Decision Support Systems 46 (2008) 61–69

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Decision Support Systems

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Shopbot 2.0: Integrating recommendations and promotions withcomparison shopping

Robert Garfinkel a,1, Ram Gopal a,2, Bhavik Pathak b,3, Fang Yin a,⁎a Department of Operations and Information Management, University of Connecticut, Storrs, CT 06269, United Statesb Department of Decision Sciences, Indiana University South Bend, South Bend, IN 46634, United States

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +1 860 486 6182.E-mail addresses: Robert.Garfinkel@business.ucon

Ram.Gopal@business.uconn.edu (R. Gopal), bkpathak@Fang.Yin@business.uconn.edu (F. Yin).

1 Tel.: +1 860 486 1289.2 Tel.: +1 860 486 2408.3 Tel.: +1 574 520 4614.

0167-9236/$ – see front matter © 2008 Elsevier B.V.doi:10.1016/j.dss.2008.05.006

a b s t r a c t

Article history:Received 17 September 2007Received in revised form 5 March 2008Accepted 21 May 2008Available online 3 June 2008

The current generation of shopbots reduce consumer search costs associated with determiningthe best purchase price and place to buy a product predetermined by the shopper. In order toprovide better service to shoppers, the service horizon of these shopbots can be extended inseveral dimensions. In this paper, we suggest that shopbots can integrate retail promotions andincorporate recommender systems in order to provide greater values to their users. Althoughthe majority of online retailers already provide recommender systems, we show that profitmaximizing retailers may not always provide transparent recommendations and argue thatshopbots are in the better position to offer such recommendations. We develop integerprogramming models for shopbots to integrate sales promotions and productrecommendations. We validate our model by using product recommendation data from twopopular online retailers, Amazon.com and Buy.com, to show that our model providesrecommendations that offer better value to the price sensitive shopbot customers.

© 2008 Elsevier B.V. All rights reserved.

Keywords:Recommender systemsSales promotionsShopbotsOnline retailing

1. Introduction

Shopbots or comparison shopping agents, which arewebsites providing online comparison shopping services tomillions of shoppers every day, have been helping reduce thesearch costof price and retailer-related informationon theWeb,and thereby improving market efficiency. They allow shoppersto search for and compare prices and inventory information ofgoods across a large number of sellers. Some shopbots alsoprovide information on sellers' service quality. Research onshopbots has focused on technical issues such as shopbot in-terface [12], consumer behavior issues such as price sensitivity

n.edu (R. Garfinkel),iusb.edu (B. Pathak),

All rights reserved.

and brand loyalty [5,18], and retailer behavior issues such asprice dispersion and competition [5,10,17].

The service currently provided by themajority of shopbotsis relatively narrow in the sense that it only allows shoppers tocompare prices of a single product of which they are alreadyaware. The service horizon of shopbots could possibly beexpanded in several dimensions. For example, since currentshopbots focus almost exclusively on searches for a singleitem, models for finding the best price for a bundle of itemshave been proposed [8]. Such models may reduce the searchcosts for those shoppers who are interested in purchasing abundle of items. Another possible dimension to expand theshopbot service horizon could be to integrate sales promotioninformation into the search results presented to shoppers. Asbusiness to consumer electronic commerce continues to growand the competition among online retailers becomes moreintense, retailers turn to various strategies to attract sales onthe Web. One common strategy is to offer promotions tocompete with other retailers on prices. For example, manyretailers offer free shipping for a minimum purchase. “Dollar-off” and “percentage-off” promotions for aminimumpurchaseare also very common among online retailers. As shopbots are

4 In addition to being an online retailer, Amazon.com is also a marketprovider and service provider. However, in the context of this paper, we onlyrefer to the aspect of Amazon.com as a pure retailer.

Fig. 1. A book webpage from Amazon.com.

62 R. Garfinkel et al. / Decision Support Systems 46 (2008) 61–69

particularly appealing to price sensitive shoppers [15] whousually seek various sales promotions, it would increase thevalue of shopbot service to shoppers to integrate sales pro-motion information into the price quotes presented byshopbots.

Yet another possible way to expand shopbots service is toincorporate recommender systems. The proliferation of SKUsat various online retailers has made shoppers' product selec-tion problem more complex. Online retailers have startedproviding value-added services such as product reviews andratings as well as recommendations in order to facilitateshoppers' purchase decision making. These different forms ofdigital word of mouth reduce the uncertainty that isassociated with the purchase of unfamiliar products, espe-cially those products for which quality is difficult or im-possible to evaluate before purchase and usage. Given the highvolume of traffic to popular shopbot websites, integration ofconsumer-centric, product-specific decision support systems,such as recommender systems, into shopbots may providemore value to the shoppers by reducing their product-specificsearch costs. None of the popular shopbots provides state-of-the-art recommender service similar to those provided bymajor e-tailors such as Amazon. Therefore, this research pro-poses an approach based on integer programming to integrateboth sales promotion information and product recommenda-

tion servicewith current price comparison service of shopbotsto create a next generation shopping agent, which we termshopbots 2.0. Next, we use a simple example from therecommender system implemented by Amazon.com to illus-trate our approach to integrating promotions and recommen-der systems with shopbots and to provide context for thediscussion in later sections.

Amazon.com4 has become the dominant player in theonline book industry with a market share of more than 60%[11]. Amazon.comwas also among the first online retailers tooffer recommendations. On every product page as shown inFig. 1, the pricing, availability, and version information arelisted first for a given book (the base item). Then a singlerecommended item (hereafter termed best bet) is offeredunder “Better Together”, along with the total price of the two-book package. Furthermore, a group of five related books(hereafter termed choice set) is also provided under the title“Customers who bought this item also bought”. The items inthe choice set are those that were purchased the most byother shoppers who also purchased the base item. The order

Fig. 2. The best bet is not from the choice set.

5 According to the prices on November 30, 2006.

63R. Garfinkel et al. / Decision Support Systems 46 (2008) 61–69

of the items in the choice set is transparently determined by apublished recommendation algorithm in order of the relat-edness of those items to the base item. Therefore, when thebest bet is a member of the choice set, the resulting two-bookbundle consists of strongly related books. Selecting a best betis a business decision of Amazon.com and is therefore nottransparent to the customer. Simply selecting a best bet fromthe choice set may increase the probability of it being sold,but it may not fulfill the possible profit maximizationobjective of a retailer. As a profit maximizing economicagent, Amazon.com may select a best bet that may providemaximum profitability based on the contribution from acandidate best bet and probability of it being sold. Wewill seethat there are many instances in which the best bet does notcome from the choice set, as illustrated in Fig. 2.

While many online retailers offer savings in the form ofpromotions as well as using recommender services, we will seethat there aremany instances inwhich the latter donot considerthe former in their decision making. In particular, based on ourobservation of Amazon.com, when the best bet comes from thechoice set its selection is basedpurely on its relatedness score. Inmany situations, an item from the choice set that is slightlydifferent in terms of its relatedness but, combinedwith the baseitem, takes advantage of current promotions, could have beenthe best bet, andwould undoubtedly have beenmore appealingto price sensitive shoppers.

For example, for the book “The Blind Side” as the baseitem, Amazon's best bet is “Money Ball”, which is first in thechoice set. However, the total order value of these two books

is $24.88, twelve cents less than the minimum order amountto qualify for free shipping, and thus a shopper would have topay $29.86 in total including the shipping cost of $4.98.

This leaves open the possibility of selecting another itemfrom the choice set costing as little as thirteen cents more andthus saving almost five dollars.5 However, if the base item ispurchased with the book “Losers” from the choice set, thetotal cost is only $25.62 with free shipping. Obviously, if theshopper is price sensitive, and the relatedness of the tworecommended items are not too different from each other,which is very likely since both are from the choice set,recommending the second item would more likely lead to asale. Since there are many different types of promotionsoffered by retailers such as x dollars off order of y dollars ormore and buy one product and get another for free, etc., it is anon-trivial task to identify the optimal recommendation thatwould provide the most value for shoppers. We develop aninteger programming model to solve this problem. It wouldalso be interesting to seewhether the outcome of our solutionwould result in significant possible savings for shopperscompared to the recommendation yielded by current recom-mender systems.

In summary, we introduce a new research perspective forshopbots: the possibility of integrating recommendationspartially based on retailer sales promotions into currentshopbots services. Wemotivate our research by observing the

Table 1Results of logistic regressions (**pb .01; *pb .05)

Dependent variable Best bet is from the choice set

Sales rank 0.0173*(0.00799)

0.00415**(0.000825)

0.00249**(0.000338)

List price −0.2705(0.1617)

−0.1499**(0.0543)

−0.1486**(0.0482)

Amazon price 0.4001(0.2799)

0.2071*(0.0891)

0.2260**(0.0803)

No. of reviews −0.00211**(0.000735)

−0.00084**(0.000286)

−0.00049**(0.000158)

No. of stars −0.0222(0.4659)

0.0828(0.2037)

0.3598*(0.1666)

Number ofobservations

100 477 949

Likelihood ratioChi-square

27.826(pb .0001)

64.9157(pb .0001)

112.7013(pb .0001)

Odds ratio estimatefor sales rank

1.017 1.004 1.002

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current practice of retailer recommendations. We also arguethat shopbots are in the best position to integrate the two, andwe provide an integer programming model that could beimplemented for such integration. The potential savings areverified using a data set assembled from online book retailers.The remainder of the paper proceeds as follows. Relatedliterature and industry background are discussed in Section 2.Section 3 presents an integer programmingmodel to optimizethe recommendations for shopbots and the empirical valida-tion of the model and conclusions are given in Section 4.

2. Recommender systems and the online book industry

Following the seminal paper of Nelson [14], there has beena rich literature on consumer behavior in purchasingexperience goods such as books, movies, and concerts, forwhich it is relatively easier to assess quality after actualconsumption. The basic theme of the theory is that since it isusually very costly, and sometimes even impossible, toevaluate the quality of experience goods, shoppers turn toother sources of information on product quality whenmakingpurchase decisions. Empirical studies have shown the impactof product information on demand from various sources suchas pricing [2], advertising [15], and expert reviews [4,16].Since the explosion of Internet usage, this line of research hasexpanded to study the impact of digital word of mouth [3]and even peer-to-peer file sharing [9].

Online recommendation systems can be considered to beanother source of product quality information that is based onthe past purchasing/browsing behavior of shoppers. It bearsclose resemblance to word of mouth. However, in contrast tothe lack of control of word of mouth, a retailer has full controlover what algorithm to use for the recommendation systemand how to present the recommendations. One is not requiredto purchase a product before reviewing it. Recommendationsdiffer in that they are typically made based on actualpurchases, and therefore can be considered to be more ob-jective than customer reviews and ratings.

Shoppers follow two-stage decision making while pur-chasing a book. The first stage consists of finding a preferredbook among many alternatives and in the second stageshoppers seek the best place to purchase a book and usuallythis decision is based on price. Books are experience goodsduring the first stage of decision making and quasi-commod-ity in the second stage of decision making [6]. Recommendersystems assist shoppers in the first stage of decision makingand hence in the context of our work, we consider books asexperience goods. As a typical example of experience goods,books have been the object of numerous studies on electroniccommerce theory and practice because of the homogeneity ofthe product across different retailers, and the large number ofproducts available for sampling. These are also the reasons forchoosing books as the object of observation in this study. Inaddition, there is one more feature of books that is uniquelyappealing in the current context. Recommended items forbooks are almost always other books, whichmakes it easier tocompare across different retailers. In contrast, recommenda-tions for other products could be from completely differentproduct categories across different retailers.

The effectiveness of recommendations has been studiedextensively, based on various technical measures of the

accuracy of recommendations. However, these measures donot reflect the business value of recommendations toshoppers [1]. While the relatedness of a recommended itemto the base item should be an important criterion of theusefulness of the recommendation, its appeal to a shopper isalso crucial. If, for instance, a shopper decides not to purchasethe best bet, its business value is zero nomatter how closely itis related to the base item. On the other hand, if an item that isa little less related to the base item but much more appealingto the shopper is substituted, it would more likely result in asale. To many shoppers, the appeal of an item is very muchrelated to the price of, and potential savings resulting from itspurchase.

The Amazon free shipping example of the previous sectionshows that recommendations provided by retailers may notbe fully aligned with other initiatives and incentives tomaximize their value to shoppers. Although there could bemany measures of the appeal of an item to a shopper, webelieve that price and resulting savings are always among themost important ones for homogeneous products like books. Ingeneral the items in the choice set are not very different interms of how related they are to the base item. Thus, when thebest bet comes from the choice set, it may be possible tochoose an alternative member of that choice set to increasethe value of the recommendation to a shopper by conservingrelatedness and simultaneously yielding financial benefits.

Retailers as profit maximizers may also adopt a recom-mendation strategy to select alternate best bets from outsideof the choice sets. For instance they may be interested incross-selling slow-moving items by tagging them along withfast-moving items. Retailers could also use the best bet toprovide a subtle way for publishers or authors to promotetheir own books [7,13]. In order to understand such strategies,we collected Amazon.com's recommendation data to run alogistic regression. The binary dependent variable indicateswhether the best bet is from the choice set (value is one) ornot. The independent variables are sales rank, list price,Amazon price, number of reviews, and average star ratings ofthe base item. Here, the sales rank of a book is a numberspecifying the relative position of a book in terms of its salesquantity on Amazon.com, number of reviews is the total

65R. Garfinkel et al. / Decision Support Systems 46 (2008) 61–69

number of customer feedbacks received for a product onAmazon.com, average star ratings is the aggregated rating fora product, on the scale of 1 to 5, provided by customers. Weran the regression on the top 100, 500, and 1000 books on arandomly chosen day. The results are shown in Table 1.

The coefficients for sales rank and number of reviews aresignificant across the three regressions, indicating that themore popular a base item book is (lower sales rank and highernumber of reviews), the more likely that the correspondingbest bet will come from outside of the choice set. It isimportant to note here that as the sales rank specifies therelative position of the book in terms of the sales quantity,lower sales rank implies higher sales quantity and hencemorepopularity. The impact gets stronger as popularity increases.The odds ratio measures the marginal increase in probabilityper unit increase of rank, which is 1.7% for the top 100, 0.4%for the top 500, and 0.2% for the top 1000 ranked books. Theseresults suggest that there exists a correlation between thepopularity of the base item and the likelihood that items notnecessarily related to the base item being recommended.Even though we cannot identify the real motive behind suchbusiness practice, it is obvious that retailers do not alwaysrecommend the most related items.

3. Choosing the optimal best bet from the choice set

Since discounts and promotions can be of various types,such as: free shipping; fixed amount or fixed percentage ofprice reduction for a bundle; or “buy one get one for free”, theproblem of finding an “optimal” best bet for a price sensitiveshopper is complex. We provide an integer programmingmodel to solve this problem. The model can be implementedby any entity to provide additional service to shoppers. Inparticular we argue that shopbots are in an ideal position toprovide this value-added service to millions of shoppers.

3.1. Savings are possible

When best bets are chosen from the choice set, the defaultchoice for Amazon.com is always the top book in the list. Toverify that savings can result by relaxing that restriction, wecollected data from Amazon.com during June 2005. First, wecalculated the baseline savings resulting from the Amazon bestbuy as the difference between the list prices and the pricescharged by Amazon (henceforth called retailer prices) for thebundle. Then, starting from the top of the choice set, wecalculated the savings resulting from purchasing the bundle ofthebase itemwith each item in the choice set. If the savingswerehigher than the baseline savings, this item was marked as analternatebest bet. The shipping cost is included in the calculationof savings.We also restricted the alternate bundles to thosewithtotal cost no greater than the baseline bundle total cost, so thatthe resulting extra savings are comparable to the baselinesavings. The only available promotion during the data collectionperiod was free shipping for a minimum purchase of $25.

We did the above analysis for the top 100 selling books ona randomly chosen day. Out of these 100 books, we foundalternate best buys with greater savings for 62 books. Theaverage savings from purchasing the alternate bundle were$14.98, which is significantly higher than the average baselinesavings of $12.07. These results show that there exists the

possibility of modifying best buys to increase savings whilemaintaining a high degree of usefulness as measured byrelatedness.

3.2. The role of shopbots

The current generation of shopbots provide price andretailer based search-cost solutions to primarily price sensi-tive shoppers. They are not designed to take into accounteither promotions or recommendations. That is, through theirWeb searches, they are not able to tell the shoppers whichitems are recommended by various retailers to go with therequested item. More critically, even with access to retailers'recommendations, they are not designed to find the best priceamong all retailers that takes into account all of the pro-motions that are available, while restricting the purchase tothe base item plus a recommended item. This provides a greatopportunity for improving shopbot design by incorporatingboth mechanisms – promotions and recommendations – intocurrent shopbot services.

In contrast, even though retailers could implement the samemechanism in their recommender systems, as indicated earlierthis might not be in their own best interest in terms of profitmaximization. Retailers might recommend items based onconcerns other than the relatedness of items, such as inventoryclearance, targeted promotions of writers or books, etc. On theother hand, since shopbots do not possess or sell the items butonly provide information that may lead to sales, shopbots canalways provide such recommendations based on relatednessand promotions to shoppers. Furthermore, since each retailer'srecommendations are uniquely based on the historical dataeach retailer possesses, recommendations are likely to varyacross retailers. Shopbots can aggregate recommendations fromdifferent retailers to provide a more accurate prediction. Basedon the same logic, shopbots might even be able to integrate acustomer's purchase data from different retailers, which alsoleads to potentially more accurate prediction. In addition,shopbot shoppers are shown to be particularly price sensitive[5]. Therefore, we introduce a new research perspective forshopbots: the possible role of shopbots to integrate recommen-dations and sales promotions into their current service.

3.3. An integer programming model

Next we develop an integer programming model that ismeant to be implemented by a shopbot to combine variouspromotions with recommendations to maximize the savings ofrecommended items to shoppers. The model is specific to agiven retailer, so that the overall model for a shopbot shouldconsist of a number of independent models, one for eachretailer since the base item is a singleton.

3.3.1. PromotionsHere we enumerate three of the most common types of

promotions offered by retailers. These are incorporated intothe integer programming model. Naturally, other promotiontypes could be modeled as well.

Free Items: One free item can be received for any order thatconsists of at least another givennumberof purchased items,and where at least a certain amount of money is spent.

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“Dollars off” coupons: Aminimumpurchase amount gets theshopper a coupon that can be used against the purchaseprice of all items.Free shipping: A minimum purchase amount gets the shop-per free shipping.

3.3.2. An integer programming modelHere are the constraints and objective function developed

for Amazon. Let the items in the choice set be indexed by ia{1,⋯,n} and let xi be a binary variable indicating whether ornot the ith book is purchased and paid for, and similarly fiindicates whether that book is chosen to be received free. Theretailer price of the ith book in the choice set is pi, while p0 isthe price of the base item.

The following constraint holds if the shopper specifies thatno more than u books should be purchased, including thebase book;

∑n

i¼1xi � u−1 ð1Þ

The following deal with three types of promotions.

Free items:

A book cannot be both paid for and free;

xi þ fi � 1; i ¼ 1; N ;n ð2Þ

No more than one free book can be received per order;

∑n

i¼1fi � 1 ð3Þ

At least A books must be purchased in order to qualify for afree book;

1þ ∑n

i¼1xi � A ∑

n

i¼1fi ð4Þ

The total amount spent must be at least as great as theprice of the free book;

p0 þ ∑n

i¼1pixi � ∑

n

i¼1pifi ð5Þ

Dollars off coupons: There is a set of “dollar-off” coupons,indexed by k=1,..., ℓ, where an order of total expenditureno less than tk dollars yields a cost reduction of dk dollarsoff the total price. Let yk be a binary variable indicatingwhether or not the kth coupon is used.

No more than one coupon can be received per order;

∑ℓ

k¼1yk � 1 ð6Þ

There is a minimum purchase amount to qualify for eachcoupon;

p0 þ ∑n

i¼1pixi � ∑

ℓ

k¼1tkyk ð7Þ

Free shipping: Let z be a binary variable indicating whetheror not the shipping is free. If the total value of the orderexceeds F dollars then shipping is free, otherwise theshipping cost is fixed at s dollars.

∑n

i¼1pixi− ∑

ℓ

k¼1dkyk � Fz−p0 ð8Þ

Overall budget: The maximum amount that the shopper iswilling to pay is b dollars;

p0 þ ∑n

i¼1pixi− ∑

ℓ

k¼1dkyk þ sz � b ð9Þ

Objective: Let Li denote the list price of the ith book. Theobjective for the shopper's economic gain maximization isto maximize the total savings from the list price plus anysavings from applicable promotions;

max ∑n

i¼1Li−pið Þxi þ ∑

ℓ

k¼1dkyk þ szþ ∑

n

i¼1Lifi ð10Þ

The overall integer programming problem for Amazon isgiven below.

max ∑n

i¼1Li−pið Þxi þ ∑

ℓ

k¼1dkyk þ szþ ∑

n

i¼1Lifi ð11Þ

s.t.

∑n

i¼1xi � u−1 ð12Þ

∑n

i¼1pixi− ∑

ℓ

k¼1dkyk þ sz � b−p0 ð13Þ

∑ℓ

k¼1tkyk− ∑

n

i¼1pixi � p0 ð14Þ

∑ℓ

k¼1yk � 1 ð15Þ

Fzþ ∑ℓ

k¼1dkyk− ∑

n

i¼1pixi � p0 ð16Þ

xi þ fi � 1; i ¼ 1; N ;n ð17Þ

∑n

i¼1fi � 1 ð18Þ

A ∑n

i¼1fi− ∑

n

i¼1xi � 1 ð19Þ

∑n

i¼1pifi− ∑

n

i¼1pixi � p0 ð20Þ

z; xi; fi; yk binary;i ¼ 1; N ;n; k ¼ 1; N ;ℓ ð21Þ

Here n is the size of the choice set and is typically a smallnumber. The additional n+ℓ+1 variables are specific to theAmazon model. The same distinction can be made for the

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constraints. There are a total of n+8 constraints, but any ofthese could be flexible based on the desires of the shopper.The most robust, that do not deal with any particular kind ofpromotion, are the constraints (12) that limit the number ofitems purchased and some variation of the budget constraint(13).

Some retailers also offer percent-off coupons. Usually, dol-lars off coupons are not stackable with any other coupons,including percent-off coupons. Therefore, we can replacedollar-off coupon constraints with percent-off coupon con-straints and get a new formulation, which is provided inEqs. (22)–(26) in Appendix A.

In the formulation (11)–(21) we assume fixed shippingcosts per order. The shipping cost structure of many retailersincludes both fixed and variable charges. For example,Amazon.com charges $3.99 for the first item and $0.99 foreach additional item as long as the total order value is lessthan $25. In the presence of both fixed and variable shippingcharges, our objective function and some of the constraintsbecome nonlinear. A formulation for fixed and variable ship-ping cost is provided in Appendix B.

3.4. Computational results from Amazon and Buy.com

It would be interesting to see howmuch savings can resultfrom the model (11)–(21) and whether the savings aresignificant. We tested the previous model using data collectedfrom both Amazon.com and another online retailer Buy.com.We chose Buy.com as another data source because it offeredvarious dollar-off coupons ($5 off $25, $7.50 off $50, and $10off $70) as well as free shipping during the data collectionperiod, which allows us to test our model with more forms ofpromotions. Amazon.com only offered free shipping promo-tion during the data collection period. Neither offered any freeitem promotion so constraints (17)–(20) did not apply.

We first collected data of the top 100 ranking books fromAmazon in June, 2005. These data include the title, list price,and actual price of the base item, and the price of recom-mended item and choice set items. Data were also collectedfrom Buy.com for the same books. However, Buy.com did notprovide a choice set for 13 books so the actual sample size is 87.

For each book we determine the benchmark savings of thecurrent best bet as the difference between the sum of the listprices of the two books (base item and best bet) and the totalorder cost (including shipping if the purchase does not qualifyfor free shipping). We then solve the integer programming

Table 2Savings of our best bets

Amazon.com Buy.com

Benchmark Our RS Benchmark Our RS

Sample size 87 46Number of booksrecommended

1 1 1 1 (43), 2 (3)

Average order value 30.91 30.04 28.73 24.49Max order value 87.29 87.29 61.59 53.97Min order value 20.96 11.98 16.53 16.40Average savings 12.19 16.23 15.67 21.45Max savings 58.49 58.49 28.40 35.46Min savings −4.98 −3.99 4.41 4.54

model using all the choice set items and restricting the budgetto be no more than the total order cost under the current bestbet. We also limit the maximum number of items to be 5although only in two cases is more than one item recom-mended by the integer programming solution. The resultingsavings are compared to the benchmarks and are summarizedin Table 2.

The average savings from our best bets for Amazon.com($16.23) are 33% higher than the benchmark ($12.19) with aslightly lower average actual cost. The savings for Buy.com are37% higher with actual cost 15% lower than the benchmark.Both differences are statistically significant at pb .001 level.Note that negative savings can occur if the retailer price ishigher than the list price.

In our method, best bets in a recommendation bundleneed not to be the most related item, hence there is a tradeoffbetween savings and relatedness. However, these recom-mendations are for price sensitive shopbot consumers. Theseshoppers will be more responsive to the recommendationsthat offer more savings than those that are more related aslong as the recommendations are not completely unrelated totheir interests. As such, these recommendations are selectedfrom the top n related items so as long as this n remainswithin some threshold value. Thus the recommendationsgenerated from our model will not be completely unrelated tothe shopper's interests.

4. Conclusions

In this researchwe studyhow to enhance the business valueof recommendations by incorporating various sales promotionmechanisms into recommendations so that a synergy can beformed among the two.We explore the potential misalignmentbetween sales promotions and recommendations in the onlinebook industry by showing that recommendations could havebeen modified to be more appealing to certain shoppers whilepreserving the relatedness of the recommendations.

We point out the potential conflict between retailers'incentives to provide recommendations and shoppers' desireto find related items. On the one hand, retailers want toenhance shopping experience and increase customer loyaltyby providing accurate recommendations. On the other hand,as profit maximizers, retailers do have the incentive tomodifyrecommendations to either convey quality related informa-tion to shoppers or serve their own operational purposes suchas inventory clearance through cross-selling. Using thelimited data, we show that the action of modifying recom-mendations is correlated with the ranking of base items,therefore suggesting that retailers are taking advantage of thepopularity of base items for their own economic goal. In aseparate research on the impact of recommendations onsales, it is found that demand elasticity of recommendationsdoes not change when the best bet recommended items arenot from the choice set vs. when they are from the choice set.This provides feasibility to retailers if they do wish to utilizerecommendations for their operational purposes withoutnegatively affecting the perceived trustworthiness of theoutcome of recommender systems.

In contrast, shopbots are in a better position to serve theinterests of shoppers due to their role of infomediary. Shopbotsdo not own and sell items but only provide information that

68 R. Garfinkel et al. / Decision Support Systems 46 (2008) 61–69

facilitates transactions. Therefore, shopbots recommender sys-tems are impartial in the sense that they would not benefit fromrecommending items that are not based on the relatedness ofitems. Furthermore, shopbots have access to data from multipleretailers and therefore they can provide more accurate recom-mendations. We propose that shopbots could become theintegrator of recommendations and sales promotionseven though none of these are part of the services they currentlyprovide. An integerprogrammingmodel is developed that canbeimplemented by a shopbot to solve for the optimal recom-mendations. Using data from two major online retailers, thismodel is shown to generate significant savings for shoppers.

Coming back to what retailers can do to align the recom-mendations with their own economic goals, one implication ofthe current research is that retailers could benefit fromcustomized recommendations for different customer segmentsor preferences. For price sensitive shoppers, retailers couldimplement the same model we propose for shopbots to gene-rate recommendations that would more likely lead to actualsales. For shoppers who are willing to pay more to get a real fitfor their taste, it could be beneficial to recommend the itemsbased purely on relatedness. When the need of moving slowinventory items becomes paramount, retailers could userecommendations as a mechanism to be combined withexisting price discounts. In this case, the shopbot model canbe used as a base for retailers to design their discount strategyso that the effect of promotions are not negated but strength-ened by recommendations.

Note that our integer programming model is based onmaximization of potential savings to cater to the need of deal-hunting shoppers. Therefore, it might not be in the bestinterest of those shoppers who are willing to pay more for abetter-matched recommendation. This is a limitation of ourresearch. However, we believe it would not be too difficult tomodify the objective function and constraints so that itsatisfies other needs of shoppers as well, which could result inmultiple configurations of the same recommender systems.

One possible extension to the current study is to introducemeasures of business values of recommendations from theretailer's perspective, such as profit of the recommended bun-dle, or reduction in inventory cost. This will enable retailers tobetter align the recommendations with their own economicgoals. Another interesting direction for future research is tostudy the reaction of shoppers to thosemodified recommenda-tions. Specifically, if themodified recommendationsdo increasethe demand for the recommended items, retailers shouldconsider using recommendations for their own benefit. Also dodifferent types of shoppers react differently tomodified recom-mendations? The findings could help any recommender sys-tems customize their recommendations.

Appendix A. Constraints for percent-off coupon

In the case of a percent-off coupon with α off the totalvalue over G, our formulation will be changed as follows.

max ∑n

i¼1Li−pið Þxi þ α ∑

n

i¼1pixi þ ∑

n

i¼1Lifi þ sz ð22Þ

will be the new objective function. The following will replaceEqs. (12), (13), (14), and (16) respectively.

Maximum items in a recommendation bundle:

∑n

i¼1xi � u−1 ð23Þ

Budget constraint:

∑n

i¼1pi 1−αð Þxi þ sz � b−p0 ð24Þ

Percent-off eligibility constraint:

∑n

i¼1pixi � G−p0 ð25Þ

Free shipping eligibility constraint:

∑n

i¼1pi 1−αð Þxi � F 1−zð Þ−p0 ð26Þ

We can determine the optimal solution for Eq. (11) andthen solve Eq. (22). If we find a feasible solution for Eq. (22),then we will further compare the solutions from Eq. (22) andselect the one that provides greater savings.

Appendix B. Non-linear shipping promotions

Max ∑n

i¼1Li−pið Þxi þ ∑

l

k¼1dkyk− sþ v ∑

n

i¼1xi

� �zþ ∑

n

i¼1Lifi ð27Þ

s.t.Maximum items in a recommendation bundle:

∑n

i¼1xi � u−1 ð28Þ

Consumer's budgetary constraint:

∑n

i¼1pixi− ∑

l

k¼1dkyk þ sþ v ∑

n

i¼1xi

� �z � b−p0 ð29Þ

Discount eligibility constraint:

p0 þ ∑n

i¼1pixi � ∑

l

k¼1tkyk ð30Þ

Coupon stackability constraint:

∑l

k¼1yk � 1 ð31Þ

Free shipping eligibility constraint:

∑n

i¼1pixi− ∑

l

k¼1dkyk � F 1−zð Þ−p0 ð32Þ

Buy-A-get-one-free constraints remain the same as Eqs. (17)–(20).

When a retailer offers both types of coupons, we need tosolve the variable shipping costs case in two stages and for thesecond stage, a model similar to Eqs. (22)–(26) can beformulated. As can be seen from the formulation our objectivefunction (27) as well as consumer budget constraint (29) hasbecome nonlinear in the presence of variable shipping costs.

R. Garfinkel et al. / Decision Suppor

References

[1] G. Adomavicius, A. Tuzhilin, Toward the next generation of recommen-der systems: a survey of the state-of-the-art and possible extensions,IEEE Transactions on Knowledge and Data Engineering 17 (6) (2005)734–749.

[2] R.E. Caves, D.P. Greene, Brands' quality levels, prices, and advertisingoutlays: empirical evidence on signals and information costs, Interna-tional Journal of Industrial Organization 14 (1) (1996) 29–52.

[3] J.A. Chevalier, D. Mayzlin, The Effect of Word of Mouth on Sales: OnlineBook Reviews, 2004.

[4] J. Eliashberg, S.M. Shugan, Film critics: influencers or predictors? Journalof Marketing 61 (2) (1997) 68–78.

[5] G. Ellison, S.F. Ellison, Search, Obfuscation, and Price Elasticities on theInternet, 2004.

[6] J.M. de Figueiredo, Finding sustainable profitability in electroniccommerce, Sloan Management Review 41 (4) (2000) 41–52.

[7] L.J. Flynn, Like This? You'll Hate That. (Not All Web RecommendationsAre Welcome.), New York Times, 2006 pp. 1; Section C; Column 2;Business/Financial Desk.

[8] R. Garfinkel, R.D. Gopal, A.K. Tripathi, F. Yin, Design of a bundle shopbot,Decision Support Systems 42 (3) (2006) 1974–1986.

[9] R.D. Gopal, S. Bhattacharjee, G.L. Sanders, Do artists benefit from onlinemusic sharing? Journal of Business 79 (3) (2006) 1503–1533.

[10] G. Iyer, A. Pazgal, Internet shopping agents: virtual co-location andcompetition, Marketing Science 22 (1) (2003) 85–106.

[11] S. Latcovich, H. Smith, Pricing, sunk costs, and market structure online:evidence from book retailing, Oxford Review of Economic Policy 17 (2)(2001) 217–234.

[12] A.L. Montgomery, K. Hosanagar, R. Krishnan, K.B. Clay, Designing abetter shopbot, Management Science 50 (2) (2004) 189–206.

[13] Y.Q. Mui, Wal-Mart blames web site incident on employee's error,Washington Post, 2006 pp. Financial D01.

[14] P. Nelson, Information and consumer behavior, The Journal of PoliticalEconomy 78 (2) (1970) 311–329.

[15] P. Nelson, Advertising as Information, The Journal of Political Economy82 (4) (1974) 729–754.

[16] D.A. Reinstein, C.M. Snyder, The influence of expert reviews onconsumer demand for experience goods: a case study of movie critics,Journal of Industrial Economics 53 (1) (2005) 27–51.

[17] M.D. Smith, The impact of shopbots on electronic markets, Journal of theAcademy of Marketing Science 30 (4) (2002) 442–450.

[18] M.D. Smith, E. Brynjolfsson, Consumer decision making at an internetshopbot: brand still matters, The Journal of Industrial Economics 49 (4)(2001) 541–558.

Dr. Robert Garfinkel is a professor in theOperations and Information ManagementDepartment of the School of Business atthe University of Connecticut. Hiswork ona variety of problems in operations re-search, mainly involving combinatorialoptimization, has appeared in such jour-nals as: Operations Research; Manage-ment Science; Informs Journal onComputing; Decision Support Systems,

and Mathematical Programming. His current research hasfocused heavily on the problem of optimally balancing validsecurity concerns against the desire to provide users of adatabase with valuable information. Other ongoing researchstreams include: improvingefficiency inhospital settings; designof markets for grid computing; construction of recommendersystems for shopbots; optimization problems in microfluidicsystems; and optimization in vehicle routing. He is also coauthorof the book Integer Programming with George Nemhauser.

Dr. Ram D. Gopal is currently the GE

Endowed Professor of Business and anAckerman Research Scholar. His currentresearch interests are in the areas of datasecurity, privacy and valuation, databasemanagement, intellectual property rightsand economics of software and musicpiracy, online market design and perfor-mance evaluation, economics of onlineadvertising, technology integration, and

69t Systems 46 (2008) 61–69

business impacts of technology. His research has appeared inManagement Science, Operations Research, INFORMS Journal onComputing, Information Systems Research, Journal of Business,Journal of LawandEconomics, Communications of theACM, IEEETransactions on Knowledge and Data Engineering, Journal ofManagement Information Systems, Decision Support Systems,and other journals and conference proceedings. He currentlyserves as the Ph.D. director for the department and is on theeditorial board of Information Systems Research, Journal ofDatabase Management, Information Systems Frontiers, andJournal of Management Sciences.

Dr. Bhavik Pathak is an assistant professorof Decision Sciences at the School ofBusiness and Economics at Indiana Uni-versity South Bend. Dr. Pathak receivedhisPh.D. in Operations and InformationMan-agement from the University of Connecti-cut in 2006. Dr. Pathak's teaching interestsare in the areas of electronic commerce,decision support systems, managementinformation systems, and data mining. Dr.

Pathak's research interests are in the areas of electronic com-merce, online recommender systems, social networking, shop-bots, and online promotions. His research has been published inthe Journal of Retailing, Communications of the AIS, and In-dustrial Management and Data Systems.

Dr. Fang Yin is an Assistant Professor inthe Operations and InformationManage-ment Department of the School of Busi-ness at the University of Connecticut. Hisresearch interests are in the businessvalue of IT investment, online sales pro-motion, shopbots design, and online re-commender systems. Hehas published inMIS Quarterly, Decision Support Systems,Journal of Retailing, Sloan Management

Review, and several other journals. He holds a Ph.D. from theUniversity of Texas at Austin.