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Competition and Cannibalization of Brand Keywords∗
Andrey SimonovUniversity of Chicago (Booth)
Chris NoskoUniversity of Chicago (Booth)
Justin M. RaoMicrosoft Research
September 4, 2015
We describe and quantify the effects of competition on search advertising brand key-words. In this market, firms compete over advertising on their own keywords as well asthose of their competitors. Traffic often has a direct substitute: a similar organic link thatappears directly below the paid advertisements. Given this potential crowd out, real andperceived (non-incremental) metrics of ad performance will diverge, complicating the op-timization problem for firms. We utilize a large-scale, fully randomized experiment on theBing platform where paid search advertisements were selectively removed for subsets of users.The experiment gives us good data coverage for over 2,500 firms. We find that there is sub-stantial heterogeneity in the causal effect of one’s own brand keyword advertisements, whichis driven in part by how well known the brand is. Competitors greatly impact the market.First, if the focal brand is not present in the top slot, they can steal a substantial fraction ofclicks. Second, when the focal brand is in the top slot, the firms can steal a modest amountof clicks, but the primary effect is to dramatically shift the focal brand’s traffic from theorganic link to the paid link, thus increasing cannibalization and raising the cost of the focalbrand’s advertising.
∗We thank Christian Perez, Matthew Goldman and Giorgos Zervas for helpful comments.
1
1 Introduction
Advertising on brand keywords, defined as bidding on queries related to a company’s trade-
marked name, is a substantial fraction of advertising expenditure on sponsored search plat-
forms. At first glance, this practice seems to make a lot of sense: Consumers who search for a
firm’s brand name are signaling a high degree of product awareness and click-through-rates
(CTR) for the focal brand on brand keywords are consequently considerably 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. Bidding
aggressively can ensure the focal brand occupies the top slot in an effort to minimize “traffic
stealing.” On the other hand, the focal brand also occupies the first “organic” result shown
just below the paid link, creating the possibility that the paid link can crowd-out free clicks.
Indeed a recent paper, Blake et al. (2015), find almost complete crowd-out studying a sin-
gle, well-known brand, eBay. Using a controlled experiment, they document that when eBay
stopped bidding on its own keyword, 99.5% of traffic was retained via the organic link.
We are thus left with a bit of a mystery. Advertising on brand keywords remains big
business, yet published evidence on the topic indicates that it is practically worthless for the
focal brand. Moreover, the paper had a compelling story: users who intentionally seek out a
particular brand do not need to be continued to be advertised to. Competing explanations
of why firms bid on brand advertisements 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 the ad’s CTR, rather
than the causal impact on customer visits, which leads to an irrational interest in keywords
that appear attractive through this nominal lens. The second class of explanations points to
eBay being unrepresentative in some way. For example, eBay, as the second largest e-retailer
with a very established brand, 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 disparate explanations with a large-scale, fully
randomized experiment run on Bing.com. 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 (cap 0 through 3). For example, for a query where one
2
ad cleared the auction reserve price, comparing conditions “Cap 0” and “Cap 1” 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.
Even within this group, firms vary widely in terms of size and other brand quality metrics.
We start by measuring the extent that organic search results serve as substitutes for paid
search links. We find that the results in Blake et al. (2015) are 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, or about 5–10% baseline CTR
of the organic link. This effect is significantly larger for lesser known brands, while the
strongest brands in our sample show effects closest to that of eBay.
Although incremental traffic is non-zero, “cannibalization” or “crowd out” of free (organic
link) clicks is generally an order of magnitude larger than the casual impact of the ad. Given
the large crowd out, the nominal “cost per click” (CPC), the standard pricing metric reported
by online advertising platforms, and effective cost per incremental click (CPIC) strongly
diverge. When the firm does not enjoy a high position in the organic ranking, which is true
of competing firms on brand queries and most firms on generic commercial queries, these two
quantities are roughly equal. In contrast, all the brand firms in our study occupied the top
organic position. We measure the wedge between CPIC and CPC for a broad cross-section
of firms advertising on brand keywords in the absence of competing bidders that clear the
reserve price. In this case, the vast majority of the time the focal brand is the only ad shown,
making is easy to form the counterfactual of what would happen if they stopped advertising
(at least in the short-run).
For firms that show a significant ad effect, CPIC is on average 11 times larger than the
CPC. For all firms, we establish lower bound of 22 times CPC. While these multipliers are
large, it is not immediately clear what conclusions we should draw since the pricing rule
of the generalized second price (GSP) auction rewards link relevancy and clickability with
lower prices—the focal brand almost always pays a much lower CPC than competitors. To
put our CPIC estimates in context we compare them to the CPCs focal brands pay on
keywords when they do not occupy a high organic position (meaning CPC represents the
real price) and their bids for brand keywords. CPIC exceeds CPC on non-brand keywords
in 90% of cases and their bid in the brand keyword auction in 73% of cases. Since we cannot
3
observe the potential differences in the value of an ad click versus an organic link click,1 nor
the overall value of a click (given bid shading in the GSP Gomes and Sweeney (2014)), we
cannot definitely conclude that firms do not fully understand average versus marginal CPC,
but the evidence does strongly point in that direction.
All of the results presented so far have focused on brands that do not face aggressively
bidding competing firms. Bidding on a competitor’s brand keyword may be an attractive
strategy to “steal” traffic from the focal brand and it is quite common in our data. We
find when the focal brand continues to occupy the top slot, competitors can steal 3-5% of
the focal brand’s traffic by bidding into the second through fourth slots. Weaker brands are
more susceptible to competitors and indeed face competition more frequently.
This competitive siphoning, while statistically significant, might be considered relatively
modest, especially for stronger brands. However, these ads have a much larger impact
on cannibalization rates. When facing no competitors (in our experimental condition that
eliminated them), 50% of clicks to the focal brand’s website go through the paid link and
the remaining half are free clicks on the organic link. Holding the set of firms fixed, the
fraction of paid clicks increases to 61% when one competitor is present; 73% when two
competitors are present and 80% when 3 competitors are advertising. So while competitors
might steal relatively few clicks, they cause the brand to pay for substantially more. A
natural explanation is that more competitors creates a larger ad space pushing the brand’s
organic link down the page into a less visually prominent position.
Given that the focal brand has to pay for a large fraction of clicks, many of which are
non-incremental, why would it continue to advertise? In our final set of results, we look
at cases where the focal brand is not present but competitors are. These firms come in
two varieties. The first are firms that produce a product and sell it themselves, but other
retailers sell it as well, such as a laptop computer. These firms typically only get 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 upwards of 80% of total clicks.
It turns out that, in both cases, competing advertisers garner 20% of total clicks—far higher
than the 3-5% we observed earlier when the focal brand was present in the top slot. For
the first type of firms, only about one-third of this 20% comes from substitution from the
1We note that more than 90% of firms have the same “landing page” (where a user goes after clicking onboth.)
4
focal brand’s links. The rest come from clicks that would have gone to other firms present in
the organic links or from searches that would have resulted in no clicks. In contrast, for the
second type of firm, much like in the case of firms who bid on their own keywords, nearly all
the competing ad clicks come from the focal brand.
These results indicate that the returns to advertising for the focal brand depends critically
on the presence of competitors. In the absence of competitors, CPIC typically exceeds the
nominal CPC a focal brand pays on other keywords. Computing CPIC for firms that occupy
the top slot in the presence of competitors is more difficult because it requires us to estimate
a counterfactual we cannot directly observe at the firm level. Nonetheless, using the average
estimates from other firms predicts that firms of the second variety would lose a substantial
fraction of traffic—roughly one-fourth of total clicks—if they discontinued their ad. The
effects on firms of the first variety are more muted.2
The evidence brings us to something of a resolution to the question posed at the outset.
When competing bidders are present, occupying the top slot can effectively, but not com-
pletely, fend off competition. For these brands, advertising is primarily defensive. As more
competitors enter, the cost of this defense rises because cannibalization increases, giving
rise to an interesting tradeoff firms must navigate. When no competitors are present, the
paid ad does causally increase total clicks, but effect sizes are modest and decrease with a
firm’s brand capital. In this case, roughly speaking, for every 8 clicks a firm pays for, only
one represents a causal increase. We saw that the effective real cost typically exceeds other
quantities, such as the bid and the firm’s CPCs on non-brand keywords, that we would not
expect it to if firms behave rationally, although this is admittedly a highly tentative con-
clusion. Without the defensive value induced by competition, the real cost of these clicks
appears quite high and the overall returns are, at the very least, questionable.
2 Background and Context
Early experimental evidence, Reiley et al. (2010), showed that organic links and ads are
substitutes for each other. This substitution pattern is overwhelmingly present in our ex-
2Indeed the returns to these firms is more complex, since “competiting firms” are advertising the productthe focal brand produce, although they almost always sell other brands on their website.
5
perimental data as well, and has also been found in structural work (Jeziorski and Segal,
2014).3 Reiley et al. (2010) further show that more ads can increase total CTR on the top
slot because organic links act as (slightly better) substitutes for ads in their study.
We define brand keywords as queries that consists 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. A 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. Indeed, firms have
sued Google, claiming trademark infringement, but their core claims have consistently been
rejected by the courts.4 In 2009, Google began allowing resellers to use trademarked terms
in their ads. 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.
2.1 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. Clearly, 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, conducted on a fraction of U.S.-located users over nine days in January
of 2014, randomized searches 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,
3One paper, Yang and Ghose (2010), sharply diverges from all other papers we are aware and uses astructural model to assert that there is a positive interdependence between organic ranking and searchclick-through rate.
4Competing ads cannot use trademarked terms in their ad text, as would reasonably confuse a consumer.For example, while travel website Expedia is free to bid on “priceline,” it cannot include “priceline” in theirad text. This rule does not apply to licensed resellers, however.
6
and 3. The fifth condition was a control group, and corresponded to the maximum of 4
mainline ads. The control group corresponded to the situation that would have occurred in
the absence of the experiment. We note that just because a treatment group limited the
number of ads that could be shown, that doesn’t necessarily correspond to that number
of ads that actually appeared. For instance, in the treatment group that limited mainline
ads to a maximum of 3 (cap 3 to employ the 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 Direc-
tory Project.5 Search is characterized as brand search 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. This restriction assures us that we are getting true
brand search behavior and represents an underestimate of the true set of brand searches that
may be going on.
Figure 1 provides an example of a brand search query. The queries are simplified using
standard techniques, e.g. we treat macys.com, macys, www.macys.com and macy’s as the
same query. We focus on searches with 0 or 1 clicks on the page, ignoring queries with 2 or
more clicks.6
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. E.g., 64.7% of
all brands in the control group have less than 10 exposures but represent only 0.19% of all
traffic.
The large majority of traffic is generated by a relatively small subset of the total brands:
Almost 96% of traffic comes from the 1045 brands that have 1000 or more exposures. In
5dmoz.org, the project uses volunteer annotators to “classify the web.”6In these rather rare occurrences, the searcher often visits all advertisers, making it less interesting to
study. Further, search engines often refund clicks from such patterns.
7
Figure 1: Brand search example
This example has two mainline ads: own brand ad in mainline 1 and competitor’s ad in mainline2.
8
Table 1: Majority of brands have few exposures and less own ads in mainline 1(based on Control condition)
Number of Number of Percentage Percentage Percentage Percentageexposures brands of brands of traffic of own ads of competitor’s adsin Control in Control (%) (%) in ML1 (%) in ML1 (%)
1 4869 23.1 0.02 3 30.62 2773 13.1 0.02 4.1 32.43 1686 8 0.02 6.3 30.8
4 - 10 4315 20.5 0.12 10.2 34.511 - 100 4200 19.9 0.64 19.8 34.6
101 - 1000 2202 10.4 3.6 42.64 28.5> 1000 1045 5 95.6 43.8 13.6Total 21090 100 100 14.4 31.4
Percentage of ads is computed across companies. For example, companies with 4 exposures andcompanies with 10 exposures are given the same weight in group 4-10. Total frequency is also
computed across companies, unweighted.
particular, the starting point for our sample selection is the 2517 companies with over 350
exposures.7 These firms offer us relatively precise estimates and cover 98.7% of the market
activity.
In Figure 12 (Appendix A) we show that for this sample, 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.
3 Results: Causal Effect of Ads
In this section we look at how effective advertising on one’s own brand keyword is in driving
traffic to a brand’s website.
7With this selection rule we are balancing the number of firms against the inclusion of brands that don’tprovide meaningful information because they are so small. We have done substantial robustness around thisthreshold and very little is affected.
9
3.1 In the absence of competing ads
We begin by looking at the case of firms that choose to advertise on their own keyword
over 90% of the time. For the 2517 high exposure companies, this case corresponds to
32.7% of companies.8 For 42.7% of this set (352 companies), competitor is occupying 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 and there is no competitor bidding to
disrupt this substitution. To estimate effects for this case, we compare our treatment group
that limited advertising to one mainline ad (cap 1) and this ad went to the brand website
that was bidding on the paid link to our treatment condition where no advertisements (cap
0) were shown for the same set of queries. Effectively we forcibly and experimentally remove
the paid search advertisement despite the fact that the firm was still bidding on it. Our
primary measure of interest is the probability that an individual arrives at the website of
the searched brand. We estimate this probability based on a user’s behavior on the search
page – clicks to any own organic or paid link on the first search page indicates arrival at that
brand’s website. No click after the search or clicks on other organic links indicates that the
user did not arrive to the brand’s website.
Figure 2 plots the probability that a user arrived at a brand’s website either from organic
or paid search links by the cap 0 vs. cap 1 groups. As shown, advertising on one’s one
keyword drives an incremental 2.27% of traffic to a brand’s website. This average effect is
statistically significant, although perhaps economically small.
Next, we look at whether this effect varies by observables in the data. We focus on
a subsample of companies with sufficient amount of traffic to get reliable company-specific
estimates of the probability to get a click9. This provides us with a sample of 493 companies.
Figure 3 presents an estimate of the density of the estimates. The average effect is 0.0214,
and the average standard deviation of the estimate is 0.0488. We plot the corresponding
normal density over the distribution of effects. The distribution of effects has heavier tails,
which suggests there is heterogeneity. We test it formally and reject the hypothesis that
8If we focus on companies that advertise on their own keyword at least once, these companies correspondto 53.6% of companies and 19.6% of traffic
9We keep companies with more than 80 exposures in each condition.
10
Figure 2: The Effect of Advertising on One’s Own Keyword
there is no heterogeneity10.
What is the source of this heterogeneity? We first ask whether the effect is more incre-
mental for firms with brand queries in more competitive environments. In many cases, firms
bid on their own keywords but competitors also bid on them. We might expect that in this
environment the incremental effect of having one’s own advertisement in mainline 1 would
be more incremental than compared to queries where competitors don’t bid. Importantly,
regardless of the number of bidders, we are comparing a condition where only at most one’s
own ad appears in mainline 1 – we’ve experimentally eliminated the competitors ads for the
purposes of this comparison, and are simply comparing the cap 0 to the cap 1 condition
for firms that would have had competitors show up on their keyword in the absence of the
experiment. Below, we systematically explore the effect of adding the competitors’ ads back
in. Here we are simply looking at the selection effects across different types of firms.
Table 2 divides the sample into groups depending on the probability (across queries and
time) that a competitor would have ended up in mainline 2 in the absence of the experiment.
The table shows that there is clearly significant heterogeneity in the incremental effect of
10We perform a series of standard tests for normality, including Shapiro-Wilk, Jarque-Bera, DAgostinoand other tests. All of them reject normality of the distribution.
11
Figure 3: There is heterogeneity in the effect of own ad in mainline 1
advertising on one’s own keyword. For firms whose competitors never bid on their keyword,
the incremental effect is around 1.4%, whereas for companies who almost always (above 90%)
have competitors bid on their keyword, the effect is around 4.9%.
We can also cut the data by the number of other competitive bidders for a given query.
Table 3 shows the estimates binned by the number of bidders on a brand’s keyword. The
similar pattern emerge: effect of one’s own brand ad in mainline 1 increases from 1.4% to
2.7% as the number of bidders increases.
Table 4 provides regression results of competition on the effect of own brand ad in mainline
1. There is a significant correlation between the frequency of a competitor’s ad in mainline
2 and the magnitude of the effect. The number of bidders does not correlate significantly
with the effect of own brand advertisement.
A second observable descriptor is the amount of brand capital that a firm has. Blake et
al. (2015) showed that bidding on one’s own brand ad does not have much of an effect for
eBay, a company with substantial brand capital. With our broader sample we can explore
how this effect differs with the level of brand capital of the firm.
12
Table 2: Effect is higher for brands with more competitors on their keywordsTotal Competitors’ ads frequency in mainline 2
0-0.1 0.1 - 0.25 0.25 - 0.75 0.75 - 0.9 0.9 - 1
Ncomp 493 192 99 125 34 43
pnoad 0.779 0.811 0.790 0.756 0.739 0.708(0.001) (0.002) (0.003) (0.003) (0.005) (0.005)
pad 0.800 0.825 0.808 0.776 0.783 0.757(0.001) (0.001) (0.002) (0.002) (0.004) (0.003)
pad − pnoad 0.021 0.014 0.018 0.020 0.044 0.049(0.002) (0.002) (0.003) (0.004) (0.007) (0.006)
whereNcomp - number of companiespnoad - probability to get to brand’s website when there are no adspad - probability to get to brand’s website when there is own ad in mainline 1 and no competitor’sads
To investigate the relationship between brand capital and the effect of brand advertising,
we examine the relationship between the own brand effect and measures of brand capital
collected from Alexa.com. In particular, we collect US and global rankings, bounce rate,
the fraction of traffic from search, pages viewed per day and time spend per day. Table 10
(Appendix B) provides a summary of brand capital measures.
Table 5 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 well-known company like amazon.com or alibaba.com (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 “dcards” (detailed informational panels including things like maps and
ratings), and the number of organic links of one’s own brand on the first page11. Finally, in
specification (4) we also control for the level of competition.
We conclude that brands with higher brand capital gain less from advertising on their
11For a description and examples of deeplinks and dcard please see Appendix C
13
Table 3: Effect is higher for brands with more bidders on their keywordsTotal Quantiles of number of bidders
0-0.1 0.1 - 0.25 0.25 - 0.75 0.75 - 0.9 0.9 - 1
Ncomp 493 48 74 246 74 51# of bidders 11.55 3.7 6.22 10.88 16.72 22.42
pnoad 0.779 0.819 0.807 0.780 0.743 0.749(0.001) (0.003) (0.003) (0.002) (0.004) (0.005)
pad 0.800 0.833 0.825 0.802 0.766 0.776(0.001) (0.002) (0.002) (0.001) (0.003) (0.003)
pad − pnoad 0.021 0.014 0.018 0.022 0.023 0.027(0.001) (0.004) (0.004) (0.002) (0.005) (0.006)
Table 4: Companies with higher competition have higher own ad effect
Dependent variable:
Effect of own ad in ML1
(1) (2) (3)
Frequency of competitor in ML2 0.032∗∗∗ 0.034∗∗∗
(0.007) (0.007)Number of bidders 0.001 −0.0002
(0.0004) (0.0004)Constant 0.012∗∗∗ 0.014∗∗∗ 0.013∗∗∗
(0.003) (0.005) (0.005)
Observations 493 493 493R2 0.043 0.005 0.044Adjusted R2 0.041 0.003 0.040
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
own ad in mainline 1. This could be either because consumers are less likely to substitute
away from going to a firm’s website when that firm has higher brand capital or because the
organic link of these firms take up more room on the page, thus making the organic link
more of a substitute for the paid link. In either case, the implication for managers remains
the same – firms with higher brand capital benefit less from bidding on their own brand
keywords.
14
Table 5: Companies with higher brand capital have lower own ad effect
Dependent variable:
Effect of own ad in ML1
(1) (2) (3) (4)
log(us) 0.004∗∗ 0.009∗ 0.006 0.006(0.001) (0.005) (0.005) (0.005)
Deeplinks −0.002∗ −0.002(0.001) (0.001)
Dcard −0.002∗∗∗ −0.001∗∗
(0.001) (0.001)Number of own organic links −0.001 −0.0002
(0.002) (0.002)Frequency of competitor in ML2 0.017∗∗
(0.008)Constant −0.008 0.024 0.062∗∗∗ 0.052∗∗
(0.012) (0.020) (0.024) (0.024)Other brand capital controls No Yes Yes Yes
Observations 492 492 492 492R2 0.013 0.038 0.079 0.089Adjusted R2 0.011 0.026 0.062 0.070
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
15
3.2 In the Presence of Competition
First, we look at situations where a competitor is the only bidder that clears the reserve
price and they therefore end up in the mainline. This is a selected sample because only firms
that do not bid on their own keyword are included in it.12 However, for this set of firms we
can use the experiment to look at what happens when a competitor’s ad appears in mainline
1 but using the experimental variation that removes that competitor’s ad. Second, we look
at the situation where, conditional on competitors bidding and ending up in mainline slots
2-4, we experimentally removed those ads.
3.2.1 Competitors in the Top Position
We select the set of firms who do not bid on their own keywords but where a competitor
does and explore the effect that the competitive advertisement has on traffic both to the
competitor’s website and to the firm’s own website (via the unpaid organic link). We note
that this is clearly a selected group of firms. And in particular that firms that choose to not
“protect” their own brand keywords by bidding on them are fundamentally of a different
type. For instance, consider the case of brand keywords for upstream firms that do not
have their own web retail presence. In this case, the mainline slots for these keywords might
be bid on by downstream retailers that can actually sell the product. Consumers in this
particular case might not be interested in going to the brand’s own website. Rather, they
are interested in shopping for the product. A firm advertising on its own keywords wouldn’t
make any sense.
One marker of this form of heterogeneity is the probability, in the absence of any adver-
tisements (a case created by the cap 0 condition in our experiment), that consumers click on
the firm’s organic unpaid link. In the case where consumers don’t click on the organic link
for the brand, even in the absence of advertisements, we might think that they are typing
in the brand keyword in an attempt to get to a different type of website. In the case where
we can match the probability of clicking on the organic link between firms that advertise on
their own keywords and those that don’t, we might think that we are somewhat effectively
controlling for some of the heterogeneity.
12Recall that one of the downsides of the experiment is that we were not able to place ads in situationswhere they wouldn’t have existed otherwise – we were only able to subtract them.
16
Figure 4 displays the probability of a consumer arriving (through an unpaid link) on a
brand’s website for the 49 firms that fit the criteria described in the data description, who do
not bid on their own keywords, and who have a competitor in mainline 1 above 90% of the
time in the control group for our experiment. It’s clear that 1) The probability of arriving
at a firm’s website through an organic link is lower for these firms on average than for firms
that advertise on their own keyword and 2) That there is substantially more heterogeneity
in this effect relative to those firms that advertise on their own brand keyword13.
Figure 4: Heterogeneity in firms that do not bid on their own brand keywords
We now explore the causal effect of a competitor appearing in the mainline for this set
of firms. Based on the data in figure 4, we divide the sample into firms that receive a low
percentage of traffic on their own keywords (to the left of the first red line) and those that
receive a high percentage (those to the right of the second red line). We surmise that the
second of these groups is more like firms that do advertise on their own keywords although
note that there is still selection into this sample.
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 (no advertisements) and cap 1 (only the competitor
appears in the mainline area).
13Figure 14 in Appendix E shows a similar histogram for 493 companies which advertise on their keywordmore than 90% of the time
17
Figure 5: The incremental effect of bidding on one’s competitors’ keywords
In the case of low organic traffic (the left-hand panel), on average, 30% of clicks in the
cap 0 condition (when there are no ads) end up at the brand’s website. In contrast, in the
high organic traffic case (the right-hand panel), on average, around 80% of clicks are on
the brand’s website – a number very similar to the case when firms do advertise on their
own keyword discussed above. When we experimentally add the competitor’s advertisement
(the cap 1 case), two things happen. First, the percentage of traffic to the brand’s website
drops. For the low organic companies it drops by 8 percentage points. For the high organic
case by about 17 percentage points. This is traffic that a competitor is “stealing” from the
branded website. Second, the competitor gains a large number of clicks – around 20% in
either case. In both cases the number of clicks that the competitor gains is larger than the
amount that is lost by the brand website indicating that there is an overall increase in clicks
to the combination of mainline advertisements (in this case 1) and the top organic link (the
brand’s own website). Some traffic was diverted from links further down on the page or from
clicks that wouldn’t have happened in the absence of the competitive advertisement.
18
3.2.2 Effect when competitors occupy positions 2–4
Conditional on a the focal firm winning the brand keyword auction, what is the effect of
competitors in mainline slots 2, 3, and 4? Do competitors capture any traffic and does that
decrease the probability of clicks on the brand’s paid or organic links? Again, we deal with
a bit of a selected sample – keywords where both the brand and competitors are already
bidding. But conditional on this selection, we use the experimental variation to remove each
of the competitive advertisements in the mainline slots.
Figure 6 displays the causal effect of competitive bidding in each of the mainline slots.
We narrow our sample down to firms where, in the absence of the experiment, 4 mainline
ads would show up. We then systematically remove each one of those advertisements and
ask what happens to traffic to the brand’s website. Looking at the left panel, the first point
in the graph displays the total traffic that goes through both the organic and paid link to
the brand’s website. This is the experimental condition where all other advertisements were
removed. The second through fourth points display the traffic to the brand’s website adding
in competitors into the mainline slots 2 through 4, respectively. Going from no competitive
advertisements to 3 (slots 2-4) reduces the traffic to a brand’s own website by around 4
percentage points. In the right panel we separate advertisers by a median split of website
traffic. The lower, blue line represents firms below median size and the red above. 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 6 demonstrates that competitive advertising can affect traffic to a brand’s own
website, but does this benefit the competitors who are bidding? Figure 7 shows the traffic
that these competitive advertisements generate to the competitors’ websites. The top (red)
line shows the amount of traffic that is siphoned off by the competitor in the 2nd mainline slot.
The first point is for the situation where we place only 1 competitive advertisement (and the
brand’s own advertisement in the first mainline slot). The second point is the traffic that goes
to the competitor in the mainline 2 slot when there are 2 competitive advertisements, and the
3rd point for when there are three. Interestingly, adding more competitive advertisements
does not decrease the traffic that goes to the competitive advertiser in the 2nd mainline slot.
It stays static at around 2.5%. The 2nd (blue) line does the same thing for the advertiser in
mainline slot 3, and the green dot for the competitive advertiser in mainline slot 4. All told,
19
Figure 6: The effect of competitive ads in mainline slots 2-4
(a) Average effect (b) Upper and Lower 50% by Brand Capital (USRanking)
competitors in all slots are able to causally siphon off around 4.3% of traffic from the focal
brand by bidding on its keyword.
In this section we’ve documented some of the effects of competition on brand advertising.
For 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.
When we remove these advertisements, on average, clicks on the brand’s own organic link
increases by between 8 and 17 percentage points. These numbers provide some justification
for the common belief that firms should bid on their own keyword in order to protect their
brand keywords from competitors. For the cases in the data where competitors have this
opportunity, they gain a significant number of clicks at the expense of traffic to the brand’s
website. In the case where a brand does bid on its own keyword and therefore ends up in the
mainline 1 slot, we examine the effect that competitors have in slots 2-4. Here the evidence
is much more muted. Competitors gain around 5% percent of clicks, split between the 3
advertising competitors. Most traffic still goes to the brand’s website, either through the
paid link or the organic link.
20
Figure 7: The effect of competitive ads in mainline slots 2-4
4 Results: Cannibalization, CPIC, and Bidding
Up until now we have focused on the overall probability that a searcher ended up at either the
brand’s own website or a competitor’s site, combining the paid and organic links together.
But, of course, whether or not this traffic goes through the organic or paid links matters a
lot to the firms. 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. Bidding on one’s own keyword not
only affects the overall probability that searches end up on the brand’s website (as described
above), it also affects the percentage of traffic that goes through each of these links on
the search results page. Furthermore, competitors not only steal clicks, they also push the
organic links further down on the page, forcing more traffic through the brand paid link.
This section documents both of these effects and argues that the right measure for thinking
about the success of one’s campaign is not ROI computed with the cost per click metric
(CPC), rather firms should be computing the cost per incremental click (CPIC). We define
and compute this metric and measure the difference between the CPIC and the CPC for a
wide variety of firms.
21
4.1 Cannibalization without Competitors
We earlier documented that without competitors brand advertising does generate incremen-
tal lift—on average around 2.27%, but that comes at the expense of driving a significant
portion of traffic through the paid link instead of the organic link. 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 always advertising on their keyword. On average, 46.3% of clicks go
through the paid link, with up to 80% for some companies. This corresponds to an average
cannibalization rate of 44.6%.
Figure 8: Histogram of the fractions of total clicks going through paid links
Figure 9 uses the experimental variation and compares the cap 0 (no advertising) con-
dition with the cap 1 (brand advertising on one’s own keyword) condition, emphasizing the
cannibalization that occurs when advertising on one’s own brand keyword. The difference
in the overall height of the bars between the cap 0 and cap 1 conditions represents the incre-
mental clicks to a brand’s website as discussed above (average effect of 2.27%). The drop in
the pink bar shows the amount that paid search cannibalizes organic clicks for our sample.
Half of the traffic now goes through the paid search and almost all of it would have ended
22
up at the brand’s website in the absence of brand keyword advertising.
Figure 9: Organic click cannibalization compared to incremental clicks
High cannibalization rates mean that companies are paying more than just the cost
per click (CPC) for traffic. Instead, to figure out the actual costs that firms are incurring
we need to compute the ratio of the incremental clicks that occur because of paid search
advertising to the overall amount that the firms spend on paid search. To compute the costs
per incremental click we need to form the counter-factual: what would happen if the firm
did not advertise. For firms that typically do not face competing ads, this can be computed
by a simple comparison of the Cap 0 to Cap 1 conditions. As such we restrict our analysis
to ad listings that face competition less than 20% of the time (the results are not sensitive
to this threshold). In this case, we can compute CPIC as
CPIC =xp1
(p1 − p0)CPC
where x is the fraction of clicks going through an ad, p1 is the probability of getting to one’s
own website with the own brand ad in mainline 1, and p0 is the probability of getting to
one’s own website without the own brand ad in mainline 1.
23
We compute the CPIC/CPC ratio based on estimates of x, p0 and p1 from the analysis
above. Notice that it is only possible to compute CPIC if there is a positive effect of own
brand advertisement on traffic to company’s website. In our sample, effect of advertisement
was positive and significant for only 16% of companies. This restricts us from computing the
average CPIC/CPC ratio across all companies. Instead, we compute CPIC/CPC ratio for a
company with average effect size. For this subset of companies, average effect of own brand
advertisement is 1.6%14, and average amount of traffic going to own brand website through
the paid link is 36%. The resulting estimate of CPIC to CPC ratio is 22.5. We notice that
this estimate is a lower bound on the expected CPIC/CPC ratio, given that the CPIC/CPC
function is convex in the advertisement effect, p1 − p015.
Additionally, we compute the CPIC/CPC ratio for companies which had significant effect
of own brand advertisement in mainline 1. This corresponds to a 43 companies. Figure 10
shows the histogram of the CPIC/CPC ratio estimates for these companies.
Figure 10: Histogram of CPIC/CPC estimates for firms that do not typically face competingads
14Effects for these companies are presented in columns 1 and 2 of Table 215We provide more details on this in the Appendix D
24
The median CPIC for the 43 companies with a significant impact of the ad is 9.1, and
the mean is 11.07. This means that for each marginal click, brands on average pay 9.1 times
their CPC. For companies with an insignificant effect of their own brand ad in mainline 1,
the CPIC/CPC ratio is either bigger (if we do not have enough power to show significance),
or does not make sense (if there is no effect).
Table 6: Summaries of CPC, CPIC and bids
Unweighted average Weighted averageAll brands Significant All brands Significant
Ncomp 268 43 268 43¯CPCown ($) 0.15 0.15 0.09 0.06
¯CPCcompet ($) 0.86 0.78 0.61 0.47¯CPCown−other ($) 1.36 1.43 1.14 0.92
¯bidown ($) 11.48 6.45 4.8 1.9¯bidcompet ($) 1.8 1.97 1.15 0.87
¯bidown−other ($) 2.42 2.26 1.96 1.49¯CPICown ($) 1.12 1.42
CPICown > CPCcompet (%) 51.16 92.73CPICown > CPCown−other (%) 55.88 90.62
CPICown > bidown (%) 34.88 73.72The weights are based on the number of exposures
¯CPCown−other is computed for about 85% of companies in the sample
Table 6 present the summary of bids and CPC. A number of things are apparent from
this table. First, the per click price on the focal brand’s own keyword is relatively low, the
average unweighted CPC is 15 cents (the average weighted CPC is 9 cents). But given the
amount of traffic that goes through these links, the amount of money involved is far from
trivial. Second, the average CPIC for the focal brand on one’s own keyword is much higher
– almost $1.42 per click. This is substantially higher than the $0.47 that competitors are
paying per click. Third, for 93% (51% unweighted) of the companies in the sample, the
CPIC that they are paying is higher than the CPC that their competitors are paying on
those same keywords.
All of this strikes us as being rather curious. Firms are spending a substantial amount of
money on brand keyword advertising when the CPIC calculations indicate that they would
actually pay less per incremental click for non-brand keywords. Similarly, firms are paying
more to attract these customers than their competitors are. Why would a firm pay so much
25
relative to the competition to maintain a customer’s click that is presumably already very
loyal? One explanation is that these customers are more valuable to the firm and that
firms are doing the right thing in spending large amounts on marketing to these customers.
Another explanation is that firms do not fully understand the difference between CPC and
CPIC. When they bid, they use CPC metrics given to them by the search engine.16 While
none of these numbers conclusively show that firms do not understand the difference between
CPC and CPIC, it starts to paint a picture that tips in that direction. We leave the unpacking
the underlying causes, such as principle agent problems, for future work.
4.2 Cannibalization with competitors
One effect of competition on brand keywords is that a competitor may steal some traffic
from the brand owner. Another is that paid search competition shifts the organic links
further down the page. This pushes more traffic through the paid search link for the brand,
increasing cannibalization. To the best of our knowledge this paper is the first one to point
this out and provides evidence for this additional effect that competition can have on its
rivals. For a competitor, simply bidding on a brand’s keyword can cause them harm through
this increased cannibalization channel, even if no customers click on the competitive link.
In fact, from the competitor’s perspective, it works better if no customers click on the ad
because then the competitor doesn’t pay anything.
To examine the magnitude of this effect, we go back to the set of keywords that, in the
absence of the experiment, would have generated 4 different mainline advertisements. We
then experimentally put ads in slot 1 (the brand advertisement) and slots 2-4 (advertisements
from competitors). This compares the experimental cap 0 through cap 4 conditions. 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 plots these effects.
The height of the overall bar indicates the overall traffic that makes it to the brand’s
16We 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. Our sense, based on workingwith large firms on their keyword bidding strategies, is that many of them train machine learning models tomaximize ROI using the CPC and not the CPIC metric. If the difference between these two metrics is large,as we are arguing in this paper, then these machine learning algorithms will over-bid on brand keywordsrelative to non-brand (for a fixed marketing budget).
26
Figure 11: The effect of competition in increasing cannibalization
website. Even in the face of 3 competitive advertisements, consumers tend to make it to
the brand’s website. However, there is a dramatic difference in the percentage of that traffic
that goes through the paid link.
Table 7 extends the sample of firms to include those that would have only had a competi-
tive ad in the 2nd mainline slot, those that would have competitive advertisements in slots 2
and 3, and those that would have had ads in all 4 slots. The last column thus corresponds to
the numbers in figure 11. Having a competitor’s ad in mainline 2 increases the fraction of ads
going through the paid link of one’s own brand by 10 percentage points, having competitors’
ads in mainline slots 2 and 3 - by 9 percentage points more, and having competitors’ ad in
mainline slot 4 - by 6 percentage points more. The average fraction of paid clicks for one’s
own brand ad with 3 competitor’s in the mainlines is 0.843 which implies that over 4 out of
5 clicks on one’s own brand keyword are paid.
We conclude that competitors can hurt a brand not only by stealing clicks, but also by
increasing a brand’s costs. It might really make sense from a competitive standpoint to bid
on rivals’ keywords. If the competitor gets a lot of clicks, then their decision to advertise
27
Table 7: Competitors’ ads in mainlines 2, 3 and 4 increase cannibalization
Effect of competitors’ ads inML 2 ML 2-3 ML 2-4
Ncomp 784 682 564x1 0.539 0.566 0.595
(0.0029) (0.0042) (0.0133)x12 0.655 0.68 0.699
(0.0032) (0.0039) (0.013)x13 0.773 0.782
(0.0038) (0.0118)x14 0.843
(0.0091)
whereNcomp - number of companiesx∗ - fraction of clicks leading to own brand website through ad in mainline 1 with- x1 no competitors’ ads- x12 competitor’s ad in mainline 2- x13 competitors’ ads in mainlines 2 and 3- x14 competitors’ ads in mainlines 2, 3 and 4
will depend on the CPC and the quality of these clicks. However, even if competitors do not
get many clicks (imagine they get no clicks), they might want to advertise, as they would
increase the costs of the focal brand without paying anything.
5 Conclusion
In this paper, we’ve described and documented the competitive environment for brand key-
word advertising on Bing. Brand keyword advertising has an interesting property in that a
clear free substitute exists in close proximity to the paid advertisement. While this substi-
tute is more dramatic and obvious relative to other advertising situations, the general point
extends well beyond brand keywords. Consider, for example, the decision of a large firm
such as Amazon of whether or not to advertise on Bing. A consumer search process might
begin at a search engine but also include other websites that have Amazon advertisements
on them. Similarly, it might also include direct navigation to Amazon’s own home page.
In the absence of search advertisements, these other substitute channels might continue to
28
drive traffic, creating a wedge between the CPC of a search advertisement and the true
incrementality (CPIC) of those ads. Organic links provide a convenient lens to study this
wider phenomenon.
In the presence of substitute channels, questions about whether or not a firm should
advertise to either its own customers or try and advertise on the keywords of competitors
becomes more complex.
We demonstrated that advertising on one’s own keyword drives a significant amount
of traffic to one’s own website, but it comes at a cost – much of that traffic would have
flowed through the organic link in the absence of that advertisement. There is substantial
heterogeneity in the true incrementality of own brand keyword advertising. For large, known,
brands, this advertising drives little to no incremental traffic. For less well-known brands,
this number can be larger. In the face of this large amount of cannibalization, firms should
not look at the CPC of their advertisements, rather they should compute the cost per
incremental click. We demonstrated that the wedge between these metrics is quite large and
briefly discussed whether firms seem to understand this metric.
One reason for bidding on one’s own keyword is to prevent competitors from doing so
and appearing in the first mainline slot—“defensive advertising.” We demonstrated that
for a selected sample, when a competitor bids on its rival’s keywords, it can capture a
significant amount of traffic – around 20%. Most of that comes from traffic that would have
gone to the focal brand’s website through its organic link in the absence of the competitive
advertisement. Even when a focal firm is bidding and winning its own keyword, a competitor
can still significantly affect the search environment. By advertising on its rival’s keyword
and ending up in mainline slots 2-4, it can both steal some traffic (on average around 5%)
and, importantly, push the focal firm’s organic link further down on the page, driving a
much higher percentage of traffic through their paid link. This raises the costs of the focal
firm dramatically. This raising rival’s cost phenomena might apply to many other setting of
internet advertising and is a yet unexplored but we think interesting competitive element.
29
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6 Appendices
6.1 Appendix A: Matching with Auction Data
Experiment we use randomly restricts the number of possible paid links on top of the search
page. The control group corresponds do the default, which is a maximum of 4 advertisements
in the mainline (Cap 4). There are 4 experimental conditions: Cap 0, 1, 2 and 3. The idea
is similar to the control: e.g. Cap 0 does not allow any advertisements in the mainline, and
Cap 3 allows at most 3 advertisement in the mainline.
This design of the experiment restricts us to studying only the cases where advertisement
is eligible to be shown in the mainline, which means that in the absence of experiment
advertisement will be shown in the mainline. E.g. we cannot study the effect of advertisement
for a company that does not advertise on its own keyword: there will be no own brand
advertisements in both Cap 0 and Cap 1 condition.
Thus, we restrict our attention to cases where companies advertise. We are still facing a
challenge: if a company advertises only 50% of the time on a given query and selects search
traffic where the effect will be higher17, we cannot compare occasions with advertisement to
the treatment condition where the ad will be removed. E.g. if we would like to estimate
the effect of own brand advertisement in mainline 1 when in 50% of the cases company
advertises, and in 50% of the cases there is no advertisement, comparing occasions in Cap 1
conditions with a paid link shown to the entire Cap 0 conditions will bias the estimates.
To find the right treatment group, we need to allocate the occasions where ad was actually
removed from the mainline. In the example above, we would like to compare occasions with
own brand paid link in Cap 1 to occasions in Cap 0 when own brand paid link would have
been shown. To find such occasions, we collect the auction data for the search queries in the
experiment. The allocation of positions in mainline follows the standard GSP auction rules:
players submit the bids for price of a click, platform computes the ”rankscore” of a given
player, and players are allocated the positions in mainline based on their rankscores. Given
that the reservation level is cleared, company with the highest rankscore gets position 1,
company with the second highest rankscore gets position 2, etc. Rankscore is proportional
17E.g. using geo-targeting
31
to the bid and a probability of click on the ad as computed by the platform
RSj ∝ bjpclickαj (1)
where bj is a bid of company j, pclickj - probability of company j to get a click, and α is
the tuning parameter.
This implies that knowing the rankscores of bidders and reservation level for a search
query allows to say which advertisement would be shown in the mainline in the absence of
the experiment. To get this information, we exploit auction data collected by the advertising
team. Experiment that we use was designed by removing the potential advertising slots from
mainline, but bidding data was still collected.
Table 8: Summary of matching of the experimental and auction data
Condition Searches Searches % of eligible ads inTotal Matched Matched, % ML 1 ML 2 ML 3 ML 4
Cap 0 3162615 1506827 47.6 30.6 9.2 4.6 2.4Cap 1 6342073 3568054 56.3 41.8 11.2 4.7 2.9Cap 2 6338914 3568918 56.3 41.8 10.9 6.1 3.2Cap 3 6348311 3577819 56.4 41.9 11 5.7 4.1
Control 22209220 12506083 56.3 41.9 11 5.7 3.5
Table 8 presents the summary of matching experimental data and collected auction data
for Cap 0-4. For Cap 1, 2, 3 and 4, around 56.3% of search queries in experimental data
where matched with the auction data. A search query will not be recored in the auction
data if no advertiser submitted a non-trivial bid18, so the unmatched data can correspond to
queries with no bidders.The majority of unmatched queries correspond to occasions where
no advertisements were shown, which supports this explanation.
Cap 0 condition has a higher percentage of unmatched search queries. This indicates
the problem with the matching, given that the experiment was constructed to be balanced
between the treatment and control groups. We further find that percent of advertisement
eligible for the mainline 1 position in Cap 0 is substantially different from the percent of
advertisements eligible for mainline 1 position in Cap 1, 2, 3 and 4.
18As defined by the platform
32
This creates a potential problem for using the occasions with eligible brand advertise-
ments for Cap 0 condition. Consider the case of estimating the effect of own brand ad-
vertisement in mainline 1. Using the matched data, we would like to compare occasions
with own brand advertisement from Cap 1 condition to occasions with eligible own brand
advertisement from Cap 0 conditions. We know that some occasions with eligible own brand
advertisement are missing from Cap 0. If this mismatch is correlated with the probability
of a click on own brand weblink, our estimate of advertisement effect will be biased.
To check if there is a selection problem in Cap 0 matching, we estimate the effect of own
brand advertisement in mainline 1 for companies which always advertise in mainline 1 on
their own keyword19. For these companies, comparison of Cap 0 to Cap 1 provides the casual
effect of own brand advertisement: we know that, if not the experiment, search results in
Cap 0 will have own brand advertisement in mainline 1. We also can estimate the effect
using only eligible own advertisement occasions in Cap 0 and Cap 1. If the estimates of the
effect based on two methods are different, we can confirm that the occasion in Cap 0 which
have eligible own brand advertisement in mainline 1 are correlated with the probability of
click on own brand website.
Table 9: Effect of own brand ad in mainline 1 is significantly underestimated when usingeligible ads
All queries When own ad is eligibleNcomp 391 391pown0 0.7867 0.8115
(0.0022) (0.0029)pown1 0.8035 0.8179
(0.0015) (0.0015)pown1 − pown0 0.0168 0.0063
(0.0027) (0.0033)pown0 is the probability of a click on own brand link in Cap 0
pown1 is a similar probability in Cap 1
Table 9 presents the estimation results. Advertisement effect estimate based on all traffic
is 1.68 percent points. Advertisement effect estimated based only on traffic with eligible
own advertisement is 0.63 percent points. The difference in two estimates is statistically
19Companies that have own brand advertisement in mainline 1 more than 99% of the time
33
significant20.
We thus confirm that the occasions of eligible own brand ads in mainline 1 in Cap 0 are
correlated with the probability to get a click on own brand weblink. This restricts us from
using the eligible advertisements occasions to compare Cap 0 and Cap 1. Instead, we focus
only on companies that have a paid link in mainline 1 more than 90% of the time. For these
companies, comparison of Cap 0 and Cap 1 gives casual effect of advertisement.
Figure 12 shows that around 50% of companies advertise at least 10% of the time, with
around 33% advertising more than 90% of the times. Restricting the analysis to the later
group gives us 824 companies which always advertise on their keyword.
20Difference in estimates is 0.0105, with standard error of the difference being 0.0043, which correspondsto a t-stat of 2.46
34
Figure 12: Frequency of ads in mainline 1 for 2517 most popular brand queries
(a) Frequency of own ads in ML1 (b) Frequency of competitor’s ads in ML1
(c) Frequency of ads in ML1
35
6.2 Appendix B: Brand Capital Measures
Table 10: Brand capital measures
Mean Standard deviationlog(expos) 7.59 1.41
log(Rankglobal) 10.52 2.14log(RankUS) 9.05 1.96
bounce rate, (%) 35.1 14.3time spent per day (minutes) 4.6 2.98
pages viewed per day 4.5 2.76search traffic (%) 19.85 9.19
36
6.3 Appendix C: Deeplinks and Dcard Example
Figure 13: Deeplinks example
37
6.4 Appendix D: Lower bound on CPIC/CPC
We compute the CPIC/CPC ratio for a company with average effect size. This estimate
gives a lower bound on the average CPIC/CPC ratio as CPIC/CPC is a convex function in
the effect size:
CPIC/CPC =xp1
p1 − p0Fix the xp1 and assume the effect size p1 − p0 > 0. Denote the LHS of the CPIC/CPC
equation as f(∆(p)), where ∆(p) = p1 − p0 Then, by Jensen’s inequality, E(f(∆(p))) >
f(E(∆(p))). Hence, computing CPIC/CPC in the average effect point E(∆(p)) gives a
lower bound on the expected value of CPIC/CPC.
38
6.5 Appendix E
Figure 14: Probability to get a click for firms that bid on their own brand keywords
(a) Cap 0 (b) Cap 0 versus Cap 1
39
6.6 Appendix F: Competition and Brand Capital Relationship
Table 11: Companies with higher brand capital have less competition
Dependent variable:
Frequency of competitor in ML2
(1) (2)
log(us) 0.052∗∗∗ 0.028∗∗∗
(0.009) (0.009)Deeplinks −0.023∗∗∗
(0.008)Dcard −0.022∗∗∗
(0.003)Number of own organic links −0.039∗∗∗
(0.012)Constant −0.126∗ 0.483∗∗∗
(0.075) (0.109)
Observations 492 492R2 0.065 0.216Adjusted R2 0.063 0.209
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
40