1,584)

Time Relative to Customized Test Group Control Group Nonredeemers Redeemers

Coupon Program (n = 1,584) (n = 916) (n = 1,150) (n = 434)

Weekly Trip Revenue per Customer ($)Before (weeks 136) 38 9 39 36During (weeks 37102) 47 10 47 48

Weekly Revenue per Customer for Promoted Brands ($)Before (weeks 136) 347 02 343 365During (weeks 37102) 410 03 408 423

Weekly Revenue per Customer for Nonpromoted Brands in Promoted Categories ($)a

Before (weeks 136) 347 09 350 340During (weeks 37102) 352 07 350 360

Weekly Revenue per Customer for Nonpromoted Categories ($)Before (weeks 136) 31 9 32 29During (weeks 37102) 39 10 39 40

aOnly categories with at least one promoted brand were included in the analyses.Notes: Means in bold were significantly different at p< 01.

our model framework were accomplished by adopting ahierarchical Type III Tobit model (Amemiya 1998) that

simultaneously allowed for moderators and accounted forthe endogeneity of customized coupon campaigns usingthe incidental parameter approach (Manchanda, Rossi, andChintagunta 2004).

Customer Behavior

Let TIit be a binary variable that indicates whether cus-tomer i visited a store in week t. Let TRit and CCRit rep-resent trip revenue and customized coupon redemptions forcustomer i in week t, respectively.8 Because in any week t,we observed trip revenue and customized coupon redemp-tions from customer i only if that customer visited a store(TIit > 0), we modeled customer behavior using a Tobit IIIspecification (Amemiya 1998). This specification allowedtrip revenue and customized coupon redemptions to be con-ditional on trip incidence TIit. Let TI

it be a latent variablethat represents customer is utility for visiting the retailerin week t. We assumed that a customer would visit a store(TIit = 1) if and only if his or her utility for visiting thestore was positive (TIit > 0). The main effects of customizedcoupon campaigns and the control factors affected trip inci-dence, as follows:

TIit = 10i+1RiStock_R1it+1EiStock_E1it+11Q2(1a)

+12Q3+13Q4+14Recit+15TIit1+16CBit

+17FD_Promit+18FSI_Redit+19Ret_Discit

+

110NPP_Prefit+

1itTIit

= 1 if TIit >0 and 0 otherwise

where

TIit = a latent variable indicating customer is utility forvisiting the store in week t;

8Exploratory tests rejected the null hypothesis of unit roots

in the weekly data, indicating that it is not necessary to model

changes in the dependent variables.

1i = 10i 1Ri1Ei, or customer-specific coefficientsthat capture the intercept and the redemption and

exposure effects on trip incidence;1 = 11 12 110, or coefficients that capture

the effect of control factors on trip incidence;

Stock_R1it = stock of the customized coupon redemptions forcustomer i in week t;

Stock_E1it = stock of the exposure to customized coupon cam-paigns for customer i in week t;

Q2, Q3, Q4 = dummy variables for quarters 2 through 4 (quar-ter 1 is the base case);

Rec = recency of customer transactions;

CB = level of cross-buying;

FD_Prom = propensity of the customer to respond to featuresor displays;

FSI_Red = propensity of the customer to redeem FSI

coupons;Ret_Disc = propensity of the customer to purchase products

with retailer discounts; and

NPP_Pref= preference-weighted nonprice promotions.

Let LTRit and LCCRit indicate natural logarithms of triprevenue and the number of customized coupons redeemed,respectively, by customer i in week t.9 Then, LTRit andLCCRit are classified as unobserved when customer i doesnot visit a store in week t (TI it = 0). We modeled the naturallogarithm of trip revenue and customized coupon redemp-tions for observations with TIit = 1, as follows:

LTRit=

20i+

2RiStock_R

2it+

2EiStock_E

2it+

21Q2(1b)

+22Q3+23Q4+24Recit +25TRit 1

9We obtain LTRit and LCCRit by taking the logarithm of

TRit +1 +1 and CCRit +1 +1, respectively, to avoid taking logs of 0.

The AndersenDarling test statistic was less than 1.93 for both

LTR and LCCR, thus failing to reject the null hypotheses of nor-

mal distribution.

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+26CBit +27FD_Promit +28FSI_Redit

+29Ret_Discit +210NPP_Prefit + 2it and

LCCRit = 30i +3RiStock_R3it 1 +3EiStock_E3it +31Q2(1c)

+32Q3+33Q4+34Recit +35TRit 1 +36CBit

+37FD_Promit +38CP3it +39NPP_Prefit + 3it

Error distribution. The subscripts 1, 2, and 3 in Equa-tions 1a1c label the effect of the drivers on trip incidence,

trip revenue, and customized coupon redemptions, respec-tively. The linkages among the three equations in a TypeIII Tobit model are captured by the error structure. Specifi-cally, we assume that the unobserved error terms, 1it , 2it ,and 3it , are distributed multivariate normal, with mean 0and a variancecovariance matrix as specified in Equa-tion 2. Equations 1a2 represent a Tobit III specification.

=

112

13

22

2323

(2)

Identification. Because our model includes a simultane-ous system of equations (Equations 1a1c), the estimatedcoefficients can be identified only if there is at least oneunique variable in each equation. We used the lagged valueof the redemption stock (Stock_R3it 1) to model customizedcoupon redemptions in week t (CCRit) in Equation 1c,because as we explain subsequently, we used the depen-dent variable (CCRit) to calculate the redemption stock attime t. Furthermore, the lagged redemption stock capturesthe reinforcing effect of past customized coupon redemp-tions on future redemption. We used lagged revenue insteadof lagged trip incidence to model revenue and customizedcoupon redemption. We used the cumulative number of

FSI redemptions until time t

1 as the conventional pro-motions variable for the customized coupon redemptionsequation (CP3it). The cumulative redemption value capturescustomers tendency to redeem coupons, which is moreappropriate for predicting a customers level of customizedcoupon redemptions. The conventional promotion variablein the customized coupon redemption model (CP3it andthe lagged revenue (TRit 1) in the trip revenue model alsoensure identification of the simultaneous system (Equa-tions 1a1c) used to model customer decisions.

Unobserved customer and environmental factors mayaffect the dependent and control variables, leading to acorrelation between the control variables and the errors inthe regressions. Therefore, we used lagged variables of all

the control factors (except seasonality) as instruments. TheLagrange multiplier test indicated that the errors in the triprevenue and customized coupon regressions were not seri-ally correlated, confirming that the lagged variables werevalid instruments for the control factors (Greene 1993).10

10The Lagrange-multiplier test for serial correlation allows for

lagged dependent variables in the panel data model. The substan-

tive results of our study did not change when we included two-

period lagged control variables instead of a single-period lagged

Stock of redemption and exposure effects. Similar toAnsari, Mela, and Neslin (2008), we accommodated thepotential carryover effect of customized coupon campaignsby including a stock variable for redemption and exposure.In week t, the stock variable for an exposure effect to cus-tomized coupon campaigns is given by

StockE1it = CCit +1EStockE1it 1(3)

where

CCit = number of customized coupon campaigns mailed to cus-tomer i in week t, and

1E = the carryover of exposure to customized coupon cam-paigns on trip incidence.

Carryover or dynamics were captured by the weights inthe stock variable, 1E. Large values of 1E imply that thecustomized coupon campaign has an effect well into thefuture. In the first week (t = 1), the exposure stock equalsthe number of customized coupon campaigns in that week(CCi1). The exposure stock in the second week (StockE1i2)equals the sum of the number of customized coupon cam-paigns in the second week (CCi2) and the carryover effect

times the exposure stock in week 1 (1ECCi1). Equation 3provides a generalization of this recursive process for anyarbitrary week t.

The section Derivation of Stock Variables and Esti-mation Algorithm in the Web Appendix provides furtherdetails on the derivation of the stock variable (see http://www.marketingpower.com/jm_webappendix ). We modeledthe exposure effect of coupon campaigns on trip revenueand customized coupon redemption similar to Equation 3.We used the number of redemptions by customer i in week t(CCRit), instead of the number of customized coupon cam-paigns (CCit), to measure the stock of the redemption effect.The expiration date of the coupons was not relevant for our

model, because only coupons that have not expired con-tribute to the number of customized coupon redemptionsin a week (CCRit), and the redemption of coupons and theexposure to campaigns affect customer behavior beyond theexpiration date through the carryover effect.

Moderators of Redemption and Exposure Effects

Our specification for the moderators allowed for both amain effect and an interaction of the moderators with theredemption and exposure effects. Moderator effects wereassessed through a hierarchical model for the customer-specific coefficients, as follows:

i=

0+

1FVali+

2NumCi+

4RegCi(4)+4RewPi +5NCi +6NumNCi +I

variable. We also estimated a model that allowed for serially cor-

related errors over a single period, to accommodate lagged depen-

dent variables and endogeneity of the customized coupon cam-

paigns. However, the coefficient of the serial correlation was not

significantly different from 0. The results are available on request.

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where

i = 10i1Ri3Ei= a 91 column vector of the ninecustomer-specific coefficients in Equations 1a1c;

FVali = average customized coupon face value received bycustomer i;

NumCi =number of customized coupons per campaign receivedby customer i;

RegCi = regularity of the campaigns received by customer i;

RewPi =proportion of reward-focused campaigns received bycustomer i;

NCi =1 if majority of the coupons received by customer iare for niche products;

NumNCi =number of noncustomized coupons received by cus-tomer i;

= 01 6 = a 97 matrix of coefficients of themoderators, where each 0, 1, and so on, are 91column vectors; and

i = is a customer i-specific error term that is distributednormal with 0 mean and variance V, i N0V.

Endogenous Selection of Customers

The customer-specific intercept and the number of cus-tomized coupon campaigns a customer receives (and thusthe stock variables) may be correlated because customers

who spend more also receive more customized coupon cam-paigns. Also, if the retailers mailed customized couponcampaigns to customers they expected to be more respon-sive, the coefficient of the exposure and redemption stocks(i.e., slope coefficients) would be correlated with the stockvariables. The preexisting differences among customersand retailer decision rules for selecting customers for thecustomized coupon campaigns represent omitted variablesthat are correlated with the stock variables in our model.This situation could lead to a correlation among the inter-cepts, the stock variables, and the unobserved error terms(1it, 2it, 3it in Equations 1a1c, resulting in inconsistentestimates of the customer-specific coefficients, i.

11

We adopted recent developments in econometrics to

account for the correlation of the stock variables with boththe intercept and the slope coefficients (Donkers et al. 2006;Manchanda, Rossi, and Chintagunta 2004). This methodol-ogy acknowledges that customized coupon campaigns arenot random variables but are outcomes in line with retail-ers knowledge about customers past purchase behavior.The intuition is to model the joint distribution of customerbehavior variables in Equations 1a1c and the numberof customized coupon campaigns the customers received.We modeled the effect of the omitted variables regardingretailer decisions in the distribution of the number of cus-tomized coupon campaigns customer i received in time t(CCit. The customer-specific coefficients i therefore wereestimated after accounting for the omitted variables.

We included the recency, frequency, and monetary value(RFM) score (a measure of customers revenue potential),the history of customized coupon campaigns sent, and thecustomers propensity to respond to conventional promo-tions to predict CCit . We included an instrumental variable,

11The endogeneity in our study differed from price and sales

promotion endogeneity, because the customized coupon cam-

paigns varied across customers, whereas the price and sales pro-

motion decisions were common to all customers.

incidence in popular coupon categories, which correlatedwith retailer decisions but not with customer behavior.12

The instrumental variable exploited the unique characteris-tic that the availability of coupons depends on manufactur-ers willingness to provide customized coupon promotions.In our data, more coupons were available in the baby, bak-ing, frozen food, pet, frozen pizza, and frozen meat cate-gories. Retailers were more likely to select customers whopurchased in these categories for the customized couponcampaigns, but purchases in these categories were not sig-

nificantly correlated with customers trip incidence, trip rev-enue, or customized coupon redemptions. Let CCit equal 1if customer i received a customized coupon campaign inweek t and 0 otherwise. We modeled CC it using a probitmodel, as follows:

CCit CC

it(5)

CCit = 1IPCi +2RFMit 1 +3CCit 1 +4CP4it(6)

+Unobserved factorsi + 4it

where

= cumulative normal distribution;

CCit = retailers latent utility from sending a cus-

tomized coupon campaign to customer i inweek t;

IPCi = incidence of purchases in popular couponcategories for customer i in weeks 136;

RFMit 1 = average of the RFM value of customer i inweek t 1;

Recency = number of weeks since customer is lastpurchase;

Frequency = number of trips made by customer i duringweeks t1 through t 12;

Monetary value = average revenue per trip incidence providedby customer i during weeks t 1 throught12;

CP4it = cumulative number of conventional promo-

tion redemptions until week t, measured sim-ilarly to CP3it in Equation 1c; and

Unobserved factors = 10i 1Ri 3Ei, or the nine customer-specific coefficients in Equations 1a1c.

The unobserved factors related to retailers judgment,not captured by the customers purchase data. In Equa-tion 6, we used the customer-specific intercepts and slopecoefficients of the stock variables from Equations 1a1cto capture unobserved factors. We simultaneously esti-mated the parameters in the customer decision models(Equations 1a3), the hierarchical model of the moderators(Equation 4), and the retailer decision model (Equations 5and 6) using a Markov chain Monte Carlo algorithm.Details on the likelihood function, the prior distributions

of the model parameters, and the estimation algorithmappear in the Derivation of Stock Variables and Esti-mation Algorithm section in the Web Appendix (http://www.marketingpower.com/jm_webappendix ). The ModelComparisons and Post Hoc Analyses section in the Web

12Instrumental variables were not required to account satis-

factorily for the correlation among the customer-specific inter-

cepts, slopes, and covariates in the customer decision model

(Wooldrige 2010).

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switched to the focal store in the week of coupon redemp-tion and went back to competitive stores in other weeks.Our primary objective in this study was to assess theredemption effects of customized coupons on customer pur-chases. Our results provide motivation for additional stud-ies to evaluate the process that leads to redemption effects,such as purchase acceleration and store switching.

We also estimated our model on a subsample of 1,150test group customers who were nonredeemers of the cus-tomized coupon campaigns. The campaigns had a positive,

significant exposure effect on trip incidence (5.27, p

measuring and managing

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