J. Serv. Sci. & Management, 2008,1: 233-243
Published Online December 2008 in SciRes (www.SciRP.org/journal/jssm)
1
Combining Keyword Search Advertisement and Site-
Targeted Advertisement in Search Engine Advertising
Li Chen
Department of Operations and Information Management, School of Business, University of Connecticut
Received June 3
rd
, 2008; revised November 17
th
, 2008; accepted November 27
th
, 2008.
ABSTRACT
Internet advertising has seen a strong growth in recent years and search engine advertising has played an important
role in that growth. Search engines continue to expand their business by providing new options of advertising. For ex-
ample, Google provides a new advertisement mechanism based on cost-per-thousand-impression (CPM) payment
called “site-targeted advertisement” in addition to the famous “keyword search advertisement” based on cost-per-click
(CPC). While keyword search advertisement is a cost-efficient way of advertising, site-targeted advertisement provides
a quicker alternative to expose the ad to a mass population at a higher expense. This paper studies a mixed strategy of
optimization by combining these mechanisms to exploit their corresponding advantages. We set up a general model to
find optimal starting and ending times for both methods. Closed-form solutions are calculated for two applications: (1)
Advertising of perishable information with click-based revenue only; and (2) Site-targeted advertisement first and key-
word search advertisement last. Comparative static analysis provides an analysis of properties of each application. We
also develop a computational experiment based on Google AdWords to illustrate the application of the model.
Keywords: online advertising, search engine, keyword auction, cost per click
1. Introduction
An increasingly popular approach for firms to develop
e-commerce is to advertise on search engines such as
Google, Yahoo, and MSN. For most of people using
Internet, search engine websites are a “must-see” when
surfing online. A survey from iProspect shows that 56
percent of respondents used search engines at least once a
day [1]. Although search engine is a relatively new me-
dium for advertising compared with newspaper, TV and
radio, search engines provide potential buyers and sellers
a worldwide and 24-7 access to each other. Search en-
gine advertising has seen a solid and continuous growth
in recent years. During the second quarter of 2008, search
engine advertising witnessed an increase of 24 percent
from the second quarter of 2007 to reach $2.5 billion,
around 44 percent of total US online advertising spending
[2]. The search engine advertising market can be seen as
a duopoly. According to comScore.com, Google took 57
percent of the market in September 2008 and Yahoo took
23.7 percent [3].
In this paper, we base our research on advertising
mechanism of Google AdWords, the advertising program
of the largest player in this market. Currently Google
AdWords provides two types of advertisements: keyword
search advertisement and site-targeted advertisement [4].
Keyword search advertisement (hereinafter as “keyword-
ad”) refers to advertisements that appear side by side with
search results on the Google web pages. Advertisers who
are interested in putting this type of ad on Google web
pages need to participate in a keyword-ad campaign and
win ad slots (see a simplified process in Figure 1).
For each ad campaign, Google adopts a modified sec-
ond-price auction mechanism to allocate the ad slots. In
ranking the advertisements, Google not only considers
the bid amount but also the quality of the advertisements.
Under this auction mechanism, an advertiser with a
higher ranking wins an ad slot but only needs to pay the
necessary amount to rank over the advertiser with the
next highest ranking. Since keyword-ad is based on a
cost-per-click (CPC) payment mechanism, an advertiser
only needs to pay for every click on the sponsored link. If
no click ever occurs, no payment will be charged. (See
Figure 2 for an example of keyword-ad).
Google AdWords began Site-targeted advertisement
(hereinafter “site-ad”) in April 2005, allowing advertisers
to choose individual sites in the Google network where
they would like their ads to appear. (see Figure 3 for an
example).
Register as an
AdWords member Create an ad to
attract clicks
Review and save
the ad
Choose keywords
or web sites
Set limits like
daily budget
Figure 1. Process flow of ad campaign setup

234 Li Chen
Before the site-ad mechanism was introduced, the only
way to advertise through Google was through CPC-based
keyword-ad auction. This method was simple and easy to
apply, but it had two drawbacks: (1) only visitors who
search information through Google had a chance to see
the ad and click on it; (2) click fraud may increase the
advertising cost [4]. After the introduction of site-ads,
Google expected to provide AdWords users with more
choices of location of their ads and increase Google’s
revenues at the same time. With site-ads available, adver-
tisers can be seen more widely over the Internet. The al-
location of ad slots in a site-ads auction also uses the
modified second-price auction mechanism. However,
unlike a keyword auction, advertisers are charged based
on a cost-per-thousand- impressions (CPM) payment
method rather than cost-per-click (CPC) method. Each
time an ad is displayed on a web page, the advertiser will
be charged [5].
With the presence of site-ads, advertisers can realize
their revenues through CPC-based keyword-ads, CPM-
based site-ads, or both. Given these choices, our goal in
this paper is to study whether a mixed strategy of com-
bining the two ad mechanisms will help advertisers in-
crease their revenues. We develop a general budget- con-
strained, nonlinear optimization model to maximize ad-
vertisers’ revenues using this “mixed strategy”. In the
model, we determine starting and ending points for the
time intervals during which keyword-ad and site-ad ad-
vertising campaigns should hold. These intervals might
overlap. After formulating the optimality conditions of
the model, we concentrate on the study of two particular
scenarios: (1) advertising of perishable information with
click-based revenue only; and (2) site-ads first and key-
word-ads last.
Figure 2. Example of Google AdWords keyword search ads
Figure 3. Example of google adwords site-targeted ads (New York Times website)
Keyword
-
ad
Site
-
ad

Combining Keyword Search Advertisement and Site-Targeted Advertisement in Search Engine Advertising 235
In the first scenario, we assume that the advertiser only
makes revenues from people visiting the advertiser’s web
site, which is generated by clicks. Also, we assume that
the content on the web site is perishable and its value de-
creases over time. The scenario is typical of newspaper
websites, where just looking at the ads linking to a news
story page does not generate revenues for the newspaper.
In the second scenario, we assume that the advertiser
starts using site-ads and then switches to keyword-ads
without overlapping. This situation applies to advertising
of new products or services where the advertiser wants to
aggressively expose her ads to a large population of po-
tential customers, even though it might be more expen-
sive.
We find closed-form solutions for both scenarios, and
then we perform comparative static analysis to study how
the changing model parameters affect the solution. Based
on this analysis, we provide some managerial insights
under two conditions: non-binding budget and binding
budget. Under the condition of non-binding budget, for a
manager facing a high content depreciation rate and low
number of visits and click-through rate, it would be a bet-
ter strategy to open new advertising campaigns and com-
pete for more popular keyword-ads or site-ads on a more
popular web site or both.
Under the condition of binding budget, for the first
scenario, advertisers who see a higher depreciation rate, a
lower number of visits of both types of ad, or a lower
click-through rate of both types of ads would have to ex-
tend the keyword-ad duration. For the second scenario,
we find that advertisers who see a higher number of visits
on site-ad would have to move the switch time earlier,
and they would have to delay the switch time while seeing
a lower number of visits of site-ad.
The remainder of this paper proceeds as follows. Sec-
tion 2 provides a review of recent literature in related ar-
eas. Section 3 discusses the general model for the mixed
strategy of combining both types of ad mechanisms. Sec-
tion 4 details the analysis of the two scenarios discussed
above and offers numerical examples. Section 5 contains
the conclusion. In the Appendix, we present the optimal-
ity conditions for the general model.
2. Literature Review
Search engine advertising has become a hot topic and
attracted significant research interests [4,6,7,10,14,15,16].
There are multiple streams of related literature studying
search engine advertising. In particular, Jansen and
Mullen [6] provided an extensive review discussing issues
such as auction properties, competitive landscape, how to
rank ads, how to set slot prices, payment mechanisms, etc.
Even though they considered three types of participants:
content providers, search engines and searchers; we only
consider the point of view of the content providers who
want to optimize their revenues subject to a budget con-
straint. We review the following areas that are more re-
lated to our research.
2.1. Advertising Allocation Mechanisms
In search engine advertising, auction is the major mecha-
nism to allocate ad slots. Feng and Bhargava [7] used
simulation to study four allocation mechanisms of ad slots,
including those used by Overture (Yahoo) and Google
AdWords. They found that performance of mechanisms
used by Yahoo and Google is better under certain scenario,
and their performance also depended on the degree of
correlation between providers’ willingness to pay and
relevance. Edelman et al. [8] focused on “generalized
second-price” (GSP) auction, in which the bidder who
wins an ad slot only needs to pay the next highest bidders’
price plus an increment. GSP is attractive to search engines
because it helps to maximize profit. In addition, Feng et al. [9]
developed a simultaneous pooled auction (SPA) mechanism
and showed that using reserve price in SPA significantly in-
creased a search engine’s revenue.
2.2. Payment Mechanism
There are multiple payment methods in search engine
advertising, such as cost-per-click, cost-per- thousand-
impression (CPM) and cost-per-action [10]. While CPM
comes from traditional print media, cost-per-click and
cost-per-action are based on the search engines’ and ad-
vertisers’ measurement. Hu [11] applied the economic
theory of incentive contracts to show that perform-
ance-based pricing models improve effectiveness of ad-
vertising campaigns. After investigating the implementa-
tion of paid placement strategies, Weber and Zheng [12]
found that revenue-maximizing search engines ranked ads
on a weighted average of relative performance and bid
amount. Kumar et al. [13] studied an interesting problem
of the optimal advertising schedule in ads slots of the web
sites based on a hybrid pricing model.
2.3. Bidder Strategies
Advertisers need to determine how to respond to competi-
tion in auctions. One common observation is cycle bidding,
where bidders revise their bids to compete for ad slots
(Edelman and Ostrovsky 2007 [14] and Zhang and Feng
[15]). One possible explanation is gap jamming, which
refers to the behavior of bidders’ raising bids to some point
just below competitors’ bids. When gap jamming is pre-
sent, competitors will be charged for the highest possible
amount. Another important strategy for bidders is how to
allocate their funds across advertising campaigns such as
keyword-ad campaigns. Özlük and Cholette [16] suggested a
model for advertisers who have a fixed daily budget limit to select
keywords to maximize productivity and then determine the bid
for each keyword selected.
2.4. Optimization in Internet Advertising
Internet advertising also faces optimization problems. For
example, Dewan et al. [17] found a tradeoff between ads
and content for web sites: more ads generate more revenue
but may turn viewers off. Their findings suggest that
websites put fewer ads and more content, and get com-
pensated for by future profits. Another example comes
from Fruchter and Dou [18], which studied how to dy-

236 Li Chen
namically assign budget of banner ads between the two
types of portals (generic vs. specialized).
Our work is different from previous research in that we
consider a mixed keyword-ad and site-ad strategy, we only
consider the content provider’s point of view, we provide a
new budget-constrained, nonlinear optimization model to
maximize advertisers’ revenues, and we study two par-
ticular scenarios that have not been considered before:
advertising of perishable information with click-based
revenue only, and site-ad first and keyword-ad last.
3. General Model
In this paper, we develop an optimization model of a
mixed strategy which combines the keyword-ad and
site-ad mechanisms to help advertisers maximize their
revenue. Keyword-ad is a cost-efficient advertising strat-
egy because advertisers are only charged for clicks on
their ads. Therefore, we regard keyword-ad as a “waiting
strategy” because only visitors with relatively strong in-
terest will search information through search engines and
advertisers have no control of the number of search re-
quests and clicks based on these visits. For site-ad, we
regard it as a “showing strategy” because after putting an
ad on a targeted website, viewers of the website are ex-
posed to that site-ad even if they do not plan to search that
information. Compared with keyword-ad, site-ad is a
more aggressive approach in terms of generating a large
number of impressions in a short time, but the related
expense is usually also higher.
In our model, we assume that there is fixed time period
of length
T
during which the decision maker will decide
the starting and ending times of each type of ad mecha-
nisms. We denote by
1
y
and
3
y
the starting times of
keyword-ad and site-ad respectively; and by
2
y
and
4
y
the ending times of keyword-ad and site-ad, respectively.
Notice that
1 3
0
yy T
≤ ≤≤
,
2 4
0
yy T
≤≤≤
, and the two
intervals may overlap. We denote the time vector of deci-
sion variables by Y= (y
1
,y
2
,y
3
,y
4
).
Our model uses a set of exogenous smooth functions as
described in Table 1. The idea is to use these functions to
capture the behavior of the searchers as well as the pay-
ment mechanism of the advertiser. We use subscript “1”
to refer to CPC-based keyword-ads and “2” to refer to
CPM-based site-ads. Consistent with previous work [7],
we assume that click-through rate only depends on the
location of ad slot on the web page. Also we assume that
the click-through rate of keyword-ad is higher than that of
site-ad. This is because people who search for the infor-
mation are more likely to click on the ad. Although there
maybe a higher number of clicks generated from site-ads,
the click-through rate may not be as high as that of key-
word-ad. For example, if 100 people search information
of “2008 Olympics”, and there are 30 people click on the
keyword-ad, then the click-through rate is 30 percent. At
the same time, there maybe 1000 people see the site-ad of
Table 1. Notation used in the general model
Ad Type
Visits in time
period t Payment per ad in time period t
Click-through rate
in time period t
Keyword
1
( )
S t
P
1
(t)
1
( )
l t
Site
2
( )
S t
P
2
(t)
2
( )
l t
the same content but only 100 people click the ad. Al-
though the site-ad generates more clicks (100＞ 30), the
click-through rate is only 10 percent, smaller than that of
keyword-ad (30 percent).
Our goal is to maximize advertiser’s total revenue
( )
R Y
through the period for a given choice of starting
and ending times
12 3 4
( ,,,)
Yy yyy
= . In addition, we
assume that the advertiser is subject to a budget limit
B
,
that is, the advertiser cannot spend more than
B
through
the whole period.
We compute total revenue
( )
R Y
as follows:
2 4
11 122
1 3
()( )( )( )( )
y y
y y
RYrStltdtStltdt
 
= +
 
 
∫ ∫
2 4
2 12
1 3
( )( )
y y
y y
rSt dtStdt
 
+ +
 
 
∫ ∫
In the definition of total revenue,
1
( )
S t
denotes the
number of visits on the keyword-ad in time period t and
2
( )
S t
denotes to the number of visits on site-ad in time
period t.
1 1
( )()
St lt
denotes the number of clicks gener-
ated from keyword-ad in time period
t
, and
2 2
( )( )
Stlt
denotes to the number of clicks generated from site-ad in
time period
t
. We take the integral to calculate the total
number of clicks and total number of visits during the
whole ad campaign duration. Finally, we multiply by r1
and r2 the integrals, respectively, to obtain the total reve-
nue.
To account for the budget constraint, we use the expression:
2 4
1 1122
1 3
( )()( )( )( )
y y
y y
Stltpt dtStpt dt
+
∫ ∫
where
1 11
( )( )( )
St ltpt
denotes the cost of keyword-ad
and
2 2
( )( )
St pt
denotes the cost of site-ad. As before, the
integrals are used to calculate the total amount of payment.
The resulting model is the following:
2 4
11 122
1 3
()( )( )( )( )
y y
Yy y
MaxRYrSt ltdtSt ltdt
 
= +
 
 
∫ ∫
2 4
2 12
1 3
( )( )
y y
y y
rSt dtSt dt
 
+ +
 
 
∫ ∫
(1)
subject to:
0, 1,...,4
i
y Ti≤ ≤= (2)
2 4
1 1122
1 3
( )()( )( )( )
y y
y y
Stlt ptdtSt ptdtB
+ ≤
∫ ∫
(3)

Combining Keyword Search Advertisement and Site-Targeted Advertisement in Search Engine Advertising 237
JSSM
Constraint (2) implies that the time points should be
nonnegative and do not exceed the length of the decision
period; and constraint (3) is the firm’s budget constraint
T
.
We solve this nonlinear optimization problem by using
Lagrangian multipliers, Karush-Kuhn-Tucker conditions
and Leibniz rules (see the details in Appendix).
4. Two Specific Applications
In this section, we discuss the application of the general
model on two specific applications of search engine ad-
vertising: (1) Advertising of perishable information with
click-based revenue only; and (2) Site-targeted ad first
and keyword search ad last.
4.1. Advertising of Perishable Information with
Click-Based Revenue Only
For the first application, advertisers are advertising per-
ishable information and their revenue comes only from
clicks generated. This scenario is common among online
content providers such as online newspaper websites who
have a strong incentive to attract visitors to their websites.
According to the Newspaper Association of America
(NAA) (www.naa.org), the audience of online newspaper
websites reaches 3.6 million per month in 2007 [19]. A
new method of attracting visitors to online newspaper
websites is to advertise latest news stories through search
engines such as Google. For example, when there is a
breaking news story such as “Powell endorses Obama”,
online newspapers such as New York Times put keyword-
ads on Google that might lead to their latest news story
online (see Figure 4). However, news stories, like sea-
sonal products or fashion goods, depreciate in value fairly
quickly after people hear enough of them and lose interest.
Therefore, online content providers such as online news-
paper are willing to maximize the influence of a news
story before the “news story” loses value to audience.
Figure 4(a). Example of New York Times’ ad on Google
Figure 4(b). New York Time’s news story linked by the keyword-ad in Figure 4(a)

238 Li Chen
JSSM
As mentioned earlier, it is difficult for a keyword-ad
strategy to rapidly generate the expected level of attention
because it is a “waiting strategy” where advertisers have
to wait people viewing their ads and clicks on them. In
this scenario, a mixed strategy that combines site-ad with
keyword-ad may be a better alternative. The rationale is
that visitors of the targeted site are exposed to the ad of
the news story. These visitors might not know the news or
they know but have not an interest strong enough to go to
Google.com to search “Powell endorses Obama”. How-
ever, they are likely to click the ad linked to the online
newspaper to read the story. In other words, such a strat-
egy is more likely to impress potential visitors whose
search cost of news is relatively high but are willing to
read the news when a link is in front of them.
Under this scenario, our main concern is to find the op-
timal ending time of both types of ads to maximize of the
advertisers’ revenue given a budget constraint.
4.1.1. Modified Mathematical Model
We make two modifications on the general model from
Section 3 to meet requirements of this specific application:
(1) we assume that advertisers want to begin both key-
word-ad and site-ad at the beginning of the advertising
campaign. Thus, we set the starting time of both key-
word-ad and site-ad to zero, which implies that both types
of ad will be adopted from the beginning; and (2) we as-
sume that the advertisers in this specific application are
interested only in the number of the clicks on their web-
site. Impressions of both keyword-ad and site-ad on
viewers will not bring value to the advertisers. For that
reason, we only consider the first expression in the defini-
tion of
( )
R Z
in (1).
We use z
1
and z
2
to denote the ending time of keyword-
ad and site-ad respectively, and
1 2
( ,)
Zz z
= to denote
the corresponding decision vector.
The general model is modified as follows:
1 2
11 122
0 0
(){()()( )( )}
z z
MaxRZrSt lt dtSt ltdt
= +
∫ ∫
(4)
subject to:
1 2
0 ,
z zT
≤ ≤
(5)
1 2
11 122
0 0
( )( )( )
z z
pSt lt dtpStdtB
+ ≤
∫ ∫
(6)
We assume that
1
( )
S t
decreases exponentially with a de-
preciation rate
θ
so that
1 1
( )
t
Stse
θ
−
= , where
1
s
denotes
to the number of initial visits. A higher value of
θ
indicates a
fast speed people lose interest on the information.
We also assume
2
( )
S t
is fairly stable in terms of the number
of visits per unit of time so we can use average number of
impressions
2
S
as an approximation. As before, the
click-through rate is assumed to be relatively stable at
1
l
and
2
l
during the whole advertising period.
In this study, we only consider situations under which
both
1
z
∗
and
2
z
∗
are positive. Using Karush-Kuhn-
Tucker condition, we get closed forms for three optimal
solutions
* *
1 2
( ,)
z z
with economic meaning.
a)
* *
1 2
zz T
= =
In this case, the optimal ending time
for both types of ads is the end of the advertising period,
which implies that the budget constraint is not binding.
b)
1
z T
∗
=
and
211 122
1
{(1)} /
T
z Be
ps lps
θ
θ
∗− −
= −− .
In this case, the best choice is to stop the site-ad before
the end of the advertising period, and let keyword-ad con-
tinue to the end.
c)
2 2
11 11
( )
1
log{1 }
Bps T
zps l
θ
θ
−
∗ −
= −− and
*
2
z T
=
.
In this case, the best choice is to stop keyword-ad be-
fore the end of the advertising period and let site-ads con-
tinue to the end.
4.1.2. Comparative Static Analysis
We investigate the impact of changes of parameter values
under two conditions: whether budget constraint is bind-
ing or not. The rationale to discuss the scenario of
non-binding budget is that for large online content pro-
viders such as New York Times, their interests are proba-
bly not saving money but fully utilizing the budget to
generate attention and clicks, especially when a news
story is still of interesting to public.
(1) The budget constraint is not binding
Let
1
11 122
(1 )
T
BBeps lpsT
θ
θ
− −
= −−−
%
denotes re-
maining budget. As mentioned above, we are interested to
see how changes of parameters will influence advertisers’
revenue and remaining budget. Comparative analysis on
parameters can shed lights on what decision to make (see
Table 2).
We see that an increase in on
θ
leads to less revenue
and a higher remaining budget, while an increase in
1
l
,
1
S
and
2
S
leads to more revenue and less remaining
budget. The result is intuitive because an increase in
θ
implies that public’s interest on the online content such as
a news story depreciates faster, and fewer clicks are gen-
erated. On the other hand, increases in
1
l
,
1
S
and
2
S
imply that either more people are interested in the news
story or more people are exposed to the site-ad or people
who see the keyword-ad are more likely to click on the ad
and observe the information linked to the ad. Obviously,
all these changes will lead to more revenue and less re-
maining budget.
Using these results, a manager can determine which
strategy to choose. For example, in case of a high depre-
ciation rate and low number of visits and click-through rate,
advertisers should bid aggressively so as to open new advertising
Table 2. Comparative static analysis under non-binding
budget
Effects of an increase in
Variable
of interest
θ
1
s
2
s
1
p
,
2
p
1
l
2
l
R
- + + N/A + +
B
%
+ - - - - N/A

Combining Keyword Search Advertisement and Site-Targeted Advertisement in Search Engine Advertising 239
JSSM
campaigns and compete for more popular keyword-ads or
site-ads on a more popular website or both.
(2) The budget constraint is binding
In this scenario, advertisers have consumed all their
advertising budget resource before the end of advertising
period (case (b) and case (c)). The difference is key-
word-ad stops before the end of advertising period in case
(c) and site-ad stops before the end of advertising period
in case (b). Advertisers’ interest here is what changing
model parameters’ influence will be. Table 3 shows the
effect of parameters change on revenue, and optimal ending
time of
1
z
∗
in case (a) and
2
z
∗
in case (b).
Table 3. Comparative static analysis under binding budget
Effects of an increase in parameters
Variable of
interest
θ
1
s
2
s
1
p
,
2
p
1
l
2
l
R
- +
+ N/A + +
1
z
∗
in case (a)
+ - - - - N/A
2
z
∗
in case (b)
+ - - - - N/A
We find that under this scenario an increase in
θ
,
1
l
,
1
S
and
2
S
have the same impact on revenue as in the
non-binding budget case. As for the impact on duration of
both types of ad, our analysis yielded two interesting
findings: (1) lower revenue may happen after a longer
advertising period. This result seems counter-intuitive.
However, the rationale here is that if people lose interest
on the news story faster, then keyword-ads fail to generate
the expected number of clicks. However, since advertisers
are charged based on clicks, the budget is consumed
slower, which leads to a longer advertising period; (2)
The click-through rate of site-ad has no certain impact on
the duration. This is because site-ad is charged based on
impression. Whether click-though rate is high or not will
not influence the speed advertising budget is consumed.
The managerial insight here is that advertisers who see
a higher depreciation rate, a lower number of visits of
both types of ad, or a lower click-through rate of both
types of ads would have to extend keyword-ad duration.
On the other side, advertisers who see a lower deprecia-
tion rate, a higher number of visits, or a higher
click-through rate would have to extend site-ad duration.
Figure 5. Example of combining keyword-ad and site-ad

240 Li Chen
JSSM
We have observed real examples of using both types of
ads (see Figure 5). However, it will be easier to show the
implication of our model using real data from advertising
campaigns.
To illustrate our model to advertisers who want to ap-
ply the “mixed strategy”, we use a numerical experiment
to explain the general solution methodology. We put an
ad of “Enjoy everyday in Shanghai
1
” which links to the
top news story on entertainment in Shanghai to an online
version of a local newspaper www.shanghaistar.com. In
order to maximize the value of the news story, we com-
bine keyword-ad and site-ad campaign through Google
AdWords. Following the model, we begin both types of
ads at the same time and our purpose is to obtain the op-
timal stop time for both advertising strategies.
Data are collected from Jan 9
th
, to Feb 22
nd
, 2006 from
Google AdWords records of both ads. Before we apply
the model, we use the data to validate our exponential
decay assumption of the keyword-ad visit S
1
(t) and site-ad
visit S
2
(t) functions. Results of MS Excel to estimate S
1
(t)
using the best-fit exponential curve (red curve in Figure 6)
and estimate S
2
(t) using average (red curve in Figure 7) fit
our assumptions well. (See Figure 6 and Figure 7).
Then we used the detailed information about both
keyword-ad and site-ad to calculate values of parameters
in our model (see Table 4).
Finally, we set time period
100
T
=
days and the
budget limit
$40
B
=
. Advertisers who want to apply our
model just need to set their own numerical values of these
exogenous variables. The optimal solution is
*
1
100
y=,
*
2
76.32
y= and the estimated number of clicks is 44.
Thus, the optimal decision for advertisers is to hold the
keyword-ad to the end of the advertising period, but end
the site-ad around two and half months.
4.2. Site-Targeted Advertisement First, Keyword
Search Advertisement Last
For second specific application, advertisers want to start
their advertising campaign by using site-ad to aggres-
sively impress the public and then switch to keyword-ad
without overlapping. This strategy can be applied to ad-
vertisement of new products and services where the ad-
vertiser wants to expose her ads to a mass population of
potential customers more quickly.
In a global economy with intense competition, firms face
Table 4. Summary of google adwords’ report
Ad Type Clicks Impression CTR (percent) CPC CPM
Keyword 9 1276 0.69 $0.05 -
Site 8 2959 0.27 - $7.66
1
Shanghai is a big city in China.
strong pressure to continuously exploit new product or
services and effectively advertising those new products
and services to potential consumers. At the beginning
period of advertising, companies are not only interested in
how many purchases have been made, but also how many
consumers are aware of the new product or services. In
other words, either a visit to the advertiser’s website or an
impression on the ad to potential customers brings bene-
fits to the company. Consumers who visit firms’ website
may immediately make a purchase, while potential con-
sumers exposed to the ad without clicking may come back
and purchase the product or service later.
Site-ad meets the requirement to wildly and quickly
impress the public in the early stage of advertising period.
Although keyword-ad is not an effective marketing tool at
the beginning because potential customers are unfamiliar
with the new product or brand, it is a cost-efficient mar-
keting method after certain level of awareness is achieved.
Therefore, we suggest that site-ad first and keyword-ad
last might be a better strategy for advertisers. Then the
question for advertisers is how to determine optimal
switching time from site-ad to keyword-ad.
4.2.1. Modified Mathematical Model
For this specific application, we assume that advertisers
will begin with site-ad only, and then switch to key-
word-ad without overlapping. To meet requirements of
this specific application, we modify the general model in
section 3 as follows: (1) we set the start time of site-ad as
zero and the ending time of keyword-ad as the end of the
whole advertising period
T
; (2) we use
z
as the
switching time of a site-ad and a keyword-ad.
The general model is modified as follows:
Max
11 122
0
(){( )( )}
T z
z
RzrSt ldtSt ldt
= +
∫ ∫
2 12
0
{( )( )}
T z
z
rSt dtSt dt
+ +
∫ ∫
(7)
subject to:
0
z T
≤ ≤
(8)
11122
0
( )( )
T z
z
pStl dtpStdtB
+ ≤
∫ ∫
(9)
Similar to the application of advertising perishable in-
formation with click-based revenue only, we estimate the
visit of site-ads using average visits
2 2
( )
S ts
=
and
steady click-through rate
1
l
and
2
l
over the advertising
period
[0, ]
z
∗
.We also assume that
1
( )()
Stsg z
′
= +,
where
( )
g z
refers to visits due to awareness of site-ad.
A linear function ()
g zazb
= +
is used to estimate the
value of
1
( )
S t
. The trade-off in this model is that longer
site-ads duration leads to a higher number of impressions
at the beginning of keyword-ad, but runs out the budget
more rapidly at the same time.
We get two optimal solutions for switching time
z
∗
.

Combining Keyword Search Advertisement and Site-Targeted Advertisement in Search Engine Advertising 241
JSSM
Figure 6. Summary of keyword-ad impression Figure 7. Summary of site-ad impression
a)
221 1
1 1
( )
2
spplbsaT
zap l
∗
′
−+−− ∆
=
b)
221 1
1 1
( )
2
spplbsaT
zap l
∗
′
−+−+ ∆
=
where
2
1 11 1221 1
4(()) (())
ap lbsp l TBspp lbsaT
′ ′ ∆ =+−+−+− .
In both cases, advertisers switch from site-ads towards
keyword-ads at
z
∗
.
4.2.2. Comparative Static Analysis
Similar to the case in section 4.1, we investigate the impact
of changing parameter values under two conditions:
whether the budget constraint is binding or not. We also
assume here that advertisers want to make full use of the
budget to maximize the advantages of both keyword-ad
and site-ad.
(1) The budget constraint is not binding
Let
2 2
1 122
(()()())
2
a
BBp lsbTzTzpsz
′
= −+−+−−
%
denotes remaining budget. In this scenario, advertisers fail
to use up their advertising budget on site-ad. Table 5
shows how an increase in value of parameters will influ-
ence both revenue and remaining budget.
We see from the table that only
2
s
has impact on both
revenue and remaining budget. Intuitively, when the
budget is not used up, an increase in visits of site-ad
2
s
to some value but lower than
2
/
Bp T
will consume
more advertising budget and increase revenue. Therefore,
Table 5. Comparative static analysis under non- binding
budget
Effects of an increase in
Variable of
interest
a
s
′
,
b
2
s
1
p
,
2
p
1
l
2
l
R
N/A
N/A + N/A N/A +
B
%
N/A
N/A - - N/A N/A
advertisers with advertising dollar left can transform
budget resource into revenue by selecting a more popular
web site or start new site-ads.
(2) The budget constraint is binding
Under the condition that budget constraint is binding,
advertiser is interested to see how change in value of one
parameter will influence the optimal switching time, such
as moving the optimal switching time earlier or later (see
Table 6).
We can see from Table 6 that an increase in site-ads
visits
2
s
will force the advertiser to move the optimal
switch time earlier because the expected level of aware-
ness is achieved earlier in time and advertising resources
is used more quickly. On the other hand, an increase of
value of parameters
s
′
or
b
have the opposite effect
on switch time although they have the same effect on
revenue as S
2
. However, an increase in value of parame-
ters like payment for each click p
1
and payment for each
impression p
2
has uncertain effect on switch time because
the closed-form solution is not available.
Table 6. Comparative static analysis under binding budget
Effects of an increase in parameters
Variable
of interest
a
s
′
,
b
2
s
1
p
,
2
p
1
l
2
l
R
+ + + N/A +
+
z
∗
? + - ? ? N/A
*Question mark means no close-form solution available
Table 7. Parameters used in numerical example
Parameter Parameter Parameter
1
r
=$0.25
2
r
=$10.00 per thousand
a
=20
s
′
=50
2
s
=7500
B
=$250
1
p
=$0.20
2
p
=$8.00 per thousand
b
=40
1
l
=1%
2
l
=0.03%
T
=20

242 Li Chen
JSSM
We use a numerical example to illustrate the applica-
tion of our model. Similar to the application of advertising
perishable information with click-based revenue only, we
can calculate values of parameters using the information
from search engines. Using the values in Table 7, we ob-
tain that the switch time is
4.1
z
∗
= and the revenue is
$342.10 for the advertiser.
5. Conclusions
Internet advertising, especially search engine advertising
has quickly become vital for businesses to succeed in
e-commerce. Google, the largest search engine, provides
site-targeted ads to advertisers in addition to its traditional
pay-per-click model. In spite of the fact that more and
more firms put site-targeted ads on Google-networked
websites, very little research has attempted to analyze how
advertisers can make use of this new type of ads such as a
mixed strategy of combining it with the CPC-based key-
word search advertisement. This research attempts to fill
this gap by suggesting and formally modeling the strategy
of combining both types of ads.
We developed a general model to address the research
problem of what is the optimal time to start and end both
keyword-ads and site-ads. This model would help adver-
tisers to maximize their revenues. We modify the general
model for two specific scenarios: (1) Advertising of per-
ishable content with click-based revenue only; and (2)
Site-ads first and keyword-ads last. We provide
closed-form solutions for these two applications and pro-
vide managerial insights under the situation of bind-
ing-budget and non-binding budget. Computational ex-
periment and numerical example is also provided to illus-
trate the implementation of the model.
This research focuses on the mixed strategy of both
keyword-ad and site-ad in Google AdWords framework.
As for future research, it will be interesting to study
strategies of advertising across different search engines
when they adopt different mechanisms. Another promis-
ing area will be how search engines can help advertisers
when they observe that advertisers using a mixed strategy.
6. Acknowledgment
The author would like to thank Professor Manuel Nunez
of University of Connecticut for his great help on im-
proving the early versions of this paper.
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Combining Keyword Search Advertisement and Site-Targeted Advertisement in Search Engine Advertising 243
JSSM
Appendix
We use the Karush-Kuhn-Tucker conditions to solve the
general model of Equation (1) on page 3.
Step 1: We get the following Lagrangian equation:
1 2 3 4 0 12 3 4
(,,,,,, ,, )
L yyyy λ λλ λ λ
=
242 4
131 3
11 122212
{( )( )()()}{( )( )}
yyy y
yyy y
rStltdtStlt dtrSt dtStdt
++ +
∫∫∫ ∫
2 4
1 3
01 1122
{( )( )( )( )()}
y y
y y
BStltpt dtStpt dt
λ + −+
∫ ∫
11 2233 44
()() () ()
T yT yT yT y
λ λλλ +− +−+−+−
Step 2: Using Leibniz rule, we get the Kuhn-Tucker
conditions:
1 11 101 111 12111
1 1
( )(){( )}()0;0;0;
L L
S ylypyrSyryy
y y
λ λ
∂ ∂
=− −− ≤≥=
∂ ∂
1 21 21012122222
2 2
( )( ){()}( )0;0;0;
L L
Syl yrp ySy ryy
y y
λ λ
∂ ∂
=−+− ≤≥=
∂ ∂
2 302312 32 32333
3 3
( ){( )( )}( )0;0;0;
L L
SypyrlySy ryy
y y
λ λ
∂ ∂
=−−−≤≥=
∂ ∂
2 412 40242 42444
4 4
( ){( )( )}( )0;0;0;
L L
SyrlypySyryy
y y
λ λ
∂ ∂
=−+−≤≥=
∂ ∂
2 4
1 3
1 112200
0 0
( )( )( )( )( )0;0;0;
y y
y y
L L
BStlt ptdtStptdt λ λ
λ λ
∂ ∂
= −−≥≥=
∂ ∂
∫ ∫
1 1 1
1 1
0; 0;0;
L L
T y λ λ
λ λ
∂ ∂
= −≥≥=
∂ ∂
2 2 2
2 2
0; 0;0;
L L
T y λ λ
λ λ
∂ ∂
= −≥≥=
∂ ∂
333
3 3
0; 0;0;
L L
T y λ λ
λ λ
∂ ∂
= −≥≥=
∂ ∂
4 4 4
4 4
0; 0;0;
L L
T y λ λ
λ λ
∂ ∂
= −≥≥=
∂ ∂
Since we only consider solutions with economic meanings,
we make several restrictions on both
'
y s
and
'
s
λ
:
(1)
1
λ
and
3
λ
cannot be positive, which means no
advertisement.
(2)
1
y
is always less than or equal to
2
y
, and
3
y
is
always equal to or less than
4
y
.
(3) One of
1
y
and
3
y
must equal to zero, which
makes sure that at least one type of ads begin.
Let
1 11
( )
( )( )( )
h t
St ltpt
t
∂=
∂
,
2 2
( )
( )( )
g t
St pt
t
∂=
∂
,
110 112
( )( )(())
ftl tp trr
λ
=− +
,
2110 12
( )( )(( ))
ftl trp tr
λ
= −+
,
3021 22
( )( )( )
f tptrltr
λ
=− +
,
41 2022
( )( )()
ftrltp tr
λ
= −+
.
Step 3: Assume the reverse function of
( )
f t
i
,
1,2,3,4
i = , exist and
( )
g t
and
( )
h t
exist, we get closed-form
solution as followings:
When the budget condition is not binding:
1)
1 234
0 0
yyTyyT
= == =
;
When the budget condition is binding, there are 14
possible cases:
2)
1
1 2 34
0((0));
yyyyhBh
−
= = ==+
3)
1
1 3 42
0((0));
yy yy gBg
−
= ===+
4)
1
1 324
0(()(0)(0));
yyyTyhBg Tgh
−
= ===−++
5)
1
1 324
0((0)(0)( ));
yyygBghh TyT
−
= ==++−=
6)
1
1243
0((0)( )())
yyyTyhBgg Th T
−
== ==+−− ;
7)
1
12 43
((0)( )( ))0
ygBhgTh TyyTy
−
=+−−= ==
;
8)
1
1 3
0(0) 2,4
i i
y yy fi
−
= ===
9)
1
1
0(0), 2,3,4
i i
yy fi
−
= ==
10)
1
1 4
0(0), 2,3
i i
yyfiy T
−
= ===
11)
1
1 2
0(0), 3,4
i i
yyTyfi
−
= = ==
12)
12 3
(0), 1,40
i i
yfiyTy
−
=== =
13)
13
(0), 1,2,40
i i
y fiy
−
= ==
14)
134
(0), 1,20
i i
yfiyy T
−
=== =
15)
14
(0), 1,2,3
i i
yfiy T
−
= ==
The revenue function:
2 4
1 3
11 122
{( )( )( )( )}
y y
y y
RrStlt dtStlt dt
= +
∫ ∫
2 4
1 3
2 12
{( )( )}
y y
y y
rSt dtSt dt
+ +
∫ ∫
The remaining budget
2 4
1 3
1 1122
( )( )( )( )( )}
y y
y y
BBStltptdtSt ptdt
= −−
∫ ∫
%
1 324
()()( )()
Bg yh yg yh y
= ++−−
(a) Starting time
1 11 11112
1
( )()()0;
RSylyrSy r
y
∂
= −−<
∂
11 11 111
1 1
( )
( )()( )0;
Bg ySy lypy
y y
∂ ∂
= =>
∂ ∂
%
2 3131232
3
( )( )( )0;
RSyly rSy r
y
∂
= −−<
∂
32 323
3 3
( )
( )( ) 0;
h y BSy p y
y y
∂ ∂
= =>
∂ ∂
%
which shows an increase in starting time decreases
revenue but increases the remaining budget.
(b) Ending time
1 21 211 22
2
( )()()0;
RSyly rS y r
y
∂
=+ >
∂
21 21 2 1 2
2 2
( )
()( )( )0;
Bg yS y l yp y
y y
∂ ∂
= −=−<
∂ ∂
%
2 42412 42
4
( )( )()0;
RSy lyrSyr
y
∂
=+>
∂
42 42 4
4 4
( )
( )( )0;
Bh yS y p y
y y
∂ ∂
= −=−<
∂ ∂
%
which shows that increase in ending time increase
revenue but decrease the remaining budget.
Specific problems like the two problems discussed in
Section 4 can be solved using the approach above.