Does Television Advertising Influence Online Search?
Mingyu Joo,1 Kenneth C. Wilbur,2 and Yi Zhu3
April 21, 2011
Abstract: This paper finds a significant association between television advertising for financial
services brands and consumers’ tendency to search branded keywords (e.g. ―Fidelity‖) rather
than generic category-related keywords (e.g. ―stocks‖). The effect is largest for young brands
during standard business hours with an elasticity, .07, comparable to extant measurements of
advertising’s impact on sales. However, television advertising is not correlated with category
search incidence. These findings confirm the external validity of previous experimental findings
and suggest that practitioners should account for these effects when planning, executing, and
evaluating both television and search advertising campaigns.
Keywords: Advertising, Information Search, Media, Search Engine Marketing, Television
1
Doctoral Student, Syracuse University Whitman School of Management, mjoo@syr.edu,
http://www.mingyujoo.com.
2
Assistant Professor, Duke University Fuqua School of Business, kennethcwilbur@gmail.com.
http://kennethcwilbur.com.
3
Doctoral Student, University of Southern California Marshall School of Business,
zhuy@usc.edu, http://www-scf.usc.edu/~zhuy.
The authors gratefully acknowledge financial support from MSI/WIMI multichannel research
award #4-1645.
Electronic copy available at: http://ssrn.com/abstract=1720713
1
1. Introduction.
The average American household contains 2.6 people and 2.5 televisions.1 The average
American watches 5.1 hours of television per day, more than the 4.6 self-reported daily hours
spent on all work-related, educational and housework activities.2 The typical consumer is
exposed to about 29 minutes of paid television advertising per day.3 It is difficult to overstate the
importance of television.
Also remarkable is the growth of online search. Americans entered 16.4 billion queries at
core search engines like Google, Yahoo and Bing in December 2010, or about 1.7 searches per
person per day.4 In other words, the average consumer now practices, about twice per day, an
activity which hardly existed fifteen years ago. Perhaps the best evidence that search converts
prospects to customers is revealed by changes in advertising budgets. Marketers spent over $12
billion on search advertising in 2010; this amount is substantial in light of the $59 billion spent
on television advertising in the US.5
Two decades of research and practice on integrated marketing communications have
shown that delivering a consistent message through multiple consumer touchpoints is more
effective than managing disparate, medium-specific campaigns. So one might expect that
advertisers benefit from coordinating their television advertising and search advertising
campaigns. After all, synchronizing ―push‖ and ―pull‖ tactics is an old topic in marketing.
Despite these apparent incentives, coordination appears to be uncommon. Agencies have
continued their 20-year trend toward specialization. Conversations with practitioners, reviews of
―traditional‖ and ―digital‖ agency websites and case studies, and reading the academic literature
and textbooks in marketing all give a consistent impression: coordination of television
advertising and search advertising is unusual.
This article will investigate the simplest possible explanation for this apparent lack of
coordination: perhaps television advertising does not influence online search. The purpose of this
paper is to investigate field data in the financial services product category to determine whether
and how television advertising expenditures are correlated with online search behavior. It
1
Nielsen (2011), U. S. Census Bureau (2011).
Nielsen (2011), BLS American Time Use Survey (2011).
3
This inference assumes 11.3 minutes of advertising per hour, as is typical in prime time, and an advertising
avoidance rate of 50%.
4
comScore (2011).
5
Lee (2011).
2
Electronic copy available at: http://ssrn.com/abstract=1720713
2
explores this problem by aggregating over a large individual-level search dataset, and then
relating intertemporal variation in brands’ television advertising to intertemporal variation in
brand-specific search data.
While there appears to be no academic work looking for these effects in field data, a few
practitioner studies have examined the topic. For example, a survey found that 37% of internet
users reported that a television ad had prompted them to conduct an internet search (iProspect
2007). However, these studies are typically based on self-reports, include limited controls for
competing explanations and often focus on a single brand. Most importantly, they do not
disentangle TV advertising’s effects on the probability of searching in the category from the
probability of choosing a branded keyword.
The empirical results indicate that television advertising is positively correlated with
consumers’ choice of branded keywords. This association is largest for younger brands during
standard business hours, with an elasticity of .07, comparable to extant findings of the effect of
advertising on sales. However, advertising expenditure is not found to be correlated with the
number of searches in the category. That is, television advertising appears to shift share among
competing brands’ keywords, but it does not appear to increase the total number of people
searching for financial services products. These results may be given a causal interpretation
under the assumption that the brands’ television advertising was independent of consumer search
behavior.
Next, previous research on how advertising influences consumer search is reviewed.
Sections two and three present the data, model and estimation strategy. Section four contains the
empirical results. Section five discusses how the results might practice and the academic
literature.
1.1. Theoretical Background and Related Literature
Difficult though it may be to remember, consumers searched for information before there were
search engines. Marketing academics have studied this behavior extensively since the 1970’s.
This literature implies three primary reasons that television advertising is likely to influence
online search: objective knowledge, perceived knowledge, and incidental exposure.
First, television advertising may increase consumers’ objective knowledge of product or
category features and benefits, and this influences consumer search. For example, Brucks (1985)
3
found that objective knowledge increases consumers’ ability to acquire additional knowledge
and makes search more efficient. Several studies have related these effects directly to objective
knowledge provided by advertising. Newman and Staelin (1973) showed that advertising may
enlarge the set of brands a consumer can recall easily. The literature suggests that objective
knowledge stimulates consumer search in new product categories about which consumers are
unknowledgeable, but not in established product categories. Bettman and Park (1980) found that
consumers with moderate amounts of prior product information are more likely to search for a
brand than those with little prior information. Swasy and Rethans (1986) found that ads for new
products stimulated more curiosity among consumers with greater prior category knowledge.
However, Wood and Lynch (2002) found that consumers with high levels of prior knowledge are
less motivated and learn less than those with little prior knowledge.
Second, advertising may alter how much a consumer thinks she knows (―perceived
knowledge‖), which may influence how the consumer searches. Perceived knowledge has
repeatedly been shown to differ from objective knowledge with correlations ranging from .05
(Radecki and Jaccard 1995) to .65 (Park, Mothersbaugh and Feick 1994). Moorman et al. (2004)
experimentally manipulated perceived knowledge by first testing subjects’ knowledge in a
particular domain, then randomly giving artificially low test results to some knowledgeable
subjects and artificially high test results to some unknowledgeable subjects. Perceived
knowledge led to search strategies which were less likely to uncover disconfirming information.
This result is strongly parallel to the result below that television advertising may be associated
with consumers’ choice of branded keywords rather than generic keywords.
Third, incidental exposure to advertising may influence consumers outside their
conscious awareness. Incidental exposure refers to advertising which is perceived but not
processed. It commonly occurs when consumers redirect their attention during television
commercial breaks. Janiszewski (1993) found that incidental exposure enhances brand liking.
Shapiro, MacInnis and Heckler (1997) found that incidental exposure to advertising influenced
the products that enter consumers’ consideration sets, even when the consumers are not
consciously aware that they saw the ads. The experiment was conducted in three stages. At the
start, subjects focused on reading a quickly-scrolling article in the middle column of an online
magazine. Ads for carrots and can openers appeared in a peripheral area of the screen for a
treatment group, while blocks of text appeared for a control group. Next, subjects were asked, in
4
an apparently unrelated task, to select among a set of items they would consider purchasing in
two purchase situations (e.g. ―food you would buy for a snack one hour before dinner‖). Finally,
subjects were asked to identify which ad, among a set of four, had appeared on screen while they
were reading the article. The randomized treatment in the experimental design showed that
incidental ad exposure influenced consideration sets, as one would typically expect. However,
the surprise was that the treated subjects were unable to identify which ads they had been
exposed to while reading the article. This showed that incidental advertising exposures may
influence a consumer’s consideration set outside her conscious awareness. Shapiro (1999) took
this a step further, showing that incidental ad exposure led to consideration set inclusion, even
when subjects explicitly instructed to avoid choosing products depicted in ads.
The literature on consumer search has mainly relied on laboratory experiments. The
current paper provides a rare test of the external validity of its findings. It further indicates the
possible duration of these effects in the field.
2. Data
This section explains why we chose to study the financial services category, introduces the
datasets, and describes how their characteristics influence the modeling choices presented below.
2.1. Research Context: Product Category Choice
A product category suitable for determining how offline advertising is associated with online
search should exhibit four characteristics:
1. Category brands’ offline advertising must be measured with high frequency.
Television advertising expenditures may be observed by day and exact time, as are online search
data. Advertising expenditure data for other offline media such as radio, magazines and
billboards are typically only observed to vary monthly. Variation in advertising expenditure over
time is critical to identify the effect of advertising on search behavior.
2. Consumers must search category brand names online. High-involvement categories
may be most appropriate since consumers are likely to gather information related to brands and
products within those categories. It also suggests that categories with infrequent choices or high
prices might be most appropriate, since consumers cannot gather information easily through
product trial.
5
3. Category brand names should not overlap with common words or names.
Otherwise, category searches cannot be separated from unrelated searches. For example, it is not
clear whether a search for ―Sequoia‖ is for a tree or the Toyota sports utility vehicle of the same
name. Many well-known brands—like Apple, Miller, Target, and Visa—present this problem.
4. The category should not be subject to obvious simultaneity concerns. For example,
advertising and consumer search for a movie both peak around the date the movie is released in
theaters. It would be difficult to tease apart the effect of advertising from the effect of the movie
release date without good instrumental variables.6 Ideally, the researcher should be able to get
data on exogenous time-varying factors that may shift consumers’ tendency to search for brands
in the category in order to separately identify the effect of television advertising from other
factors that vary over time.
The financial services product category scores well on all four criteria. It is the seventhmost advertised product category on television. It is a high-involvement category with infrequent
choice, as consumers tend to stay with investment brokerages for long periods of time. Major
financial services brand names, such as Schwab and Ameritrade, do not overlap with common
words or names, as shown in Table 4 below. The data do not suggest simultaneity, as section 2.7
below explores in depth. Stock index returns, such as the Dow Jones Industrial Average (DJIA),
offer exogenous time-varying instruments to help separate the effect of advertising on search
from other factors that may vary over time.7
2.2. Online Search Data
The online search data include all queries entered by several hundred thousand randomly
selected users of AOL.com between March 1 and May 30, 2006.8 In the following discussion, a
―query‖ is a search term and a ―keyword‖ is a word within a query. Each query consists of one or
6
It is important to note that the data analyzed here is not from a single-source panel of advertising exposures and
internet usage data. Several companies measure each variable individually, but the only source of simultaneous
measurement currently available in the US is Nielsen Cross-Platform Homes (Nielsen 2010). Nielsen declined to
provide those data, citing an expected commercial data price measured in millions of dollars.
7
Many other product categories were considered, but not analyzed. The automotive category scores well on all
criteria except the third. The pharmaceutical category scores well on all criteria except the second, since most
consumers get information about drugs from their doctors rather than from search engines. Movies and video games
score well on all criteria except the fourth.
8
AOL did not actively solicit consumer consent before providing the data. Under our institutions’ rules about
research on human subjects, these data are permissible to be analyzed so long as we do not try to examine or infer
any personally identifying information (e.g., social security numbers, IP address, or geographic location). We have
taken great care to comply with this policy.
6
more keywords and most keywords appear in multiple queries. For each query, the data include
an anonymous user ID, the constituent keyword(s), the date and time of the search, and if the
consumer clicked a result, the link and position of the result clicked.9
A critical aspect of the study is to distinguish between advertising’s possible effects on
total category searches and keyword choice. A naïve approach would be to look simply at the
search queries that contain the brand name, since these are readily available. This might lead to
an erroneous conclusion about the rate at which television advertising creates new searches that
would not otherwise have occurred, when in fact consumers would have searched anyway, but
are now using branded keywords in favor of generic keywords.
This is an important mistake. A customer who searches a branded keyword is cheaper to
acquire and more likely to convert than a customer who searches a generic keyword. Branded
keywords limit the amount of competitive information the consumer is exposed to and reduce
competition in the keyword auction.
To avoid this mistake, it is necessary to identify the full set of product category-related
searches. This is a challenge because the researcher may not know the full set of brand- or
category-related keywords. For example, consumers may frequently misspell a brand name or
may use unanticipated generic keywords.
A query mining algorithm was developed to identify the full set of product categoryrelated keywords. It is aligned with the literature on keyword suggestions (Abhishek and
Hosanagar 2007, Chen et al. 2008, Fuxman et al. 2008) and is described in detail in Appendix 1.
The algorithm had three purposes: (1) identify unanticipated keywords for each brand, (2)
identify generic keywords related to any brand in the product category, and (3) filter out
unrelated queries. The results indicate that the procedure worked well. Table 1 displays some
sample brand keywords identified by this procedure. They include several unanticipated
misspelled brand keywords such as ―Charlesswab‖ and ―Fedelity.‖ Table 2 displays some sample
generic keywords identified by this procedure. It includes many keywords one would expect,
such as 401K, Stocks, Bonds, and Dividends. The query mining technique also identified some
reasonable generic keywords which were not anticipated, such as Equity, Liquidity, and UGMA,
9
AOL displayed zero, one or two sponsored links above the organic search results and did not display sponsored
links on the side of the page. The sponsored links were typically the same as the highest-ranking organic results and
directed consumers to the same landing pages. The paid links appeared in a lightly shaded area of the page but were
otherwise similar in appearance to the organic links. The data do not distinguish how many sponsored links were
presented on each page of search results.
7
a tax shelter for intergenerational wealth transfers. Finally, Table 3 shows some keywords which
were identified as unrelated to the category, such as calculators, careers, and government.10
2.3. Television Advertising Data.
Television advertising expenditure data are gathered from Kantar Media’s ―Stradegy‖ database.
The data record all paid advertisements in national broadcast and cable networks and all local
broadcast stations in the US. Each advertisement is assigned to a brand and a program-specific
estimated advertising cost.11
2.4. Consumer Segmentation
A question of particular interest is how advertising’s impact on online search behavior varies
across consumer segments. The search data do not provide consumer demographics, so
consumers are segmented according to usage. Discrete consumer segmentation is used so that
segment-specific results have clear interpretations, consistent with the descriptive purpose of the
study.
Consumer segments are defined by dividing consumers along two dimensions: frequency
of search (Frequent vs. Infrequent) and time elapsed since last search. A consumer is categorized
as a ―Frequent Searcher‖ if she enters more non-financial-services-related queries during the
sample than average; this distinction is often labeled ―heavy/light‖ in the scanner panel data
literature. A consumer is categorized as a ―Recent Searcher‖ if she has entered any query within
the previous sixty minutes. Otherwise, she is classified as ―not recent‖ (NR). Using nonfinancial-services-related queries ensures that the segmentation does not rely on the dependent
variables in the regression. The four segments (Frequent/NR, Frequent/Recent, Infrequent/NR,
Infrequent/Recent) have average sizes of 48%, 13%, 34%, and 5%, respectively.
2.5. Brand Characteristics, Advertising Content, Time Effects, and Time-Varying Shifters
10
It would have been preferable to add in website traffic data. Alexa and Comscore were contacted to obtain web
traffic data matching the 2006 search data. However, these traffic data were no longer available and these two
companies restrict sales of disaggregated data as a service to clients, including several large brands in the sample.
11
Advertising ratings and demographics would be preferable to expenditures, but these additional data were cost
prohibitive. In their absence, it is necessary to presume that ad prices correlate with program audiences. This
presumption is common in the literature and has substantial empirical support (e.g. Wilbur 2008).
8
Several brand characteristics were considered: brand awareness, total assets under management,
and asset growth in the previous twelve months. Brand awareness is unavailable for most brands
in the category, so brand age is used in its place. After gathering data on these three brand
characteristics, it was found that age and total assets are highly correlated and that total assets
and recent asset growth are almost perfectly correlated. Therefore brand age is the only brand
characteristic included in the analysis, but the results change little if total assets or recent asset
growth is used in its place.
Similarly, a number of advertising content characteristics were considered. Intuitively, if
an advertisement emphasizes the brand’s website, it would likely increase web traffic and search
activity for the brand. ―Web Emphasis‖ was defined to indicate that the web address was spoken
during the course of the commercial while ―Phone Emphasis‖ indicates that the phone number
was spoken during the course of the commercial. These variables are very highly correlated with
other measures of web and phone prominence such as font size and duration on screen. Several
subjective measures were considered, such as whether the ad contained a celebrity spokesperson
or a humor appeal, but preliminary analysis did not reveal any impact of these effects on
consumer search behavior, so they were excluded from the analysis since they are more
susceptible to coding error.
A stock performance index, the Dow Jones Industrial Average (DJIA), is used as an
exogenous variable to control for unobserved time-varying determinants of consumers’ online
search actions. DJIA levels are widely reported in the media, and recent movements in the stock
index may lead consumers to check their account balances by searching their financial service
providers’ brand names. Two variables based on DJIA are included: 1) the absolute positive
percentage change since the most recent trading day’s opening value; and 2) the absolute
negative percentage change since the most recent trading day’s opening value. These variables
allow the effect to be asymmetric around zero and proved to fit the data better than several
alternate specifications.
Finally, the model relies on brand dummies and time fixed effects to estimate baseline
consumer tendencies to search. The time controls consist of week fixed effects, to allow search
tendencies to vary across weeks in the sample, and two sets of hourly fixed effects: one for
weekdays and another for weekends. Specification tests preferred these weekday-invariant hour
dummies and to a full set of weekday/hour dummies. This likely reflects differences in white-
9
collar workers’ internet access between weekdays and weekends and variation in relevant
television programming schedules across days. The hours between 2:00AM and 9:00 AM are
dropped due to near-zero advertising and search activity in these hours.
2.6. Descriptive Statistics
Table 4 describes the brand-level advertising and search data. Keyword search and result click
rates vary considerably across brands. Some brands, like Van Kampen, targeted financial
advisors and other intermediaries. Others, such as E-Trade and TD Ameritrade, focused
primarily on providing web services. These differences demonstrate the importance of estimating
brand-specific search tendencies to cleanly identify how these actions change with variation in
advertising over time. Search tendency is higher on weekdays than on weekend days, helping to
explain the structure of the day/hour fixed effects described in section 2.5.
2.7. Simultaneity and Causality
A key question in any regression of a variable y (search behavior) on another variable x (TV
advertising expenditures) is whether x causes y or y causes x. In the present application, the
temporal ordering that advertisements must be purchased prior to airing rules out the possibility
that consumer search data (y) directly cause TV advertising placements (x). However, it could be
that companies anticipated when consumers would search and placed ads at points in time most
likely to influence search. To explore this possibility, Figure 1 shows aggregate category
searches and advertising expenditures for each day in the sample. The correlation between the
two variables is nearly zero (-.02) and statistically insignificant. Figure 2 shows how average
search tendency and advertising expenditures moved over the day/hour effects in the analysis.
Again, the correlation is near zero (.08) and statistically insignificant.
It is also notable that sample advertisements emphasized telephone contact more than
twice as often as web contact, and several frequently searched brands with high advertising
spending did not even give their web addresses in their advertising, e.g., Charles Schwab. These
observations are consistent with typical advertising practice, in which television and search
advertising campaigns typically are run by different agencies and designed to achieve different
objectives.
10
While it is impossible to prove exogeneity, the data do not suggest that firms planned
their advertising expenditures based on consumer search tendencies.12 Under the assumption that
television advertising was planned independently of search behavior, the estimation results
below can be interpreted as causal. The next section describes the model used to estimate
relationships among the data.
3. Model and Estimation
The goals of the model are to derive estimating equations that describe how advertising is related
to search behavior. Since the search data are recorded at the individual level and do not record
individual exposures to television advertising, they are aggregated over individuals within each
segment, brand and hour. Thus, the level of variation in the search data is made to match the
level of variation in the advertising data. The parameters of primary interest are identified by
correlations between intertemporal variation in television advertising expenditures and
intertemporal variation in aggregate search data.
Numerous online search behaviors were considered and the search data were investigated
extensively. Three variables of primary interest were defined. The first is the number of
consumers in each segment who conducted at least one product category-related search in each
time period. The second variable is the number of people in each segment who, conditional on
having searched at least once in the hour, chose at least one branded keyword. This second
choice is parsimonious and reflects the data. Consumers virtually never use multiple specific
searches for competing brands within the same hour; it is easier to get information on multiple
brands by searching once using a generic keyword. Further, a consumer often quickly narrows
the scope of her search from a generic keyword to a branded keyword. However, consumers
virtually never broaden a search from a branded keyword to a generic keyword (Pass et al. 2006).
The third variable is number of consumers in each segment who, conditional on having searched
at least one branded keyword in a given hour, clicked at least one result link.
All three search behaviors count the number of consumers who took an action rather than
the number of actions themselves. Counting users rather than search actions gives a truer
indication of the magnitude of the effect.
12
This paper was presented to a mixed academic/practitioner conference audience. A marketing researcher from one
of the three highest-spending brands in this study was asked if his brand had planned its 2006 television advertising
expenditures based on its expectations of consumers’ search behavior. His reply was ―absolutely not.‖
11
Consistent with the descriptive purpose of the study, the model does not impose a
specific ordering on these three behaviors, but it does allow them to be interrelated through
correlated errors. A different approach would be to include the expected utility of future
decisions in the utility of each decision. This approach would require strong assumptions about
dynamic decision making and uncertainty. However, the imposition of such assumptions would
contravene the descriptive purpose of the paper
3.1. Search Behavior
The first behavior modeled is whether to search a financial services keyword. This is a zero-one
variable, so a binary logit model is used. A consumer in segment z searches at time t with
probability
exp( 1z  ln(1  At ) 1zt  DJIAt1z  X t1z  1zt )
Pr 
,
1  exp( 1z  ln(1  At ) 1zt  DJIAt1z  X t1z  1zt )
z
1t
(1)
where  1z is a segment-specific baseline tendency to search, At is category advertising stock,
DJIAt is a vector containing the two stock index variables described in section 2.5, X t is a
vector containing week and day-hour fixed effects as described in section 2.5, and 1zt represents
unobserved deviations from baseline search tendency.
Advertising stock is given by the standard exponential smoothing function
At  advt   z At 1 ,
(2)
z
where advt is the cumulative category advertising expenditure at time t, and  is an advertising
carryover parameter for segment z measuring persistence of effects in hours.13 Advertising
responsiveness 1zt is
1zt  1z  xt1zt ,
(3)
where 1z represents the baseline effect of category advertising on search probability, and xt
allows this effect to vary across weekday-hours and weekend-hours as described in section 2.5.
13
Distributed lag carryover specifications were also estimated. The parameter estimates showed patterns quite
similar to exponential decay, so the simpler model is presented. Several alternate carryover assumptions were tested,
including allowing each brand’s advertising to have a separate decay on category search incidence, allowing
carryover to fall overnight, and allowing carryover parameters to vary across behavioral models. The assumptions
presented here represent the best fit in terms of AIC and BIC.
12
Let ntz be the number of people in segment z at time t, and let c1zt be the number of
people in segment z who search at least one financial services keyword in time t. The probability
of this event is binomial with probability Pr1zt ,
L1zt 
z
z
z
ntz !
(Pr1zt ) c1t (1  Pr1zt ) nt c1t .
z
z
z
c1t !(nt  c1t )!
(4)
3.2. Keyword Choice
The second behavior modeled is what query to enter in the search engine. Therefore, a
multinomial choice model allows consumers to search a query related to one of the k  1,..., K
brands in the data. The ―outside option‖ is to choose not to search any brand-related queries in
time t, searching only generic keywords such as ―stocks‖ or ―retirement‖ ( k  0 ).
The conditional probability that a consumer in segment z chooses a keyword associated
with brand k at time t is
z
2 kt
Pr
exp( 2zk  ln(1  Akt )  2zkt  DJIAt2z  X t2z   2zkt )
,

1   exp( 2zk '  ln(1  Ak 't )  2zk 't  DJIAt2z  X t2z   2zk 't )
(5)
k'
z
where  2zk are baseline brand search tendencies, Akt  advkt   Akt1 is advertising stock for
brand k at time t, and  2zk t represents unobserved factors that influence choice of brand keyword k
K
at time t. The standard assumption that Pr20z t  1   Pr2zkt is made so that the probabilities sum to
k 1
one.  2zk t relates advertising impact on brand keyword choice to brand and ad content
characteristics:
 2zkt   2z  webkt 2z, web  phonekt 2z, phone  agek 2z,age  xt 2zt ,
(6)
where  2z represents the baseline impact of brand advertising on brand keyword choice, webkt is
the fraction of advertising spending by brand k at time t that emphasized web contact and
phonekt is the fraction of advertising spending by brand k at time t that emphasized telephone
contact. agek is the age of brand k, which proxies for brand recognition and assets under
management.
13
Let c2zk t be the number of people in segment z at time t who searched at least once for
brand k. Out of the c1zt people who searched in time t, the probability of observing that each of
the k  0,...,K options is searched at least once by ( c20z t ,..., c2zk t ,..., c2z Kt ) people is given by a
multinomial distribution,
Lz2t 
c1zt !
K
c
K
 (Pr
c2z kt
z
2 kt
)
.
(7)
z
k 0
2 kt
!
k 0
3.3. Click Behavior
The third behavior modeled is, conditional on searching at least once for brand k at time t,
whether to click at least one result. This behavior is categorical and therefore is modeled using a
binary logit.14 A consumer in segment z clicks at least one search result for brand k at time t with
probability
z
3 kt
Pr
exp( 3zk  ln(1  Akt )  3zkt  DJIAt3z  X t3z   3zkt )
,

1  exp( 3zk  ln(1  Akt )  3zkt  DJIAt3z  X t3z   3zkt )
(8)
where  3zk represents the baseline tendency to click at least one link after searching brand k,  3zk t
represents unobserved factors that may influence result click probability, and advertising effect
 3zk t has the same structure as  2zk t given in equation (6) above.
Let c3zkt be the number of those people who clicked at least one result link after searching
brand keyword k at time t. Out of the c2zk t people who searched at least once for brand k at time t,
the probability of observing that c3zkt people clicked at least one link is binomial,
L3z kt 
z
z
z
c2zkt!
(Pr3zkt ) c3 kt (1  Pr3zkt ) c2 kt c3 kt
z
z
z
c3kt!(c2 kt  c3kt )!
(9)
3.4. Estimation
The error structure allows for correlation across time, search behaviors, and segments. m  1,2,3
indexes search, keyword, and click behaviors. The full error structure is
14
Alternate Tobit and Poisson specifications were considered, but the results were qualitatively identical to those of
the binary logit model, so the simpler model is presented.
14
1zt  u1zt  1z u1z,t 1   zz 'u1zt' ,
(10)
 2zkt  u2zkt   2z u2zk ,t 1   zz 'u2zkt'  12u1zt  23u3zkt ,
(11)
3zkt  u3zkt  3z u3zk ,t 1   zz 'u3zkt'  13u1zt  23u2zkt ,
(12)
z ' z
z ' z
z ' z
z
where u mtz and umkt
are ―white noise‖ error shocks independently and identically distributed
2
N (0,  mz
) ,  mz is a segment- and behavior-specific shock persistence parameter,  zz ' allows for
contemporaneous error correlation across segments, and  mm ' allows for contemporaneous error
correlation across behaviors.
z
Let  t be a vector containing all 1zt and  mkt
terms. Equations (10)-(12) make it clear
that  t is multivariate normal. It is straightforward but space-consuming to write out  , the
time-invariant covariance matrix of  t , so it is omitted for brevity. The unconditional likelihood
function is obtained by integrating  t over brands, time periods, and consumer segments using
partial likelihood functions in equations (4), (7), and (9).
L     L1zt Lz2t  L3z ktF (1 ,...,T )
z
t
k
(13)
Parameter estimates are obtained by using Simulated Maximum Likelihood to approximate the
log of (13).
4. Results
This section shows how television advertising is associated with search, keyword choice, and
click behaviors. It then presents advertising persistence parameters, keyword choice advertising
elasticities, model fit and holdout sample results. The non-advertising parameter estimates are
reasonable, but not the focus of the study, so they are presented in Appendix 2.
4.1. Advertising and Search Behavior Results
The results show almost no evidence that television advertising is correlated with the number of
financial product category searches, as can be seen in Table 5. Intuitively, this finding makes a
lot of sense for the financial services category. It suggests that television advertising alone
15
cannot lead consumers to enter the product category; this seems likely, as information search for
financial services is more likely driven by personal factors than by advertising. However, if the
set of category-related generic keywords had not been identified, the study might have led us to
the erroneous conclusion that television advertising increases the number of searches in the
category.
One should be cautious before extending this finding to other categories. The financial
services product category is mature, featuring well-established brands that advertise heavily. It is
likely that television advertising for a new product or a new brand does generate new category
searches.
4.2. Advertising and Keyword Choice Results
In contrast to search behavior, it turns out that television advertising is associated with
consumers’ keyword choice behavior, as shown in Table 6.
Advertising is more highly correlated with keyword choice for consumers who have not
searched within the past hour. It is also more highly correlated with keyword choice for
consumers who search frequently.
Branded keyword choice is most strongly correlated with advertising expenditures during
business hours on weekdays. Since many financial services consumers are white-collar
professionals, it seems intuitive that their online response to advertising may be most easily
observed during business hours, as they would have easy access to a computer. Weekday hours
between 9 AM and 5 PM are when the number of searches is largest. There are also a few
statistically significant late-evening dummies for frequent searchers who have not searched
within the past hour.
Advertising for older brands is significantly negatively correlated with most segments’
keyword choices. That is, the association between a brand’s television advertising and
consumers’ choice of that brand’s keywords is much larger for relatively younger firms. Of the
brands in the sample, younger firms such as TD Ameritrade and E-Trade started delivering
services online earlier than older firms such as Merrill Lynch. It therefore stands to reason that
consumers would be more likely to respond to younger firms’ advertising by going online. It is
also possible that younger firms’ advertising targets younger consumers, and that younger
consumers are more likely to respond via search than via telephone.
16
Among the advertising content variables, phone emphasis is significantly negatively
correlated with brand keyword choice for infrequent searchers who have searched within the past
hour. However, web emphasis had no significant impacts on any segments’ behaviors. Since web
emphasis and phone emphasis were not present in most category advertisements (7% and 16% of
spending, respectively), one should hesitate to generalize the ad content results to other
categories.
Overall, the finding that television advertising is insignificantly correlated with the
number of people who searched in the category, but is significantly correlated with keyword
choice, suggests that perhaps television advertising does not expand the market but does shift
query share among existing brands. This is similar to findings in the advertising effectiveness
literature about how advertising influences sales in mature product categories (Tellis 2004).
4.3. Advertising and Click Behavior Results
Table 7 shows that television advertising has few significant correlations with consumers’
tendency to click branded search results.
Just two advertising variables have significant correlations with click tendency. First,
frequent searchers are less prone to click search results in periods when brands have advertised
heavily. It is possible that advertising leads to increased brand keyword choice, but that those
searches are less likely to convert to website visits. This would happen if consumers are less
satisfied with branded search results than with generic keyword search results. Second,
consumers’ tendency to click results after searching older brands is significantly associated with
television advertising; this is the opposite of the pattern observed in the keyword choice behavior
results. This is likely due to higher levels of brand recognition and easier identification of the
brand website among the search results.
4.4. Advertising Carryover
The associations between television advertising and search behavior persist for hours, not days,
as shown in Table 8. Infrequent searchers who have searched recently have the shortest shelf life,
as 90% of the carryover has been spent within the first two hours, while frequent searchers who
have searched recently have the longest memories (13 hours). In general, carryover estimates are
larger for frequent searchers than for infrequent searchers.
17
4.5. Keyword Choice Advertising Elasticities
It appears the primary effect of television advertising is on consumers’ keyword choice. How big
is that effect? A simple way to measure effect magnitudes is to use elasticity calculations, as in
the advertising effectiveness literature, e.g., Ataman, van Heerde, and Mela (2010) and Lodish et
al. (1995). The interpretation of these figures is the percentage change in branded keyword
choice probability, given a 1% change in the brand’s television advertising expenditures.
Table 9 shows brand-specific point estimates and confidence intervals for the elasticity of
keyword choice during standard business hours (Monday-Friday, 9 AM-5 PM) with respect to
television advertising. Consistent with the empirical results presented earlier, younger brands
have stronger associations between televisio advertising and branded keyword choice. For brands
which are less than fifty years old, the average elasticity is .07, with a confidence interval
ranging from .03 to .10.
This overall elasticity is larger than the short-term market share elasticity of advertising
(.05) and smaller than the long-term market share elasticity of advertising (.10) reported by
Lodish et al. (1995). It similarly lies between the short-run (.01) and long-run (.12) advertising
elasticities of Ataman, van Heerde and Mela (2010).
4.6. Model Validation
Table 10 presents goodness-of-fit statistics for each model, segment, and overall. Pseudo Rsquared statistics are based on Likelihood Ratio statistics, as in McFadden (1974). Overall, the
model explains about 70% of the variation in the dependent variables.
A large number of covariates have been used in the estimation, so the model was reestimated on an 80% subsample of the data and prediction errors were compared to a 20%
holdout sample. Advertising carryover prevents a random holdout validation exercise, so holdout
validation was done via a nonrandom split (Keane and Wolpin 2007). The model is re-estimated
using data from March and April and fitted values are produced for May.
Despite the nonrandom split, the results are very encouraging. Table 11 presents Root
Mean Square Error (RMSE) comparisons between in-sample and out-of-sample observed and
predicted choice probabilities, by model and segment. Error magnitudes are comparable across
18
dependent variables and segments; the largest single difference is about 10%, and six of twelve
model/segments have differences smaller than 5%.
5. Discussion
This paper has presented the first investigation of how television advertising expenditures are
associated with multiple aspects of online search behavior. It found no evidence that advertising
increases the number of people who search financial services-related queries, but it is positively
correlated with changes in consumers’ tendency to search brand-related keywords. For younger
brands during business hours, television advertising’s association with keyword choice is
comparable to extant measurements of the elasticity of advertising on sales.
5.1. Implications for Practice
The most important implication of these results is the need for firms to consider how television
advertising may impact their search advertising campaigns. Search engine marketing is typically
run as a standalone activity which seeks to maximize its incremental profits. If it is indeed the
case that television advertising increases branded keyword choice, then it will reduce the number
of expensive clicks on generic keyword searches and increase the number of clicks paid for on
cheaper branded keywords. It also should increase the conversion rate of both branded and
generic search keywords. The marketer who is ignorant of these effects is likely to underspend
on television advertising and overspend on search advertising.
The best way for a brand to investigate these effects is to use A/B testing. There are two
straightforward ways to do this. First, a brand could randomize television advertising weight
across geographic markets and examine variability on search behavior across markets. Second, a
brand could randomize the timing of national ads, and then correlate geographic variation in
search behavior with geographic variation in exposure to national television ads, since local
audiences of national programs vary considerably across markets.
The estimated durations of these effects suggest that they last hours but not days. This
implies that the daily search data commonly reported by search engines would provide a better
measure of the effect than the short windows of time available from website traffic data.
If A/B testing confirms that these effects exist for a brand, then the next steps are to
investigate how they vary across user segments, creatives, programs and times of day. This may
19
lead the marketer to alter its pulsing, creatives, budgets and return-on-investment metrics for
both television and search advertising. It is likely advisable to facilitate coordination between the
two agencies executing the advertising campaigns in each medium. As Swasy and Rethans
(1986) suggested, ―advertisers should begin to incorporate a measure of curiosity generation and
question solicitation in their new-product ad concept and ad-testing procedures.‖
5.2. Implications for Academic Literature
The quickly-growing academic literature on search engine marketing has mostly ignored the
effects of offline advertising on online outcomes, with a rare exception in Kim and Balachander
(2010). Future academic work on search advertising could consider that search advertisers may
be able to influence consumer search via offline marketing. Perhaps the default assumption about
how media advertising influences consumer search for established brands in mature product
categories should be that it shifts consumers’ keyword choices, rather than their tendencies to
search or click.
5.3. Limitations and Extensions
This study has a number of limitations which suggest future research opportunities. It is based on
data for a mature product category and established brands; different patterns of effects might be
found for new brands or evolving product categories. It seems especially likely that television
advertising for new brands or for evolving categories would increase the number of searches, in
contrast to the results shown here.
The available data limited the scope of the research questions, as data on brand website,
additional search engines, social networks, and paid search advertisements were unavailable. The
effects estimated here are based on time series identification and do not come from a singlesource panel in which advertising exposures and search behavior are observed within a single
household, so the results are subject to alternate explanations. Many steps were taken to rule out
alternative explanations—allowing baseline search behaviors to vary with brand and time, using
exogenous time-varying factors to isolate the effect of advertising, and selecting a category that
is not subject to obvious temporal endogeneity and that did not promote its online offerings to a
significant extent. Still, this is a descriptive approach without presumptions of causality. We
20
hope to spark field experiments or additional research in this new area as better data become
available to academics in the future.
Appendix 1: Query Mining Technique to Find Category Keywords
This appendix describes the query mining algorithm. It draws on three papers in particular.
Abhishek and Hosanagar (2007) introduced the concept of semantic similarity, using the
similarity in the results returned by two different search queries to identify commonalities among
the queries. Buidling on this, Fuxman et al (2008) found that keyword suggestions are maximally
relevant when the analyst uses clickstream data to identify similarities in result clicks to identify
similarities in search queries. Chen et al (2008) added concept hierarchy to semantic similarity,
showing that keyword context is an important determinant of keyword meaning, a technique
which helps to screen out irrelevant keywords. The algorithm used here draws on all three of
these papers. Its purpose is to identify queries containing brand-related and generic keywords.
This technique will work for any set of search queries that includes both query information and
result click information. It is described in the present context of the financial services category,
but may be used for any category for which a partial set of brands can be identified.
The basic idea of the technique is to iterate between sets of brand-related websites and
the keywords that led to clicks on those sites to identify relevant queries, refining the data at
several points. Three examples will facilitate the exposition. Suppose consumer A searches
―Information about Fidelity retirement‖ and clicks Fidelity.com. Suppose consumer B searches
―Fidelity NetBenefits 401K‖ and clicks a Fidelity subsidiary, NetBenefits.com. Assume, for the
purposes of this example, that the researcher does not know NetBenefits is affiliated with
Fidelity. Consumer C searches the word ―retirement homes in Chicago‖ and clicks
brookdaleliving.com. Starting from Fidelity.com, the query mining procedure will classify
―Fidelity‖ and ―NetBenefits‖ as keywords related to the Fidelity brand, ―retirement‖ and ―401K‖
as generic keywords, and ―Information,‖ ―about,‖ ―homes,‖ and ―Chicago‖ as irrelevant
keywords. It will identify Fidelity.com and Netbenefits.com as brand-related websites, and
brookdaleliving.com as an irrelevant website. The discussion below uses these examples to
illustrate how the procedure works.
Figure A1.1 displays the logical structure of the query mining technique. The analyst
starts with a list of Known Brand Names, such as ―Fidelity.‖ From this list, a Preliminary
21
Website List is compiled by searching each of the known brand names, and including the first
brand-owned website returned as an organic result to a search on the brand name. From a Known
Brand List that only contains ―Fidelity,‖ this step would produce a Preliminary Website List that
only contains Fidelity.com.
The Preliminary Query Set is identified as all queries in the data that led to result clicks
to any website in the Preliminary Website List. In the examples above, the Preliminary Query
Set would include only consumer A’s query. The Preliminary Keyword Set is identified as all
unique keywords contained in this Preliminary Query Set.15 In the examples above, this set is
―information,‖ ―about,‖ ―Fidelity,‖ and ―retirement.‖
The Revised Query Set is defined as all queries containing any member of the
Preliminary Keyword Set. In the examples above, the Revised Query Set would contain all three
consumers’ queries, since consumer B’s query includes the word ―Fidelity‖ and consumer C’s
query includes the word ―retirement.‖
The Website Filter generates a list of category-related websites. It works in three steps.
First, the analyst compiles a list of unique websites consumers clicked after searching queries in
the Revised Query Set. In the examples above, this set would include fidelity.com,
netbenefits.com, and brookdaleliving.com. Second, for each website, the analyst calculates the
aggregate click-through rate from any query in the Revised Query Set to the site in question.
Sites with low aggregate click-through rates are dropped. If the above example were replicated
on a large scale, this step would drop brookdaleliving.com. Third, the analyst manually visits
each remaining site and classifies it as either (i) associated with a particular brand, (ii) a generic
site, or (iii) unrelated; unrelated sites are dropped. This completes the Revised Website List.
The Keyword Filter generates a list of category-related keywords from the Revised
Website List. Like the Website Filter, it works in 3 steps. First, the analyst compiles the list of
unique keywords contained in queries that led to clicks on the Revised Website List. In the
examples above, this step includes ―Information,‖ ―about,‖ ―Fidelity,‖ ―retirement,‖
―Netbenefits,‖ and ―401K.‖ Second, for each keyword, the analyst calculates the aggregate clickthrough rate from all queries containing that keyword to any site in the Revised Website List and
drops those with low aggregate click-through rates. This step removes most unrelated keywords.
15
Infrequently occurring keywords are excluded from the preliminary keyword set. In the current application,
keywords that appeared in fewer than four queries were dropped.
22
In the examples above, this would drop ―about.‖ Third, the analyst classifies each remaining
keyword as either belonging to a particular brand or as a generic keyword. For each keyword w
and each brand k, the analyst calculates CTR wk as the aggregate click-through rate from all
queries containing keyword w to any website associated with brand k. When the ratio of CTR wk
to CTR wk ' is larger than the threshold for all brands k '  k , then keyword w is associated with
brand k; otherwise keyword w is classified as a generic keyword. In the examples above, this step
would associate ―Fidelity‖ and ―Netbenefits‖ with the brand Fidelity, and would classify
―retirement‖ and ―401K‖ as generic keywords. The output of this step is the Revised Keyword
List, which should be reviewed by the analyst for reasonableness.16
The Final Query Set is defined as all queries containing any member of the Revised
Keyword Set. A branded query is any query that contains at least one brand-related keyword. A
generic query is any query that does not contain any brand-related keywords. In the current
application, it was virtually never the case that a single query contains keywords associated two
or more different brands, but many queries contain multiple brand-related keywords for the same
brand, and many queries combine brand-related keywords for a single brand with generic
keywords. Note that the Final Query Set will contain many queries that did not lead to any result
click.
In the current research, 22 known brands were identified from the advertising database.
The Preliminary Website List for these brands produced 37,398 queries in the Preliminary Query
Set. This query set included 1,843 unique keywords in the Preliminary Keyword Set. The
Revised Query Set contained 906,538 queries. These queries produced clicks to 2,389 unique
URLs, which the Website Filter reduced to 53 sites in the Revised Website List. The Keyword
Filter resulted in 199 unique keywords in the Revised Keyword List, of which 92 were generic
and 107 were branded. The generic keywords produced 245,419 queries in the Final Query Set,
and the branded keywords contributed 67,733 queries.
Appendix 2: Additional Empirical Results
This appendix presents the non-advertising parameter estimates that are not included in the main
text: brand fixed effects, time fixed effects, and error covariance parameters. For brevity, the
16
It would be possible to iterate query selection, website filtering and keyword filtering as needed if the analyst
suspects that some important category keywords have not been included.
23
discussion focuses on the keyword choice model parameter estimates in Table A2.2. The brandspecific keyword choice effects are estimated precisely for the most part, with t-statistics as high
as 46. Citigroup, Fidelity, and Wachovia were the most-searched brands. Positive changes in the
DJIA are found to increase the probability that the third segment searches a brand-related
keyword, but otherwise DJIA does not have significant effects. Brand-related searches vary
across weeks in the sample, for example falling somewhere near the federal tax deadline of April
15. And the day/hour effects show that consumers who have not searched recently are more
likely to use brand-related keywords than generic keywords.
Table 1. Sample Brand Keywords
Charles Schwab
SCHWA
SCHWAB
SCWAB
SHWAB
CHARLESSCHWAB
CHARLESSCHWABB
CHARLESSWAB
WWWCHARLESSCHWAB
Fidelity
FEDELITY
FID
FIDELITY
FIDELITYADVISOR
FIDELITYINVESTMENT
FIDELITYINVESTMENTS
FIDELTY
Table 2. Sample Generic Keywords
401K
401-K
BANK
BANKING
BANKS
BOND
BONDS
BROKER
BROKERAGE
BROKERS
DIVIDENDS
EQUITY
FINANCE
FINANCIAL
FUND
FUNDS
INCOME
INVEST
INVESTING
INVESTMENT
INVESTMENT
INVESTMENTS
INVESTOR
INVESTORS
IRA
LIABILITY
LIQUIDITY
LOAN
LOANS
MONEY
MORTGAGE
SECURITIES
SECURITY
SHARES
STOCK
STOCKS
TAX
TRUSTS
UGMA
YIELD
24
Table 3. Sample Unrelated Keywords
ABOUT
ADVANTAGE
AFTER
ARE
BEST
BUY
BUYING
CALCULATOR
CALCULATORS
CALIFORNIA
CAREERS
CEO
CHAINS
CHARTS
CITY
CLOSED
CODE
COLLEGE
COMHTTP
COMMON
COMMUNITY
CONNECTICUT
CORPORATION
DEAN
DEATH
DELTA
DISTRIBUTION
DVD
EDUCATION
EXAMPLES
FIRST
FOR
FORM
GOVERNMENT
GROUP
HALL
HARBOR
HIGH
HISTORY
HIV
Table 4. Descriptive Statistics
Mon.-Fri. Daily Averages
Brand
AG Edwards
Charles Schwab
CitiGroup
E-Trade
Edward Jones
Fidelity
Forex
FXCM
Legg Mason
Merrill Lynch
Morgan Stanley
Nuveen
Oppenheimer
OptionsXpress
Raymond James
ScottTrade
ShareBuilder
T. Rowe Price
TD Ameritrade
Vanguard
Van Kampen
Wachovia
All Brand Keywords
All Generic Keywords
Number ClickAdv.
of
Through Exp.
Queries
Rate
($000)
4
38
85
35
10
189
2
1
1
25
12
1
4
2
3
25
4
3
68
33
1
155
699
2,586
51.6%
58.0%
60.6%
54.7%
60.6%
74.1%
51.0%
71.4%
84.2%
36.5%
65.1%
75.5%
60.9%
76.7%
68.7%
27.6%
32.3%
69.4%
56.0%
28.5%
78.6%
56.7%
58.9%
61.6%
13.7
160.8
0.0
67.4
56.0
122.8
0.0
0.0
0.0
19.4
0.0
0.0
21.8
0.0
10.6
14.0
0.0
65.9
313.6
0.0
2.3
20.3
888.7
--
Saturday-Sunday Averages
Number ClickAdv. % Adv. Exp. % Adv. Exp. Brand Age
of
Through Exp. Emphasizing Emphasizing (Years in
Queries
Rate
($000)
Web
Phone
2006)
2
18
68
13
7
97
3
1
0
18
6
1
2
1
2
7
2
2
32
28
0
115
424
2,108
47.9%
48.4%
61.4%
52.9%
71.4%
72.7%
51.3%
68.8%
66.7%
32.3%
66.7%
84.6%
70.5%
75.8%
55.8%
17.5%
14.8%
63.6%
50.8%
32.1%
100.0%
60.0%
58.0%
63.4%
45.3
298.8
0.0
71.5
45.5
337.5
0.0
0.0
0.0
344.5
0.0
0.0
55.7
0.0
8.7
1.5
0.0
153.8
452.6
0.0
39.9
182.0
2037.2
--
16.4%
0.0%
-5.8%
12.3%
7.3%
---7.3%
--7.2%
-6.0%
16.8%
-12.5%
9.3%
-7.5%
3.7%
7%
--
9.1%
0.0%
-13.5%
22.8%
18.3%
---12.6%
--11.1%
-33.2%
11.4%
-22.6%
23.2%
-22.6%
22.8%
16%
--
119
35
194
24
84
60
7
7
107
92
71
108
46
6
44
26
6
69
35
31
79
98
---
25
Table 5. Search Behavior Advertising Parameter Estimates
Consumer Segment
Frequent, NR
Est. St.Err.
Intercept
Weekday 9:00am
Weekday 10:00am
Weekday 11:00am
Weekday 12:00pm
Weekday 1:00pm
Weekday 2:00pm
Weekday 3:00pm
Weekday 4:00pm
Weekday 5:00pm
Weekday 6:00pm
Weekday 7:00pm
Weekday 8:00pm
Weekday 9:00pm
Weekday 10:00pm
Weekday 11:00pm
Weekday 12:00am
Weekday 1:00am
Weekend 9:00am
Weekend 10:00am
Weekend 11:00am
Weekend 12:00pm
Weekend 1:00pm
Weekend 2:00pm
Weekend 3:00pm
Weekend 4:00pm
Weekend 5:00pm
Weekend 6:00pm
Weekend 7:00pm
Weekend 8:00pm
Weekend 9:00pm
Weekend 10:00pm
Weekend 11:00pm
Weekend 12:00am
.133
-.087
-.082
-.180
-.028
-.092
-.092
-.045
-.006
-.075
-.080
-.092
-.137
-.105
-.091
-.065
-.136
-.250
-.341
-.213
-.222
-.270
-.093
-.148
-.017
-.169
-.025
-.080
-.169
-.154
-.210
-.015
-.117
-.236
.145
.151
.153
.154
.152
.155
.158
.161
.161
.153
.155
.154
.150
.148
.149
.151
.154
.162
.158
.155
.158
.160
.157
.156
.154
.152
.155
.158
.161
.160
.160
.160
.168
.188
Frequent, Recent
Est. St.Err.
*
-.161
.163
.352
.229
.295
.211
.107
.372
.189
.166
.426
.280
.106
.175
.312
.109
.289
.047
-.032
.401
.037
.092
.026
.070
.336
.335
.079
.157
.161
-.059
-.025
.159
.228
.328
.204
.223
.221
.219
.220
.221
.224
.227
.229
.231
.240
.236
.229
.217
.215
.220
.223
.230
.271
.258
.248
.246
.246
.239
.240
.236
.237
.244
.246
.242
.243
.250
.265
.266
Infrequent, NR
Est. St.Err.
-.112
.159
.189
.215
.113
.030
.141
.169
.179
.124
.075
.056
.105
.133
.099
.165
.109
.225
.023
.007
.031
.159
.128
.155
.135
.109
.178
.157
.172
.099
.131
.116
.149
.008
.130
.137
.139
.139
.135
.137
.138
.140
.140
.135
.136
.135
.133
.132
.132
.134
.137
.144
.135
.135
.137
.138
.136
.135
.135
.134
.136
.138
.139
.138
.139
.139
.145
.164
* Significant at the 95% confidence level. ** Significant at the 99% confidence level.
Infrequent, Recent
Est. St.Err.
.165
-.341
-.007
-.124
-.230
.021
-.229
-.495
-.012
-.205
-.159
-.296
-.196
-.186
-.098
-.061
-.014
-.221
-.211
-.291
-.350
-.173
-.064
-.120
-.095
-.071
-.030
.058
-.177
.011
-.110
-.121
-.187
-.174
.247
.294
.287
.270
.259
.266
.274
.281
.280
.263
.267
.266
.254
.252
.253
.259
.268
.281
.261
.263
.274
.272
.261
.261
.258
.260
.266
.269
.269
.264
.266
.264
.286
.327
26
Table 6. Keyword Choice Advertising Parameter Estimates
Consumer Segment
Frequent, NR
Est. St.Err.
Intercept
Weekday 9:00am
Weekday 10:00am
Weekday 11:00am
Weekday 12:00pm
Weekday 1:00pm
Weekday 2:00pm
Weekday 3:00pm
Weekday 4:00pm
Weekday 5:00pm
Weekday 6:00pm
Weekday 7:00pm
Weekday 8:00pm
Weekday 9:00pm
Weekday 10:00pm
Weekday 11:00pm
Weekday 12:00am
Weekday 1:00am
Weekend 9:00am
Weekend 10:00am
Weekend 11:00am
Weekend 12:00pm
Weekend 1:00pm
Weekend 2:00pm
Weekend 3:00pm
Weekend 4:00pm
Weekend 5:00pm
Weekend 6:00pm
Weekend 7:00pm
Weekend 8:00pm
Weekend 9:00pm
Weekend 10:00pm
Weekend 11:00pm
Weekend 12:00am
Brand Age
Web Emphasis
Phone Emphasis
.019
.111
.112
.107
.107
.092
.099
.111
.103
.074
.080
.055
.057
.079
.059
.062
.034
.087
.068
.053
.028
.041
.048
.035
.032
.053
.063
.065
.037
.019
.066
.070
.034
.053
-.001
.009
-.007
.034
.030
.030
.030
.030
.030
.030
.030
.030
.030
.030
.030
.030
.030
.030
.031
.032
.034
.033
.033
.033
.034
.034
.034
.033
.033
.032
.033
.033
.034
.033
.034
.037
.037
.000
.005
.004
Frequent, Recent
Est. St.Err.
**
**
**
**
**
**
**
**
*
**
**
*
*
*
*
*
**
.076
.117
.138
.090
.090
.076
.078
.060
.086
.043
.025
.041
.030
.049
.021
.050
-.018
.011
-.010
.019
.053
.017
.010
.045
.032
-.021
.017
-.031
.044
.012
.042
.006
-.017
.015
-.001
-.018
.000
.221
.050
.050
.049
.049
.049
.049
.049
.049
.050
.049
.049
.049
.049
.050
.051
.051
.054
.063
.057
.057
.058
.058
.055
.061
.057
.057
.058
.056
.058
.059
.056
.057
.068
.002
.011
.007
*
**
Infrequent, NR
Est. St.Err.
.020
.080
.090
.061
.057
.084
.064
.080
.057
.046
.037
.039
.032
.044
.055
.035
.032
-.006
.033
.033
.019
.020
.010
.001
.021
.026
.015
.033
.023
.035
.031
.019
-.004
.002
-.001
.000
-.004
.025
.024
.024
.024
.024
.024
.024
.024
.024
.024
.024
.024
.024
.024
.024
.024
.025
.027
.026
.026
.026
.026
.026
.026
.026
.026
.025
.026
.026
.026
.026
.026
.028
.029
.000
.003
.002
**
**
*
*
**
**
**
*
*
**
* Significant at the 95% confidence level. ** Significant at the 99% confidence level.
Infrequent, Recent
Est. St.Err.
.103
.087
.075
.064
.008
.016
.017
-.007
.006
.017
-.063
.022
-.038
-.023
-.030
-.001
-.018
-.101
-.046
-.008
.036
-.027
-.042
-.052
-.044
-.040
.004
.031
-.020
.026
-.013
-.016
-.025
.011
-.001
.003
-.024
.079
.078
.077
.076
.076
.077
.077
.077
.076
.077
.077
.076
.076
.075
.076
.077
.080
.086
.088
.082
.082
.082
.082
.084
.083
.080
.080
.082
.085
.083
.080
.083
.086
.095
.000
.012
.009
**
**
27
Table 7. Click Behavior Advertising Parameter Estimates
Consumer Segment
Frequent, NR
Est. St.Err.
Intercept
Weekday 9:00am
Weekday 10:00am
Weekday 11:00am
Weekday 12:00pm
Weekday 1:00pm
Weekday 2:00pm
Weekday 3:00pm
Weekday 4:00pm
Weekday 5:00pm
Weekday 6:00pm
Weekday 7:00pm
Weekday 8:00pm
Weekday 9:00pm
Weekday 10:00pm
Weekday 11:00pm
Weekday 12:00am
Weekday 1:00am
Weekend 9:00am
Weekend 10:00am
Weekend 11:00am
Weekend 12:00pm
Weekend 1:00pm
Weekend 2:00pm
Weekend 3:00pm
Weekend 4:00pm
Weekend 5:00pm
Weekend 6:00pm
Weekend 7:00pm
Weekend 8:00pm
Weekend 9:00pm
Weekend 10:00pm
Weekend 11:00pm
Weekend 12:00am
Brand Age
Web Emphasis
Phone Emphasis
-.320
-.030
.002
-.065
.031
-.020
-.069
-.033
-.036
-.031
-.004
.001
.042
-.015
-.015
-.008
-.058
-.041
.093
-.076
-.061
.048
-.070
-.097
-.099
-.029
-.027
-.065
-.057
.026
-.002
-.054
-.018
.036
.004
.012
-.004
.073
.064
.064
.065
.065
.065
.066
.065
.065
.065
.064
.064
.065
.065
.065
.067
.070
.077
.074
.072
.073
.073
.076
.076
.071
.071
.070
.073
.074
.074
.071
.072
.084
.084
.000
.010
.008
Frequent, Recent
Est. St.Err.
**
**
-.253
-.025
-.074
-.059
-.065
-.028
-.096
-.054
-.170
-.056
-.154
-.152
-.100
-.125
-.141
-.108
-.091
-.066
-.186
-.107
-.116
-.012
-.127
-.194
.038
-.113
-.192
-.139
-.123
-.010
.028
-.080
-.107
-.032
.004
.015
-.010
.115
.100
.103
.100
.100
.101
.101
.101
.101
.104
.101
.102
.101
.101
.103
.105
.102
.114
.118
.113
.116
.117
.120
.109
.129
.113
.113
.112
.111
.119
.112
.106
.111
.132
.001
.020
.014
*
**
Infrequent, NR
Est. St.Err.
.004
-.078
-.058
-.049
-.066
-.093
-.088
-.094
-.078
-.084
-.044
-.078
-.079
-.083
-.058
-.070
-.120
-.063
-.067
-.091
-.056
-.041
-.083
-.082
-.102
-.044
-.099
-.054
-.067
-.071
-.068
-.119
-.095
-.093
.001
.002
-.001
.049
.048
.048
.048
.048
.048
.048
.048
.048
.048
.047
.047
.047
.047
.048
.048
.050
.053
.051
.051
.051
.051
.052
.051
.050
.050
.050
.051
.051
.051
.052
.051
.055
.056
.000
.006
.004
*
*
*
*
**
* Significant at the 95% confidence level. ** Significant at the 99% confidence level.
Infrequent, Recent
Est. St.Err.
-.058
-.001
-.051
-.047
-.124
-.072
-.125
.020
-.147
-.145
-.062
-.072
-.113
-.104
-.122
-.181
-.069
.018
-.094
-.037
-.177
-.172
-.252
-.084
-.065
-.040
-.107
-.146
-.180
.014
-.110
-.122
-.225
-.082
.002
.011
.023
.154
.155
.151
.150
.150
.150
.151
.151
.150
.151
.150
.150
.149
.149
.149
.151
.156
.171
.175
.160
.158
.155
.155
.157
.166
.154
.157
.155
.169
.161
.160
.165
.165
.187
.001
.021
.016
**
28
Table 8. Advertising Carryover Parameter Estimates
Carryover Est. St.Err.
Frequent, Not Recent
Frequent, Recent
Infrequent, Not Recent
Infrequent, Recent
.630
.830
.500
.300
90% Life
(.060)
(.037)
(.075)
(.030)
**
**
**
**
5
13
4
2
** Significant at the 99% confidence level.
Table 9. Influence of Advertising on Keyword Choice during Business Hours
Est.
95% Bootstrap CI
Firm Age
Adv. Exp.
($000)
AG Edwards1
Wachovia
Merrill Lynch
Edward Jones
Van Kampen
T. Rowe Price
Fidelity
Oppenheimer
Raymond James
-.018
.004
.011
.020
.026
.037
.043
.062
.065
(
(
(
(
(
(
(
(
(
-.054
-.025
-.021
-.013
-.008
.001
.007
.019
.021
, .012
, .029
, .037
, .046
, .053
, .066
, .073
, .101
, .104
)
)
)
)
)
)
)
)
)
119
98
92
84
79
69
60
46
44
2,085
6,073
10,236
4,877
1,188
8,349
16,881
2,888
926
Charles Schwab
TD Ameritrade
Scottrade
E-Trade
.073
.072
.083
.084
.074
(
(
(
(
(
.026
.026
.032
.033
.037
, .116
, .113
, .129
, .133
, .109
)
)
)
)
)
35
31
26
15
18,381
32,465
964
6,309
Young Brands 2
1. Only brands with average daily spending in excess of $10,000 are shown.
2. Young brand stands for brands of which ages are less than 50 years as of 2006,
and business hours are from 9am to 5pm weekdays.
Note: Estimates in bold are significantly different from zero.
29
Table 10. Goodness of Fit
Search Behavior
Keyword Choice
Click Behavior
All Models
Pseudo R-Squared
Freq., NR Freq./Rec. Infreq., NR Infreq./Rec. Overall
.806
.790
.619
.630
.732
.615
.685
.550
.670
.588
.191
.160
.067
.069
.106
.778
.771
.589
.630
.697
Table 11. Holdout Validation
Search
Behavior
In-Sample
Out-of-Sample
Keyword
Choice
In-Sample
Out-of-Sample
In-Sample
Out-of-Sample
Click
Behavior
Root Mean Squared Error
Freq., NR Freq./Rec. Infreq., NR Infreq./Rec.
.006
.012
.011
.027
.006
.011
.011
.028
.021
.022
.158
.170
.035
.036
.106
.112
.019
.020
.211
.214
.043
.044
.110
.116
30
Table A2.1. Time-varying Effects in Search Behavior Model
Consumer Segment
Frequent, NR
Est. St.Err.
Intercept
DJIA Changes (+)
DJIA Changes (-)
Week2 (March)
Week3 (March)
Week4 (March)
Week5 (March/April)
Week6 (April)
Week7 (April)
Week8 (April)
Week9 (April)
Week10 (April/May)
Week11 (May)
Week12 (May)
Week13 (May)
Week14 (May)
Weekday 9:00am
Weekday 10:00am
Weekday 11:00am
Weekday 12:00pm
Weekday 1:00pm
Weekday 2:00pm
Weekday 3:00pm
Weekday 4:00pm
Weekday 5:00pm
Weekday 6:00pm
Weekday 7:00pm
Weekday 8:00pm
Weekday 9:00pm
Weekday 10:00pm
Weekday 11:00pm
Weekday 1:00am
Weekend 9:00am
Weekend 10:00am
Weekend 11:00am
Weekend 12:00pm
Weekend 1:00pm
Weekend 2:00pm
Weekend 3:00pm
Weekend 4:00pm
Weekend 5:00pm
Weekend 6:00pm
Weekend 7:00pm
Weekend 8:00pm
Weekend 9:00pm
Weekend 10:00pm
Weekend 11:00pm
Weekend 12:00am
Weekend 1:00am
-3.49
0.03
0.02
-0.08
-0.05
-0.04
-0.01
-0.02
0.01
-0.05
-0.07
-0.01
0.00
-0.10
-0.13
-0.34
-0.19
-0.24
0.80
-0.92
-0.25
-0.30
-0.79
-1.25
-0.50
-0.54
-0.46
0.10
-0.34
-0.58
-0.91
1.40
2.49
0.98
0.97
1.53
-0.67
-0.01
-1.59
0.31
-1.60
-0.89
0.27
0.07
0.80
-1.73
-0.48
1.04
-1.69
0.67
0.02
0.02
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.04
0.84
0.87
0.89
0.85
0.90
0.96
1.00
1.01
0.86
0.93
0.91
0.83
0.77
0.79
0.85
1.09
1.01
0.94
1.02
1.04
0.98
0.96
0.95
0.89
0.99
1.06
1.11
1.10
1.11
1.11
1.27
1.60
1.85
**
**
*
**
**
**
*
Frequent, Recent
Est. St.Err.
-5.09
0.04
0.03
-0.08
-0.10
-0.01
0.00
0.01
-0.05
-0.02
-0.15
-0.07
-0.14
-0.14
-0.17
-0.27
2.03
-0.49
1.08
0.28
1.37
2.50
-0.74
1.53
1.68
-1.43
0.28
2.42
1.59
-0.23
2.35
3.24
4.18
-1.61
3.34
2.62
3.34
2.92
-0.65
-0.73
2.74
1.58
1.58
4.66
4.22
1.55
0.55
-0.60
3.72
1.21
0.04
0.03
0.05
0.05
0.05
0.05
0.05
0.06
0.05
0.06
0.05
0.06
0.06
0.05
0.06
1.68
1.63
1.59
1.59
1.59
1.67
1.72
1.75
1.78
1.98
1.91
1.79
1.55
1.52
1.62
1.83
2.61
2.40
2.19
2.16
2.15
2.03
2.08
2.01
2.04
2.19
2.23
2.14
2.17
2.32
2.60
2.56
2.89
**
**
*
*
**
**
*
Infrequent, NR
Est. St.Err.
-2.53
0.02
0.04
-0.06
-0.03
-0.09
-0.08
-0.07
-0.06
-0.08
-0.13
-0.05
-0.11
-0.19
-0.26
-0.39
-0.14
-0.49
-0.77
0.26
1.06
-0.09
-0.38
-0.53
0.05
0.54
0.77
0.13
-0.21
0.16
-0.64
-1.28
1.17
1.23
0.97
-0.49
-0.19
-0.51
-0.36
-0.03
-0.97
-0.73
-0.88
-0.01
-0.42
-0.26
-0.72
1.00
1.05
* Significant at the 95% confidence level. ** Significant at the 99% confidence level.
0.51
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.03
0.68
0.72
0.72
0.62
0.67
0.69
0.73
0.73
0.62
0.67
0.66
0.60
0.57
0.58
0.64
0.86
0.65
0.66
0.71
0.73
0.67
0.65
0.66
0.64
0.72
0.78
0.78
0.76
0.79
0.79
0.92
1.27
1.56
**
**
**
**
**
**
**
**
**
*
**
**
**
**
Infrequent, Recent
Est. St.Err.
-4.34
-0.01
-0.04
-0.01
-0.01
-0.04
-0.04
-0.02
-0.04
-0.11
-0.13
-0.11
-0.11
-0.13
-0.19
-0.28
3.86
0.52
1.63
2.63
0.27
2.56
5.22
0.43
2.24
1.79
3.34
2.23
2.05
1.01
0.60
2.24
2.25
3.31
3.91
1.95
0.77
1.20
0.98
0.58
-0.06
-1.00
1.78
-0.45
1.08
1.23
1.93
1.81
-0.14
1.16
0.04
0.03
0.05
0.05
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.07
1.99
1.85
1.58
1.39
1.51
1.65
1.77
1.78
1.47
1.63
1.59
1.34
1.30
1.33
1.47
1.82
1.48
1.54
1.75
1.70
1.48
1.50
1.47
1.53
1.70
1.75
1.72
1.64
1.68
1.62
2.04
2.64
2.87
**
*
*
**
**
**
*
*
*
31
Table A2.2. Brand and Time-varying Effects in Keyword Choice Model
Consumer Segment
Frequent, NR
Est. St.Err.
Intercept
Charles Schwab
CitiGroup
E-Trade
Edward Jones
Fidelity
Forex
FXCM
Legg Mason
Merrill Lynch
Morgan Stanley
Nuveen
Oppenheimer
OptionsXpress
Raymond James
Scottrade
ShareBuilder
T. Rowe Price
TD Ameritrade
Vanguard
Van Kampen
Wachovia
DJIA Changes (+)
DJIA Changes (-)
Week2 (March)
Week3 (March)
Week4 (March)
Week5 (March/April)
Week6 (April)
Week7 (April)
Week8 (April)
Week9 (April)
Week10 (April/May)
Week11 (May)
Week12 (May)
Week13 (May)
Week14 (May)
Weekday 9:00am
Weekday 10:00am
Weekday 11:00am
Weekday 12:00pm
Weekday 1:00pm
Weekday 2:00pm
Weekday 3:00pm
Weekday 4:00pm
Weekday 5:00pm
Weekday 6:00pm
Weekday 7:00pm
Weekday 8:00pm
Weekday 9:00pm
Weekday 10:00pm
Weekday 11:00pm
Weekday 1:00am
Weekend 9:00am
Weekend 10:00am
Weekend 11:00am
Weekend 12:00pm
Weekend 1:00pm
Weekend 2:00pm
Weekend 3:00pm
Weekend 4:00pm
Weekend 5:00pm
Weekend 6:00pm
Weekend 7:00pm
Weekend 8:00pm
Weekend 9:00pm
Weekend 10:00pm
Weekend 11:00pm
Weekend 12:00am
Weekend 1:00am
-6.73
1.96
3.57
1.91
0.87
3.66
-0.10
-3.36
-1.34
1.29
1.33
-1.65
0.01
0.05
-1.09
2.04
0.12
0.13
2.49
2.58
-2.46
4.13
0.05
0.01
0.02
0.08
-0.01
0.07
0.11
-0.06
0.10
0.02
0.16
0.10
-0.01
0.05
-0.13
0.55
0.47
0.34
0.26
0.29
0.28
0.25
0.23
0.47
0.38
0.38
0.36
0.15
0.28
0.11
-0.17
0.56
0.47
0.52
0.39
0.38
0.27
0.35
0.22
0.32
0.15
0.11
0.12
0.11
0.07
-0.11
0.25
0.47
0.20
0.20
0.15
0.21
0.18
0.17
0.21
0.75
0.31
0.17
0.17
0.35
0.21
0.20
0.27
0.19
0.20
0.20
0.21
0.16
0.49
0.15
0.04
0.03
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.07
0.06
0.08
0.13
0.13
0.14
0.14
0.14
0.14
0.14
0.14
0.13
0.13
0.13
0.13
0.14
0.14
0.15
0.19
0.17
0.17
0.17
0.17
0.18
0.18
0.18
0.18
0.18
0.19
0.19
0.18
0.19
0.20
0.22
0.21
0.22
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
*
**
**
*
*
*
**
**
**
**
*
**
**
**
*
*
*
Frequent, Recent
Est. St.Err.
-6.07
0.92
3.08
1.12
0.65
2.88
0.09
-3.12
-1.20
1.18
0.68
-1.75
-0.23
-0.63
-1.02
1.44
-0.27
-0.65
1.77
2.13
-2.05
3.51
0.05
0.16
-0.24
-0.13
-0.08
-0.19
0.34
-0.11
-0.09
-0.21
0.08
-0.08
-0.11
-0.16
-0.47
0.12
-0.30
-0.12
-0.17
-0.39
-0.27
-0.02
-0.22
-0.25
-0.12
-0.22
-0.14
-0.42
-0.57
-0.46
-0.33
0.18
0.27
-0.18
-0.29
-0.09
-0.19
-0.66
-0.05
-0.36
-0.06
-0.39
-0.53
-0.76
-0.09
0.08
-0.71
-0.04
0.60
1.82
0.56
2.01
0.66
1.27
0.67
1.22
0.70
0.52
0.58
0.78
1.45
0.64
1.16
1.69
0.61
1.00
1.95
0.56
0.92
0.43
0.09
0.07
0.13
0.13
0.13
0.13
0.13
0.14
0.14
0.14
0.13
0.13
0.14
0.13
0.17
0.25
0.26
0.24
0.24
0.25
0.25
0.24
0.25
0.25
0.24
0.25
0.24
0.25
0.26
0.26
0.30
0.37
0.34
0.34
0.34
0.34
0.32
0.39
0.33
0.35
0.33
0.36
0.36
0.38
0.33
0.34
0.44
0.39
**
**
*
*
*
*
**
*
**
*
**
**
*
*
Infrequent, NR
Est. St.Err.
-5.95
1.86
2.94
1.72
0.79
3.76
-1.10
-1.75
-2.15
1.94
0.77
-1.86
-0.64
-0.90
-0.29
1.26
-0.06
-0.61
2.44
2.06
-1.64
3.76
0.14
0.06
0.01
0.11
0.02
0.01
0.09
0.02
0.10
0.05
0.13
0.09
-0.03
-0.04
-0.29
0.46
0.27
0.33
0.31
0.05
0.23
0.11
0.24
0.29
0.27
0.24
0.31
0.20
0.15
0.08
0.02
0.45
0.47
0.33
0.31
0.31
0.31
0.25
0.17
0.26
0.10
0.15
0.03
-0.02
0.14
0.08
0.26
0.11
* Significant at the 95% confidence level. ** Significant at the 99% confidence level.
0.12
0.11
0.09
0.11
0.10
0.10
0.16
0.20
0.24
0.09
0.10
0.21
0.14
0.15
0.12
0.11
0.12
0.14
0.11
0.09
0.19
0.09
0.03
0.02
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.04
0.05
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.09
0.09
0.09
0.09
0.09
0.09
0.10
0.10
0.13
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.13
0.12
0.13
0.13
0.13
0.13
0.13
0.15
0.15
0.18
**
**
**
**
**
**
**
**
**
**
**
**
**
**
*
**
**
**
**
**
**
**
*
**
*
*
**
*
**
**
**
**
**
*
*
**
**
**
**
*
**
**
**
**
**
**
*
*
Infrequent, Recent
Est. St.Err.
-6.44
1.60
3.00
1.17
1.06
3.33
-0.57
-1.82
-2.35
1.48
0.75
-0.87
-0.56
-1.19
-0.74
1.00
0.02
-0.64
1.99
2.00
-6.81
3.57
-0.04
0.01
0.09
0.21
-0.01
0.10
0.01
0.04
0.07
0.11
0.31
0.16
0.07
-0.14
-0.30
0.30
0.53
0.63
0.71
0.38
0.34
0.37
0.30
0.23
0.21
-0.06
0.39
0.42
0.12
-0.01
0.30
0.79
0.64
0.30
0.59
0.46
0.36
-0.01
0.43
0.37
-0.14
-0.07
-0.29
0.23
-0.09
0.11
-0.46
-0.16
0.35
0.31
0.27
0.34
0.30
0.28
0.40
0.63
0.79
0.28
0.30
0.44
0.40
0.49
0.42
0.32
0.35
0.41
0.33
0.27
7.03
0.26
0.09
0.08
0.12
0.12
0.13
0.13
0.14
0.14
0.14
0.14
0.13
0.13
0.14
0.14
0.17
0.30
0.27
0.26
0.26
0.26
0.26
0.26
0.26
0.27
0.27
0.27
0.26
0.26
0.26
0.28
0.32
0.36
0.34
0.35
0.32
0.32
0.34
0.36
0.34
0.35
0.39
0.39
0.39
0.34
0.37
0.37
0.46
0.50
**
**
**
**
**
**
**
**
**
*
*
*
**
**
**
**
*
*
**
*
32
Table A2.3. Brand and Time-varying Effects in Click Behavior Model
Consumer Segment
Frequent, NR
Est. St.Err.
(Intercept)
Charles Schwab
CitiGroup
E-Trade
Edward Jones
Fidelity
Forex
Merrill Lynch
Morgan Stanley
Oppenheimer
OptionsXpress
1.10
1.92
0.16
1.88
0.77
1.42
-0.02
-1.64
0.79
0.98
1.98
Raymond James
Scottrade
ShareBuilder
T. Rowe Price
TD Ameritrade
Vanguard
Wachovia
DJIA Changes (+)
DJIA Changes (-)
Week2 (March)
Week3 (March)
Week4 (March)
Week5 (March/April)
Week6 (April)
Week7 (April)
Week8 (April)
Week9 (April)
Week10 (April/May)
Week11 (May)
Week12 (May)
Week13 (May)
Week14 (May)
Weekday 9:00am
Weekday 10:00am
Weekday 11:00am
Weekday 12:00pm
Weekday 1:00pm
Weekday 2:00pm
Weekday 3:00pm
Weekday 4:00pm
Weekday 5:00pm
Weekday 6:00pm
Weekday 7:00pm
Weekday 8:00pm
Weekday 9:00pm
Weekday 10:00pm
Weekday 11:00pm
Weekday 1:00am
Weekend 9:00am
Weekend 10:00am
Weekend 11:00am
Weekend 12:00pm
Weekend 1:00pm
Weekend 2:00pm
Weekend 3:00pm
Weekend 4:00pm
Weekend 5:00pm
Weekend 6:00pm
Weekend 7:00pm
Weekend 8:00pm
Weekend 9:00pm
Weekend 10:00pm
Weekend 11:00pm
Weekend 12:00am
Weekend 1:00am
--1
-0.19
-2.08
0.50
1.99
-1.87
-0.16
0.35
0.24
-0.15
0.00
-0.08
0.18
0.20
-0.46
-0.08
-0.07
-0.08
-0.40
-0.76
-0.29
-0.34
0.13
-0.15
0.11
-0.48
-0.27
0.13
-0.17
-0.25
-0.22
-0.42
-0.60
-0.36
-0.16
-0.41
-0.24
-0.26
-0.28
0.14
0.11
-0.49
0.58
0.22
0.20
-0.14
0.02
0.19
0.17
-0.10
-0.15
0.19
0.13
-0.25
-0.14
0.33
0.31
0.22
0.34
0.30
0.25
0.34
0.25
0.28
0.34
0.58
0.30
0.35
0.33
0.33
0.23
0.22
0.08
0.06
0.11
0.11
0.11
0.11
0.12
0.12
0.12
0.12
0.11
0.11
0.12
0.11
0.15
0.28
0.28
0.29
0.28
0.28
0.29
0.28
0.28
0.28
0.28
0.28
0.28
0.29
0.28
0.31
0.41
0.33
0.36
0.36
0.34
0.39
0.40
0.36
0.38
0.38
0.42
0.41
0.38
0.38
0.41
0.49
0.45
0.43
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
*
*
*
Frequent, Recent
Est. St.Err.
0.88
1.50
0.21
2.51
0.52
1.47
--1.63
-0.60
---0.51
--2.02
-1.32
-0.44
0.15
-0.11
-0.39
0.07
-0.19
-0.39
-0.10
-0.82
-0.35
0.27
-0.19
-0.32
-0.95
-0.35
-1.06
-0.15
0.45
-0.09
0.24
-0.32
0.34
0.28
0.25
0.13
0.55
0.67
0.31
0.32
0.62
0.72
0.09
-0.06
0.43
0.25
-0.87
0.13
0.55
-0.10
0.34
0.23
-0.25
-0.14
-0.04
-0.76
-0.33
-0.73
-0.26
-0.09
0.40
0.38
0.21
0.44
0.42
0.25
*
**
**
**
0.28 **
0.32
0.36
0.40
0.23
0.24
0.16
0.11
0.23
0.24
0.22
0.23
0.21
0.23
0.24
0.27
0.23
0.24
0.24
0.23
0.30
0.42
0.49
0.40
0.41
0.43
0.41
0.42
0.43
0.43
0.41
0.44
0.41
0.43
0.46
0.50
0.58
0.62
0.57
0.61
0.63
0.65
0.55
0.71
0.58
0.64
0.58
0.63
0.59
0.60
0.51
0.51
0.79
0.64
**
**
**
**
**
Infrequent, NR
Est. St.Err.
0.48
0.00
-0.21
-0.08
-0.01
0.86
-0.96
-0.92
0.02
-0.01
0.55
0.19
0.14
0.11
0.15
0.14
0.12
0.25
0.12
0.14
0.20
0.25
*
0.48
-1.43
-1.15
0.58
-0.06
-1.35
-0.29
0.09
0.15
-0.20
-0.04
0.02
0.07
0.20
-0.32
-0.02
0.09
-0.02
-0.19
-0.42
-0.18
-0.26
0.10
-0.18
-0.37
-0.11
-0.10
-0.13
-0.07
-0.02
-0.04
-0.36
0.02
-0.09
-0.07
-0.25
-0.18
-0.26
0.07
-0.05
-0.36
-0.44
0.10
-0.07
0.06
-0.20
0.05
-0.21
-0.17
-0.19
-0.12
0.07
-0.07
0.07
-0.27
0.20
0.15
0.20
0.23
0.15
0.12
0.11
0.04
0.04
0.06
0.06
0.06
0.06
0.07
0.07
0.07
0.07
0.06
0.06
0.07
0.07
0.09
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.16
0.17
0.17
0.17
0.17
0.18
0.23
0.21
0.21
0.20
0.21
0.21
0.21
0.22
0.22
0.22
0.23
0.23
0.23
0.25
0.24
0.27
0.26
0.32
*
**
**
*
**
**
**
*
**
**
*
**
**
**
**
**
**
**
**
*
*
*
Infrequent, Recent
Est. St.Err.
-0.07
0.08
-0.09
0.51
-0.24
0.75
--0.90
-0.11
----1.04
--0.23
-0.73
-0.45
-0.20
-0.09
0.07
-0.01
0.15
0.33
0.02
-0.17
0.19
-0.02
0.17
0.14
-0.68
0.10
-0.97
-0.09
0.15
0.20
0.66
0.36
0.65
-0.12
0.52
0.83
0.23
0.29
0.31
0.38
0.51
0.75
0.33
1.06
0.96
0.86
0.61
0.76
0.29
0.37
-0.48
0.50
0.81
1.82
-0.45
1.00
1.32
1.16
1.11
0.15
0.45
0.29
0.22
0.36
0.31
0.24 **
0.27 **
0.32
0.34 **
0.32
0.25
0.22
0.14
0.12
0.20
0.19
0.20
0.20
0.21
0.22
0.22
0.22
0.20
0.21
0.21
0.22
0.26
0.54
0.47
0.45
0.44
0.45
0.45
0.45
0.46
0.46
0.45
0.48
0.45
0.45
0.46
0.51
0.53
0.62
0.54
0.58
0.53
0.56
0.54
0.67
0.55
0.63
0.63
0.78
0.68
0.64
0.67
0.68
0.92
0.87
**
*
**
**
*
* Significant at the 95% confidence level. ** Significant at the 99% confidence level.
"--" indicates the segment searched this keyword in less than 3% of sample time periods, so the brand/segment dummy was dropped.
33
Table A2.4. Error Variance and Persistence Parameters
Error Variance Parameters (σ)
Frequent, NR
Est. St.Err.
Search Behavior
Keyword Choice
Click Behavior
0.010
0.075
0.003
0.055
0.037 *
0.156
Frequent, Recent
Est. St.Err.
0.056
0.095
0.010
0.085
0.149
0.280
Infrequent, NR
Est. St.Err.
0.016
0.003
0.012
Infrequent, Recent
Est. St.Err.
0.034
0.026
0.088
0.100
0.281
0.012
0.081
0.083 **
0.279
Persistence Parameters (ρ)
Frequent, NR
Est. St.Err.
Search Behavior
Keyword Choice
Click Behavior
1.834
-0.341
0.001
11.951
0.740
39.826
Frequent, Recent
Est. St.Err.
1.617
2.967
0.010
3.170
5.173
27.627
Infrequent, NR
Est. St.Err.
1.693
2.402
0.010
Infrequent, Recent
Est. St.Err.
4.329
24.143
6.900
0.364 1.142
0.462 0.658
0.010 25.693
Table A2.5. Error Covariance Parameters across Models
Covariance Parameters (ν)
Search Behavior
Est. St.Err.
Keyword Choice
Click Behavior
0.0099
0.0100
0.8247
2.2305
Keyword Choice
Est. St.Err.
0.0099
0.7687
Table A2.6. Error Covariance Parameters across Consumer Segments
Covariance Parameters (τ)
Frequent, NR
Est. St.Err.
Frequent, Recent
Infrequent, Not Recent
Infrequent, Recent
0.0102
0.0094
0.0106
0.4573
0.3490
0.1737
Frequent, Recent
Est. St.Err.
0.0099
0.0098
0.2507
0.4517
Infrequent, NR
Est. St.Err.
0.0085
0.0884
M-F 9AM
M-F 10AM
M-F 11AM
M-F Noon
M-F 1PM
M-F 2PM
M-F 3PM
M-F 4PM
M-F 5PM
M-F 6PM
M-F 7PM
M-F 8PM
M-F 9PM
M-F 10PM
M-F 11PM
M-F 12AM
M-F 1AM
Sa-Su 9AM
Sa-Su 10AM
Sa-Su 11AM
Sa-Su Noon
Sa-Su 1PM
Sa-Su 2PM
Sa-Su 3PM
Sa-Su 4PM
Sa-Su 5PM
Sa-Su 6PM
Sa-Su 7PM
Sa-Su 8PM
Sa-Su 9PM
Sa-Su 10PM
Sa-Su 11PM
3/01/06
3/04/06
3/07/06
3/10/06
3/13/06
3/16/06
3/19/06
3/22/06
3/25/06
3/28/06
3/31/06
4/03/06
4/06/06
4/09/06
4/12/06
4/15/06
4/18/06
4/21/06
4/24/06
4/27/06
4/30/06
5/03/06
5/06/06
5/09/06
5/12/06
5/15/06
5/18/06
5/21/06
5/24/06
5/27/06
5/30/06
34
Figure 1. Average Category Search and Ad Spending by Date
5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
Aggregate Category Searches
Aggregate Category Searches
Category Advertising Expenditures ($000)
Figure 2. Aggregated Category Search and Ad Spending by Day/Hour
250
200
150
100
50
0
Category Advertising Expenditures ($000)
Figure A1.1. Query Mining Logical Structure
35
Preliminary
Keyword Set
Preliminary
Query Set
Revised
Query Set
Website
Filter
Preliminary Brand
Keyword List
Revised Brand
Website List
Keyword
Filter
Known Brand Names
START
Revised
Keyword
Set
Final
Query
Set
END
References
Abhishek, V. and K. Hosanagar. 2007. Keyword Generation for Search Engine Advertising
using Semantic Similarity between Terms. Proceedings of the ICEC 2007.
Ataman, M. B., H. J. van Heerde, C. F. Mela. 2010. The Long-term Effect of Marketing Strategy
on Brand Performance. Journal of Marketing Research, 47 (5), 866-882.
Athey, S. and G. Ellison. 2008. Position Auctions with Consumer Search. Unpublished
manuscript, MIT. http://econ-www.mit.edu/files/2879.
Bettman, J., C. W. Park. 1980. Effects of Prior Knowledge and Experience and Phase of the
Choice Process on Consumer Decision Processes: A Protocol Analysis. Journal of
Consumer Research, 7, 234-248.
Brucks, M. 1985. The Effects of Product Class Knowledge on Information Search Behavior.
Journal of Consumer Research, 12, 1, 1-16.
Bureau of Labor Statistics (BLS). 2011. American Time Use Survey, Table 1. http://www.bls.
gov/news.release/atus.t01.htm. Accessed March 2011.
Census Bureau. 2011. US QuickFacts. http://quickfacts.census.gov/qfd/states/00000.html.
Accessed March 2011.
Chen, Y. , G. Xue and Y. Yu. 2008. Advertising Keyword Suggestion Based on Concept
Hierarchy. Proceedings of the WSWD 2008.
comScore, Inc. 2011. comScore Releases December 2010 U.S. Search Engine Rankings.
http://www.comscore.com/Press_Events/Press_Releases/2011/1/comScore_Releases_De
cember_2010_U.S._Search_Engine_Rankings. Accessed March 2011.
Danaher, P. J., J. R. Rossiter. 2009. Comparing Perceptions of Marketing Channels. European
Journal of Marketing, forthcoming.
Edelman, B., M. Ostrovsky, M. Schwarz. 2007. Internet Advertising and the Generalized
Second-Price Auction: Selling Billions of Dollars Worth of Keywords. American
Economic Review, 97(1), 242-259.
Fuxman, A., P. Tsaparas, K. Achan and R. Agrawal. Using the wisdom of the crowds for
keyword generation. Proceedings of the WWW 2008.
Goldfarb, A., C. E. Tucker. 2010. Search Engine Advertising: Channel Substitution when Pricing
Ads to Context. http://ssrn.com/abstract=1021451
36
Guo, C. 2001. A Review on Consumer External Search: Amount and Determinants. Journal of
Business and Psychology, =5, 3, 505-519.
IAB. 2009. Internet Advertising Revenue Report. http://www.iab.net/media/file/IAB-AdRevenue-Full-Year-2009.pdf
iProspect. 2007. Offline Channel Influence on Online Search Behavior Study. http://www.
iprospect.com/premiumPDFs/researchstudy_2007_offlinechannelinfluence.pdf. Accessed
March 2011.
Janiszewski, C. 1993. Preattentive Mere Exposure Effects. Journal of Consumer Research, 20, 3,
376-392.
Katona, Z., M. Sarvary. 2010. The Race for Sponsored Links: Bidding Patterns for Search
Advertising. Marketing Science, 29 (2), 199-215.
Keane, M. P., K. I. Wolpin. 2007. Exploring the Usefulness of a Nonrandom Holdout Sample for
Model Validation: Welfare Effects on Female Behavior, International Economic Review,
48(4), 1351-1378.
Kim, A., S. Balachander. 2010. Coordinating Traditional Media Advertising and Search
Advertising. Unpublished manuscript, Purdue University.
Lee, E. 2011. Among Media, TV Is Still on Top. Advertising http://adage.com/article/
mediaworks/media-tv-top-ad-dollars-viewers/149613/. Accessed March 2011.
Lin, C., S. Venkataraman, S. D. Jap. 2010. Media Multiplexing Behavior: Implications for
Targeting and Media Planning. Unpublished manuscript, Emory University.
Lodish, L. M., M. Abraham, S. Kalmenson, J. Livelsberger, B. Lubetkin, B. Richardson, M. E.
Stevens. 1995. How TV Advertising Works: A Meta-Analysis of 389 Real World Split
Cable TV Advertising Experiments. Journal of Marketing Research, 32 (2), 125-139.
Mayzlin, D., J. Shin. 2010. Uninformative Advertising as an Invitation to Search. Unpublished
manuscript, Yale University.
McFadden, D. 1974. The Measurement of Urban Travel Demand. Journal of Public Economics,
3, 303-328.
Moorman, C., K. Diehl, D. Brinberg, B. Kidwell. 2004. Subjective Knowledge, Search
Locations, and Consumer Choice. Journal of Consumer Research, 31, 3, 673-680.
Naik, P. A., K. Raman. 2003. Understanding the Impact of Synergy in Multimedia
Communications. Journal of Marketing Research, 13 (4), 375-388.
Newcomb, K. 2007. Report: Offline Ads are Heavy Drivers of Search. ClickZ, August 20.
http://www.clickz.com/clickz/news/1707177/report-offline-ads-are-heavy-drivers-search
Newman, J. W., R. Staelin. 1973. Information Sources for Durable Goods. Journal of
Advertising Research, 13, 19-29.
Nielsen. 2011. State of the Media 2010: Audiences & Devices. http://blog.nielsen.com/
nielsenwire/wp-content/uploads/2011/01/nielsen-media-fact-sheet-jan-11.pdf. Accessed
March 2011.
Nielsen. 2010. Three Screens Report. http://en-us.nielsen.com/content/nielsen/en_us/
measurement/ a2m2_three_screens.html
Park, C. W., D. L. Mothersbaugh, L. Feick. 1994. Consumer Knowledge Assessment. Journal of
Consumer Research, 21, 1, 71-82.
Pass, G., A. Chowdhury, C. Torgeson. 2006. A Picture of Search. Proc. of the First International
Conference on Scalable Information Systems, June, Hong Kong.
Punj, G. N., R. Staelin. 1983. A Model of Consumer Information Search Behavior for New
Automobiles. Journal of Consumer Research, 9, 4, 366-380.
37
Radecki, C. M., J. Jaccard. 1995. Perceptions of Knowledge, Actual Knowledge and Information
Search Behavior. Journal of Experimental Social Psychology, 31, 2, 107-138.
Rutz, O., R. Bucklin. 2010. From Generic to Branded: A Model of Spillover in Paid Search
Advertising. Journal of Marketing Research, forthcoming.
Rutz, O., M. Trusov (2010), ―Zooming In on Paid Search Ads – An Individual-level Model
Calibrated on Aggregated Data,‖ Unpublished manuscript, Yale University.
Shapiro, S. 1999. When an Ad’s Influence is beyond Our Conscious Control: Perceptual and
Conceptual Fluency Effects Caused by Incidental Ad Exposure. Journal of Consumer
Research, 26, 1, 16-36.
Shapiro, S., D. J. MacInnis, S. E. Heckler. 1997. The Effects of Incidental Ad Exposure on the
Formation of Consideration Sets. Journal of Consumer Research, 24, 1, 94-104.
Shin,W. 2009. The Company that You Keep: When to Buy a Competitor’s Keyword.
Unpublished manuscript, Duke University.
Swasy, J. L., A. J. Rethans. 1986. Knowledge Effects on Curiosity and New Product
Advertising. Journal of Advertising, 15, 4, 28-34.
Tellis, G. J. 2004. Effective Advertising: Understanding When, How and Why Advertising
Works. Thousand Oaks: Sage Publications.
Varian, H. 2007. Position Auctions. International Journal of Industrial Organization, 25(6),
1163-1178.
Wilbur, K. C. 2008. A Two-Sided, Empirical Model of Television Advertising and Viewing
Markets. Marketing Science, 27 (3), 356-378.
Wood, S. L., J. G. Lynch. 2002. Prior Knowledge and Complacency in New Product Learning.
Journal of Consumer Research, 29, 3, 416-426.
Yang, S., A. Ghose. 2010. Analyzing the Relationship Between Organic and Sponsored Search
Advertising: Positive, Negative, or Zero Interdependence? Marketing Science,
forthcoming.
Yao, S., C. F. Mela. 2009. A Dynamic Model of Sponsored Search Advertising. Unpublished
manuscript, Duke University.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1285775.
Zentner, A. 2010. The Effect of the Internet on Advertising Expenditures: an Empirical Analysis
Using a Panel of Countries. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1792789.