Competition and Crowd-out for Brand Keywords in Sponsored Search

Transcription

1 Competition and Crowd-out for Brand Keywords in Sponsored Search Andrey Simonov University of Chicago (Booth) Justin M. Rao Microsoft Research Chris Nosko University of Chicago (Booth) September 30, 2015 Abstract On search keywords that include trademarked terms, the brand owner (focal brand) and other relevant firms compete for consumers. For the focal brand, paid clicks have a direct substitute in the organic link that appears directly below the advertisements. This crowd-out can create a large wedge between the real causal effect and commonlyreported nominal measures, such as click-through rates. We quantify this wedge using a large-scale, fully randomized experiment on Bing, which allows us to study 2,500 brands with a sufficient sample size. We find a positive, statistically significant impact of brand ads, with a modest size, on the order of 1 4%. There is significant heterogeneity, with more well-known brands showing a smaller causal effect. Competitors greatly impact the market. First, if the focal brand is not present in the top slot, competitors steal 10-20% of clicks on average. Second, when the focal brand is in the top slot, competitors steal only a modest fraction of clicks, 1-5%, but dramatically shift the focal brand s traffic from the organic link to the paid link, raising the focal brand s costs. Taken together, the results indicate the primary value of branded paid links is defensive in nature. We thank Christian Perez, Matthew Goldman and Giorgos Zervas for helpful comments. Most of this work was completed when Simonov was an intern at Microsoft Research. All opinions represent our own and not those of our employers.

2 1 Introduction In sponsored search advertising, brand keywords, queries that include a company s trademarked name, are a substantial portion of advertising expenditure. At first glance this practice seems sensible: Consumers learn about products through a variety of channels and seek them out online using a search engine. Entering a specific brand signals product awareness. Click-through rates (CTR) tend to be higher than similar non-brand keywords. Further, since bidding on another firm s trademarked term is legal, competing firms can step in and siphon off traffic from the focal brand (as in theoretical model of Sayedi et al. (2014)). The focal brand might want to bid aggressively to minimize traffic stealing. On the other hand, the focal brand also occupies the first organic result, which is shown just below the paid link(s), creating the possibility that the paid link simply crowds-out free clicks. Indeed a recent paper, Blake, Nosko and Tadelis (2015), find almost complete crowd-out for a single, well-known brand, ebay. Using a controlled experiment, they document that when ebay stopped bidding on its own keywords 99.5% of traffic was retained via the organic link. We are thus left with a bit of a mystery. Advertising on brand keywords is substantial, yet the presence of an almost complete substitute (the organic link) suggests that it is practically worthless for the focal brand. Moreover, there may be no need to continue to advertise to users who intentionally seek out a particular brand these are folks that will find their way to your product regardless of search advertising. Competing explanations of why firms continue to bid on brand keywords have fundamentally different implications for the marketplace and our understanding of firm behavior. The first class of explanations posits that advertisers focus on nominal measures of ad performance, such as CTR, rather than the causal impact on customer visits, which leads to an irrational interest in keywords that appear attractive through this lens. The second class of explanations points to ebay as being unrepresentative in some way. It is the second largest e-retailer with a very established brand and thus may be immune to competitive effects among customers using ebay in a search query. Under this explanation, other firms very well could be making rational choices. In this paper we arbitrate between these explanations with a large-scale, fully randomized experiment run on Bing. On both Bing and Google, the maximum allowable number of ads placed above the organic search results is 4. In the experiment this cap was exogenously reduced to 0, 1, 2 or 3. For example, if a query had a single ad that cleared the auction 1

3 reserve price, comparing conditions Cap 0 (0 ads allowed) and Cap 1 (only 1 ad allowed) effectively replicates the ebay experiment. We have such experiments for tens of thousands of brands, but focus our analysis on the top 2,500 brands, which account for 98.7% of brand searches. The included firms have widely varying brand quality metrics. We start by measuring the extent to which organic search results serve as substitutes for paid search links when no competing ads are present (matching the setting of Blake et al.). We find that the results in Blake et al. are a bit of an outlier. In our sample of firms, sponsored links on branded search queries drives significant incremental traffic total clicks to a firm s website increases by 2-3 percentage points on average. This effect is significantly larger for lesser known brands, while the strongest brands in our sample show effects closest to that of ebay. But while incremental traffic is relatively modest, crowd-out of free clicks on the brand s link in the top organic slot tends to be quite large. 1 When we experimentally introduce the brand ad about half the traffic, or 40 percentage points of CTR, is shifted from the organic link to the paid ad. Since crowd-out exceeds the causal effect by more than a factor of ten, the nominal cost per click (CPC), the standard pricing metric reported by online advertising platforms, strongly diverges from the real cost per incremental click (CPIC). For the case where brands do not face competing ads we measure the wedge between CPIC and CPC for a broad cross-section of firms. For firms that show a significant ad effect, CPIC is on average 11 times larger than the CPC. For all firms, we establish a lower bound ratio of While these multipliers might seem too high to rationalize, we must keep in mind that the focal brand always pays a much lower CPC than competitors because the pricing rule of the generalized second price (GSP) auction rewards link relevancy and clickability. To put our CPIC estimates in context we compare them to two quantities that should capture the real costs of customer acquisition on the platform: 1) the CPC focal brands pay on keywords when they do not occupy a high organic position 2) the CPC competitors pay on brand keywords. CPIC estimates exceed these comparison measures in the vast majority of cases. 3 1 All the brands in our sample always occupied the top slot. 2 We cannot reliably compute CPIC at the firm level when the estimated causal effect is close to zero. As such, we rely on the convexity of the underlying function and use Jensen s equality to produce a lower bound by estimating the function at the average value of the arguments. 3 We cannot observe the potential differences in the value of an ad click on different search queries or click type, nor the overall value of a click (given bid shading in the GSP Gomes and Sweeney (2014)), so these comparisons are by no means perfect. We note that more than 90% of firms have the same landing page 2

4 We have thus far focused on the case of focal brands not facing aggressively bidding rival firms. If competitors are present a situation commonly observed in our data the focal brand can choose to 1) either defend and bid aggressively to occupy the top slot or 2) not bid and allow competitors to fill the top of the page, pushing the brand s organic link further down. 4 In the first case, the focal brand s ad in the top slot provides a largely effective defense competitors are able to steal only 1-5% of clicks, which depends on the number of competitors present and brand attributes. It turns out that in this case competing ads have a much larger impact on crowd-out rates. When facing no competitors (Cap 1 condition), about 50-60% of clicks to the focal brand s website go through the paid link and the remaining half or so are free. When we randomly add in competitors, the fraction of paid clicks increases by 10% for the first competitor added, 9% for the second and 5% for the third, reaching 84% for a full slate. Thus increased competitors both lower the effectiveness (relatively small effect) and raise the costs (relatively large effect) of the focal brand s defensive position. Now we look at the case where the focal brand does not advertise but competitors do. These firms come in two varieties. The first, which we ll call reseller types, are firms that produce a product and sell it themselves, but allow other licensed retailers to sell it too (e.g. laptop manufacturers). These firms typically get only about 30% of total clicks on brand searches. The second variety is the exclusive seller, such as a travel website or car insurance company. These firms typically get more than 80% of total clicks. Using the experimental variation to forcibly remove competitors ads reveals that, in both cases, competing advertisers get 20% of total clicks far higher than the 1-5% we observed earlier when the focal brand was present in the top slot. For reseller types, only about one-third of this 20% comes from substitution from the focal brand s links, with the rest coming from clicks that would have gone to other firms present in the organic links. In contrast, for exclusive types, nearly all the competing ad clicks come from the focal brand. Based on observed click metrics, exclusive types are a much better match to the brands that choose to advertise, indicating that competitors pose a real threat to traffic acquisition if the brand is not present. Our results indicate that the returns to advertising for the focal brand hinge on presence of competitors. In the absence of competitors, ads have a positive and statistically significant (where a user goes after clicking on both.) 4 It is exceedingly rare that a brand bids and does not occupy the top slot. Conditional on bidding, they bid to win. 3

5 effect, but associated crowd-out produces CPIC/CPC ratios (which give a multiplier on nominal costs to get real costs) that are suspiciously high: 11 for firms with a significant ad effect and 22 overall. Computing CPIC when both the brand and competitors are present is more difficult because it requires us to estimate a counterfactual we cannot directly observe at the firm level. Nonetheless, using average estimates from other firms indicates that exclusive type firms would lose a substantial fraction of traffic roughly 25% of total clicks if they discontinued their ad, while the impact on reseller type firms is more muted. 5 This translates to CPIC/CPC ratios of 3 5 for exclusive types, with the exact figure depending on the number of competitors present and brand attributes. Given the relatively low CPC paid by focal brands, these multipliers generally place real costs below our comparison measures and thus are rationalizable. How does firm behavior align with our findings? We saw that without the defensive value induced by competition, the real costs of brand ad clicks are quite high and the overall returns are, at the very least, questionable. In this case, a slight majority, 54%, of firms choose not to advertise and only a small fraction of the firms that do advertise show ad effects and crowd-out rates that rationalize their choices. The remaining firms appear to excessively focus on average rather than marginal costs, as has been previously been documented in other contexts (de Bartolome, 1995; Ito, 2014), or suffer from an incentives failure of some sort. When competitors are present, the real-to-nominal cost ratios are generally easy to rationalize by the data, and here we observe that the vast majority exclusive-type brands choose to advertise. Finally, advertising on a rival s keywords produces a double-dividend: it necessitates a relatively costly defense, which increases with the number of competitors, and generates a modest amount of stolen clicks. Consistent with these observations, we observe the practice is widespread. Taken together, the majority of firms exhibit behavior that is broadly consistent with our findings, but a non-negligible fraction make choices that do not appear to be supported by the data. 5 The returns to these firms is more complex, since competing firms are advertising the product the focal brand produce, although they almost always sell other brands on their website. 4

6 2 Background and Context We define brand keywords as queries that consist of a trademarked term and where the trademark holder occupies the top organic slot. Competitors using a trademarked term to guide their bidding is a contentious practice as the focal brand dislikes the fact that their competitors can target a user that has expressed a particular interest in them. Indeed the brand firm may use other forms of advertising to generate searches for their products, as shown in Lewis and Nguyen (2014), and not want this traffic prone to competition. Although there have been trademark infringement lawsuits on the matter courts have consistently upheld the practice. The use of trademarked terms within a competitors ad is not allowed 6, an exception was granted in 2009 to licensed resellers, since a reasonable consumer would not be confused by the practice (the good being sold be the reseller is genuine). Chiou and Tucker (2012) study this change and find that this change did not damage the focal brand because it made the competing resellers less distinct. In empirical studies of brand search, early experimental evidence comes from Reiley et al. (2010), which showed that organic links and ads are substitutes for each other. This substitution pattern is overwhelmingly present in our experimental data as well, and has also been found in structural work (Jeziorski and Segal, 2014). 7 Reiley et al. (2010) further show that more ads can increase total CTR for the ad placed in the top slot because organic links act as slightly better substitutes for ads in their study. As discussed in the introduction, Blake et al. (2015) ran a large experiment with ebay and the results of the experiments led the firm to discontinue brand search ads. In the experimental period ebay did not face competing ads. The final relevant strand of literature involves user behavior in selecting options on the search results page and how this relates to bidding incentives. The first set of papers in this literature help explain why the position of a competitor s ad on the page causally influences a user. Granka et al. (2004) presents eye-tracking evidence that most users use a top-down cascade approach to searching the page. Craswell et al. (2008) takes this result to the field and shows that position effects can be large near the top of the page the same link 6 For example, while travel website Expedia is free to bid on priceline, it cannot include priceline in their ad text. 7 One paper, Yang and Ghose (2010), sharply diverges from this result. Using a structural model, they assert that there is a positive interdependence between organic ranking and search click-through rate. 5

7 gets substantially more clicks based on position alone. Later work has shown that search is more complex than simple top-down traversals (Dupret and Piwowarski, 2008), but still supports the finding that being pushed further down the page tends to lower CTR. In a (mostly) theory paper, Desai et al. (2014) model interactions between competitors with brand keywords. They discuss the prisoner s dilemma nature of branded keyword bidding. Jerath et al. (2011) present a related theoretical model where there are low and high quality firms and discuss why inferior firms have more incentive to locate at the top of the page because otherwise no one will find them. In contrast, a superior firm may get more clicks despite being lower down the page. In our setting this could correspond to the brand s organic link still getting the majority of clicks even if there are rival ads above it. One could broadly read our empirical evidence as consistent with many of these theoretical predictions Experiment and Data Description The data in our study come from a fully randomized experiment on the Bing search engine. On Bing, the sponsored listings that appear at the top of the page, above the organic listings, are known as the mainline. A maximum of four mainline ads could be shown on a given query the same practice employed by Google. Absent an experiment, the number of ads and their composition is endogenously determined by firms bids. A cross sectional regression that looked at differences in the number of advertisements by keyword would conflate true effectiveness of advertisements with differing environments across keywords. We use our experimental variation to control for these confounding factors. The experiment was conducted on a small fraction of U.S.-located users over nine days in January of Users searches were randomized into one of five conditions. Four of these conditions had some treatment, and corresponded to limiting the maximum number of mainline ads at 0, 1, 2, and 3. The fifth condition was a control group, and corresponded to the maximum of 4 mainline ads, the typical production setting. 9 The treatment limited the number of ads that could be shown, but often this cap did not bind. For instance, in the treatment group that limited mainline ads to a maximum of 3 ( cap 3 to employ the 8 We do not, however, observe brands occupying the lower adverting slots. The context of brand search is somewhat different than generic product search, so we would not expect all the predictions to be borne out. 9 The reason all the rest of the traffic is not the control group is that other experiments are running concurrently, so a clean control is preferred. 6

8 terminology we will use throughout), if there were not enough bidders that met the reserve price to fill the 3 slots, then fewer than 3 ads were shown. We carefully control for this issue by selecting only queries that matched into bidding data where an ad would have been shown in the absence of the experiment. See the Appendix A for more detail on this process. To identify brands, we extracted 87,000 retailer and brand names from the Open Directory Project. 10 A search is characterized as a brand query if and only if (1) the query is in this list, meaning it is a verified firm brand, and (2) the query matches the domain name in the first organic position. We focus only on brands that are in the first organic link because this selects true brand keywords. Queries that generate brands that are not in the first organic position might be searches of a different nature, perhaps not meant to get directly to the brand page, but to a broader set of sites. Figure 1: Brand search example This example has two mainline ads: own brand ad in mainline 1 and competitor s ad in mainline dmoz.org, the project uses volunteer annotators to classify the web. 7

9 Figure 1 provides an example of a brand query. Queries are simplified using standard techniques, e.g. we treat Macy s, macys.com, macys, and macy s as the same query. We focus on searches with 0 or 1 clicks on the page, ignoring rare instances of 2 or more clicks. 11 The majority of brands have very few exposures in the dataset. Table 1 presents the number of brands binned by the number of observations for those brands. 64.7% of all brands in the control group have less than 10 exposures but represent only 0.19% of all traffic, whereas 96% of traffic comes from the 1045 brands that have 1000 or more exposures. The starting point for our sample selection will be the 2517 companies with over 350 exposures. 12 These firms offer us relatively precise estimates and cover 98.7% of market activity. Table 1: Ad coverage in the control condition Number of Number of Percentage Percentage Percentage Percentage exposures brands of brands of traffic of own ads of competitor s ads in Control in Control (%) (%) in ML1 (%) in ML1 (%) > Total Percentage of ads is computed across companies. For example, companies with 4 exposures and companies with 10 exposures are given the same weight in group Total frequency is also computed across companies, unweighted. In our working sample of firms, 32.7% of companies advertise on their own brand keywords more than 90% of the time. 39% of brands do not advertise on their own keyword at all. The remaining brands advertise selectively, turning brand advertising on and off, or (more likely) advertising in some geographic regions but not others. More detailed information is given in the Appendix A, Figure In these occurrences, the searcher often visits all advertisers, making it less interesting to study. Further, search engines often refund clicks from such patterns. 12 With this selection rule we are balancing the number of firms against the inclusion of brands that don t provide meaningful information because they are so small. We have done substantial robustness around this threshold and the results are not materially impacted. 8

10 3 Results: Causal Effect of Brand Advertisements In this section we measure how effective advertising is in driving traffic to the focal brand s website. 3.1 Ad Effect without Competitors Present We begin by looking at firms that chose to advertise on their own keyword over 90% of the time during our sample period. 32.7% of the 2,517 high-exposure firms are in this set. 13 For 42.7% of this set (352) a competitor occupied the second paid slot less than 20% of the time. This represents the most extreme form of potential substitution between paid and organic search links because the link directly below the mainline advertisement goes to the brand s website with limited-to-no competition to stand in the way of the firm getting the click. To estimate ad effectiveness for this case, we compare the cap 1 treatment group for cases when the top ad went to the brand website to the cap 0 treatment condition where no advertisements were shown for the same set of queries because the experiment forcibly (and randomly) removed all paid search links. Our primary outcome of interest is the probability that an individual arrives at the website of the searched brand. We estimate this probability using the server logs that separately record clicks on a brand s organic link and those on the paid link. If a user does not click one of these links it is either because she chose to visit a competitor or did not click any link. Figure 2 plots the probability that a user arrived at a brand s website by the cap 0 vs. cap 1 groups. The figure shows that advertising on one s one keyword drives an incremental 2.27% of traffic to a brand s website. This average effect is overwhelmingly statistically significant, but we note that the y-axis is zoomed in and the overall magnitude of the effect is perhaps economically small. Next, we look at whether the ad effect varies by observable features in the data, focusing on a sub-sample of 493 companies with a sufficient amount of traffic to get reliable company-specific estimates of the probability to get a click (the results are not sensitive to 13 If we focus on companies that advertise on their own keyword at least once, these companies correspond to 53.6% of companies and 19.6% of traffic 9

11 Figure 2: The Effect of Advertising on One s Own Keyword this choice). 14 The first step is to check if there is more variance in our estimates than would be expected from sampling variation alone. Figure 3 presents an estimate of the density across firms. In this sub-sample, the average effect is (this is consistent with a smaller effect for larger firms), and the standard deviation is We plot a fitted normal density over the distribution of effects, which shows the empirical distribution has heavier tails. We formally test and readily reject the hypothesis that there is no heterogeneity. 15 Having verified there is real heterogeneity, what is the source? In our comparison of experimental conditions cap 0 vs. cap 1, we use all firms with a sufficient sample size in the top position this includes both those firms that would have faced competing ads in slots 2 4 and those that are typically alone on the page. The presence of competitors is presumably endogenous to many unobservable factors such as the brand capital (as in Simon and Sullivan (1993)) of the focal firm. A natural hypothesis is that stronger brands are less susceptible to competition and thus competing bidders is a signal of a weaker brand. If weaker brands benefit more from advertising, then we expect a larger effect for those brands that face competing bidders in the normal state of affairs. 14 We keep companies with more than 80 exposures in each condition. 15 We perform a series of standard tests for normality, including Shapiro-Wilk, Jarque-Bera, DÁgostino and other tests. All of them reject normality of the distribution. 10

12 Figure 3: There is heterogeneity in the effect of own ad in mainline 1 Table 2 divides the sample into groups depending on the probability (across queries and time) that a competitor would have occupied the 2nd slot in the absence of the experiment. For firms whose competitors never clear the reserve price on their keyword, the incremental effect is 1.4% on average, whereas for firms who almost always face competition, the effect is 4.9% on average. These differences are significant well beyond conventional levels and the results are also confirmed in a regression framework given in Appendix B, Table 10. We can also cut the data by the number of other competitive bidders for a given query, as given in Table 9 in Appendix B. The table shows shows that greater competition, in terms of bidders that clear the reserve price, is associated with a large experimental estimate. We now examine if the effect size varies with a firm s brand capital. Blake et al. showed that ebay s brand ads had almost no impact on website traffic. As the authors note, ebay is a very strong brand it is the second largest e-retailer. Our broader sample allows us to look at a wide range of firms, from those that are quite similar to ebay to those that the vast majority of consumers would not recognize by name. We proxy for brand capital with data collected from Alexa.com, a widely used website ranking service. In particular, we collect US and global rankings, bounce rate (the probability of a visit less than 30 seconds), the fraction of traffic from search (well-known firms get more direct navigation via the URL window), pages viewed per day and time spent per day. Table 8 (Appendix B) provides a 11

13 Table 2: Effect is higher for brands with more competitors on their keywords Total Competitors ads frequency in mainline N comp p noad (0.001) (0.002) (0.003) (0.003) (0.005) (0.005) p ad (0.001) (0.001) (0.002) (0.002) (0.004) (0.003) p ad p noad (0.002) (0.002) (0.003) (0.004) (0.007) (0.006) where N comp - number of companies p noad - probability to get to brand s website when there are no ads p ad - probability to get to brand s website when there is own ad in mainline 1 and no competitor s ads summary of brand capital measures. Table 3 presents the results of four regressions of own brand ad effect on brand capital measures. Specification (1) shows that companies with higher rankings tend to have a lower incremental effect of one s own ad in mainline 1. On average, the effect of an own brand ad for a very well-known company (log(us) ranking around 2.5) is 3 percentage points lower than for an unknown brand (log(us) ranking around 11). As we add more controls for our brand capital measure in specification (2), the coefficient on log ranking is still significant. In specification (3) we add controls for how much space the brand links have on the webpage: the number of deep links (sub links below the main link), the number of detail cards (detailed informational panels including things like maps and ratings), and the number of organic links of one s own brand on the first page. 16 The coefficient falls by roughly 30% and is no longer statistically significant. This drop combined with the negative coefficients on the features that indicate richer and larger content for the focal brand indicate that some of the effect was coming through stronger brands getting better visual representations. Finally, in specification (4) we add in the frequency of times competing bidders occupy slot 2. The coefficients do not change much and we can use this specification to confirm the larger effect when the ad typically faces competition. 16 For a description and examples of deeplinks and dcard please see Appendix C 12

14 Table 3: Companies with higher brand capital have lower own ad effect Dependent variable: Effect of own ad in Position 1 (1) (2) (3) (4) Log US Website Traffic (0.001) (0.005) (0.005) (0.005) Deep links (0.001) (0.001) Details card (0.001) (0.001) # brand organic links Freq. competitor in Slot 2 (0.002) (0.002) (0.008) Constant (0.012) (0.020) (0.024) (0.024) Other brand capital controls No Yes Yes Yes Observations R Adjusted R Note: p<0.1; p<0.05; p<

15 We conclude that brands with higher brand capital gain less from occupying the top adverting slot. This appears to be both a combination of consumers being less likely to substitute away from going to a firm s website when that firm has higher brand capital and because stronger brands get more visually appealing organic search results. Regardless of the precise causal channel the implication for managers remains the same firms with higher brand capital benefit less from bidding on their own brand keywords. 3.2 Ad Effect with Competitors Present First, we look at situations where competitors are the only bidder that clears the reserve price. In this case, competing links are above the focal brand s first link (the organic result) on the search results page. This is clearly a selected sample because the brand s decision not to advertise is a choice. In particular firms that choose to not protect their own brand keywords are presumably different than those that do. 17 However, for firms that chose not to advertise on their own keyword the experiment gives us clean variation to estimate the causal competitive effect. We start by comparing cap 1 to cap 0, where the competing ad has been removed at random. In order to mitigate selection effects we break up the set of firms that do not advertise on their own keywords but face competition into two sets. To do so we use the probability, in the absence of any advertisements (a case created by the cap 0 condition in our experiment), that consumers click on the firm s organic unpaid link. The case where consumers click relatively infrequently on the brand s organic link indicates that many firms sell a genuine version of the intended good. For example, one can buy a Dell laptop directly from Dell or through many e-retailers. We will call this the reseller case. In contrast, consider a search for a travel booking tool like Expedia.com. For these types of searches there is only one genuine version of the good and the branded firm garners a high fraction of clicks. We thus refer to such cases as the exclusive type. We assign types using the probability of arrival distribution, which is given in Figure 4 for the 49 firms that do not bid on their own keywords and have a competitor in mainline 1 above 90% of the time in the control group for our experiment. The rates here are much 17 Recall that one of the downsides of the experiment is that we were not able to place ads in situations where they wouldn t have existed otherwise we were only able to subtract them. 14

16 Figure 4: Heterogeneity in firms that do not bid on their own brand keywords lower as compared to the average arrival rate of about 80% observed in the last section. There is also substantially more more heterogeneity, with a large mass of firms below 0.30 (Figure 14 in Appendix E shows a similar histogram for 493 companies which advertise on their keyword more than 90% of the time). We divide the firms with the two vertical lines shown at arrival rates of 50% and 70%. Based on click behavior, the exclusive types form a more suitable comparison group for the firms that typically advertise on their keywords. Figure 5 graphs the difference in the number of clicks on the brand s own organic link and the competitor s ads across the cap 0 and cap 1 conditions. The left-panel gives the reseller type and the right panel the exclusive type. When we experimentally add the competitor s advertisement two effects occur. First, the percentage of traffic to the brand s website drops. The drop is 8 percentage points for the reseller case and 17 percentage points for the exclusive case. These drops represent traffic stealing. Second, the competitor gains a large number of clicks around 20% in both cases. This means that for the exclusive case, almost all the clicks garnered by the competing ad come at the expense of the focal brand, whereas for the reseller case less than half of the clicks came from the focal brand, with the rest coming from other resellers with links further down the page. We now move to the case where the focal firm wins the auction and competitors occupy 15

17 Figure 5: The incremental effect of bidding on one s competitors keywords for reseller type (left panel) and exclusive type (right panel) firms. slots 2, 3, and 4. We narrow our sample down to firms where, in the absence of the experiment, 4 mainline ads would be shown. 18 This holds the set of firms constant and the experimental conditions systematically remove advertisements. Figure 6 displays the causal effect of occupying each of the mainline slots. Starting with the left panel, the first point displays the total traffic that goes through both the organic and paid link to the brand s website when only the brand ad is present. The second through fourth points display the traffic to the brand s website adding in competitors into the mainline slots 2 through 4, respectively. The relationship is downward sloping, as expected and the full slate of competitive ads reduces the traffic to a brand s own website by 4.3 percentage points on average. In the right panel we separate advertisers by a median split of website traffic. The lower line represents below-median firms. We can see the smaller firms have a lower baseline CTR and a slightly greater loss of clicks when facing 3 or 4 competitors. Figure 7 breaks down the stolen traffic by competitor position. Two points are worth noting. First, the amount of traffic garnered by the second and third place bidders is not 18 Again, we deal with a bit of a selected sample keywords where both the brand and competitors are already bidding. We have already shown these firms tend to be weaker brands, for instance. However these are presumably the firms for which these estimates matter the most. 16

18 Figure 6: The effect of competitive ads in mainline slots 2-4 (a) Average effect (b) Upper and Lower 50% by Brand Capital (US Ranking) significantly affected by the number of additional ads below them, though there is slight evidence that the second ad prefers to face more ads below it, consistent with Reiley et al. (2010). Second, the higher placed bidders secure more clicks. This is a natural consequence of the auction logic and should not be interpreted as a causal effect of position, though some of the effect is likely due position effects, Goldman and Rao (2015). We started by examining the impact of the focal brand ad in isolation and observed a statistically significant effect of about 2.5 percentage points. This effect tended to be larger for weaker or more susceptible brands. Within the sample of firms that we observe that do not bid on their own keyword and have a competitor in mainline 1, this competitor receives around 20% of clicks for those keywords. Taken together, our estimates provide some justification for the notion that firms should bid on their own keyword in order to protect their brand keywords from competitors. In the absence of a focal brand, ad clicks to the brand s website decrease by an average of between 8 and 17 percentage points depending on the type of firm. We showed that such defensive positions are relatively effective. When the focal brand is present, competitors in slots

19 Figure 7: The effect of competitive ads in mainline slots 2-4 steal far fewer clicks, around 4 percentage points, or one-fourth of the effect for the exclusive types (the appropriate comparison group) when the brand ad is not present. This evidence indicates that the marginal effect of the brand ad is quite high in this case on the order of 15 percentage points. In contrast when competitors are not present the effect is around 2 percentage points. This comparison suggests the primary value of a focal brand s ad is defensive. 4 Results: Crowd-out and Cost Per Incremental Click Up until now we have combined paid and organic clicks together in order to measure the causal impact of the ads. Of course the firm is not agnostic to the source of traffic: If the traffic goes through the paid link, they must pay the search engine for each click; if it goes through the organic link, it is free. When crowd out occurs, for every marginal click there are inframarginal clicks that the firm must pay for as well. In this section we define cost per incremental click, which accounts for the real cost of acquiring traffic. 18

20 4.1 Crowd-out and CPIC without Competitors Present Figure 8 shows the percentage of traffic going to one s own brand website through the paid link in mainline 1 for the sample of focal firms consistently advertising on their keywords. On average, 46.3% of clicks go through the paid link and most of the firms fall between 30% and 70%. Figure 8: Histogram of the fractions of total clicks going through paid links Figure 9 visually shows how many of these clicks are cannabilizing organic clicks by using the experimental variation induced by the cap 0 vs. cap 1 conditions. The difference in the overall height of the bars represents incremental clicks to a brand s website, i.e. the average effect of 2.27% we previously documented. The drop in the organic segment (pink) shows the degree to which the paid link cannibalizes organic clicks roughly half of the traffic now goes through the paid link. Putting both figures together, on average for every incremental click the firm pays for 19 inframarginal ones. It is important to note, however, that this calculation implicitly sets the counterfactual of no competetive ads. Since our interest is to compute an accurate costs per incremental click at the firm level, we need to choose the appropriate counter-factual: what would happen if the firm did not advertise. 19

21 For firms that typically do not face competing ads, the simple Cap 0 to Cap 1 comparison is valid and we use this as a starting point. Figure 9: Organic click cannibalization compared to incremental clicks We compute CPIC as: CP IC = xp 1 (p 1 p 0 ) CP C where x is the fraction of clicks going through an ad, p 1 is the probability of getting to one s own website with the own brand ad in mainline 1, and p 0 is the probability of getting to one s own website without the own brand ad in mainline 1. The CPIC/CPC ratio is a natural measure of crowdout it gives how many clicks a firm has to pay for to garner one incremental click. However, a challenge in computing this ratio is that CPIC is only a coherent object if there is a positive effect of own brand ad (otherwise it is infinite or negative). In our sample, the estimate is significantly positive for 16% of companies. We start by computing CPIC for these 43 firms. The median is 9.1, the mean is and the distribution is shown in Figure 10. To estimate this ratio for all firms we rely on the assumption of a non-negative ad effect and the convexity of the relationship between CPIC and effect size. This convexity is due 20

22 to effect size showing up in the denominator of CPIC. Using Jensen s inequality, evaluating the CPIC function at the expected value of it s arguments will be a lower bound for the expectation of the function. 19 For these firms, the average average effect of an own brand advertisement is 1.6,% 20 and the average amount of traffic going to the own brand website through the paid link is 36%. The resulting estimate of the CPIC to CPC ratio is Figure 10: Histogram of CPIC/CPC ratio for the estimates for the 43 firms that do not typically face competing ads and show a significantly positive ad effect. Table 4 presents a detailed summary of the costs paid per click by the focal brand and competing firms. The focal brand s nominal CPC is low, between $ depending on the sample and the weighting. But given the amount of traffic that goes through these links, the amount of money involved is far from trivial. Competitors typically pay many multiples of the brand s price, between $ on average, despite occupying lower positions. This disparity is a function of the pricing rule of the GSP auction more relevant and clickable ads get a lower per click price. Real costs, given by CPIC, are much higher. We can estimate CPIC at the firm level for those with a significant ad effect. It comes in at $1.12 in the unweighted average and $1.42 when we weight the average by searches. To etimate the average across all firms, we use the lower bound approach previously described and estimate $3.50 and $2.52 for the unweighted and weighted figure respectively. All of these averages 19 We provide more details on this in the Appendix D. 20 Recall these are only firms that do not face competition. Effects for these companies are presented in columns 1 and 2 of Table 2. 21

23 are higher than the CPC competitors are paying, both at the average and firm level, which is especially true when weighting by impressions. A second natural comparison point for CPIC is the CPC focal brands pay on keywords when they do not occupy high organic position and thus nearly all clicks are marginal. Even for the firms with a significant effect, CPIC exceeds this figure for 93% (51% unweighted) of the companies that show a significantly positive ad effect. We are unable to make firm level comparisons when the effect size is not significant and positive, but note that the lower bound given by evaluating the CPIC at average values well exceeds the comparison measures of CPC. Table 4: Cost of Clicks for Focal Brands and Competitors Unweighted average Weighted average Measure All brands Significant All brands Significant CP C brand ($) CP C compet ($) CP C brand other ($) CP IC brand ($) CP IC brand > CP C compet (%) CP IC brand > CP C own other (%) N Weights are number of exposures CPC paid by the brand on other keywords when they are not high in the organic listing. It is computed for 85% of companies in the sample where a match is possible. Lower bound computed with average values as described in the text. Taken together, the evidence indicates that these firms, which did not face competition in our sample period, are spending a substantial amount of money on brand keyword advertising when the CPIC calculations indicate that they could acquire clicks for a lower real cost via other keywords. Similarly, firms are paying more to attract these customers than their competitors are. What explains this pattern of results? One way to rationalize behavior is to posit a higher valuation for clicks for brand searches as compared to more generic product searches. For any given effect size there is a valuation premium that rationalizes the implicit CPIC. While we cannot rule such valuations out, it seems questionable that users already familiar enough with a brand to enter a targeted query are substantially more valuable than users acquired through more generic keywords or those specifically searching for competitors. Indeed, since 22

24 brand searchers are already aware of the focal brand, the brand awareness channel of advertising effectiveness is shut down (Laurent et al., 1995), which would point to a prior that these users are less valuable. The second class of explanations points to bounded rationality on the part of the decision makers, such as the online media buyer at firms. 21 If decision makers focus on vanilla CPC, the familiar and widely reported metric, as opposed to CPIC, which must be computed via experimentation, then the low CPC, often ten times less than what the brand pays on generic keywords, makes brand searches look quite attractive. This explanation is supported by past work showing that people struggle to think marginally, sub-optimally focusing on average as opposed marginal measures (de Bartolome, 1995). 22 Finally, it could be that the proximate decision makers know the CPIC but choose to report CPC to their less-savvy superiors in order to frame the purchase as a good deal (potentially by adding them to a larger portfolio to reduce the average (nominal) customer acquisition cost of online advertising). While we cannot conclusively reject the first explanation, the scales seem to tip against it. We have no way to differentiate between the bounded rationality and principle-agent explanations and thus leave this unpacking too future work. Finally we note that among the set of firms in our sample that do not face competition, 54.4% choose not to advertise and some that do advertise have observable behavior that is easily rationalizable. This indicates that if there are indeed biases (or incentive problems) at play they are by no means universally present. 4.2 Crowd-out with Competitors Present We now shift our focus to the case when one or more competitors clears the auction s reserve price and the brand s ad still occupies the top slot. 23 Competitors impact crowd-out because their ads appear at the top of the page, shifting the brand s organic link further down. Given that top-down visual search is common, this pushes more traffic through the paid search link for the brand. 21 At smaller firms, this individual would buy the advertising through the platforms online portal. For larger firms, they likely handle a relationship with a search engine management firm. 22 We wouldn t argue that firms don t intuitively understand that some of this cannibalization is happening. The question is to what extent firms measure and adjust their bids based on it. 23 It is exceedingly rare for the brand to occupy slots 2 4. Bids reveal that if they choose to bid, they win the auction easily. 23

25 To examine the magnitude of this effect, we extract the set of keywords that, in the absence of the experiment, have 4 ads present. We then use experimental variation to study the effect of ads in slot 1 (focal brand) and slots 2-4 (competitors). In each case we compare the total traffic that goes to the brand s own website and the fraction of that traffic that goes through the organic versus the paid link. Figure 11: The effect of competition in increasing cannibalization Figure 11 plots these effects. The relative uniformity in the bar height confirms what we previously documented in terms of a modest brand ad effect and minimial traffic stealing when the brand occupies the top slot. Far more dramatic differences emerge when we focus on the paid/organic traffic split. When only the brand ad is present, 60% of clicks go through the paid link. (This is higher than our previous estimates because the sample of firms here is restricted to those that face 3 competing ads in the control condition.) This increases to 70%, 78% and 84% with one, two and three competitors present respectively. Table 5 extends the sample of firms to include those that typically face one (first column) two (second column) and three (corresponding to Figure 11) competing ads. The effect is consistent across these samples of firms. A competitor s ad in mainline 2 causally increases 24

26 the fraction of ads going through the focal brand s paid link by percentage points, an additional 8 9 percentage points for the third slot and an additional 6 percentage points for slot 4. When facing a full slate of competitors, the ad gets 84.3% of brand clicks. Table 5: Crowd-out by the number of ads a brand faced in the control and number of competitors present Number of competitors in Control Condition Cap % 56.6% 59.5% (0.29) (0.42) (1.33) Cap % 68.0% 69.9% (0.32) (0.39) (1.30) Cap % 78.2% (0.38) (1.18) Cap % (0.91) N Standard errors in parentheses. In Cap 0, not shown, is 0% in all cases. It is clear why brand s have fought to ban competing firms from bidding on their trademarked terms. If competitors do not advertise, a slim majority of brands choose not to advertise, meaning all clicks are free. Those that do advertisers have crowd-out levels of roughly 54 60%. If competitors bid aggressively, the vast majority of companies choose to advertise in order to defend their turf. This practice can be explained by the large click stealing we observed if no defense is present. Such defense is largely effective, but, as competition increases, would-be free clicks are causally pushed to the paid link, raising costs for the focal brand. A competitor can tax a rival through this channel, while stealing a few clicks along the way. It is not surprising, then, that this is a commonly observed practice CPIC with Competitors Present To compute CPIC, we need the relevant counterfactual of what would happen if the focal brand did not advertise. When competitors are absent, if the firm stopped bidding, then no ads would be present and thus we can measure CPIC at the firm level by comparing Cap 0 to 24 One could imagine that either collusion or repeated game dynamics lead to a don t bid on mine and I won t bid on yours situation. To the extent that this occurs, it does not appear to be common. 25