00b7d528845e62e610000000

Journal of Retailing 87S (1, 2011) S43S52

Innovations in Retail Pricing aDhruv Grewal a,, Kusum L. Ailawadi b,1, Dine

Praveen Kopalle b,3, Jane R. Roa 213 Malloy Hall, Babson College, Babson Park, MA

all, Hrsity AStated Stat5 Ap

Abstract

Retailers e topin pricing an effecenhanced ab is artfindings as t andenabling tec 2011 New

Keywords: Pr

In 2008, total U.S. retail sales climbed over $3.9 trillion (U.S.Census Bureau 2008), of which approximately $1.4 trillion camefrom food, beverage, drug, and department stores and approx-imately $2promotionsretailers usGiven theirthe marketiedge abouoften focusrich dataseeral producinstead usements of pr

RecentBolton, Sh

CorresponE-mail ad

kusum.ailawakopalle@dart

1 Tel.: +1 62 Tel.: +1 33 Tel.: +1 6

2009), Grewal, Levy, and Kumar (2009), Grewal et al. (2010),Kopalle et al. (2009a), Levy et al. (2004), Neslin (2002),Puccinelli et al. (2009), and van Heerde and Neslin (2008), sum-

0022-4359/$doi:10.1016/j27 billion from online and mail-order stores. Priceare a key marketing instrument that on- and offline

e to generate sales and increase their market share.importance and long history, it is not surprising thatng literature has accumulated a vast body of knowl-

t how promotions work. Quantitative research hased on the consumer packaged goods industry, wherets covering long periods and purchases across sev-t categories are available. Most behavioral researchs experimental settings to manipulate various ele-omotional designs and isolate their effects.reviews, such as those by Ailawadi et al. (2009),ankar, and Montoya (2007), Grewal and Levy (2007,

ding author. Tel.: +1 781 239 3902.dresses: [email protected] (D. Grewal),[email protected] (K.L. Ailawadi), [email protected] (D. Gauri),mouth.edu (J.R. Robertson).03 646 2845.15 443 3672.03 646 3612.

marize the findings of both types of research. Rather than reviewpast research again, we focus our attention on retail price andpromotion innovations, many of which have received significantattention in the press, though they have just started to provokeacademic interest. As shown in the organizing framework ofFig. 1, we group these innovations based on their relevance tothree questions:

whom to target? what promotions and pricing models to use? how design elements can increase the effectiveness of these

promotions?

This organization around three questions or areas of inquiryguides our review. We wish to note that it is not intended as aframework for analyzing innovations. Within each area, we iden-tify major innovations and their technological enablers, highlightrecent research that provides insights on these innovations, andpose questions that should be addressed in future research area.

The first research area involves innovations that are aimedat determining whom to target? Two key areas of improved

see front matter 2011 New York University. Published by Elsevier Inc. All rights reserved..jretai.2011.04.008b Tuck School at Dartmouth, Dartmouth College, 100 Tuck Hc Whitman School of Management, Syracuse University, 721 Unive

d The Integer Group, Unitede Dallas Market Center, Unite

Received 22 April 2011; accepted 2

confront a seemingly impossible dual competitive challenge: grow thd promotion provide considerable opportunities to target customers

ilities to measure and improve the effectiveness of their promotions. Thhey pertain to enhanced targeting, new price and promotion models,hnologies and suggests important avenues for further research.

York University. Published by Elsevier Inc. All rights reserved.

icing; Promotion; Value; Retail strategy; Targeting; Retail technologynd Promotionssh Gauri c,2, Kevin Hall d,bertson e02457, United Statesanover, NH 03755, United Statesve, Syracuse, NY 13215, United States

sesril 2011

line while also preserving their bottom line. Innovationstively both offline and online. Retailers also have gainedicle synthesizes recent advances in pricing and promotionsimproved effectiveness. It also highlights the role of new

S44 D. Grewal et al. / Journal of Retailing 87S (1, 2011) S43S52

Research Area 1: Whom to Target?

Organizing Framework

ork.

targeting adevelopingtory for deindividualthese targetsonal shoppassistants hconsequencspecific pri

The secemerging mdynamic prlimited timtions basedenablers intification (R

The thiring the effedesign elemmessagingmeasure co

Research

Retailerquestion: wfocused andata they hIn similar fthe usage o

ed re

ailerstomlty ptancLoyalty Data Based Promotions

Online History Based Promotions

Technological Enablers:

Mobile Applications

Personal Shopping Assistant

Kiosks

Research Area 3: How to Design Effective

Promotions?

Online Promotion Design Elements

Offline Promotion Design Elements


In-Store Digital Messaging

Eye tracking

Research Area 2: What Price & Promotion

Model to Use?

Dynamic pricing,

New Promotions Methods

Volume Based Discounts


Electronic Price Tags

RFID

Fig. 1. Organizing framew

ctivities involve the use of loyalty program data forloyalty-based promotions and the use of online his-

veloping and offering online promotions targeted atcustomers. Technology enablers that are likely to aiding activities include mobile applications, online per-ing assistants and kiosks. Online personal shopping

Target

Retual cuof loyaFor insave access to shoppers purchase history and, as ae, can generate personal shopping lists and displayces and promotions.ond research area that we address involves what the

odels of price and promotions are. These includeicing models, promotions based on exclusivity, i.e.,e and limited merchandise (e.g., Gilt), and promo-on volume discounts (e.g., GroupOn). Technology

clude electronic price tags and radio frequency iden-FID).

d research area pertains to how retailers are increas-ctiveness of online and offline promotions throughents. Technology enablers include in-store digital

and eye-tracking equipment that retailers can use tonsumer response in greater detail than ever before.

area 1: more focused and targeted promotions

s are steadily innovating to address the most centralho to target with their promotions? They are bringingalytics together with the wealth of loyalty programave at their disposal to develop targeted promotions.ashion, they are using online analytic tools to increasef targeted online promotions.

untargetedpromotionsretailer catetargeted prmation aboother commof its promofor each proof the treatmthe basis ogeneralizatand those ting work.examine coversus thosshow, in atrust and falower price

The avato partner wthat encourthe brand iredeem forof its targetValue

Creation

&

Appropriation

tail promotions using loyalty data

s increasingly target specific promotions to individ-ers or customer segments, driven by the availabilityrogram data and retailers ability to mine such data.e, the drugstore chain CVS offers not only traditional,

promotions through its weekly flyer but also targetedbased on its Extra Care loyalty program data. Thegorizes its customers into several segments, designs

omotions for each segment, and disseminates infor-ut those promotions through personalized e-mail andunications. It continually evaluates the effectivenesstions by gathering data from matched control groupsmotional offer and comparing the purchase behaviorent and control groups. Research could generate, on

f these controlled field experiments, some empiricalions about the types of targeting strategies that workhat do not. These insights would complement exist-For example, Feinberg, Krishna, and Zhang (2002)nditions in which it is better to target loyal customerse likely to switch. Grewal, Hardesty, and Iyer (2004)scenario-based study that respondents indicate moreirness if customers who buy more frequently receives, rather than new customers.ilability of loyalty program data also enables retailers

ith their manufacturer vendors to offer promotionsage store loyalty. Instead of receiving a discount on

tself, consumers earn extra loyalty program points topurchases in the retailers stores. CVS funds someed promotions through its vendors and offers Extra

D. Grewal et al. / Journal of Retailing 87S (1, 2011) S43S52 S45

Bucks when the customer buys a particular vendors brands.It is important to evaluate how customers perceive such promo-tions and hretailer. Ifprice (Winerepresent awho are tyand Breugepromotions

Finally,of a loyaltwith retaileabsence ofalso evaluaexample, Vness of retaredemptionexposure eand overallthe profitab

Targeted on

Retailertomize proconsumer

ing steadilyonline (e.g.Zhang andthe functioindividualover their ppaign easilytheir objecredeemed).also helpsand compecampaigngration ofchallengingcost and be(Neslin anof multi-ch(2009) and

It is imtargeting ithe improvtions target2009; FrucCheng andand demonprices in thimproves pRossi, McCshow the imZhang andprofitability

greater benefits than segment-level customization, especiallyoffline.

re ationscon

e incoupes. Tr andimpld th

on re

lso nest

logi

bileberacq

terneed (eminbram). Thti-waand i. Mtingp pro. In tant sao, ae r

ngntsiasmobes c

siges/ipholitiesthe

logietha

h thefor

ies.s li

tive,storet othretaises oriedalmow effective they are for the manufacturer and thethey are effective and can reduce negative referencer 1986) effects associated with promotions, they maywinwin proposition for manufacturers and retailers,pically at odds when it comes to promotions. Zhanglmans (2010) also examine the effectiveness of suchfor an online retailer.promotions can be targeted even in the absence

y program. Companies such as Catalina now workrs to offer targeted coupon promotions, even in thea retailer-specific loyalty program. Research shouldte the effectiveness of such targeted promotions. Forenkatesan and Farris (2010) quantify the effective-iler coupon promotions by including not only theireffect but also their exposure effect. They find that

ffects can be more important than redemption effects, such targeted promotions can significantly improveility of customers to a retailer.

line promotions

s sometimes use past purchase history data to cus-motions for individual consumers, not just for

segments. Such customized promotions are grow-in all retail channels, though they are most notable

, Ansari and Mela 2003; Syam, Ruan, and Hess 2005;Wedel 2009), probably because the Internet providesnality and specific features to cost-effectively targetcustomers. For example, e-tailers enjoy great controlromotions, such that they can initiate an online cam-and then end the campaign the moment they achieve

tives (e.g., a pre-determined number of couponsAccess to real-time promotional effectiveness data

these firms tailor their offers and integrate customertitor responses immediately into their promotionaldesigns. However, for multi-channel retailers, inte-real-time promotional effectiveness data is quite(Neslin et al., 2006). Firms need to understand the

nefits associated with a single view of their customersd Shankar 2009). For a more detailed discussionannel research, see reviews by Neslin and ShankarNeslin et al. (2006).portant to examine the extent to which individuals profitable. Extant analytical research has showned profitability of one-to-one and reward promo-ed at the individual customer level (Chen and Zhanghter and Zhang 2004; Shaffer and Zhang 2002).Dogan (2008) extend this analysis to the Internet

strate that even when consumers actively seek lowere future, dynamic targeted pricing is beneficial androfitability. However, empirical evidence is mixed.ulloch, and Allenby (1996) support this result andproved profitability of customized promotions. But,

Wedel (2009) find that, although targeting improves, individually customized promotions do not offer

Thepromo(2008)increasonlinedurablretailewhichthey fineffecttions a(e.g., B

Techno

Moto numsumers

the Inexplodis becoBalasureviewor mula firmnologymarkedevelo(SMS)importXu, Li

SomshoppiassistaVictor(http://providphone,Christithe-gocapabiresentstechnocationsthroug(2011)activit

Firminteracin theand geto thepurchanot carBuy, Wre also potential differences in online and offlinewhich should be examined. Chiou-Wei and Inman

sider online coupons and find that redemption ratesresponse to greater coupon face value, such thatons appear more effective for large-ticket items andhey further note that greater distance between thethe consumer results in lower coupon redemption,

ies locational relevance, even in an online setting. Yetat the coupon expiration date did not have a significantdemption rates. The effectiveness of online promo-eeds to be examined in non-grocery contexts/stores

Buy, Staples, Macys).

cal enablers

Internet users are growing rapidly and are anticipatedat least 1.4 billion by 2013 (Bucher 2009). As con-uire more next-generation smart phones and accesst through them, interest in mobile marketing has.g., Sultan and Rohm 2005, 2008). Mobile marketing

g increasingly important in retailing (see Shankar andanian 2009 and Shankar et al., 2010 for a detailedese authors define mobile marketing as the two-wayy communication and promotion of an offer betweents customers using a mobile medium, device or tech-ore and more firms have started to integrate mobileinto their integrated marketing communications andmotional campaigns based on short message servicesurn, mobile advertising is becoming an increasinglyource of revenue for cellular carriers (Gauntt 2008;nd Li 2008).etailers have mounted touchscreen tablets ontocarts, which then serve as personal shopping(PSA) (Kalyanam, Lal, and Wolfram 2006).Secret launched a mobile Web site in 2009ile/victoriassecret.com) that acts like a PSA andonsumers with a means to browse and order byn up for text alerts, or receive price promotions.iPhone application (http://www.christies.com/on-ne) provides real-time auction results, browsing, and integration with camera telephones, which rep-famous auction houses attempt to leverage digitals. Coupon Sherpa and other firms offer mobile appli-t consumers can use to view and redeem couponsir cellular devices. We refer readers to Shankar et al.a more detailed discussion on innovations in digital

ke CVS and Staples have visibly embraced the use ofstand-alone kiosks. CVS customers can use a kioskto print out their loyalty program reward coupons

er promotional discounts. By connecting customerslers Web site, the kiosks at Staples stores facilitatef out-of-stock items, as well as products and servicesin the store. Other retailers (e.g., Metro, CVS, Bestart) have incorporated kiosks to handle service needs


(e.g., printing personalized online coupons). These interactivekiosks clearly enhance the retailers ability to provide customerstargeted prstore, but reof these prwhether kimultiple ch

Res

A seconpromotionsnology. Thpricing aspromotions

Dynamic p

Traditiopricing, usterion (Retcomputersemphasis ohave fine-ters use sof(AssociatedACNielsenetary modeset optimumsupports astion and cothat might

Most redynamic por companDynamic pchanging sZale 2010)First, muchprise dataas Oracle aelasticitiescan be custDynamic pelasticitiesselling higof dynamicgrowth asbased on hpurchase b

In devemotion sch(Kopalle 2expansion aperformancfunctions, oship, and s

Dynamic pricing models allow companies to price discrim-inate on a small scale, even at a single customer level, which

themuchrks,

oflty Ito pe trsticegorvanlike

rs. Ip anspeits m

enta ca

et acon

ininegor

futreas

cingy.l, Knamgoryicinizesman

taileandsear

thesuchty.ther,thatey trs agopampoande priproer tieve

, thepriceee Cs con

. Thprivomotions that they can access while they are at thesearch is needed on the profitability (or lack thereof)omotions. Further, it would be interesting to studyosks increase the effectiveness of promotions acrossannels?

earch area 2: price and promotion models

d important area pertains to what new price andmodels have emerged as a result of emerging tech-

ese new models include the expansion of dynamicwell as the development of entirely new types of.

ricing models

nally, retailers have taken a one-note approach toing cost as the primary, and sometimes the only, cri-ail Industry Report 2000). But increasingly powerfuland more sophisticated software, as well as renewedn training senior managers in quantitative analysis,

uned pricing strategies in recent years. Some retail-tware to determine optimal markups and discounts

Press 2007) for manufacturers. Symphony-IRI andprovide promotional software that builds on propri-ls featuring both price and promotion elasticities to

prices. Estimation and optimization software alsosessments of tricky details, such as product substitu-mplementarity effects, and they often uncover resultsnot be clear from a spreadsheet.cently, some retailers have employed sophisticatedricing models that use data from Internet purchasesy enterprise resource planning systems to set prices.ricing models update prices frequently, based on

upply or demand characteristics (Nagle, Hogan, and. The driving forces behind this trend are threefold.more data are available today. Managers use enter-

management systems, produced by companies suchnd SAP, to spot purchase patterns and estimate pricein transaction data. Second, price analytic softwareomized to a particular market context or data sources.ricing software, which provides data to determinemore reliably, is especially important to companiesh volume, high frequency products. Third, the use

pricing models has grown, alongside the Internetsa distribution channel. However, these models areistorical data, which may not be indicative of futureehavior.loping an optimal dynamic retail pricing and pro-edule, retailers should keep several issues in mind010): inter- and intra-category optimization, marketnd contraction effects, modeling frameworks, modele, the psychological aspects of pricing, objectiveptimization, parameter estimation, product relation-

calability.

makesgies, snetwoappealCasuapricesdistancdiagno(McGrand adand isretaileshift usumer

allows2010).

Recing inDubeshoulddetermof catthat asers incsacrifiitabilit

Halfor dya cateand prmaximsidersthat reown-

The reaboutsionsquanti

Furgramsthat thpetitorfirm (Ktwo coinum)A morrewardcustom

Howpricingaboutness, s

well ahistoryceivedparticularly attractive to retailers. New technolo-as radio frequency identification (RFID), wirelessand global positioning (GPS), further enhance thedynamic pricing in the retail arena. Progressivensurance Company, for example, offers differentolicyholders based in part on data (e.g., speed,

aveled) uploaded from devices that plug into theports of cars operated by its MyRate customers2009). RFID technology has broader implications

tages for retailers implementing dynamic pricingly to be beneficial for both EDLP and Hi-Low

n the future, prices in stores might automaticallyd down based on costs, inventory levels, and con-nding habits. One New York restaurant already

enu prices to fluctuate according to demand (Katz

pricing research has focused on dynamic pric-tegory based on consumer state-dependent utility.l. (2008) suggest that dynamic pricing modelssider the evolution of consumer brand loyalty ing optimal prices over time. In their examinationy pricing, Fox, Postrel, and Semple (2009) noteure traffic becomes more sensitive to price, retail-ingly should consider lowering current prices andcurrent profits for increased future traffic and prof-

opalle, and Krishna (2010) present a frameworkic pricing and ordering decisions by retailers in-management setting. Their multi-brand orderingg model incorporates retailer forward buying andprofitability for the category. The model also con-ufacturer trade deals to retailers, ordering costsrs incur, retailer forward-buying behavior, and thecross-price effects of all brands in the category.ch thus derives implications in a dynamic settingimpact of interdependence among brands on deci-

as pass-through of trade deals and retailer order

we can infer from emerging literature on loyalty pro-consumers are strategic and forward looking, suchade off immediate price discounts offered by com-ainst loyalty rewards in the future from the targetlle et al., 2009b). Most loyalty programs comprisenents: customer tiers (e.g., Silver, Gold and Plat-frequency rewards (e.g., buy n and get n + 1 free).ce-oriented customer segment values the frequencygram more; the service-oriented segment prefers theer program.r, as retailers and manufacturers adopt such dynamicy also need to realize that it might increase concernsand promotion fairness (for research on price fair-

ampbell 1999; Grewal, Hardesty, and Iyer, 2004), assumer concerns about the privacy of their shopping

e perceived fairness of the price (and potentially per-acy) is likely depends on factors such as the retailers


explicit or inferred motives, as well as their reputation (Campbell1999).

New types

SeveralHauteLooktime periodto the site.has been inexclusivenesive promoMoreover,provide refand Roy (greatest apthan collec

These inother consuor begin to(often 50 ptually alwa(i.e., compavailable fodise sells oa cue of thretailers arepolicy perserence pricthan deceiv

Conditwhere somof the discness of concontrol of tchases exceffective wBut, there aconditionsumer. SomDonuts or rland furnitu(Bortman 2can tie in wcess. Varioconditional(i.e., invitathe Worldan uncertaiof such proshort versuhas disappe

Volume-ba

Volumeprice per ou

(e.g., a 12-pack is cheaper than a 6-pack on a per item basis).An innovative adaptation of volume-based pricing on the Web

es ainedsite

on.cometr

reladve pe

n fomedany

rtingemenent.haningo pon.cobilitya re

ponailern ne

t!ereascouer itse disuchealsey cox orkno

new

erstaals oals (erchadealrminmpah exineinatanddelthe

ersbuyilly rbe firlyers

actuproof promotions

online retailers, such as Gilt, RueLaLa, and, offer a limited set of fashion products for limiteds to select groups of consumers who must subscribeSome allow subscriptions only after the customer

vited by another subscriber, which creates a sense ofss. Recent research suggests consumers value exclu-tions over inclusive ones (Barone and Roy 2010).the sites offer rewards to existing customers if theyerrals to others (see also Ryu and Feick 2007). Barone2010) suggest that exclusive promotions have thepeal to consumers who adopt an independent rathertivist self-construal.vitation-only promotions also reduce the chance thatmers will see the offer and form adverse perceptionsexpect to find these fashion items at sharp discountsercent or more). Yet on the sites, the descriptions vir-ys contain a comparison of the regular and sale pricearative price format). The offers generally remainr a limited duration, such as 4 h or until the merchan-

ut. By using reference prices that provide consumerse quality and value of the merchandise, these online

likely enhancing consumer demand. From a publicpective, societies need to ensure that advertised ref-es are genuine, to confirm they are informing rathering customers.ional promotions are another type of promotione condition has to be met for the consumer to availount. Lee and Ariely (2006) examine the effective-ditional promotions where the condition is under thehe consumer (e.g., consumers receive $1 off on pur-eeding $6), and find that these promotions are mosthen consumers have less concrete shopping goals.re newer types of conditional promotions where theis an outside event beyond the control of the con-

e recent examples are free coffee offered by Dunkinefunds on furniture purchases offered by a New Eng-re store if the Boston Red Sox win the World Series009). Such a promotion illustrates how local brandsith others and free ride on, say, the Red Soxs suc-

us factors could form the basis for and influence suchpromotions, such as the exclusivity of the audience

tion only), reference or tie-in to a specific event (e.g.,Series), and the entertainment value of winning ifn event happens. Research is needed on the viabilitymotions. How successful are these promotions in thes the long runi.e., after the novelty of the promotionared?

sed pricing

discounts are common; in most grocery stores, thence is lower for larger packages or greater quantities

providdetermbuyingGroupmanyreceiveto taketain timsign osocialpate. Mor spocomplthe evmore tlion. Jare als(rakuteprofitaing toa Grouthe retdraw iamoun

Whume dito covvolumsites soffer dthat thTJMax

Wetivelyto undthe dethe denic, puinhibitto detebers cothrougdetermdissem

Jinging mosharingthe offgroupnorma

tend to(i.e., faconsum

of theservicelarge discount on a given product or services if a pre-number of consumers agree to purchase it. Group

s appear mostly in big cities (Boehret 2010). Bothm and LivingSocial.com are especially popular in

opolitan areas, where consumers register online toevant deals and coupons. They can decide whetherantage of each deal and must do so within a cer-riod. To ensure the minimum number of other users

r the deal, users rely on their e-mail contacts andia to encourage others in their network to partici-of the deals involve social events, such as restaurantsevents, i.e., encouraging others to participate is atary action; they even might meet as a group at

According to Groupons own statistics, it has solda million such deals and saved consumers $42 mil-and Xie (2009) note that such group buying sitespular in China (e.g., TeamBuy.com.cn) and Japan.jp/groupbuy). Further research should examine thefor retailers using such sites. For example, accord-

cent Wall Street Journal article (January 7, 2011)offer drew nearly 2800 customers to a retailer butwas lamenting that the program not only did not

w customers, but they spent less than their average

s pay-what-you-wish pricing represents a form of vol-nt in which the firm hopes to achieve enough volumemarginal costs, group buying Web sites provide the

count only after enough people register. Alternatively,as Tippr.com, Woot.com, Gilt.com, and Ideeli.comregardless of the number of customers, which impliesmpete more directly with discount retailers such asMarshalls.

w very little about the effectiveness of these rela-forms of price promotion models. It would be usefulnd, for instance, how (and how quickly) users diffuseffered by group buying sites. Certain elements of.g., product versus service, utilitarian versus hedo-se price, magnitude, reputation) might enhance oracceptance among the group; it would also be usefule what percentage of customers are already mem-red with new members who have access to the offeristing members. Another stream of research mightwhich forms of social media are more effective foring the deal.Xie (2009) suggest that retailers using a group buy-

outperform referral reward programs if the cost ofinformation is low (e.g., through social media) and

are fairly mature. These insights may explain whyng sites have been able to grow rapidly. These usersely heavily on social media, and the typical offersor restaurants, spas, and other entertainment optionsmature categories). These are categories with whichare generally familiar with or have some knowledgeal regular price and the reputation of the retailer orvider.


Technology enablers

A numbthe abilityregard to dtronic priceimplement

As retaimization, aare far behmization anbecause dyand ESLsthose chanand thus foCorp. (Silicdisseminatsolutions toalso has beusing ESLproduct inf(Chain Stoto understarapid pricein their shothey are nothe adoptio

To be salize the SFor examption suppor(http://wwwtus and allosite (http://a customerwithout evemobile appbased priceand promoa store that

Howevelocationalsomewhatdespite thecomputingapplicationRaman, an

If they cdata througbecome a cet al., 20092008). Recallow for ldynamic pAs consumtraceable, rlocations to

tomers. They also could devise improved pricing and promotioncampaigns that reflect lessons learned from RFID data with

ing c

Res

ally,tilizeectiv

and

desiretaricesrdingthe bisedoth aeau aoffli

r valnlinrifyses intionselatedemter tnaltion,er, T

t froep prpric

ful tve.

sumey-Ma $1r-tic(e.g.m),10e, anas p

ationeep toffere co

er mer e

di etdesial val 199ta-aner of technological innovations have allowed retailersto create and offer these new business models. Withynamic pricing, newer technologies such as elec-tags and mobile applications have made it easier to

marketplace offers.lers become more interested in dynamic price opti-n obvious question is whether electronic shelf labelsind (Supermarket News 2005). Dynamic price opti-d electronic shelf labels (ESL) are a very good matchnamic pricing involves many rapid price changes,make it easier and more cost efficient to executeges, especially for retailers who change prices oftenr whom price changes have become costly. Altierreon India 2008) offers ESLs that automate the price-

ion process in retail stores and present promisingimplement truly dynamic pricing solutions. Sears

en conducting tests of electronic signage solutions,s in place of traditional printed ones, with price andormation updated using a simple wireless networkre Age 2008). At the same time, research is needednd the consumer implications of dynamic pricing,changes, and ESLs. Will such ESLs aid consumerspping process or will they add to their confusion? Ift well accepted by consumers, it is likely to hindern of such technology in the retail market.uccessful, such mobile campaigns should person-MS and enhance time and locational relevance.

le, Searss (http://www.sears.com) mobile applica-ts barcode scanning for coupons; eBays mobile Web.m.ebay.com) informs customers about their bid sta-

ws payments through PayPal mobile; Ralph Laurensm.ralphlauren.com) can be accessed immediately bywho uses his or her camera phone to scan a bar code,r typing in or clicking into the Web site. Many suchlication technologies offer the potential for location-

promotions, in which customers receive couponstions directly on their cell phones when they are nearsells items they may be interested in purchasing.r, the roles of personalization, time relevance, andrelevance for sales promotion effectiveness remainunclear and demand further research. In addition,widespread use of fuzzy logic, neural networks, soft, and collaborative filtering in other disciplines, their

in the retail domain appears minimal (Vadlamani,d Mantrala 2006).ontain built-in RFID readers, systems can exchangehout the store; this technology has the potential toommon and important element of retailing (Ganesan; Hui, Fader and Bradlow 2009; Sorensen 2003; Webbent advances in RFID technology and its speed couldong-range readers that feature simplified payments,ricing, and greater product history data collections.er items, such as clothing and accessories, becomeetailers could install low-cost readers at various store

learn more about the shopping habits of their cus-

match

Finhave uthe eff

Ofine

Tooffers,sales poff wonicateadvertcates bCompthat ingreatein an oand veincreapercep

A r(2007)as greaputatiopromoHowevbenefione-straisingbe useeffecti

Con(Linds$5 offsmalleterms$10 ite(e.g., $Monromationexpectmust ktionalbe morgle flyconsum

Ailawaing orthe deGrewafor meustomer purchase information.

earch area 3: promotional design elements

we focus on addressing the question of how retailersd price and promotional design elements to increaseeness of price promotions.

online promotion design elements

gn their offline and online promotional flyers andilers must determine whether to use reference and, the phrasing of the offer, dollar versus percentage, and colors. In weekly flyers, retailers often commu-enefits of the deal they are offering by indicating the

reference prices. Therefore, the price offer communi-regular price and a limited time, lower sale price (seend Grewal 1998). Past research has demonstratedne ads, as the advertised price increases, it conveys

ue (Grewal, Monroe, and Krishnan 1998). However,e arena, consumers have the ability to check pricesthe veracity of the advertised reference price. Thus,

advertised reference prices may not enhance valueand may even reduce them.

d issue involves sequential discounts. Chen and Raoonstrate that consumers regard sequential discountshan one big discount, largely due to their own com-errors. On the other hand, when a retailer ends athe price usually jumps back to the original price.siros and Hardesty (2010) reveal that retailers would

m slowly increasing the price rather than a larger,ice hike. Both sequential elements (decreasing andes) appear to promise benefits to retailers and it wouldo understand the conditions in which they are most

ers process their price savings in a relative fashionullikin and Grewal 2006); they are likely to value0 item more than they do $5 off a $100 item. For

ket items, they also may prefer savings in percentage, 50 percent off) than in dollar terms (e.g., $5 off awhereas for larger purchases, they prefer dollar termsoff $250 rather than 4 percent savings) (see Chen,d Lou 1998). This suggests that presenting the infor-ercentage off versus dollar off influences future prices (DelVecchio, Krishnan, and Smith 2007). Retailershese insights in mind when they design price promo-s. In particular, the offers for department stores maymplicated than those for grocery stores, because a sin-ight include both low (e.g., cosmetics) and high (e.g.,lectronics) price items. Past behavioral research (seeal., 2009 for a recent review) has shown that the fram-

gn of the deal influences consumer perception aboutlue, search, and purchase intent (see Compeau and8 and Krishna, Briesch, Lehmann, and Yuan 2002alytic research in this area). This research suggests


the importance of framing the deal. Going forward, researchcould empirically investigate the relative effectiveness of mul-tiple wayswith sale pOne Get Ocomplemensome disco

In a seristrate thatcommunicared conveyresult applia differencecircumstanas a heuristthat consumsometimesResearch sthese cues

Technology

Recent rmarketingand Ferraroconsumer pelements. Sthat consumto spend onchase. Theslack) enhaon unplannunplanned

Althougbeen wellGinter 199Mason, anon in-storeThese newinteractivehelp retaileWalmart emnicate offerlaundry deon actual inlifts for proexpose conto the mercwithout plalikely to attAppropriatbrand switc

One recthe measureye-trackinsupport itsand Zhang

movements consist of fixations, during which the eye remainsrelatively still for about 200300 ms, separated by rapid move-

calle (i.g eq

act caps(e.g

entsingTheion,centosenheselanogleft of valentsnt cas we

es thts reshophichectiv

gene orgation

n thedeavof nxam

stomtiona

otiypesor orrgani.

ther,ouldturetionsvidin

Furthe

ant is Kiohe aunitizatioin d

w prand channels of communicating different deals butrices of items that are effectively similar, viz., Buyne Free (BOGO), offering mail-in rebates, bundlingtary items versus selling individual items, offeringunts for online purchases, etc.es of studies, Chandrashekeran et al. (2011) demon-consumers use the color of the prices in marketingtions as a signal. Their findings show that prices inhigher savings than do prices in black, though thises only to male consumers. Women do not perceivein the savings amount based on color. Yet in certain

ces, consumers appear to use the color of the priceic to evaluate the offer value. The multitude of flyersers receive display prices in a vast array of colors,

using many different colors within the same flyer.hould continue to investigate how consumers react toin settings with different or inconsistent colors.

enablers

esearch has demonstrated the importance of in-store(see review by Shankar et al., 2011). Inman, Winer,

(2009) demonstrate that more than 40 percent ofurchase decisions depend on price and promotiontilley, Inman, and Wakefield (2010a, 2010b) showers maintain a mental budget of what they expecta given grocery trip and the items they plan to pur-

refore, savings on planned purchases (i.e., in-storence the quantity of items purchased while savingsed purchases may influence the purchase of moreitems.h the role of traditional displays on brand choice hasestablished (Ainslie and Rossi 1998; Allenby and5; Boatwright, Dhar, and Rossi 2004; Wilkinson,d Paksoy 1982), the influence of new technologiesshopping behaviors requires additional exploration.technologies, such as in-store digital messaging,

kiosks, and personal shopping assistants, also mayrs target their customers better. For example, BJs andploy in-store digital advertising displays to commu-

s or describe the use of a promoted product (e.g., newtergent). Kalyanam, Lal, and Wolfram (2006) report-store signage experiments suggests promising salesmoted items. In-store digital signage technology cansumers to dynamic messaging in the store, adjacenthandise. Consumers buy a great deal of merchandisenning to do so in advance, so this technology seemsract their attention and increase purchase behaviors.e comparative and usage messages could encouragehing. All these issues warrant additional research.

ent advance holds particular promise for addressingement issues associated with promotions, namely,g software. Advances in eye-tracking technologymore widespread use in marketing (Pieters, Wedel,2007; Van Der Lans, Pieters, and Wedel 2008). Eye

ments,distanctrackinthe exthen mpicture

Recings, u2009).sideratin thatand chtion. Tin a pto thetions odocumdiffereucts, aexcludproducvinceinto wand eff

TheferencInnovtions ithis engorieshave eing cupromoof promthree tbasis fmay ofashion

Furthat shing, fupromoas propoints.of howimportsuch a

In topportoptimieffectsand need saccades, which average three to five degrees ine., degrees of visual angle) and last 4050 ms. Eye-uipment records the duration of each eye fixation andoordinates of the central two degrees of vision, andthe coordinates to the location of each area in the., brands on a supermarket shelf).

research has examined the role of in-store shelf fac-pictures and eye-tracking equipment (Chandon et al.,number of facings has a strong impact on recall, con-and choice; the location of the facing also is critical,er facings are more likely to be noticed, reexamined,, whereas top facings enhance attention and evalua-results highlight the importance of item placementram. Also, whether the price of the item appearsr right of the item can influence customers percep-

ue (Suri and Grewal 2011). This technology thereforeexactly what shoppers see and miss as they look attegories. Some shoppers may fail to see certain prod-ll as promotional information, which immediatelyem from the relevant set of purchasers. These unseenmain unsold, without ever having had a chance to con-pers. Eye-tracking software could provide insightspromotional design elements are the most importante for attracting attention.

Research issues

esis of this article was in a Thought Leadership Con-anized at Texas A&M University, with the topicin Retailing. In our effort to understand innova-

domains of pricing and promotion, we have limitedor to recent innovations. Across the broad cate-

ew promotion types and technological advances, weined three types, from innovations aimed at target-ers to those that have evolved out of new price andl models to methods for increasing the effectiveness

ons. The guiding framework in Fig. 1 includes theseof pricing and promotion innovations and serves as aganizing our discussion. Certainly, other researchersze pricing and promotions innovations in a different

in Fig. 2, for each section, we summarize avenuesbe explored in further research. In the area of Target-research issues include the effectiveness of targeted, comparing better implementation strategies suchg an immediate discount versus giving out loyaltyther, we also need to have a better understandingresults generalize to non-grocery settings. Equallys to study the effectiveness of interactive productssks, shopping assistants, and mobile applications.

rea of price and promotion models, future researches include consideration of inter- and intra-categoryn, incorporating market expansion and contractionynamic pricing models, a relative comparison of oldicing and promotion models on retailer performance,


Research Issues:

ectivenmparisontsaden th

ectivenpping a

onsidertimizanamic

elative odels octors i

ole of plevance

ffectivensumeitching

ffectiveacking

Research Areas and Issues

ssues

the long tering the sucstrategies (vant researcontext, onand locatio

With reresearch istal signageusage rate,of promoti

In summresearch anareas. Furthvide foundto be doneand promoretail pricinthe effort roverviewedthese threepursued. Win price anand develothe paper a

The autthe RetailiUniversity,reviewers.

i, Kukateshiling:85 (1A. anuct C, GregFeatuarchResearch Area 1: Targeting

Research Area 3: Promotional Design

Research Area 2: Price & Promotion

Models

1. Eff2. Co

poi3. Bro4. Eff

sho

1. Copdy

2. Rm

3. Fa4. R

re

1. Ecosw

2. Etr

Fig. 2. Research areas and i

m impact on reference prices and the factors impact-cess or failure of the newer price and promotion

more details on innovative business models and rele-ch issues are available in Sorescu et al., 2011). In thise could also study the role of personalization, timenal relevance on the effectiveness of sales promotion.spect to promotional design, we identify two keysues: (1) Examine the effectiveness of in-store digi-technology on consumer shopping behavior such as

AilawadVenRetaing,

Ainslie,Prod

AllenbyandResebrand switching, etc. and (2) Study the effectiveness

onal design elements using eye-tracking software.ary, we have outlined insights gleaned from prior

d practice as it pertains to the above three researcher, we also highlight research issues that could pro-

ations for further research. Thus, while work remains, the ideas of better customer targeting, new pricetion models, and promotional design elements ing and promotions arena seem viable and worthy ofequired to more fully understand it. We have alsorelevant technological enablers as they pertain todomains and the related research that needs to bee hope that our article outlining recent innovations

d promotion serves as a catalyst for future researchpment on the research issues described throughoutnd outlined in Fig. 2.

Acknowledgements

hors appreciate the feedback from participants atng Thought leadership Conference at Texas A&Mthe special issue editors and the three anonymous

Ansari, AsimResearch,

Associated PrValley New

Barone, MichExclusive74 (2), 12

Boatwright, PCompetitimotional

Boehret, KathThe Wall

Bolton, Ruth Nand Emertury: CurrGermany:

Bortman, Elible?, Spo

Bucher, Dto Surphttp://ww

Campbell, Mand Conse

Chain Store AChandon, Pie

ChaoF K.Effects ofess of targeted promotionsn of providing immediate discount vs. loyalty

e understanding to non-grocery settingess of interactive products such as Kiosks, ssistants, mobile apps

Research Issues:

the impact of inter- and intra category tion, market expansion & contraction effects in pricing modelscomparison of new price and promotional n retailer performancempacting the success/failure of the new modelsersonalization, time relevance and locational for sales promotion effectiveness

Research Issues:

ness of in-store digital signage technology on r shopping behavior (increased usage, brand etc.)

ness of promotional design elements using eye-technology

.

References

sum, J.P. Beauchamp, Naveen Donthu, Dinesh Gauri andShankar (2009), Communication and Promotion Decisions in

A Review and Directions for Future Research, Journal of Retail-), 4255.d Peter E. Rossi (1998), Similarities in Choice Behavior Acrossategories, Marketing Science, 17 (2), 91106.M. and James L. Ginter (1995), The Effects of In-Store Display

re Advertising on Consideration Sets, International Journal ofin Marketing, 12 (1), 6780.

and Carl F. Mela (2003), E-Customization, Journal ofMarketing40 (May), 13145.ess (2007), Inside the World of Price Optimization Software,s, April 29, E2.

ael and Tirthankar Roy (2010), Does Exclusivity Always Pay Off?Price Promotions and Consumer Response, Journal ofMarketing,132.eter, Sanjay Dhar and Peter Rossi (2004), The Role of Retailon, Demographics and Account Retail Strategy as Drivers of Pro-Sensitivity, Quantitative Marketing and Economics, 2, 16990.erine (2010), A Deal on a Haircut? Thats What Friends Are For,Street Journal, March 24, D3.

., Venkatesh Shankar and Detra Montoya (2007), Recent Trendsging Practices in Retail Pricing, in Retailing in the 21st Cen-ent and Future Trends, Kraft M. and Mantrala M., eds (2nd ed.).Springer Verlag Publishers(2009), The Jordans Furniture Monster Deal: A Legal Gam-rt Marketing Quarterly, (December), 21821.an (2009), Mobile Social Networkers in U.S.ass 56M by 2013, eMarketer, November 16,

w.mobilemarketer.com/cms/news/research/4648.html.argaret C. (1999), Perceptions of Price Unfairness: Antecedentsquences, Journal of Marketing Research, 36 (May), 18799.ge (2008), Signage, 84 (2), 91.rre, J. Wesley Hutchinson, Stephen J. Heyman, Eric T. Bradlow,P. and Scott H. Young (2009), Does In-Store Marketing Work?the Number and Position of Shelf Facings on Brand Attention


and Evaluation at the Point of Purchase, Journal of Marketing, 73 (6),117.

Chandrashekeran, Rajesh, Dhruv Grewal, Rajneesh D. Suri and Nancy M.Puccinelliworking p

Chen, Haipento Four: EConsumer

Chen, Yuxin agic Consu4350.

Chen, S.-F., KPromotionJournal o

Cheng, Hsingwith Inter

Chiou-Wei, SElectronic(Septemb

Compeau, Laing: An In25773.

DelVecchio,or PercenChoice, J

Dube, Jean-P(2008), C27 (3), 41

Feinberg, FreOthers GeMarketing

Fox, E.J., S. PEndogeno

Fruchter, GilaA CustomResearch,

Ganesan, Shaton WeitzEmergingnal of Ret

Gauntt, Johning Pains2000452.a

Grewal, Dhruof Buyerof Trust, PMarketing

Grewal, Dhruford, ReoOnline antive Mark

Grewal, DhruFuture, J

RetailingGrewal, Dhru

Managem114.

Grewal, DhruComparisTransactio

Hall, JosephRetailerment vers17283.

Hui, Sam, PeAn IntegrScience, 2

Inman, J. Jeffrey, Russell S. Winer and Rosellina Ferraro (2009), The Interplaybetween Category Characteristics, Customer Characteristics, and CustomerActivities on In-Store Decision Making, Journal of Marketing, 73, 192.

aoqinugh Gm, Kgies atury:Newasil//abcnl 29,, Pravetailin, Prav

els,petiti

, Pravminatomerol of

, Aradeta-Anal oand Dnal Cichaeerginnal o-Mullistencketingor, Jek, Aprhomics ofle Ri

Scotttute.Scottling,Oppoices RScottomerteracRik,g Des28.

lli, Nahubiriling:0.ndustrsed Peter Eurcha40.

ngseoeward.Gregagem, Venkhesish Ann, Veney (20(2011), Color of the Price: Gender Differences, unpublishedaper, Babson College.g and Akshay Rao (2007), When Two Plus Two Is Not Equalrrors in Processing Multiple Percentage Changes, Journal ofResearch, 34 (3), 32740.

nd John Z. Zhang (2009), Dynamic Targeted Pricing with Strate-mers, International Journal of Industrial Organization, 27 (1),

ent B. Monroe and Y.-C. Lou (1998), The Effects of Framing PriceMessages on Consumers Perceptions and Purchase Intentions,

f Retailing, 74 (3), 35372.Kenneth and Kutsal Dogan (2008), Customer-Centric Marketingnet Coupons, Decision Support Systems, 44 (3), 60620.ong-Zan and J. Jeffrey Inman (2008), Do Shoppers LikeCoupons? A Panel Data Analysis, Journal of Retailing, 84

er), 29730.rry D. and Dhruv Grewal (1998), Comparative Price Advertis-tegrative Review, Journal of Public Policy & Marketing, 17 (2),

Devon, Shanker Krishnan and Daniel C. Smith (2007), Centst? The Effects of Promotion Framing on Price Expectations andournal of Marketing, 71 (3), 15870.ierre, Gunter J. Hitsch, Peter E. Rossi and Maria Ana Vitorinoategory Pricing with State Dependent Utility, Marketing Science,

729.d M., Aradhna Krishna and Z.J. Zhang (2002), Do We Care Whatt? A Behaviorist Approach to Targeted Promotions, Journal ofResearch, 39 (3), 27791.ostrel and J.H. Semple (2009), Optimal Category Pricing with

us Store Traffic, Marketing Science, 28 (4), 70920.E. and John Z. Zhang (2004), Dynamic Targeted Promotions:er Retention and Acquisition Perspective, Journal of Service7 (1), 319.nkar, Morris George, Sandy Jap, Robert W. Palmatier and Bar-(2009), Supply Chain Management and Retailer Performance:Trends, Issues, and Implications for Research and Practice, Jour-ailing, 85 (4), 849.

du Pre (2008), Mobile Advertising: After the Grow-, eMarketer http://www.emarketer.com/Reports/All/Emarketerspx.v, David Hardesty and Gopalkrishnan Iyer (2004), The EffectsIdentification and Purchase Timing on Consumers Perceptionsrice Fairness, and Repurchase Intentions, Journal of Interactive, 18 (4), 87100.v, Ram Janakiraman, Kirthi Kalyanam, P.K Kannan, Brian Ratch-Song and Stephen Tolerico (2010), Strategic Management of

d Offline Retail Pricing: A Research Agenda, Journal of Interac-eting, 24 (May), 13854.v and Michael Levy (2007), Retailing Research: Past, Present andournal of Retailing, 83 (4), 44764.

and (2009), Emerging Issue inResearch, Journal of Retailing, 85 (4), 5226.v, Michael Levy and V. Kumar (2009), Customer Experienceent: An Organizing Framework, Journal of Retailing, 85 (1),

v, Kent B. Monroe and R. Krishnan (1998), The Effects of Priceon Advertising on Buyers Perceptions of Acquisition Value andn Value, Journal of Marketing, 62 (2), 4660.

M., Praveen K. Kopalle and Aradhna Krishna (2010),Dynamic Pricing and Ordering Decisions: Category Manage-us Brand-by-Brand Approaches, Journal of Retailing, 86 (June),

ter Fader and Eric Bradlow (2009), Path Data in Marketing:ative Framework and Prospectus for Model Building, Marketing8 (2), 32035.

Jing, Xithro

KalyananoloCeneds.

Katz, Bhttp:Apri

Kopalleof R

KopallePauwCom

KopalleSwaCustScho

KrishnaA MJour

Lee, L.ditio

Levy, MEmJour

LindseyPersMar

McGregWee

Nagle, TTactSadd

Neslin,Insti

Neslin,TeerandServ

Neslin,Custof In

Pieters,tisin1815

PuccineRagReta153

Retail IFocu

Rossi, Pof P321

Ryu, Garal R849

Shaffer,Man

ShankarSyntTent

ShankarRizlg and Jinhong Xie (2009), Customers as Sales Agents: Sellingroup Buying, working paper, University of Florida.irthi, Rajiv Lal and Gerd Wolfram (2006), Future Store Tech-nd Their Impact on Grocery Retailing, in Retailing in the 21stCurrent and Future Trends, Manfred Kraft and Murali Mantrala,York: Springer Berlin Heidelberg, 95112.(2010), New York Bar to Set Menu Prices Like Stocks,ews.go.com/Travel/wireStory?id=10190154, March 24 (accessed

2010).een K. (2010), Modeling Retail Phenomenon: Editorial, Journalg, 86 (June), 11724.een, Dipayan Biswas, Pradeep K. Chintagunta, Jia Fan, KoenBrian Ratchford and James Sills (2009a), Retailer Pricing andve Effects, Journal of Retailing, 85 (1), 5670.een K., Scott A. Neslin, Baohong Sun, Yacheng Sun and Vanithahan (2009b), The Joint Sales Impact of Frequency Reward andTier Components of Loyalty Programs, working paper, TuckBusiness, Dartmouth College.hna, Richard Briesch, Donald R. Lehmann and Hong Yuan (2002),nalysis of the Impact of Price Presentation on Perceived Savings,

f Retailing, 78 (2), 10118.. Ariely (2006), Shopping Goals, Goal Concreteness, and Con-oupons, Journal of Consumer Research, 33 (1), 607.l, Dhruv Grewal, Praveen K. Kopalle and James Hess (2004),g Trends in Retail Pricing Practice: Implications for Research,f Retailing, 80 (3), 1651165.ikin, Joan and Dhruv Grewal (2006), Imperfect Information: Thee of Price Dispersion on the Web, Journal of the Academy ofScience, 34 (2), 23643.

na (2009), Matching Prices to Demand, in Real Time, Businessil 7.

as T., John E. Hogan and Joseph Zale (2010), The Strategy andPricing: A Guide to Growing More Protably, 5th ed. Upper

ver, NJ: Prentice Hall.A. (2002), Sales Promotion, Cambridge, MA: Marketing Science

A., Dhruv Grewal, Robert Leghorn, Venkatesh Shankar, Miriji L.Jacquelyn S. Thomas and Peter C. Verhoef (2006), Challengesrtunities in Multi-Channel Customer Management, Journal ofesearch, 9 (2), 95112.A. and Venkatesh Shankar (2009), Key Issues in MultichannelManagement: Current Knowledge and Future Directions, Journaltive Marketing, 23 (1), 7081.Michel Wedel and Jie Zhang (2007), Optimal Feature Adver-ign Under Competitive Clutter, Management Science, 53 (11),

ncy M., Ronald C. Goodstein, Dhruv Grewal, Robert Price, Priyaand David Stewart (2009), Customer Experience Management inUnderstanding the Buying Process, Journal of Retailing, 85 (1),

y Report (2000) At What Price? Guidelines for a Customer-ricing Strategy, Arthur Andersen, Report 1, No. 14.., Robert E. McCulloch and Greg M. Allenby (1996), The Valuese History Data Target Marketing, Marketing Science, 15 (4),

g and Lawrence Feick (2007), A Penny for Your Thoughts: Refer-Programs and Referral Likelihood, Journal ofMarketing, 71 (1),

and John Z. Zhang (2002), Competitive One-to-One Promotions,ent Science, 48 (9), 114360.atesh and Sridhar Balasubramanian (2009), Mobile Marketing:

and Prognosis, Journal of Interactive Marketing, 23 (2), 11829.iversary Special Issue

katesh, Jeffrey Inman, Eileen Kelly, Murali Mantrala and Ross11), Innovations in Shopper Marketing, Journal of Retailing,.


Shankar, Venkatesh, Alladi Venkatesh, Charles Hofacker and Prasad Naik(2010), Mobile Marketing in the Retailing Environment: Current Insightsand Future Research Avenues, Journal of Interactive Marketing, 24 (2),11120.

Silicon India (2008), Altierre: Revolutionizing Retail Space, 11 (10), 38.Sorensen, Herb (2003), The Science of Shopping, Marketing Research, 15 (3),

305.Sorescu, Alina, Ruud Frambach, Jagdip Singh, Arvind Rangaswamy and

Cheryl Bridges (2011), Innovations in Retail Business Models, Journal ofRetailing,.

Stilley, Karen M., J. Jeffrey Inman and Kirk Wakefield (2010a), Spending onthe Fly: Mental Budgets, Promotions, and Spending Behavior, Journal ofMarketing, 74 (May), 3447.

, and(2010b), Planning to Make Unplanned Purchases? The Role of In-StoreSlack in Budget Deviation, Journal of Consumer Research, 37 (August),26478.

Sultan, F. and A.J. Rohm (2005), The Coming Era of Brand in the HandMarketing, MIT Sloan Management Review, 47 (Fall), 8390.

and (2008), How to Market toGeneration m(obile), MIT Sloan Management Review, 49 (4), 3541.

Supermarket News (2005), The ESL Connection, May 30, 43.Suri, Rajneesh, D. and Dhruv Grewal (2011), When Left Is Right and Right

Maybe Wrong, working paper, Babson College.Syam, Niladri B., Ranran Ruan and James D. Hess (2005), Cus-

tomized Products: A Competitive Analysis, Marketing Science, 24 (4),56984.

Tsiros, Michael and David M. Hardesty (2010), Ending a Price Promotion:Retracting It in One Step or Phasing It Out Gradually, Journal ofMarketing,74 (9), 4964.

U.S. Census Bureau (2008), Annual Retail Trade Report, available athttp://www2.census.gov/retail/releases/current/arts/sales.pdf.

Vadlamani, Ravi, Kalyan Raman and Murali K. Mantrala (2006), Applicationsof Intelligent Technologies in Retail Marketing, in Retailing in the 21stCentury: Current and Future Trends, Manfred Krafft and Murali Mantrala,eds. New York: Springer Berlin Heidelberg, 17388.

Van Der Lans, Raif, Rik Pieters and Michel Wedel (2008), Eye-MovementAnalysis of Search Effectiveness, Journal of the American Statistical Asso-ciation, 103 (482), 45261.

van Heerde, Harald J. and Scott A. Neslin (2008), Sales Promotion Models,in Handbook of Marketing Decision Models, Wierenga Berend ed. SpringerPublishers, 10762.

Venkatesan, Rajkumar and Paul Farris (2010), Measuring and Managing theCustomer Profitability of Retailer-Targeted Coupon Campaigns, workingpaper, University of Virginia.

Wall Street Journal (2011), Groupon Flaw In the Business, January 7, p. B1.Webb, Warren (2008), RFID in Embedded Designs, Electronics Weekly, 2365

(December 17), 147.Wilkinson, J.B., J. Barry Mason and Christie H. Paksoy (1982), Assessing the

Impact of Short-Term Supermarket Strategy Variables, Journal of Market-ing Research, 19 (February), 7286.

Winer, Russell S. (1986), A Reference Price Model of Demand for Frequently-Purchased Products, Journal of Consumer Research, 13 (September),2506.

Xu, David Jingjun, Stephen Shaoyi Liao and Qiudan Li (2008), CombiningEmpirical Experimentation and Modeling Techniques: A Design ResearchApproach for Personalized Mobile Advertising Applications,Decision Sup-port Systems, 44 (3), 71024.

Zhang, Jie and Els Breugelmans (2010), The Impact of an Item-Based LoyaltyProgram, Unpublished working paper, University of Maryland.

Zhang, Jie and Michel Wedel (2009), The Effectiveness of Customized Pro-motions in Online and Offline Stores, Journal of Marketing Research, 46(April), 190206.

Innovations in Retail Pricing and PromotionsResearch area 1: more focused and targeted promotionsTargeted retail promotions using loyalty dataTargeted online promotionsTechnological enablers

Research area 2: price and promotion modelsDynamic pricing modelsNew types of promotionsVolume-based pricingTechnology enablers

Research area 3: promotional design elementsOffline and online promotion design elementsTechnology enablers

Research issuesAcknowledgementsReferences

Documents

00b7d528845e62e610000000