Return on Quality Improvements in Search Engine

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Journal of Interactive Marketing 26 (2012) 141 – 154 www.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, sealed bid auctions that weight the submitted bids for each keyword by the quality of an advertisement. Conventional wisdom suggests that advertisers can only benefit from improving their advertisement's quality. With an empirical study, this article shows that quality improvements have complex effects 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 improvements lead to higher weighted bids, which only lower prices if they do not improve the ranking of the advertisement. Otherwise, better ranks likely lead to higher 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 the Internet, the means by which consumers obtain information has changed fundamentally. Search engines have become the main tool consumers use to locate information (Hennig-Thurau et al. 2010; Rangaswamy, Giles, and Seres 2009), and this shift has been accompanied by the launch of a new and extremely popular online advertising format, known widely as search engine marketing (SEM), keyword advertising, and paid or sponsored search. Such tactics accounted for 47% of total worldwide online advertising spending in 2009, and U.S. advertisers alone spent $10.7 billion (IAB 2010). The mechanism supporting SEM works as follows (Abou Nabout et al. forthcoming; Skiera and Abou Nabout 2011; Yao and Mela 2008): A consumer types a keyword, such as “cruise vacation,” into a search engine (e.g., Google) and receives two types of results (see Fig. 1). The lower, left-hand part of the screen shows unsponsored search results, whose ranking reflects the relevance that the search algorithm assigns to these different results. ⁎ Corresponding author. E-mail addresses: [email protected] (N. Abou Nabout), [email protected] (B. Skiera).

The other parts, on the top and right-hand side, present sponsored search results. The display of the unsponsored (organic) search results is free of charge, whereas advertisers pay for each click on their ads that appears among the sponsored (paid) search results (Bucklin 2008; Rangaswamy, Giles, and Seres 2009). For the sponsored search ads, the ranking and prices paid per click depend on keyword auctions, which are generalized, second-price, sealed bid auctions (Edelman, Ostrovsky, and Schwarz 2007; Varian 2007). The two market leaders, Google and Yahoo, use similar auction designs (Zhou and Lukose 2006): advertisers submit a bid for each keyword at the price they are willing to pay for each click. The search engine provider weights the submitted bids according to the ad's quality, measured by a proprietary quality score (QS), and ranks the ads accordingly (Agarwal, Hosanager, and Smith (2011); Kinshuk et al. 2011; Yao and Mela 2011). From the search engine provider's point of view, the introduction of ad quality to the auction design provides a means to deal with the hidden cost of user dissatisfaction with poor quality ads (Abrams and Schwarz 2008; Varian 2010). Despite the massive importance of the QS, neither Google nor the other search engine providers publish their exact algorithms for determining the scores. However, Google states that “the higher your Quality Score, the lower your costs and the better your

1094-9968/$ -see front matter © 2012 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.intmar.2011.11.001

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sponsored search results

ranks

keyword 1

2

3

4

5

6

unsponsored search results Fig. 1. Search results in Google.

ad position” (Google Adwords Help 2009b), which suggests that advertisers can only benefit from improving their ad quality. 1 This suggestion is in line with the assumption embraced by many search marketers (Danuloff 2009; Soxman 2009). With our empirical study though, we show that quality improvements have complex effects whose returns are actually unclear: 4.84% of all quality improvements to an ad lead to higher prices (measured by prices per click) per keyword, 100% to a higher number of clicks, and 52.57% create higher costs for SEM. Furthermore, 37.23% lower profits. The reason is that quality improvements lead to higher weighted bids, which decrease prices per click only if the weighted bids do not improve the ad ranking. Otherwise, better ranks likely lead to higher prices per click and higher costs for SEM, with ambiguous consequences for profit. Thus, this article aims to analyze the consequences of improvements in ad quality on prices per click, the number of clicks, costs for SEM, and profits. In particular, we develop an approach to decompose the different effects of changes in ad quality on the outcomes of the keyword auction. In turn, we empirically estimate the impact of changes in ad quality on rank, price per click, number of clicks, and thus costs for SEM and profits. We apply the proposed decomposition method to two real-world SEM campaigns that consider the results of 4,354 changes of ad quality across 162 days.

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

Although we focus on SEM, the results are interesting for multiple other areas that aim to match buyers (here, searchers) with sellers (here, advertisers). Online comparison shopping Web sites such as Kelkoo and shopping.com establish retailer rankings to match consumers' needs. Although many Web sites currently issue ranks by product prices or retailers' reputation, they recently started to do so on the basis of retailers' bids for the price of each click on their listings. These prices per click then are weighted by the retailers' reputation or product prices. Similar arguments would apply to dating and auction Web sites. Furthermore, whereas we focus on weights linked to ad quality, similar weights could be linked to factors such as the speed of Web sites, delivery times, or retailers' reputation. Thus, our results should hold for areas beyond SEM. To accomplish our research aims, we organize the remainder of this article as follows: we briefly review literature on SEM, then formally describe how ad quality influences the outcomes of the keyword auction. In particular, we show how to disentangle direct and indirect price and quantity effects using our proposed decomposition method. Next, we present the results of our empirical study, which we conduct in two different industries, the business-to-consumer travel market and the business-to-business industrial goods market. We simultaneously model the search engine's keyword rank-allocating and pricing behavior and consumers' click behaviors. The results reveal the effects of changes in actual ad quality on prices per click, number of clicks, SEM costs, and profit. We conclude with a summary of the results, managerial implications, and directions for further research.

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Previous Research on Search Engine Marketing Despite growing attention among search advertisers (Danuloff 2009; Soxman 2009), ad quality continues to be ignored by academic researchers. Emerging streams of theoretical research instead deal with the optimal design of keyword auctions (Edelman, Ostrovsky, and Schwarz 2007; Feng 2008; Varian 2007), possible improvements to current designs (Blumrosen, Jason, and Nong 2008; Chen, Liu, and Whinston 2009; Gunawardana, Meek, and Biggs 2008), or bidding behavior in keyword auctions (Edelman and Ostrovsky 2007; Kleinberg 2005; Zhou and Lukose 2006). These results have relevance primarily for search engine providers. Another research stream analyzes key questions from the advertisers' perspective, including forecasts of the performance of single keywords depending on specific ad properties (Rutz and Trusov 2011), spillover effects from generic to branded keywords (Rutz and Bucklin 2011), and indirect effects of SEM (Rutz, Trusov, and Bucklin 2011). Goldfarb and Tucker (2011) explore substitution patterns across advertising platforms and show that search engine marketing substitutes for offline advertising when lawyers cannot contact clients by mail. Yang and Ghose (2010) analyze the relationship between organic and sponsored search and find that clickthroughs on organic listings have a positive interdependence with clickthroughs on paid listings, and vice-versa. Skiera, Eckert, and Hinz (2010) analyze the long tail in SEM, which they define as the vast number of less popular keywords employed by users to search the Internet. They find that advertisers can largely ignore the performance of keywords that fall in this long tail. Still other studies empirically analyze SEM performance and model the relationships between rank and bids, rank and clickthrough rate (CTR), or rank and the percentage of consumers who 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 of research, Abou Nabout et al. (forthcoming) analyze the performance of fee-based compensation plans in SEM and recommend compensation plans that rely on the idea of sharing profits. Finally, Ghose and Yang (2009) estimate the impact of various keyword attributes on consumers' clickthrough and purchase propensities, the advertiser's bid, and the search engine provider's ranking decision. Their study is the only one that incorporates past CTR and landing page quality as a proxy for ad quality to model the search engine provider's ranking decision. These authors show that the search engine provider incorporates prior CTR to determine the rank of the keyword; they also find that bids have a greater role in determining the rank than do ad quality-related factors. Ad Quality in Search Engine Marketing Search Engine's Ranking and Pricing Decision In an unweighted, generalized, second-price auction, advertisers submit bids for a specific keyword by stating the price that they are willing to pay for each click on their ad

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(Edelman and Ostrovsky 2007). The sponsored search results then display in decreasing order of submitted bids for the respective keyword, such that the ad with the highest bid appears at the top (i.e., first rank, r = 1), the ad with the next highest bid is in the second rank (r = 2), and so on (Yao and Mela 2008). If a user clicks on an ad at rank r, the corresponding advertiser pays the search engine provider an amount equal to the next highest bid (Hanson and Kalyanam 2007), that is, the bid offered by the advertiser at rank (r + 1). Thus, each advertiser must just pay enough to beat the competition (by one cent). Followed by Yahoo in late 2006, Google first launched a weighted auction format that incorporated the QS as a measure of ad quality in 2002 (Liu, Chen, and Whinston 2010). Google officially states that it uses three determinants to calculate this weight for each advertiser and each keyword (Google Adwords Help 2009b). Historical CTR represents the major determinant of QS and comprises the CTR of the keyword, the CTR of all ads and keywords in the ad group, and the CTR of all ads and keywords in the campaign. A second determinant is the relevance of the keyword to the ads in its ad group, as well as the relevance of the keyword and the matched ad to the search query. Finally, landing page quality constitutes the third determinant of QS. However, the exact weighting that Google uses for calculating the QS according to these three determinants is not publicly available. To make a ranking decision, the search engine provider calculates a weighted bid WBidi for each advertiser j (j = 1,…J) in the keyword auction, equal to the product of the bid, Bidj, and the Quality Score, QSj: WBidj ¼ Bidj ⋅Q Sj

ð1Þ

All advertisers are then ranked in decreasing order of their weighted bids, WBidj, so that the ad of each advertiser is assigned a rank (also called a slot) in the sponsored search results. The price per click, or more commonly the cost per click (CPC), for the advertiser assigned to rank r (CPCr) includes the weighted bid of the advertiser who scored at rank r + 1. Its price per click CPCr is therefore calculated according to Eq. (2) and is just large enough to beat the competition (therefore $.01 is added): CPC r ¼ :01$ þ WBidrþ1 =Q Sr :

ð2Þ

Numerical Example of Price Effects and Quantity Effects To illustrate how this process works in practice, imagine that three advertisers, A, B, and C, with different QS, all bid $.50 on a keyword. Columns (1) and (2) in Table 1 represent the inputs to the keyword auction: bids and the QS of all advertisers. Columns (3)–(7) represent the outcomes of the keyword auction. The weighted bids in Column (3) are calculated according to Eq. (1) and used to rank the advertisers in decreasing order of their weighted bid [see Column (4)]. The prices per click in Column (5a) are then calculated according to this ranking,

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Table 1 Numerical example: Effects of ad quality improvements on outcomes of keyword auctions. Input (1) Bid Situation 0 A $ .50 B $ .50 C $ .50 Situation A1: A $ .50 B $ .50 C $ .50 Situation A2: A $ .50 B $ .50 C $ .50 Situation B1: A $ .50 B $ .50 C $ .50 Situation B2: A $ .50 B $ .50 C $ .50

Outcome (2) Quality Score

(3) = (1)⋅(2) Weighted Bid

(4) Rank

(5a) Cost per Click

(5b)

(6a)

Cost per Click′

a

Clicks

(6b) Clicks′

b

(7a) = (5a) ⋅ (6a)

(7b) = ($1−(5a))⋅(6a)

SEM costs

Profit after SEM costsc

8 4.0 1 3.0/8 + .01 = $.39 ./. 100 ./. ./. 6 3.0 2 1.5/6 + .01 = $.26 ./. 90 ./. $23.40 3 1.5 3 min price = $.05 ./. 80 ./. ./. Direct Price Effect = −$.04, Indirect Price Effect = Indirect Quantity Effect = Direct Quantity Effect = 0 8 4.0 1 3.5/8 + .01 = $.45 ./. 100 ./. ./. 7 3.5 2 1.5/7 + .01 = $.22 1.5/7 + .01 = $.22 90 90 $20.19 3 1.5 3 min price = $.05 ./. 80 ./. ./. Direct Price Effect = −$.04, Indirect Price Effect = Indirect Quantity Effect = 0, Direct Quantity Effect ≠ 0 8 4.0 1 3.5/8 + .01 = $.45 ./. 100 ./. ./. 7 3.5 2 1.5/7 + .01 = $.22 1.5/7 + .01 = $.22 92 92 $20.63 3 1.5 3 min price = $.05 ./. 80 ./. ./. Direct Price Effect = −$.08, Indirect Price Effect ≠ 0, Indirect Quantity Effect = Direct Quantity Effect = 0 8 4.0 2 1.5/8 + .01 = $.20 ./. ./. ./. ./. 9 4.5 1 4.0/9 + .01 = $.45 1.5/9 + .01 = $.18 90 90 $40.90 3 1.5 3 min price = $.05 ./. 80 ./. ./. Direct Price Effect = −$.08, Indirect Price Effect ≠ 0, Indirect Quantity Effect ≠ 0, Direct Quantity Effect ≠ 0 8 4.0 2 1.5/8 + .01 = $.20 ./. 90 ./. ./. 9 4.5 1 4.0/9 + .01 = $.45 1.5/9 + .01 = $.18 102 92 $46.35 3 1.5 3 min price = $.05 ./. 80 ./. ./.

./. $66.60 ./. ./. $69.81 ./. ./. $71.37 ./. ./. $49.10 ./. ./. $55.65 ./.

a

Cost 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 cost per 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 to derive the cost per click. b Clicks′ 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 quality has 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 the higher appeal of the ad and to 10 additional clicks in B2 due to the better rank. c Advertiser 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). All calculations are done by using exact numbers instead of the rounded values that are displayed in the table.

following Eq. (2). We first assume that the best rank (i.e., rank 1) receives 100 clicks, rank 2 receives 90 clicks, and rank 3 receives 80 clicks. Situation 0 represents the base case; we particularly investigate advertiser B. In Situation A1, B's QS increases from 6 to 7, and its price per click decreases from $.26 to $.22 (− 14%). The number of clicks remains the same (quantity effect = 0), and its SEM costs decrease by 14%. In Situation B1, B's QS increases from 6 to 9, which puts B in rank 1, for a 75% higher price per click and 75% higher SEM costs. The reason for this seemingly surprising result is that the increase in quality has two effects (these arguments are straightforward for the reverse case of decreased quality). First, it has a direct effect and reduces the price per click, with the assumption that the rank does not change (similar to Situations A1 and A2). Second, the indirect effect on price appears because the increase in quality improves the rank, which leads to a higher price per click. Only if the direct effect is greater than the indirect effect will the quality improvement lead to lower prices per click. We disentangle the total price effect into these two effects by calculating 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 ($.26) reveals the direct effect that decreases the advertiser's price; the difference between the total effect and the direct effect is, in essence, the indirect effect that increases the advertiser's price. Thus, in Situation B1 we observe a negative direct effect of –$.08 (=$.18–$.26) and a positive indirect effect of $.27 (=$.45– $.26–[−$.08]). In real-world SEM campaigns it is then an empirical question how large these effects actually are. Situations A2 and B2 further complicate our calculations because improvements in ad quality might also result in changes to the number of clicks. In Situation A2, the number of clicks is assumed to increase by 2 though the ad ranking remains the same because the higher quality of the ad is more appealing to consumers. In Situation B2 [Column (6a)], we assume that the number of clicks increases by 12, which is the sum of 10 clicks added by the better rank and 2 clicks added because of the more appealing ad. In turn, this higher number of clicks increases the SEM costs in Situations A2 and B2 (from $20.19 to $20.63 and from $40.90 to $46.35); however, profits also increase resulting from the additionally acquired clicks (from $69.81 to $71.37 and from $49.10 to $55.65). Again, it is an empirical question how SEM profits are affected by improvements in ad quality.

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Formal Decomposition of Price and Quantity Effects Thus, ad quality might influence prices per click and quantity, and their respective effects on the total costs for SEM per keyword can be disentangled as follows: ΔSEM Costs ¼ ðCPC 1 ⋅Clicks1 Þ−ðCPC 0 ⋅Clicks0 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 and B, 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 keyword. Thus, the total price effect (PE) and total quantity effect (QE) can be given by: Total Price Effect: PE ¼ ðCPC 1 −CPC 0 Þ⋅Clicks0 ;

ð4Þ

Total Quantity Effect: Q E ¼ ðClicks1 −Clicks0 ÞCPC 0

ð5Þ

With Eq. (4), we can further decompose the total price effect (PE) into direct and indirect price effects, DPE and IPE:   0 Direct Price Effect: DPE ¼ CPC 1 −CPC 0 ⋅Clicks0 ;

effects [Eqs. (4) and (5)] equals the interaction effect (IE). For comparable approaches in different contexts, see van Heerde and Bijmolt (2005); Wiesel, Skiera, and Villanueva (2008). Interaction Ef f ect: IE ¼ ðCPC 1 −CPC 0 Þ⋅ðClicks1 −Clicks0 Þ ¼ ΔSEM Costs  PE  Q E:

ð6Þ

In line with Eq. (3), we measure the return on quality improvements by calculating the difference in profit after SEM costs per keyword as follows: ΔProf it af ter SEM Costs ¼ PC⋅ðClicks1 −Clicks0 Þ−ΔSEM Costs;

ð7Þ

Where PC refers to profit contribution per click and PC ⋅ (Clicks1 − Clicks0) equals the ΔProfitbeforeSEMCosts. Fig. 2 summarizes the various effects of ad quality improvements (and declines) on profit. A multitude of effects occur, but the number of clicks always increases as a result of quality improvements. As a consequence, the advertiser's profit is likely to increase if the CPC decreases as a result of the quality improvement and the profit contribution per click is greater than this cost per click. However, if the CPC increases due to a dominating indirect price effect, the consequences for the advertiser's profit are less obvious because such a quality improvement could lead to increases or decreases in profit. It is thus an empirical question how all these different effects influence the return on quality improvements. Return on Quality Improvements in the Numerical Example

ð4aÞ

  0 Indirect Price Effect: IPE ¼ CPC 1 −CPC 1 ⋅Clicks0 ; ¼ PE−DPE;

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ð4bÞ

where CPC1' is the cost per click after the QS change if the rank does not change [Column (5b)]. Similarly, we calculate the direct and indirect quantity effects, DQE and IQE, according to Eqs. (5a) and (5b): Direct Quantity Effect:  DQ E ¼ Clicks1 −Clicks0 ⋅CPC 0 and

ð5aÞ

Indirect  Quality Effect:  IQ E 0 ¼ Clicks1 −Clicks1 ⋅CPC 0 ¼ Q E−DQ E;

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

Table 2 reports the results for the effects described in Eqs. (3)–(7) for the numerical example outlined in Table 1. In Situations A1 and A2, the indirect effects equal zero because no rank change occurs in response to the quality improvement. Situation A2 suggests that the direct price effect is much stronger than the direct quantity effect, such that the reduction in price is much larger than the increase in the number of clicks. Therefore, the additional profit contribution generated by the higher number of clicks leads to an increase in profit after SEM costs, even though the SEM costs are higher than in Situation A1. In Situations B1 and B2, the results indicate that the strongest effect is the indirect price effect because prices per click have increased very strongly and thus so have SEM costs. The result is an overall loss in profit after SEM costs. In Situation B2 (positive quantity effects), the loss in profit is slightly smaller than in Situation B1 (quantity effects of zero) because the additional profit contribution generated by the higher number of clicks partly compensates for the price increases due to the high indirect price effects. This numerical example illustrates that a quality improvement (decline) may have complex and unexpected effects on prices per click, SEM costs, and ultimately profit after SEM costs.

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

Empirical Examination Purpose This empirical study aims to analyze the consequences of improvements in ad quality on prices per click, the number of clicks, costs for SEM, and profits. Using data pertaining to two real-world SEM campaigns that consider the results of 4,354 changes of ad quality across 162 days, we first estimate the impact of changes in ad quality on rank, prices per click, number of clicks, costs for SEM, and profits. We simultaneously model the search engine's keyword rank-allocating and pricing behavior and consumers' click behaviors. The obtained estimation results enable us to run counterfactual analyses on all QS changes in our data and thereby answer the following research questions: ▪ How great is the effect of a quality improvement (decline) on prices per click? In how many cases does a quality Table 2 Decomposition of the return on quality improvements for Advertiser B in the numerical example. Situations

A1

A2

(1) Total Price Effect (PE) Direct Price Effect (DPE) Indirect Price Effect (IPE) (2) Total Quantity Effect (QE) Direct Quantity Effect (DQE) Indirect Quantity Effect (IQE) (3) Interaction Effect Δ Search engine marketing costs = (1) + (2) + (3) Δ Profit after search engine marketing costs a

–$3.21 –$3.21 $.00 $.00 $.00 $.00 $.00 –$3.21

–$3.21 –$3.21 $.00 $.52 $.52 $.00 –$.07 –$2.76

$17.50 –$7.50 $25.00 $.00 $.00 $.00 $.00 $17.50

$17.50 –$7.50 $25.00 $3.12 $.52 $2.60 $2.33 $22.95

$3.21

$4.77

–$17.50

–$10.95

a

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

B1

B2

improvement (decline) result in a reduction (increase) in prices 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 quantity effect, as well as the direct and indirect quantity effects? ▪ How great is the effect of a quality improvement (decline) on SEM costs and thus on profit? In how many cases does a quality improvement (decline) result in reduced (increased) costs for SEM and profit? Which effect drives the difference in SEM costs and profit: price or quantity? Because some SEM campaigns are more profitably managed than others, we study two real-world SEM campaigns that differ largely in their share of profitable keywords in order to understand how quality improvements (declines) affect SEM campaigns under different conditions. In practice, many keywords do not yield positive profits after SEM costs because keyword prices are too high compared to the profit contributions per click. But advertisers frequently subsidize the losses that occur for some keywords with high gains earned from other (typically branded) keywords. The obtained results therefore enable us to answer the last research question: ▪ How are campaigns with different shares of profitable keywords affected by quality improvements (declines)? Data Our data set contains daily information about SEM on Google for two advertisers in two industries, travel and industrial goods, whose campaigns are managed by the same German performance marketing agency on behalf of their clients. The

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

travel advertiser is a global cruise line brand that provides excursions to more than 50 countries and allows customers to book cruises online; the industrial goods advertiser is a leading manufacturer of industrial equipment that operates globally but does not sell its goods online. In the former case, conversions correspond to actual sales online; in the latter case, they are contacts, such as requests for quotes or opportunities for sales calls. 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 differ across matching types, 2 we control for these differences by treating keywords associated with the different matching types separately. Our final data set includes 97,781 observations of 5,230 unique keywords. In Table 3, we report summary statistics of our data set for each advertiser, including the number of searches, the number of clicks, the number of conversions, the cost per click, the rank, and the QS (from 1 to 10). The average number of searches per keyword during the study phase is 375.21 for travel, of which 37.89 lead to a click (CTR = 10.10%) and .80 lead to a purchase (conversion rate = 2.11%). The average number of searches per keyword is much higher for industrial goods (3,648.92), but only 121.06 of those lead to a click (CTR = 3.32%) and .42 to a conversion (conversion rate = .35%). The average CPC for a given keyword is €.45 in the travel industry and €.39 for industrial goods. The average rank of these keywords is 2.60 for travel and 1.46 for industrial goods. Finally, we find a high average QS of 9.13 in the travel campaign and an average QS of 7.78 in the industrial goods campaign. Overall, we observe 4,354 QS changes—that is, differences across two consecutive days—from January to June. For each keyword, the mean number of QS changes during the observation period is approximately 1. Investigating the QS changes more closely, we find that out of 90 possible  types  of changes (e.g., 10 1 →2, 1 →3, 2→ 1, 2→ 3; in total, ⋅2 ¼ 90Þ, only 28 2 3 (31%) actually occur during the study phase. As we illustrate in Fig. 3, approximately 40% of the 4,354 QS changes in our data set are associated with an increase from either 8 or 9 to 10. Furthermore, the 3,038 positive QS changes are more than twice as frequent as the 1,316 negative changes. Finally, the (confidential) profit contribution per click is much higher for the industrial goods campaign than for the travel campaign. Combined with a relatively high cost per click, which often is higher than the corresponding profit contribution per click, many keywords in the travel campaign are unprofitable. Whereas the share of unprofitable keywords equals 92.47% for the travel advertiser, the industrial goods

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advertiser only has 28.58% unprofitable keywords in its campaign. But the 7.53% profitable keywords in the travel campaign account for 19.08% of all searches, 45.53% of all clicks, 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 the costs for SEM and a profit after SEM costs of €1,975.21. This campaign therefore reflects a situation, in which less than 10% of the keywords generate over two thirds of the conversions. In contrast, the industrial goods campaign is much more balanced because 71.42% profitable keywords account for 64.35% of all searches, 81.63% of all clicks, 73.75% of all conversions, and 50.31% of the costs for SEM. While all profitable 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 are managed by the same agency, the two campaigns therefore reflect different conditions encountered in real-world SEM campaigns. Model Following Ghose and Yang (2009), we simultaneously model the search engine's keyword rank-allocating and pricing behavior and consumers' click behaviors, but we focus on the search engine's pricing decision because we are interested in the direct and indirect effects of ad quality on prices per click. Similar to Ghose and Yang (2009), we treat the advertiser's bidding decision as exogenously determined because it is influenced by the advertiser's past performance with the same keyword and its campaign and not its present performance (i.e., its present ranking, price per click, clickthrough and conversion rate). The same applies to ad quality (i.e., QS value), which also depends on the advertiser's past performance as opposed to its present performance and is thus modeled as exogenously determined. First, we model the search engine's decision to assign ranks for a paid keyword ad. During the auction, search engines decide on the keyword rank by taking into account both the current bid and the QS. We use a time trend to control for changes in the extent of competition in the auction bidding process over time and unobserved industry dynamics. As Ghose and Yang (2009) note, heterogeneity from various sources is present when modeling SEM data. We therefore capture unobserved heterogeneity across keywords with a fixed effect on the intercept and model the rank, using a log-linear form that depends on the two covariates, Bidit and QSit, and the time trend, Timeit (Ghose and Yang 2009): lnðRankit Þ ¼ α i þ β1 Bidit þ β2 Q Sit þ β3 Timeit þ ε it ;

ð8Þ

2

Search engines, such as Google, typically allow the advertiser to choose from four matching types when adding keywords to a campaign: (1) “broad match” 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 match the exact phrase exclusively, and (4) “negative match” means the ad does not appear 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.

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 per click: CPCit. During the auction, search engines decide on the keyword price per click of the ad at rank r by taking into account the weighted bid of the advertiser who scored just

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Table 3 Summary statistics. Travel (N = 29,339)

Searches per keyword (total 10,392,146) Clicks per keyword (total 412,325) Conversions per keyword (total 3212) Cost per click (in €) Rank Quality score (QS) QS changes (total 4354)

Industrial goods (N = 68,442)

Mean

10th percentile

90th percentile

Mean

10th percentile

90th percentile

375.21

1.00

405.00

3,648.92

2.00

3,840

37.89

1.00

50.00

121.06

1.00

217.00

.80

.00

.00

.42

.00

1.00

.45

.17

.80

.39

.06

.82

2.60 9.13 .98

1.00 8.00 .00

4.67 10.00 2.00

1.46 7.78 1.20

1.00 7.00 .00

2.32 10.00 3.00

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

below the advertiser at rank r. Because we lack data about competition, we model the price per click as a function of rank, which is a frequent way to deal with this condition (Abou Nabout et al. forthcoming; Ganchev et al. 2007; Kitts and LeBlanc 2004). Better ranks generally are associated with higher prices per click. We extend these models by adding the QS as a covariate and including a time trend. We again capture unobserved heterogeneity across keywords with a fixed effect on the intercept and model the price per click as dependent on two covariates, Rankit and QSit, and the time trend, Timeit:

Eq. (9) using two-stage least squares (2SLS), with Bidit as an instrument for Rankit. Note that Eq. (9) accounts for direct as well as indirect price effects through QSit and Rankit, respectively. Third, we model consumers' click behaviors depending on the 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 a time trend and capture unobserved heterogeneity across keywords with a fixed effect on the intercept:

lnðCPC it Þ ¼ γ i þ δ1 Rankit þ δ2 Q Sit þ δ3 Timeit þ ϑit : ð9Þ

lnðClicksit Þ ¼ μ i þ λ1 Rankit þ λ2 Q Sit þ λ3 Timeit þ ζ it :

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

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

1000

927 870

Number of Quality Score Changes

900 800 700 600

488

500

439

410 364

400 300 200

131

100

51 2

3

7

1

7 11 7

3 14

153

49 4

3

4

130

123

115 22

0 Notes: Quality Score changes from x to y (x y). Fig. 3. Distribution of quality score changes.

15

1

ð10Þ

N. Abou Nabout, B. Skiera / Journal of Interactive Marketing 26 (2012) 141–154 Table 4 Profitability of keywords.

Share of profitable keywords —In total searches —In total clicks —In total conversions —In total SEM costs Profits after SEM costs for all keywords Profits after SEM costs for profitable keywords

Travel (N = 29,339)

Industrial Goods (N = 68,442)

7.53%

71.42%

19.08% 45.53% 73.90% 21.02% − €15,743.03

64.35% 81.63% 73.75% 50.31% +€59,405.53

+€1,975.21

+€80,016.84

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

that an ad receives because we are interested in changes in the number of clicks that result from an improved QS. To again account for the determination of Rankit by Eq. (8) and the potential correlation with the error terms in Eq. (10), ζit, we estimate Eq. (10) using 2SLS, with Bidit as an instrument for Rankit. Note again that Eq. (10) takes into account direct and indirect quantity effects through QSit and Rankit, respectively. Results In Table 5, we present the industry-specific estimation results of Eqs. (8)–(10). From our analysis of rank [Eq. (8)], we find that both covariates, bid and QS, have a statistically significant 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 advertiser achieves. Next, we turn to the search engine's pricing behavior. Both covariates, rank and QS, have a statistically significant and negative relationship with the price per click. This finding reflects a positive indirect price effect and suggests that better ranks at the top of the screen, which may result from higher QS, are associated with higher prices per click. Furthermore, it indicates a

149

negative direct price effect; the higher the QS, the lower is the price that the advertiser pays per click. This finding is therefore in line with our proposed framework for the return on quality improvements (see Fig. 2). In contrast with industrial goods, the prices per click in the travel industry clearly have increased over time. The main difference between these industries is their sales target; the latter is a business-to-business (B2B) industry, whereas the former involves business-to-consumer (B2C) interactions. The decreasing keyword prices per click in a B2B context might result from the worldwide economic crisis, which has provoked reduced marketing budgets in an industry in which demand for costly industrial goods has deteriorated dramatically. Finally, we find that rank has a statistically significant and negative relationship with the number of clicks (indirect quantity effect), which confirms that better ranks (resulting from higher QS) are generally associated with more clicks. This finding is again in line with our proposed framework (see Fig. 2). We find that the QS has a significant and positive relationship with the number of clicks in the travel industry (positive direct quantity effect). However, this association is not true for industrial goods, for which the number of clicks is not affected by higher ad quality. The reason might have to do with the audience; a procurement manager, searching for industrial goods on a search engine, might not be as heavily influenced by ad quality but instead rely on brand recognition. We also find that the number of clicks has increased over time for industrial goods but not for travel, which suggests that competition might have increased in the travel industry. For our data set from January to June 2009, this result seems plausible because, according to Google Insights for Search, cruise line searches have fallen from their peak in June–July 2009. Counterfactual Analysis Finally, we conduct a counterfactual analysis to derive insights into the sign and magnitude of the effects of ad quality on prices per click, quantity, SEM costs, and profit after SEM costs. All else being equal, we calculate ranks, prices per

Table 5 Estimation results by industry. Travel

Industrial goods

ln(Rankit)

ln(CPCit)

ln(Clicksit)

ln(Rankit)

ln(CPCit)

ln(Clicksit)

./.

./.

./.

./.

R2 F-value Number of observations Number of keywords

65.61% 334 28,558 1,874

.0482*** (.0069) –.0008*** (.0001) –.2851*** (.0140) 65.80% 168 28,558 1,874

–.0224*** (.0053) –.0003*** (.0001) −1.1233*** (.0605) 59.50% 157 67,956 2,089

n.s.

Rankit

–.0427*** (.0049) .0002** (.0001) –.2992*** (.0100) 71.82% 311 28,558 1,874

–.3583*** (.0131) –.0070*** (.0013) n.s.

./.

Timeit

− 1.2937*** (.0422) –.0300*** (.0044) n.s.

Bidit QSit

./. 84.17% 261 67,956 2,089

*** p b .01, ** p b .05, * p b .1, n.s. not significant. Standard errors are in parentheses. QS: Quality Score, CPC: Cost per click.

.0005*** (.0001) –.4148*** (.0504) 68.40% 61 67,956 2,089

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Table 6 Decomposition of the returns on quality improvements and declines by industry. Travel (N = 1,827) [N = 205]

(1) Total Price Effect (PE) Direct Price Effect (DPE) Indirect Price Effect (IPE) (2) Total Quantity Effect (QE) Direct Quantity Effect (DQE) Indirect Quantity Effect (IQE) (3) Interaction Effect Δ SEM costs = (1) + (2) + (3) Δ Profit after SEM costs (Percentage increase in profits)

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

∑ in € a

∑ in € a

∑ in € a

Negative Effect b

Negative Effect b

Negative Effect b

Negative Effect b

− 499.30 [− 33.84] − 1,219.30 [− 57.86] 720.00 [24.02] 2,251.71

1378 [147] 1435 [147] 0 [0] 0

224.57 [6.23] 957.70 [10.34] − 733.13 [− 4.11] − 1,667.72

27 [0] 0 [0] 392 [58] 392

− 289.63 [−218.75] − 679.23 [−424.14] 389.60 [205.38] 148.01

1513 [1283] 1603 [1313] 0 [0] 0

116.83 [87.10] 310.67 [163.80] −193.84 [−76.70] − 69.96

59 [13] 0 [0] 924 [738] 924

[93.81] 1,467.30

[0] 0

[− 14.72] − 1,030.95

[58] 392

[78.04] .00

[0] ./.

[−27.49] .00

[738] ./.

[68.54] 784.41

[0] 0

[− 11.14] − 636.77

[58] 392

[.00] 148.01

[./.] 0

[.00] − 69.96

[./.] 924

[25.27] − 46.91 [− 2.40] 1,705.50 [57.57] − 809.00 (−95.34%) [59.16] [(+486.57%)]

[0] 1378 [147] 0 [0] 1035

[− 3.57] − 16.06 [−.38] − 1459.21 [− 8.87] 1,050.20 (+206.18%) [− 10.98] [(− 155.55%)]

[58] 365 [58] 392 [58] 124

[78.04] –.97 [−.82] − 142.59 [−141.53] 360.06 (+42.62%) [328.14] [(+ 30.62%)]

[0] 1513 [1283] 1441 [1251] 96

[−27.49] –.43 [−.41] 46.44 [59.20] −129.30 (− 23.78%) [− 120.42] [(− 18.24%)]

[738] 865 [725] 98 [22] 861

[0]

[58]

[20]

[729]

Notes: Results for profitable keywords are given in square brackets. Percentage increase in profits compared to situation before quality score change is given in parentheses. 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 of 1,435 cases and profits decrease in 1,035 of 1,435 cases.

click, and the number of clicks on an ad before and after each of the 4,354 QS changes in our data using the estimated models as presented in Table 5. Applying Eqs. (4)–(5), we then calculate the direct and indirect price and quantity effects for each QS change. We derive the interaction effect in Eq. (6), as well as the overall effect on SEM costs [Eq. (3)]. Finally, we determine the effect on profit after SEM costs according to Eq. (7). We calculate not only the short-term effect of a QS change from one day to another but a long-term effect, taking into account the number of days for which the QS remained the same. With Table 6, we provide the decomposition of the returns on all quality improvements and declines in both industries. In Columns “∑ in €,” we report the sum of the effects in Euros for quality improvements and quality declines separately. We also report the number of quality improvements and declines, for which the price per click, quantity, SEM costs, and profit 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 on SEM costs of − €499.30. This total price effect comprises the dominant direct price effect (DPE), which reduces SEM costs by − €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 positive in the case of a quality improvement. This finding suggests that most quality improvements actually lead to an overall reduction in prices per click and thus SEM costs because the DPE is typically larger than the IPE. The direct and indirect quantity effects are always positive in case of a quality improvement and result in increased SEM costs by €2,251.71. Because the positive total quantity effect (€2,251.71) dominates the negative total price effect (− €499.30), SEM costs increase by €1,705.50 as a result of the 1,435 quality improvements; this finding contradicts conventional wisdom. The more appealing an ad is in the travel industry, the more frequent consumers click on it, which results in overall higher SEM costs because the total price effect is smaller than the total quantity effect. In addition, we find that SEM costs never decrease for any of the 1,435 quality improvements. As a result, the travel advertiser's profits decrease by €809.00 (− 95.34%) in response to 1,435 quality improvements, such that 1,035 cases reveal decreases in profits (72.13%). For the industrial goods advertiser, we find that 1,513 of 1,603 quality improvements (94.39%) result in a decrease in prices per

N. 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 leads to a reduction in SEM costs by −€679.23. The indirect price effect (IPE) instead increases SEM costs by €389.60. This finding again confirms that most quality improvements reduce overall prices per click and thus SEM costs as the direct price effect is larger than the indirect price effect. The direct quantity effect (DQE) is insignificant for the industrial goods advertiser (see Table 5); however, we find that the indirect quantity effect (IQE) results in increased SEM costs by €148.01. As the negative total price effect (−€289.63) dominates the positive total quality effect (€148.01), SEM costs decrease by −€142.60 as a result of the 1,603 quality improvements. In contrast to the travel industry, more appealing ads do not influence the click behavior of procurement managers; the total price effect is therefore larger than the total quantity effect, which results in lower SEM costs in the industrial goods industry. Finally, SEM costs decrease in 1,441 of 1,603 cases (89.89%), so the industrial goods advertiser's profits increase by €360.06 (+ 42.62%) in response to the 1,603 quality improvements, and only 96 cases suffer decreases in profits (5.99%). This number certainly is not negligible, but it is much lower than that experienced by the travel advertiser. There are fewer profit decreases because of the lack of direct quantity effects (see Table 5), so this result is driven by the reductions 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 quality improvements lead to higher prices per click (147 of 3,038), 100% to a higher number of clicks (3,038), 52.57% to higher costs 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% for the travel campaign, the industrial goods campaign only has 28.58% unprofitable keywords in its campaign. Beyond higher costs for SEM, the strong decrease in profits in the travel campaign might therefore be driven by the high average cost per click of €.45 (see Table 3), which often is higher than the advertiser's corresponding profit contribution per click. Thus, a higher number of clicks likely leads to profit losses after SEM costs. We therefore additionally analyze the returns on quality improvements and declines for profitable keywords only (see Table 6 with the corresponding results given in square brackets). Our results indicate that the different price and quantity effects (PE, DPE, IPE, and QE, IQE, DQE) point into the same direction as the previous analysis. Again, most of the quality improvements result in a decrease in price (147 of 147 in the travel campaign and 1,283 of 1,313 in the industrial goods campaign). Additionally, none of the quality improvements in the travel industry results in a decrease in SEM costs, but all lead to an increase in profits after SEM costs (+ €59.16 and + 486.57%). For industrial goods, SEM costs decrease in 1,251 of 1,313 cases and profits after SEM costs increase by

151

€328.14 (+ 30.62%); only 20 quality improvements result in a loss in profits after SEM costs. Adjustment of Bids Eq. (1) suggests though that changes in the weighted bids do not occur if the bids are adjusted as follows: Bid1 ¼ Bid0 ⋅

Q S0 ; Q S1

ð11Þ

where: Bid1: Bid0: QS0: QS1:

Bid after the QS change, Bid before the QS change, Quality score before the QS change, 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 use Eq. (11) to calculate the appropriate adjustment in bids. Thus, quality improvements lead to lower bids (and vice versa). We test this simple heuristic for profitable keywords in our data set that yield positive profits after SEM costs before the QS change. Note that the performance marketing agency who manages the campaigns does not currently adjust its bids as a response to a change in ad quality (95% of the QS changes do not result in any adjustment of the bid). We then derive the corresponding 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 SEM as a result of the 147 quality improvements. However, its profits then would increase by €78.91 (+ 648.93%). 31 of the 147 quality improvements even lead to decreases in SEM costs, but profit would increase in all 147 cases as a result of the quality improvement and bid adjustment. In contrast, the industrial goods advertiser bears − €1,117.08 lower SEM costs as a result of 1,313 quality improvements; its profits increase by €784.23 (+ 73.18%). All 1,313 quality improvements lead to lower SEM costs and profits decrease in only 14 of the cases. This finding supports the idea of adjusting the bid according to Eq. (11). A quality decline always results in lower profits for the travel advertiser (−€14.18 and –200.85%). For the industrial goods advertiser, a quality decline almost always results in lower profits (732 of 738); the difference in profits after SEM costs equals − €374.72 (− 56.76%). The reason for this negative result is that—according to Eq. (11)—a quality decline requires the advertiser to adjust the bid upward, which increases the cost per click beyond the corresponding profit contribution per 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 result from generalized, second-price, sealed bid auctions that consider both submitted bids for each keyword and ad quality.

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Table 7 Return on quality improvements and declines by industry with quality adjusted bids. Travel (N = 205)

Δ SEM costs Δ Profit after SEM costs

Industrial Goods (N = 2,051)

Quality Improvement (N = 147)

Quality Decline (N = 58)

Quality Improvement (N = 1,313)

Quality Decline (N = 738)

∑ in € a

Negative Effect b

∑ in € a

Negative Effect b

∑ in € a

Negative Effect b

∑ in € a

Negative Effect b

5.55

31

–.54

34

−1,117.08

1,313

526.76

0

78.91 (+648.93%)

0

− 14.18 (− 200.85%)

58

784.23 (+73.18%)

14

−374.72 (− 56.76%)

732

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.

Conventional wisdom suggests that advertisers can only benefit from improving their advertisement's quality but our empirical study reveals that quality improvements have complex effects whose returns are actually unclear: Google asserts that higher quality scores mean lower costs and stronger ad positions (Google Adwords Help 2009b), but our empirical study reveals that at least 4.84% of all quality improvements to an ad lead to higher prices per keyword, 100% to a higher number of clicks, and 52.57% mean higher SEM costs, with 37.23% of these improvements leading to lower profits. Quality improvements lead to higher weighted bids, which lower prices only if they do not improve the ad ranking. Otherwise, better ranks likely lead to higher prices and higher SEM costs, with ambiguous profit consequences. To disentangle the multitude of effects resulting from improvements to ad quality, we have proposed a decomposition to understand the joint effects on SEM costs and profits. We differentiate between direct and indirect price effects (DPE and IPE): The former leads to a decrease in the price per click and SEM costs, while the latter increases prices paid per click and SEM costs because of the better ad ranking. We also differentiate between direct and indirect quantity effects (DQE and IQE), both of which lead to an increased number of clicks on an ad. Improvement in ad quality benefits the advertiser as long as the negative DPE, which lowers prices, dominates the positive IPE, which leads to higher prices, and the resulting cost per click is lower than the corresponding profit contribution per click. 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 most cases. The result is lower prices per click. In contrast with the B2B industry, higher ad quality directly increases the number of clicks in the B2C industry, through the effect of more appealing ads on consumers' behavior. This direct quantity effect (DQE) significantly increases SEM costs in the B2C industry and is one reason for the losses in profits after SEM costs. This association is not true in the B2B industry though, in which ad appeal has less impact on

the behavior of procurement managers. Therefore, the DPE dominates the other effects in this industry and reduces SEM costs. Finally, the two campaigns differ substantially in their share of profitable keywords. While the travel advertiser has a large number of unprofitable keywords in its campaign (92.47%), the industrial goods advertiser only bears 28.58% unprofitable keywords. In this industry, costs per click are typically lower than the corresponding profit contribution per click, which means the additional clicks on an ad actually pay off. In contrast, advertisers whose campaigns contain a higher number of unprofitable keywords (e.g., travel advertiser) suffer more from quality improvements because the price for an additional click frequently exceeds the additionally generated profit contribution per click. In summary, considering only prices per click, we show that the return on quality improvements is positive in most cases, yet for SEM costs, i.e., prices per click times the number of clicks, the return is mixed at best. Ultimately, quantifying profits after SEM costs reveals that the return on quality improvements is positive for far less than two thirds of the quality improvements. Interestingly, campaigns with a large share of unprofitable keywords suffer more from quality improvements because the price for an additional click exceeds the additionally generated profit contribution. For advertisers to benefit from improvements in ad quality, we propose that bids should be adjusted in response to a change in quality, which is currently not the case in the two campaigns managed by a German performance marketing agency. This adjustment follows the ratio of the preceding QS to the current QS. Thus, the bids should decrease in case of a quality improvement and increase in case of a quality decrease. Our data analysis indicates that this heuristic works very well, especially for quality improvements. Although we focus on SEM, the results are interesting for multiple other areas, such as online comparison shopping Web sites. Many of these sites currently rank offerings according to product prices or retailers' reputation; they could add retailers' bids for each click. As we noted previously, we focus on

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

weights linked to the quality of an ad, but these weights also might be linked to factors such as download or delivery speeds. Improvements to respective weights do not necessarily yield better prices, lower costs, or higher profits, so providers of such matching mechanisms should be very cautious in their claims about the consequences of improvements, to avoid the threat of litigation. Advertisers also should consider carefully whether improvements to one element of the matching mechanism will require them to adjust other elements (e.g., prices). We acknowledge several limitations to our study. First, we take ad quality as given by Google and do not analyze its determinants. In order to help advertisers understand how to influence their keywords' QS, future research should further investigate the determinants of ad quality. Google states that historical CTR, the relevance of the ad, and landing page quality constitute QS. While historical CTR is typically reported in traditional SEM data sets, bounce rate is not but might be a good proxy for landing page quality. However, it is rather unclear, which determinants potentially influence an ad's relevance such that reverse engineering Google'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 related to the advertiser's effort to improve it.

Acknowledgments The authors thank Oliver Hinz and Jochen Reiner for their valuable comments on an earlier draft of the article. They also gratefully acknowledge financial support from the E-Finance Lab at Goethe-University Frankfurt.

References Abou Nabout, Nadia, Bernd Skiera, Tanja Stepanchuk, and Eva Gerstmeier (forthcoming), “How Free-based Compensation Lowers Profit in Search Engine Marketing,” International Journal of Research in Marketing. Abrams, Zoe and Michael Schwarz (2008), “Ad Auction Design and User Experience,” 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 in Online Advertising Markets,” Journal of Marketing Research, 48, 6, 1057–73. Blumrosen, Liad, D. Hartline Jason, and Shuzhen Nong (2008), “Position Auctions and Non-uniform Conversion Rates,” Paper presented at the 4th Workshop 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 Keywords in Online Search,” Journal of Marketing, 73, 4, 125–41. Danuloff, Craig (2009), “Is Hype Over Google AdWords Quality Score Justified?” Retrieved 30 July, 2011, from http://searchengineland.com/is-thehype-over-google-adwords-quality-score-justified-18031. Edelman, Benjamin and Michael Ostrovsky (2007), “Strategic Bidder Behavior in Sponsored Search Auctions,” Decision Support Systems, 43, 1, 192–8.

153

———,———, and Michael Schwarz (2007), “Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords,” The American Economic Review, 97, 1, 242–59. Feng, Juan, Hemant K. Bhargava, and David M. Pennock (2007), “Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms,” INFORMS Journal on Computing, 19, 1, 137–48. ——— (2008), “Optimal Mechanism for Selling a Set of Commonly Ranked Objects,” Marketing Science, 27, 3, 501–12. Ganchev, Kuzman, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, and Michael 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 Engine Advertising: 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 Qualitybased Auction for Search Ad Markets with Aggregators,” paper presented at the 4th Workshop on Ad Auctions, ACM Conference on Electronic Commerce. Evanston, IL, USA, Kellogg School of Management, Northwestern University. 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, Lara Lobschat, and Arvind Rangaswamy (2010), “The Impact of New Media on Customer Relationships: From Bowling to Pinball,” Journal of Service Research, 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-AdRevenue-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 Keyword Auctions,” Electronic Markets, 14, 3, 186–201. Kleinberg, Robert D. (2005), “A Multiple-Choice Secretary Algorithm with Applications to Online Auctions (pp. 630–631),” paper presented at the 16th ACM-SIAM Symposium on Discrete Algorithms, Vancouver, British Columbia, Canada. Liu, De, Jianqing Chen, and Andrew B. Whinston (2010), “Ex Ante Information and the Design of Keyword Auctions,” Information Systems Research, 21, 1, 133–53. Misra, Sanjog, Edieal Pinker, and Alan Rimm-Kaufman (2006), “An Empirical Study of Search Engine Advertising Effectiveness,” paper presented at the 7th International Conference in Web Information Systems Engineering. Evanston, IL, USA: Kellogg School of Management, Northwestern University. Rangaswamy, Arvind, C. Lee Giles, and Silvija Seres (2009), “A Strategic Perspective on Search Engines: Thought Candies for Practioners and Researchers,” Journal of Interactive Marketing, 23, 1, 49–60. Rutz, Oliver J. and Randolph E. Bucklin (2011), “From Generic to Branded: A Model of Spillover in Paid Search Advertising,” Journal of Marketing Research, 48, 1, 87–102. ———, Michael Trusov, and Randolph E. Bucklin (2011), “Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?” Marketing Science, 30, 4, 646–65. ——— and ——— (2011), “Zooming In on Paid Search Ads—A Consumerlevel Model Calibrated on Aggregated Data,” Marketing Science, 30, 5. Skiera, Bernd, Jochen Eckert, and Oliver Hinz (2010), “An Analysis of the Importance of the Long Tail in Search Engine Marketing,” Electronic Commerce 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 System for Profitable Search Engine Marketing. Frankfurt: Goethe University Frankfurt. Soxman, David (2009), “Yes, In Search Marketing Quality Does Matter,” Retrieved 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), “Decomposing the Promotional Revenue Bump for Loyalty Program Members Versus Nonmembers,” Journal of Marketing Research, 42, November, 443–57. Varian, Hal R. (2007), “Position Auctions,” International Journal of Industrial Organization, 25, 6, 1163–78. ——— (2010), “Computer Mediated Transactions,” The American Economic Review, 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 Between Organic and Sponsored Search Advertising: Positive, Negative or Zero Interdependence?” Marketing Science, 29, 4, 602–23. Yao, S. and C.F. Mela (2008), “Sponsored Search Auctions: Research Opportunities in Marketing, Foundation and Trends in Marketing, Vol. 3. ——— and C.F. Mela (2011), “A Dynamic Model of Sponsored Search Advertising,” Marketing Science, 30, 3, 447–68. Zhou, Yunhong and Rajan Lukose (2006), “Vindictive Bidding in Keyword Auctions,” paper presented at the 2nd Workshop on Sponsored Search Auctions, ACM Conference on Electronic Commerce. Ann Arbor, MI, USA: University of Michigan.