ORIGINAL ARTICLE A framework for intermediated online targeted advertising with banner ranking mechanism Kai Li • Efosa C. Idemudia • Zhangxi Lin • Yang Yu Received: 10 January 2009 / Revised: 15 September 2009 / Accepted: 4 October 2009 / Published online: 26 June 2010 Ó Springer-Verlag 2010 Abstract Reinforced by the fast growth of electronic commerce, even during the current global economic downturn, intermediated online targeted advertising (IOTA) has emerged as a promising electronic business model empowered by the Web 2.0 principle. IOTA maximizes the profit of online targeted advertising ser- vices by displaying the proper banner contents to certain types of Web users in real time in order to increase the click-through rate (CTR). However, due to severe competition in the online advertising market, the principles and algorithms of IOTA remain highly confidential. This paper is intended to unveil the nature of IOTA. We propose an IOTA service system framework and present its implementation scheme. Specifically, we address the advertisement allocation problem, using an advertisement ranking mechanism and considering the ads impression quota and the time-of-day (TOD) effect. Simulation results show that advertisement ranking in a subset of clusters that actively estimates the quota situation is feasible and efficient. Keywords Targeted advertising Intermediated online services Advertisement allocation Advertisement ranking K. Li (&) Department of Industrial Engineering, Teda College, Nankai University, 300071 Tianjin, People’s Republic of China e-mail: [email protected]E. C. Idemudia Z. Lin Y. Yu Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, TX, USA e-mail: [email protected]Z. Lin e-mail: [email protected]Y. Yu e-mail: [email protected]123 Inf Syst E-Bus Manage (2012) 10:183–200 DOI 10.1007/s10257-010-0134-4
Transcript
ORI GIN AL ARTICLE
A framework for intermediated online targetedadvertising with banner ranking mechanism
Kai Li • Efosa C. Idemudia • Zhangxi Lin • Yang Yu
Received: 10 January 2009 / Revised: 15 September 2009 / Accepted: 4 October 2009 /
Published online: 26 June 2010
Springer-Verlag 2010
Abstract Reinforced by the fast growth of electronic commerce, even during the
current global economic downturn, intermediated online targeted advertising
(IOTA) has emerged as a promising electronic business model empowered by the
Web 2.0 principle. IOTA maximizes the profit of online targeted advertising ser-
vices by displaying the proper banner contents to certain types of Web users in real
time in order to increase the click-through rate (CTR). However, due to severe
competition in the online advertising market, the principles and algorithms of IOTA
remain highly confidential. This paper is intended to unveil the nature of IOTA.
We propose an IOTA service system framework and present its implementation
scheme. Specifically, we address the advertisement allocation problem, using an
advertisement ranking mechanism and considering the ads impression quota and the
time-of-day (TOD) effect. Simulation results show that advertisement ranking in a
subset of clusters that actively estimates the quota situation is feasible and efficient.
Since the first banner advertisement appeared on HotWired in 1994, online
advertising has become an important channel of modern marketing (Adams 1995).
According to the Interactive Advertising Bureau (IAB), Internet advertising revenue
was $23.4 billion in 2008, with a 10.6% increase from that of 2007 (IAB 2007,
2008). Among the most common online advertising approaches, banner advertising
has gained popularity due to its ease of implementation. However, the ad publishers
have been facing an imminent problem behind the prosperity of banner ads. The
average click-through rate (CTR)1 of banner ads has been decreasing consistently,
from 3% in the mid-1990s to the recent 0.2% (eMarketer 2004). In the meantime,
the resources of online advertising are limited, as the total number of browsed Web
pages is restricted. This has made online advertising a sellers’ market with
increasing banner advertising demands (Bruner 2005). Driven by the above
scenario, online targeted advertising (OTA), which attempts to improve the
accuracy of online banner advertising, has become a new trend in the electronic
market.
The basic idea of targeted advertising resides in the ‘‘using Web users’ public
knowledge’’ principle of Web 2.0. OTA implements a business model that reaches
potential online customers whose past behaviors have demonstrated their interests in
specific online products and services. OTA uses consumers’ online purchase history
and Web-surfing records to select the most relevant advertisements for banner slots.
Data mining techniques help divide these online behaviors into distinct segments or
key profiles in order to display the advertisements that address the needs of specific
groups of Web users and to yield the highest possible CTR (Gallagher and Parsons
1997). The Web users who are potential customers and who fit the key profiles will
be targeted in the future.
The provision of OTA service requires expert techniques that are beyond the
advertisers’ and the publishers’ capability. Also, the extra costs incurred may not
justify the resources beyond the publishers’ core business. Therefore, intermediated
online targeted advertising (IOTA) has emerged as a new business model in the
advertising market in recent years (Idemudia et al. 2007). IOTA providers can work
on the precision of advertising and offer value-added services, such as reporting the
effectiveness of an advertising campaign to advertisers. Moreover, ad publishers can
focus on increasing the visits to their pages (Fig. 1). The IOTA business model
allows service providers to make use of all data resources from different ad
publishers in order to further improve the accuracy of banner advertising. Then
advertisers can derive higher satisfaction from the value-added services, such as
cross-publisher advertising effectiveness reports. In this way, overall marketing
efficiency is improved.
IOTA service providers have achieved great successes in the past few years and
have even stimulated a new type of gold rush. There are many successful stories,
1 Click-through rate is widely used to measure the effect of online advertisements. CTR is the ratio of the
number of users who clicked on an ad on a Web page to the number of times the ad was delivered
(impressions).
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123
such as shopping.com (acquired by eBay in 2005 for $620 million),2 aQuantive
(acquired by Microsoft for $6 billion in 2007),3 TradeDoubler (acquired by AOL
for $900 million in 2007),4 Real Media Inc. (acquired by WPP Group PLC in 2007
for $649 million),5 Right Media Inc. (acquired by Yahoo! In 2007 for $680
million),6 and DoubleClick (acquired by Google in 2007 for $3.1 billion).7 The
astonishingly high prices for the acquisitions of these online targeted advertising
companies mark their value and indicate the viability of the IOTA model. These
acquired IOTA providers still exist in the market, either in their original names or as
merged with the principal owner company. Nevertheless, there are still quite a few
IOTA companies in the market looking for acquisition opportunities, such as X ? 1
(http://www.Xplusone.com), Pay Popup (http://www.paypopup.com/), Cyber Mark
International (http://www.cybermarkintl.com/), RupizMedia (http://www.rupizme
dia.com/), and so on.
The techniques used in implementing IOTA range widely, including data mining,
optimal resource allocation, creativity optimization, pricing strategies, etc. Every
(a) (b)
Fig. 1 An IOTA business model. a Without IOTA, the relationship between publishers and advertisers ismany-to-many. b IOTA becomes a hub between groups of publishers and advertisers
successful IOTA service provider has its own know-how related to its unique
business model, which is often confidential. Therefore, the published reports from
the industry normally contain limited information about the practical systems. For
example, linear programming and simple clustering are reported to be available to
match an advertisement from several choices to a banner slot (Chickering and
Heckerman 2003).
In this paper, we focus on two research issues. One is to reveal the framework for an
online targeted advertising system. The IOTA service system framework presented in
this paper is supported by a mixture of data mining and optimization models for
targeting online consumers. The framework shows how these models can work
together as a system. The other one is advertisement allocation, which is the most
important technical issue in IOTA service system implementation. The objective in
solving this issue is to maximize the click-through rate of banner advertisements with
the practical constraints. This paper introduces a banner-ranking mechanism, which is
tested under the simulation experiments especially to show effectiveness, after taking
into account other factors such as the time of day (TOD).
The rest of this paper is organized as follows: Sect. 2 covers related works;
Sect. 3 proposes a system framework of IOTA implementation and presents an
advertisement allocation mechanism; Sect. 4 presents the simulation and results;
and Sect. 5 concludes the paper.
2 Related works
The related areas that also contribute to IOTA implementation include the general
schemes for traditional targeted marketing, the techniques used in banner
advertising, and relevant methods used in other types of advertising. In banner
advertising, it is well accepted that the right person should receive the right message
at the right time in order to increase the effectiveness of advertisements (Adam
2002). Early targeted marketing techniques only used IP addresses to obtain the
location of the Web user or used registration information to judge the user’s
intention (Gallagher and Parsons 1997). Therefore, these techniques were not
widely adopted. Some targeting techniques are based on Web page contents in
banner advertising. Previous research has found that banner advertiser-Web site
context congruity affects Web user’s attitudes and attentions (Moore et al. 2005).
Clearly, if a banner advertisement is relevant to the content of the Web page, Web
users who visit the page will more likely be attracted by the banner. However,
posting relevant advertisements to the banners in content-related Web pages is not
sufficient for the accuracy of targeted advertising. Web users’ online behavior data,
such as the time spent on the page and the browsing path to the Web site, have been
found to be useful for predicting clicks on the advertisements in conjunction with
the Web page contents (Chatterjee et al. 2003). This way of using information about
Web users’ online behavior to predict the likelihood of click-through is also called
behavioral targeted advertising (Li et al. 2007).
Two related issues about targeted advertising are discussed in the literature. One
is the targeting techniques used in targeted advertising, and the other is the
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scheduling of advertisements. A lot of work has been done in developing different
decision models that can accurately display online advertisements targeted at the
right Web users to increase CTR. For example, Kumar et al. (2007) built a hybrid
pricing model to maximize revenue through the optimal selection and placement of
these advertisements. Data mining is used as a targeting technique to discover Web
access patterns for identifying Web users (Srivastava et al. 2000). In some studies,
detailed segmentation and clustering models are used in targeted marketing for
analyzing customer patterns (Bayus and Mehta 1995) and online advertising
problems (e.g., Bhatnagar and Papatla 2001; Chickering and Heckerman 2003). The
classification model is also used to match Web sessions with optimal advertisements
(Li et al. 2007).
Maximizing total CTR by appropriately managing time and advertising space on
the Web page is a common objective for all online advertising service providers
(Amiri and Menon 2003). Early advertisement scheduling models, referred to as
exposure models, are originally from the traditional media advertising area
(Danaher 1992a, b), but they focus on the dimensions of reach and frequency
(Huang and Lin 2006). Recent literature shows that integer programming is the
primary tool for solving advertisement allocation problems. Given inventory-
management constraints, Adler et al. (2002) first used integer programming to solve
the problem of banner advertisements allocation. Amiri and Menon (2003)
described the problem in more detail and solved it in the same integer programming
approach but based on Lagrangean decomposition. Deriving material from former
studies, Kumar et al. (2006, 2007) formulated a heuristic of this advertisement
allocation problem and reached alternative heuristic solutions with a genetic
algorithm. Two researchers from the industry, Chickering and Heckerman (2003),
discussed how both publishers and advertisers could maximize advertisement
revenues, regardless of the pricing models they adopted. Advertisement impression
quotas were taken into account in their integer programming model as an important
constraint.
Alternatively, in this research, we intend to use advertisement ranking to solve
the advertisement allocation problem. Advertisement ranking has been used
frequently in search engine advertising. There are two main ranking mechanisms:
ranking based only on the bid and ranking based on both the bid and relevance. Feng
et al. (2007) modeled and compared several main mechanisms in the industry. They
found that weighting clicks on lower ranked items more than clicks on higher ones
is the optimal method. Google’s ranking (based on both bids and relevance) seems
to work more efficiently than Yahoo’s ranking (based only on bids). Banner
advertisement ranking is quite different from search engine advertisement ranking.
Its objective is to allocate the most suitable advertisements to the banner slots on a
Web page while maintaining fairness to every advertising client. In order to provide
good advertising services, both revenue and uniformity (the fairness of service to a
wide range of advertisers’ advertisements) are important to the ranking mechanism.
Although the above mentioned literature has contributed significantly to targeted
advertising, little research thus far directly addresses the main problems associated
with the IOTA business model, where a trusted third party provides online targeted
advertising services to advertisers. In addition to the normal technical problems in
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online targeted advertising, the trusted third party in the IOTA business must be able
to deal with the data from different Web sites and to mass-customize the decision
models for different types of advertisements. Motivated by these problems, we
present a novel scheme in this paper for implementation of the IOTA service
system.
3 Intermediated online targeted advertising
We first clarify the definitions of different objects in this research. Four similar
concepts of targeted objects are referred to in different online advertising contexts:
Web user, session, online customer, and client. Web users are individual persons
who visit publishers’ Web sites. The same individual may use different computers
to visit the Web at different time for different purposes. A Web session then
becomes the atomic unit to distinguish different series of Web browsing activities
by the same Web user. According to W3C (1999), a session is defined as a group of
browsing activities performed by a user from the moment he enters the Web site to
the moment he exits. The Web log in a Web server records these Web access
sessions. The Web browsing history can also be collected by cookies, which are
small temporary files created by applications from Web servers and left on clients’
computers (Spiliopoulou et al. 2003). An online customer is defined as a Web user
who has clicked an online advertisement. Clients are user-end terminals connected
to the Web servers when referring to the Client/Server computing architecture.
Generally, the same Web user can go to the Internet at different times with different
client computers, and engage in sessions for different purposes. In this case, we use
a session as the basic unit for this study.
In this section, we will present an IOTA framework, discuss the key issues in its
implementation, and propose a step-by-step implementation scheme for the IOTA
service. We will leave the more detailed advertisement allocation mechanism for
Sect. 3.3.
3.1 The anatomy of an IOTA service system
In an IOTA service system, there are four agents (Fig. 2): (1) Web users who
browse Web pages and are targeted for advertisements; (2) publishers who provide
advertisement placement slots for publishing advertisements; (3) advertisers who
are clients of IOTA and also the main source of revenue; and (4) an IOTA service
provider who intermediates online advertising business between advertisers and
publishers.
The IOTA service system includes three main modules: Ads Management
System, Optimum Decision Modeling, and Realtime Ads Publishing. The Ads
Management System is used to facilitate the management of the Ads Inventory.
Advertisers can use it to input and edit their ads, and to retrieve ad performance
reports. An IOTA provider can plan its services with regard to the dynamically
changed advertisement inventory data maintained by the Ads Management System.
The Optimum Decision Modeling module uses the data collected in the Web Access
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History database to build advertisement decision models. The Realtime Ads
Publishing module retrieves ads from the Ads Inventory based on the Optimum
Decision Modeling in accordance with the click-through flows of Web users in real-
time.
The pricing model is one of the critical factors underpinning the IOTA services.
As a trusted third party, an IOTA service provider is engaged in two advertising
pricing issues with advertisers and publishers. The first involves how the IOTA
provider charges the advertisers, and the other is how it pays for the use of
publishers’ banner slots. Two commonly known online advertising pricing models
are cost per click (CPC) and cost per thousand impressions (CPM). Each of these
models has its own advantages and weaknesses. With the CPM model, even though
a Web user may pay no attention to the banner ads, the publisher will still charge a
fee to the owner of the banner advertisements for each page view. With the CPC
model, advertisers are charged only when their ads have been clicked. Therefore, in
general, publishers prefer CPM to CPC, because the CPC scheme may place them
under pressure to keep up the click-through rate while they have to focus on selling
different types of advertising placement resources. Advertisers prefer CPC to CPM,
because they are looking for better effectiveness in advertising their service, as long
as the fraud from computer-generated clicks can be filtered out (Shen 2002). In fact,
the CPC pricing method has become the more widely used among the pricing
options. In 2001, 53% of advertising networks used the CPC method (eMarketer
2001). In order to attract and retain advertisers, publishers sometimes try to enhance
their CPM services by providing an analytical report of their advertising
effectiveness, but this is uncommon due to the extra cost. An advertiser may also
simultaneously post its advertisements to multiple publishing sites operated by
different publishers. Working as an intermediary, IOTA provides a good solution to
this problem.
Web Access History Optimum
Decision Modeling
AdsInventory
AdsPublishing
Banners
Ads ManagementSystem
LandingPage
AdvertisersIOTA ServiceProvider
Web Users
RealtimeAds Publishing
AdsPublisher
Fig. 2 IOTA service system
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The IOTA business model is highly effective. An IOTA provider can have
contracts with advertisers in the CPC model at the same time it has contracts with
advertisement publishers in the CPM model. The publishers then can maintain their
CPM pricing model while advertisers are charged based on the CPC pricing model
to guarantee that ‘‘what they get is what they pay for.’’ Furthermore, the multiple-
link of an IOTA provider to several advertisers helps them to obtain the
comprehensive data on advertising effectiveness that is needed to generate the
effect reports requested by the advertisers. This can save advertisers’ efforts to
identify adequate publishers by comparing the effectiveness of different advertising
services. In this way, all business partners of the IOTA provider will be satisfied.
3.2 Implementing intermediated online targeted advertising service
The key issue in implementing an IOTA service system is how to choose the right
advertisement in real-time and place it in a proper banner slot of a Web page for a
Web browsing session. IOTA service providers usually have thousands of
advertisements that are dynamically maintained in the Ads Inventory (Fig. 2). It
is practically impossible to create the same number of classifiers for targeted
advertising. Intuitively, these advertisements can be grouped according to their
similarities to reduce the number of classifiers. In this way, the issue becomes how
to allocate an advertisement from a much smaller advertisement set to the banner
slot in the current Web page once a right group is identified for a Web session.
Chickering and Heckerman (2003) use predictive segments of advertisements in
conjunction with integer programming to perform a constrained optimization.
However, the number of classifiers for the segments could be still too big,
particularly when an IOTA service provider accumulatively handles the demands
from multiple advertisers.
To tackle this problem we introduce an approach using a mixture of data mining
methods. First the hierarchical clustering method is used to group advertisements;
then a group of advertisements is identified for a specific Web session by a process
consisting of a series of classifications; and finally an advertisement is selected for
the banner slot based on an advertisement ranking mechanism. For example, a set of
10,000 advertisements could be segmented into 125 clusters with 80 advertisements
in each cluster on average. These clusters can be organized into a hierarchical
structure with regard to the properties of advertisements, such as category, color
(Wilkie 1990), size (Cho 2003), interactivity (Fortin and Dholakia 2005), and
vividness (Fortin and Dholakia 2005). In the case of 125 clusters, we can have five
clusters at level 2, five sub-clusters at level 3 for each cluster, and five leaf clusters
at level 4 for each sub-cluster. Thus, by using this model, a Web browsing session
can be classified to a destination leaf cluster of advertisements in three consecutive
steps. Then allocating an advertisement from a much smaller ad set to a banner slot
becomes a much easier job.
Based the above idea, we propose an IOTA implementation process as shown in
Fig. 3. The whole process can be divided into two parts: offline process and online
process. The offline process mainly includes data preparing, advertisement
clustering, classification model training, and advertisement managing. The online
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process includes data acquiring, real time matching, advertisement allocating, and
advertisement publishing. There are four stages in implementing intermediated
online targeted advertising.
• Stage One: Preprocessing advertisements with the proposed hierarchical clus-
tering method.
• Stage Two: Collecting data from Web users’ Web browsing activities. Three
types of data are available: Web log data, cookie data, and user profiles.
• Stage Three: Building classification models with the extracted attributes from the
data collected in Stage two, using the hierarchical advertisement clusters as the
targets. An implementation of the above scheme was reported in Li et al. (2007).
• Stage Four: Using an advertisement allocation mechanism to assign a suitable
advertisement in the selected cluster to the slot of the Web page. We propose an
advertisement-ranking algorithm in terms of the CTR, the impression quota, and
the time-of-day effect to resolve the imperfections in the integer programming
approach. Details are covered in the next subsection.
The proposed sequential classification based on the hierarchically organized
advertisements may only reach a suboptimal solution. Therefore, carefully organizing
the hierarchical structure for advertisements clusters is critical to effectively improve
the performance of targeted advertising.
3.3 Advertisement allocation
Advertisement allocation is an important issue in implementing the IOTA business
model. Although the advertisement allocation obtained from the integer program-
ming approach is theoretically optimal, it is not uniform regarding different types of
advertisements, as noted by Chickering and Heckerman (2003). As a common
sense, it is inappropriate to allocate all impressions to those advertisements having
historically higher CTR. Therefore, a trade-off between resource allocation
Fig. 3 IOTA implementing process
A framework for intermediated online targeted advertising 191
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uniformity and nominal profit maximization for an allocation solution is critical to a
feasible targeted advertising system, because overly servicing some advertisements
will result in significantly reduced CTR according to the practice of the industry. In
addition, the integer programming approach to allocating advertisements could
incur too much computational overhead which is inappropriate in a real-time
context. Therefore, in this section, we propose an advertisement ranking approach
for allocating advertisements from a selected advertisement group to the presently
available banner slots. This approach resolves not only the uniformity problem but
also the problem of computational overhead caused by the integer programming
algorithm.
Advertisement ranking has been studied widely in search engine advertising but
not examined yet for banner advertising. In search engine advertising, ranking is
used for allocating advertisements to sponsored slots which are located on the right
side of the search page. Slots located at the top of the paid place usually receive
more clicks. When a Web user inputs a query, the search engine selects all the
advertisements that match the keywords, ranks the advertisements, and places them
in the sponsored slots from top to bottom in terms of their ranking order. There are
two main ranking mechanisms in search engine advertising for allocating sponsored
slots in the industry (Feng et al. 2007). One is to rank advertisements solely based
on the bid price, like the previously discussed approach by Yahoo, and the other is
based on both bids and relevance, like those by Google and Microsoft. Banner
advertisement ranking is quite different from search engine advertisement ranking.
It picks the most suitable advertisements in a selected cluster and posts them to the
slots on the page that the Web user is requesting. Banner advertisement ranking is
an important step in IOTA implementing. After an active Web browsing session is
dynamically classified into an appropriate leaf advertisement cluster, a banner slot
allocation program will be activated to choose the proper advertisements for the
available banner slots. Then these advertisements will be posted to the banner slots
on the Web page, taking into account all constraints requested by advertisers. The
constraints are mainly the impression quotas of advertisements contracted between
advertisers and IOTA service providers. That is, the IOTA service provider delivers
at least the requested minimum number of impressions to Web users.
3.3.1 The advertisements ranking mechanism
Before we derive the ranking mechanism, we must look at two important
assumptions. First, the position of a slot on a Web page does not have a joint effect
with the characteristics of advertisements on the click-through rate in an IOTA
situation. Second, the total number of all impressions is far greater than the sum of
each advertisement’s quota contracted between an IOTA service provider and its
advertisers. This is because a responsible IOTA service provider must have enough
resources to commit to overall advertising demands.
Suppose that there are K banner slots on a Web page, and s advertisements in a
matched leaf cluster of a hierarchical advertisement tree, which is the result from
running a series of classification models. Denote the set of the leaf cluster as S. We
have s = |S|. Qi is the quota of advertisement i in a certain time period. Denote v as
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the expected number of accesses of a page in a given time period T. The total
impression-slot is K*v, where K vPs
i¼1 Qi, in the period. Our allocation
problem is how to pick K advertisements from s possible choices for the available
banner slots, when a Web user visits the Web page. We use Ni to denote the number
of clicks on advertisement i in time period T and use P to denote the price of CPC.
Therefore, the revenue of the IOTA service provider from a leaf cluster can be
denoted as P ¼Ps
i¼1 P Ni, to represent the effect of the allocation model. We use
Mi to denote the number of impressions on advertisement i in time period T and use
N 0i to denote the historical total number of clicks on advertisement i, M0i to denote
the historical total number of impressions on advertisement i. Therefore, the CTR of
advertisement i is CTRi ¼ N0iM0i
, and the CTR after time period T is CTRi ¼ N 0iþNi
M0iþMi.
The general idea in implementing the mechanism for advertisement allocation is
to rank the matched advertisements and post the first K advertisements in the
ranking stack. Denote Ri (Ri [ 1, 2, 3,…, s) as the rank of advertisement i in the
ranking result of the leaf cluster. Ri is determined by the order of its ranking
weight Wi in the cluster after all advertisements are sorted in descending order by Wi.
Define each advertisement’s ranking weight Wi as a function of its CTRi and its degree of
quota fulfillment Di, where Di = Mi/Qi. Let ranking weight Wi = CTRi * (Di)-a,
where we can adjust a to change the relative effect of Di on Wi.Denote hi as the probability that a Web user will click advertisement i in real
time. In the basic ranking mechanism, we assume that hi is equal to advertisement
i’s historical CTR, hi = CTRi. The basic advertisement ranking mechanism does
not consider that the probability hi changes throughout the day, so there is no time-
of-day effect. The model containing the effect of TOD will be discussed later in this
section.
Quota fulfillment is very important in the mechanism, because it is guaranteed by
the contract. Here quota fulfillment is set at top priority before allocating the
residual impression opportunities to the best performed advertisements with the
ranking algorithm. The basic ranking mechanism includes the following steps:
Step 0: S0 = S, where S0contains the advertisements with the unfulfilled quota.
Step 1: Sort the advertisements in ascending order in terms of Di, for each
advertisement i [ S0. Every advertisement is assigned a rank R0i.Step 2: While R0i\K, post the advertisement i to the Web page, and
Mi = Mi ? 1. If advertisement i is clicked by the Web user, Ni = Ni ? 1,
recalculate the CTRi.
Step 3: Recalculate Di. If Di [ 1, meaning the quota of advertisement i is
fulfilled, advertisement i is then removed from S0. Otherwise, it is kept in S0.Step 4: Repeat Step 1 to Step 3, until all advertisements’ quotas are fulfilled. This
means that the queue of top priority advertisements is empty.
Step 5: Sort the advertisements in descending order based on Wi = CTRi*(Di)-a,
i [ S. Thus, every advertisement will be assigned a rank Ri.
Step 6: If Ri \ K, post the advertisement i to the Web page, and Mi = Mi ? 1.
If advertisement i is clicked by the Web user, Ni = Ni ? 1, recalculate the
CTRi.
A framework for intermediated online targeted advertising 193
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Step 7: Recalculate Di. If Di \ 1, meaning the quota of advertisement i is not
fulfilled, advertisement i is then added to S0.Step 8: If it is the end of period T, exit. Otherwise, if S0 is not empty, go to Step 1,
or go to Step 5.
We denote the above basic advertisements ranking mechanism as BARM.
3.3.2 The time-of-day extended mechanism
Time of day (TOD) means that advertising in different time periods in a day may have
different effects on the same type of users. It has been studied in a broad range of disciplines
for years, such as psychology (Dunne and Roche 1990), health (Lange et al. 2005), and
electric power consumption (Schwarz 1984). Specifically, TOD has been an important
factor in traditional marketing, for example, its effect on consumer’s purchase behaviors
(Wirl 1990; Roehm and Roehm 2004). Customers who have similar preferences and
characteristics may demonstrate similar behavioral patterns in terms of time. For example,
they tend to surf the Web during the same period of time in a day. Accordingly, the
probability that Web users will click a certain advertisement changes with different times
in the day. In the BARM mechanism, hi = CTRi = Ni/Mi is a constant. Now we take into
account the TOD effect on that, which means that hi varies periodically. Assume that
the periodical change of hi is subject to a sine wave.8 Thus, we define hi =
CTRi * (1 ? Asinb), where A is a constant much smaller than 1, b ¼ Bi þ 2p tT, and Bi
represents TOD effects on the advertisement CTR in different times. In other words,
the function implies that some advertisements with a low CTR may have a higher
probability to be clicked than a higher CTR advertisement in a certain time period.
Assigning real-time sessions with the right advertisements in accordance with
different time periods leads to more realistic targeted advertising. Therefore, it is
reasonable that IOTA service providers consider the TOD effect in the ranking
mechanism to improve the accuracy of targeted advertising in real time. In practice the
changing pattern of advertisements’ hi can be predicted based on its historical data. We
denote the TOD extended ranking mechanism proposed in this section as TERM.
4 Simulation and results
The main objective of the conducted simulation experiments is to verify the
feasibility and efficiency of the banner advertisement ranking mechanism and to
find the general rules in the ranking process, so that banner advertisement ranking
can be better applied in the e-business.
4.1 Simulation design
Following the discussions in the former subsection, in the simulation study we adopt
a strategy in which the percentages of quotas are fulfilled based on priority in
8 Actually, many studies in other areas have revealed that the TOD effect is a sine wave, but not a simple
sine wave (Hursh 2003).
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accordance with the proportion of time elapsed in a service period. Denoting the
elapsed time in a period as t, the proportion of the time elapsed is L = t/T, where Tis the length of the period. The status of temporal quota fulfillment Di = Mi/Qi can
be compared to L to decide the ranking of advertisement i. If Di \ L, advertisement
i must be set at a higher priority to be posted than must the advertisements with a
fulfilled quota. Considering the ad publishing as a queuing system, there are two
queues in the system with different priorities. If an ad has the impression ratio
Di \ L, it is placed in queue 1, the higher prioritized one. Otherwise, it is placed in
queue 2, the lower prioritized one. Only when queue 1 is empty can the
advertisements in queue 2 be serviced. Advertisements in queue 1 are ranked with
regard to their ratio of Di. The lower the Di, the higher it is ranked. Advertisements
with the same Di will be ranked according to their CTRs. In queue 2, ranking weight
Wi = CTRi * Di-a = CTRi * (Qi/Mi)
a, where a is a discounting factor to make the
ranking more fair.
We tested two ranking mechanisms, BARM and TERM with the simulation
system, and compared them with the one-by-one mechanism (OBOM), which is the
basic and simple allocating mechanism with equal opportunities for every
advertisement. In the OBOM mechanism, an advertisement is posted when it is
its turn to be placed to the Web page. Therefore, every advertisement will get a
fairly average number of impressions.
BARM and TERM mechanisms share the same expression of ranking weight
Wi = CTRi * Di-a = CTRi * (Qi/Mi)
a in queue 2. To generate CTR, we use
Bernoulli distribution (Pr(X = 1) = 1-Pr(X = 0) = h)) to represent a random
Web user’s click-through decisions, where X = 1 is for a click-through of the
advertisement, and X = 0 is for a non click-through. The difference is that in
the BARM mechanism hi = CTRi, and in the TERM mechanism hi = CTRi
* (1 ? Asinb). The TERM mechanism is expected to perform better than the BARMmechanism, as the ranking method can dynamically adjust to the best choices.
4.2 Simulation experiment results
First, we compute the revenue of each ranking mechanism with K = 1 (the number
of available slots on a Web page), s = 25 (the number of advertisements in a leaf
advertisements cluster), a = -0.5, and A = 0.1. The CPC price is set to 1. Then the
revenue equals the sum of clicks (P ¼Ps
i¼1 P Ni ¼Ps
i¼1 1 Ni ¼Ps
i¼1 Ni).
Every advertisement is preset to a corresponding historical CTRi, which is subject to
the normal distribution where the average CTR = 0.190. Also every advertisement
is given a Bi as an inherent character, which is subject to the normal distribution.
Every advertisement is assigned a uniform quota Qi = 40, i ¼ 1; 2; . . .; 25: The
experiment was run over v = 1,500 times.
The results in Fig. 4 show that the performance of advertisement ranking
mechanisms BARM and TERM is superior to that of the OBOM mechanism. This is
intuitive, because both the BARM mechanism and the TERM mechanism adapt to
the conceived context better than the OBOM mechanism. In Fig. 4 we can see that
the experiment based on the OBOM mechanism can get only the basic revenue
(about 280), since the initialized average CTR of the advertisements is 0.190. The
A framework for intermediated online targeted advertising 195
123
revenue of experiment based on either BARM or TERM is clearly much higher than
that from the OBOM mechanism.
Also we can see that the consideration of the TOD effect makes the ranking
mechanism more effective. The TERM mechanism adapts better to the practical
situation. Since the BARM mechanism does not cover the TOD effect, the total
number of clicks in the BARM mechanism is lower than for the TERM. Figure 4 also
shows the revenue’s growing process when using two ranking mechanisms. The
advantage of the TERM mechanism is obvious. This indicates that the TOD effect is
quite important to online targeted advertising. Therefore, it will be more efficient to
consider the TOD effect in advertisement allocation.
There is an interesting issue from the practical perspective that how the total
revenue is influenced by the weight of Di in ranking, which is denoted as a. When aequals 0, ranking weight Wi = CTRi * Di
-a = CTRi. This means that ranking totally
depends on an advertisement’s CTR when the quota is fulfilled. When a equals 1,
ranking weight Wi = CTRi * Di-a = CTRi/Di. This means Wi is more inversely
proportional to Di. As a changes from 0 to 1, Di is weighted more and more in the
ranking formula for queue 2 to suppress the overly-serviced advertisements. When
advertisement i’s Di is much greater than other advertisements’ Di, is given the same
CTR, Wi is smaller, and the advertisement i is ranked lower in the queue. This, in
turn, reduces the possibility that advertisement i will be posted. That is why revenue
decreases as a change from 0 to 1 (Fig. 5).
Fig. 4 Revenue for the three advertisement allocation mechanisms
Fig. 5 Revenue versus a for the TOD ranking mechanisms
196 K. Li et al.
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Finding 1 Revenue generated in period of time T decreases as the mechanismconsiders more uniformity.
From Fig. 5 we can also analyze the trend from the MA curve and see that the
revenue is negatively related to P. Ranking weight is the key to a ranking-based
mechanism, and a is the most important parameter that decides the uniformity of
ranking mechanisms. In other words, as a is close to 1, the ranking mechanism tends
to count uniformity more than revenue. Uniformity implies that advertisements with
low CTR will also get the chance to be posted. If we pursue only short-time
revenue, we can use an integer programming model to reach a mathematically
optimal solution with a = 1, which is theoretically the most profitable. In that case,
the advertisements with low CTR can only get posted at the quota level, while
advertisements with the highest CTR will receive all the extra impressions besides
the quota. Obviously, this is unfair for the low CTR advertisements. Over a long
time period, the IOTA service provider will lose those customers with low CTR
advertisements, and its long-time revenue will decrease.
Figure 6 shows the relationship between the historical CTR and the number of
clicks and impressions after the simulation. The data is produced by the TERMmechanism.
Finding 2 The number of clicks for advertisements is positively related toadvertisements’ historical CTR.
Finding 3 The impression of advertisements is positively related to theadvertisements’ historical CTR.
According to Findings 2 and 3, in order to reach maximum total clicks, the
advertisements with a higher historical CTR must have a higher chance for being
posted than those advertisements with a lower historical CTR, because they are
more likely to be clicked by online customers. Findings 2 and 3 show that the ads
with high historical CTRs will have a higher probability to be posted in both
mechanisms and will also get more clicks. Thus, the intrinsic properties of online
ads, which influence CTR, are very important, such as color, interactivity, vividness
and so on. However, another interesting observation remains. In an integer
programming model, maximizing revenue is the objective, since its objective
Clicks
Impressions
Historical CTR
Fig. 6 Historical CTR versus number of clicks and impressions for the TOD extended mechanism
A framework for intermediated online targeted advertising 197
123
function is based on the number of clicks. Therefore, the impressions of
advertisements with low CTR are just fulfilled at their quota levels, and the
advertisement with the highest CTR may take all the residual impressions over the
total quota Q. In this case, the total number of clicks can be at maximum, meaning
that the total revenue is maximized. In the TERM mechanism, both revenue and
uniformity of advertisement allocation are considered. Analytically, it is difficult to
derive a mathematical solution for the optimum impression allocation among
advertisements, but it is possible to reach a relatively and practically more feasible
one.
Another interesting question is how many slots an IOTA service provider should
buy on a specific Web page. If there are K slots on the Web page, it means the IOTA
service provider can post K advertisements from a selected leaf cluster for a Web
accessing session. Intuitively, if there is more than one slot on the page, it means
that more than one advertisement will be posted for the Web session. In that case,
the Web user has multiple choices. This will increase the likelihood that a Web user
will click any given advertisement. However, if there are too many slots, the CPM
cost will be too high. This will reduce the revenue. The number of slots that an
IOTA service provider should buy from a publisher on a Web page poses a
challenging question depending on multiple factors, such as the traffic of the page,
the advertisements’ creativity, the Web users’ characteristics, and so on. In spite of
that, the answer can come from experimenting. Continuously adjusting the number
can ultimately lead to a satisfactory strategy in practice.
5 Conclusion
This paper addresses the implementation issues of the emerging intermediated
online targeted advertising system. In the beginning, we discuss the business model
for intermediated online targeted advertising, abbreviated as IOTA. Then we present
the IOTA service system process: cluster all advertisements into a hierarchical tree
and apply classification data mining techniques to find the most suitable sub-cluster
in a hierarchical cluster tree according to the online behaviors of Web users.
Moreover, we tackle the advertisement allocation problem, which is one of the most
important issues in implementing the IOTA service system. An advertisement
ranking method is utilized in the allocation mechanism. The time-of-day effect,
which is proposed by the industry, has also been taken into account. Finally, we use
simulation to verify the feasibility of our advertisements ranking mechanisms.
Simulation results show that TOD is a critical factor in advertisement ranking.
Considering TOD effect in the mechanism can further optimize the outcomes of
online advertising.
Targeted marketing is becoming an emerging trend in electronic commerce with
an increasing number of online companies requiring this type of service. The
increasing demand calls for more effective services with lower costs. This opens a
considerable market for IOTA service providers. The implementation of the IOTA
business model is very important, and it can lead to a ‘‘Win-Win-Win’’ situation for
advertisers, advertisement publishers, and IOTA providers. Hence, our work offers a
198 K. Li et al.
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valuable practical contribution. In the industry, most IOTA service providers are
using a uniform CPC price. Therefore, the CPC price is the same for every
advertisement in this research. However, if IOTA service providers can offer
differentiated advertising services and charge different prices, it will be interesting
to find out whether profits will increase and whether advertisers will be better off.
The answers to these further research questions will provide insight for the industry
in conceiving a better pricing strategy to enhance the widely adopted uniform
pricing method. Furthermore, along with the development of information technol-
ogy, a dynamic IOTA service system will be needed in the future, which means that
real-time data analysis and active model improvements can be carried out.
Acknowledgment We are grateful to Dr. Kai Jin for her involvement in the early stage of this research
project.
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