Available online at www.sciencedirect.com

Journal of Interactive Marketing 26 (2012) 141–154www.elsevier.com/locate/intmar

Return on Quality Improvements in Search Engine Marketing

Nadia Abou Nabout & Bernd Skiera ⁎

Department of Marketing, Faculty of Business and Economics, Goethe-University Frankfurt am Main, Grueneburgplatz 1, 60629 Frankfurt, Germany

Available online 24 April 2012

Abstract

In search engine marketing, such as on Google, advertisements' ranking and prices paid per click result from generalized, second-price, sealedbid auctions that weight the submitted bids for each keyword by the quality of an advertisement. Conventional wisdom suggests that advertiserscan only benefit from improving their advertisement's quality. With an empirical study, this article shows that quality improvements have complexeffects whose returns are actually unclear: 5% of all quality improvements to an advertisement lead to higher prices (measured by price per click)per keyword, 100% to a higher number of clicks, 53% to higher costs for search engine marketing, and 37% to lower profits. Quality improvementslead to higher weighted bids, which only lower prices if they do not improve the ranking of the advertisement. Otherwise, better ranks likely lead tohigher prices. A decomposition method can disentangle these effects and explain their effects on search engine marketing costs and profits. Finally,the results indicate that advertisers benefit if they lower their bids after improvements to advertising quality.© 2012 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Keywords: Search engine marketing; Keyword advertising; Online marketing

Introduction

Since the advent and subsequent far-reaching diffusion of theInternet, the means by which consumers obtain information haschanged fundamentally. Search engines have become the maintool consumers use to locate information (Hennig-Thurauet al. 2010; Rangaswamy, Giles, and Seres 2009), and thisshift has been accompanied by the launch of a new and extreme-ly popular online advertising format, known widely as searchengine marketing (SEM), keyword advertising, and paid orsponsored search. Such tactics accounted for 47% of totalworldwide online advertising spending in 2009, and U.S. adver-tisers alone spent $10.7 billion (IAB 2010).

The mechanism supporting SEM works as follows (AbouNabout et al. forthcoming; Skiera and Abou Nabout 2011; Yaoand Mela 2008): A consumer types a keyword, such as “cruisevacation,” into a search engine (e.g., Google) and receives twotypes of results (see Fig. 1). The lower, left-hand part of the screenshows unsponsored search results, whose ranking reflects the rel-evance that the search algorithm assigns to these different results.

⁎ Corresponding author.E-mail addresses: abounabout@wiwi.uni-frankfurt.de (N. Abou Nabout),

skiera@skiera.de (B. Skiera).

1094-9968/$ -see front matter © 2012 Direct Marketing Educational Foundation, Indoi:10.1016/j.intmar.2011.11.001

The other parts, on the top and right-hand side, present sponsoredsearch results. The display of the unsponsored (organic) searchresults is free of charge, whereas advertisers pay for each clickon their ads that appears among the sponsored (paid) search re-sults (Bucklin 2008; Rangaswamy, Giles, and Seres 2009).

For the sponsored search ads, the ranking and prices paid perclick depend on keyword auctions, which are generalized,second-price, sealed bid auctions (Edelman, Ostrovsky, andSchwarz 2007; Varian 2007). The two market leaders, Googleand Yahoo, use similar auction designs (Zhou and Lukose2006): advertisers submit a bid for each keyword at the pricethey are willing to pay for each click. The search engine provid-er weights the submitted bids according to the ad's quality,measured by a proprietary quality score (QS), and ranks theads accordingly (Agarwal, Hosanager, and Smith (2011);Kinshuk et al. 2011; Yao and Mela 2011).

From the search engine provider's point of view, the intro-duction of ad quality to the auction design provides a meansto deal with the hidden cost of user dissatisfaction with poorquality ads (Abrams and Schwarz 2008; Varian 2010). Despitethe massive importance of the QS, neither Google nor the othersearch engine providers publish their exact algorithms for deter-mining the scores. However, Google states that “the higheryour Quality Score, the lower your costs and the better your

c. Published by Elsevier Inc. All rights reserved.

keyword

unsponsored search results

2

3

4

5

6

1

sponsored search results ranks

Fig. 1. Search results in Google.

142 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

ad position” (Google Adwords Help 2009b), which suggeststhat advertisers can only benefit from improving their adquality.1 This suggestion is in line with the assumption em-braced by many search marketers (Danuloff 2009; Soxman2009).

With our empirical study though, we show that quality im-provements have complex effects whose returns are actuallyunclear: 4.84% of all quality improvements to an ad lead tohigher prices (measured by prices per click) per keyword,100% to a higher number of clicks, and 52.57% create highercosts for SEM. Furthermore, 37.23% lower profits. The reasonis that quality improvements lead to higher weighted bids,which decrease prices per click only if the weighted bids donot improve the ad ranking. Otherwise, better ranks likelylead to higher prices per click and higher costs for SEM, withambiguous consequences for profit.

Thus, this article aims to analyze the consequences of im-provements in ad quality on prices per click, the number ofclicks, costs for SEM, and profits. In particular, we developan approach to decompose the different effects of changes inad quality on the outcomes of the keyword auction. In turn,we empirically estimate the impact of changes in ad qualityon rank, price per click, number of clicks, and thus costs forSEM and profits. We apply the proposed decomposition meth-od to two real-world SEM campaigns that consider the resultsof 4,354 changes of ad quality across 162 days.

1 Google's statements about the benefits of a quality improvement are evenstronger in other languages.

Although we focus on SEM, the results are interesting formultiple other areas that aim to match buyers (here, searchers)with sellers (here, advertisers). Online comparison shoppingWeb sites such as Kelkoo and shopping.com establish retailerrankings to match consumers' needs. Although many Websites currently issue ranks by product prices or retailers' rep-utation, they recently started to do so on the basis of retailers'bids for the price of each click on their listings. These pricesper click then are weighted by the retailers' reputation orproduct prices. Similar arguments would apply to dating andauction Web sites. Furthermore, whereas we focus on weightslinked to ad quality, similar weights could be linked to factorssuch as the speed of Web sites, delivery times, or retailers'reputation. Thus, our results should hold for areas beyondSEM.

To accomplish our research aims, we organize the remain-der of this article as follows: we briefly review literature onSEM, then formally describe how ad quality influences the out-comes of the keyword auction. In particular, we show how todisentangle direct and indirect price and quantity effectsusing our proposed decomposition method. Next, we presentthe results of our empirical study, which we conduct in two dif-ferent industries, the business-to-consumer travel market andthe business-to-business industrial goods market. We simulta-neously model the search engine's keyword rank-allocatingand pricing behavior and consumers' click behaviors. The re-sults reveal the effects of changes in actual ad quality on pricesper click, number of clicks, SEM costs, and profit. We con-clude with a summary of the results, managerial implications,and directions for further research.

143N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

Previous Research on Search Engine Marketing

Despite growing attention among search advertisers(Danuloff 2009; Soxman 2009), ad quality continues to be ig-nored by academic researchers. Emerging streams of theoreticalresearch instead deal with the optimal design of keyword auctions(Edelman, Ostrovsky, and Schwarz 2007; Feng 2008; Varian2007), possible improvements to current designs (Blumrosen,Jason, and Nong 2008; Chen, Liu, and Whinston 2009;Gunawardana, Meek, and Biggs 2008), or bidding behavior inkeyword auctions (Edelman and Ostrovsky 2007; Kleinberg2005; Zhou and Lukose 2006). These results have relevance pri-marily for search engine providers.

Another research stream analyzes key questions from the ad-vertisers' perspective, including forecasts of the performance ofsingle keywords depending on specific ad properties (Rutz andTrusov 2011), spillover effects from generic to branded key-words (Rutz and Bucklin 2011), and indirect effects of SEM(Rutz, Trusov, and Bucklin 2011). Goldfarb and Tucker(2011) explore substitution patterns across advertising platformsand show that search engine marketing substitutes for offline ad-vertising when lawyers cannot contact clients by mail. Yang andGhose (2010) analyze the relationship between organic andsponsored search and find that clickthroughs on organic listingshave a positive interdependence with clickthroughs on paid list-ings, and vice-versa. Skiera, Eckert, and Hinz (2010) analyzethe long tail in SEM, which they define as the vast number ofless popular keywords employed by users to search the Internet.They find that advertisers can largely ignore the performance ofkeywords that fall in this long tail.

Still other studies empirically analyze SEM performance andmodel the relationships between rank and bids, rank and click-through rate (CTR), or rank and the percentage of consumerswho click on an ad and then finally become customers (Feng,Bhargava, and Pennock 2007; Ganchev et al. 2007; Misra,Pinker, and Rimm-Kaufman 2006). Building on this stream ofresearch, Abou Nabout et al. (forthcoming) analyze the perfor-mance of fee-based compensation plans in SEM and recom-mend compensation plans that rely on the idea of sharingprofits. Finally, Ghose and Yang (2009) estimate the impact ofvarious keyword attributes on consumers' clickthrough and pur-chase propensities, the advertiser's bid, and the search engineprovider's ranking decision. Their study is the only one that in-corporates past CTR and landing page quality as a proxy for adquality to model the search engine provider's ranking decision.These authors show that the search engine provider incorporatesprior CTR to determine the rank of the keyword; they also findthat bids have a greater role in determining the rank than do adquality-related factors.

Ad Quality in Search Engine Marketing

Search Engine's Ranking and Pricing Decision

In an unweighted, generalized, second-price auction, adver-tisers submit bids for a specific keyword by stating the pricethat they are willing to pay for each click on their ad

(Edelman and Ostrovsky 2007). The sponsored search resultsthen display in decreasing order of submitted bids for the re-spective keyword, such that the ad with the highest bid appearsat the top (i.e., first rank, r=1), the ad with the next highestbid is in the second rank (r=2), and so on (Yao and Mela2008). If a user clicks on an ad at rank r, the correspondingadvertiser pays the search engine provider an amount equalto the next highest bid (Hanson and Kalyanam 2007), that is,the bid offered by the advertiser at rank (r+1). Thus, each ad-vertiser must just pay enough to beat the competition (by onecent).

Followed by Yahoo in late 2006, Google first launched aweighted auction format that incorporated the QS as a measureof ad quality in 2002 (Liu, Chen, and Whinston 2010). Googleofficially states that it uses three determinants to calculate thisweight for each advertiser and each keyword (GoogleAdwords Help 2009b). Historical CTR represents the major de-terminant of QS and comprises the CTR of the keyword, theCTR of all ads and keywords in the ad group, and the CTRof all ads and keywords in the campaign. A second determinantis the relevance of the keyword to the ads in its ad group, aswell as the relevance of the keyword and the matched ad tothe search query. Finally, landing page quality constitutes thethird determinant of QS. However, the exact weighting thatGoogle uses for calculating the QS according to these three de-terminants is not publicly available.

To make a ranking decision, the search engine provider cal-culates a weighted bid WBidi for each advertiser j (j=1,…J) inthe keyword auction, equal to the product of the bid, Bidj, andthe Quality Score, QSj:

WBidj ¼ Bidj⋅QSj ð1Þ

All advertisers are then ranked in decreasing order of theirweighted bids, WBidj, so that the ad of each advertiser isassigned a rank (also called a slot) in the sponsored searchresults.

The price per click, or more commonly the cost per click(CPC), for the advertiser assigned to rank r (CPCr) includesthe weighted bid of the advertiser who scored at rank r+1. Itsprice per click CPCr is therefore calculated according toEq. (2) and is just large enough to beat the competition (there-fore $.01 is added):

CPCr ¼ :01$þWBidrþ1=QSr: ð2Þ

Numerical Example of Price Effects and Quantity Effects

To illustrate how this process works in practice, imagine thatthree advertisers, A, B, and C, with different QS, all bid $.50 ona keyword. Columns (1) and (2) in Table 1 represent the inputsto the keyword auction: bids and the QS of all advertisers. Col-umns (3)–(7) represent the outcomes of the keyword auction.The weighted bids in Column (3) are calculated according toEq. (1) and used to rank the advertisers in decreasing order oftheir weighted bid [see Column (4)]. The prices per click inColumn (5a) are then calculated according to this ranking,

Table 1Numerical example: Effects of ad quality improvements on outcomes of keyword auctions.

Input Outcome

(1) (2) (3)=(1)⋅(2) (4) (5a) (5b) (6a) (6b) (7a)= (5a)⋅ (6a) (7b)= ($1−(5a))⋅(6a)

Bid Quality Score Weighted Bid Rank Cost per Click Cost per Click′a Clicks Clicks′b SEM costs Profit after SEM costsc

Situation 0A $ .50 8 4.0 1 3.0/8+ .01=$.39 ./. 100 ./. ./. ./.B $ .50 6 3.0 2 1.5/6+ .01=$.26 ./. 90 ./. $23.40 $66.60C $ .50 3 1.5 3 min price=$.05 ./. 80 ./. ./. ./.Situation A1: Direct Price Effect=−$.04, Indirect Price Effect=Indirect Quantity Effect=Direct Quantity Effect=0A $ .50 8 4.0 1 3.5/8+ .01=$.45 ./. 100 ./. ./. ./.B $ .50 7 3.5 2 1.5/7+ .01=$.22 1.5/7+ .01=$.22 90 90 $20.19 $69.81C $ .50 3 1.5 3 min price=$.05 ./. 80 ./. ./. ./.Situation A2: Direct Price Effect=−$.04, Indirect Price Effect=Indirect Quantity Effect=0, Direct Quantity Effect≠0A $ .50 8 4.0 1 3.5/8+ .01=$.45 ./. 100 ./. ./. ./.B $ .50 7 3.5 2 1.5/7+ .01=$.22 1.5/7+ .01=$.22 92 92 $20.63 $71.37C $ .50 3 1.5 3 min price=$.05 ./. 80 ./. ./. ./.Situation B1: Direct Price Effect=−$.08, Indirect Price Effect≠0, Indirect Quantity Effect=Direct Quantity Effect=0A $ .50 8 4.0 2 1.5/8+ .01=$.20 ./. ./. ./. ./. ./.B $ .50 9 4.5 1 4.0/9+ .01=$.45 1.5/9+ .01=$.18 90 90 $40.90 $49.10C $ .50 3 1.5 3 min price=$.05 ./. 80 ./. ./. ./.Situation B2: Direct Price Effect=−$.08, Indirect Price Effect≠0, Indirect Quantity Effect≠0, Direct Quantity Effect≠0A $ .50 8 4.0 2 1.5/8+ .01=$.20 ./. 90 ./. ./. ./.B $ .50 9 4.5 1 4.0/9+ .01=$.45 1.5/9+ .01=$.18 102 92 $46.35 $55.65C $ .50 3 1.5 3 min price=$.05 ./. 80 ./. ./. ./.aCost per Click′ is the predicted cost per click for the case in which the rank has not changed such that advertiser C still scores below advertiser B. Because B's costper click is then calculated as advertiser C's weighted bid divided by B's Quality Score (+$.01), Column (5b) differs from (5a) where the actual ranking is used toderive the cost per click.bClicks′ are the predicted number of clicks for the case in which the rank has not changed. We assume for the first situations (A1 and B1) that the increase in qualityhas no additional effect on the number of clicks. In the second situations (A2 and B2), we assume that the increase in quality leads to two additional clicks due to thehigher appeal of the ad and to 10 additional clicks in B2 due to the better rank.cAdvertiser B's profit contribution per click=$1.Notes: Bold numbers indicate changes compared with Situation 0. The “min price” describes the minimum price that must be paid for each click (here, $.05). Allcalculations are done by using exact numbers instead of the rounded values that are displayed in the table.

144 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

following Eq. (2). We first assume that the best rank (i.e., rank1) receives 100 clicks, rank 2 receives 90 clicks, and rank 3 re-ceives 80 clicks.

Situation 0 represents the base case; we particularly investi-gate advertiser B. In Situation A1, B's QS increases from 6 to 7,and its price per click decreases from $.26 to $.22 (−14%). Thenumber of clicks remains the same (quantity effect=0), and itsSEM costs decrease by 14%. In Situation B1, B's QS increasesfrom 6 to 9, which puts B in rank 1, for a 75% higher price perclick and 75% higher SEM costs. The reason for this seeminglysurprising result is that the increase in quality has two effects(these arguments are straightforward for the reverse case of de-creased quality). First, it has a direct effect and reduces theprice per click, with the assumption that the rank does notchange (similar to Situations A1 and A2). Second, the indirecteffect on price appears because the increase in quality improvesthe rank, which leads to a higher price per click. Only if the di-rect effect is greater than the indirect effect will the quality im-provement lead to lower prices per click.

We disentangle the total price effect into these two effects bycalculating the price per click that would result if advertisers'ranking did not change in response to the quality improvement[Column (5b)]. The difference between this price [Column

(5b)] and the price paid in Situation 0 ($.26) reveals the directeffect that decreases the advertiser's price; the difference be-tween the total effect and the direct effect is, in essence, the in-direct effect that increases the advertiser's price. Thus, inSituation B1 we observe a negative direct effect of –$.08(=$.18–$.26) and a positive indirect effect of $.27 (=$.45–$.26–[−$.08]). In real-world SEM campaigns it is then anempirical question how large these effects actually are.

Situations A2 and B2 further complicate our calculations be-cause improvements in ad quality might also result in changesto the number of clicks. In Situation A2, the number of clicksis assumed to increase by 2 though the ad ranking remains thesame because the higher quality of the ad is more appealingto consumers. In Situation B2 [Column (6a)], we assume thatthe number of clicks increases by 12, which is the sum of 10clicks added by the better rank and 2 clicks added because ofthe more appealing ad. In turn, this higher number of clicks in-creases the SEM costs in Situations A2 and B2 (from $20.19 to$20.63 and from $40.90 to $46.35); however, profits also in-crease resulting from the additionally acquired clicks (from$69.81 to $71.37 and from $49.10 to $55.65). Again, it is anempirical question how SEM profits are affected by improve-ments in ad quality.

145N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

Formal Decomposition of Price and Quantity Effects

Thus, ad quality might influence prices per click and quanti-ty, and their respective effects on the total costs for SEM perkeyword can be disentangled as follows:

ΔSEMCosts ¼ CPC1⋅Clicks1ð Þ− CPC0⋅Clicks0ð Þ; ð3Þ

where:

CPC0 Cost per click before the QS change (Situation 0, Column(5a)),

CPC1 Cost per click after the QS change (Situations A andB, Column (5a)),

Clicks0 Clicks before the QS change (Situation 0, Column(6a)),

Clicks1 `Clicks after the QS change (Situations A and B, Column(6a)).

Eq. (3) describes the difference in total costs for SEM per key-word. Thus, the total price effect (PE) and total quantity effect(QE) can be given by:

Total Price Effect: PE ¼ CPC1−CPC0ð Þ⋅Clicks0; ð4Þ

Total Quantity Effect: QE ¼ Clicks1−Clicks0ð ÞCPC0 ð5Þ

With Eq. (4), we can further decompose the total price effect(PE) into direct and indirect price effects, DPE and IPE:

Direct Price Effect: DPE ¼ CPC01−CPC0

� �⋅Clicks0; ð4aÞ

Indirect Price Effect: IPE ¼ CPC1−CPC01

� �⋅Clicks0;

¼ PE−DPE; ð4bÞ

where CPC1' is the cost per click after the QS change if the rank

does not change [Column (5b)]. Similarly, we calculate the di-rect and indirect quantity effects, DQE and IQE, according toEqs. (5a) and (5b):

Direct Quantity Effect: DQE¼ Clicks1−Clicks0

� �⋅CPC0and ð5aÞ

Indirect Quality Effect: IQE

¼ Clicks1−Clicks01

� �⋅CPC0 ¼ QE−DQE; ð5bÞ

where Clicks1' is the clicks after the QS change if the rank does

not change [Column (6b)]. The difference between the ΔSEM -Costs in Eq. (3) and the sum of the total price and quantity

effects [Eqs. (4) and (5)] equals the interaction effect (IE). Forcomparable approaches in different contexts, see van Heerdeand Bijmolt (2005); Wiesel, Skiera, and Villanueva (2008).

Interaction Ef f ect: IE ¼ CPC1−CPC0ð Þ⋅ Clicks1−Clicks0ð Þ¼ ΔSEM Costs� PE � QE:

ð6Þ

In line with Eq. (3), we measure the return on quality im-provements by calculating the difference in profit after SEMcosts per keyword as follows:

ΔProf it af ter SEM Costs¼ PC⋅ Clicks1−Clicks0ð Þ−ΔSEM Costs; ð7Þ

Where PC refers to profit contribution per click andPC ⋅ (Clicks1−Clicks0) equals the ΔProfitbeforeSEMCosts.

Fig. 2 summarizes the various effects of ad quality improve-ments (and declines) on profit. A multitude of effects occur, butthe number of clicks always increases as a result of quality im-provements. As a consequence, the advertiser's profit is likelyto increase if the CPC decreases as a result of the quality improve-ment and the profit contribution per click is greater than this costper click. However, if the CPC increases due to a dominating in-direct price effect, the consequences for the advertiser's profit areless obvious because such a quality improvement could lead toincreases or decreases in profit. It is thus an empirical questionhow all these different effects influence the return on qualityimprovements.

Return on Quality Improvements in the Numerical Example

Table 2 reports the results for the effects described inEqs. (3)–(7) for the numerical example outlined in Table 1.In Situations A1 and A2, the indirect effects equal zero be-cause no rank change occurs in response to the quality im-provement. Situation A2 suggests that the direct price effectis much stronger than the direct quantity effect, such that thereduction in price is much larger than the increase in the num-ber of clicks. Therefore, the additional profit contributiongenerated by the higher number of clicks leads to an increasein profit after SEM costs, even though the SEM costs arehigher than in Situation A1. In Situations B1 and B2, the re-sults indicate that the strongest effect is the indirect price ef-fect because prices per click have increased very stronglyand thus so have SEM costs. The result is an overall loss inprofit after SEM costs. In Situation B2 (positive quantity ef-fects), the loss in profit is slightly smaller than in SituationB1 (quantity effects of zero) because the additional profit con-tribution generated by the higher number of clicks partly com-pensates for the price increases due to the high indirect priceeffects.

This numerical example illustrates that a quality improve-ment (decline) may have complex and unexpected effects onprices per click, SEM costs, and ultimately profit after SEMcosts.

Fig. 2. Framework for the return on quality improvements in search engine marketing (SEM).

146 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

Empirical Examination

Purpose

This empirical study aims to analyze the consequences ofimprovements in ad quality on prices per click, the numberof clicks, costs for SEM, and profits. Using data pertainingto two real-world SEM campaigns that consider the resultsof 4,354 changes of ad quality across 162 days, we first esti-mate the impact of changes in ad quality on rank, prices perclick, number of clicks, costs for SEM, and profits. We simul-taneously model the search engine's keyword rank-allocatingand pricing behavior and consumers' click behaviors. Theobtained estimation results enable us to run counterfactual an-alyses on all QS changes in our data and thereby answer thefollowing research questions:

▪ How great is the effect of a quality improvement (decline)on prices per click? In how many cases does a quality

Table 2Decomposition of the return on quality improvements for Advertiser B in thenumerical example.

Situations A1 A2 B1 B2

(1) Total Price Effect (PE) –$3.21 –$3.21 $17.50 $17.50Direct Price Effect (DPE) –$3.21 –$3.21 –$7.50 –$7.50Indirect Price Effect (IPE) $.00 $.00 $25.00 $25.00(2) Total Quantity Effect (QE) $.00 $.52 $.00 $3.12Direct Quantity Effect (DQE) $.00 $.52 $.00 $.52Indirect Quantity Effect (IQE) $.00 $.00 $.00 $2.60(3) Interaction Effect $.00 –$.07 $.00 $2.33Δ Search engine marketingcosts= (1)+ (2)+ (3)

–$3.21 –$2.76 $17.50 $22.95

Δ Profit after search enginemarketing costs a

$3.21 $4.77 –$17.50 –$10.95

a Advertiser B's profit contribution per click=$1.

improvement (decline) result in a reduction (increase) inprices per click? What is the size of the total price effect,as well as the direct and indirect price effects?

▪ How great is the effect of a quality improvement (decline)on the number of clicks? What is the size of the total quan-tity effect, as well as the direct and indirect quantity effects?

▪ How great is the effect of a quality improvement (decline) onSEM costs and thus on profit? In howmany cases does a qual-ity improvement (decline) result in reduced (increased) costsfor SEM and profit? Which effect drives the difference inSEM costs and profit: price or quantity?

Because some SEM campaigns are more profitably man-aged than others, we study two real-world SEM campaignsthat differ largely in their share of profitable keywords inorder to understand how quality improvements (declines) af-fect SEM campaigns under different conditions. In practice,many keywords do not yield positive profits after SEM costsbecause keyword prices are too high compared to the profitcontributions per click. But advertisers frequently subsidizethe losses that occur for some keywords with high gainsearned from other (typically branded) keywords. The obtainedresults therefore enable us to answer the last researchquestion:

▪ How are campaigns with different shares of profitable key-words affected by quality improvements (declines)?

Data

Our data set contains daily information about SEM on Goo-gle for two advertisers in two industries, travel and industrialgoods, whose campaigns are managed by the same Germanperformance marketing agency on behalf of their clients. The

147N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

travel advertiser is a global cruise line brand that provides ex-cursions to more than 50 countries and allows customers tobook cruises online; the industrial goods advertiser is a leadingmanufacturer of industrial equipment that operates globally butdoes not sell its goods online. In the former case, conversionscorrespond to actual sales online; in the latter case, they arecontacts, such as requests for quotes or opportunities for salescalls.

The data pertain to each given keyword on each given day,between 13 January 2009 and 23 June 2009 (162 days total).Because the prices per click related to a keyword may differacross matching types,2 we control for these differences bytreating keywords associated with the different matchingtypes separately. Our final data set includes 97,781 observa-tions of 5,230 unique keywords. In Table 3, we report summarystatistics of our data set for each advertiser, including the num-ber of searches, the number of clicks, the number of conver-sions, the cost per click, the rank, and the QS (from 1 to 10).

The average number of searches per keyword during thestudy phase is 375.21 for travel, of which 37.89 lead to aclick (CTR=10.10%) and .80 lead to a purchase (conversionrate=2.11%). The average number of searches per keywordis much higher for industrial goods (3,648.92), but only121.06 of those lead to a click (CTR=3.32%) and .42 to aconversion (conversion rate= .35%). The average CPC for agiven keyword is €.45 in the travel industry and €.39 for in-dustrial goods. The average rank of these keywords is 2.60 fortravel and 1.46 for industrial goods. Finally, we find a highaverage QS of 9.13 in the travel campaign and an averageQS of 7.78 in the industrial goods campaign.

Overall, we observe 4,354 QS changes—that is, differencesacross two consecutive days—from January to June. For each key-word, the mean number of QS changes during the observation pe-riod is approximately 1. Investigating the QS changes moreclosely, we find that out of 90 possible types of changes (e.g.,

1→2, 1→3, 2→1, 2→3; in total,102

� �⋅2 ¼ 90Þ, only 28

(31%) actually occur during the study phase.3 As we illustrate inFig. 3, approximately 40% of the 4,354 QS changes in our dataset are associated with an increase from either 8 or 9 to 10. Further-more, the 3,038 positive QS changes are more than twice as fre-quent as the 1,316 negative changes.

Finally, the (confidential) profit contribution per click ismuch higher for the industrial goods campaign than for thetravel campaign. Combined with a relatively high cost perclick, which often is higher than the corresponding profit con-tribution per click, many keywords in the travel campaign areunprofitable. Whereas the share of unprofitable keywordsequals 92.47% for the travel advertiser, the industrial goods

2 Search engines, such as Google, typically allow the advertiser to choosefrom four matching types when adding keywords to a campaign: (1) “broadmatch” shows the ad in response to similar phrases and relevant variations,(2) “phrase match” shows the ad in response to searches that match the phrase,(3) “exact match” means the ad shows only in response to searches that matchthe exact phrase exclusively, and (4) “negative match” means the ad does notappear for any search that includes that term (Google Adwords Help 2009a).3 For example, 1→2 means that the quality score changed from 1 to 2.

advertiser only has 28.58% unprofitable keywords in its cam-paign. But the 7.53% profitable keywords in the travel cam-paign account for 19.08% of all searches, 45.53% of allclicks, and even 73.90% of all conversions in the campaign(Table 4). Whereas the whole campaign generates a loss of–€15,743.03, these keywords generate only 21.02% of thecosts for SEM and a profit after SEM costs of €1,975.21.This campaign therefore reflects a situation, in which lessthan 10% of the keywords generate over two thirds of the con-versions. In contrast, the industrial goods campaign is muchmore balanced because 71.42% profitable keywords accountfor 64.35% of all searches, 81.63% of all clicks, 73.75% ofall conversions, and 50.31% of the costs for SEM. While allprofitable keywords generate a profit after SEM costs of€80,016.84, the overall profit after SEM costs equals€59,405.53 for all keywords. Because these campaigns aremanaged by the same agency, the two campaigns therefore re-flect different conditions encountered in real-world SEMcampaigns.

Model

Following Ghose and Yang (2009), we simultaneouslymodel the search engine's keyword rank-allocating and pricingbehavior and consumers' click behaviors, but we focus on thesearch engine's pricing decision because we are interested inthe direct and indirect effects of ad quality on prices per click.Similar to Ghose and Yang (2009), we treat the advertiser'sbidding decision as exogenously determined because it is influ-enced by the advertiser's past performance with the same key-word and its campaign and not its present performance (i.e., itspresent ranking, price per click, clickthrough and conversionrate). The same applies to ad quality (i.e., QS value), whichalso depends on the advertiser's past performance as opposedto its present performance and is thus modeled as exogenouslydetermined.

First, we model the search engine's decision to assign ranksfor a paid keyword ad. During the auction, search engines de-cide on the keyword rank by taking into account both the cur-rent bid and the QS. We use a time trend to control forchanges in the extent of competition in the auction bidding pro-cess over time and unobserved industry dynamics. As Ghoseand Yang (2009) note, heterogeneity from various sources ispresent when modeling SEM data. We therefore capture unob-served heterogeneity across keywords with a fixed effect on theintercept and model the rank, using a log-linear form that de-pends on the two covariates, Bidit and QSit, and the timetrend, Timeit (Ghose and Yang 2009):

ln Rankitð Þ ¼ αi þ β1Bidit þ β2QSit þ β3Timeit þ εit ; ð8Þ

where keyword i=1, …, I, and time t=1, …, T.Second, we model the search engine's decision about the

price per click for a paid keyword ad, denoted as cost perclick: CPCit. During the auction, search engines decide onthe keyword price per click of the ad at rank r by taking intoaccount the weighted bid of the advertiser who scored just

Table 3Summary statistics.

Travel (N=29,339) Industrial goods (N=68,442)

Mean 10thpercentile

90thpercentile

Mean 10thpercentile

90thpercentile

Searches per keyword(total 10,392,146)

375.21 1.00 405.00 3,648.92 2.00 3,840

Clicks per keyword(total 412,325)

37.89 1.00 50.00 121.06 1.00 217.00

Conversions per keyword(total 3212)

.80 .00 .00 .42 .00 1.00

Cost per click(in €)

.45 .17 .80 .39 .06 .82

RankQuality score (QS)

2.60 1.00 4.67 1.46 1.00 2.329.13 8.00 10.00 7.78 7.00 10.00

QS changes(total 4354)

.98 .00 2.00 1.20 .00 3.00

Notes: Number of keywords: 5,230 (travel: 2,655, industrial goods: 2,575).

148 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

below the advertiser at rank r. Because we lack data aboutcompetition, we model the price per click as a function ofrank, which is a frequent way to deal with this condition(Abou Nabout et al. forthcoming; Ganchev et al. 2007; Kittsand LeBlanc 2004). Better ranks generally are associatedwith higher prices per click. We extend these models by add-ing the QS as a covariate and including a time trend. We againcapture unobserved heterogeneity across keywords with afixed effect on the intercept and model the price per click asdependent on two covariates, Rankit and QSit, and the timetrend, Timeit:

ln CPCitð Þ ¼ γi þ δ1Rankit þ δ2QSit þ δ3Timeit þ ϑit: ð9Þ

Because Rankit is determined by Eq. (8) and potentiallycorrelated with the error terms in Eq. (9), υit, we estimate

2 3 7 1 7 11 7 3 1451

4 349

0

100

200

300

400

500

600

700

800

900

1000

Num

ber

of Q

ualit

y Sc

ore

Cha

nges

Notes: Quality Score changes from x to y (x y).

Fig. 3. Distribution of qu

Eq. (9) using two-stage least squares (2SLS), with Bidit asan instrument for Rankit. Note that Eq. (9) accounts for directas well as indirect price effects through QSit and Rankit,respectively.

Third, we model consumers' click behaviors depending onthe rank of the keyword (Abou Nabout et al. forthcoming;Ghose and Yang 2009) and ad quality, as measured by the QS.We again control for unobserved consumer dynamics using atime trend and capture unobserved heterogeneity across key-words with a fixed effect on the intercept:

ln Clicksitð Þ ¼ μ i þ λ1Rankit þ λ2QSit þ λ3Timeitþ ζ it : ð10Þ

In contrast with Ghose and Yang (2009), we do not model theclickthrough probability (CTR) but rather the number of clicks

131153

4

115

439

22

870

488

410

927

15

364

123

1

130

ality score changes.

Table 4Profitability of keywords.

Travel(N=29,339)

Industrial Goods(N=68,442)

Share of profitablekeywords

7.53% 71.42%

—In total searches 19.08% 64.35%—In total clicks 45.53% 81.63%—In total conversions 73.90% 73.75%—In total SEM costs 21.02% 50.31%

Profits after SEM costsfor all keywords

−€15,743.03 +€59,405.53

Profits after SEM costs forprofitable keywords

+€1,975.21 +€80,016.84

Notes: Number of keywords: 5,230 (travel: 2,655, industrial goods: 2,575). SEM:Search Engine Marketing.

149N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

that an ad receives because we are interested in changes in thenumber of clicks that result from an improved QS. To again ac-count for the determination of Rankit by Eq. (8) and the potentialcorrelation with the error terms in Eq. (10), ζit, we estimateEq. (10) using 2SLS, with Bidit as an instrument for Rankit.Note again that Eq. (10) takes into account direct and indirectquantity effects through QSit and Rankit, respectively.

Results

In Table 5, we present the industry-specific estimation re-sults of Eqs. (8)–(10). From our analysis of rank [Eq. (8)], wefind that both covariates, bid and QS, have a statistically signif-icant and negative relationship with rank, which suggests that(i) the higher the bid, the better is the ranking of the advertiser,and (ii) the higher the QS, the better rank the advertiserachieves.

Next, we turn to the search engine's pricing behavior. Bothcovariates, rank and QS, have a statistically significant and neg-ative relationship with the price per click. This finding reflects apositive indirect price effect and suggests that better ranks at thetop of the screen, which may result from higher QS, are associ-ated with higher prices per click. Furthermore, it indicates a

Table 5Estimation results by industry.

Travel

ln(Rankit) ln(CPCit) ln

Bidit −1.2937*** ./. ./(.0422)

QSit –.0300*** –.0427*** .0(.0044) (.0049) (.

Timeit n.s. .0002** –(.0001) (.

Rankit ./. –.2992*** –(.0100) (.

R2 65.61% 71.82% 6F-value 334 311 1Number of observations 28,558 28,558 2Number of keywords 1,874 1,874 1

*** pb .01, ** pb .05, * pb .1, n.s. not significant. Standard errors are in parenthese

negative direct price effect; the higher the QS, the lower is theprice that the advertiser pays per click. This finding is thereforein line with our proposed framework for the return on qualityimprovements (see Fig. 2). In contrast with industrial goods,the prices per click in the travel industry clearly have increasedover time. The main difference between these industries is theirsales target; the latter is a business-to-business (B2B) industry,whereas the former involves business-to-consumer (B2C) inter-actions. The decreasing keyword prices per click in a B2B con-text might result from the worldwide economic crisis, whichhas provoked reduced marketing budgets in an industry inwhich demand for costly industrial goods has deteriorateddramatically.

Finally, we find that rank has a statistically significant andnegative relationship with the number of clicks (indirect quan-tity effect), which confirms that better ranks (resulting fromhigher QS) are generally associated with more clicks. This find-ing is again in line with our proposed framework (see Fig. 2).We find that the QS has a significant and positive relationshipwith the number of clicks in the travel industry (positive directquantity effect). However, this association is not true for indus-trial goods, for which the number of clicks is not affected byhigher ad quality. The reason might have to do with the audi-ence; a procurement manager, searching for industrial goodson a search engine, might not be as heavily influenced by adquality but instead rely on brand recognition. We also findthat the number of clicks has increased over time for industrialgoods but not for travel, which suggests that competition mighthave increased in the travel industry. For our data set from Jan-uary to June 2009, this result seems plausible because, accord-ing to Google Insights for Search, cruise line searches havefallen from their peak in June–July 2009.

Counterfactual Analysis

Finally, we conduct a counterfactual analysis to derive in-sights into the sign and magnitude of the effects of ad qualityon prices per click, quantity, SEM costs, and profit after SEMcosts. All else being equal, we calculate ranks, prices per

Industrial goods

(Clicksit) ln(Rankit) ln(CPCit) ln(Clicksit)

. –.3583*** ./. ./.(.0131)

482*** –.0070*** –.0224*** n.s.0069) (.0013) (.0053).0008*** n.s. –.0003*** .0005***0001) (.0001) (.0001).2851*** ./. −1.1233*** –.4148***0140) (.0605) (.0504)5.80% 84.17% 59.50% 68.40%68 261 157 618,558 67,956 67,956 67,956,874 2,089 2,089 2,089

s. QS: Quality Score, CPC: Cost per click.

Table 6Decomposition of the returns on quality improvements and declines by industry.

Travel (N=1,827) [N=205] Industrial Goods (N=2,527) [N=2,051]

Quality Improvement(N=1,435) [N=147]

Quality Decline (N=392)[N=58]

Quality Improvement(N=1,603) [N=1,313]

Quality Decline (N=924)[N=738]

∑ in € a NegativeEffect b

∑ in € a NegativeEffect b

∑ in € a NegativeEffect b

∑ in € a NegativeEffect b

(1) Total PriceEffect (PE)

−499.30 1378 224.57 27 −289.63 1513 116.83 59[−33.84] [147] [6.23] [0] [−218.75] [1283] [87.10] [13]

Direct Price Effect(DPE)

−1,219.30 1435 957.70 0 −679.23 1603 310.67 0[−57.86] [147] [10.34] [0] [−424.14] [1313] [163.80] [0]

Indirect Price Effect(IPE)

720.00 0 −733.13 392 389.60 0 −193.84 924[24.02] [0] [−4.11] [58] [205.38] [0] [−76.70] [738]

(2) Total QuantityEffect

2,251.71 0 −1,667.72 392 148.01 0 −69.96 924

(QE) [93.81] [0] [−14.72] [58] [78.04] [0] [−27.49] [738]Direct QuantityEffect

1,467.30 0 −1,030.95 392 .00 ./. .00 ./.

(DQE) [68.54] [0] [−11.14] [58] [.00] [./.] [.00] [./.]Indirect QuantityEffect

784.41 0 −636.77 392 148.01 0 −69.96 924

(IQE) [25.27] [0] [−3.57] [58] [78.04] [0] [−27.49] [738](3) Interaction Effect −46.91 1378 −16.06 365 –.97 1513 –.43 865

[−2.40] [147] [−.38] [58] [−.82] [1283] [−.41] [725]Δ SEM costs= (1)+(2)+ (3)

1,705.50 0 −1459.21 392 −142.59 1441 46.44 98[57.57] [0] [−8.87] [58] [−141.53] [1251] [59.20] [22]

Δ Profit after SEM costs −809.00 1035 1,050.20 124 360.06 96 −129.30 861(Percentage increasein profits)

(−95.34%) (+206.18%) (+42.62%) (−23.78%)[59.16] [0] [−10.98] [58] [328.14] [20] [−120.42] [729]

[(+486.57%)] [(−155.55%)] [(+30.62%)] [(−18.24%)]

Notes: Results for profitable keywords are given in square brackets. Percentage increase in profits compared to situation before quality score change is given in pa-rentheses. SEM: Search Engine Marketing.a Sum of effects for quality improvements and quality declines separately. For example, for all 1,435 quality improvements in the travel industry, the direct price

effect leads to a drop in SEM costs by −€1,219.30.b Number of negative effects (i.e., reduction in price per click, quantity, SEM costs, or profit after SEM costs) out of all quality improvements and declines per

campaign. For example, prices per click decrease for 1,378 of 1,435 quality improvements in the travel industry. At the same time, SEM costs decrease in 0 of1,435 cases and profits decrease in 1,035 of 1,435 cases.

150 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

click, and the number of clicks on an ad before and after each ofthe 4,354 QS changes in our data using the estimated models aspresented in Table 5. Applying Eqs. (4)–(5), we then calculatethe direct and indirect price and quantity effects for each QSchange. We derive the interaction effect in Eq. (6), as well asthe overall effect on SEM costs [Eq. (3)]. Finally, we determinethe effect on profit after SEM costs according to Eq. (7). Wecalculate not only the short-term effect of a QS changefrom one day to another but a long-term effect, taking intoaccount the number of days for which the QS remained thesame.

With Table 6, we provide the decomposition of the returnson all quality improvements and declines in both industries.In Columns “∑ in €,” we report the sum of the effects inEuros for quality improvements and quality declines separately.We also report the number of quality improvements and de-clines, for which the price per click, quantity, SEM costs, andprofit have decreased (Columns “Negative Effect”).

In the travel industry, 1,378 of 1,435 quality improvements(96.03%) result in a price decrease, with a total price effect onSEM costs of −€499.30. This total price effect comprises thedominant direct price effect (DPE), which reduces SEM costsby −€1,219.30, and an indirect price effect (IPE), which leads

to an increase in SEM costs by only €720.00. As expected,the DPE is always negative, whereas the IPE is always positivein the case of a quality improvement. This finding suggests thatmost quality improvements actually lead to an overall reductionin prices per click and thus SEM costs because the DPE is typ-ically larger than the IPE.

The direct and indirect quantity effects are always positivein case of a quality improvement and result in increasedSEM costs by €2,251.71. Because the positive total quantityeffect (€2,251.71) dominates the negative total price effect(−€499.30), SEM costs increase by €1,705.50 as a result ofthe 1,435 quality improvements; this finding contradicts con-ventional wisdom. The more appealing an ad is in the travel in-dustry, the more frequent consumers click on it, which resultsin overall higher SEM costs because the total price effect issmaller than the total quantity effect.

In addition, we find that SEM costs never decrease for anyof the 1,435 quality improvements. As a result, the travel adver-tiser's profits decrease by €809.00 (−95.34%) in response to1,435 quality improvements, such that 1,035 cases reveal de-creases in profits (72.13%).

For the industrial goods advertiser, we find that 1,513 of 1,603quality improvements (94.39%) result in a decrease in prices per

151N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

click, with a total price effect on SEM costs of –€289.63. Again,the dominant effect on price is the direct price effect, which leadsto a reduction in SEM costs by −€679.23. The indirect price ef-fect (IPE) instead increases SEM costs by€389.60. This findingagain confirms that most quality improvements reduce overallprices per click and thus SEM costs as the direct price effect islarger than the indirect price effect.

The direct quantity effect (DQE) is insignificant for the indus-trial goods advertiser (see Table 5); however, we find that the in-direct quantity effect (IQE) results in increased SEM costs by€148.01. As the negative total price effect (−€289.63) domi-nates the positive total quality effect (€148.01), SEM costsdecrease by −€142.60 as a result of the 1,603 quality improve-ments. In contrast to the travel industry, more appealing adsdo not influence the click behavior of procurement managers;the total price effect is therefore larger than the total quantityeffect, which results in lower SEM costs in the industrial goodsindustry.

Finally, SEM costs decrease in 1,441 of 1,603 cases(89.89%), so the industrial goods advertiser's profits increaseby €360.06 (+42.62%) in response to the 1,603 quality im-provements, and only 96 cases suffer decreases in profits(5.99%). This number certainly is not negligible, but it ismuch lower than that experienced by the travel advertiser.There are fewer profit decreases because of the lack of directquantity effects (see Table 5), so this result is driven by the re-ductions in prices per click in 1,513 of 1,603 cases.

Influence of Keyword Profitability

Overall, our empirical study shows that 4.84% of all qualityimprovements lead to higher prices per click (147 of 3,038),100% to a higher number of clicks (3,038), 52.57% to highercosts for SEM (1,597), and 37.23% to lower profits (1,131).But the two campaigns differ largely in their profitability.While the share of unprofitable keywords equals 92.47% forthe travel campaign, the industrial goods campaign only has28.58% unprofitable keywords in its campaign. Beyond highercosts for SEM, the strong decrease in profits in the travel cam-paign might therefore be driven by the high average cost perclick of€.45 (see Table 3), which often is higher than the adver-tiser's corresponding profit contribution per click. Thus, a highernumber of clicks likely leads to profit losses after SEM costs. Wetherefore additionally analyze the returns on quality improve-ments and declines for profitable keywords only (see Table 6with the corresponding results given in square brackets).

Our results indicate that the different price and quantity ef-fects (PE, DPE, IPE, and QE, IQE, DQE) point into the samedirection as the previous analysis. Again, most of the qualityimprovements result in a decrease in price (147 of 147 inthe travel campaign and 1,283 of 1,313 in the industrialgoods campaign). Additionally, none of the quality improve-ments in the travel industry results in a decrease in SEM costs,but all lead to an increase in profits after SEM costs (+€59.16and +486.57%). For industrial goods, SEM costs decrease in1,251 of 1,313 cases and profits after SEM costs increase by

€328.14 (+30.62%); only 20 quality improvements result ina loss in profits after SEM costs.

Adjustment of Bids

Eq. (1) suggests though that changes in the weighted bids donot occur if the bids are adjusted as follows:

Bid1 ¼ Bid0⋅QS0QS1

; ð11Þ

where:

Bid1: Bid after the QS change,Bid0: Bid before the QS change,QS0: Quality score before the QS change,QS1: Quality score after the QS change.

Thus, if an advertiser believes that its bids were optimal (i.e.,profit maximizing) before it changed its ad quality, it can useEq. (11) to calculate the appropriate adjustment in bids. Thus,quality improvements lead to lower bids (and vice versa). Wetest this simple heuristic for profitable keywords in our dataset that yield positive profits after SEM costs before the QSchange. Note that the performance marketing agency who man-ages the campaigns does not currently adjust its bids as a re-sponse to a change in ad quality (95% of the QS changes donot result in any adjustment of the bid). We then derive the cor-responding differences in SEM costs (Δ SEM costs) and profits(Δ Profit after SEM costs) and present the results in Table 7.

The travel advertiser would only spend €5.55 more on SEMas a result of the 147 quality improvements. However, itsprofits then would increase by €78.91 (+648.93%). 31 of the147 quality improvements even lead to decreases in SEMcosts, but profit would increase in all 147 cases as a result ofthe quality improvement and bid adjustment. In contrast, the in-dustrial goods advertiser bears −€1,117.08 lower SEM costsas a result of 1,313 quality improvements; its profits increaseby €784.23 (+73.18%). All 1,313 quality improvements leadto lower SEM costs and profits decrease in only 14 of thecases. This finding supports the idea of adjusting the bidaccording to Eq. (11).

A quality decline always results in lower profits for the trav-el advertiser (−€14.18 and –200.85%). For the industrialgoods advertiser, a quality decline almost always results inlower profits (732 of 738); the difference in profits after SEMcosts equals −€374.72 (−56.76%). The reason for this nega-tive result is that—according to Eq. (11)—a quality decline re-quires the advertiser to adjust the bid upward, which increasesthe cost per click beyond the corresponding profit contributionper click and thus produce losses in profits after SEM costs.

Summary, Conclusions, and Implications

In SEM, the ranking of ads and the prices paid per click re-sult from generalized, second-price, sealed bid auctions thatconsider both submitted bids for each keyword and ad quality.

Table 7Return on quality improvements and declines by industry with quality adjusted bids.

Travel (N=205) Industrial Goods (N=2,051)

Quality Improvement(N=147)

Quality Decline (N=58) Quality Improvement(N=1,313)

Quality Decline (N=738)

∑ in € a NegativeEffect b

∑ in € a NegativeEffect b

∑ in € a NegativeEffect b

∑ in € a NegativeEffect b

Δ SEM costs 5.55 31 –.54 34 −1,117.08 1,313 526.76 0Δ Profit afterSEM costs 78.91 0 −14.18 58 784.23 14 −374.72 732

(+648.93%) (−200.85%) (+73.18%) (−56.76%)

SEM: Search Engine Marketing.a Sum of effects for quality improvements and quality declines separately. For example, for all 147 quality improvements in the travel industry, SEM costs increase

by €5.55 and profits increase by €78.91.b Number of negative effects (i.e., reduction in price per click, quantity, SEM costs, or profit after SEM costs) out of all quality improvements and declines per

campaign. For example, SEM costs decrease in 31 of 147 cases and profits always increase for travel.

152 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

Conventional wisdom suggests that advertisers can only benefitfrom improving their advertisement's quality but our empiricalstudy reveals that quality improvements have complex effectswhose returns are actually unclear: Google asserts that higherquality scores mean lower costs and stronger ad positions(Google Adwords Help 2009b), but our empirical study revealsthat at least 4.84% of all quality improvements to an ad lead tohigher prices per keyword, 100% to a higher number of clicks,and 52.57% mean higher SEM costs, with 37.23% of these im-provements leading to lower profits. Quality improvementslead to higher weighted bids, which lower prices only if theydo not improve the ad ranking. Otherwise, better ranks likelylead to higher prices and higher SEM costs, with ambiguousprofit consequences.

To disentangle the multitude of effects resulting from im-provements to ad quality, we have proposed a decompositionto understand the joint effects on SEM costs and profits. Wedifferentiate between direct and indirect price effects (DPEand IPE): The former leads to a decrease in the price per clickand SEM costs, while the latter increases prices paid per clickand SEM costs because of the better ad ranking. We also differ-entiate between direct and indirect quantity effects (DQE andIQE), both of which lead to an increased number of clicks onan ad.

Improvement in ad quality benefits the advertiser as long asthe negative DPE, which lowers prices, dominates the positiveIPE, which leads to higher prices, and the resulting cost perclick is lower than the corresponding profit contribution perclick. Our empirical examination of two different industries(B2C and B2B) shows that this dominance frequently occurs.Consequently, the total price effect (PE) is negative in mostcases. The result is lower prices per click.

In contrast with the B2B industry, higher ad quality direct-ly increases the number of clicks in the B2C industry, throughthe effect of more appealing ads on consumers' behavior. Thisdirect quantity effect (DQE) significantly increases SEMcosts in the B2C industry and is one reason for the losses inprofits after SEM costs. This association is not true in theB2B industry though, in which ad appeal has less impact on

the behavior of procurement managers. Therefore, the DPEdominates the other effects in this industry and reducesSEM costs.

Finally, the two campaigns differ substantially in their shareof profitable keywords. While the travel advertiser has a largenumber of unprofitable keywords in its campaign (92.47%),the industrial goods advertiser only bears 28.58% unprofitablekeywords. In this industry, costs per click are typically lowerthan the corresponding profit contribution per click, whichmeans the additional clicks on an ad actually pay off. In con-trast, advertisers whose campaigns contain a higher number ofunprofitable keywords (e.g., travel advertiser) suffer morefrom quality improvements because the price for an additionalclick frequently exceeds the additionally generated profit con-tribution per click.

In summary, considering only prices per click, we show thatthe return on quality improvements is positive in most cases,yet for SEM costs, i.e., prices per click times the number ofclicks, the return is mixed at best. Ultimately, quantifyingprofits after SEM costs reveals that the return on quality im-provements is positive for far less than two thirds of the qualityimprovements. Interestingly, campaigns with a large share ofunprofitable keywords suffer more from quality improvementsbecause the price for an additional click exceeds the additional-ly generated profit contribution.

For advertisers to benefit from improvements in ad quality,we propose that bids should be adjusted in response to a changein quality, which is currently not the case in the two campaignsmanaged by a German performance marketing agency. This ad-justment follows the ratio of the preceding QS to the currentQS. Thus, the bids should decrease in case of a quality im-provement and increase in case of a quality decrease. Ourdata analysis indicates that this heuristic works very well, espe-cially for quality improvements.

Although we focus on SEM, the results are interesting formultiple other areas, such as online comparison shoppingWeb sites. Many of these sites currently rank offerings accord-ing to product prices or retailers' reputation; they could add re-tailers' bids for each click. As we noted previously, we focus on

153N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

weights linked to the quality of an ad, but these weights alsomight be linked to factors such as download or delivery speeds.Improvements to respective weights do not necessarily yieldbetter prices, lower costs, or higher profits, so providers ofsuch matching mechanisms should be very cautious in theirclaims about the consequences of improvements, to avoid thethreat of litigation. Advertisers also should consider carefullywhether improvements to one element of the matching mecha-nism will require them to adjust other elements (e.g., prices).

We acknowledge several limitations to our study. First, we takead quality as given by Google and do not analyze its determinants.In order to help advertisers understand how to influence theirkeywords' QS, future research should further investigate the deter-minants of ad quality. Google states that historical CTR, the rele-vance of the ad, and landing page quality constitute QS. Whilehistorical CTR is typically reported in traditional SEM data sets,bounce rate is not but might be a good proxy for landing pagequality. However, it is rather unclear, which determinants poten-tially influence an ad's relevance such that reverse engineeringGoogle's QS is a challenging research path to pursue. In addition,QS can be seen as an attempt by Google to adjust the advertisers'ranking in a way that it provides Google with the highest revenue.Thus, the degree of sensitivity of QS to the advertiser's effort (e.g.,in terms of money spent) to improve ad quality is actually unclear.Further research should therefore elaborate on how QS is relatedto the advertiser's effort to improve it.

Acknowledgments

The authors thank Oliver Hinz and Jochen Reiner for theirvaluable comments on an earlier draft of the article. They alsogratefully acknowledge financial support from the E-FinanceLab at Goethe-University Frankfurt.

References

Abou Nabout, Nadia, Bernd Skiera, Tanja Stepanchuk, and Eva Gerstmeier(forthcoming), “How Free-based Compensation Lowers Profit in SearchEngine Marketing,” International Journal of Research in Marketing.

Abrams, Zoe and Michael Schwarz (2008), “Ad Auction Design and UserExperience,” Applied Economics Research Bulletin, 2, 1, 98–105.

Agarwal, Ashish, Kartik Hosanagar, and Michael D. Smith (2011)“Location, Location, Location: An Analysis of Profitability of Position inOnline Advertising Markets,” Journal of Marketing Research, 48, 6,1057–73.

Blumrosen, Liad, D. Hartline Jason, and Shuzhen Nong (2008), “Position Auc-tions and Non-uniform Conversion Rates,” Paper presented at the 4th Work-shop on Ad Auctions, ACM Conference on Electronic Commerce. Evanston,IL, USA: Kellogg School of Management, Northwestern University.

Bucklin, Randolph E. (2008), “Marketing Models for Electronic Commerce,”Handbook of Marketing Decision Models,” B. Wierenga, editor. Berlin/Heidelberg: Springer, 327–69.

Chen, Jianqing, De Liu, and Andrew B.Whinston (2009), “Auctioning Keywordsin Online Search,” Journal of Marketing, 73, 4, 125–41.

Danuloff, Craig (2009), “Is Hype Over Google AdWords Quality Score Justi-fied?” Retrieved 30 July, 2011, from http://searchengineland.com/is-the-hype-over-google-adwords-quality-score-justified-18031.

Edelman, Benjamin and Michael Ostrovsky (2007), “Strategic Bidder Behaviorin Sponsored Search Auctions,” Decision Support Systems, 43, 1, 192–8.

———,———, and Michael Schwarz (2007), “Internet Advertising and theGeneralized Second-Price Auction: Selling Billions of Dollars Worth of Key-words,” The American Economic Review, 97, 1, 242–59.

Feng, Juan, Hemant K. Bhargava, and David M. Pennock (2007), “ImplementingSponsored Search inWeb Search Engines: Computational Evaluation of Alter-native Mechanisms,” INFORMS Journal on Computing, 19, 1, 137–48.

——— (2008), “Optimal Mechanism for Selling a Set of Commonly RankedObjects,” Marketing Science, 27, 3, 501–12.

Ganchev, Kuzman, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, andMichael Kearns (2007), “Empirical Price Modeling for Sponsored Search,”Internet and Network Economics, Xiaotie Deng, Fan Chung Graham, eds.Berlin/Heidelberg: Springer, 541–8.

Ghose, Anindya and Sha Yang (2009), “An Empirical Analysis of Search EngineAdvertising: Sponsored Search in Electronic Markets,” Management Science,55, 10, 1605–22.

Goldfarb, Avi and Catherine E. Tucker (2011), “Search Engine Advertising:Channel Substitution When Pricing Ads to Context,” Management Science,57, 3, 458–70.

Google Adwords Help (2009a), “What Are Keyword Matching Options?”Retrieved 30 July, 2011, from http://adwords.google.com/support/aw/bin/answer.py?hl=en&answer=6100.

——— (2009b), “What is ‘Quality Score’ and How is it Calculated?”Retrieved 30 July, 2011, from http://adwords.google.com/support/bin/answer.py?answer=10215.

Gunawardana, Asela, Christopher Meek, and Jody Biggs (2008), “A Quality-based Auction for Search Ad Markets with Aggregators,” paper presentedat the 4th Workshop on Ad Auctions, ACM Conference on Electronic Com-merce. Evanston, IL, USA, Kellogg School of Management, NorthwesternUniversity.

Hanson, Ward and Kirthi Kalyanam (2007), Internet Marketing and e-Commerce.Mason, Ohio: Thomson South-Western.

Hennig-Thurau, Thorsten, Ed Malthouse, Christian Friege, Sonja Gensler, LaraLobschat, and Arvind Rangaswamy (2010), “The Impact of New Media onCustomer Relationships: From Bowling to Pinball,” Journal of Service Re-search, 13, 3, 311–30.

IAB (2010), “IAB Internet Advertising Revenue Report: 2009 Full-Year Results,”Retrieved 1 November, 2011, from http://www.iab.net/media/file/IAB-Ad-Revenue-Full-Year-2009.pdf.

Kinshuk, Jerath, Liye Ma, Young-Hoon Park, and Kannan Srinivasan (2011),“A ‘Position Paradox' in Sponsored Search Auction,” Marketing Science,30, 4, 612–27.

Kitts, Brendan and Benjamin LeBlanc (2004), “Optimal Bidding on KeywordAuctions,” Electronic Markets, 14, 3, 186–201.

Kleinberg, Robert D. (2005), “A Multiple-Choice Secretary Algorithm withApplications to Online Auctions (pp. 630–631),” paper presented at the16th ACM-SIAM Symposium on Discrete Algorithms, Vancouver, BritishColumbia, Canada.

Liu, De, Jianqing Chen, and Andrew B. Whinston (2010), “Ex Ante Informa-tion and the Design of Keyword Auctions,” Information Systems Research,21, 1, 133–53.

Misra, Sanjog, Edieal Pinker, and Alan Rimm-Kaufman (2006), “An EmpiricalStudy of Search Engine Advertising Effectiveness,” paper presented at the7th International Conference in Web Information Systems Engineering. Ev-anston, IL, USA: Kellogg School of Management, Northwestern University.

Rangaswamy, Arvind, C. Lee Giles, and Silvija Seres (2009), “A Strategic Per-spective on Search Engines: Thought Candies for Practioners and Re-searchers,” Journal of Interactive Marketing, 23, 1, 49–60.

Rutz, Oliver J. and Randolph E. Bucklin (2011), “From Generic to Branded: AModel of Spillover in Paid Search Advertising,” Journal of Marketing Research,48, 1, 87–102.

———, Michael Trusov, and Randolph E. Bucklin (2011), “Modeling IndirectEffects of Paid Search Advertising: Which Keywords Lead to More FutureVisits?” Marketing Science, 30, 4, 646–65.

——— and ——— (2011), “Zooming In on Paid Search Ads—A Consumer-level Model Calibrated on Aggregated Data,” Marketing Science, 30, 5.

Skiera, Bernd, Jochen Eckert, and Oliver Hinz (2010), “An Analysis of the Im-portance of the Long Tail in Search Engine Marketing,” Electronic Com-merce Research and Applications, 9, 6, 488–94.

154 N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154

——— and Nadia Abou Nabout (2011), A Bidding Decision Support Systemfor Profitable Search Engine Marketing. Frankfurt: Goethe UniversityFrankfurt.

Soxman, David (2009), “Yes, In Search Marketing Quality Does Matter,” Re-trieved 30 July 2011 from http://www.thinkbigshot.com/blog/marketing/208-yes-in-search-marketing-quality-does-matter.html.

van Heerde, Harald and Tammo H.A. Bijmolt (2005), “Decomposingthe Promotional Revenue Bump for Loyalty Program Members Ver-sus Nonmembers,” Journal of Marketing Research, 42, November,443–57.

Varian, Hal R. (2007), “Position Auctions,” International Journal of IndustrialOrganization, 25, 6, 1163–78.

——— (2010), “Computer Mediated Transactions,” The American EconomicReview, 100, 2, 1–10.

Wiesel, Thorsten, Bernd Skiera, and Julian Villanueva (2008), “Customer Equity—An Integral Part of Financial Reporting,” Journal of Marketing, 72, March,1–14.

Yang, Sha and Anindya Ghose (2010), “Analyzing the Relationship BetweenOrganic and Sponsored Search Advertising: Positive, Negative or ZeroInterdependence?” Marketing Science, 29, 4, 602–23.

Yao, S. and C.F. Mela (2008), “Sponsored Search Auctions: Research Opportuni-ties in Marketing, Foundation and Trends in Marketing, Vol. 3.

——— and C.F. Mela (2011), “A Dynamic Model of Sponsored SearchAdvertising,” Marketing Science, 30, 3, 447–68.

Zhou, Yunhong and Rajan Lukose (2006), “Vindictive Bidding in KeywordAuctions,” paper presented at the 2nd Workshop on Sponsored Search Auc-tions, ACM Conference on Electronic Commerce. Ann Arbor, MI, USA:University of Michigan.